Monitoring Data Assessment report for the 3 original - M3-life: Project

Modelling Monitoring Management
Monitoring data assessment report
Application of integrative modelling and monitoring
approaches for river basin management evaluation
www.life-m3.eu
The M3 project is financed by LIFE+ Programme of the European Commission. The report does not necessarily express the views of the European Commission.

Contents
1. Executive Summary ........................................................................................................... 2
2. Introduction ........................................................................................................................ 4
3. Monitoring design evaluation ............................................................................................. 7
3.1. Criteria for an efficient monitoring ............................................................................ 8
3.2. Load calculation methods ......................................................................................... 11
3.3. Analysing grab sample data ..................................................................................... 12
3.3.1. Cumulative distribution of discharge ................................................................. 12
3.3.2. Rating curves and confidence intervals .............................................................. 13
3.3.3. The influence of sampling frequency ................................................................. 16
3.3.4. Conclusions and recommendations .................................................................... 17
4. Regional survey: Delfland ................................................................................................ 18
4.1. Pressures in Delfland ................................................................................................ 19
4.1.1. Nitrogen and Phosphorus ................................................................................... 20
4.1.2. Copper and Zinc ................................................................................................. 20
4.1.3. Pesticides and glasshouse emissions .................................................................. 21
4.2. Delfland Programs of Measures. .............................................................................. 26
4.3. Monitoring in Delfland ............................................................................................. 27
4.3.1. Surface water quality data .................................................................................. 27
4.3.2. Sediment quality data ......................................................................................... 32
4.3.3. Biological monitoring ........................................................................................ 40
4.4. Delfland summary .................................................................................................... 42
5. Regional survey: Erft ....................................................................................................... 47
5.1. Pressures in the Erft Catchment ............................................................................... 48
5.2. Monitoring in the Erft Catchment ............................................................................ 51
5.2.1. Surface water quality data .................................................................................. 51
5.2.2. Sediment quality data ......................................................................................... 60
5.2.1. Biological survey data ........................................................................................ 65
5.3. Erft summary ............................................................................................................ 69
6. Regional survey: Luxembourg ......................................................................................... 73
6.1. Pressures in Luxembourg ......................................................................................... 73
6.2. Programs of measures .............................................................................................. 74
6.3. Monitoring in Luxembourg ...................................................................................... 74
6.3.1. Surface water quality data .................................................................................. 74
6.3.2. Suspended matter quality data............................................................................ 83
6.3.3. Biological survey data ........................................................................................ 85
6.4. Luxembourg summary ............................................................................................. 87
7. Comparison of Micro-pollutants results by Region ......................................................... 91
8. References ........................................................................................................................ 94
9. Appendices ....................................................................................................................... 96
Monitoring data assessment report M 3 1/109

1. EXECUTIVE SUMMARY
Common deficits to monitoring concepts in the 3 evaluated regions
Although EU-guidelines have been published to steer member states through different
steps of WFD implementation, the documents remained vague on most quantitative
aspects of evaluating pressures and adapting monitoring networks to provide a sound
database for Program of Measures (POM) definition. A major reason for the lack of
clarity in linking different steps are the large knowledge gaps in the relationship between
morphological, ecological and chemical drivers leading to the endpoint metrics of good
ecological status (including the appropriateness of those metrics). Regulators and river
basin managers have traditionally been populated with distinct services of chemists and
biologists with little common understanding of a holistic approach to address the WFD
challenge to achieve good ecological status. Cause-effect relationships are still largely
obscure and this translates in inconsequent monitoring that is rather motivated by
minimal documentation compliance than a real result oriented search for causes of
impairment. Left to their own devices, most regional stakeholders stick to a classical
spot-analysis and threshold exceedance philosophy rather than an ecosystem approach as
intended by the original idea of good ecological status. It has to be pointed out here that
EU guidelines carry a good deal of contradictions in that respect. As a consequence it is
to be expected that vaguely motivated POMs are in acute danger of failure, jeopardizing
the goals of the WFD altogether.
Based on this general background we have identified the following deficits in the 3
evaluated regions.
1. Link between pressures and monitoring concepts
� Pressures that have been identified in the regions are censored in the sense that
only obvious and commonly accepted pressures have been accounted for
(morphology, connectivity of streams, eutrophication)
� Diffuse chemical pollution has in general not been addressed unless very obvious
(e.g. horticulture in Delfland)
� Pressures have not really been quantified in terms of loads or a sound immission
characterization (exposure of biota)
� Ranking of pressures seems to have relied on expert knowledge or “gut feeling”
� Monitoring networks seem to follow rather historic river network locations than
operational criteria of pressure localization.
� Monitoring has generally not been used to validate initial pressure identification.
� Monitoring design has not been intended to measure the success of POM
� Monitoring eutrophication is still addressing nutrient threshold concepts instead
of ecosystem properties.
Monitoring data assessment report M 3 2/109

2. Pressures related to chemical pollution
� There is an apparent lack in identifying substances related to diffuse chemical
pressures
� Analyte lists are not adapted to local immission situation, most often they cover
Priority and Dangerous substance lists (regardless of local relevance) completed by
historically covered substances (mostly solvents and chlorinated products)
� Some regions are making an effort in including locally applied pesticides
� Micro-pollutants originating from urban pressures (surface runoff, WWTPs) are
insufficiently addressed.
3. Dynamics of pressure indicator occurrence
� Regular grab sampling routines are the rule regardless of the targeted pollutant’s
dynamics.
� There’s a tendency to sample with great spatial detail, very low frequency and little
apparent relation to the pollutants’ probable occurrence.
� Fixed continuous monitoring stations are no real alternative: they are maintenance
intensive and cover an incomplete picture of pollution (particle-bound pollutants,
micro-pollutants).
4. Suitability for load calculation and model validation
� Load calculation is most often impeded by the absence of close-by discharge gauges
� Non-adapted sampling schemes can lead to large biases in load calculation, especially
for particle-bound pollutants and substances which are strongly driven by recent
application (pesticides).
� Sampling frequency and analyte coverage are too low to validate emission models
� Grab or spot sampling is not suited to validate water quality models in their
description of ecosystem processes.
� In absence of longitudinal profiles or tracer tests, transport and attenuation of
pollutants in rivers cannot be truly validated in water quality models
5. Link between chemical and ecological monitoring
� Monitoring locations for chemicals and biota do not sufficiently coincide.
� Infrequent grab samplings and the absence of ecological metrics (primary production,
respiration) don’t allow for an appropriate exposure characterization.
� In the absence of more in-depth pressure analysis locally adapted xenobiotic exposure
is not covered.
� The lack of representative and consistent chemical monitoring precludes any fruitful
statistical analysis of chemical impairment causes.
Monitoring data assessment report M 3 3/109

2. INTRODUCTION
This first M 3 report examines existing monitoring programmes in the three study partner
regions and assesses the value of their existing data for the purposes of meeting the objectives
of the Water Framework Directive (WFD) and the M 3 project. In general, the WFD requires
that EU member states meet the requirement of achieving good ecological status of their
waterbodies by 2015, and that they establish Programmes of Measures (PoMs) which are
management interventions designed to reduce the impact of human activity and hence lead to
an improvement in the condition of their water bodies. The WFD Common Implementation
Strategy provides procedural guidance documents to assist water managers and practitioners
with meeting the WFD objectives but leaves many practical questions on “who to do it" as
well as the scientific background open.
The M 3 project aims to provide demonstration monitoring programmes, modelling case
studies and scenario testing to assist water managers and practitioners in applying the WFD
and helping towards achieving the difficult goal set forth by Directive. The economic costs of
meeting the objectives of the WFD are likely to be very high and may even be unattainable in
the time-frame available. In order to ensure management actions are most effective it is of key
importance that water managers have a structured approach to gaining an understanding of
what the key pressures and impacts on their water bodies are and that their monitoring of
emissions (inputs) and immission situations (exposure conditions within the water body)
situations adequately characterises these. Beyond this, managers need a strategy to assess the
effectiveness of their programmes of measures, and the PoMs themselves need to be realistic
and achievable and have a time-frame for implementation against which to test any
improvement in condition. The monitoring needs to be set within an adaptive management
framework so that the programme receives on-going re-evaluation to ensure that the
monitoring and PoMs are meeting the needs for the water body ecosystem.
In assessing existing monitoring in relation to new objectives, such as the WFD, it is apparent
that existing monitoring, in many cases, will not be sufficient to enable an adequate
understanding of the key pressures and impacts on a water body and, consequently, it will not
be possible to devise appropriate PoMs. Figure 2.1demonstrates typical monitoring pathways
associated with general monitoring objectives. In general, existing monitoring may follow
Paths 3 or 4 (Figure 2.1), for ambient or routine monitoring and “state of the environment”
reporting. In this case a few water samples may be taken, often at low flow, to give an
“indication” of system condition; their results may be compared against prior observations for
the same site, or against results for other similar water bodies. Very often there will be no
pressure-impact model for the system, and as will be shown later, occasional grab samples at
low flow have little value in determining true system condition or the influence of pressures
on the system.
Pathways 1 and 2 (Figure 2.1) provide the components necessary to gain sufficient
information about water-body functioning, in order that meaningful PoMs may be devised,
and Figure 2.2 provides a structural frame for ensuring that the necessary information is
gathered by monitoring in order to gain an improved system understanding.
As indicated in Figure 2.1, in order to devise appropriate PoMs and adequate monitoring, a
valid conceptual model of the system functioning and key processes is required (e.g. Figure
Monitoring data assessment report M 3 4/109

2.2). A key prior requirement for such assessments is the ability to measure the driving
variables and characteristics that impact on the system. It is necessary to
� define the physical habitat and the upstream land uses that may determine the
condition at the location of interest.
� determine the fluvial status, i.e. the relative river flow or water level, since many water
quality and ecosystem factors and emissions are influenced by the flow conditions.
� An adequate pressure impact model for the system is needed in order to decide what
should be monitored and what is not so important.
� A truly integrated approach to monitoring is required, such that the hydrometric team,
the water quality officers and the biologists work together to build a sound
understanding and conceptual model of their system which they update as they gain
new insights with experience.
What is commonly observed with ambient monitoring or state of the environment monitoring,
is that the timing and or location of monitoring components, i.e. flow, water quality, and
biological surveys are not coincident, in which case it is not possible to say what the causal
factors in the system are.
Figure 2.1: Objective-based choice of water body monitoring components.
If the system is adequately characterised it is then possible to devise effective programmes of
measures. This is where modelling comes in. Modelling offers the potential to examine the
Monitoring data assessment report M 3 5/109

impacts of management scenarios, i.e. PoMs, on the system, provided the system variability
and sensitivity to pressures has been adequately characterised, and the model is capable of
incorporating these processes and sensitivities.
Figure 2.2: Pressure and monitoring model for river management (modified from Wilkinson et al., 2007).
Temporal and spatial scale issues are important in devising monitoring programmes. It is of
no value to sample at either a very high intensity or a low intensity, if the sampling frequency
does not match the typical dynamics of the system under consideration. Figure 2.3 gives an
example of the temporal variation in nitrate-nitrogen concentration from a clear-felled forestry
area in Mid-Wales in the United Kingdom. In this case weekly sampling over 14 years
highlights dynamic variations in nitrogen at three temporal scales:
� the long-term (7 year) response from the beginning of tree harvesting, to when the
harvest impacts have subsided;
� the seasonal response associated with the annual growth and decay cycle, and;
� the rainfall-runoff event response that results in enhanced leaching of soil nitrogen into
the river.
A daily nitrogen dynamic may also have been observed if higher intensity sampling were
undertaken over diurnal cycles.
Monitoring data assessment report M 3 6/109

NO3_N NO3_N (mg/L) (mg/L)
5.0 5.0
4.0 4.0
3.0 3.0
2.0 2.0
1.0 1.0
0.0 0.0
Apr-83 Apr-83
Purturbation Purturbation response response (7 (7 years) years)
Mar-84 Mar-84
Apr-85 Apr-85
Apr-86 Apr-86
Apr-87 Apr-87
Apr-88 Apr-88
Apr-89 Apr-89
Apr-90 Apr-90
Monitoring data assessment report M 3 7/109
Apr-91 Apr-91
Seasonal Seasonal cycle cycle (1 (1 year) year)
Apr-92 Apr-92
Apr-93 Apr-93
Event Event responses responses
(days) (days)
Figure 2.3: Stream nitrate concentration (Afon Hore, Plynlimon, Wales) demonstrating response
dynamics at three temporal scales (adapted from: Wilkinson et al, 1997).
This provides an example of temporal dynamics for just one substance, and it can easily be
recognised that grab water quality samples collected at some long time interval along the
above nitrogen time-series would most likely produce radically different results. This would
also be the case for many other water quality variables, and especially those substances that
are influenced by rainfall-runoff (storm) events, associated with runoff, combined sewer
overflows (CSOs), or with strong affinity to the solid phase. In the latter case, they may be
characterised by low dry weather concentrations when they settle and accumulate on river
beds, and are subsequently turbulently stirred back into the flowing water during storm flows.
Such substances include phosphorus and many micro-pollutants such as metals and persistent
organic chemicals (POPs). There is little value in testing for these substances under low flow
conditions with occasional grab samples in the water column, since they are unlikely to be
present in concentrations that reflect the true degree of contamination of the system. Sediment
samples, or suspended matter samples, as will be shown later, provide a far more reliable and
valuable indication of the presence of these substances.
3. MONITORING DESIGN EVALUATION
Characterizing the ecological and chemical status in a water body requires a profound
understanding of its ecosystem functioning in order to be able to select the timing, the
location, and the medium of sampling that can provide unbiased and repeatable information
on the water body’s status. By setting good ecological status as a measure of the presence of a
certain diversity and abundance of biota being present in a water body, the WFD inherently
presumes that it is feasible to identify and quantify the causes of ecological status impairment.
This claim for causality caught water managers and the supporting scientific community
desperately unprepared to assess the complexity of the freshwater ecosystems with metrics
that would make a controlled management realisable. Nevertheless by imposing such an
integrative approach, the WFD fostered a new momentum in ecological and ecotoxicological
research in surface and waters that, given the huge challenges, will prevail for some years if
not decades.
This report is about evaluating the appropriateness of monitoring schemes to inform about the
evolution of pressures and hence the success of programs of measures. Monitoring the
ecological and chemical status is at the centre of the WFD implementation as they are the
endpoint metrics of the whole endeavour. Realizing an unbiased monitoring should therefore
be regarded as a key element of successful implementation.
Apr-94 Apr-94
Apr-95 Apr-95
Apr-96 Apr-96
Apr-97 Apr-97

When formulating this work point in the project proposal we had the conviction that
traditional sampling methods neglected hydrological and ecosystem processes. During the
evaluation we rapidly gained certainty that monitoring and its current deficits are the core of
the matter: poor ecosystem understanding and ecotoxicological shortcomings jeopardize a
holistic approach of evaluating environmental health of water bodies.
In the following we need to take a look at the criteria for an unbiased monitoring first, which
are depending on its purpose. We then will present ways of analyzing grab sample data and
will enumerate current scientific deficits that impede efficient monitoring designs.
3.1. Criteria for an efficient monitoring
Good ecological status is the ultimate endpoint of the WFD. It requires a certain diversity and
abundance of macroinvertebrates, diatoms and macrophytes to be classified as good.
Although the metrics are in most cases still heavily focussed on saprobic, i.e. oxygen
depleting pollution criteria, they do reflect at least to some degree other chemical impairment.
The chemical status, which is strongly related to the compliance with the priority substances
list, is a complementary control set, which seems to follow more general chemical phasing out
objectives than a true link to the causes of ecological status impairment. Both goals need a
different perspective: While an ecotoxicological evaluation requires a representative time
record of biota exposure to pollutants, phasing out of chemicals or reducing emissions in a
catchment are based on load estimations which are strongly governed by discharge. The
pollutant’s partitioning or speciation are of different relevance for both monitoring goals:
while load calculations need total concentrations, ecotoxicology relies on bioavailable
species. The differences and implications are detailed in the following.
� Monitoring exposure in a water body
This monitoring aims at collecting data on biota exposure to chemicals which should
be representative in terms of the chemical species (phases) and the time-frame in
which biota encounter the chemicals. The exposure should ideally characterize both
ambient environment (water column, pore waters) and the contamination of the diet
(biofilms, sediments). It raises questions on the bioavailability of pollutants and the
best way to mimic this with extraction or speciation methods.
Monitoring should ideally cover periods which are relevant for the biota life-cycles
(for instance: macroinvertebrates).
� Monitoring emissions in a water body catchment
This could also be paraphrased as source control monitoring. There are steadily
flowing sources like ground water or WWTP outlets and event based emissions like
surface run-off. If the aim is to reduce emissions, water managers should be able to
quantify the loads that are passing certain knots in the hydrological network. Whether
that needs to be at the immediate source or somewhere in the catchment is (amongst
others) a matter of the representativeness of emission loads. Steadily flowing
emissions underlie a certain variability which can be seasonally driven or subject to
day/night shifts. Event-triggered emissions are often governed by antecedent build-up
Monitoring data assessment report M 3 8/109

(or attenuation) of pollutants at the source. The rhythm and the intensity of events
have a strong influence on the loads carried. Within flood waves there can be
substantial variation of pollutant concentrations due to source proximity, transport
effects like kinematic wave effect, dispersion and sedimentation.
� The link between loads and exposure
The loads emitted in a catchment are only transient in the water column or bed
sediment. In the annual time scale flood events only occupy a very short exposure time
but carry a large contaminant load. Little is known about the activity of biota during
these extreme conditions and whether the exposure can be compared to quiescent
mode conditions. Sediments can experience transient storage within a river stretch and
induce an effect on biota during this time. Sediments of lower order rivers are in
general exported out of a catchment within a year (with winter flood waves). Biofilms
and sediments can also accumulate pollutants from the water column during low-flow
and make these accessible to biota via dietary uptake routes.
� Choosing monitoring sites
Exposure monitoring should logically be undertaken at locations of ecological status
metrics. This does not mean every water body necessarily needs intensive exposure
characterization. The process can be simplified using representative groupings of sites
according to the analysis of pressures and impacts.
The same applies to load calculation sites. These should be driven by spatial analysis
of pressures and catchment properties (like geology, soils and land use). In addition,
hydraulic effects such as dispersion and transport losses should be taken into account
when considering the scale of the monitored catchment.
The current practice of most regulators and water managers is still largely based on
techniques aiming at water column spot sampling which have little capacity to characterize
true exposure or loads. Sediment and suspended matter sampling suffer from biases due to
temporal and spatial representativeness but also due to bioavailability of pollutant
concentrations.
Grab sampling in general has several drawbacks, both for exposure and load calculations.
Other sampling techniques might be better suited (see Table 3.1.).
Monitoring data assessment report M 3 9/109

Table 3.1: Shortcomings for grab samplings and alternatives
Type of pollution Drawbacks
sampling
grab
Nutrient status Dissolved species
like o-PO4 often at a
minimum due to
continuous uptake
Pesticides Exposure is
potentially highly
variable, not assessed
by grab sampling
Urban runoff Grab sampling
misses first flush
Particle bound
contaminants (water
column)
Sediment sampling
(spot sampling).
phenomena
Pollutants with a high
particle affinity fall
below detection
limits at base-flow
(low suspended
matter concentration)
Spatial and temporal
representativeness
Alternative Comments
River metabolism
Online measurement
Passive sampling
with membranes
Discharge triggered
autosamplers
Integrative sampling
with silk nets or
flow-through
centrifuges
Ecosystem metrics
like Gross Primary
production and
ecosystem respiration
are better indicators
of impairment
Provides mean
exposure
concentration; ability
to representatively
sample high-flow
extreme
concentration not
known
Needs events of
different size to
characterize inputs
Collect superficial
remobilizable
sediments; to be
checked for
ecological relevance
No real alternatives Analyze spatial and
temporal variability;
sieve to

3.2. Load calculation methods
A substantial body of literature has been published on the most accurate way to calculate
loads from discrete samplings (Littlewood 1992, Preston et al. 1992, Coats et al. 2002, Johnes
2007). These studies relied on intensively sampled sites and usually evaluate estimation
methods (averaging, Beale’s ratio, rating curves) with decimated datasets as compared to the
full dataset (often daily values). The results are not very surprising: Averaging methods suffer
strongly due to infrequent sampling while the accuracy of rating curves (with discharge as
regressor) perform well if stratified sampling approaches with more effort during high flows
are included. In the end, the validity of the estimate is strongly related to sampling design.
None of these studies make any suggestions on how to plan sampling for optimal load
accuracy and precision from scratch. This is because they depend on intense measurement to
provide information on the fitness of sampling approach and load calculation method.
For a water manger the following questions emerge:
� To what extent does optimal sampling design depend on the substances to be
quantified?
� To what extent do the size of the catchment and the nature of the hydrologic network
determine the dynamics of pollutant response?
� How far away from a source are pollutant flows measurable with accuracy?
� How many samples are needed to assess the true statistical distribution of a parameter?
It is clear that, for example, suspended matter concentrations are much more variable than
nitrate concentrations. Depending on the ranking of pressures sampling should be oriented
towards the needs of the most critical components.
Small homogeneous catchments hydrographs are quite flashy which increases the risk of
missing phases of preferential conveyance with regular sampling. Catchments of intermediate
size are probably the most problematic to sample as heterogeneity of precipitation (kinematic
wave effects) and dispersion as well as transient sedimentation losses render timing and
location of sampling most challenging.
The evaluation of data needs much more care. Large rivers absorb smaller inputs and
dispersion makes chemical signal variability much more predictable. The boundary between
the different types of catchments is difficult to draw, since this aspect has not been
investigated to any great extent in the literature.
Monitoring data assessment report M 3 11/109

3.3. Analysing grab sample data
Grab sampling has a lot of shortcomings when addressing the pollution of a surface water,
especially when they are infrequent. They do carry information about the variability of
parameters within a certain catchment but there are no documented methods on how to
systematically analyze grab sampling records. For that reason, an approach is proposed here
which will raise questions of limits in accuracy and precision in characterizing exposure and
substance flows in rivers.
The method is based on the rating curve as an analytical tool, using confidence and prediction
intervals for uncertainty evaluation. Its basic assumption is that pollutant concentration is
mainly governed by hydrology. Other influential parameters are expressed through the
variability in the confidence interval. The method combines the rating curve with cumulative
distribution (CD) of discharge during the observation period to calculate an discharge
weighted yearly average concentration with its 95% confidence interval. This average can be
multiplied with the cumulated discharge to give the mass flow. It therefore allows the
comparison of parameters for one site or between sites in an objective way.
3.3.1. Cumulative distribution of discharge
The basic question behind the cumulative distribution of discharge is the representativeness of
samplings for hydrological situations. Is routine grab sampling systematically missing high
flows? Are the samplings
adequately distributed over the
different situations occurring
during the observation period?
For that purpose the discharge
record (e.g. 15 min values) are
sorted in ascending order and
transformed to a cumulative
distribution. The discharge at
sampling times are sorted in a
same way and plotted together
with the overall discharge
distribution. In that way it is
possible to compare the
representativeness of sampling.
Fig. 3.1 shows the data for a bimonthly
sampling in
Kautenbach/Luxembourg. The
discharge distribution shows that
half of the year a discharge of
Cumulative distibution discharge
1.0
0.8
0.6
0.4
0.2
0.0
Cumul discharge 2007
Cumul sampling 2007
0 5 10 15 20 25 30 35 40
Discharge [m 3 /s]
Figure 3.1: Cumulative distributions for discharge and discharge at
sampling dates
less than 2.5 m 3 /s prevails while only during 10% of the time a discharge of greater than 10
m 3 /s has been recorded. The bimonthly sampling covers the discharge distribution quite well,
a deviation to the left of the sampling distribution shows that these situations are relatively
Monitoring data assessment report M 3 12/109

NO 3 [mg/l]
42
40
38
36
34
32
30
28
oversampled. The distribution also shows that the high-flow extremes are not covered. The
question that arises now is whether the variability of parameters at different discharge levels
can be described by the sampling.
3.3.2. Rating curves and confidence intervals
Rating curves are used to determine the mean concentration at each discharge level and to
calculate the corresponding confidence intervals. We use the allometric function because of
its versatility. It is irrelevant if the coefficient of determination R 2 is high or not. At low R 2 an
arithmetic mean is approximated but the
confidence intervals remain valid. Rating
curves of conductivity, nitrate and total
phosphorus are displayed Fig. 3.2-3.4).
Conductivity is well suited to detect
hydrological phenomena such as kinematic
wave effects (values that don’t follow the
general dilution tendency). Nitrate seems to
be mobilized from the low-permeability
soils in the catchment, but the relationship is
weak compared to the variability of the data.
Total phosphorus is impacted by a low-flow
source, but the relative variability is high.
26
Chi^2/DoF = 17.02603
R^2 = 0.31282
24
a 29.63572 ±1.02001
b 0.07706 ±0.02159
22
-2 0 2 4 6 8 10 12 14 16 18 20
Discharge [m 3 /s]
NO3
Data: Data10_C
Model: Allometric1
Equation:
y = a*x^b
Weighting:
y No weighting
150
-2 0 2 4 6 8 10 12 14 16 18 20
Monitoring data assessment report M 3 13/109
P tot [mg/l]
Conductivity [�S/cm]
550
500
450
400
350
300
250
200
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0
Discharge [m 3 /s]
Data: Data10_B
Model: Allometric1
Equation:
y = a*x^b
Weighting:
y No weighting
Conductivity
Chi^2/DoF = 2771.38269
R^2 = 0.59143
a 409.91065 ±12.4808
b -0.15768 ±0.02478
Figure 3.2: Rating curve for conductivity
-2 0 2 4 6 8 10 12 14 16 18 20
Discharge [m 3 /s]
Figure 3.3: Rating curve for Nitrate Figure 3.4: Rating curve for Ptot
Ptot
Data: Data10_D
Model: Allometric1
Equation:
y = a*x^b
Weighting:
y No weighting
Chi^2/DoF = 0.01038
R^2 = 0.62886
a 0.33834 ±0.02305
b -0.44689 ±0.07535

When plotting the relative uncertainties
against the cumulative discharge distribution
the error of concentration estimation at
different discharges appears. All three
compounds show higher estimate
uncertainty at low and high flow with a
minimum at intermediate discharges. While
nitrate and conductivity are at a similar error
level, the Ptot estimate is much more
uncertain. This may in part be due to the
proximity to limit of detection. The
approach allows for an objective evaluation
of weaknesses in certain discharge ranges in
terms of estimation error and
representativeness.
5
0 5 10 15 20 25 30 35 40
A water manager will be interested to measure the evolution of emission loads from year to
year to document the effectiveness of the river basin management and programs of measures.
Figure 3.4 shows the allometric fits of discharge-concentration curves (A+B) for 4 separate
years and the corresponding 95% confidence error (C+D). The year 2005 had a smaller data
set missing higher flow events which leads to much higher error for 2005 than for the
following years. Total phosphorus shows a clear difference in rating curve shape between
2005-2006 and 2007-2008. This is mainly due to lower concentrations at low flow, a
consequence of improved waste water treatment. The shape of the error curve also changes
with lower error at high flows. It is questionable if the difference in high flow Ptot
concentration is not a consequence of the choice of the function (steeper fit in 2005-2006).
NO 3 [mg/l]
C95 relative error [%]
50
45
40
35
30
25
20
15
10
5
0
0 5 10 15 20 25 30 35 40
20
18
16
14
12
10
8
6
Rating curves NO 3
Discharge [m 3 /s]
2005
2006
2007
2008
4
-2 0 2 4 6 8 10 12 14 16 18 20
Discharge [m 3 /s]
C95 NO3 2005
C95 NO3 2006
C95 NO3 2007
C95 NO3 2008
Monitoring data assessment report M 3 14/109
Cumulative distibution
1.0
0.8
0.6
0.4
0.2
0.0
Discharge [m 3 /s]
Cumul discharge 2007
Cumul sampling 2007
C95 conductivity 2007
C95 NO3 2007
C95 Ptot 2007
Figure 3.3: Cumulative distributions of discharge
vs. C95 error of parameters
P tot [mg/l]
0.6
0.5
0.4
0.3
0.2
0.1
A B
C95 relative error [%]
Rating Curves P tot
0.0
0 5 10 15 20 25 30 35 40
50
45
40
35
30
25
20
15
10
C95 Ptot 2005
C95 Ptot 2006
C95 Ptot 2007
C95 Ptot 2008
Discharge [m 3 /s]
2005
2006
2007
2008
-2 0 2 4 6 8 10 12 14 16 18 20
Discharge [m 3 /s]
Figure 3.4: Rating curves (A+B) and error distributions for 2005-2008 for NO3 and Ptot
C
D
30
25
20
15
10
C95 relative error [%]

The weighted means are calculated by
allocating the rating curve derived
concentration and C95 proportionally to the
discharges of the cumulative distribution of
every year. The error of nitrate is below 10 %
while Ptot has 20% uncertainty in the weighted
average. The trend for total phosphorus is
clearly visible although not statistically
significant (overlapping errors). The discharge
weighted mean can be multiplied directly with
the discharge sum over the observation period
to produce the mass load.
A separate dataset for suspended matter exists for the Kautenbach sampling location with
monthly frequency but still a reasonable distribution (Fig.3.6). The C95 error distribution
shows that estimate uncertainties for this parameter are much larger than for those previously
discussed. At these low sampling frequencies rating curves are depending more strongly on
high flow values and can lead to strong deviations (as in 2006, Fig. 3.7.).
Cumulative distribution
1.0
0.8
0.6
0.4
0.2
0.0
Cumul discharge 2007
SuspM CD sampling 2007
SuspM error 2007
0 5 10 15 20 25 30 35
Discharge [m 3 /s]
Suspended matter weighted annual means carry
a high uncertainty with errors ranging from 40-
100% (Fig. 3.8). The monitoring of erosion is
therefore strongly dependent on sampling
strategy and needs a better coverage of high
flow situations. If higher accuracy and precision
is needed, the use of turbidity probes and
autosampler devices is recommended.
180
160
140
120
100
80
60
40
20
C95 relative error [%]
0
0.0
3 4 5 6 7
Monitoring data assessment report M 3 15/109
NO 3 weighted mean [mg/l]
Susp. Matter [mg/l]
40
35
30
25
20
15
10
5
NO 3 weighted mean CD current year
P tot weigted mean CD current year
Mean discharge [m 3 /s]
Figure 3.5: Discharge weighted means for NO3 and Ptot
100
80
60
40
20
RC SuspM 2006
RC SuspM 2007
RC SuspM 2008
0
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30
Discharge [m 3 /s]
Figure 3.6: Figure Cumulative 3.7: Rating discharge curves for distribution the years for 2006-2008
suspended matter sampling and C95 error distribution
Suspended matter [mg/l]
15
10
5
0
99%
87%
2006 2007 2008
41%
0.5
0.4
0.3
0.2
0.1
P tot weighted mean [mg/l]
Weighted annual mean SuspM
Figure 3.8: Discharge weighted means for susp. matter

3.3.3. The influence of sampling frequency
The Erfverband runs several online monitoring
stations with automated analysis of some basic
parameters and (dissolved) nutrients. Of the latter
orthophosphate and ammonium often fell below
detection limit which eliminated a
straightforward load calculation (censored data
issue). The nitrate dataset was fit for use and
different sampling frequencies were tested for
their effect on mean weighted load and relative
error. For that purpose daily data were decimated
to weekly, bimonthly and monthly pace. The
cumulative distribution plots show that the
weekly sampling rate matches the daily reference
very well, while monthly frequency misses the
upper 10% high flow situations (Fig. 3.9). There
is very little difference in the rating curves but the
range covered and the C95 error distribution
decreases from monthly to daily (Figs 3.10 &
3.11).
NO 3 -N [mg/l]
8
6
4
2
Allometric fit NO3 daily
Allometric fit NO3 weekly
Allometric fit NO3 bi-monthly
Allometric fit NO3 monthly
6 8 10 12 14 16 18 20 22 24
Discharge [m 3 /s]
Figure 3.11: Rating curves for Nitrate at different
sampling frequencies
6 8 10 12 14 16 18 20 22 24
Monitoring data assessment report M 3 16/109
Cumulative distribution
Cumulative distribution discharge[-]
1.0
0.8
0.6
0.4
0.2
0.0
1.0
0.8
0.6
0.4
0.2
0.0
Discharge [m 3 /s]
Cumul daily
Cumul weekly
Cumul monthly
Cumul Bimonthly
Figure 3.9: Cumulative discharge distribution
for decimated sampling frequencies
C95 rel error NO3 daily [%]
C95 rel error NO3 weekly [%]
C95 rel error NO3 bi-monthly [%]
C95 rel error NO3 monthly [%]
6 8 10 12 14 16 18 20 22 24
Discharge [m 3 /s]
Cumul daily
Figure 3.10: Distributions of C95 error at
different sampling frequencies
35
30
25
20
15
10
5
C95 relative error [%]

The weighted means for the different sampling frequencies differ only slightly but their
confidence increases with higher pace. If one does the same exercise with he prediction
interval the 4 cases don’t improve in
precision (data not shown). This means that
the ability to predict the true value is as bad
with a daily sampling pace than with a
monthly one. It also suggests that at least for
nitrate a monthly sampling frequency
adequately pictures the true distribution of
concentrations and the improvement of
confidence of the mean of the rating curve is
a sheer number effect. This questions the
usefulness of the “significant difference
concept” for load calculations.
It would have been interesting to run the
same experiment for turbidity but
unfortunately these data were biased by
fouling of the optics.
3.3.4. Conclusions and recommendations
The obvious question of how often one has to sample to make reliable load calculations
remains unanswered. The dependence on parameter properties and catchment scale needs to
be investigated in order to give data supported advice to water managers. The data available
in the M3 project regions were not numerous enough to inquire further on this subject.
The following conclusions are drawn.
Daily Weekly Bimonthly Monthly
� Grab sampling is not adapted to characterize intermittent and strongly event-triggered
pollution (e.g. CSO overflows). Grab samples tend to cover proportionally more
descending flood-wave limbs and miss first-flush effects;
� Integrative sampling methods like passive sampling devices or suspended matter
collection methods are an alternative;
� The use rating curves and cumulative distributions to evaluate the representativeness
of sampling and the variability of concentrations at different discharges is suggested;
� Sampling design has to be adapted to pollutant properties and hence their expected
dynamics. It is not appropriate to address all pollutants with the same sampling
scheme;
� Bimonthly sampling frequency seems to be a minimum to guarantee an acceptable
accuracy and precision for most pollutants. This needs to be further investigated;
� Care should be taken to have several repeats at higher flows (> 0.75 of the cumulative
distribution). A minimum of 6 samples should be taken in this range at bimonthly
pace.
Monitoring data assessment report M 3 17/109
Discharge weighted mean NO 3 -N value [mg/l]
5
4
3
2
1
0
4 %
11.26 %
15.33 %
Sampling frequency
27.6 %
Figure 3.12: Discharge weighted means for different
sampling frequencies

4. REGIONAL SURVEY: DELFLAND
The Delfland system is different to the Erft and Luxembourg regions in that it does not
conform to hydrological norms, the system is entirely man-made based on the drainage and
recovery of marginal coastal land (Map 4.1). The hydraulic system of boezems (drainage
canals) and polders (land units below sea-level) is intensively managed to balance inputs of
water from rain and wastewater discharges, while at the same time ensuring that the ingress of
saline water is not encouraged (by over-pumping). The system does not, therefore, have well
defined catchments with a watershed and drainage network and flow in the drainage canals is
not necessarily unidirectional since it may be necessary to pump water to or from where it is
not wanted. The polder channels maintain the water table below the polder land surface,
pumps lift water from the polder level up to the major drainage canal system of boezems,
these channels are pumped under appropriate conditions out to sea or into the Rhine estuary.
Pumping data has been requested for the Delfland system and is still awaited.
Table 4.1: Summary of WFD water bodies in Delfland.summarises the five WFD water
bodies in Delfland (Map 4.1). The Netherlands opted for a water body typology based on
abiotic descriptors such as geological substrate, channel depth, width and acid buffering
capacity.
Table 4.1: Summary of WFD water bodies in Delfland.
Name
Measurements WFD Classification
Length (km)
Area (Ha)
Width (m)
Channel
substrate class
East Boezem 71 33 2 to 17 50
pebble
%
West Boezem 112 50 2 to 12 50
pebble
%
Berkel Polder 11 3.8 2 to 5 50
pebble
%
Hook of Holland 3 1.5 5 50 %
and Saltpeat
organic
Polders
material
South Polder Delft 2.1 2 6 50 %
border
pebble
Total 199 90
Monitoring data assessment report M 3 18/109
Water depth
class
Channel width
class (m)
Buffering
capacity
(meq/L)
WFD water
body type
> 3 m > 15 1 to 4 M7 – large deep
canals
< 3 m 8 to 15 1 to 4 M3 – buffered
regional canal
< 3 m 8 to 15 1 to 4 M3 – buffered
regional canal
< 3 m 8 to 15 M10 – fens
waterways
canals
and
< 3 m 8 to 15 1 to 4 M3 – buffered
regional canal

Map 4.1: Locations of the 5 named WFD water bodies in Delfland (as presented in HHRa, 2008).
4.1. Pressures in Delfland
The Delfland Water Board (Hoogheemraadschap van Delfland - HHR) identifies N and P and
Cu and Zn as the key pressures in all water bodies (HHRa, 2008). Horticulture is a major
industry in Delfland and a major contributor of emissions of N and P. Pesticide emissions
from green houses are also a significant pressure. PAHs are ubiquitous in Delfland and the
HHR have not yet characterised their main sources (HHRa, 2008). Delfland WWTP
emissions are discharged into the Rhein Estuary and do not directly contribute to the water
body pollutant loadings. CSO emissions occur in Delfland and mainly result in odour
complaints, fish-kill (due to oxygen depletion), and microbial contamination, but are not a
major contributor of N (3%), P (6%), or Cu and Zn to total water body loads of these
substances (HHRa, 2008). Further information about Delfland pressures is provided below.
Monitoring data assessment report M 3 19/109

4.1.1. Nitrogen and Phosphorus
Control and reduction of N and P is one of the key objectives for HHR within the WFD with
the intention to achieve a 50 % reduction in nitrogen concentrations in Delfland water bodies
by 2015. In 2005 glasshouses accounted for around 50 % of the 1128 tons nitrogen load into
the Delfland waterbodies and connection of the Delfland glasshouses to the waste water
treatment system is expected to achieve a dramatic improvement in the nitrogen emissions
situation (HHRb, 2008). Leaching from agricultural land accounts for a further 30% of N
emissions in Delfland and a reduction in agricultural activity (phasing-out of agriculture)
combined with an organic manure policy, and the expanding urban area, are expected to
reduce emissions from this source (HHRb, 2008).
Figure 4.1: Emissions by source of nitrogen and phosphorus into the Delfland polder/boezem system,
glasshouse horticulture accounts for around 50% of the total reported 1128 tons of nitrogen and around
42 % of the 120 tons of phosphorus.
Denitrification is estimated (HHRb, 2008) to gas-off around a third of all nitrogen from the
Delfland system. Summer nitrogen concentrations are commonly around 6 mgN/l, the
improved scenarios are estimated to achieve the standard of 1.8 mg N/l in most areas other
than central Delfland and the Delft area and the Schie (HHRb, 2008).
The Delfland phosphorus emissions total around 120 tons per year, soil losses account for
around 45 % of emissions and glasshouses around 40 %. The connection of glasshouses to the
WWTP system, and the aforementioned changes in agricultural activity, land area, and
practices are expected to reduce average summer P concentrations, which are typically greater
than 1 mgP/l, closer to the 0.3 mgP/l standard.
4.1.2. Copper and Zinc
Although copper and zinc are identified as a problem in the Delfland area, HHRa (2008)
claim/state that since these metals may come from a variety of sources and the relative extent
of this is not currently known, it is difficult to determine objectives for improving their
emission levels. A range of known sources of copper include building materials, vehicle brake
linings, antifouling chemicals, copper baths, animal feed and manure, and fireworks. For zinc,
galvanised building and road related structures, tyres, antifouling products, animal feed and
manure, are all known sources. Given experiences elsewhere it might be expected that urban
Monitoring data assessment report M 3 20/109

unoff from built-up areas and road surfaces are the main sources of these metals and
consequently their control is not straightforward.
4.1.3. Pesticides and glasshouse emissions
Pesticide emissions in Delfland are predominantly from glasshouses, although the use of
herbicides to prevent weed growth on municipal paved surfaces is still a widespread practice
and problem for water bodies. HHR also suggest that the connection of glasshouses to the
WWTP system would reduce direct regional emissions of pesticides, but that improved
treatment technology would be required to reduce their concentration in effluents. Delfland
WWTP effluent is discharged directly to the Rhine estuary, so these emissions do not impact
on the “inland” water bodies.
Delfland had a little over 3000 glasshouses for horticulture in 2003/4 (Teunissen, 2005),
which is approaching a third of all glasshouses in the Netherlands (>9600). A wide range of
pesticides are used in glasshouse horticulture to control fungi, insects, root diseases.
Pesticide emissions from glasshouses in the Netherlands are via the following main pathways
(Teunissen 2005);
� flushing or percolation of drainage water from ground cropped areas;
� flushing or discharges drained from crops in artificial substrates;
� discharges resulting from filter backwash;
� direct vent escape of mist internal applications or drainage of condensate waters;
� first-flush run-off of rainwater from the greenhouse;
� atmospheric deposition.
The pesticide situation in Delfland in relation to Glasshouse emissions is well documented
and the following section provides further information on these.
Pesticides in Delfland
Pesticide emissions from Delfland glasshouse horticulture are one of the major pressures for
the water board area. The following section introduces this subject regarding pesticide use
and on ground practices and presents summaries of pesticide data for Delfland grab samples.
Flushing and discharge of recirculating water used in pesticide application via the root
environment is probably the most important role in polluting surface waters (WHD, 2008).
Root environment pesticide treatments may include combinations of many different
substances (Table 4.2). Tomatoes and paprika receive 7 to 8 out of 10 substances. Roses and
gerbera pot plants 6 of 10 listed substances and the majority of gerbera growers use
imidacloprid, through the root environment and 6% pymetrozine to control white-fly. These
substances are also used for cucumber, tomato and eggplant for white-fly, and lice. For the
control of root diseases, in cucumber, tomato and peppers, propamocarb and etridiazol are
used, for roses propamocarb, dimethomorph and metalaxyl, and for gerbera propamocarb and
metalaxyl are applied.
Monitoring data assessment report M 3 21/109

Table 4.2: Crops and numbers of growers using pesticides applied to the root environment (after WHD,
2008).
Cucumber
Paprika
Aubergine
Root environment
applications
propamocarb 56 52 42 42 3 34 11 7
imidacloprid 44 60 41
34 94 8 4
pymetrozine 44 28 55 38
3 6
Etridiazole
44 10 40 30
carbendazim 11 4 4 19 1 21 44 16 4
Spinosad
16 17
4
tolclophos-methyl
27
Metalaxyl
13 33 4 4
cyromazine 22
4 26
6
dimethomorph 6 31 2
With surface applications, powdery mildew in cucumbers is prevented using imazalil (72% of
all use), and is applied with a proprietary smoke generator, which is also regularly used to
control aphids in paprika growing houses. Deltamethrin and methomyl (Table 4.3) are used as
part of crop rotation procedures (for vegetables and ornamentals) or are applied to products
prior to delivery for auction (WHD, 2008).
Monitoring data assessment report M 3 22/109
Tomato
Chrysanthemums
Table 4.3: Crops and numbers of growers using pesticides applied aerially (after WHD, 2008).
Cucumber
Paprika
Aubergine
Aerial applications
Abamectin 33 36 42 13 21 21 11 27 42
Deltamethrin
4 3
33 7 6 2 27
Methomyl 11 36 1 9 21 28
17 4
Pirimicarb 22
56 2
17 12
Imazalil
72
2
2 22
Teunissen (2005) reported on pesticide use in Netherlands glasshouse horticulture and
summarised the mass of the products commonly used at that time (Table 4.4) and the
associated emission loads to surface waters from major glasshouse crop types (Table 4.5).
While these figures might not be accurate for the current situation, and the substances in use
have changed due to various products being banned, they give an indication of the intensity of
pesticide use and the resultant fluxes to surface waters. Table 4.6reproduces data from
Teunissen (2005) recording pesticide exceedances of threshold water quality standards
(MTR), indicating the number of times greater than the MTR the worst observed
concentrations were, in Delfland in the three to four years prior to the date of publication.
Teunissen notes that pesticide usage varies according to emergent diseases as the seasons
progress. Table 3.8 includes the year in which certain substances were banned from
commercial use, and it is apparent that, where measured, some of these are still being detected
frequently in sediment dredge samples.
Tomato
Chrysanthemums
Roses
Roses
Gerbera
Gerbera
Potpl.
Potpl.
Orchids
Orchids

Table 4.4: Top 15 active substances with the greatest emissions to surface waters 1997.
Emissions to Water Percentage of total emissions to
Substance Usage (kg)
(kg)
water from glasshouses
Etridiazol 13331 66.4 15.4
Tolclofos-methyl 8774 46.6 10.8
Propoxur* 581 26.9 6.2
Propamocarb-HCl 13221 21.3 4.9
Thiram1) 6726 18.7 4.3
Daminozide 23227 18.3 4.2
Methiocarb 16768 17.8 4.1
Parathion* 5976 16.9 3.9
Carbofuran 3150 15.8 3.7
Oxamyl 1875 14.7 3.4
Methomyl 5621 12.9 3
Dichloorvos* 3430 12.1 2.8
Cyromazine 841 11.6 2.7
Mevinfos* 2188 11.3 2.6
Fosethyl-aluminium 11091 10.1 2.3
*denotes banned substances (after Lieffijn, 2000)
Table 4.5: Crop related total emissions and equivalent areal emission rates 1997.
Emissions to water
Product Emissions to water (kg) Product
(kg/Ha)
Chrysanthemums 128.9 Chrysanthemums 173
Other plants 124.7 Other plants 134
Pot plants 68.2 Alstroemeria 64
Rose 52.8 Rose 58
Lily/Iris 11.4 Lily/Iris 52
Fresia 11.3 Fresias 45
Cucumber 9.2 Pot plants 34
Alstroemeria 7.2 Anthurium 17
Tomato 6.1 Orchids 13
Paprika 3.2 Gerbera 10
Orchids 2.6 Carnations 9
Gerbera 2.1 Cucumber 9
Anthurium 1.6 Aubergine 6
Carnations 1.3 Tomato 4
Aubergine
(after Lieffijn, 2000)
0.8 Paprika 2
First flush and condensate waters are collected and reused by over 80% of glasshouse
operating companies, and 60% of growers claim no discharges, but around 10% report
discharges to sewer and approaching 30% were still discharging directly to surface water
(WHD, 2008). The majority of growers that discharge are substrate growers, whereas ground
growers account for only 20% of companies discharging, and the volumes.
The times of discharging are highly erratic. Table 4.7 indicates the number of growers that
discharge pesticides to surface water or sewers at the given frequencies. Daily discharges are
made by 28% of growers, 9% discharge weekly, 21% of growers discharge monthly, and 48%
occasionally (WHD, 2008). The daily discharge volumes are relatively small (< 10 m 3 ) but
occasional discharges, e.g. at the end of a particular crop cycle can be greater than 5000 m 3 .
Monitoring data assessment report M 3 23/109

Table 4.6: Exceedances of water quality objectives for specific pesticides at Delfland (2001-4) surface
water monitoring sites and year at which certain substances were banned (after Teunissen, 2005).
Substance Year banned Exceedance of MTR
Bromofos-methyl 1992 5
Carbofuran
27
Chloorfenvinvos
990
Chloorthalonil
1088
Diazinon 2001 12
Dichloorvos 1999 78000
Dieldrin 1985 2
Endosulfansulfaat
3
a-Endosulfan 1988 2
b-Endosulfan 1988 2
Endrin 1987 14
Etridiazol
402
Flutolanil 2004 8
Heptenofos 1998 19
Malathion
18
Mevinfos 1999 30
Parathion-ethyl 2002 5297
Parathion-methyl 2004 566
Permethrin 2000 1067
Pirimicarb
14
Pirimifosmethyl
283
Pyrazofos 2000 2
Toclofosmethyl
4
Tolylflanide
6
Triazofos 1998 24
Vinchlozolin
1
Table 4.7: The number of glasshouse growers that flush or discharge to surface water or sewer systems at
given intervals and the substances present (after WHD, 2008).
Pesticides applied to the root Number of companies and frequency of application
environment
daily weekly monthly occasional total
azoxystrobin (Amistar) - - 2 - 2
carbendazim 3 4 11 22 40
cyromazine (Trigard) 1 - 2 11 14
dimethomorph (Paraat) 1 2 6 4 13
etridiazool (Aaterra) 5 3 7 23 38
imidacloprid (Admire) 6 7 15 25 53
mancozeb/metalaxyl
(Fubol Gold/Ridomil)
5 - 2 2 9
propamocarb (Previcur) 7 4 14 33 58
pymetrozine (Plenum) 5 - 8 25 38
pyrimethanil (Scala) - 1 2 4 7
spinosad (Conserve/Tracer) 3 - 4 12 19
tolclofos-methyl (Rizolex) 3 2 4 3 12
Table 4.8 provides responses from growers regarding the volumes of their discharges and
their regularity, although WHD (2008) report that not all growers provided information on
their practices. The responses suggest that ornamental growers tend to discharge higher
volumes than growers of vegetables.
Monitoring data assessment report M 3 24/109

Table 4.8: Indicative discharge volumes for growers of ornamentals and vegetables at various repeat
frequencies.
Numbers of growers
Vegetables Ornamentals
Frequency < 10 m3 10 - 100 m3 > 100 m3 < 10 m3 10 - 100 m3 > 100 m3
Daily 4 4 1 6 4 8
Weekly no data no data no data 3 2 2
Monthly 3 5 2 4 8 4
Occasionally 8 15 13 3 13 no data
Appendix V presents a tabular summary of pesticide data for Delfland grab water samples,
including total numbers of analyses per substance, number and percentage positive detections,
number of locations where each substance was tested for and detected. Average “site mean”
concentrations are presented, and the concentration value for the location with the highest
“site mean”. Site means were calculated for each location for all data available, excluding less
than detection values, thus the site means only reflect those samples where positive detections
were made. The table also indicates the year when certain substances were banned and the
literature source cited in this report that refers to each substance.
The data indicate that HHR have analysed and detect a wider range of pesticides than those
cited as being used in greenhouses by WHD (2008), Teunissen (2005) and Lijffen (2000), and
there also a number of substances, cited in the literature, that have not been tested for by
HHR. A number of the banned substances had a relatively high level of detection; flutolanil
13%, pyrimethanil 39%, dichlorvos 8%, diethyl-methyl-benzamide 23%, diazinon 11%,
endosulphansulphate 19%, and others were detected to a less often.
Figure 4.2 presents the substances that are most frequently detected and/or have the highest
site mean and average site mean concentrations; these include many substances not listed by
WHD, Teunissen or Lieffijn. For example, furalaxyl has the highest site mean at 5.6 μg/l.
Iprodione and parathion-methyl also had high site mean values, but all three substances were
infrequently detected.
Some pesticides that were frequently detected often had low site mean concentrations (e.g.
pirimicarb, toclophos-methyl, pirimethanyl, diethophencarb and diuron). The substances
detected most frequently and with the highest concentrations tend to be those identified in the
literature (e.g. imidacloprid, carbendazim, dimethomorph, metalaxyl, etridiazol). Glyphosate
is one substance which was commonly detected, and was present at 4 out of 5 locations where
it was tested, the mean concentrations were also relatively high. An examination of the
correlation of the substance means, and their geographic location, shows that there is great
variability between substances, and some similarities in pesticide profiles between locations.
There is good potential for a more detailed investigation of these data. Many of the substances
are very use specific, and the variability in occurrence in surface waters in Delfland suggests
that there is a high degree of operator/on-ground practice differences.
Monitoring data assessment report M 3 25/109

Figure 4.2: Mean pesticide concentrations in Delfland, the number of sampling locations where each
substance is detected and the percentage detection for each substance (substances marked * are
mentioned in the literature cited above).
4.2. Delfland Programs of Measures.
Delfland has already outlined their programs of measures for contributing to the WFD
objectives (HHRa, 2008) as follows:
1. Measures for water treatment – improve sewer connections, upgrade sewer overflows,
uncouple paved areas from foul sewer network. (Although sewer overflows are not
considered to be a major pressure for nutrient and metal emissions, they have a significant
transient impact and are included amongst the PoMs for Delfland by HRRa (2008);
2. Reduce agricultural land fertilisation, by 2015 N and P fertilizer application should be
balanced with vegetative uptake;
3. Construction of vegetated channel bank buffer strips to maximise nutrient uptake;
4. Measures to increase water residence time within polders, this is known to maximise N
and P uptake.
Monitoring data assessment report M 3 26/109

Additional politically driven measures expected to contribute to the WFD goals for 2015
include:
5. The establishment of national standards for emissions from glasshouses, in particular N
and P to be effective from 2010;
6. Changes in flushing regimes of boezems to reduce N and P;
7. Improve understanding of the eutrophic impacts of dredging, i.e. the resuspension of N
and P associated with settled materials;
8. Research into the impact of varying polder water levels to control N and P (process not
specified).
Exisiting monitoring in Delfland is well targeted for the identified pressures in the water
board area, the following section describes the monitoring and identifies areas where
improvements would enhance the existing programme.
4.3. Monitoring in Delfland
Delfland operates a comprehensive program of ecological and chemical monitoring which
includes ambient monthly to weekly water quality grab sampling, biological surveys which
are undertaken at around 5 yearly intervals, and analyses of dredge sediments provides a
useful insight into the sinks and reservoirs of many micro-pollutants. The datasets provided to
this project do not include any event-response based sampling. Existing HHR reports have
highlighted some areas where further knowledge is required, this is in the transport and fate of
glasshouse pesticides and sourcing of PAH contamination (HRRa, 2008).
4.3.1. Surface water quality data
Delfland surface water quality data is grouped under four main monitoring themes,
Glasshouses, Urban Areas, Base Network and WFD Monitoring (Table 4.9).
Glasshouse monitoring is the most intensive monitoring undertaken in Delfland, and a large
suite of organic micropollutants, mainly pesticides, is analysed (see Appendix III).
As identified in Section 4.1.3, the operational aspects of pesticide use in glasshouses mean
that the emission of these substances is significantly dependent on on-ground operational
practices. Under these circumstances it is clear that routine grab sampling is unlikely to
capture emissions associated with transient applications of pesticide cocktails, or intra-crop
clean-down dressings, or inter-seasonal sterilization operations. These operationally
influenced emission episodes need carefully planned and targeted surveys which are guided
by direct knowledge of application times, and if possible with the cooperation of glasshouse
operators. The passive detector method proposed for pesticide detection in Luxembourg
Monitoring data assessment report M 3 27/109

would be well suited for this purpose and further investigations might examine the utility of
this approach.
The Base network and urban areas are the least intensively monitored and have the smallest
suite of contaminants analysed. An examination of the time-series of a common analyte,
conductivity (it is measured at all sites), demonstrates the occurrence of sampling visits for a
typical glasshouse, urban and WFD monitoring site in Delfland (Figure 4.3). In general,
monthly sampling is undertaken, but more recently, sampling at the WFD sites was increased
to fortnightly and then from April 2009 to weekly sampling. Weekly sampling will increase
the probability of sampling during rainfall runoff events, however, appropriate high frequency
event sampling would be necessary to characterise storm event responses.
Table 4.9: Number of water quality monitoring locations within specific monitoring schemes.
Monitoring Theme Glasshouses Urban Areas Base network WFD monitoring
Number of sites 21 (monthly) 14 (monthly9 12 (monthly9 3 (weekly)
Electrical Conductivity (mS)
1.9
1.7
1.5
1.3
1.1
0.9
0.7
0.5
Glasshouse OW058-001
WFD OW111-000
Base/Urban OW216-002
Figure 4.3: Water quality sampling visits at three sites in Delfland, representing the main monitoring
themes (note that sampling frequency at the WFD sites has been increased from monthly to weekly).
Data preparation issues with the Delfland datasets were relatively minor. Analyte names had
to be translated into English and some date formatting issues had to be corrected. The main
datasets provided for Delfland were surface water quality, sediment quality and benthic
invertebrate data. The Delfland surface water quality dataset includes records for 52 sites
(Map 4.2). The database includes some 347065 records for 5877 samples and includes 207
analytes. Figure 3.3 provides a site by site breakdown of the numbers of records, samples and
analytes. The purpose of this plot is to demonstrate the distribution of data and hence
information. Clearly, some sites have much more information than others. For example only
half of the sites have more than 50 analytes tested for, and slightly fewer sites have more than
50 samples taken, consequently only 8 locations have more than 20000 data records and most
Monitoring data assessment report M 3 28/109

sites have a factor of ten less. This difference is a consequence of the sampling program
undertaken.
An investigation of the occurrence of values at or below the analytical limit of detection,
demonstrates that many substances tested for are not detected (Figure 4.4). Some of these
substances may be absent or tend to adsorb readily to particulates and are settled-out of the
water column. Analysis of Delfland dredge materials (Section 4.3.2, below) demonstrates that
many of these substances, infrequently detected in the water column, are present in channel
sediments.
Map 4.2: The location of sampling points and the Polder/Boezem system of Delfland, showing water
quality grab sampling locations, macroinvertebrate survey points and channel dredging locations.
Monitoring data assessment report M 3 29/109

Figure 4.4: Numbers of samples and analytes for surface water quality sites in the Delfland study area.
Monitoring data assessment report M 3 30/109

Figure 4.5: The detection of substances analysed for in surface waters collected in the Delfland region (this
information is summarised in more detail in Appendix II).
Monitoring data assessment report M 3 31/109

4.3.2. Sediment quality data
The Delfland study partner provided sediment quality data for dredge materials sampled to
assess the degree of contamination prior to removal and disposal. The boezems accumulate
settled materials and need to be dredged on a regular basis to avoid water-logging of adjacent
land, maintain drainage potential, and the fluvial capacity of the channels when pumping is
necessary to draw away runoff from rainfall events. Sediment reservoirs of contaminants and
nutrients in static water bodies such as the canals and drainage channels may represent source
zones for resuspension and onward transport, and accumulated contaminated material must
enter the food chain for functional organisms that feed in the bottom sediments.
Data from 1995 to 2007 was initially collated. This includes around 689000 records for 6321
samples taken from 259 areas. There are 136 analyte codes in the dataset. Figure 4.6 shows 37
locations where more than 50 samples have been collected. Samples tend to be taken in
Spring or early Summer (Figure 4.7).
Figure 4.6: Numbers of dredge sediment samples from from locations where more than 50 samples have
been analysed.
Monitoring data assessment report M 3 32/109

Figure 4.7: Numbers of samples taken each month from all Delfland channel dredging locations between
1995 and 2007.
Heavy metals and organic micro-pollutants often attach, adsorb, or complex preferentially
with particulate phase materials, and hence they may be more readily detected in sediments
than in water column grab samples. Figure 4.8 demonstrates the high levels of detection for
most substances. Section 4.3.2.1 demonstrates the relative detection of these substances in
sediment and water samples.
Monitoring data assessment report M 3 33/109

Figure 4.8: Numbers of records per analyte and numbers of results greater than the analytical detection
limit for sediment samples collected by the Delfland study partner between 1995 and 2007.
The following sections provide some more analysis of the relative success of micro-pollutant
detection in dredged materials compared to surface water grab samples, and also examine
PAH profiles in dredge sediment samples, showing some patterns that may be useful to assist
in tracing different sources of these substances.
Monitoring data assessment report M 3 34/109

4.3.2.1. Micro-pollutant Positive Results for water samples and dredge sediments
of Delfland.
The Delfland dataset includes both sediment and water column samples analysed for micropollutants
providing an opportunity to compare the rates of positive detection, and highlight
the inadequacy of grab water samples for indicating the presence of such contamination.
The significance of settled particulate matter
Heavy metals and persistent organic pollutants generally attach, adsorb, or complex with
particulate phase materials, which tend to settle out of the water column under dry weather
flow conditions. Consequently the detection of these substances is much more reliable
from sediments than the water column as demonstrated in the Delfland data:
Group of substances % Detection in Dredge
% Detection on Surface water
Sediments
grab samples
Heavy metals >85 % 75 to 17 %
PAHs >95 % 85%).
Arsenic, nickel, zinc and copper had the highest detection rates in water samples (>75%), but
lead, chromium, cadmium and mercury were detected in only 46 to 17 % of results in water
samples, respectively. The high molecular weight PAHs were detected in greater than 95 % of
sediment results, and less than 32 % of results for water samples, and the low molecular
weight PAHs, which tend to be less abundant in the PAH mix, had relatively low detection in
Monitoring data assessment report M 3 35/109

sediments and were rarely detected in water. The majority of pesticides had between 60 and
80 % recovery in sediment samples, but only a few substances had greater than 4 % recovery,
with the majority being detected in around 0.5 to 2 % of water samples. The PCBs had
positive results in between 66 and approaching 80 % of dredge sediment samples, but in water
samples greater than 99 % of results were negative.
These findings are not new, but they graphically demonstrate that sampling the water column
does not detect a range of substances which are detrimental in the aquatic environment. It
further raises a number of key points, relevant to sampling and monitoring throughout Europe
within the Water Framework Directive and, pertinent to the M3 project:
� The immission situation for certain aquatic organisms that live in the benthic
environment cannot be adequately characterised by water column sampling alone.
This is especially likely to be the case in the Delfland region and other similar canal
and drainage systems elsewhere, where normal hydrological behaviour and
associated hydraulic flushing of channels does not occur;
� Ambient or routine sampling is rarely adequate to characterise storm-event responses,
when particulate associated substances are resuspended from bottom reservoirs within
the channel. Storm event sampling and analysis of raw water samples will detect
resuspended particulate associated substances (which may facilitate assessment of
worst-case exposure to these substances of water column dwelling organisms);
� Sediment sampling and characterisation of typical contaminant levels in bottom
sediments can assist in the assessment of pollutant loads, if suspended load and
emissions are known. If suspended sediment is sampled through rainfall-runoff events
and the average contaminant concentrations are well characterised then ranges of
anticipated storm related contaminant concentrations can be estimated;
This issue is closely associated with the matter of estimating contaminant loads through
catchments and drainage networks. The characterisation of storm response events and
sediment associated contaminant levels is critical to determining fluxes of these substances,
and in addition their sources from emissions or from dry weather accumulated reservoirs
within the channel.
Monitoring data assessment report M 3 36/109

Figure 4.9: Percentage of Delfland sediment and water sample results for micro-pollutants with
positive detections.
Monitoring data assessment report M 3 37/109

4.3.2.2. Delfland PAH Sediment Profiles
The Delfland Water Board, in a recent report HHRa (2008) remarked that PAHs were not
adequately characterised for surface waters to be able to assess their sources, but suggest that
their levels are worst in the East Boezem, and link this with commercial shipping activities.
PAHs are often derived from the incomplete combustion of liquid and solid fuels, e.g. from
diesel fuel and wood or coal smoke, and enter surface waters via runoff from impervious
surfaces. Sources from shipping may include marine coatings, lubricants and exhaust and
bilge emissions (HHRa, 2008). The relative proportions of the PAH compounds, their
geographical distribution may help identify differing emission sources (Wilkinson, 2007;
Kochbach et al., 2006; Kennedy, 1999). An initial examination of the PAH relationships for
Delfland dredge sediment samples suggests two main sources materials, those high in
naphthalene and those which are not (Figure 4.10). The 500 samples (out of 1890) with the
greatest naphthalene and pyrene are used in the analysis. This was done to eliminate samples
close to detection limit, since the analytical discretisation interval (i.e. the jumps in data
values) at very low values interferes with the substance inter-relationships. Anthracene,
dibenzo(a,h)anthracene, fluorene, acenapthene and acenapthylene were excluded from the
analysis because their concentrations were small relative to the resolution of the analytical
method (i.e. the steps in values were too great).
% Pyrene
25.00
20.00
15.00
10.00
5.00
0.00
y = -0.1862x + 17.42
R² = 0.6536
0.00 20.00 40.00 60.00 80.00 100.00
% Napthalene
Figure 4.10: Percentage pyrene against percentage naphthalene in Delfland dredge sediment samples.
Figure 4.10 demonstrates the approximately 100 samples that were dominated by naphthalene
in concentrations well above detection. The remaining 400 samples were dominated by the
other PAHs (and these substances correlate highly with the sum of PAHs, Table 4.10)
Monitoring data assessment report M 3 38/109

Table 4.10: Correlation coefficient of each PAH as a predictor of the sum of PAHs in dredge sediment
samples from Delfland.
substance r 2 correlation sum of PAHs
fluoranthene 0.979
pyrene 0.972
phenanthrene 0.909
benzo(b)fluoranthene 0.927
chrysene 0.968
benzo(a)anthracene 0.925
benzo(a)pyrene 0.911
benzo(g.h.i)perylene 0.882
indeno(1.2.3-c.d)pyrene 0.892
benzo(k)fluoranthene 0.916
naphthalene 0.392
Map 4.3 shows the locations of 885 of the dredge sediment samples for which point locations
are available. Although samples with greater than 5 % naphthalene occur across Delfland, two
distinct “hot-spots” are apparent where naphthalene makes up more than 60 % of the sum of
PAHs.
Map 4.3: Locations of dredge sediment samples highlighting where naphthalene dominates the PAH
composition.
Monitoring data assessment report M 3 39/109

Napthalene is well-known to be highly volatile and hence its use as an indicator for sourcing
pollution might be questioned. In the case presented here, however, naphthalene is so
dominant in certain samples, and the other PAHs in these samples are in concentrations on
average around 30-50 % lower than in the non-naphthalene samples. In this case, it is
therefore difficult to support the argument that the compositional differences are purely a
function of differential volatilisation.
Possible general PAH sources, that have already been suggested, are soot from vehicle
exhaust emissions and materials used in commercial shipping. It is also likely that heavy fuel
oils (bunker fuel) used to power large vessels will give a different PAH signature than
common petrol (benzin), diesel (gas-oil) and jet-fuel (kerosene) combustion products.
Naphthalene, specifically, is also used as a soil fumigant, however, further analysis of it’s
uses and potential sources in Delfland would need to be undertaken to establish the cause of
the patterns seen in the dredge sediment samples.
4.3.3. Biological monitoring
The Delfland biological monitoring data includes results for 239 locations (Map 4.4) each
surveyed twice in the period between 1997 and 2007. Surveying collects a wide range of
information on diatoms, phytoplankton, zooplankton, macrophytes and macroinvertebrates
identified to species level, and this is combined with information about the habitat and water
chemistry to estimate a series of scores in the following areas (Franken et al., 2006):
� Trophic condition - degree of eutrophication;
� Saprobic status - oxygen status;
� Comparative status - degree of correspondence of the animals/plants that occur in
relation to the original type of water (for example ditch in clay,
or canal in sand);
� Acid status - condition of pH (determined using the animals/plants);
� Brackishness - condition of salinity (determined using the animals/plants);
� Management - management of the water body (for example straight edges,
possibility for plants to grow on the banks);
� Toxicology - degree to which the water is influenced by toxicological
substances (for example hazardous substances like imacloprid).
In the final scoring of a site, the worst result from each characteristic is used to summarise the
overall condition, in a “one-out, all-out” manner. Values are between 1 to 5; 1 being very
poor, and 5 being very good condition. Table 4.11 is an excerpt of the summary table for the
biological scores for Delfland (translated into English).
This scoring approach, which assigns a site to poor status on the basis on any one bad value,
has the disadvantage that most sites score poorly for some reason or another, when in reality
there may be aspects of the condition at a given location which are good and offer good
ecological potential. It might be more appropriate to use a weighted combination of the
scores and hence provide a more tiered indication of ecological potential.
Monitoring data assessment report M 3 40/109

Table 4.11 demonstrates that the sites presented generally have moderate to poor oxygen and
nutrient status. Map 4.4 provides a total of all the scores showing those sites that score most
poorly, these mainly occur in an arc from west to north to east covering the most developed
areas, i.e. those with the greatest urban area or percentage of greenhouse land-cover.
Table 4.11: An excerpt of the EBEO-System ecological health scoring for sites in Delfland.
Site number Location Xcoord Ycoord Year Water type Total Chemistry Structure Oxygen Trophic Toxicity Brackish Comparative Acid
OW001-000 Kerstanje, spoorbrug 83351 448531 2000 KANAAL 1 3 1 2 2 4 3
OW001-000 Kerstanje, spoorbrug 83351 448531 2004 KANAAL 2 3 2 2 2 3 4
OW001B000 De Spieringw atering, Spieringw eteringw eg 82146 448779 2000 SLOOT 2 4 2 3 3 3 5 2 3
OW001B000 De Spieringw atering, Spieringw eteringw eg 82146 448779 2004 SLOOT 2 4 2 3 2 5 5 2 5
OW004-000 Kromme Zw eth, Noordlierw eg afbuiging 77028 445195 1998 KANAAL 1 4 1 3 2 5 1
OW004-000 Kromme Zw eth, Noordlierw eg afbuiging 77028 445195 2002 KANAAL 2 4 2 2 3 5 1
OW004-001 Zw eth, Dorpskade 80270 447486 2000 KANAAL 3 4 3 2 2 4 2
OW004-001 Zw eth, Dorpskade 80270 447486 2004 KANAAL 3 4 3 2 2 4 3
OW004-001 Zw eth, Dorpskade 80270 447486 2005 KANAAL 2 2 2 2 1 4 2
OW004-002 Zeven gaten van Lingen 77497 445401 1998 SLOOT 2 4 2 3 2 5 5 2 3
OW004-002 Zeven gaten van Lingen 77497 445401 2002 SLOOT 3 4 3 3 2 5 5 2 5
OW005-000 Zw ethkanaal, rijksw eg N 213 74701 443818 1998 KANAAL 2 3 2 2 3 4 1
OW005-000 Zw ethkanaal, rijksw eg N 213 74701 443818 2002 KANAAL 3 4 3 3 3 4 2
OW006-003 Oranjekanaal, 700 m. tnv spoorbrug 72099 441918 1998 KANAAL 2 2 2 3 2 4 2
OW006-003 Oranjekanaal, 700 m. tnv spoorbrug 72099 441918 2002 KANAAL 3 4 3 2 2 5 3
OW006-016 Oude Spui, einde w eg bij schuur 72522 441981 1998 SLOOT 2 3 2 3 1 2 5 2 3
OW006-016 Oude Spui, einde w eg bij schuur 72522 441981 2002 SLOOT 2 4 2 2 2 3 5 2 3
OW007-000 De Strijp, Zw aansheul 75588 446164 1999 SLOOT 1 2 1 3 2 3 5 2 2
OW007-000 De Strijp, Zw aansheul 75588 446164 2003 SLOOT 1 3 1 2 2 3 5 2 3
OW007-001 Verlengde Strijp, Veilingw eg 77480 444629 1998 KANAAL 2 4 2 3 2 4 1
OW007-001 Verlengde Strijp, Veilingw eg 77480 444629 2002 KANAAL 1 4 1 3 2 5 2
OW008-000 Naaldw ijksevaart, Verdilaan 74308 445529 1999 KANAAL 2 4 2 2 2 5 2
OW008-000 Naaldw ijksevaart, Verdilaan 74308 445529 2003 KANAAL 2 4 2 2 3 4 2
OW008-002 Zijsloot Naaldw ijkse Vaart, singel Waterlelie 73220 445630 1999 SLOOT 1 3 1 3 1 2 5 2 3
OW008-002 Zijsloot Naaldw ijkse Vaart, singel Waterlelie 73220 445630 2003 SLOOT 2 4 2 3 2 3 4 2 3
Map 4.4: Locations of the biological survey sites in Delfland which have the poorest combined score (red
spots). Green spots indicate those sites with the best scores.
Monitoring data assessment report M 3 41/109

An informative further examination of these data would be to link the biology, water and
sediment quality data to reveal the presence and extent of the various pressures in relation to
the biological status.
The following section provides a summary of the findings for Delfland.
4.4. Delfland summary
An overview is provided for the study partner regions. Since so much information has been
provided the summary for each region has generated a large amount of material. For this
reason quick look-up tables with information about the driving forces, stresses and impacts in
each region and their nature in terms of spatial extent and temporal dynamics have been
provided along with a more elaborated narrative.
Figure 4.11 provides a key to the symbols used in the data summary tables. The temporal
dynamics symbols are intended to give an “indication” of the nature of emissions, and the
variability of the analyte concentrations, and the quality of grab sampling in relation to the
system being sampled. The good, moderate, poor smileys are intended to be interpreted in the
context of their subject heading, and generally indicate what is bad/worse/poor/inadequate,
moderate, and good.
Figure 4.11: Key to the data/monitoring summary tables.
Monitoring data assessment report M 3 42/109

The Delfland summary
The Delfland monitoring programme offers a well balanced monitoring situation in relation to
the pressures identified. Table 4.12 presents the basic overview of pressures and impacts, and
associated monitoring. The following text provides a summary of some general and specific
points relevant to the data situation and monitoring in Delfland.
� Greenhouses present a key stress in Delfland, due to nutrient and pesticide emissions
o Emissions are intermittent and their composition, timing and volume is closely
related to the grower’s practices, which in turn are driven by the cycles of
crops, the nature of the crops, their pests and seasons and cycles of cropping.
Different crops require different pesticide dressings and different pests require
different cocktails of chemicals, and greenhouse disinfection between crops
requires a major clean-down of facilities;
o In general suites of analytes for pesticides appear to be well targeted, but these
will change over time as some products are banned and new products enter the
market;
o
Greenhouse emissions require some targeted sampling in cooperation with
willing growers – this would entail sampling during the wash down of
facilities, and at times when different pesticide applications are being made;
� Basic physico-chemical water quality monitoring is carried out at all sites, but
coverage of certain micro-pollutants namely the polycyclic aromatic hydrocarbon
(PAHs) is limited to a small number of sites (in water samples). It is not certain
whether PAHs are specifically a problem in Delfland, but are ubiquitous as another
aspect of general water body contamination. Since these substances are on the priority
list, greater attention to their sources and impact should be provided. The dredge data
shows significant PAH compositional variation and this variability may offer some
emission source diagnostic potential;
� Limits of detection for many micro-pollutants in grab water quality samples are rarely
exceeded, this can mean that they are absent, or it may be that sampling does not occur
when they are emitted and/or resuspended from channel sediments (see following
point);
� Dredge sediment analysis data augments the water quality data in a useful way, in that
it provides an indication of the material entering and settling in the boezem channels.
This material is available into the food-web and impacts on the stream biota, and may
be entrained if channel flows ever locally increase sufficiently. Many substances that
are rarely detected by surface water monitoring (as mentioned above) are found in the
dredge samples;
� A large amount of model application work with SOBEK to characterise the hydraulic
behaviour of the polder/boezem system has already been undertaken. The linkages
with water quality and loads of contaminants are not fully developed. Since rainfall
Monitoring data assessment report M 3 43/109

events result in pumping responses to drain the polders, sampling to establish the
extent of sediment remobilisation in the region of pumping stations, as well as to
establish fluxes of materials up and through the system out into the Rhein and North
Sea is needed.
� Although monitoring may be adequate to provide a basic characterisation of indicators
of certain stresses in the Delfland channels, in order to meet the purposes of the
M 3 /WFD objectives the existing datasets should be augmented by event-based and
diurnal cycle monitoring to adequately characterise substance emission loads and
immission characteristics and transport fluxes. (In general, dynamic modelling
requires high intensity monitoring to provide sufficient data to build, calibrate and
validate models, before scenario testing can be carried out);
Monitoring data assessment report M 3 44/109

Table 4.12: Pressure and monitoring summary table for Delfland.
Monitoring data assessment report M 3 45/109

assessed? Not yet. Although, simple improvements in emissions may be adequate to observe measureable ecological improvements.
Data suited for modelling?
Can PoM objectives be
Due to the above, without pumping information and detailed observation estimation of loads is very difficult.
The existing SOBEC hydrodynamic modelling may be used with the available WQ data, however, any conclusions about the value of this work have yet to be
Is flow available at or nearby?
Data adequate to estimate
loads?
The "hydrological" nature of the Delfland water-bodies means that conventional uni-directional flow estimates are not available. The system is completely
Do WQ and biology sites match?
No additional comments
5. Other questions relevant to M3 objectives and WFD issues (where additional comment is required)
indiv idual discharge episodes)
appropriate for the dynamics of the
emissions and immission situation
associated with each pressure?
below the limit of detection. (Not
targeted or sufficient to capture
Spatial cov erage good. Pesticide
lev els in ambient samples are generally
Spatial cov erage captures
main drainage network.
Not directly monitored. As abov e As abov e
4. Is the spatial and temporal
frequency of monitoring
Ambient monthly grab sampling can represent the general situation, but will not characterise rainfall related runoff and flushing of substances.
winter, efficiency of leaf clearing
in autumn.
not tend to cause major episodic
changes unless a major plant
failure occurs
spills can result in sporadic
emissions
pesticides, spills, and wash down of plant
and equipment can result in episodic
emissions
Application of weedkillers in
public spaces, road salting in
plant operations do cause
fluctuations in quality, but they do
down, flushing and other
uncontrolled activ ities, and
pesticides with (longer dynamic – days,
weeks). Seasonal dressings of fertilisers and
different phases of growth or crop type.
Major “clean-down” of glasshouses
between crops or seasons
particulate matter, no microbial
contaminants.
Spatially extensiv e, effectiv ely
Monitoring data assessment report M 3 46/109
(hours to days). Sporadic
spillage potential (hours).
v ariation. Rainfall response CSO
emissions (hours to days). WWTP
days). Sporadic spillage
potential (hours). Washing
bound P and pesticides (days). Percolation
of mobile substances such as N and certain
and do any practice based fractors
influence emission patterns?
urbanisation. Episodic rainfallev
ent response based emissions
(daily, weekly to months). Pesticide
dressings to suit different problems at
Temporally relativ ely constant with
often significant diurnal load
Episodic rainfall-ev ent response
based emissions (hours to
discharges etc. Episodic rainfall-runoff of
turbidity and associated substances, i.e.
3. What are the spatio-temporal
characteristics of the emissions,
diffuse, but v arying in intensity
according to degree of
Localised point-source emissions (100s
meters). Routine and episodic emissions
Spatially limited point sources with
effects spanning 100s to 10s km.
Spatially limited to industrial
areas, can be extensiv e.
Generally diffuse sources, but specific point
emissions present due to hard-standing
substances associated with the
pressures / emissions?
of pesticides measured, including
v arious reported as commonly used in
Netherlands greenhouses.
2. Are the measured analytes
adapted to the contaminants and
Most phys-chem parameters and
nutrients measured. A v ery wide range
Cu, Ni, Zn (not Pb), no organic
micro-pollutants,
turbidity/clarity, oxygen
depleting substances,
substances, thermal pollution,
particulate matter
No micro-pollutants, oxygen
depleting substances, eutrophying
As urban areas. Yes - Nutrients, microbial contaminants,
turbidity, pesticides analysed. Specificity o
pesicides needs checking
impacts and associated
substances?
litter, microbial contamination,
oils, PAHs
substances, thermal pollution,
particulate matter
Type of pressure present Greenhouse Horticulture Urban areas, road systems Waste water treatment facilities Industrial areas Farmland, grazed and cultivated
Pesticides, fertilisers, organic matter, Heav y metals, pesticides, Endochrine disruptors, oxygen Heav y metals, solv ents, oils, Nutrients, microbial contaminants, turbidity,
1. What common pressures /
turbidity
turbidity, particulate matter, depleting substances, eutrophying PAHs
pesticides
emissions exist and what are their

5. REGIONAL SURVEY: ERFT
The second M3 study partner is the Erftverband who manage all matters relating to water in
the Erft catchment, river system and groundwater bodies. Erftverband, a non-profit
organisation, which under public law, is responsible for the management of groundwater,
water supply, waste water and surface water. The Federal German State of North Rhine-
Westphalia, within whose borders the Erft river system lies, is the responsible authority
regarding reporting under the water framework directive to the EU.
The Erft catchment covers an area of 1930 km² and has a total river length of 682 km which is
subdivided into 92 water bodies. The human population is 529,000 and land use is dominated
by farmland, forest and pasture (Figure 5.1).
Landuse Area (Ha) % area sum
Arable farmland 109894.7 55.01 55.0
Woodland and Forest 33171 16.60 71.6
Pasture 15834 7.93 79.5
Residential area 11203 5.61 85.1
Industrial and Commercial area 10031 5.02 90.2
Open cast mines 6738 3.37 93.5
Mixed use areas 5050 2.53 96.1
Horticulture 1839 0.92 97.0
All other uses 6007 3.01 100.0
Residential
area, 5.61
Mixed use
Open cast areas, 2.53
mines, 3.37
Industrial and
Commercial
area, 5.02
Pasture, 7.93
Woodland
and Forest,
16.60
Horticulture,
0.92
All other
uses, 3.01
Arable
farmland,
55.01
Figure 5.1: Land use in the Erft catchment.
There are 7 water body classifications for the Erft catchment (Figure 5.2), lowland
watercourses dominate, with gravelly lowland watercourses (types 16 and 17) accounting for
over 40% of the water bodies. One third of the 92 water bodies are classified as heavily
modified because of e.g. housing or dams, hydropower plants or altered hydrological regime
due to mining activities. There are five artificial water bodies with a total length of 50km,
mainly mill streams and canals. The Erft is classified as heavily modified or artificial for
about 80% of its length (http://www.niederrhein.nrw.de/erft/kap_4/kap_4_2.html), although this is not
strongly represented by the water body classifications to which they have been assigned.
Monitoring data assessment report M 3 47/109

Erft - types of water bodies
18
20%
19
13%
17
21%
5
2%
5.1
13%
16
20%
7
11%
type no.
(%)
description
5 (2%) siliceous submountainous streams rich
in coarse material
5.1 (13%) siliceous submountainous streams rich
in fine material
7 (11%) carbonate submountainous streams
rich in coarse material
16 (20%) gravelly lowland streams
17 (21%) gravelly lowland rivers
18 (20%) loess-loamy lowland streams
19 (13%) small lowland water courses in river
valleys
Figure 5.2: The proportions of WFD water body types in the Erft catchment as assigned by MUNLV.
5.1. Pressures in the Erft Catchment
The main pressures identified by the North Rhine-Westphalian Ministerium für Umwelt und
Naturschutz, Landwirtschaft und Verbraucherschutz (MUNLV) in the process of the WFD
include:
� physico-chemical pressures: communal and industrial discharges, diffuse pollution by
agriculture, sump water discharges of lignite open pit mining and heavy metal
contamination by former ore mining activities;
� quantitative pressures: sump water discharges (500 million m 3 of groundwater per year
is pumped into the Erft to keep open cast lignite mines dry);
� morphology: transverse structures (which impound suspended material and reduce the
upstream passage of fish).
Of these identified pressures, 54% are related to morphology, i.e. transverse structures, flow
regulation, backwater effects or fish passage. A further 37% are emissions quality related (e.g.
waste water treatment plants, erosion) or emissions quantity and quality (e.g. rain water and
sump water discharges, and inflows from tributaries). Pressures related to abstractions and
emissions quantity alone (abstraction and cooling water discharge) account for 3% of
pressures, while 6% are not clearly specified (labelled as “unknown” or “other significant
anthropogenic pressure”). Water abstractions from the Erft are used to irrigate intensive fruit
and vegetable production areas. Water supply for domestic and industrial uses (620 million
m 3 per year) comes predominantly from groundwater sources.
Mine groundwater drainage contributes 7 m 3 /sec to the mean runoff of 10 m 3 /sec at the mouth
of the Erft, and of the 500 million m 3 discharged, around one half of this water is discharged
to the middle reaches of the river (Christoffels, 2008).
Monitoring data assessment report M 3 48/109

Erftverband operates 42 waste water treatment plants (WWTPs) serving a population
equivalent of 1.2 million people and the reported reduction in nitrogen and phosphorus from
waste water are approximately 76% for nitrogen and 91% for phosphorus.
Since agricultural land accounts for around 63 % of the Erft catchment area (Figure 5.1), the
majority of which is arable farmland, agricultural landuse can be expected to be a significant
pressure in the Erft Catchment. Major cropping groups on this land are wheat, rye, barley and
sugar beet (Christoffels, 2008). Figure 5.3 presents some water quality status maps reported
by MUNLV, these indicate the river network status for total nitrogen, total phosphorus,
oxygen, zinc and 2 pesticides, isoproturon and metribuzin. The maps demonstrate that the
river network shows nitrogen impacts throughout the agricultural area. The status for
phosphorus is not indicated to be as bad, but this may be a consequence of the monitoring
accuracy. Oxygen status is generally acceptable, but zinc is elevated through a good part of
the main river valley, which coincides with the main motorway (autobahn) route. The
pesticide isoproturon is elevated along most of the main Erft channel. Metribuzin is elevated
mainly in the Finkelbach, which also has elevated nitrogen, phosphorus and marginally
impacted oxygen status. In the presentation of the online monitoring carried-out by
Erftverband, Christoffels (2008), describes the pressures related to specific monitoring
locations in more detail (see Section 5.2.1.2 below). Figure 5.4 indicates the areas where the
likelihood of high levels of phosphorus erosion and nitrogen leaching exist.
The next section presents a little background on theabout the administrative arrangements
pertinent to the Erft system and introduces and describes the datasets held by Erftverband and
presented for the M3 project.
Monitoring data assessment report M 3 49/109

Figure 5.3: Maps of river status for 2004 as reported by MUNLV showing results for a variety of
parameters indicating nutrients N and P, and oxygen, zinc and two example pesticides, isoproturon and
metribuzin.
Leaching (N) Erosion (P)
Figure 5.4: The potential for diffuse pollution by leaching (nitrogen) and erosion (phosphorus) in the Erft
catchment (downloaded from http://193.159.219.153/bestandsaufn/daten/erft/abb/abb3_1_3_1 & 2.pdf).
Monitoring data assessment report M 3 50/109

5.2. Monitoring in the Erft Catchment
Christoffels (2008) lists the main pressures on the Erft system as highlighted in the section
above, these are agriculture, coal mining discharges, WWTP emissions, abstractions for
irrigation and aquatic uses. Recreation is also an important use of the Erft, with fishing and
boating being two recreational activities quoted (Christoffels, 2008). The monitoring strategy
in the Erft is aimed at quantifying river condition in respect to these pressures and uses of the
river system.
The existing monitoring locations are not specifically located to meet objectives associated
with the WFD (operational, surveillance and investigative monitoring). Of the total of 124
monitoring locations operated by Erftverband, only 59 directly characterise a WFD water
body. The other locations are side-streams and multiple locations within water-bodies.
Erftverband monitors biological and physico-chemical parameters with grab samples and
operates five online monitoring stations. Sediment grab samples are also collected along the
Erft mainstream, as discussed below.
5.2.1. Surface water quality data
5.2.1.1. Grab samples
Water quality grab samples from the Erft system are collected at least twice a year at the
beginning of the growing season in spring and in Autumn at the end of the season. Samples
are tested for a range of up to 125 analytes falling into 10 groups (Table 5.1). On average, 186
samples are collected each year but only 9 locations are sampled 5 times or more often per
year (Figure 5.6). 28 sites are sampled 3 times a year, 21 sites 2 times a year and one site has
irregular sampling. As Christoffels (2008) notes, the grab water quality samples are only a
valid “snap-shot” for the moment of sampling, and suggests that they are insufficient on their
own to characterise the highly dynamic processes occurring in the river, and that biological
examinations give a better integrated reflection of longer-term conditions. Online monitoring
in the Erft is intended to characterise the true dynamics of the system (see below).
The parameters tested for in the grab samples (Table 5.1) give an instantaneous indication of
the oxygen status, nutrient conditions, WWTP impacts, major ion chemistry, as well as, heavy
metals, microbial indicators, and organic micro pollutants. In this sense the analyte suite is
well adapted to characterise the pressures and impacts on the system, it is merely the sampling
frequency which is, as acknowledged by Christoffels (2008), not suited to provide a
characterisation of system dynamics.
In terms of the bulk of grab sample data, a total of 1241 grab samples were collected in the
time period January 2003 to August 2009 and 74,673 measurements/analyses performed.
Figure 5.5 provides a site by site breakdown of the numbers of records, samples and analytes.
For example there are two sites with nearly 80 samples each but very few records in the
database. These locations are used to determine the effects of the flood retention basin
Eicherscheid and are tested only for temperature, pH, conductivity, ammonium and
phosphorus. Most sites have 20 or 14 samples taken and are tested for around 50 analytes.
Monitoring data assessment report M 3 51/109

Table 5.1: Water quality parameter groupings of Erftverband analyses and general % greater than the
detection limit.
Parameter grouping number of example analytes no. of % > detection limit
analytes
records
general parameters 27 temperature, DO, hardness,
suspended solids
21,466 99,7%
organoleptic parameters 5 smell, colour 6,009 100%
oxygen depleting substances 4 BOD, TOC 4,314 68%
nitrogen compounds 6 Norg, NH4-N 6,586 39%
phosphorus 3 total P, ortho-phosphate 2,475 63%
salt content 4 conductivity, chloride 3,397 100%
Metals 20 iron, cadmium, nickel 20,510 35%
hygiene-relevant param. 4 E. coli, bacteria count 1,616 99,9%
organic halogen compounds 1 AOX 232 77%
pesticides & pharmaceuticals 51 Atrazine, Diclofenac 13,023 4.7%
3000
2500
2000
1500
1000
90
80
70
60
50
40
30
20
10
500
0
0
100
90
80
70
60
50
40
30
20
10
0
N. records
751
735
330
314
325
754
621
630
633
771
776
310
641
337
624
628
769
645
631
616
615
622
626
629
642
647
637
635
623
625
634
639
644
768
774
327
752
332
608
614
331
313
336
607
609
329
360
361
328
340
341
356
357
753
323
326
620
7950
666
N. samples
751
735
330
314
325
754
621
630
633
771
776
310
641
337
624
628
769
645
631
616
615
622
626
629
642
647
637
635
623
625
634
639
644
768
774
327
752
332
608
614
331
313
336
607
609
329
360
361
328
340
341
356
357
753
323
326
620
7950
666
N. analytes
751
735
330
314
325
754
621
630
633
771
776
310
641
337
624
628
769
645
631
616
615
622
626
629
642
647
637
635
623
625
634
639
644
768
774
327
752
332
608
614
331
313
336
607
609
329
360
361
328
340
341
356
357
753
323
326
620
7950
666
Figure 5.5: Numbers of database records, samples and analytes for surface water quality sites in the Erft
study area.
As can be seen in Figure 5.7, most samples are collected during the first three quarters of each
year with very few samples collected during October, November and December.
Monitoring data assessment report M 3 52/109

average nr. of samples/ year
12
10
8
6
4
2
0
620
7950
314
337
735
751
325
330
754
616
631
637
310
615
621
622
623
624
625
626
628
629
630
633
634
635
639
641
642
644
645
647
768
769
771
774
776
327
313
331
332
336
607
608
609
614
752
323
326
328
329
340
341
356
357
360
361
753
666
sampling location
Figure 5.6: Average numbers of samples taken annually from Erftverband sampling locations.
Samples per month
60
50
40
30
20
10
0
4 5
25
37
2
37
15
17
37
2
7 11 16
2
37
15
52
35
2
3
4
2
7
2
19
43
2
36
37
15
17
2
2
7
Monitoring data assessment report M 3 53/109
19
43
2
38
15
17
37
2
2
7
25
37
2 5
48
17
37
2
2
7
20
37
8
37
16
17
37
2
5
4
Jan 03
Mrz 03
Mai 03
Jul 03
Sep 03
Nov 03
Jan 04
Mrz 04
Mai 04
Jul 04
Sep 04
Nov 04
Jan 05
Mrz 05
Mai 05
Jul 05
Sep 05
Nov 05
Jan 06
Mrz 06
Mai 06
Jul 06
Sep 06
Nov 06
Jan 07
Mrz 07
Mai 07
Jul 07
Sep 07
Nov 07
Jan 08
Mrz 08
Mai 08
Jul 08
Sep 08
Nov 08
Jan 09
Mrz 09
Mai 09
Jul 09
Figure 5.7: Water quality sampling effort by Erftverband by month (i.e. numbers of grab samples taken
per month).
Out of the 51 pesticides and pharmaceuticals measured only 30 were above detection limit in
water samples (Figure 5.8). Concerning pesticides, Isoproturon (winter and summer cereals)
and Diuron (herbicide used on roads) were detected most often but still in only 30% of
samples. These last two are also classified as priority substances under the WFD. Most
measurements above detection limit in the pharmaceutical group were for Pentoxifyllin (a
blood circulation drug) and Diclofenac (an analgesic).
Erftverband is the only M 3 study partner to analyse for dissolved metals as well as total
metals. Elevated levels of iron and manganese occur mainly in the lower reaches of the Erft
and are due to mining activities and mine drainage water discharges. Another major source
for metals, especially Mn, Zn, Ni and Cd is the Burgfeyer Stollen, a disused gallery
discharging into the Veybach in the south-western part of the Erft region.
15
37
9
7
50

Isoproturon
Diuron
Chloridazon
Metamitron
Metazachlor
Metolachlor
Atrazin
Metribuzin
Simazin
Desethylatrazin
Hexazinon
Terbuthylazin
Metoxuron
Metobromuron
Chloroxuron
Linuron
Chlortoluron
Sebuthylazin
Methabenzthiazuron
Desethylterbuthylazin
Propazin
Monuron
Monolinuron
Desisopropylatrazin
Cyanazin
Terbutryn
Prometryn
Pentoxifyllin
Diclofenac
MCPA
Mecoprop (=MCPP)
Carbamazepin
Ibuprofen
Naproxen
Dichlorprop
Fenoprofen
Clofibrinsäure
2,4-D
2,4,5-T
MCPB
Fenoprop
2,4-DB
Metalaxyl
Terbumeton
Tebuconazol
Flurtamon
Fluroxypyr
Ethofumesat
Carbetamid
Bromacil
Gemfibrozil
> L.o.D
Records
pesticides and pharmaceuticals
0 50 100 150 200 250 300 350 400
numbers of results
total manganese
total iron
total nickel
total zinc
total lead
total cobalt
total copper
total cadmium
total chromium
diss. manganese
diss. iron
diss. nickel
diss. zinc
diss. cobalt
diss. cadmium
diss. copper
diss. lead
diss. chromium
total mercury
total arsenic
metals
0 200 400 600 800 1000 1200
numbers of results
Monitoring data assessment report M 3 54/109
> L.o.D
Records
Figure 5.8: Total records and results greater than the limit of detection for pesticides, pharmaceuticals
and metals in Erft water quality grab samples.
To recap, the grab samples provide a well pressure-adapted but very sparse snap-shot of water
quality conditions in the Erft water-bodies. At the other extreme, online monitoring offers a
major contrast, with a switch in philosophy from many locations sampled infrequently to a
few locations with a very high intensity observations.
5.2.1.2. Online data
The Erftverband operates 6 online measurement stations in the Erft system, four of them are
along the Erft and one (Metternich) is located in the River Swist (Table 5.2). These stations
are aimed at complimenting the multiple location grab sampled data (see above), by providing
measurements at an intensity that facilitates the description of diurnal cycles in water quality

and the impact of rainfall-runoff / flood wave events and the associated dynamic variations in
water quality (Christoffels (2008). Two methods of online monitoring are distinguished; “insitu”
stations make measurements directly in the flowing water for a limited range of
parameters; “on-site” stations collect and pump water to a monitoring station that may use
reagent based analyses for a wider range of parameters beyond those which are measured insitu.
In-situ stations are visited on a weekly basis for maintenance and to download data.
A key issue with the installation and use of online approaches is the necessity to protect, what
is often expensive, equipment from damage by flood debris, boating, or acts of vandalism.
This often limits the location of such stations to sites that limit the potential for flood damage,
and which have limited or no public access or that are otherwise protected. The majority of
the on-site stations are located on former WWTP sites and are hence secure.
Christoffels (2008) summaries the advantages and disadvantages of on-site stations compared
to in-situ stations. Advantages include:
� Greater protection from outside interference;
� Less impact of weather conditions;
� Ease of operation and maintenance;
� Use of reagents affords wider parameter set.
Disadvantages include:
� High operating costs;
� Lack of mobility;
� Risk of water quality transformations en-route from river to station.
Figure 5.9 presents a schematic representation of the Erft online monitoring zones and
relationship between the stations, the stream network and the presence of WWTP emissions.
Table 5.2 summarises the pressures and parameters associated with each monitoring station.
The Euskirchen monitoring zone includes three measurement stations, the Veybach at
Mechernich, the Erft at Eicherscheid and at Euskirchen itself. The Veybach station is an “insitu”
station and is located at the outlet of the Burgfeyer Stollen, the outlet of a disused mine
containing ores rich in Ni, Zn, Co and Cd. The purpose of the station is to monitor potential
disturbances in the mine and possible discharges of toxic mine-water.
The Veybach in-situ monitor is a YSI sensor, and the first of this kind to be installed in the
Erft system. The sensor is located in the stilling well of mine outlet and was installed in April
2002. It recorded oxygen, conductivity and water depth until April 2003, since then it has
additionally recorded turbidity, pH and temperature. The instrument is a compact water-proof
unit which includes power cells, data logger and all instruments, and can be immersed and
kept out of site, and is hence much less prone to interference and vandalism (Christoffels,
2008). This also improves the potential for exact siting of the probe and the two month
operational period reduces the need for maintenance (Christoffels, 2008), although care is
required to ensure that fouling of optical instruments does not impinge on data quality.
The second in-siu station is in the Erft headwaters and is sited downstream of a flood
retention basin at a location prone to algal blooms in summer and poor oxygenation
conditions. The outlet of the euskirchen zone is monitored by an on-site station at Euskirchen
and this site captures the impacts of five WWTPs and also measures Ni in order to monitor
Monitoring data assessment report M 3 55/109

the downstream transport of drainage from the Burgfeyer Stollen in the Veybach, as well as,
onward impacts from the upper Erft.
Figure 5.9: Schematic representation of the online monitoring stations in the Erft system, showing
monitoring zones, whether stations are “in-situ” or “on-site”, and the location of WWTPs.
The Metternich Zone includes the River Swist and is monitored near to it’s confluence with
the Erft using an on-site monitoring station. The main impacts in the catchment are from the
sewer network and WWTPs of the suburbs of Bonn. As indicated in Figure 5.3 (above) the
Swist suffers from poor nitrogen, phosphorus and pesticide contamination.
Mining pressures and impacts dominate the Bergheim monitoring zone, which also includes a
high density of WWTPs. Rotbach and Bleibach (Blei is German for lead) also drain former
metals mine workings and contribute lead to the channel sediments (see later, Section 5.2.2).
The largest mine drainage emission also enters the Erft at this point. The online monitoring
station is an on-site station and is located downstream of the mine drainage discharge. This
discharge accounts for 50 % of all mine drainage in the Erft system and is necessary to keep
the open-cast lignite (brown coal) mining operations dry. The discharge drains groundwater
which is high in iron (which is additionally monitored at this location), the iron once oxidised
Monitoring data assessment report M 3 56/109

in the river to iron oxyhydroxide dominates the sediment composition in the Erft from this
location downstream (see Section 5.2.2).
The final online monitored location on the Erft is an on-site station located at Münchrath
village, major pressures in this zone are three large WWTPs. The Gillbach and Norf
tributaries of the Erft are downstream of the monitoring site and is not included because no
suitable secure location was available to site the instrumentation and analysis cabin.
Table 5.2: Locations of online measurement stations and the pressures and monitored parameters at each
site.
Monitoring
zone
Euskirchen Metternich Bergheim Münchrath
Station name Erft @
Eicherscheid
Veybach @
Mechernich
Erft @
Euskirchen
Swist @
Metternich
Erft @
Bergheim
Erft @
Münchrath
Type of station In-situ In-situ On-site On-site On-site On-site
Main pressures /
reasons for
monitoring
identified by
Erftverband
Main pressure
identified by
NRW (MUNLV)
Downstream of
flood storage
basin; low
oxygen, algal
blooms
WWTP,
(rain water)
discharges,
cooling water,
abstraction,
morphology
At disused
mine
“Burgfeyer
Stollen” outlet;
Zn, Ni, Co, Cd
WWTPs, CSOs,
Veybach
heavy metals
(Ni)
WWTP,
(rain water)
discharge,
abstraction,
morphology,
upper reaches,
inflow of
tributary
Sewer
networks and
WWTPs of
Bonn suburbs
discharges,
morphology,
unknown
Lignite mine
discharge (inc.
Fe), high
density of
WWTPs
WWTP,
rain water
discharge,
morphology,
upper reaches
solar radiation in
preparation
x
air
temperature
x x x x x x
water
temperature
x x x x x x
O2 concentr. x x x x x x
O2 saturation x x x x x x
pH X x x x x x
conductivity X x x x x x
turbidity
NH4-N x
NO3-N x
o-PO4-P
Fe tot
Ni tot
Biomonitor
Chl a
water level
in
preparation
in
preparation
x x x x x
x x x x
x x x x
x x x x
x
3 large WWTPs,
Erft catchment
outlet
morphology,
other
significant
anthrop.
influences,
upper reaches
The parameters shown in Table 5.2 are recorded as five minute averages of values polled
every 5 seconds. Table 5.3 provides a summary of data availability for two example online
Monitoring data assessment report M 3 57/109
X
X

stations, the Erft at Bergheim and at Münchrath. The percentage availability of the data is
quite variable, but generally above 70 %, excepting some parameters and specific years, e.g.
conductivity at Bergheim. It is not clear why the low availability of data occurs, although
instrumental monitoring often suffers from low reliability, however, in this case regular
maintenance and downloading of data should be followed-up by data visualisation to capture
any problems shortly after they occur, and hence reduce the duration of holes in the data sets.
For orthophosphate and ammonium ions in solution, the low levels of availability are a
consequence of the low concentrations encountered, less than the sensitivity of the analytical
method. They may also result from the exhaustion of reagents, but this, again, would be an
issue of maintenance. In the analysis presented in Section 2 of this report, Gallé found that the
online turbidity records suffered from drift associated with optical fouling of the instrument.
Gallé suggests that these data may be of limited validity for the purposes of the analysis
carried-out in Section 2. This does not invalidate the data. It is often possible to reconstruct
missing data periods based on autocorrelation with other variables, and problems due to
sensor drift can be corrected provided that the operator appropriately flags the data to show
that it has been corrected.
Table 5.3: Summary of the percentage availability of various parameters measured at two example online
monitoring stations in the Erft system for 2004 onwards.
air
conduc
temper
temper tivity turbidit
o-PO4- ammon
ature
ature [uS/cm y oxygen nitrate P ium
Year [°C] pH [°C] ] [TE(F)] [mg/l] [mg/l] [mg/l] [mg/l]
Water quality station 1: Erft at Bergheim
2004 100.0% 97% 97.5% 95.3% 96.8% 97.5% 85.5% 48.7% 87.4%
2005 100.0% 99.9% 100.0% 85.1% 57.1% 98.1% 91.3% 52.9% 80.5%
2006 99.5% 99.5% 99.5% 0.2% 77.2% 99.4% 85.3% 64.0% 74.4%
2007 90.3% 86.1% 88.3% 68.5% 77.0% 88.0% 85.9% 55.1% 84.7%
2008 97.5% 85.8% 86.0% 86.0% 73.5% 85.9% 85.3% 66.8% 65.9%
2009 99.0% 99.0% 99.0% 98.7% 95.8% 99.0% 96.7% 98.9% 95.7%
mean 97.7% 94.6% 95.0% 72.3% 79.6% 94.6% 88.3% 64.4% 81.4%
Water quality station 3: Erft at Münchrath
2004 93.9% 93.6% 93.6% 91.9% 78.4% 92.8% 93.5% 58.1% 29.1%
2005 78.0% 77.1% 77.5% 76.8% 76.3% 77.5% 76.9% 48.9% 11.0%
2006 96.3% 99.8% 99.8% 99.8% 99.3% 96.0% 99.4% 64.7% 25.2%
2007 79.0% 73.9% 79.6% 79.6% 77.9% 79.4% 79.0% 36.4% 19.7%
2008 91.2% 93.8% 93.8% 84.5% 93.4% 93.8% 90.4% 22.8% 18.1%
2009 85.7% 85.8% 85.8% 83.4% 79.8% 85.5% 83.4% 23.8% 29.3%
mean 87.4% 87.3% 88.4% 86.0% 84.2% 87.5% 87.1% 42.4% 22.1%
Figure 5.10 and Figure 5.11 show online monitoring results for nutrient responses over
seasonal cycles at Bergheim and also the influence of a combined sewer overflow on
dissolved oxygen, turbidity and ammonium-N concentration. In addition, these stations permit
an examination of diurnal variations in concentration. Although the six stations do not
characterise every water body in the Erft catchment, they characterise the main Erft River at
Monitoring data assessment report M 3 58/109

important nodes along the system and allow the impact of major pressures to be observed in
the dynamic variation of a range of relevant parameters.
Figure 5.10: Time series of nutrient concentrations for the Erft at Bergheim (as presented by Christoffels,
2008).
Figure 5.11: Time series of dissolved oxygen, turbidity and ammonium-N concentrations for the Erft at
Bergheim, showing the impact of a combined sewer overflow following intense rainfall (as presented by
Christoffels, 2008).
Monitoring data assessment report M 3 59/109

Christoffels (2008) highlights some general advantages of the continuous monitoring system,
these are:
� an ability to observe short and longer term changes in water quality as they occur (and
hence make appropriate investigations and remedial management actions);
� a greater ability to observe the direct impacts of water management practices;
� an enhanced ability to perform effectiveness monitoring for remedial management
actions.
In addition to the water quality analyses carried-out, Erftverband also collect annual grab
samples of river channel sediments in the Erft. Since mining has been such a major activity in
the Erft system, the river sediments through their metals composition provide an integrated
record of the impact of mine related emissions into the Erft, and these data compliment the
grab samples and online data as shown in the next section.
5.2.2. Sediment quality data
Sediment sample data have been provided by Erftverband for 38 locations spanning 100 km
along the Erft river (Table 5.4). Sampling surveys took place in 2001, 2002, 2004 and 2008.
The material collected was classification into 11 particle size classes, and the following
parameters were quantified at all locations: calcium, cadmium, cobalt, chromium, copper,
iron, manganese, nickel, phosphorus, lead, zinc and mercury.
The Erftverband sediment data for the Erft River has been analysed for a range of metals. The
profiles of the metals concentrations in sediments along the river reflect the nature of
emissions, or inputs, to the river from various sources related to past and current mining
activities in the catchment. These contaminated sediments contribute to the pressures on
organisms feeding on the river bed, such as the macroinvertebrates, and the low abundance of
pollutant sensitive species appears to coincide with the contaminated sediments.
This section of the report provides a brief initial assessment of these data.
Figure 5.12 includes a longitudinal profile showing the changes in concentrations of
substances in sediment samples sieved at the 63 micron size fraction, for the parameters listed
above. Erftverband provided data for two profiles for 2004 and 2008, these were provided as
concentrations in mg/kg and have been converted to the molar concentrations (in order to
assess the relative numbers of atoms of each substance). The relative contribution of each
substance to the sum of the measured substances was estimated and is helpful to illustrate the
changing sediment composition.
Monitoring data assessment report M 3 60/109

Table 5.4: Description of the locations of Erft sediment sampling locations
Site Description of Location
Distance from
S-1
S – 1 Erftforking above Schönau left branch 0.0
S – 2 Erft before Barrage Eicherscheid 5.0
S – 3 Erft above Bad Muenstereifel before bridge at the pool 7.0
S – 4 Erft before military base above Fa.Greven above 10.5
Iversheimer
S – 5 Erft above Iversheimer 10.8
S – 6 Kuchenheimer mill-race above Fa.Kalff 16.5
S – 7 Erft at the sugar factory Euskirchen 21.8
S – 8 Erft above Veybach 23.0
S – 9 Erft in Euskirchen below Veybach confluence 24.7
S – 10 Erft before military base Bodenheim 26.3
S – 11 Erft before military base Ottensheim 27.5
S – 12 Erft below weir Hausweiler 29.5
S - 13 Erft on military base Steinrausche 33.5
S - 14 Erft in Bliesheim above bridge Weilerswist mill-race branch 37.8
S – 15 Erft above weir at km 55.4, below Rotbach tributary 44.6
S - 16 Greater Erft branch at Brüggen 46.4
S - 17 Erft before military base Brüggen, Türnich mill-race branch 46.6
S - 18 Guech ditch before entrance into the Erft 49.0
S - 19 Erft, junction Kaltes Wasser 50.7
S - 20 Little Erft on the Horrem-Sindorf road 55.8
S - 21 Erft flood channel on the Horrem-Sindorf road 55.8
S - 22 Greater Erft on the Horrem-Sindorf road 55.8
S - 23 Connecting channel Erft / Erft flood channel at the 63.0
fishpond
S - 24 Erft flood channel above confluence with Erft at Paffendorf 64.5
S - 25 Erft in Bedburg at the sugar factory 67.8
S - 26 Erft below Bedburg 69.4
S - 27 Erft below RWE Frimmersdorf, Gustorf Mill 77.8
S - 28 Pond water at the Erft Gustorf 78.5
S - 29 Erft above Grevenbroich 80.3
S - 30 Mühlenerft in Grevenbroich 80.8
S - 31 Erft in Wevelinghoven, Kottmann Mill 84.4
S - 32 Erft at the Hemmerden town park 84.9
S - 33 Erft at Untermühle 86.0
S - 34 Erftmäander at Wevelinghoven WWTP 87.8
S - 35 Erft at Grabenmeisterei Münchrath 90.4
S - 36 Erft at Reuschenberg town park 95.9
S - 37 Erft below Gnadenthaler Mill 98.0
S - 38 Erft before confluence at Grimlinghausen-Neuss, Rhein
Strasse
99.5
Monitoring data assessment report M 3 61/109

Concentration of constituents (mg/kg = p.p.m)
1000000
100000
10000
1000
100
10
1
Ca Fe+P
Cd,Co,Ni,Zn Pb
Cu Cr
100 x Hg
0 20 40 60 80 100
Distance from S1 (km)
Figure 5.12: Locations of sediment sample sites and associated profiles of sediment concentrations
showing three entry points where specific metals emissions result in elevated sediment concentrations
which may be localised (e.g. lead at site 15) or continue for some distance downstream (e.g Zn etc at site 9,
and iron at site 23).
Calcium and iron are the dominant measured constituents of the sediments and are indicators
for the natural mineral weathering source material (calcium) and for mining operation
emissions (iron), they account for between 75 and 99 % of the measured constituents. The
longitudinal surveys provide a useful indication of the changing emission/immission situation
in the Erft. In general, with distance downstream the proportion of iron increases and that of
calcium decreases, demonstrating the increasing impact of mining related emissions. Zinc,
nickel, cobalt and cadmium are highly autocorrelated, as shown later, suggesting a common
source (the Burgfeyer Stollen disused mine in Veybach), the sum of these substances account
for up to around 20 % of the measured constituents from sample location 9 to 14. Lead also
increased at location 9, but to a greater extent at location 15, suggesting a source of additional
lead, but not zinc and its correlates. Between sites 22 and 23 the sump water discharge that
drains groundwater from the open cast lignite mining area enters the Erft at Bergheim. The
calcium concentration continues to decline with distance downstream, but the
iron/phosphorus concentration increases sharply, and with it so do the concentrations of
copper and lead.
Monitoring data assessment report M 3 62/109
9
15
23

Inter-annual variation
Figure 5.13 shows the correlation of constituents in Erft river sediments, for the 63 micron
size fraction, collected during the years 2004 and 2008.
Iron and phosphorus in Erft sediments were highly autocorrelated (Figure 5.14 a & b) and
have been lumped together in subsequent analyses. Their profile did not vary greatly between
the two survey years (Figure 5.13 and Figure 5.14a), although the absolute concentrations did
vary between years. Since the lignite mine sump-water discharge at Bergheim is the single
largest input of iron in the Erft, it is possible that the oxidising iron molecules collect (adsorb)
phosphorus. As previously mentioned, zinc, nickel, cobalt and cadmium, sourced from the
Burgfeyer Stollen, in the Veybach were highly auto-correlated (Figure 5.15a) and their
profiles varied between years to a greater extent than that for iron and phosphorus (Figure
5.13). This may be a consequence of hydrometeorological variability and its impact on
sediment flushing from the Burgfeyer Stollen mine working.
The profiles of the other analysed metals varied to an even greater extent, suggesting greater
variations in source over time and space. These variations may be a consequence of
hydrometeorological variations between years driving the runoff and transport of these
substances. It is beyond the scope and remit of this current report to undertake an in depth
investigation of this.
Iron + Phosphorus 2008 (nMoles/kkg)
3500.0
3000.0
2500.0
2000.0
1500.0
1000.0
500.0
0.0
y = 0.5298x + 495.86
R² = 0.8533
0.0 1000.0 2000.0 3000.0 4000.0 5000.0
Iron + Phosphorus 2004 (mMoles/kg)
Zn, Ni, Co, Cd 2008 (nMoles/kkg)
y = 1.4789x + 13.671
R² = 0.8157
Monitoring data assessment report M 3 63/109
400.0
350.0
300.0
250.0
200.0
150.0
100.0
50.0
0.0
0.0 100.0 200.0
Zn, Ni, Co, Cd 2004 (mMoles/kg)
Figure 5.13: Relationships between sediment metals concentrations and between monitored years.
Calcium, Iron and Phosphorus (and Chromium)
As mentioned above iron and phosphorus (Fe+P) concentrations in Erft sediments were
highly autocorrelated (Figure 5.14 b) and are summed in this analysis. The relationship
between calcium (Ca) and Fe+P shows a “mutual exclusivity”, i.e. when there is a high Ca
concentration there is alow Fe+P concentration and vice versa (Figure 5.14c). When the
samples are represented as the percentage composition by mass Ca and Fe+P are inversely
related (Figure 5.14d).

When X-Y plotting the proportional relationships for the various parameters against each
other, it became apparent that there was something unusual with the proportion of Chromium
(Cr) (Figure 5.14e), which does not exhibit an apparent relationship with any of the
parameters. This apparent “relationship” of Cr with Ca and Fe+P (Figure 5.14a, d & e) occurs
as a consequence of the fact that Cr is relatively invariant in concentration (Figure 5.14a), and
hence its variation as a proportion of the measured constituents is merely an artefact of the
relative increases and decreases in Ca and Fe+P. As indicated by Figure 5.14a Cr is closely
related to the iron and phosphorus upstream of the sump water discharge which indicates that
the sump water does not contribute significantly to Erft sediment chromium loads.
Iron + Phosphorus (nMoles/kg)
Calcium (nMoles/kg)
5000
4500
4000
3500
3000
2500
2000
1500
1000
500
0
2004 2008
23
a. b.
3000.0
2500.0
2000.0
1500.0
1000.0
500.0
0.0
1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76
0.0 2000.0 4000.0 6000.0
Iron + Phosphorus (mMoles/kg)
% Calcium
100.0
80.0
60.0
40.0
20.0
0.0
23
fe+p
cr
Monitoring data assessment report M 3 64/109
6.00
5.00
4.00
3.00
2.00
1.00
0.00
Chromium (mMoles/kg)
y = -0.921x + 90.441
R² = 0.9279
Phosphorus (mMoles/kg)
0.0 20.0 40.0 60.0 80.0 100.0
350.0
300.0
250.0
200.0
150.0
100.0
c. d. e.
% Iron + Phosphorus
% Chromium
50.0
0.0
0.09
0.08
0.07
0.06
0.05
0.04
0.03
0.02
0.01
0.00
0.0 20.0 40.0 60.0 80.0 100.0
% Iron + Phosphorus
y = 0.074x + 7.313
R² = 0.8575
0.0 2000.0 4000.0 6000.0
Iron (mMoles/kg)
Figure 5.14: The relationship between calcium and iron plus phosphorus, and the “apparent” relationship
with chromium.
Lead and Cadmium, Cobalt, Nickel and Zinc
Figure 5.15a demonstrates the high degree of autocorrelation between zinc, nickel, cobalt and
cadmium. Nickel and zinc correlate with an R 2 of 0.99, suggesting that these metals have the
same source in all samples, and cobalt and cadmium are only slightly less well correlated, and
must essentially be derived from the same source material. Location 9 is downstream of the
confluence with the Veybach which delivers a large increase in sediment Zn, Ni, Co and Cd
derived from a disused mine gallery, the Burgfeyer Stollen (Figure 5.15b). The increase in
lead is only small (Figure 5.15b). Lead increases sharply below the confluence with the
Rotbach (which is fed by the appropriately named “Bleibach” or “Lead Stream”) at site S15,
from which point zinc and its correlates and lead continue downstream with close ratios

(Figure 5.15b to d). Although under some circumstances lead is known to have a high affinity
for iron oxyhydroxides, in this situation, the sediment variations in lead and iron appear to be
largely independent and hence the influencing sources and processes can be assumed to be
different.
% Cd, o, Ni, Zn
Zinc
14000
12000
10000
8000
6000
4000
2000
25.0
20.0
15.0
10.0
5.0
0.0
0
9
2004 2008
cd,co,ni,zn
pb
15
Nickel
R² = 0.9902
1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73
9
15
a.
b.
2.5
2.0
1.5
1.0
0.5
0.0
% Lead
Cd, Co, Ni, Zn
1000.0
100.0
10.0
1.0
R² = 0.9749
Source with low
Pb relative to Cd,
Co, Ni and Zn
0.1 1.0 10.0 100.0
Figure 5.15: Major changes and inputs of zinc and its correlates, and lead.
Cobalt
4000
3500
3000
2500
2000
1500
1000
500
0
Monitoring data assessment report M 3 65/109
Nickel
Lead
c.
% Cd, Co, Ni, Zn
Cadmium
50
45
40
35
30
25
20
15
10
100.0
10.0
1.0
0.1
5
0
y = 7.642x 0.8346
R² = 0.9189
Nickel
0.0 0.1 1.0 10.0
A compositional analysis of this kind is a useful step in highlighting changes in emissions and
immissions situations and in identifying sources for certain substances. A further step in this
process is to identify the degree to which the concentrations of metals present in sediments
are toxic to aquatic organisms. The association of phosphorus with iron is also informative.
While bound with iron salts, the P may be unavailable, but “transverse structures”, i.e. weirs
and locks, create quiescent zones where accumulated decomposing organic material is likely
to lead to the seasonal development of anaerobic conditions and the solubilisation of iron,
manganese and associated metals and phosphorus, which may contribute to both eutrophic
and toxic pressures for aquatic life. The Erft sediment samples were not analysed for organic
micro-pollutants; since these tend to bind to particulates, it would be informative to include
analyses for an appropriate range of these in future surveys.
5.2.1. Biological survey data
Erftverband carry out surveying for macroinvertebrates at 62 locations (see Table 5.5), 24 of
them along the main Erft, where macrophyte surveys are also conducted. 274 surveys were
made in the 6 years for which data has been provided (2003-08). Up to the end of 2004, all
locations were sampled once a year, after that, only 29 sites were sampled annually, while 10
sites in 8 water courses were discontinued. Further information about the macroinvertebrate
survey data is presented in Section 6.5.
% Lead
R² = 0.964
d.

Erft Macroinvertebrate Survey Results and Metals Profiles
Macroinvertebrates are identified to the species level and from their counts, a number of
metrics are calculated (Table 5.5), these include the total number of taxa found and the
saprobic index, which responds to the oxygen sensitivity of the organisms present. An
abundance score is calculated and a measure of scatter (both not shown here). In addition to
these metrics, for this report, the % EPT (mayfly (Ephemeroptera), stonefly larvae
(Plecoptera) and caddisfly (Trichoptera) ) has been estimated, and the percentage of total taxa
that are EPT (EPTrichness/Ntaxa %). Since EPT are particularly sensitive to pollution, this is
a useful additional measure. The ratio of EPT to total taxa accounts for the fact that some sites
may have low EPT, but EPT may dominate the count, therefore the EPT alone gives the
impression of an absence of intolerant species, when, in fact they dominate the community
structure.
Figure 5.16 shows the distribution of macroinvertebrate scores EPTrichness/Ntaxa (%) and
sediment �metals (Fe, Zn, Ni, Pb, Co, Cd) + phosphorus. As is commonly observed, the
greatest proportion of EPT is observed in the upper reaches of the catchment where pollution
levels tend to be less than in the lower reaches. The entry of the iron rich sump water
discharge at point 23 is clearly seen (for locations refer to Figure 5.12), however, the
proportion of EPT was already reduced well upstream of this point. As discussed above there
are distinct increases in metals inputs at locations 9 (zinc, nickel, cobalt and cadmium) and 15
(lead) (Figure 5.12), and the EPT proportions downstream of these locations are certainly
reduced. Having said this, there are a great many other potential factors involved and other
pollutants that impact on sensitive macroinvertebrate species. This simple illustration merely
demonstrates the increase in contamination and decrease in sensitive species numbers with
distance through the catchment. Iron and phosphorus are not actually toxic, however their
presence in elevated concentrations in the sediments indicates some degree of disturbance
from a “natural” condition and hence reduction in capacity to support a full range of
macroinvertebrate taxa, and it is well known that nutrient enrichment and oxygen depletion
result in a loss of sensitive macroinvertebrate species. The saprobic index appeared to be
relatively insensitive to pollution indicators, and it is suggested that the raw macroinvertebrate
data be further interrogated with respect to other metrics. Metrics that take account of species
tolerance to pollution and relative abundance might be of greater value as measures of
ecological health, for example the BWMP score (Friedrich et al., 1996), ASPT (Armitage et
al., 1983), SIGNAL (Chessman et al., 1997), or MCI (Stark and Maxted, 2007).
Since a key aim of the WFD is to improve river ecological health, the choice of a pollution
sensitive index for macroinvertebrate community structure in the Erft, combined with a more
detailed examination of the water and sediment quality data might be helpful to identify
strong causal factors associated with the loss of sensitive species.
A summary of the Erft monitoring programme is presented in the following section.
Monitoring data assessment report M 3 66/109

Table 5.5: Summary macroinvertebrate metrics for the Erft system for 2008, including additional EPT
metrics calculated for this report.
River /
Stream
Elsdorfer Fliessenoberhalb Einmündung Escher Fliessen e3 12 2.26 II-III 2.04 8.3
Elsdorfer Fliessenan der Strasse Niederembt - Glesch e4 14 2.34 II-III 0 0.0
Erft oberhalb Schönau 1 24 1.62 I-II 30.61 62.5
Erft am Mühlenstau Iversheim 3 25 1.62 I-II 30.61 60.0
Erft vor Zuckerfabrik Euskirchen 5 23 2 II 22.45 47.8
Erft oberhalb Veybachmündung 6 23 2.05 II 22.45 47.8
Erft Kuchenheimer Mühlengraben vor der Mündung 10 19 1.93 II 14.29 36.8
Erft bei Klein-Vernich 11 11 1.9 II 14.29 63.6
Erft in Bliesheim 13 8 1.89 (II) 10.2 62.5
Erft Strassenbrücke Gymnich/ Brüggen 15 17 2.28 II-III 16.33 47.1
Erft Grosse Erft in Thorr 18 20 2.26 II-III 10.2 25.0
Erft Strasse Sindorf/Horrem 19 26 2.21 II 22.45 42.3
Erft Kleine Erft, Brücke Bergheim 20 15 2.19 II 10.2 33.3
Erft unterhalb Bedburg 25 13 2.25 II-III 8.16 30.8
Erft an der Gustorfer Mühle 28 12 2.35 II-III 4.08 16.7
Erft an der Obermühle W evelinghoven 29 8 2.16 II 4.08 25.0
Erft am Pegel Neubrück 31 12 2.17 II 8.16 33.3
Erft in Neuss-Reuschenberg 34 6 2.15 II 4.08 33.3
Erft unterhalb W iebacheinleitung 20b 16 2.19 II 10.2 31.3
Erft 1 km unterhalb Paffendorf 21b 12 2.31 II-III 6.12 25.0
Finkelbach vor Mündung 23=f3 3 2.45 II-III 2.04 33.3
Finkelbach unterhalb Güsten f1 14 2.42 II-III 8.16 28.6
Jüchener Bach unterhalb Jüchen bei Herberath j1 13 2.29 II-III 6.12 23.1
Jüchener Bach bei Bedburgdyck j2 11 2.28 II-III 2.04 9.1
Jüchener Bach unterhalb Glehn j3 12 2.54 II-III 6.12 25.0
Neffelbach vor Mündung 16=nf4 11 2.37 II-III 4.08 18.2
Neffelbach unterhalb Juntersdorf nf1 23 1.83 II 22.45 47.8
Neffelbach unterhalb KA Bessenich in Sievernich nf2 14 2.44 II-III 8.16 28.6
Nordkanal Unterhalb Einmündung Juchener Bach nk1 9 2.47 II-III 2.04 11.1
Nordkanal unterhalb Kläranlage Nordkanal nk2 7 2.26 II-III 4.08 28.6
Nordkanal an der Stadthalle in Neuss nk3 11 2.32 II-III 6.12 27.3
Norf am Derikumer Hof 35=no1 13 2.48 II-III 4.08 15.4
Rotbach vor Mündung 14=r4 15 2.34 II-III 4.08 13.3
Rotbach oberhalb Eicks r1 14 1.65 I-II 14.29 50.0
Rotbach bei Lövenich oberhalb Vlattener Bach r2 25 2.14 II 14.29 28.0
Rotbach unterhalb Bleibach r3 12 2.19 II 8.16 33.3
Swist vor Mündung 12=s6 13 2.18 II 8.16 30.8
Swist oberhalb Meckenheim s1 21 2.13 II 16.33 38.1
Swist Strassenbrücke Miel/Buschhoven s3 18 2.3 II-III 14.29 38.9
Veybach vor Mündung 7=v4 4 1.91 II 8.16 100.0
Veybach bei Breitenbenden v2 21 1.59 I-II 24.49 57.1
Location
description
Monitoring data assessment report M 3 67/109
Location
N taxa
Saprobic
index
Water
classification
% EPT
PET richness /
N taxa %

Figure 5.16:
(triangles) (mg/kg) in the Erft system for 2008.
Monitoring data assessment report M 3 68/109

5.3. Erft summary
Monitoring in the Erft system offers extremes of intensity. There are a few sites with highly
intense monitoring of a few instrumented variables and many sites with grab sampling for a
wider range of analytes, but at very low temporal intensity. Sediment samples analysed for
metals provide an indication of the impacts of mine drainage waters in the Erft main channel,
and biological surveys present an end-point condition with macroinvertebrate survey data.
General summary narrative for Erft
� Pressures and impacts in the Erft system include the common sources such as WWTPs
and CSO emissions and agricultural activities but also less common ones such as
drainage water from existing and closed mines. Transverse structures, i.e. weirs for
river regulation and navigation are also listed under WFD as a stress;
� Erftverband grab sampling occurs twice or three times a year at 84% of the sites, this
is effectively a random spot sample and may provide some indication of the low-flow
immission situation, but requires supplementary monitoring beyond the 6 online
monitoring stations in order to characterise the dynamic behaviour in Erft water
bodies;
� Sediment sampling along the Erft River assesses mainly metals and phosphorus and
the particulate fractions with which these substances are dominantly associated. There
is no testing for organic micro-pollutants in these samples which are well known to
accumulate in sediments. The frequency of sampling may not capture short term
dynamic variations in sediment concentration, but the sediment signal can in any case
be expected to integrate and attenuate out any short timescale variations;
� Continuous monitoring data may be suitable for at-a-point modelling and flux
estimation, but large distances between sampling sites can mean that conditions and
dynamics at one site are often quite different at another in relation to the channel
situation and the sources and types of emissions. Since the grab sampling is at a very
low frequency, these data are also insufficient to facilitate meaningful load or flux
estimations or to carry out modelling.
Specific comments
� Online data characterise large portions of the main Erft catchment but the observations
may not be transferrable to the many small component water bodies, and grab samples are
too infrequent to characterise water body conditions - they neither characterise the
emissions or immission situations nor are they sufficient to estimate loads or fluxes. These
observations highlight the need to undertake supplementary monitoring in a wider range
of smaller water bodies to characterise their behaviour as set out in the M 3 field work
programme, including diurnal and event based monitoring and using passive collectors;
Monitoring data assessment report M 3 69/109

� Water and sediment quality analyses do not include PAHs, and only 8 of 33 priority
substances (4 metals, 4 pesticides) in water samples are monitored. Inclusion of a
selection of indicator parameters would provide information about the degree of
contamination by these substances;
� In the upper reaches of Neffelbach, Rotbach and Veybach only two samples per year
(March and June) are collected. Five measurements a year are only undertaken where
these water courses end. Hygiene parameters or pesticides are not measured in the upper
reaches, only oxygen depleting substances, nitrogen and phosphorus;
� Assessment of PAHs in mining sump-water may be of interest, since brown coal may be
high in these substances, assessment in sediments would also be useful to characterise
their longitudinal distribution in the Erft system.
The third partner region in the M 3 project is Luxembourg which has some similarities to the
Erft, although mining influences are minimal. WWTP pollution and agriculture are the main
pressures. The next section of the report presents the data and monitoring situation for
Luxembourg.
Monitoring data assessment report M 3 70/109

Stress and monitoring summary table for Erftverband.
Monitoring data assessment report M 3 71/109

Monitoring data assessment report M 3 72/109

6. REGIONAL SURVEY: LUXEMBOURG
The river systems of Luxembourg generally flow in a northerly direction to the Mosel and on
to the Rhine system. The bulk of the human population and industrial industry is focussed in
the southern central parts of the country where the rivers are small streams rising in generally
gentle rolling country. Consequently the demand on these resources is greatest in this area, for
water supply and for the drainage of treated sewage effluent and CSO emissions. Effluent and
CSO discharges can be a large component of both dry weather and wet weather river flows.
As the streams grow into small rivers they pass into different geological zones where the
delivery of groundwater return flows considerably increases the flow and dilutes the water
arriving from the populated areas, so for most pollutants water quality and stream ecological
health improves in a downstream direction.
6.1. Pressures in Luxembourg
Luxembourg has not realized the analysis of impacts and pressures as required in 2004 up to
now. It shows that the Luxembourgish Water Management Administration has –at least in the
past – not grasped the interconnection of the different steps of the WFD implementation.
Despite the lack of a pressure analysis, a monitoring network has been introduced in 2006 and
a river basin management plan published by the end of 2009. Under these premises it is of no
surprise that monitoring and POMs sticked to sectorial interests (WWTPs, Ecology) and
lacked a balanced integrative approach.
Pressures in one of the main catchments in Luxembourg, the Alzette (1072 km 2 ) have been
submitted to a more in depth analysis during a research project on regional substance flow
analysis by CRTE in 2002-2006. The Alzette catchment is the most heavily impacted due to
high urban pressure. From this project a few main pressures can mentioned here:
� High input ratio of wastewater to receiving rivers in the southern catchment
o Non-conformity of a number of WWTPs with more than 10’000 PE
o High impact of xenobiotics, such as pharmaceuticals to be expected
� Dominant combined sewer systems with very limited retention structures
o High CSO inputs during summer storms (bacterial contamination, metals)
� Distinct background from steel industry and other discontinued activities
o Oxyanions (V,Mo) and PAH ubiquitous in southern Alzette catchment
� Intensive agriculture with substantial nutrient and pesticide losses
o NO3 levels with a median of 25-30 in ground and surface waters
o Seasonal pesticide peaks durind low- and high flow
o Soil erosion poorly managed (P input)
Monitoring data assessment report M 3 73/109

Northern and Eastern Luxembourg are more strongly influenced by agricultural impacts. The
Moselle might suffer from stronger vineyard pesticide inputs but this has not been
investigated.
6.2. Programs of measures
The river Basin Management Plan is very strongly focussing on the upgrading of waste water
works, where Luxembourg has a substantial delay and sewer networks (including retention
structures). A limited effort is being invested in the elimination of transverse structures or
river restoration. Agricultural emissions are intended to be tackled by means of EU common
policy instruments and the Nitrate directive (which Luxembourg currently doesn’t comply to).
6.3. Monitoring in Luxembourg
Three groupings of sample and survey data are available for Luxembourg; surface water grab
samples at 303 locations, macroinvertebrate surveys at 109 locations, and suspended matter
surveys at 4 locations. The following sections summarise the monitoring in each monitoring
group.
6.3.1. Surface water quality data
Water quality data from the Luxembourg Gestion de l’eau routine monitoring of streams and
rivers has been provided to CRTE as output from their LIMS type database system. The
LIMS export for the entire Gestion de l’eau database, as provided, has 116,212 records
covering 303 sampling locations (Figure 6.1). 103 analytes are reported for a period spanning
January 2005 to May 2009. The majority of the samples were collected for ambient routine
monitoring. This means that there are samples from high flow periods at only a few locations.
At these more frequently sampled sites trace constituents which are often associated with
particulate matter were generally less than the limit of detection (as discussed below). Dry
weather routine or ambient sampling provides only minimal information about stream
condition, is not useful for determining fluxes of contaminants, and provides little information
about process dynamics and interactions. The following text summarises the examination of
the water quality dataset.
Number of samples, analytes and records
Figure 6.1 presents ranked cover of sample numbers, numbers of analytes and numbers of
records for the Luxembourg dataset. Only a small number of sampling locations have a
relatively large number of samples (Table 6.1), around 220 out of 305 sampling locations
having less than 10 samples. Table 6.2 summarises specific locations with greater than 37
water quality samples. The table also indicates which sites have river discharge measurements
nearby, biological surveys and suspended matter data.
Table 6.1: Frequency of sampling at Luxembourg Gestion de l’eau sampling locations (2005-2009).
Number of samples > 1 > 10 > 20 > 40 > 60 > 80
Number of locations 305 83 42 23 12 7
Monitoring data assessment report M 3 74/109

Figure 6.1: Water quality sampling locations in Luxembourg, sites with greater than 29 samples.
Monitoring data assessment report M 3 75/109

Table 6.2: Luxembourg sampling locations with greater than 37 water quality samples, availability of
river discharge, biological survey data, and suspended matter surveys.
Site code
River
Location
name
L100011A01 Alzette Alzette at Esch/Alzette
frontière
Biological
surveys
Monitoring data assessment report M 3 76/109
Discharge
Suspended
matter
samples
Water Quality
samples
2005
2006
2007
2008
80 21 18 17 18 6
L100011A09 Alzette at Hespérange Yes 79 20 18 17 18 6
L100011A15 Alzette
Heisdorf
at Steinsel-
Yes 43 13 1 12 12 5
L100011A21 Alzette at Ettelbruck 10 Yes 39 120 22 32 27 30 9
L104030A11 Mamer Mamer amont
Yes 45 12 2 13 13 5
confluent
Mersch
Alzette à
L105030A04 Eisch Eisch at Steinfort 56 12 13 13 13 5
L105030A12 Eisch at Mersch 7 Yes 56 12 13 13 13 5
L106030A12 Attert Attert
Berg
aval Colmar- 9 76 20 17 16 17 6
L110030A11 Wiltz Wiltz at Kautenbach.
Yes 40 108 20 31 23 26 8
aval
Clerve
embouchure
L110040A03 Clerve Clerve
Clervaux
amont
62 14 15 14 14 5
L112010A01 Sûre Sûre at Martelange Yes 71 16 17 17 15 6
L112010A02 Sûre at Bigonville Yes 40 3 7 14 13 3
L112010A04 Sûre amont Esch sur
Sûre
Yes 71 16 17 17 15 6
L112010A07 Sûre at Dirbach 41 11 5 12 11 2
L112010A09 Sûre at Bourscheid 2 Yes 47 11 11 12 11 2
L112010A10
moulin
Sûre aval Michelau Yes 47 11 11 12 11 2
L112010A11 Sûre at Erpeldange 7 Yes 48 11 11 12 12 2
L112010A22 Sûre at Rosport Yes 38 9 5 12 10 2
L112010A23 Sûre at Born Yes 40 11 5 12 10 2
L112010A24 Sûre at Wasserbillig 9 63 115 39 19 26 24 7
L122020A07 Our Our at Vianden 8 Yes 43 7 7 13 12 4
L144030A09 Ernz
Noire
Ernz Noire at Grundhof 8 65 14 15 15 15 6
L202030A12 Syre Syre at Mertert 7 65 14 15 15 15 6
L300030A06 Chiers Chiers at Rodange 8 34 84 19 15 18 25 7
An investigation of sampling dates relative to recorded river flow heights and volumes helps
to assess whether coverage of wet weather flows and first flushes was suitable for load
estimation. Figure 6.3 provides examples of sites listed in Table 6.2 that have been sampled,
either frequently, or infrequently. The plots show where sampling occurred relative to the
flow condition in the river. This gives an indication of how many samples were collected
during dry weather flow and how many during storm runoff events. The most intensively
monitored sites offer some potential for the estimation of loads.
2009

L100011A21
L110030A11
L106030A12
L100011A01
L112010A01
L144030A09
L105030A04
L112010A11
L112010A09
L122020A07
L112010A07
L112010A23
L112010A12
L299010A03
L122030A02
L110040A08
L122030A01
L122020A04
L100011A03
L112010A03
L106030A06
L112010A21
L106030A11
L122020A06
L122020A03
L122020A01
L100011A16
L104030A06
L100011A18
L100011A12
L100011A08
L100011A04
L110030A12
L106030A10
L141030A05
L106030A08
L105030A08
L110030A03
L110040A01
L110040A07
L110030A02
L104030A09-1
N. samples
N. samples
0 50 100 150
L100011A21
L110030A11
L106030A12
L100011A01
L112010A01
L144030A09
L105030A04
L112010A11
L112010A09
L122020A07
L112010A07
L112010A23
L112010A12
L299010A03
L122030A02
L110040A08
L122030A01
L122020A04
L100011A03
L112010A03
L106030A06
L112010A21
L106030A11
L122020A06
L122020A03
L122020A01
L100011A16
L104030A06
L100011A18
L100011A12
L100011A08
L100011A04
L110030A12
L106030A10
L141030A05
L106030A08
L105030A08
L110030A03
L110040A01
L110040A07
L110030A02
L104030A0…
N. analytes
N. analytes
0 20 40 60 80 100
L100011A21
L110030A11
L106030A12
L100011A01
L112010A01
L144030A09
L105030A04
L112010A11
L112010A09
L122020A07
L112010A07
L112010A23
L112010A12
L299010A03
L122030A02
L110040A08
L122030A01
L122020A04
L100011A03
L112010A03
L106030A06
L112010A21
L106030A11
L122020A06
L122020A03
L122020A01
L100011A16
L104030A06
L100011A18
L100011A12
L100011A08
L100011A04
L110030A12
L106030A10
L141030A05
L106030A08
L105030A08
L110030A03
L110040A01
L110040A07
L110030A02
L104030A09-1
N. records
0 1000 2000 3000 4000 5000 6000 7000
Figure 6.2: Numbers of samples, analytes and date records for 82 of 305 (27 % of) sampling locations in
Luxembourg where 10 or more samples have been collected.
Monitoring data assessment report M 3 77/109
N. records

Rvier Discharge (m3/sec)
Rvier Discharge (m3/sec)
Rvier Discharge (m3/sec)
Rvier Discharge (m3/sec)
120.0
100.0
80.0
60.0
40.0
20.0
0.0
Alzette - Ettelbrück
Susp matter
WQ - Ettelbrück (n=120)
MI (bio) survey
Jan-2005 Jul-2005 Jan-2006 Jul-2006 Jan-2007 Jul-2007 Jan-2008 Jul-2008
500.0
400.0
300.0
200.0
100.0
0.0
40.0
30.0
20.0
10.0
0.0
Sûre - Rosport
Susp matter
WQ - u/s Wasserbillig (n=115)
MI (bio) survey
Jan-2005 Jul-2005 Jan-2006 Jul-2006 Jan-2007 Jul-2007 Jan-2008 Jul-2008
Wiltz - Q d/s Kautenbach
Susp matter - L110030A12
WQ - u/s Clerve confluence (n=108)
MI (bio) survey
Jan-2005 Jul-2005 Jan-2006 Jul-2006 Jan-2007 Jul-2007 Jan-2008 Jul-2008
30.0
25.0
20.0
15.0
10.0
5.0
0.0
Attert - Reichelange
WQ - STEP, Everlange (n=17)
MI (bio) survey
Jan-2005 Jul-2005 Jan-2006 Jul-2006 Jan-2007 Jul-2007 Jan-2008 Jul-2008
Figure 6.3: Discharge time-series for frequently and infrequently sampled sites showing the occurrence of
water quality samples, suspended matter and macroinvertebrate surveys.
Monitoring data assessment report M 3 78/109

Figure 6.4 demonstrates the influence of bathing waters monitoring in relation to seasonal
sampling effort; the greatest number of sites are sampled during the summer months. In the
winter months, only the 17 most intensely monitored locations (Figure 6.1 and Table 6.2)
were regularly sampled, whereas, in summer (June to August) around 130 locations were
routinely sampled.
Locations sampled per month
200
180
160
140
120
100
80
60
40
20
0
18
19
01/02/2005
01/03/2005
57
107
01/04/2005
01/05/2005
130
127
01/06/2005
01/07/2005
158
71
01/08/2005
01/09/2005
17
24
01/10/2005
01/11/2005
17
17
17
17
01/12/2005
01/01/2006
01/02/2006
01/03/2006
45
114
01/04/2006
01/05/2006
147
133
146
73
34
17
15
18
18
Monitoring data assessment report M 3 79/109
16
43
132
131
142
176
73
19
19
17
22
21
Figure 6.4: Plot of number of locations sampled per month against month.
01/06/2006
01/07/2006
01/08/2006
01/09/2006
01/10/2006
01/11/2006
01/12/2006
01/01/2007
01/02/2007
01/03/2007
16
82
72
150
134
142
105
19
19
19
16
Appendix VII provides an overview summary table of the water quality grab sampling data
for Luxembourg. The table is ranked in descending order of the most frequently sampled sites
and shows the number of samples taken in the years 2005 to 2009 inclusive. The table further
indicates the sampling themes that comprise the totals for each year. Cells coloured green
indicate the greatest numbers of samples, those in red show those years and sites with the
least samples. The table clearly demonstrates that there are many sites with few samples and
few sites with many samples.
Percentage Cover of Analytes in Luxembourg data
A broad suite of analytes has been collected. The maximum number of analytes at any one
location was just over 90, with most sites having around 40 to 60 analytes measured.
Appendix V and VI demonstrate the ranges of parameters analysed relative to the sampling
themes of the Luxembourg Water Agency. The green cells demonstrate the themes and
substances that are most commonly tested.
The basic physicochemical tests and analyses for inorganic macronutrients, and oxygen status
related determinants appear to have been made on most samples. These include pH,
temperature and electrical conductivity. Major cations and anions are recorded for most
records, as well as nutrients and measures of oxygen status and demand. Iron and manganese
are generally present in solution at higher concentrations in waters of poor oxygen status; they
were measured in less than 50% of samples.
It is important to note that the number of suspended sediment and turbidity data is relatively
low. Coverage for suspended solids was almost completely absent and turbidity was assessed
for around a third of samples (suspended solids is a key vector for contaminants, and analyses
of particulate bound metals is necessary to determine realistic metals concentrations in
polluted freshwaters).
01/04/2007
01/05/2007
01/06/2007
01/07/2007
01/08/2007
01/09/2007
01/10/2007
01/11/2007
01/12/2007
01/01/2008
01/02/2008
01/03/2008
01/04/2008
01/05/2008
01/06/2008
01/07/2008
01/08/2008
01/09/2008
01/10/2008
01/11/2008
01/12/2008
01/01/2009
16
16
01/02/2009
01/03/2009
83
65
01/04/2009
01/05/2009
0
01/06/2009

Cover for micropollutants is less complete (Appendix V and VI), with common heavy metals
being analysed for in around a third of all samples. Other contaminant metals were only
analysed for in around 20 % of samples. Measurements of micropollutants and indices of
biological condition are only present for a very small proportion of samples.
Positive detection of substances in Luxembourg Water Quality samples
Figure 6.5 provides a quick look-up indication of the results for analytes in the Luxembourg
dataset that were commonly or rarely tested for, and which generally had mainly positive
results or not. In general, the common physical and chemical tests and analytes have positive
results as would be expected. It is the micropollutants that have a low proportion of positive
detections.
The substances rarely tested for and commonly with few positive detections were the heavy
metals and the organic micropollutants; largely PAHs (which are generally by-products of
combustion) and a range of solvent and some pesticides. The pattern of detections in surface
water samples is very similar to that in the other study regions, and a comparison is provided
in Section 6.1. Section 6.7 gives a more detailed summary of the Luxembourg data in relation
limits of detection.
The Luxembourg “Priority Substances” data set exists for priority substances at 7 locations in
Luxembourg water courses (Figure 6.6). These data provided to CRTE for this project span
the period 2000 to the end of 2006 (Figure 6.7). The PAH data are included in the separately
provided general Water Agency dataset (Figure 6.5) and most of these have few positive
detections. As repeatedly stated, the need for more comprehensive sediment and particulate
phase testing for micropollutants is necessary to characterise the immissions situation for
these substances.
Monitoring data assessment report M 3 80/109

Cond.
pH
NO2
NH4
Sulfate-SO4
Nitrate-NO3
Chloride-Cl
Temp °C
P_tot
BOD_5
DO(%sat)
DO
Sodium-Na
Potassium-K
Hardness (H#CO3) °Fr
PO4-P
Cote NH4 (IPO)
Organic Pollution Index
Cote o-PO4 (IPO)
Cote NO2 (IPO)
Cote BOD-5 (IPO)
Magnesium-Mg
Calcium-Ca
Total hardness
Iron-Fe
Turbidity visual obs.
Sum cations
Sum anions
Charge balance
Manganèse-Mn
Pheopigments
Chlorophylle-a
Indice biochim.
Cote Sat O2 (Indice biochim.)
Cote BOD-5 (Indice biochim.)
Cote NH4 (Indice biochim.)
Zinc-Zn
Nickel-Ni
Lead-Pb
Copper-Cu
Chromium-Cr
Cadmium-Cd
Turbidity
Alkalinity
Hardness (H#CO3) meq./L
Intestinal enterococci
E.coli
Above DL
No. of results
0 500 1000 1500 2000 2500 3000 3500
0 500 1000 1500 2000 2500 3000 3500
Monitoring data assessment report M 3 81/109
Boron-B
Arsenic-As
Mercury-Hg
Total coliforms
Total Xylène
Vanadium-V
Silver-Ag
Fluoranthène
Benzo(k)fluoranthène
Benzo(b)fluoranthène
Benzo(a)pyrène
Indeno(1.2.3.c.d)pyrène
Benzo(g.h.i)pérylène
Tétrachloroéthylène
Faecal coliforms
Fluoride-F
Toluène
Tétrachlorométhane
Ethylbenzène
Dichlorométhane
Chloroform
Benzène
1.1.1-Trichloroéthane
Silicon-Si
Suspended solids
Cote Macrozoobenthos
Diatom poll.sens.ind.
Diatom biological index
Trichloroéthylène
Mineralisation rate
Macrophyte bio. Index
Hardness (H#CO3) mg/L
TOC
Pyrène
Phenantrène
Fluorène
Chrysène
Benzo(a)anthracène
Anthracène
Acenaphtène
Dibenzo(a.h)anthracène
HCO3
Free cyanide-CN
COD
Above DL
No. of
results
Figure 6.5: Total number of results (blue) and number of positive results (red) in the Luxembourg
surface water quality dataset download.

Number of Samples
70
60
50
40
30
20
10
0
2000 2001 2002 2003 2004 2005 2006
Figure 6.6: Number and location code of “priority substances” samples per year.
Acénaphtène
Anthracène
Benzo (a) anthracène
Benzo (a) pyrène
Benzo (b) fluoranthène
Benzo (ghi) pérylène
Benzo (k) fluoranthène
Chrysène
Dibenzo(a,h)anthracène
Fluoranthène
Fluorène
Indéno (1.2.3 cd) pyrène
Méthyl (2) fluoranthène
Méthyl (2) naphtalène
Naphtalène
Phénanthrène
Pyrène
Biphényl
Di(ethylhexyl)phtalate
Phosphate de tributyle
1-1-2-2 Tetrachloroéthane
1-2 Dichloroéthane
1-4 Dichlorobenzène
Benzène
Décabromodiphényléther
Dichloroaniline (s)
Dichlorométhane
Hexachlorobutadiène
Tetrachloroéthylène
Tetrachlorure de Carbone
Trichloréthylène
Trichlorobenzène 1,2,3
Trichlorobenzène 1,2,4
Trichlorobenzène 1,3,5
4-nonyl phénol
4-tert-octyl phénol
2-3 Dichloroaniline
2-4 Dichloroaniline
2-5 Dichloroaniline
2-6 Dichloroaniline
3-4 Dichloroaniline
3-5 Dichloroaniline
4 chloro 2 nitroaniline
Pentabromodiphényléther
0 50 100 150 200 250 300
42
36
36
36
36
36
36
42
Number of Records
150
162
162
168
186
186
210
204
228
228
228
228
228
228
228
228
228
228
228
228
228
224
221
228
228
Number of records
N > L.o.D
228
263
263
263
263
263
263
262
263
263
263
2,3,4-Trichlorophénol
2,3,5-Trichlorophénol
2,3,6-Trichlorophénol
2,4,5-Trichlorophénol
2,4,6-Trichlorophénol
3,4,5-Trichlorophénol
L300030A06
L112010A24
L100011A21
L100011A09
L106030A12
L110030A11
L100011A01
Monitoring data assessment report M 3 82/109
Alachlor
Atrazine
Bentazone
Chlordane
Chlordane béta (trans)
Chlorfenvinphos
Chloroalcanes C10-C13 (51,5%)
Chloroalcanes C10-C13 (55,5%)
Chloroalcanes C10-C13 (63%)
Chlorobenzène
Chloroforme
Chlorpyrifos éthyl
Chlorpyrifos méthyl
Chorotoluron
Delta HCH
Désethyl atrazine
Dichlorvos
Diclobényl
Diuron
Endosulfan a
Gamma HCH
Hexachlorobenzène
Isoproturon
Metalaxyl
Métazachlor
Metolachlor
Pentachlorophénol
Simazine
Tributylétain
Trichlorfon
Trifluraline
0 100 200 300
30
42
42
42
42
42
42
42
42
Number of Records
84
78
84
186
186
186
186
186
204
196
228
228
228
228
Number of records
N > L.o.D
Figure 6.7: Number of results (blue) and positive results (red) for the Luxembourg priority
substance water quality data.
228
228
227
226
228
228
228
228
228
228
222
228
227
263

6.3.2. Suspended matter quality data
The Water Agency in Luxembourg recently provided their data for suspended matter quality
for four locations (Table 6.3). The sample material was collected using the continuous
throughflow centrifugation method collecting material for each sample over a 24 hour period.
Figure 6.3 shows the occurrence of surveys for three of the locations (red spots) relative to the
river flow values. These surveys have successfully collected samples during elevated flows
and also provide dry-weather (low flow) samples. These data provide a useful supplement to
the concurrent surface water samples. Due that late arrival of these data, a comparison with
the water samples has not yet been carried-out. It may be of interest to examine the relative
high and low flow composition of the material, certain differences in composition might be
expected. The examination of suspended matter composition in Luxembourg rivers is of
interest, since it highlights one of the key shortcomings of the grab sampling water quality
programme. There are almost no suspended solids analyses carried-out at any of the sampling
locations. At the very least suspended solids data are required for those sites where
suspended matter analysis is carried-out in order to be able to relate these to the general
suspended matter concentrations. The following text provides a description of the suspended
matter data and presents mean concentrations.
In general, the samples show frequent positive detections (green cells in Table 6.3) of PAHs
and metals, but most other substances are rarely detected; a similar pattern to that observed in
surface water samples. This is unlike the results for Delfland, where dredge sediment samples
have frequent positive detections of most substances compared to water samples. The low
detection levels may be related to the substances tested for and their use, and whether changes
in substances used for various purposes means that the list of analytes should be reviewed on
a regular basis. The absence of substances may also relate to the nature of the solids collected
by the methods, and without a more detailed examination of the data it is not possible to make
a useful conclusion from the table of means presented.
Of the concentrations presented iron is the most abundant component measured, this is similar
to the sediment samples for the Erft River. Phosphorus is the second most abundant
component, which was also the case for the Erft samples. The PAHs are the next most
abundant group of measured substances, and the metals are in much lower concentrations,
except for zinc. Zinc is a an important component of the tyre making process, iron, the other
metals and PAHs are also common constituents of road runoff, but without a more detailed
examination of the data caution is appropriate before making any speculative conclusions
about the nature of this material.
Monitoring data assessment report M 3 83/109

Table 6.3: Summary of data for suspended matter collected at four sites in Luxembourg, showing the
geometric mean (in mg/kg unless otherwise stated), number of results and number of positive results.
Geometric means Chiers at
Rodange
positive results
Wiltz at
Kautenbach
Monitoring data assessment report M 3 84/109
positive results
Sûre at
Wasserbillig
positive results
Alzette at
Ettelbruck
Substance 2007-9 n 2006-9 n 2005-9 n 2006-9 n
Percentage water 71.7 34 34 64.7 40 40 69.7 63 63 65.1 39 39
Dry residue % 27.4 34 34 34.1 40 40 28.4 63 63 33.5 39 39
Grainsize > 2 mm % 7.4 35 1 1.90 37 1 28.7 46 1 36 0
Total organic carbon 137469 34 34 49046 40 40 28586 63 63 46118 38 38
Iron 47389 34 34 36388 40 40 31478 63 63 35684 39 39
Total Phosphorus 5109 34 34 1704 40 40 2639 63 63 2806 39 39
Manganese 1622 34 34 1760 40 40 2613 63 63 1658 39 39
Zinc 708.3 34 34 419.7 40 40 292.4 63 63 408.8 39 39
Copper 106.3 34 34 66.47 40 40 43.00 63 63 56.12 39 39
Chromium 79.24 34 34 159.8 40 40 60.00 63 63 62.47 39 39
Lead 65.08 34 34 49.19 40 40 42.33 63 63 53.74 39 39
Nickel 58.18 34 34 73.76 40 40 45.27 63 63 37.52 39 39
Arsenic 24.84 34 34 12.15 40 40 11.64 63 62 14.95 39 39
Tin 11.74 12 11 3.990 12 12 3.907 12 11 5.924 12 12
Fluoranthene 2.017 34 33 2.490 40 40 1.934 59 59 2.305 39 39
Benzo (b) fluoranthene 1.623 34 33 1.986 40 39 1.456 59 58 1.640 39 39
Indeno (1,2,3-c,d) pyrene 1.350 34 30 1.523 40 39 1.227 59 53 1.250 39 37
Benzo (a) pyrene 1.288 34 33 1.638 40 40 1.204 59 58 1.395 39 39
Cadmium 1.210 34 28 1.534 40 40 0.693 63 35 0.735 39 28
Benzo (k) fluoranthene 1.206 34 33 1.411 40 40 0.956 59 58 1.221 39 39
Benzo (g,h,i) perylene 1.016 34 32 1.141 40 40 0.974 59 56 0.958 39 39
Ugilec 141 0.370 34 1 40 0 0.250 60 1 0.240 39 1
Mercury 0.156 34 31 0.194 40 37 0.124 63 48 0.111 39 32
Anthracene 0.111 34 26 40 40 0.137 63 35 0.144 39 35
Pentachlorophenol 0.061 34 1 0.023 40 1 0.030 63 3 0.061 39 2
PCB 180 0.018 34 1 40 0 0.011 63 2 0.012 39 1
PCB 138 0.016 34 5 40 0 0.013 63 2 0.016 39 1
PCB 153 0.016 34 6 40 0 0.016 63 2 0.015 39 1
4-nonylphenol 35 0 41 0 2.400 63 2 40 0
1,2,4-trichlorobenzene 35 0 41 0 0.280 64 1 40 0
PCB 101 0.015 34 2 40 0 0.012 63 1 39 0
PCB 118 0.015 34 1 40 0 63 0 39 0
PCB 52 0.011 34 1 40 0 63 0 39 0
Lindane (gamma HCH) 34 0 40 0 0.006 63 2 39 0
Tributyl-tin 0.0001 35 1 41 0 64 1 40 0
1,1-dichloroethane 13 0 13 0 13 0 13 0
1,2,3-trichlorobenzene 35 0 41 0 64 0 40 0
1,3,5-trichlorobenzene 35 0 41 0 64 0 40 0
4-tert-octylphenol 35 0 41 0 63 0 40 0
Aldrin 12 0 12 0 12 0 12 0
DDT-4,4' 12 0 12 0 12 0 12 0
Dieldrin 12 0 12 0 12 0 12 0
Endosulfan alpha 12 0 12 0 12 0 12 0
Hexachlorobenzene 34 0 40 0 49 0 39 0
Hexachlorobutadiene 34 0 40 0 63 0 39 0
Isodrin 12 0 12 0 12 0 12 0
Isoproturon 13 0 13 0 13 0 13 0
Naphtalene 34 0 40 0 63 0 39 0
PCB 126 11 0 35 0 13 0 11 0
PCB 28 34 0 40 0 63 0 39 0
Pentachlorobenzene 34 0 40 0 62 0 39 0
positive results

Suspended matter is clearly a significant source of micro-pollutants, and their impact of
stream biota has been the subject of considerable research effort. In the next section a
summary of the macroinvertebrate survey data is provided.
6.3.3. Biological survey data
In Luxembourg an adaptation of the French IBGN methodology is used to quantify stream
macroinvertebrate (MI) community status. The IBGN is an abundance and sensitivity
weighted macroinvertebrate community index and the macroinvertebrate information is
summarised at the family level. At each sampling location 8 habitat patches are sampled to
ensure that areas with abundant organisms are not missed (Friberg et al., 2006). Since 1999 up
to 10 samplings of a total of 109 locations have been made, and each year a sub-set of around
40 locations is sampled (Table 6.4). Only 32 locations have been surveyed on more than 32
occasions, and 27 locations have more than 7 survey results.
Table 6.4: Numbers of locations in Luxembourg where IBGN macroinvertebrate surveys were
undertaken showing that the variability in IBGN has been small (higher IBGN indicates better condition).
Year Tot Sites Sites > 7 Mean IBGN
1999 31 28 10.2
2000 34 27 11.5
2001 44 28 12
2002 47 26 12.9
2003 38 27 12.7
2004 42 28 11.7
2005 42 28 11
2006 39 20 12.1
2007 24 5 10
Site by site examination of the IBGN results demonstrates that the year by year variations are
relatively small. This suggests that a small sample size may still provide a valid indication of
condition. In Luxembourg, however, the most frequently sampled sites are those with the
highest IBGN score and hence those showing the least evidence of human disturbance (Figure
6.8). Low IBGN sites are not often sampled and have little water quality information.
Luxembourg has 62 sites with macroinvertebrate (MI) and water quality data, 36 of these
locations have just one WQ sample. Figure 6.9 demonstrates the coincidence of Luxembourg
sites with greater than 20 water quality samples or greater than 7 macroinvertebrate surveys.
The general message in this figure is that only 16 locations have relatively frequent water
quality and MI surveys, and of the sites with frequent MI surveys, a significant number have
few water quality determinations. Table 6.2 lists the sampling locations with the most water
quality records, numbers of biological surveys and proximity to river discharge
measurements, and Figure 6.3 shows when marcoinvertebrate surveys were made relative to
river flows and water quality samples. The correspondence of water quality monitoring
locations and macroinvertebrate survey sites in Luxembourg is not ideal. Considering the
pressure and monitoring model (Figure 2.2), the macroinvertebrate survey results represent an
end-point of ecosystem condition, and water quality is one causal factor in determining this
end condition.
Monitoring data assessment report M 3 85/109

Ideally, water quality and biological survey locations need to coincide to maximise the
predictive value of the two measures.
Average IBGN and N_mi surveys
0
2
4
6
8
10
12
14
16
18
20
L110040A-08
L107030A-09
L110030A-12
L122020A-07
L202030A-12
L112010A-05
L112010A-12
L144030A-09.1
L140030A-03
L106030A-12
L105030A-12.1
L110030A-02
L106036A-01
L103030A-07
L106030A-06
L110036A-01
L202030A-08.1
L141030A-02
L106030A-02.1
L106030A-01
L106033A-01.1
L102030A-04
L100011A-18.1
L107030A-05
L110044A-03
L201033A-01
L202031A-02
L110046A-01
L122020A-04
L106030A-08
L141030A-10
L144031A-01
L200030A-11
L105031A-01
L103030A-01
L105030A-06
L144032A-01
L112010A-21
L101030A-10
L200031A-01
L100011A-03
L202040A-01
L200030A-06
L144030A-05.1
L100011A-04.1
L102030A-01
L201031A-01
L101030A-03
L105030A-01
Monitoring data assessment report M 3 86/109
N_mi
IBGN_ave
N. samples
Figure 6.8: Average IBGN score (green), number of biological surveys (N_mi, red), and number of water
quality samples (blue) for Luxembourg monitoring locations.
L112010A-09
L112010A-17
L106030A-01
L122020A-04
L110040A-04
L106030A-06
L100011A-18.1
L107030A-09
L140030A-06
L110030A-12
L141030A-13.1
L100011A-14.1
L201030A-06
L200030A-11
L102030A-04
L103030A-07
L108030A-02
L100011A-21
L112010A-24
L300030A-06.1
L106030A-12
L202030A-12
L144030A-09.1
L105030A-12.1
L112010A-11
L122020A-07
L112010A-02
L112010A-12
L110040A-08
L112010A-05
L100011A-03
L112010A-18
L104030A-10
Number of macroinvertebrate surveys
-14 WQ Samples -12 -10Macroinvertebrate -8 -6 -4 Samplings -2 0 2 4 6 8 10 12
< 7 biological surveys ,
>= 20 water samples
> = 7 biological surveys ,
< 20 water samples
>= 7 biological surveys ,
> = 20 water samples
0 20 40 60 80 100 120 140 160 180 200 220 240 260
Number of water quality samples
Figure 6.9: Correspondence of macroinvertebrate surveys and water quality sampling in Luxembourg
(sites with >= 20 WQ samples, and / or, >= 7 MI surveys).
200
180
160
140
120
100
80
60
40
20
0
Numbers of WQ samples

Water quality, macroinvertebrate and suspended matter data are currently being investigated
to examine potential relationships in Luxembourg between pressures and impacts, and this
analysis is being used to guide the M 3 monitoring strategy in Luxembourg.
6.4. Luxembourg summary
Many sites are monitored in Luxembourg, but monitoring is dominated by a small number of
frequently sampled sites, where general water quality parameters and in some cases micropollutants
are analysed. The majority of the sites have only been sampled between one and
five times a year over the past 5 years. A number of monitoring themes exist and any site can
have sampling under one or more of these schemes, therefore, monitoring is not site/pressure
specific. It would be better to have each pressure accounted for in a single monitoring suite
for each site. For this reason the monitoring summary table provides information on each
sampling theme as well as for the combined themes which at some sites add up to give
relatively intense monitoring.
General summary narrative for Luxembourg
� Residential areas, industry and agriculture all exert a significant stress on the small
streams and rivers of central and southern Luxembourg.
� Basic physico-chemical water quality monitoring is carried out at all sites, but
coverage of micro-pollutants is limited to 7 sites;
� In general there is a good geographical correspondence between biological survey and
water quality monitoring locations, it is the frequency of monitoring which is an issue.
Some locations have regular biological survey but infrequent water quality sampling
and vice versa, which means that few sites have both good biological and water
quality monitoring coverage thus the association of ecological health and water quality
is not facilitated;
� Flow gauging coverage addresses most of the major rivers and associated water
quality monitoring sites, but the majority of the minor sampling locations have no
flow estimates in their vicinity;
� Testing for suspended solids was almost completely absent and turbidity was assessed
for only around a third of samples. Suspended solids is a key vector for contaminants,
and analyses of particulate bound metals is necessary to determine realistic metals
concentrations in polluted freshwaters;
� At a small number of sites, water sampling is sufficiently intensive to afford basic
estimates of material fluxes for some substances. At most sites data are too sparse and
give an inadequate coverage of rainfall response events. The calibration of models for
water quality and load estimation should be possible for certain substances where the
short-term dynamic variation of those substances is not large with respect to the longer
term average concentration; this limits the value of the data. At the more intensively
Monitoring data assessment report M 3 87/109

monitored sites, existing monitoring needs to be supplemented with targeted event
response sampling, especially for particulate associated substances.
Some specific comments
� The data entry and input problems with the Luxembourg site and analyte names can be
overcome by having site names linked automatically to location codes, and/or having pulldowns
for each input field, additional information can be provided in a comments column;
� Sampling many sites infrequently can give an indication of conditions, but this is highly
dependent on the prevailing conditions. Stratified sampling which increases in intensity
during storm events leads to a more statistically robust characterisation of conditions at a
site;
� Many micro-pollutants are quite industry specific, the choice of analytes needs to be
tailored to suit the industries and activities present in a catchment in order to effectively
characterise the stress exerted by the emitting processes, and beyond this to identify
programs of measures to reduce these stresses.
Monitoring data assessment report M 3 88/109

Table 6.5: Stress and monitoring summary for Luxembourg.
Monitoring data assessment report M 3 89/109

Monitoring data assessment report M 3 90/109

7. COMPARISON OF MICRO-POLLUTANTS RESULTS BY REGION
The differences between micro-pollutant testing of water samples between the study regions
is presented. General findings are as follows:
� Numbers of samples and analyses varied greatly, Delfland had made the greatest
number of analyses for the widest range of analytes, and these were most relevant to
the stresses in Delfland;
� Few substances were tested for in all regions;
� Percentage detection between regions was comparably low;
� Mean concentrations for the same substances in tests where the results were greater
than detection limit varied greatly between regions;
� Too few results were greater than detection limit to afford a meaningful comparison of
the situation between regions.
A large range of micro-pollutants are monitored in the partner regions and the comparability
of monitoring between the partner regions has been investigated. Of the 225 substances
analysed in water quality samples from the three regions, only 31 of these are tested for in
more than two regions and only 7 in all three regions. Delfland tested for 159 substances,
Erftverband looked for 52 substances, and Luxembourg analyzed for 90. Infrequent grab
sampling in the Erft system and Luxembourg, very low recovery rates (Table 7.1), and
different limits of analytical detection and reporting, mean that a comparison from region to
region is almost meaningless. A far greater number of analyses have been carried-out in
Delfland, but even here the rates of recovery were very low (Table 7.1). Table 7.1 also
presents mean values for those substances tested for in more than one region (excluding PAHs
and metals), and Figure 7.1 reproduces the arithmetic mean concentrations for results greater
than the limit of detection, the numbers of records greater than detection limit for each region
is included with the variable name. Figure 7.1 is provided to demonstate the variability in
results for the substances presented. A surprising result is the apparently high values in
Luxembourg samples. Of all of the regions, Delfland appears to have the most targetted
selection of priority substance analytes, and this is not surprising given the density of
glasshouses and the intensity of chemical use by growers. In Luxembourg and Erft, the use of
these chemicals will be less intense and localised.
Monitoring data assessment report M 3 91/109

Arithmetic mean concentration (ug/L)
0.50
0.45
0.40
0.35
0.30
0.25
0.20
0.15
0.10
0.05
0.00
d e l
Figure 7.1: Mean concentrations of micro-pollutants at all locations by region.
Monitoring data assessment report M 3 92/109

Table 7.1: Micro-pollutant comparison for water quality samples from the three study partner regions.
Substance Reported L.o.D
Delfland Erftverband Luxembourg
Total records
n > L.o.D
% > L.o.D
Arithmetric mean (c >
L.o.D)
diuron 0.02/0.1 739 226 30.6 0.038 0.050 251 62 24.7 0.105 0.050 228 45 19.7 0.340
lindane 0.010 3003 136 4.5 0.011 0.005 228 14 6.1 0.033
hexachlorobutadiene 0.002/0.006 2520 90 3.6 0.005 0.005 204 2 1.0 0.780
isoproturon 0.02/0.1 733 91 12.4 0.021 0.050 251 70 27.9 0.185 0.050 42 0 0.0 na
delta-hexachlorocyclohexane0.002/0.006 2478 65 2.6 0.004 0.005 228 1 0.4 0.170
metazachlor 0.010 2091 39 1.9 0.053 0.050 251 9 3.6 0.237 0.050 0 0 0.0 na
atrazine 0.010 2119 14 0.7 0.024 0.050 251 9 3.6 0.061 0.030 228 14 6.1 0.200
metoxuron 0.010 678 20 2.9 0.019 0.050 251 1 0.4 0.050
bentazone 0.050 73 13 17.8 0.098 0.050 228 6 2.6 0.337
chloroform 0.100 55 3 5.5 0.100 0.5/2.5 263 13 4.9 3.815
trichlorobenzene 0.002/0.15 173 11 6.4 0.056 0.030 504 2 0.4 0.125
simazine 0.010 2120 10 0.5 0.071 0.050 251 4 1.6 0.205 0.050 228 0 0.0 na
desethylatrazine 0.010 745 0 0.0 na 0.050 251 3 1.2 0.237 0.050 227 4 1.8 0.165
chloridazon 0.010 1511 1 0.1 0.060 0.050 251 15 6.0 0.174
metamitron 0.050 1447 1 0.1 0.350 0.050 251 12 4.8 0.182
metolachlor 0.050 251 10 4.0 0.173 0.050 228 0 0.0 na
metribuzin 0.02/0.05 763 0 0.0 na 0.050 251 2 0.8 0.080
pentachlorophenol 0.01/0.05 38 0 0.0 na 0.005 222 27 12.2 0.015
alachlor 0.030 54 0 0.0 na 0.005 228 0 0.0 na
chlordane 0.010 38 0 0.0 na 0.010 228 0 0.0 na
chlorotoluron 0.010 55 0 0.0 na 0.050 251 0 0.0 na
trifluralin 0.010 54 0 0.0 na 0.030 42 0 0.0 na
metalaxyl 0.050 1423 636 44.7 0.373 0.050 65 0 0.0 na 0.100 228 0 0.0 na
dichlorv os 0.004/0.01 2948 247 8.4 0.163 0.025 226 0 0.0 na
benzene 0.100 20 2 10.0 0.100 10/5/0.5 263 0 0.0 na
chlorfenv inphos 0.008/0.02 3065 20 0.7 0.068 0.02/0.05 84 0 0.0 na
linuron 0.020 678 85 12.5 0.066 0.050 251 0 0.0 na
ethofumesate 0.010 745 2 0.3 0.020 0.100 66 0 0.0 na
tributyltin 0.005 25 1 4.0 0.006 0.030 84 0 0.0 na
hexachlorobenzene 0.002/0.006 2521 8 0.3 0.003 0.005 42 0 0.0 na
Reported L.o.D
Monitoring data assessment report M 3 93/109
Total records
n > L.o.D
% > L.o.D
Arithmetric mean (c >
L.o.D)
Reported L.o.D
Total records
n > L.o.D
% > L.o.D
Arithmetric mean (c >
L.o.D)

8. REFERENCES
Armitage, P.D., Moss, D, Wright, J.F., and Furse, M.T. 1983. The Performance of a new
Biological Water Quality Score System Based on Macroinvertebrates Over a Wide Range of
Unpolluted Running-Water Sites. Water Res. 17:333-47.
Chessman, BC Growns, JE & Kotlash, AR 1997, 'Objective derivation of macroinvertebrate
family sensitivity grade numbers for the SIGNAL biotic index: Application to the Hunter
River system, New South Wales', Marine and Freshwater Research, vol. 48, pp. 159-172.
Christoffels, E 2008 Online Monitoring of Water Quality on the River Erft. E-WAter. Official
Publication of the European Water Association (EWA), EWA 2008 ISSN 1994-8549.
Coats R, Liu F, Goldman CR 2002. A monte Carlo test of load calculation methods, Lake
Tahoe Basin, California-Nevada. J. Am. Wat. Resour.. Assoc38:719-730.
Franken R, Gardeniers J and Peeters E. 2006. Handboek Nederlandse Ecologische
Beoordelingssystemen (Ebeo-Systemen) Deel A. Filosofie En Beschrijving Van De
Systemen. STOWA, Utrecht, maart 2006, STOWA rapportnummer 2006-04
ISBN 90.5773.259.9
Friberg N, Sandin L, Furse M T, Søren E L, Clarke R T and Haase P. 2006. Comparison of
macroinvertebrate monitoring methods in Europe. In Furse M T, Hering D, Brabec K,
Buffagni A, Sandin L, and Verdonschot P F M (eds), The Ecological Status of European
Rivers: Evaluation and Intercalibration of Assessment Methods. Hydrobiologia 566:365-378.
Springer Verlag.
Friedrich, G., Chapman, D., and Beim, A. 1996. The Use of Biological Material in Water
Quality Assessments: A Guide to the Use of Biota, Sediments and Water in Environmental
Monitoring, 2nd ed. Deborah Chapman (ed.). E & FN Spon, New York.
WHD 2008. Emissiereductie van gewasbeschermingsmiddelen vanuit de glastuinbouw.
Sammenvattingrapport. Waterschap van Hollandse Delta.
HHRa 2008. Schoon water om van te genieten: Gebiedsrapportage van de detailanalyse van
de Europese Kaderrichtlijn water. Hoogheemraadschap van Delfland, Phoenixstraat, 32,
2601 DB Delft, The Netherlands. Drukkerij Stimuka, Rijswijk.
HHRb 2008. Schoon water om van te genieten: Technisch achtergrondrapportage.
Hoogheemraadschap van Delfland, Phoenixstraat, 32,
2601 DB Delft, The Netherlands. Drukkerij Stimuka, Rijswijk.
Johnes P 2007. Uncertainties in annual riverine phosphorus load estimation: Impact of load
estimation methodology, sampling frequency, baseflow index and catchment population
density. J. Hydrol. 332: 247-258
Monitoring data assessment report M 3 94/109

Kochbach A, Yttri KE, Cassee FR, Schwarze PE, Namorket E. 2006. Physicochemical
characterisation of combustion particles from vehicle exhaust and residential wood smoke.
Particle and Fibre Technology 2006 3:1.
Kennedy P. 1999 The Effects of Road Transport on Freshwater and Marine Ecosystems.
Kingett Mitchell Ltd. Contract report to NZ Ministry of Transport.
Lieffijn, H et al. Schatting van de emissie van bestrijdingsmiddelen uit de glastuinbouw.
Een nulmeting (1997) ten behoeve van het Milieuconvenant Glastuinbouw en Milieu.
LNV Directie Kennis (voorheen: Expertisecentrum LNV). Ede, 2000.
Littlewood IG. 1992. Estimating contaminant loads in rivers. A review. IH Report no. 117,
Institute of Hydrology, Wallingford.
Preston SD, Bierman VJ Jr., Silliman SE 1992. Impact of flow variability on error in
estimation of tributary mass loads. J. Environ. Engrg. 118:402-419
Stark JD, Maxted JR 2007. A biotic index for New Zealand’s soft-bottomed streams. New
Zealand Journal of Marine and Freshwater Research 41(1).
Teunissen RJM. 2005. Emissies van gewasbeschermingsmiddelen uit de glastuinbouw. RIZA
rapport 2005.019. ISBN 9036957044. Lelystad, The Netherlands. November 2005.
WHD. 2008. Emissiereductie van gewasbeschermingsmiddelen vanuit de glastuinbouw.
Report of Waterschap Hollandse Delta, The Netherlands. Retrieved on 3 March 2010 from:
http://www.wshd.nl/organisatie/publicaties/publicaties/publicaties_0/plannen_handboeken/rap
port.
Wilkinson J, Souter N, and Fairweather P 2007. Best Practice Framework for the Monitoring
and Evaluation of Water-Dependent Ecosystems. 1. Framework, DWLBC Report 2007/12,
Government of South Australia, through Department of Water, Land and Biodiversity
Conservation, Adelaide. Retrieved on 31 July 2007 from:
www.dwlbc.sa.gov.au/assets/files/ki_dwlbc_report_2007_12.pdf
Wilkinson J., Reynolds B. and Neal C. 1997. The impact of conifer harvesting and replanting
on upland water quality, R&D Progress Report to EA P2i502/6&7.
Diagrams retreived from the internet
http://www.niederrhein.nrw.de/erft/kap_1/kap_1_5.html
http://www.niederrhein.nrw.de/erft/kap_3/kap_3_1_3.html
http://www.niederrhein.nrw.de/erft/kap_4/kap_4_2.html
Monitoring data assessment report M 3 95/109

9. APPENDICES
Appendix I. Summary of data holdings by project partners.
Water quality Stage - Discharge
Luxembourg
Aim
Surveillance River quality
Number of spots
Frequency of
sampling
n/ year
Period
Start date-End date, no
enddate =ongoing
Period consitent
in
frequency/para
meters?
y= yes i=interruptions
p=parameter changes
h= historic (given up)
Operational
WWTP efficiency
Others
CSO 1 15 2004-2005 h f X
Eutrophication 1 12 2009- y i-l X
Floodevents 3 12 2005-2006 h f X X
Low-flow susp.
Matter
Sediments
6 12 2002-2004 h l
Pesticides: river &
WWTP
3 4 2009- i l X
Online/continuous Discharge 20 15 min 1997- (14);
2002- (6)
i i 20
Water quality 1 6 hours 1997-2000 h i
Erft
Surveillance
River quality
WWTP efficiency
Others
CSO 2 ca. 20 (event- 2002-2008
specific)
p f X X
Operational
Eutrophication 70 3 1963 - p i X X
Floodevents 70 3 1963 - p i X X
Low-flow susp.
Matter
70 3 1963 - p i X X
Sediments 40 1 1985 - y i X X
Online/continuous Discharge 40 5 min ca. 1970 -
Water quality 6 5 min 1992 - p i X X
Delft
influent/effluents
measurements
3 4 2005-2007 y i
Surveillance
WWTP
OSPAR 2 12 2006ongoing
y i 50
Boundaries control 11 12 2006- y i 50
Operational Base network 9 12 1994- y i 50
MTR 60 12 2006- i i 50
Waterquality urban
environment
17 12 2006- y i 50
Online/continuous
Monitoring data assessment report M 3 96/109
Type of sampling
l= low-flow; i= independent
of discharge; f=floodevents
Waterquality
greenhouse
environment
22 12 1994 i i 50
EKR waterquality 10 12 2006- y i 50
Waterquality 240 every 3-4
years 1/3 of
sampling
points, 4- 6
1994 i i 50
times a year
Water quality (salt) 1 15 min 2008- 1 online 50cm
Beukelsbrug
ongoing
Emission
greenhouses
(waalblok)
2 continiously march-june
2007
14 autosampl
er
Water height
Discharge

Physical and Oxygen demand related analytes
Conductivity
X= yes Blank =no
Temperature
Luxembourg
pH
Turbidity
Susp. Matter
X X X X X X
X X X X X X
X X X X X X
X X X X X X X
X X X X X X X X
X X X X X X
Erft
X X X X X X X X
X X X X X X X X X X
X X X X X X X X X X
X X X X X X X X X X
X X X X X
Delft
X X X X X X
X X X X X
X X X X X X
X X X X X X
X X X X X X X
X X X X X X
X X X X X X X X
X X X X X X
X X X X X
X X X X X X X X X
Oxygen
BOD
COD
Monitoring data assessment report M 3 97/109
POC
DOC

Monitoring data assessment report M 3 98/109
Appendix II. Numbers of results for each analyte for Delfland Water Quality grab
samples
OW043-002
OW062-008
OW058-001
OW021-003
OW006-003
OW203-111
OW202-000
OW090-000
OW004-001
OW056-000
OW907-010
OW116-012
OW119-000
OW115-012
OW110-000
OW215-024
OW301-001
OW221A012
OW080-002
OW047-001
OW310-000
OW306-023
OW306-022
OW015-003
OW111-000
OW221A013
OW215-026
OW210-003
OW411-014
OW008-002
OW213B024
OW306B012
OW220-010
OW207-002
OW221A023
OW069-000
OW215-032
OW216-002
OW208-016
OW015-012
OW218-200
OW217-000
OW202-322
OW220-000
OW050-002
OW413-001
OW211-000
OW401-003
OW208-000
OW208-001
OW221A000
OW202-100
Glass-houses
Glass-houses
Glass-houses
Glass-houses
Glass-houses
Glass-houses
Basic network
Glass-houses
Glass-houses
Glass-houses
WFD monitoring point
Glass-houses
Glass-houses
Glass-houses
Glass-houses
Glass-houses
Glass-houses
Glass-houses
Glass-houses
Glass-houses
Glass-houses
Glass-houses
Glass-houses
Glass-houses
WFD monitoring point
WFD monitoring point
Basic network
Basic network
Urban areas monitoring
Urban areas monitoring
Urban areas monitoring
Urban areas monitoring
Urban areas monitoring
Basic network
Urban areas monitoring
Urban areas monitoring
Urban areas monitoring
Urban areas monitoring
Urban areas monitoring
Urban areas monitoring
Basic network
Basic network
Basic network
Basic network
Urban areas monitoring
Urban areas monitoring
Basic network
Urban areas monitoring
Basic network
Basic network
Basic network
Basic network
zeros 20
22
27
27
28
51
51
52
54
54
63
74
75
75
75
75
77
78
78
79
80
81
83
86
88
88
88
92
113
125
177
177
178
178
179
179
179
180
181
181
181
181
181
182
183
183
183
184
184
193
193
193
Analyte name Par_code zeros
n
8083
7261
7585
6510
6416
6784
6079
6379
6577
6432
796
5223
5339
5333
5204
5042
5135
5232
5139
5417
5117
5282
5036
5198
5067
4635
3013
2447
881
980
910
812
824
367
964
905
829
938
878
810
288
177
123
270
554
554
537
842
233
92
92
79
Visible surface condition VUIL 0 2402 85 72 68 67 70 78 61 55 74 61 13 56 55 55 54 55 54 55 55 78 55 66 55 60 58 48 45 56 42 43 47 41 42 21 52 48 42 48 46 41 19 11 8 18 30 30 31 42 16 7 7 6
Weather WEERGSHD 0 2402 85 72 68 67 70 78 61 55 74 61 13 56 55 55 54 55 54 55 55 78 55 66 55 60 58 48 45 56 42 43 47 41 42 21 52 48 42 48 46 41 19 11 8 18 30 30 31 42 16 7 7 6
Fluidflowingwaters STROMSTR 0 2402 85 72 68 67 70 78 61 55 74 61 13 56 55 55 54 55 54 55 55 78 55 66 55 60 58 48 45 56 42 43 47 41 42 21 52 48 42 48 46 41 19 11 8 18 30 30 31 42 16 7 7 6
Oxygen O2 0 2398 85 72 68 66 70 78 61 55 74 61 13 56 55 55 54 55 54 55 55 78 55 66 55 60 58 48 44 56 42 43 47 41 42 21 51 48 42 48 46 41 19 11 7 18 30 30 31 42 16 7 7 6
Conductivity GELDHD 0 2398 85 72 68 66 70 78 61 55 74 61 13 56 55 55 54 55 54 55 55 78 55 66 55 60 58 48 44 56 42 43 47 41 42 21 51 48 42 48 46 41 19 11 7 18 30 30 31 42 16 7 7 6
Temperature T 0 2398 85 72 68 66 70 78 61 55 74 61 13 56 55 55 54 55 54 55 55 78 55 66 55 60 58 48 44 56 42 43 47 41 42 21 51 48 42 48 46 41 19 11 7 18 30 30 31 42 16 7 7 6
Acidity pH 0 2398 85 72 68 66 70 78 61 55 74 61 13 56 55 55 54 55 54 55 55 78 55 66 55 60 58 48 44 56 42 43 47 41 42 21 51 48 42 48 46 41 19 11 7 18 30 30 31 42 16 7 7 6
Clarity ZICHT 0 2369 82 62 68 67 67 77 61 55 72 58 13 56 55 55 54 54 54 55 55 75 55 66 55 59 58 48 44 56 41 42 47 41 42 21 51 48 42 47 46 41 19 11 7 18 30 30 31 42 16 7 7 6
Kjeldahlnitrogen NKj 0 2230 66 54 64 65 65 54 56 53 57 54 11 54 55 54 54 55 53 53 54 66 53 65 53 59 50 45 42 56 41 41 44 39 40 23 49 45 42 45 45 41 18 10 6 17 29 29 30 43 15 6 6 6
totalphosphate P 0 2229 66 54 64 65 65 54 56 53 57 54 11 54 55 54 54 55 53 53 54 66 53 65 53 59 50 45 42 56 41 41 44 39 40 23 49 45 42 45 45 41 18 10 6 17 29 29 30 43 15 6 6 5
nitrogen N 0 2215 66 54 64 65 65 54 56 53 57 54 11 54 55 54 54 55 52 52 53 64 52 64 52 58 50 45 42 56 41 41 44 39 40 21 49 45 42 45 45 41 16 10 6 15 29 29 31 43 15 6 6 5
chloride Cl 3 1939 64 49 60 60 63 48 48 48 63 50 9 48 47 48 48 48 47 47 48 62 47 59 47 53 35 40 36 49 35 34 38 34 34 17 43 39 36 39 38 36 12 4 4 12 22 22 24 36 9 0 0 0
Ortho-phosphate PO4 3 1929 61 48 60 60 60 48 48 48 49 48 9 48 48 48 48 49 47 47 48 60 47 59 47 53 36 40 36 50 36 35 39 34 35 17 44 40 36 40 39 36 12 4 4 12 23 23 24 37 9 0 0 0
ammonium NH4 3 1929 61 48 60 60 60 48 48 48 49 48 9 48 48 48 48 49 47 47 48 60 47 59 47 53 36 40 36 50 36 35 39 34 35 17 44 40 36 40 39 36 12 4 4 12 23 23 24 37 9 0 0 0
totalnitrateandnitrite sNO3NO2 3 1918 61 48 60 60 60 48 48 48 49 48 9 48 48 48 48 50 47 47 48 60 47 59 47 53 35 40 36 50 35 34 38 34 34 17 43 39 36 39 38 36 12 4 4 12 22 22 24 36 9 0 0 0
Copper Cu 3 1864 94 95 91 94 65 54 92 53 54 55 6 25 42 43 25 46 10 4 1 0 1 1 0 0 91 85 65 30 39 40 43 39 39 11 44 44 43 43 44 41 7 10 6 5 28 28 24 41 6 6 6 5
Nickel Ni 4 1809 94 95 91 94 65 54 91 53 54 55 6 25 42 43 25 42 10 0 1 0 1 1 0 0 91 81 65 30 39 41 39 39 39 7 40 40 43 40 40 41 7 6 2 5 28 28 24 29 6 6 6 5
Zinc Zn 5 1861 94 95 91 94 65 54 91 53 54 55 6 25 42 43 25 46 10 4 0 0 1 0 0 0 91 85 65 30 39 40 43 39 39 11 44 44 43 43 44 41 7 10 6 5 28 28 24 41 6 6 6 5
Calcium Ca 5 308 23 22 21 21 20 18 22 18 5 5 4 3 2 2 2 3 3 1 4 3 2 2 1 9 22 23 11 0 3 1 4 3 2 3 2 1 2 1 1 3 2 1 1 2 1 1 1 0 1 0 0 0
suspendedmatter ZS 6 1752 48 48 48 48 48 48 48 48 49 48 4 48 48 48 48 40 47 47 48 48 47 47 47 48 35 40 36 24 34 34 38 34 34 4 43 39 36 39 38 36 0 4 4 0 22 22 24 36 0 0 0 0
Bicarbonate HCO3 6 382 28 29 25 26 25 0 31 1 13 6 6 3 8 3 2 15 5 2 5 5 4 4 1 0 36 28 12 9 5 1 3 5 4 4 1 1 3 1 1 5 4 1 1 4 1 1 2 0 2 0 0 0
Sulfate SO4 6 279 26 22 21 20 23 0 23 1 7 8 4 2 2 2 3 4 3 2 3 6 2 2 1 0 22 23 12 1 3 1 3 3 2 3 1 1 2 1 1 3 2 1 1 2 1 1 1 0 1 0 0 0
Biochemicaloxygendemandover5days BZV5 13 750 30 29 2 25 0 29 29 25 30 29 0 3 24 25 0 29 2 4 2 4 0 0 4 0 24 29 25 13 23 23 27 23 23 4 25 28 25 28 27 25 0 3 3 0 11 11 0 25 0 0 0 0
Biochemicaloxygendemandofureaallythio BZV5a 16 511 16 17 0 16 4 17 16 16 17 17 0 6 17 17 4 17 0 7 0 0 0 0 0 0 22 16 17 17 16 17 16 16 16 2 16 16 17 16 17 16 0 2 1 0 17 17 0 17 0 0 0 0
Nitrate NO3 18 472 27 26 0 19 0 23 22 19 28 26 0 12 20 21 4 24 2 20 0 0 0 0 1 0 20 24 12 23 1 0 4 12 14 5 4 4 0 24 4 0 1 2 2 1 0 0 0 21 0 0 0 0
Nitrite NO2 18 471 27 26 0 19 0 23 22 19 28 26 0 12 20 21 4 24 2 20 0 0 0 0 1 0 19 24 12 23 1 0 4 12 12 5 4 4 0 24 4 0 1 3 3 1 0 0 0 21 0 0 0 0
Magnesium Mg 19 220 21 21 21 21 20 0 20 0 2 2 4 2 2 2 1 2 2 0 2 2 2 2 0 0 21 21 8 0 2 0 1 2 2 2 0 0 2 0 0 2 2 0 0 2 0 0 1 0 1 0 0 0
Sodium Na 19 220 21 21 21 21 20 0 20 0 2 2 4 2 2 2 1 2 2 0 2 2 2 2 0 0 21 21 8 0 2 0 1 2 2 2 0 0 2 0 0 2 2 0 0 2 0 0 1 0 1 0 0 0
Chlorophyll-a CHLFa 21 435 22 31 0 6 6 37 38 27 37 31 5 12 12 0 6 17 0 9 6 6 0 0 0 7 29 16 19 17 5 5 11 1 2 0 5 4 4 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0
dimethoate Dmtat 22 1375 53 54 51 53 53 54 51 53 54 54 6 53 54 54 54 55 54 55 54 54 54 54 53 54 44 40 23 26 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
simazine simzne 22 1375 53 54 51 53 53 54 51 53 54 54 6 53 54 54 54 55 54 55 54 54 54 54 53 54 44 40 23 26 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
atrazine atzne 22 1374 53 53 51 53 53 54 51 53 54 54 6 53 54 54 54 55 54 55 54 54 54 54 53 54 44 40 23 26 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
carbofuran cbfrn 22 1347 51 51 49 42 42 54 51 53 54 54 6 54 54 54 54 55 54 55 54 54 54 54 53 54 44 40 23 26 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
flutolanil flutlnl 22 1347 51 51 49 42 42 54 51 53 54 54 6 53 54 54 54 55 54 55 54 54 54 54 53 54 44 40 23 26 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
procimidon procmdn 22 1346 51 51 49 42 42 54 51 53 54 54 6 53 54 54 54 55 54 55 54 54 54 54 53 54 44 40 23 26 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
iprodione ipDon 22 1346 51 51 49 42 42 54 51 53 54 54 6 53 54 54 54 55 54 55 54 54 54 54 53 54 44 40 23 26 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
methylazinphos C1yazfs 22 1346 51 51 49 42 42 54 51 53 54 54 6 53 54 54 54 55 54 55 54 54 54 54 53 54 44 40 23 26 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
methylbromophos C1yBrfs 22 1346 51 51 49 42 42 54 51 53 54 54 6 53 54 54 54 55 54 55 54 54 54 54 53 54 44 40 23 26 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ethylazinphos C2yazfs 22 1346 51 51 49 42 42 54 51 53 54 54 6 53 54 54 54 55 54 55 54 54 54 54 53 54 44 40 23 26 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ethylbromophos C2yBrfs 22 1346 51 51 49 42 42 54 51 53 54 54 6 53 54 54 54 55 54 55 54 54 54 54 53 54 44 40 23 26 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ethylparathion C2ypton 22 1346 51 51 49 42 42 54 51 53 54 54 6 53 54 54 54 55 54 55 54 54 54 54 53 54 44 40 23 26 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
diazinon Daznn 22 1346 51 51 49 42 42 54 51 53 54 54 6 53 54 54 54 55 54 55 54 54 54 54 53 54 44 40 23 26 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
malathion malton 22 1346 51 51 49 42 42 54 51 53 54 54 6 53 54 54 54 55 54 55 54 54 54 54 53 54 44 40 23 26 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
pirimiphos-methyl pirmfC1y 22 1346 51 51 49 42 42 54 51 53 54 54 6 53 54 54 54 55 54 55 54 54 54 54 53 54 44 40 23 26 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
parathion-methyl ptonC1y 22 1346 51 51 49 42 42 54 51 53 54 54 6 53 54 54 54 55 54 55 54 54 54 54 53 54 44 40 23 26 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
pyrazophos pyrazfs 22 1346 51 51 49 42 42 54 51 53 54 54 6 53 54 54 54 55 54 55 54 54 54 54 53 54 44 40 23 26 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
tolclophos-methyl tolcfsC1y 22 1346 51 51 49 42 42 54 51 53 54 54 6 53 54 54 54 55 54 55 54 54 54 54 53 54 44 40 23 26 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
heptenophos heptnfs 22 1346 51 51 49 42 42 54 51 53 54 54 6 53 54 54 54 55 54 55 54 54 54 54 53 54 44 40 23 26 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
mevinphos mevfs 22 1346 51 51 49 42 42 54 51 53 54 54 6 53 54 54 54 55 54 55 54 54 54 54 53 54 44 40 23 26 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
chlorfenvinphos Clfvfs 22 1346 51 51 49 42 42 54 51 53 54 54 6 53 54 54 54 55 54 55 54 54 54 54 53 54 44 40 23 26 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
tetrachloorvinphos T4Clvfs 22 1346 51 51 49 42 42 54 51 53 54 54 6 53 54 54 54 55 54 55 54 54 54 54 53 54 44 40 23 26 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
triazophos Tazfs 22 1346 51 51 49 42 42 54 51 53 54 54 6 53 54 54 54 55 54 55 54 54 54 54 53 54 44 40 23 26 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
fenthion fenton 22 1346 51 51 49 42 42 54 51 53 54 54 6 53 54 54 54 55 54 55 54 54 54 54 53 54 44 40 23 26 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
disulfoton Dsftn 22 1346 51 51 49 42 42 54 51 53 54 54 6 53 54 54 54 55 54 55 54 54 54 54 53 54 44 40 23 26 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2.6-dichloorbenzamide 26DClBenAd 22 1346 51 51 49 42 42 54 51 53 54 54 6 53 54 54 54 55 54 55 54 54 54 54 53 54 44 40 23 26 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Bupirimate buprmt 22 1346 51 51 49 42 42 54 51 53 54 54 6 53 54 54 54 55 54 55 54 54 54 54 53 54 44 40 23 26 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
chlorpropham Clpfm 22 1346 51 51 49 42 42 54 51 53 54 54 6 53 54 54 54 55 54 55 54 54 54 54 53 54 44 40 23 26 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
chlorothalonil Cltlnl 22 1346 51 51 49 42 42 54 51 53 54 54 6 53 54 54 54 55 54 55 54 54 54 54 53 54 44 40 23 26 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Dimethomorph Dmtmf 22 1346 51 51 49 42 42 54 51 53 54 54 6 53 54 54 54 55 54 55 54 54 54 54 53 54 44 40 23 26 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
etridiazol eTDazl 22 1346 51 51 49 42 42 54 51 53 54 54 6 53 54 54 54 55 54 55 54 54 54 54 53 54 44 40 23 26 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
fenpropimorph fenppmf 22 1346 51 51 49 42 42 54 51 53 54 54 6 53 54 54 54 55 54 55 54 54 54 54 53 54 44 40 23 26 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Metazachlor metzCl 22 1346 51 51 49 42 42 54 51 53 54 54 6 53 54 54 54 55 54 55 54 54 54 54 53 54 44 40 23 26 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
pirimicarb pirmcb 22 1346 51 51 49 42 42 54 51 53 54 54 6 53 54 54 54 55 54 55 54 54 54 54 53 54 44 40 23 26 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
prosulfocarb prosfcb 22 1346 51 51 49 42 42 54 51 53 54 54 6 53 54 54 54 55 54 55 54 54 54 54 53 54 44 40 23 26 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Terbutryn terbtne 22 1346 51 51 49 42 42 54 51 53 54 54 6 53 54 54 54 55 54 55 54 54 54 54 53 54 44 40 23 26 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
tolylfluanide tolfande 22 1346 51 51 49 42 42 54 51 53 54 54 6 53 54 54 54 55 54 55 54 54 54 54 53 54 44 40 23 26 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
vinclozolin vinczln 22 1346 51 51 49 42 42 54 51 53 54 54 6 53 54 54 54 55 54 55 54 54 54 54 53 54 44 40 23 26 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
bitertanol bittnl 22 1346 51 51 49 42 42 54 51 53 54 54 6 53 54 54 54 55 54 55 54 54 54 54 53 54 44 40 23 26 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
metalaxyl mlxl 22 1346 51 51 49 42 42 54 51 53 54 54 6 53 54 54 54 55 54 55 54 54 54 54 53 54 44 40 23 26 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
N.N-diethyl-3-methylbenzamide DEET 22 1346 51 51 49 42 42 54 51 53 54 54 6 53 54 54 54 55 54 55 54 54 54 54 53 54 44 40 23 26 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Diethofencarb Detfcb 22 1346 51 51 49 42 42 54 51 53 54 54 6 53 54 54 54 55 54 55 54 54 54 54 53 54 44 40 23 26 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
4-dimethylaminosulphotoluidide DMST 22 1346 51 51 49 42 42 54 51 53 54 54 6 53 54 54 54 55 54 55 54 54 54 54 53 54 44 40 23 26 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
dodemorph dodmf 22 1346 51 51 49 42 42 54 51 53 54 54 6 53 54 54 54 55 54 55 54 54 54 54 53 54 44 40 23 26 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
furalaxyl furlxl 22 1346 51 51 49 42 42 54 51 53 54 54 6 53 54 54 54 55 54 55 54 54 54 54 53 54 44 40 23 26 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
metamitron mtmtn 22 1346 51 51 49 42 42 54 51 53 54 54 6 53 54 54 54 55 54 55 54 54 54 54 53 54 44 40 23 26 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
pyrimethanil pyrmtnl 22 1346 51 51 49 42 42 54 51 53 54 54 6 53 54 54 54 55 54 55 54 54 54 54 53 54 44 40 23 26 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
chloridazon Clidzn 22 1346 51 51 49 42 42 54 51 53 54 54 6 53 54 54 54 55 54 55 54 54 54 54 53 54 44 40 23 26 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
4-hydroxy-2.5.6-trichloorisoftalonitril HTI 22 1346 51 51 49 42 42 54 51 53 54 54 6 53 54 54 54 55 54 55 54 54 54 54 53 54 44 40 23 26 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Dichlorvos DClVS 23 1228 48 48 47 48 48 48 46 48 48 48 4 47 48 48 48 49 48 49 48 48 48 48 47 48 36 35 23 26 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Dichlobenil DCIBNL 23 1199 46 45 45 37 37 48 46 48 48 48 4 47 48 48 48 49 48 49 48 48 48 48 47 48 36 35 23 26 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
trans-permethrin tpermtn 23 1083 41 42 40 41 41 42 39 41 42 42 6 41 42 42 42 42 42 43 42 42 42 42 41 42 44 40 23 13 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
diuron Durn 24 739 53 54 51 53 53 29 38 24 18 18 6 19 18 18 18 18 18 18 18 18 18 18 17 18 44 40 23 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Isoproturon iptrn 24 733 52 52 50 52 52 29 38 24 18 18 6 19 18 18 18 18 18 18 18 18 18 18 17 18 44 40 23 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Aldicarb alDcb 24 678 41 41 39 41 41 29 38 24 18 18 6 19 18 18 18 18 18 18 18 18 18 18 17 18 44 40 23 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
aldicarbsulphon alDcbsfn 24 678 41 41 39 41 41 29 38 24 18 18 6 19 18 18 18 18 18 18 18 18 18 18 17 18 44 40 23 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
carbendazim cbedzm 24 678 41 41 39 41 41 29 38 24 18 18 6 19 18 18 18 18 18 18 18 18 18 18 17 18 44 40 23 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
imidacloprid imdcpd 24 678 41 41 39 41 41 29 38 24 18 18 6 19 18 18 18 18 18 18 18 18 18 18 17 18 44 40 23 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
indoxacarb indxcb 24 678 41 41 39 41 41 29 38 24 18 18 6 19 18 18 18 18 18 18 18 18 18 18 17 18 44 40 23 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
linuron linrn 24 678 41 41 39 41 41 29 38 24 18 18 6 19 18 18 18 18 18 18 18 18 18 18 17 18 44 40 23 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
methomyl metml 24 678 41 41 39 41 41 29 38 24 18 18 6 19 18 18 18 18 18 18 18 18 18 18 17 18 44 40 23 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
methiocarb metocb 24 678 41 41 39 41 41 29 38 24 18 18 6 19 18 18 18 18 18 18 18 18 18 18 17 18 44 40 23 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
methiocarbsulphon metocbsfn 24 678 41 41 39 41 41 29 38 24 18 18 6 19 18 18 18 18 18 18 18 18 18 18 17 18 44 40 23 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
methiocarbsulfoxide metocbsO 24 678 41 41 39 41 41 29 38 24 18 18 6 19 18 18 18 18 18 18 18 18 18 18 17 18 44 40 23 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
metoxuron metxrn 24 678 41 41 39 41 41 29 38 24 18 18 6 19 18 18 18 18 18 18 18 18 18 18 17 18 44 40 23 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
propoxur propxr 24 678 41 41 39 41 41 29 38 24 18 18 6 19 18 18 18 18 18 18 18 18 18 18 17 18 44 40 23 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Somphaeophytins s_FEO 24 378 22 30 0 6 6 34 30 24 30 30 5 12 6 0 6 17 0 9 6 6 0 0 0 7 15 15 18 17 5 5 11 1 0 0 3 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
alpha-endosulfan aedsfn 27 829 53 36 52 36 36 36 12 36 36 36 3 36 36 36 36 24 36 36 36 36 36 36 36 36 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
gamma-hexachlorocyclohexane(lindane) cHCH 27 829 53 36 52 36 36 36 12 36 36 36 3 36 36 36 36 24 36 36 36 36 36 36 36 36 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
endrin endn 27 829 53 36 52 36 36 36 12 36 36 36 3 36 36 36 36 24 36 36 36 36 36 36 36 36 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
hexachlorobenzene HCB 27 829 53 36 52 36 36 36 12 36 36 36 3 36 36 36 36 24 36 36 36 36 36 36 36 36 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Hexachlorobutadiene HxClbtDen 27 829 53 36 52 36 36 36 12 36 36 36 3 36 36 36 36 24 36 36 36 36 36 36 36 36 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2.2'.4.5.5'-pentachlorobiphenyl PCB101 27 829 53 36 52 36 36 36 12 36 36 36 3 36 36 36 36 24 36 36 36 36 36 36 36 36 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2.3'.4.4'.5-pentachlorobiphenyl PCB118 27 829 53 36 52 36 36 36 12 36 36 36 3 36 36 36 36 24 36 36 36 36 36 36 36 36 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2.2'.3.4.4'.5'-hexachloorbiphenyl PCB138 27 829 53 36 52 36 36 36 12 36 36 36 3 36 36 36 36 24 36 36 36 36 36 36 36 36 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2.2'.4.4'.5.5'-hexachloorbiphenyl PCB153 27 829 53 36 52 36 36 36 12 36 36 36 3 36 36 36 36 24 36 36 36 36 36 36 36 36 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2.2'.3.4.4'.5.5'-heptachloorbiphenyl PCB180 27 829 53 36 52 36 36 36 12 36 36 36 3 36 36 36 36 24 36 36 36 36 36 36 36 36 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2.2'.5.5'-tetrachloorbiphenyl PCB52 27 829 53 36 52 36 36 36 12 36 36 36 3 36 36 36 36 24 36 36 36 36 36 36 36 36 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
4.4'-dichlorodiphenyltrichloroethane 44DDT 27 829 53 36 52 36 36 36 12 36 36 36 3 36 36 36 36 24 36 36 36 36 36 36 36 36 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2.4.4'-Trichlorobiphenyl PCB28 27 829 53 36 52 36 36 36 12 36 36 36 3 36 36 36 36 24 36 36 36 36 36 36 36 36 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
pentachlorobenzene PeClBen 27 828 53 36 52 36 36 36 12 36 36 36 3 36 36 36 36 24 36 36 35 36 36 36 36 36 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
beta-endosulfan bedsfn 27 807 53 36 52 25 25 36 12 36 36 36 3 36 36 36 36 24 36 36 36 36 36 36 36 36 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2.4'-dichloordifenyldichloorethane 24DDD 27 807 53 36 52 25 25 36 12 36 36 36 3 36 36 36 36 24 36 36 36 36 36 36 36 36 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
4.4'-dichloordifenyldichloorethane 44DDD 27 807 53 36 52 25 25 36 12 36 36 36 3 36 36 36 36 24 36 36 36 36 36 36 36 36 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
4.4'-dichloordifenyldichloorethene 44DDE 27 807 53 36 52 25 25 36 12 36 36 36 3 36 36 36 36 24 36 36 36 36 36 36 36 36 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
alpha-hexachlorocyclohexane aHCH 27 807 53 36 52 25 25 36 12 36 36 36 3 36 36 36 36 24 36 36 36 36 36 36 36 36 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
aldrin aldn 27 807 53 36 52 25 25 36 12 36 36 36 3 36 36 36 36 24 36 36 36 36 36 36 36 36 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
beta-hexachlorocyclohexane bHCH 27 807 53 36 52 25 25 36 12 36 36 36 3 36 36 36 36 24 36 36 36 36 36 36 36 36 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
dieldrin dieldn 27 807 53 36 52 25 25 36 12 36 36 36 3 36 36 36 36 24 36 36 36 36 36 36 36 36 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
heptachlor HpCl 27 807 53 36 52 25 25 36 12 36 36 36 3 36 36 36 36 24 36 36 36 36 36 36 36 36 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
telodrin teldn 27 807 53 36 52 25 25 36 12 36 36 36 3 36 36 36 36 24 36 36 36 36 36 36 36 36 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
endosulfansulphate endsfSO4 27 807 53 36 52 25 25 36 12 36 36 36 3 36 36 36 36 24 36 36 36 36 36 36 36 36 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
delta-hexachlorocyclohexane dHCH 27 807 53 36 52 25 25 36 12 36 36 36 3 36 36 36 36 24 36 36 36 36 36 36 36 36 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2.4'-dichloordifenyldichloorethene 24DDE 27 807 53 36 52 25 25 36 12 36 36 36 3 36 36 36 36 24 36 36 36 36 36 36 36 36 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2.4'-dichlorodiphenyltrichloroethane 24DDT 27 807 53 36 52 25 25 36 12 36 36 36 3 36 36 36 36 24 36 36 36 36 36 36 36 36 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
isodrin idn 27 807 53 36 52 25 25 36 12 36 36 36 3 36 36 36 36 24 36 36 36 36 36 36 36 36 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
heptachlor HpClepO 27 798 48 36 48 25 25 36 12 36 36 36 3 36 36 36 36 24 36 36 36 36 36 36 36 36 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
somaldrin.dieldrin.endrinandisodrin sdrin4 27 735 44 34 44 34 33 33 11 33 34 34 3 32 29 32 31 22 29 32 33 31 33 31 31 31 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
sum-a.b.candd-HCH sHCH4 27 726 41 32 41 34 33 33 11 33 34 34 3 32 29 32 30 22 29 32 33 31 33 31 31 31 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
sum2.4'-and4.4'-DDT sDDT 27 724 44 33 44 33 33 33 11 32 34 34 3 32 27 32 31 20 31 32 31 31 33 31 29 29 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
cypermethrin cypmtn 27 263 10 9 9 1 1 12 12 12 12 12 0 12 12 12 12 13 12 12 12 12 12 12 12 12 0 0 0 13 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
sumdemetonisomers sdmtn 27 262 10 9 9 1 1 12 12 12 12 12 0 12 12 12 12 13 12 12 12 12 12 12 12 12 0 0 0 12 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
chlordane Cldn 28 611 28 28 28 17 17 28 12 29 29 29 0 29 29 29 29 17 29 29 29 29 29 29 29 29 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Lead Pb 29 651 93 66 90 66 65 12 62 12 12 13 6 1 0 1 0 0 1 0 1 0 1 1 0 0 57 52 36 0 0 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
benzo(a)pyrene BaP 38 642 53 54 52 52 53 54 52 53 54 54 4 0 0 0 0 0 0 0 0 0 0 0 0 0 44 40 23 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
benzo(b)fluoranthene BbF 38 642 53 54 52 52 53 54 52 53 54 54 4 0 0 0 0 0 0 0 0 0 0 0 0 0 44 40 23 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
benzo(g.h.i)perylene BghiPe 38 642 53 54 52 52 53 54 52 53 54 54 4 0 0 0 0 0 0 0 0 0 0 0 0 0 44 40 23 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
indeno(1.2.3-c.d)pyrene InP 38 642 53 54 52 52 53 54 52 53 54 54 4 0 0 0 0 0 0 0 0 0 0 0 0 0 44 40 23 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
naphthalene Naf 38 642 53 54 52 52 53 54 52 53 54 54 4 0 0 0 0 0 0 0 0 0 0 0 0 0 44 40 23 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
anthracene Ant 38 641 53 54 52 52 52 54 52 53 54 54 4 0 0 0 0 0 0 0 0 0 0 0 0 0 44 40 23 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
benzo(k)fluoranthene BkF 38 641 53 53 52 52 53 54 52 53 54 54 4 0 0 0 0 0 0 0 0 0 0 0 0 0 44 40 23 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
fluoranthene Flu 38 641 53 53 52 52 53 54 52 53 54 54 4 0 0 0 0 0 0 0 0 0 0 0 0 0 44 40 23 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
acenaphthene AcNe 38 593 44 44 43 42 42 54 52 53 54 54 4 0 0 0 0 0 0 0 0 0 0 0 0 0 44 40 23 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
acenaftyleen AcNy 38 593 44 44 43 42 42 54 52 53 54 54 4 0 0 0 0 0 0 0 0 0 0 0 0 0 44 40 23 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
benzo(a)anthracene BaA 38 593 44 44 43 42 42 54 52 53 54 54 4 0 0 0 0 0 0 0 0 0 0 0 0 0 44 40 23 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
chrysene Chr 38 593 44 44 43 42 42 54 52 53 54 54 4 0 0 0 0 0 0 0 0 0 0 0 0 0 44 40 23 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
dibenzo(a.h)anthracene DBahAnt 38 593 44 44 43 42 42 54 52 53 54 54 4 0 0 0 0 0 0 0 0 0 0 0 0 0 44 40 23 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
phenanthrene Fen 38 593 44 44 43 42 42 54 52 53 54 54 4 0 0 0 0 0 0 0 0 0 0 0 0 0 44 40 23 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
fluorene Fle 38 593 44 44 43 42 42 54 52 53 54 54 4 0 0 0 0 0 0 0 0 0 0 0 0 0 44 40 23 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
pyrene Pyr 38 593 44 44 43 42 42 54 52 53 54 54 4 0 0 0 0 0 0 0 0 0 0 0 0 0 44 40 23 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
chrome Cr 38 388 60 37 60 37 36 12 36 12 12 12 0 0 0 1 0 0 0 0 0 0 0 0 0 0 24 24 25 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
cadmium Cd 39 396 65 37 64 37 36 12 36 12 12 12 0 0 0 0 0 0 0 0 0 0 0 0 0 0 24 24 25 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Mercury Hg 39 181 41 12 40 12 12 12 13 12 12 12 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Dissolvedorganiccarbon DOC 41 187 22 21 22 21 22 1 22 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 21 21 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Totalorganiccarbon TOC 42 168 19 20 20 19 20 0 19 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 20 20 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
arsenic As 42 120 12 12 12 12 12 12 12 12 12 12 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
trichlorobenzene TClBen 42 118 13 13 13 13 13 0 13 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 13 13 13 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Potassium K 46 7 1 1 1 1 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
bentazone bentzn 47 73 11 17 17 11 17 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2-methyl-4-chlorophenoxyaceticacid MCPA 47 73 11 17 17 11 17 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2-methyl-4-chloorfenoxypropionzuur MCPP 47 73 11 17 17 11 17 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Chlorpyriphos Clprfs 47 59 12 12 11 12 12 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
1.2-dichloroethane 12DClC2a 47 55 11 11 11 11 11 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2.4-dichlorophenol 24DClFol 47 55 11 11 11 11 11 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
chlorotoluron Cltlrn 47 55 11 11 11 11 11 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
dichloromethane DClC1a 47 55 11 11 11 11 11 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Carbontetrachloride(tetra) T4ClC1a 47 55 11 11 11 11 11 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
tetrachloroethene(a) T4ClC2e 47 55 11 11 11 11 11 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Trichlorobenzene TCB 47 55 11 11 11 11 11 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
trichloromethane(chloroform) TClC1a 47 55 11 11 11 11 11 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
trichloroethene(Tri) TClC2e 47 55 11 11 11 11 11 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Alachlor alCl 47 54 11 11 10 11 11 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
somchloorfenvinvos s_CFVP 47 54 11 11 10 11 11 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Trifluralin TFLURLN 47 54 11 11 10 11 11 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
pentachlorophenol PeClFol 47 38 4 10 10 4 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Amino-methyl-phosphonate AMPA 47 31 7 7 7 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Gluphosinate GLUFSNT 47 31 7 7 7 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
glyphosate glyfst 47 31 7 7 7 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
tributyltin TC4ySn 47 25 4 9 4 4 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Benzene Ben 47 20 4 4 4 4 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
dibutyltin DC4ySn 47 20 4 4 4 4 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
sompentachlooranilinen s_PCAn 47 20 4 4 4 4 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
4-chloroaniline 4ClAn 47 19 4 4 3 4 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
bis(2-ethylhexyl)phthalate(DOP/DEHP) DEHP 47 19 4 4 3 4 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Thermotolerantcoliforms THERMTCL 49 44 10 0 0 0 0 23 0 0 0 0 0 0 0 0 0 0 0 0 0 11 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Totalcoliforms TTCLI 49 36 10 0 0 0 0 23 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
phosphamidon fosfmdn 49 18 0 6 6 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
metribuzin metbzn 49 18 0 6 6 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Escherichiacoli E_COLI 50 22 11 0 0 0 0 11 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Intestinalenterococci INTTNLETRCCN 50 21 10 0 0 0 0 11 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Monitoring data assessment report M 3 99/109
Appendix III. Numbers of positive detections for each analyte for Delfland Water
Quality grab samples
OW043-002
OW062-008
OW058-001
OW021-003
OW006-003
OW203-111
OW202-000
OW090-000
OW004-001
OW056-000
OW907-010
OW116-012
OW119-000
OW115-012
OW110-000
OW215-024
OW301-001
OW221A012
OW080-002
OW047-001
OW310-000
OW306-023
OW306-022
OW015-003
OW111-000
OW221A013
OW215-026
OW210-003
OW411-014
OW008-002
OW213B024
OW306B012
OW220-010
OW207-002
OW221A023
OW069-000
OW215-032
OW216-002
OW208-016
OW015-012
OW218-200
OW217-000
OW202-322
OW220-000
OW050-002
OW413-001
OW211-000
OW401-003
OW208-000
OW208-001
OW221A000
OW202-100
Glass-houses
Glass-houses
Glass-houses
Glass-houses
Glass-houses
Glass-houses
Basic network
Glass-houses
Glass-houses
Glass-houses
WFD monitoring point
Glass-houses
Glass-houses
Glass-houses
Glass-houses
Glass-houses
Glass-houses
Glass-houses
Glass-houses
Glass-houses
Glass-houses
Glass-houses
Glass-houses
Glass-houses
WFD monitoring point
WFD monitoring point
Basic network
Basic network
Urban areas monitoring
Urban areas monitoring
Urban areas monitoring
Urban areas monitoring
Urban areas monitoring
Basic network
Urban areas monitoring
Urban areas monitoring
Urban areas monitoring
Urban areas monitoring
Urban areas monitoring
Urban areas monitoring
Basic network
Basic network
Basic network
Basic network
Urban areas monitoring
Urban areas monitoring
Basic network
Urban areas monitoring
Basic network
Basic network
Basic network
Basic network
zeros 20
22
27
27
28
51
51
52
54
54
63
74
75
75
75
75
77
78
78
79
80
81
83
86
88
88
88
92
113
125
177
177
178
178
179
179
179
180
181
181
181
181
181
182
183
183
183
184
184
193
193
193
Analyte name Par_code zeros
n
8083
7261
7585
6510
6416
6784
6079
6379
6577
6432
796
5223
5339
5333
5204
5042
5135
5232
5139
5417
5117
5282
5036
5198
5067
4635
3013
2447
881
980
910
812
824
367
964
905
829
938
878
810
288
177
123
270
554
554
537
842
233
92
92
79
Visible surface condition VUIL 0 2402 85 72 68 67 70 78 61 55 74 61 13 56 55 55 54 55 54 55 55 78 55 66 55 60 58 48 45 56 42 43 47 41 42 21 52 48 42 48 46 41 19 11 8 18 30 30 31 42 16 7 7 6
Weather WEERGSHD 0 2402 85 72 68 67 70 78 61 55 74 61 13 56 55 55 54 55 54 55 55 78 55 66 55 60 58 48 45 56 42 43 47 41 42 21 52 48 42 48 46 41 19 11 8 18 30 30 31 42 16 7 7 6
Fluidflowingwaters STROMSTR 0 2402 85 72 68 67 70 78 61 55 74 61 13 56 55 55 54 55 54 55 55 78 55 66 55 60 58 48 45 56 42 43 47 41 42 21 52 48 42 48 46 41 19 11 8 18 30 30 31 42 16 7 7 6
Oxygen O2 0 2398 85 72 68 66 70 78 61 55 74 61 13 56 55 55 54 55 54 55 55 78 55 66 55 60 58 48 44 56 42 43 47 41 42 21 51 48 42 48 46 41 19 11 7 18 30 30 31 42 16 7 7 6
Conductivity GELDHD 0 2398 85 72 68 66 70 78 61 55 74 61 13 56 55 55 54 55 54 55 55 78 55 66 55 60 58 48 44 56 42 43 47 41 42 21 51 48 42 48 46 41 19 11 7 18 30 30 31 42 16 7 7 6
Temperature T 0 2398 85 72 68 66 70 78 61 55 74 61 13 56 55 55 54 55 54 55 55 78 55 66 55 60 58 48 44 56 42 43 47 41 42 21 51 48 42 48 46 41 19 11 7 18 30 30 31 42 16 7 7 6
Acidity pH 0 2398 85 72 68 66 70 78 61 55 74 61 13 56 55 55 54 55 54 55 55 78 55 66 55 60 58 48 44 56 42 43 47 41 42 21 51 48 42 48 46 41 19 11 7 18 30 30 31 42 16 7 7 6
Clarity ZICHT 0 2369 82 62 68 67 67 77 61 55 72 58 13 56 55 55 54 54 54 55 55 75 55 66 55 59 58 48 44 56 41 42 47 41 42 21 51 48 42 47 46 41 19 11 7 18 30 30 31 42 16 7 7 6
Kjeldahlnitrogen NKj 0 2230 66 54 64 65 62 53 56 39 57 54 7 54 55 54 54 55 53 53 53 66 53 65 51 59 50 45 42 56 41 41 44 39 40 23 49 45 42 45 45 41 18 10 6 17 29 29 30 43 15 6 6 6
totalphosphate P 0 2229 66 54 64 65 64 40 56 49 57 54 5 54 55 54 54 55 53 53 54 66 53 65 53 59 50 45 42 56 41 41 44 39 40 23 49 45 42 45 45 41 18 10 6 17 29 29 30 43 15 6 6 5
nitrogen N 0 2215 66 54 64 65 65 53 56 53 57 54 11 54 55 54 54 55 52 52 53 64 52 64 52 58 50 45 42 56 41 41 44 39 40 21 49 45 42 45 45 41 16 10 6 15 29 29 31 43 15 6 6 5
chloride Cl 3 1939 64 49 60 60 63 48 48 48 63 50 9 48 47 48 48 48 47 47 48 62 47 59 46 53 35 40 36 49 35 34 38 34 34 17 43 39 36 39 38 36 12 4 4 12 22 22 24 36 9 0 0 0
Ortho-phosphate PO4 3 1929 61 46 60 60 53 15 44 35 49 48 0 48 48 48 48 49 47 47 48 59 47 59 47 53 34 38 36 10 30 35 22 33 35 17 31 40 2 30 22 36 6 4 4 12 23 23 24 37 9 0 0 0
ammonium NH4 3 1929 36 19 28 25 38 13 37 12 36 24 1 22 34 18 28 32 32 41 35 53 35 29 28 17 24 30 27 11 16 27 27 23 29 12 9 26 24 33 33 31 11 4 4 9 20 21 17 37 4 0 0 0
totalnitrateandnitrite sNO3NO2 3 1918 61 48 60 58 60 18 47 48 49 48 9 48 48 48 48 50 47 47 48 58 47 59 47 39 25 38 36 22 14 31 21 24 27 14 24 37 29 28 24 14 12 4 4 11 13 16 17 24 7 0 0 0
Copper Cu 3 1864 94 95 91 93 64 4 69 45 50 51 6 25 42 42 25 46 10 4 1 0 1 1 0 0 74 77 63 6 4 29 17 7 31 7 16 38 30 35 4 4 7 7 4 5 16 17 23 18 6 4 6 5
Nickel Ni 4 1809 94 94 91 94 63 43 91 41 54 54 6 25 42 42 25 42 10 0 1 0 1 1 0 0 91 81 65 30 39 41 39 39 39 7 40 40 43 40 40 41 7 6 2 5 28 28 24 29 6 6 6 5
Zinc Zn 5 1861 94 84 91 94 55 2 87 10 54 54 0 25 42 42 25 46 10 4 0 0 1 0 0 0 61 83 64 2 9 39 37 13 32 11 23 44 38 31 37 5 7 9 6 5 25 17 23 26 6 4 6 5
Calcium Ca 5 308 23 22 21 21 20 18 22 18 5 5 4 3 2 2 2 3 3 1 4 3 2 2 1 9 22 23 11 0 3 1 4 3 2 3 2 1 2 1 1 3 2 1 1 2 1 1 1 0 1 0 0 0
suspendedmatter ZS 6 1752 35 48 48 46 46 39 48 13 49 48 1 48 48 48 48 40 46 46 43 46 47 47 34 48 35 39 34 23 34 30 34 28 24 4 31 26 36 38 35 13 0 4 4 0 13 16 24 12 0 0 0 0
Bicarbonate HCO3 6 382 28 29 25 26 25 0 31 1 13 6 6 3 8 3 2 15 5 2 5 5 4 4 1 0 36 28 12 9 5 1 3 5 4 4 1 1 3 1 1 5 4 1 1 4 1 1 2 0 2 0 0 0
Sulfate SO4 6 279 26 22 21 20 23 0 23 1 7 8 4 2 2 2 3 4 3 2 3 6 2 2 1 0 22 23 12 1 3 1 3 3 2 3 1 1 2 1 1 3 2 1 1 2 1 1 1 0 1 0 0 0
Biochemicaloxygendemandover5days BZV5 13 750 27 27 2 25 0 29 29 19 30 29 0 3 24 24 0 29 2 4 2 4 0 0 4 0 24 29 25 13 23 22 25 23 23 4 24 24 24 27 26 25 0 3 3 0 11 10 0 23 0 0 0 0
Biochemicaloxygendemandofureaallythio BZV5a 16 511 12 16 0 16 4 17 16 11 15 17 0 6 17 17 4 17 0 7 0 0 0 0 0 0 22 16 17 17 16 17 16 15 16 2 15 12 17 15 17 15 0 2 1 0 17 17 0 16 0 0 0 0
Nitrate NO3 18 472 27 26 0 19 0 10 21 19 28 26 0 12 20 21 4 24 2 20 0 0 0 0 1 0 15 23 12 11 0 0 2 10 12 5 3 4 0 18 2 0 1 2 2 1 0 0 0 13 0 0 0 0
Nitrite NO2 18 471 1 0 0 1 0 0 9 0 9 10 0 1 16 14 3 3 2 12 0 0 0 0 1 0 1 7 2 0 0 0 0 4 1 0 0 1 0 1 0 0 0 0 3 0 0 0 0 1 0 0 0 0
Magnesium Mg 19 220 21 21 21 21 20 0 20 0 2 2 4 2 2 2 1 2 2 0 2 2 2 2 0 0 21 21 8 0 2 0 1 2 2 2 0 0 2 0 0 2 2 0 0 2 0 0 1 0 1 0 0 0
Sodium Na 19 220 21 21 21 21 20 0 20 0 2 2 4 2 2 2 1 2 2 0 2 2 2 2 0 0 21 21 8 0 2 0 0 2 2 2 0 0 2 0 0 2 2 0 0 2 0 0 1 0 1 0 0 0
Chlorophyll-a CHLFa 21 435 8 19 0 4 5 30 38 10 36 31 0 12 12 0 6 17 0 8 5 4 0 0 0 6 28 14 18 17 5 5 6 0 2 0 2 3 4 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0
dimethoate Dmtat 22 1375 1 4 1 6 2 0 8 0 4 4 0 10 1 4 3 13 6 38 4 0 3 1 2 1 1 7 6 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
simazine simzne 22 1375 0 0 0 0 1 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
atrazine atzne 22 1374 0 0 0 0 1 0 0 1 0 0 0 0 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
carbofuran cbfrn 22 1347 3 8 20 10 26 0 15 3 9 9 1 12 9 18 10 13 19 9 16 2 16 21 10 7 2 6 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
flutolanil flutlnl 22 1347 0 0 20 0 1 0 0 1 0 23 1 0 0 3 0 1 13 0 9 1 3 18 13 9 1 0 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
procimidon procmdn 22 1346 0 3 0 0 3 0 6 0 4 3 0 0 19 7 2 2 13 36 5 0 9 5 9 1 0 4 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
iprodione ipDon 22 1346 1 0 4 0 4 0 0 1 0 4 0 2 2 9 4 1 5 3 8 0 3 16 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
methylazinphos C1yazfs 22 1346 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
methylbromophos C1yBrfs 22 1346 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ethylazinphos C2yazfs 22 1346 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ethylbromophos C2yBrfs 22 1346 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ethylparathion C2ypton 22 1346 0 0 2 0 0 0 0 0 0 0 0 2 0 1 0 0 2 0 0 0 2 2 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
diazinon Daznn 22 1346 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 8 0 3 0 0 0 4 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
malathion malton 22 1346 0 0 0 0 1 0 1 0 0 1 0 1 0 2 1 1 0 1 0 1 1 2 2 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
pirimiphos-methyl pirmfC1y 22 1346 2 1 4 0 5 1 2 1 11 8 0 13 2 3 7 4 16 36 2 1 5 16 1 1 0 4 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
parathion-methyl ptonC1y 22 1346 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 3 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
pyrazophos pyrazfs 22 1346 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
tolclophos-methyl tolcfsC1y 22 1346 1 1 30 9 23 0 6 0 20 33 1 33 11 43 17 7 31 0 47 1 29 47 42 8 1 1 2 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
heptenophos heptnfs 22 1346 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 4 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
mevinphos mevfs 22 1346 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
chlorfenvinphos Clfvfs 22 1346 0 0 1 0 0 1 0 0 0 0 0 0 0 13 0 0 1 0 0 0 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
tetrachloorvinphos T4Clvfs 22 1346 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
triazophos Tazfs 22 1346 0 0 1 0 0 2 1 0 0 2 0 1 1 5 0 1 1 0 2 0 3 3 0 3 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
fenthion fenton 22 1346 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
disulfoton Dsftn 22 1346 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2.6-dichloorbenzamide 26DClBenAd 22 1346 20 3 4 5 4 9 5 2 12 6 0 7 6 4 10 6 3 2 9 7 3 3 5 6 1 3 3 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Bupirimate buprmt 22 1346 0 2 0 0 0 0 5 0 2 1 1 1 3 0 2 14 0 19 0 0 0 2 0 0 0 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
chlorpropham Clpfm 22 1346 0 0 4 1 1 0 0 0 0 3 0 1 0 5 2 0 0 0 1 0 1 1 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
chlorothalonil Cltlnl 22 1346 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Dimethomorph Dmtmf 22 1346 14 12 19 30 17 0 15 3 36 23 1 37 32 16 28 9 15 40 30 2 36 35 25 18 1 19 1 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
etridiazol eTDazl 22 1346 1 1 23 15 21 0 6 1 20 29 0 33 13 38 36 26 27 3 24 1 21 39 22 13 1 2 6 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
fenpropimorph fenppmf 22 1346 1 0 0 0 0 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Metazachlor metzCl 22 1346 0 0 1 0 1 1 1 3 1 1 0 0 2 1 2 0 3 5 1 1 0 0 2 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
pirimicarb pirmcb 22 1346 14 22 46 30 31 1 47 3 47 48 1 43 46 52 51 50 51 50 51 3 54 51 51 41 6 20 17 5 1 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
prosulfocarb prosfcb 22 1346 1 1 0 1 2 1 1 2 0 1 0 2 1 2 2 2 1 0 1 1 1 1 3 2 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Terbutryn terbtne 22 1346 0 0 0 0 0 1 1 1 0 0 0 0 2 0 0 0 0 0 0 0 1 1 1 3 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
tolylfluanide tolfande 22 1346 2 1 0 0 0 1 0 1 0 1 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
vinclozolin vinczln 22 1346 0 0 1 0 0 1 0 0 0 0 0 0 1 1 0 0 4 0 7 0 0 1 11 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
bitertanol bittnl 22 1346 0 2 6 6 12 0 4 0 3 5 0 10 3 20 9 2 3 4 8 1 6 5 6 2 1 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
metalaxyl mlxl 22 1346 1 1 36 29 23 0 9 2 31 30 1 35 35 28 42 3 31 38 43 3 38 45 37 30 7 13 1 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
N.N-diethyl-3-methylbenzamide DEET 22 1346 12 16 7 9 9 38 30 17 18 9 1 4 13 11 10 16 10 15 8 15 3 3 6 8 8 8 7 5 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Diethofencarb Detfcb 22 1346 1 9 22 15 22 1 21 0 16 27 1 23 22 46 41 14 30 39 18 2 9 38 18 20 1 14 3 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
4-dimethylaminosulphotoluidide DMST 22 1346 0 2 6 2 9 0 2 0 1 6 0 1 5 10 5 3 3 6 2 0 1 7 4 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
dodemorph dodmf 22 1346 0 0 0 0 0 0 0 0 0 1 0 8 1 0 2 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
furalaxyl furlxl 22 1346 0 0 4 2 1 0 1 0 1 5 0 0 1 3 2 4 17 4 1 0 1 8 3 5 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
metamitron mtmtn 22 1346 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
pyrimethanil pyrmtnl 22 1346 11 12 34 34 26 0 19 2 36 41 1 20 25 22 37 11 17 12 28 3 12 38 40 24 12 21 8 5 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
chloridazon Clidzn 22 1346 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
4-hydroxy-2.5.6-trichloorisoftalonitril HTI 22 1346 1 0 9 0 6 0 0 0 0 4 0 1 2 6 1 0 13 1 2 0 3 1 4 2 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Dichlorvos DClVS 23 1228 0 0 0 0 4 0 0 0 2 0 0 0 3 3 0 6 0 2 1 1 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Dichlobenil DCIBNL 23 1199 15 4 4 5 3 6 7 3 16 10 0 13 3 8 12 3 6 7 11 12 7 6 10 10 3 3 4 2 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
trans-permethrin tpermtn 23 1083 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
diuron Durn 24 739 36 42 26 24 27 3 9 18 1 0 4 0 1 0 0 0 1 1 0 6 0 1 0 0 12 9 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Isoproturon iptrn 24 733 5 16 9 10 18 2 2 14 1 0 2 1 0 1 1 1 0 0 2 0 0 0 1 0 1 3 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Aldicarb alDcb 24 678 0 0 1 0 0 0 2 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
aldicarbsulphon alDcbsfn 24 678 2 1 1 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
carbendazim cbedzm 24 678 24 24 26 23 28 2 18 18 3 7 5 6 6 7 7 1 5 4 6 2 5 7 6 5 11 15 17 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
imidacloprid imdcpd 24 678 39 31 39 40 32 6 35 1 18 17 2 16 17 18 17 17 17 17 18 2 18 18 17 17 27 37 21 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
indoxacarb indxcb 24 678 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
linuron linrn 24 678 0 3 9 1 7 2 7 1 4 0 0 1 3 8 7 6 2 5 4 0 1 5 0 0 0 1 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
methomyl metml 24 678 0 3 19 12 18 0 7 1 2 3 0 6 5 11 6 3 3 5 2 0 4 7 1 1 0 1 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
methiocarb metocb 24 678 1 1 6 3 5 1 2 0 1 1 0 0 1 1 0 0 1 0 0 0 0 1 0 0 2 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
methiocarbsulphon metocbsfn 24 678 1 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
methiocarbsulfoxide metocbsO 24 678 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
metoxuron metxrn 24 678 1 1 6 0 2 0 1 0 1 1 0 0 0 1 1 0 0 0 0 0 0 3 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
propoxur propxr 24 678 1 2 2 2 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Somphaeophytins s_FEO 24 378 7 22 0 6 4 15 28 8 29 28 1 12 5 0 6 15 0 9 4 5 0 0 0 5 11 12 15 10 5 5 6 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
alpha-endosulfan aedsfn 27 829 0 0 4 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
gamma-hexachlorocyclohexane(lindane) cHCH 27 829 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
endrin endn 27 829 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
hexachlorobenzene HCB 27 829 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Hexachlorobutadiene HxClbtDen 27 829 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2.2'.4.5.5'-pentachlorobiphenyl PCB101 27 829 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2.3'.4.4'.5-pentachlorobiphenyl PCB118 27 829 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2.2'.3.4.4'.5'-hexachloorbiphenyl PCB138 27 829 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2.2'.4.4'.5.5'-hexachloorbiphenyl PCB153 27 829 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2.2'.3.4.4'.5.5'-heptachloorbiphenyl PCB180 27 829 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2.2'.5.5'-tetrachloorbiphenyl PCB52 27 829 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
4.4'-dichlorodiphenyltrichloroethane 44DDT 27 829 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2.4.4'-Trichlorobiphenyl PCB28 27 829 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
pentachlorobenzene PeClBen 27 828 2 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
beta-endosulfan bedsfn 27 807 1 0 4 0 1 0 0 2 0 1 0 0 1 1 1 1 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2.4'-dichloordifenyldichloorethane 24DDD 27 807 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
4.4'-dichloordifenyldichloorethane 44DDD 27 807 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
4.4'-dichloordifenyldichloorethene 44DDE 27 807 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
alpha-hexachlorocyclohexane aHCH 27 807 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
aldrin aldn 27 807 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
beta-hexachlorocyclohexane bHCH 27 807 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
dieldrin dieldn 27 807 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0 5 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
heptachlor HpCl 27 807 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
telodrin teldn 27 807 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
endosulfansulphate endsfSO4 27 807 3 1 25 0 3 0 0 2 0 3 0 2 7 10 1 1 5 1 3 1 2 3 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
delta-hexachlorocyclohexane dHCH 27 807 2 0 3 0 1 0 0 2 0 1 0 0 1 1 2 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2.4'-dichloordifenyldichloorethene 24DDE 27 807 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2.4'-dichlorodiphenyltrichloroethane 24DDT 27 807 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
isodrin idn 27 807 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
heptachlor HpClepO 27 798 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
somaldrin.dieldrin.endrinandisodrin sdrin4 27 735 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
sum-a.b.candd-HCH sHCH4 27 726 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
sum2.4'-and4.4'-DDT sDDT 27 724 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
cypermethrin cypmtn 27 263 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
sumdemetonisomers sdmtn 27 262 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
chlordane Cldn 28 611 28 28 28 17 17 27 11 28 28 28 0 28 28 28 28 17 28 28 28 28 28 28 28 28 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Lead Pb 29 651 56 32 57 21 27 0 17 0 2 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 18 16 20 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
benzo(a)pyrene BaP 38 642 4 46 13 0 2 0 1 1 3 11 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
benzo(b)fluoranthene BbF 38 642 3 41 18 0 5 0 2 0 4 13 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
benzo(g.h.i)perylene BghiPe 38 642 5 38 9 0 1 0 1 0 0 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
indeno(1.2.3-c.d)pyrene InP 38 642 1 40 12 0 1 0 1 1 1 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
naphthalene Naf 38 642 1 2 0 0 1 0 0 0 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
anthracene Ant 38 641 0 20 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
benzo(k)fluoranthene BkF 38 641 1 34 3 0 1 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
fluoranthene Flu 38 641 2 44 19 1 17 0 18 3 22 25 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 5 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
acenaphthene AcNe 38 593 2 1 2 2 3 0 2 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
acenaftyleen AcNy 38 593 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
benzo(a)anthracene BaA 38 593 0 36 3 0 1 0 1 0 1 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
chrysene Chr 38 593 1 38 15 0 3 0 2 0 7 16 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
dibenzo(a.h)anthracene DBahAnt 38 593 0 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
phenanthrene Fen 38 593 4 30 7 5 9 2 11 4 12 12 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 10 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
fluorene Fle 38 593 3 10 2 4 7 1 15 2 6 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 12 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
pyrene Pyr 38 593 5 39 11 0 8 0 11 2 18 22 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
chrome Cr 38 388 11 17 11 11 9 3 18 3 8 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10 8 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
cadmium Cd 39 396 44 29 53 25 25 0 18 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 18 21 21 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Mercury Hg 39 181 13 10 14 7 7 2 1 0 3 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Dissolvedorganiccarbon DOC 41 187 22 21 22 21 22 1 22 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 21 21 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Totalorganiccarbon TOC 42 168 19 20 20 19 20 0 19 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 20 20 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
arsenic As 42 120 12 11 12 12 8 7 12 10 12 12 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
trichlorobenzene TClBen 42 118 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Potassium K 46 7 1 1 1 1 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
bentazone bentzn 47 73 1 1 2 0 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2-methyl-4-chlorophenoxyaceticacid MCPA 47 73 2 4 6 6 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2-methyl-4-chloorfenoxypropionzuur MCPP 47 73 3 4 4 10 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Chlorpyriphos Clprfs 47 59 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
1.2-dichloroethane 12DClC2a 47 55 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2.4-dichlorophenol 24DClFol 47 55 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
chlorotoluron Cltlrn 47 55 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
dichloromethane DClC1a 47 55 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Carbontetrachloride(tetra) T4ClC1a 47 55 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
tetrachloroethene(a) T4ClC2e 47 55 1 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Trichlorobenzene TCB 47 55 0 1 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
trichloromethane(chloroform) TClC1a 47 55 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
trichloroethene(Tri) TClC2e 47 55 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Alachlor alCl 47 54 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
somchloorfenvinvos s_CFVP 47 54 0 0 1 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Trifluralin TFLURLN 47 54 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
pentachlorophenol PeClFol 47 38 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Amino-methyl-phosphonate AMPA 47 31 7 7 7 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Gluphosinate GLUFSNT 47 31 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
glyphosate glyfst 47 31 6 3 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
tributyltin TC4ySn 47 25 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Benzene Ben 47 20 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
dibutyltin DC4ySn 47 20 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
sompentachlooranilinen s_PCAn 47 20 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
4-chloroaniline 4ClAn 47 19 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
bis(2-ethylhexyl)phthalate(DOP/DEHP) DEHP 47 19 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Thermotolerantcoliforms THERMTCL 49 44 10 0 0 0 0 12 0 0 0 0 0 0 0 0 0 0 0 0 0 11 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Totalcoliforms TTCLI 49 36 10 0 0 0 0 19 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
phosphamidon fosfmdn 49 18 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
metribuzin metbzn 49 18 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Escherichiacoli E_COLI 50 22 11 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Intestinalenterococci INTTNLETRCCN 50 21 10 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Monitoring data assessment report M 3 100/109
Appendix IV. Percentage positive detections for each analyte for Delfland Water
Quality grab samples
OW043-002
OW062-008
OW058-001
OW021-003
OW006-003
OW203-111
OW202-000
OW090-000
OW004-001
OW056-000
OW907-010
OW116-012
OW119-000
OW115-012
OW110-000
OW215-024
OW301-001
OW221A012
OW080-002
OW047-001
OW310-000
OW306-023
OW306-022
OW015-003
OW111-000
OW221A013
OW215-026
OW210-003
OW411-014
OW008-002
OW213B024
OW306B012
OW220-010
OW207-002
OW221A023
OW069-000
OW215-032
OW216-002
OW208-016
OW015-012
OW218-200
OW217-000
OW202-322
OW220-000
OW050-002
OW413-001
OW211-000
OW401-003
OW208-000
OW208-001
OW221A000
OW202-100
Percentage
recovery by site
and analyte, 2005
to 2009 Glass-houses
Glass-houses
Glass-houses
Glass-houses
Glass-houses
Glass-houses
Basic network
Glass-houses
Glass-houses
Glass-houses
WFD monitoring point
Glass-houses
Glass-houses
Glass-houses
Glass-houses
Glass-houses
Glass-houses
Glass-houses
Glass-houses
Glass-houses
Glass-houses
Glass-houses
Glass-houses
Glass-houses
WFD monitoring point
WFD monitoring point
Basic network
Basic network
Urban areas monitoring
Urban areas monitoring
Urban areas monitoring
Urban areas monitoring
Urban areas monitoring
Basic network
Urban areas monitoring
Urban areas monitoring
Urban areas monitoring
Urban areas monitoring
Urban areas monitoring
Urban areas monitoring
Basic network
Basic network
Basic network
Basic network
Urban areas monitoring
Urban areas monitoring
Basic network
Urban areas monitoring
Basic network
Basic network
Basic network
Basic network
zeros 20
22
27
27
28
51
51
52
54
54
63
74
75
75
75
75
77
78
78
79
80
81
83
86
88
88
88
92
113
125
177
177
178
178
179
179
179
180
181
181
181
181
181
182
183
183
183
184
184
193
193
193
Analyte name Par_code zeros
n
8083
7261
7585
6510
6416
6784
6079
6379
6577
6432
796
5223
5339
5333
5204
5042
5135
5232
5139
5417
5117
5282
5036
5198
5067
4635
3013
2447
881
980
910
812
824
367
964
905
829
938
878
810
288
177
123
270
554
554
537
842
233
92
92
79
Visible surface condition VUIL 0 2402 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100
Weather WEERGSHD 0 2402 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100
Fluidflowingwaters STROMSTR 0 2402 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100
Oxygen O2 0 2398 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100
Conductivity GELDHD 0 2398 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100
Temperature T 0 2398 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100
Acidity pH 0 2398 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100
Clarity ZICHT 0 2369 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100
Kjeldahlnitrogen NKj 0 2230 100 100 100 100 95 98 100 74 100 100 64 100 100 100 100 100 100 100 98 100 100 100 96 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100
totalphosphate P 0 2229 100 100 100 100 98 74 100 92 100 100 45 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100
nitrogen N 0 2215 100 100 100 100 100 98 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100
chloride Cl 3 1939 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 98 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 0 0 0
Ortho-phosphate PO4 3 1929 100 96 100 100 88 31 92 73 100 100 0 100 100 100 100 100 100 100 100 98 100 100 100 100 94 95 100 20 83 100 56 97 100 100 70 100 6 75 56 100 50 100 100 100 100 100 100 100 100 0 0 0
ammonium NH4 3 1929 59 40 47 42 63 27 77 25 73 50 11 46 71 38 58 65 68 87 73 88 74 49 60 32 67 75 75 22 44 77 69 68 83 71 20 65 67 83 85 86 92 100 100 75 87 91 71 100 44 0 0 0
totalnitrateandnitrite sNO3NO2 3 1918 100 100 100 97 100 38 98 100 100 100 100 100 100 100 100 100 100 100 100 97 100 100 100 74 71 95 100 44 40 91 55 71 79 82 56 95 81 72 63 39 100 100 100 92 59 73 71 67 78 0 0 0
Copper Cu 3 1864 100 100 100 99 98 7 75 85 93 93 100 100 100 98 100 100 100 100 100 0 100 100 0 0 81 91 97 20 10 73 40 18 79 64 36 86 70 81 9 10 100 70 67 100 57 61 96 44 100 67 100 100
Nickel Ni 4 1809 100 99 100 100 97 80 100 77 100 98 100 100 100 98 100 100 100 0 100 0 100 100 0 0 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100
Zinc Zn 5 1861 100 88 100 100 85 4 96 19 100 98 0 100 100 98 100 100 100 100 0 0 100 0 0 0 67 98 98 7 23 98 86 33 82 100 52 100 88 72 84 12 100 90 100 100 89 61 96 63 100 67 100 100
Calcium Ca 5 308 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 0 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 0 100 0 0 0
suspendedmatter ZS 6 1752 73 100 100 96 96 81 100 27 100 100 25 100 100 100 100 100 98 98 90 96 100 100 72 100 100 98 94 96 100 88 89 82 71 100 72 67 100 97 92 36 0 100 100 0 59 73 100 33 0 0 0 0
Bicarbonate HCO3 6 382 100 100 100 100 100 0 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 0 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 0 100 0 0 0
Sulfate SO4 6 279 100 100 100 100 100 0 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 0 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 0 100 0 0 0
Biochemicaloxygendemandover5days
BZV5 13 750 90 93 100 100 0 100 100 76 100 100 0 100 100 96 0 100 100 100 100 100 0 0 100 0 100 100 100 100 100 96 93 100 100 100 96 86 96 96 96 100 0 100 100 0 100 91 0 92 0 0 0 0
Biochemicaloxygendemandofureaallythio
BZV5a 16 511 75 94 0 100 100 100 100 69 88 100 0 100 100 100 100 100 0 100 0 0 0 0 0 0 100 100 100 100 100 100 100 94 100 100 94 75 100 94 100 94 0 100 100 0 100 100 0 94 0 0 0 0
Nitrate NO3 18 472 100 100 0 100 0 43 95 100 100 100 0 100 100 100 100 100 100 100 0 0 0 0 100 0 75 96 100 48 0 0 50 83 86 100 75 100 0 75 50 0 100 100 100 100 0 0 0 62 0 0 0 0
Nitrite NO2 18 471 4 0 0 5 0 0 41 0 32 38 0 8 80 67 75 13 100 60 0 0 0 0 100 0 5 29 17 0 0 0 0 33 8 0 0 25 0 4 0 0 0 0 100 0 0 0 0 5 0 0 0 0
Magnesium Mg 19 220 100 100 100 100 100 0 100 0 100 100 100 100 100 100 100 100 100 0 100 100 100 100 0 0 100 100 100 0 100 0 100 100 100 100 0 0 100 0 0 100 100 0 0 100 0 0 100 0 100 0 0 0
Sodium Na 19 220 100 100 100 100 100 0 100 0 100 100 100 100 100 100 100 100 100 0 100 100 100 100 0 0 100 100 100 0 100 0 0 100 100 100 0 0 100 0 0 100 100 0 0 100 0 0 100 0 100 0 0 0
Chlorophyll-a CHLFa 21 435 36 61 0 67 83 81 100 37 97 100 0 100 100 0 100 100 0 89 83 67 0 0 0 86 97 88 95 100 100 100 55 0 100 0 40 75 100 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0
dimethoate Dmtat 22 1375 2 7 2 11 4 0 16 0 7 7 0 19 2 7 6 24 11 69 7 0 6 2 4 2 2 18 26 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
simazine simzne 22 1375 0 0 0 0 2 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
atrazine atzne 22 1374 0 0 0 0 2 0 0 2 0 0 0 0 2 0 0 2 0 0 2 0 0 0 0 0 0 0 0 0 0 33 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
carbofuran cbfrn 22 1347 6 16 41 24 62 0 29 6 17 17 17 22 17 33 19 24 35 16 30 4 30 39 19 13 5 15 26 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
flutolanil flutlnl 22 1347 0 0 41 0 2 0 0 2 0 43 17 0 0 6 0 2 24 0 17 2 6 33 25 17 2 0 9 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
procimidon procmdn 22 1346 0 6 0 0 7 0 12 0 7 6 0 0 35 13 4 4 24 65 9 0 17 9 17 2 0 10 9 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
iprodione ipDon 22 1346 2 0 8 0 10 0 0 2 0 7 0 4 4 17 7 2 9 5 15 0 6 30 19 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
methylazinphos C1yazfs 22 1346 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
methylbromophos C1yBrfs 22 1346 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ethylazinphos C2yazfs 22 1346 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ethylbromophos C2yBrfs 22 1346 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ethylparathion C2ypton 22 1346 0 0 4 0 0 0 0 0 0 0 0 4 0 2 0 0 4 0 0 0 4 4 2 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
diazinon Daznn 22 1346 0 2 0 0 0 0 2 0 0 0 0 0 0 0 0 15 0 5 0 0 0 7 0 0 0 3 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
malathion malton 22 1346 0 0 0 0 2 0 2 0 0 2 0 2 0 4 2 2 0 2 0 2 2 4 4 2 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
pirimiphos-methyl pirmfC1y 22 1346 4 2 8 0 12 2 4 2 20 15 0 25 4 6 13 7 30 65 4 2 9 30 2 2 0 10 4 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
parathion-methyl ptonC1y 22 1346 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 2 5 2 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
pyrazophos pyrazfs 22 1346 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
tolclophos-methyl tolcfsC1y 22 1346 2 2 61 21 55 0 12 0 37 61 17 62 20 80 31 13 57 0 87 2 54 87 79 15 2 3 9 0 0 33 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
heptenophos heptnfs 22 1346 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 11 0 0 7 11 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
mevinphos mevfs 22 1346 0 0 0 0 2 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
chlorfenvinphos Clfvfs 22 1346 0 0 2 0 0 2 0 0 0 0 0 0 0 24 0 0 2 0 0 0 2 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
tetrachloorvinphos T4Clvfs 22 1346 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
triazophos Tazfs 22 1346 0 0 2 0 0 4 2 0 0 4 0 2 2 9 0 2 2 0 4 0 6 6 0 6 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
fenthion fenton 22 1346 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
disulfoton Dsftn 22 1346 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2.6-dichloorbenzamide 26DClBenAd 22 1346 39 6 8 12 10 17 10 4 22 11 0 13 11 7 19 11 6 4 17 13 6 6 9 11 2 8 13 12 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Bupirimate buprmt 22 1346 0 4 0 0 0 0 10 0 4 2 17 2 6 0 4 25 0 35 0 0 0 4 0 0 0 5 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
chlorpropham Clpfm 22 1346 0 0 8 2 2 0 0 0 0 6 0 2 0 9 4 0 0 0 2 0 2 2 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
chlorothalonil Cltlnl 22 1346 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 11 0 2 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Dimethomorph Dmtmf 22 1346 27 24 39 71 40 0 29 6 67 43 17 70 59 30 52 16 28 73 56 4 67 65 47 33 2 48 4 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
etridiazol eTDazl 22 1346 2 2 47 36 50 0 12 2 37 54 0 62 24 70 67 47 50 5 44 2 39 72 42 24 2 5 26 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
fenpropimorph fenppmf 22 1346 2 0 0 0 0 0 2 0 2 0 0 0 2 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Metazachlor metzCl 22 1346 0 0 2 0 2 2 2 6 2 2 0 0 4 2 4 0 6 9 2 2 0 0 4 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
pirimicarb pirmcb 22 1346 27 43 94 71 74 2 92 6 87 89 17 81 85 96 94 91 94 91 94 6 100 94 96 76 14 50 74 19 100 67 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
prosulfocarb prosfcb 22 1346 2 2 0 2 5 2 2 4 0 2 0 4 2 4 4 4 2 0 2 2 2 2 6 4 2 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Terbutryn terbtne 22 1346 0 0 0 0 0 2 2 2 0 0 0 0 4 0 0 0 0 0 0 0 2 2 2 6 0 0 4 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
tolylfluanide tolfande 22 1346 4 2 0 0 0 2 0 2 0 2 0 0 0 0 2 0 0 2 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
vinclozolin vinczln 22 1346 0 0 2 0 0 2 0 0 0 0 0 0 2 2 0 0 7 0 13 0 0 2 21 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
bitertanol bittnl 22 1346 0 4 12 14 29 0 8 0 6 9 0 19 6 37 17 4 6 7 15 2 11 9 11 4 2 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
metalaxyl mlxl 22 1346 2 2 73 69 55 0 18 4 57 56 17 66 65 52 78 5 57 69 80 6 70 83 70 56 16 33 4 0 0 67 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
N.N-diethyl-3-methylbenzamide DEET 22 1346 24 31 14 21 21 70 59 32 33 17 17 8 24 20 19 29 19 27 15 28 6 6 11 15 18 20 30 19 0 67 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Diethofencarb Detfcb 22 1346 2 18 45 36 52 2 41 0 30 50 17 43 41 85 76 25 56 71 33 4 17 70 34 37 2 35 13 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
4-dimethylaminosulphotoluidide DMST 22 1346 0 4 12 5 21 0 4 0 2 11 0 2 9 19 9 5 6 11 4 0 2 13 8 2 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
dodemorph dodmf 22 1346 0 0 0 0 0 0 0 0 0 2 0 15 2 0 4 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
furalaxyl furlxl 22 1346 0 0 8 5 2 0 2 0 2 9 0 0 2 6 4 7 31 7 2 0 2 15 6 9 2 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
metamitron mtmtn 22 1346 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
pyrimethanil pyrmtnl 22 1346 22 24 69 81 62 0 37 4 67 76 17 38 46 41 69 20 31 22 52 6 22 70 75 44 27 53 35 19 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
chloridazon Clidzn 22 1346 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
4-hydroxy-2.5.6-trichloorisoftalonitril
HTI 22 1346 2 0 18 0 14 0 0 0 0 7 0 2 4 11 2 0 24 2 4 0 6 2 8 4 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Dichlorvos DClVS 23 1228 0 0 0 0 8 0 0 0 4 0 0 0 6 6 0 12 0 4 2 2 0 2 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Dichlobenil DCIBNL 23 1199 33 9 9 14 8 13 15 6 33 21 0 28 6 17 25 6 13 14 23 25 15 13 21 21 8 9 17 8 0 67 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
trans-permethrin tpermtn 23 1083 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
diuron Durn 24 739 68 78 51 45 51 10 24 75 6 0 67 0 6 0 0 0 6 6 0 33 0 6 0 0 27 23 22 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Isoproturon iptrn 24 733 10 31 18 19 35 7 5 58 6 0 33 5 0 6 6 6 0 0 11 0 0 0 6 0 2 8 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Aldicarb alDcb 24 678 0 0 3 0 0 0 5 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
aldicarbsulphon alDcbsfn 24 678 5 2 3 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
carbendazim cbedzm 24 678 59 59 67 56 68 7 47 75 17 39 83 32 33 39 39 6 28 22 33 11 28 39 35 28 25 38 74 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
imidacloprid imdcpd 24 678 95 76 100 98 78 21 92 4 100 94 33 84 94 100 94 94 94 94 100 11 100 100 100 94 61 93 91 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
indoxacarb indxcb 24 678 2 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
linuron linrn 24 678 0 7 23 2 17 7 18 4 22 0 0 5 17 44 39 33 11 28 22 0 6 28 0 0 0 3 35 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
methomyl metml 24 678 0 7 49 29 44 0 18 4 11 17 0 32 28 61 33 17 17 28 11 0 22 39 6 6 0 3 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
methiocarb metocb 24 678 2 2 15 7 12 3 5 0 6 6 0 0 6 6 0 0 6 0 0 0 0 6 0 0 5 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
methiocarbsulphon metocbsfn 24 678 2 0 3 2 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
methiocarbsulfoxide metocbsO 24 678 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
metoxuron metxrn 24 678 2 2 15 0 5 0 3 0 6 6 0 0 0 6 6 0 0 0 0 0 0 17 0 0 0 3 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
propoxur propxr 24 678 2 5 5 5 2 3 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 6 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Somphaeophytins s_FEO 24 378 32 73 0 100 67 44 93 33 97 93 20 100 83 0 100 88 0 100 67 83 0 0 0 71 73 80 83 59 100 100 55 0 0 0 33 50 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
alpha-endosulfan aedsfn 27 829 0 0 8 0 0 0 0 0 0 0 0 0 0 3 0 4 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
gamma-hexachlorocyclohexane(lindane)
cHCH 27 829 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
endrin endn 27 829 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
hexachlorobenzene HCB 27 829 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Hexachlorobutadiene HxClbtDen 27 829 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2.2'.4.5.5'-pentachlorobiphenyl PCB101 27 829 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2.3'.4.4'.5-pentachlorobiphenyl PCB118 27 829 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2.2'.3.4.4'.5'-hexachloorbiphenyl PCB138 27 829 2 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2.2'.4.4'.5.5'-hexachloorbiphenyl PCB153 27 829 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2.2'.3.4.4'.5.5'-heptachloorbiphenyl PCB180 27 829 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2.2'.5.5'-tetrachloorbiphenyl PCB52 27 829 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
4.4'-dichlorodiphenyltrichloroethane
44DDT 27 829 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2.4.4'-Trichlorobiphenyl PCB28 27 829 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
pentachlorobenzene PeClBen 27 828 4 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
beta-endosulfan bedsfn 27 807 2 0 8 0 4 0 0 6 0 3 0 0 3 3 3 4 0 0 0 3 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2.4'-dichloordifenyldichloorethane 24DDD 27 807 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
4.4'-dichloordifenyldichloorethane 44DDD 27 807 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
4.4'-dichloordifenyldichloorethene 44DDE 27 807 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
alpha-hexachlorocyclohexane aHCH 27 807 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
aldrin aldn 27 807 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
beta-hexachlorocyclohexane bHCH 27 807 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
dieldrin dieldn 27 807 0 0 0 0 4 0 8 0 0 0 0 0 0 0 3 0 14 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
heptachlor HpCl 27 807 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
telodrin teldn 27 807 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
endosulfansulphate endsfSO4 27 807 6 3 48 0 12 0 0 6 0 8 0 6 19 28 3 4 14 3 8 3 6 8 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
delta-hexachlorocyclohexane dHCH 27 807 4 0 6 0 4 0 0 6 0 3 0 0 3 3 6 0 0 0 0 3 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2.4'-dichloordifenyldichloorethene 24DDE 27 807 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2.4'-dichlorodiphenyltrichloroethane
24DDT 27 807 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
isodrin idn 27 807 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
heptachlor HpClepO 27 798 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
somaldrin.dieldrin.endrinandisodrin sdrin4 27 735 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
sum-a.b.candd-HCH sHCH4 27 726 0 0 0 0 0 0 9 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
sum2.4'-and4.4'-DDT sDDT 27 724 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
cypermethrin cypmtn 27 263 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
sumdemetonisomers sdmtn 27 262 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
chlordane Cldn 28 611 100 100 100 100 100 96 92 97 97 97 0 97 97 97 97 100 97 97 97 97 97 97 97 97 0 0 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Lead Pb 29 651 60 48 63 32 42 0 27 0 17 15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 32 31 56 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
benzo(a)pyrene BaP 38 642 8 85 25 0 4 0 2 2 6 20 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
benzo(b)fluoranthene BbF 38 642 6 76 35 0 9 0 4 0 7 24 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
benzo(g.h.i)perylene BghiPe 38 642 9 70 17 0 2 0 2 0 0 19 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
indeno(1.2.3-c.d)pyrene InP 38 642 2 74 23 0 2 0 2 2 2 19 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
naphthalene Naf 38 642 2 4 0 0 2 0 0 0 4 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
anthracene Ant 38 641 0 37 0 0 2 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
benzo(k)fluoranthene BkF 38 641 2 64 6 0 2 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
fluoranthene Flu 38 641 4 83 37 2 32 0 35 6 41 46 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 13 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
acenaphthene AcNe 38 593 5 2 5 5 7 0 4 0 2 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
acenaftyleen AcNy 38 593 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
benzo(a)anthracene BaA 38 593 0 82 7 0 2 0 2 0 2 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
chrysene Chr 38 593 2 86 35 0 7 0 4 0 13 30 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
dibenzo(a.h)anthracene DBahAnt 38 593 0 23 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
phenanthrene Fen 38 593 9 68 16 12 21 4 21 8 22 22 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 25 13 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
fluorene Fle 38 593 7 23 5 10 17 2 29 4 11 13 0 0 0 0 0 0 0 0 0 0 0 0 0 0 11 30 22 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
pyrene Pyr 38 593 11 89 26 0 19 0 21 4 33 41 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
chrome Cr 38 388 18 46 18 30 25 25 50 25 67 42 0 0 0 0 0 0 0 0 0 0 0 0 0 0 42 33 36 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
cadmium Cd 39 396 68 78 83 68 69 0 50 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 75 88 84 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Mercury Hg 39 181 32 83 35 58 58 17 8 0 25 25 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Dissolvedorganiccarbon DOC 41 187 100 100 100 100 100 100 100 0 0 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0 100 100 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Totalorganiccarbon TOC 42 168 100 100 100 100 100 0 100 0 0 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0 100 100 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
arsenic As 42 120 100 92 100 100 67 58 100 83 100 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
trichlorobenzene TClBen 42 118 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 23 23 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Potassium K 46 7 100 100 100 100 100 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
bentazone bentzn 47 73 9 6 12 0 53 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2-methyl-4-chlorophenoxyaceticacid MCPA 47 73 18 24 35 55 41 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2-methyl-4-chloorfenoxypropionzuur MCPP 47 73 27 24 24 91 53 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Chlorpyriphos Clprfs 47 59 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
1.2-dichloroethane 12DClC2a 47 55 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2.4-dichlorophenol 24DClFol 47 55 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
chlorotoluron Cltlrn 47 55 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
dichloromethane DClC1a 47 55 9 9 9 9 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Carbontetrachloride(tetra) T4ClC1a 47 55 0 0 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
tetrachloroethene(a) T4ClC2e 47 55 9 0 27 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Trichlorobenzene TCB 47 55 0 9 36 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
trichloromethane(chloroform) TClC1a 47 55 0 0 27 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
trichloroethene(Tri) TClC2e 47 55 0 0 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Alachlor alCl 47 54 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
somchloorfenvinvos s_CFVP 47 54 0 0 10 0 18 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Trifluralin TFLURLN 47 54 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
pentachlorophenol PeClFol 47 38 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Amino-methyl-phosphonate AMPA 47 31 100 100 100 0 0 0 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Gluphosinate GLUFSNT 47 31 14 0 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
glyphosate glyfst 47 31 86 43 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
tributyltin TC4ySn 47 25 0 11 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Benzene Ben 47 20 0 0 50 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
dibutyltin DC4ySn 47 20 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
sompentachlooranilinen s_PCAn 47 20 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
4-chloroaniline 4ClAn 47 19 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
bis(2-ethylhexyl)phthalate(DOP/DEHP)
DEHP 47 19 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Thermotolerantcoliforms THERMTCL 49 44 100 0 0 0 0 52 0 0 0 0 0 0 0 0 0 0 0 0 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Totalcoliforms TTCLI 49 36 100 0 0 0 0 83 0 0 0 0 0 0 0 0 0 0 0 0 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
phosphamidon fosfmdn 49 18 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
metribuzin metbzn 49 18 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Escherichiacoli E_COLI 50 22 100 0 0 0 0 27 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Intestinalenterococci INTTNLETRCCN 50 21 100 0 0 0 0 64 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

AppendixV. Table G.PPQ. Pesticide means for Delfland grab water samples,
including total analyses per substance, number and percentage positive results,
number of locations where substances were analysed and also detected, average of
the “site mean” concentrations, and the concentration value for the location with
the greatest site mean, the year the substance was banned and the literature
source cited in this report that refers to each substance.
Monitoring data assessment report M 3 101/109

Appendix VI. Summary of Erft online data for the Erft at Bergheim.
Monitoring data assessment report M 3 102/109

Appendix VII. Numbers of results for each analyte for each Luxembourg Water
Quality sampling theme
site Control of surface waters
zeros 39
Bathing waters m onitoring
39
Eutrophication
51
Basic monitoring + bacteriology
50
Priority substances
Full monitoring
Fisheries monitoring
Basic monitoring
Priority substances + euttrophication
Monitoring
Moselle Saar Protection Comission (ICPMS)
26
53
65
58
20
18
39
ICPMS and eutrophication
47
Monitoring Moselle Saar
54
Full monitoring + bacteriology
55
Diatom index / pollution sensitivity indices
90
Macroinvertebrate surveys
91
Priority substances and surface water monitoring
52
EU campaign (Water Framework Directive)
60
Macrophyte biological index
89
pollution
contrôle (influence sur captage SES)
n
Par_code zeros
Cond. 3 3069 797 588 553 190 93 154 216 169 70 123 71 15 15 6 0 0 3 3 0 2 1
NH4 3 3066 797 588 553 190 93 154 216 169 70 123 68 15 15 6 0 0 3 3 0 2 1
pH 3 3066 797 588 553 190 93 154 216 169 70 123 68 15 15 6 0 0 3 3 0 2 1
NO2 3 3066 797 588 553 190 93 154 216 169 70 123 68 15 15 6 0 0 3 3 0 2 1
Chloride-Cl 4 3064 797 588 553 190 93 154 216 169 70 123 68 15 15 6 0 0 3 3 0 0 1
Nitrate-NO3 4 3064 797 588 553 190 93 154 216 169 70 123 68 15 15 6 0 0 3 3 0 0 1
Sulfate-SO4 4 3064 797 588 553 190 93 154 216 169 70 123 68 15 15 6 0 0 3 3 0 0 1
P_tot 4 3063 797 588 553 190 93 154 216 169 70 121 68 15 15 6 0 0 3 3 0 2 0
Temp °C 4 3063 797 588 553 190 93 154 216 169 70 121 68 15 15 6 0 0 3 3 0 2 0
Sodium-Na 4 2807 797 588 553 190 93 125 0 169 70 109 68 15 15 6 0 0 3 3 0 2 1
Potassium-K 4 2807 797 588 553 190 93 125 0 169 70 109 68 15 15 6 0 0 3 3 0 2 1
BOD_5 5 3060 796 588 553 190 93 154 216 169 70 121 68 15 15 6 0 0 3 3 0 0 0
DO 5 3051 796 588 544 190 93 154 216 169 70 121 68 15 15 6 0 0 3 3 0 0 0
DO(%sat) 5 3051 796 588 544 190 93 154 216 169 70 121 68 15 15 6 0 0 3 3 0 0 0
Hardness (H#CO3) °Fr 5 2805 797 588 553 190 93 125 0 169 70 109 68 15 15 6 0 0 3 3 0 0 1
Magnesium-Mg 5 2644 797 588 537 149 82 102 0 137 70 71 68 15 15 6 0 0 3 3 0 0 1
Calcium-Ca 5 2635 797 588 537 149 82 93 0 137 70 71 68 15 15 6 0 0 3 3 0 0 1
PO4-P 6 2802 797 588 553 190 93 125 0 169 70 107 68 15 15 6 0 0 3 3 0 0 0
Total hardness 6 2545 797 588 497 190 77 37 0 96 70 88 68 15 15 0 0 0 3 3 0 0 1
Cote NH4 (IPO) 7 2717 796 588 550 190 58 125 0 169 63 84 67 15 0 6 0 0 3 3 0 0 0
Organic Pollution Index 7 2710 797 588 544 190 58 125 0 169 63 82 67 15 0 6 0 0 3 3 0 0 0
Cote NO2 (IPO) 7 2709 796 588 544 190 58 125 0 169 63 82 67 15 0 6 0 0 3 3 0 0 0
Cote BOD-5 (IPO) 7 2709 796 588 544 190 58 125 0 169 63 82 67 15 0 6 0 0 3 3 0 0 0
Cote o-PO4 (IPO) 7 2709 796 588 544 190 58 125 0 169 63 82 67 15 0 6 0 0 3 3 0 0 0
Chlorophylle-a 7 1312 16 5 447 190 69 154 32 143 63 90 67 15 15 6 0 0 0 0 0 0 0
Pheopigments 7 1312 16 5 447 190 69 154 32 143 63 90 67 15 15 6 0 0 0 0 0 0 0
Chromium-Cr 8 1167 323 96 0 19 93 128 245 0 70 77 77 15 15 6 0 0 3 0 0 0 0
Zinc-Zn 8 1167 323 96 0 19 93 128 245 0 70 77 77 15 15 6 0 0 3 0 0 0 0
Nickel-Ni 8 1167 323 96 0 19 93 128 245 0 70 77 77 15 15 6 0 0 3 0 0 0 0
Lead-Pb 8 1167 323 96 0 19 93 128 245 0 70 77 77 15 15 6 0 0 3 0 0 0 0
Cadmium-Cd 8 1167 323 96 0 19 93 128 245 0 70 77 77 15 15 6 0 0 3 0 0 0 0
Copper-Cu 8 1167 323 96 0 19 93 128 245 0 70 77 77 15 15 6 0 0 3 0 0 0 0
Iron-Fe 9 1583 578 99 0 19 32 207 320 0 88 91 103 19 15 12 0 0 0 0 0 0 0
Manganèse-Mn 10 1474 570 96 0 19 0 207 320 0 22 91 103 19 15 12 0 0 0 0 0 0 0
Charge balance 11 1480 539 420 322 0 34 0 0 0 63 16 67 15 0 0 0 0 0 3 0 0 1
Sum anions 11 1480 539 420 322 0 34 0 0 0 63 16 67 15 0 0 0 0 0 3 0 0 1
Sum cations 11 1480 539 420 322 0 34 0 0 0 63 16 67 15 0 0 0 0 0 3 0 0 1
Indice biochim. 11 1250 257 168 238 190 24 125 0 169 0 70 0 0 0 6 0 0 3 0 0 0 0
Cote BOD-5 (Indice biochim.) 11 1250 257 168 238 190 24 125 0 169 0 70 0 0 0 6 0 0 3 0 0 0 0
Cote Sat O2 (Indice biochim.) 11 1250 257 168 238 190 24 125 0 169 0 70 0 0 0 6 0 0 3 0 0 0 0
Cote NH4 (Indice biochim.) 11 1242 257 168 232 190 24 125 0 169 0 68 0 0 0 6 0 0 3 0 0 0 0
Turbidity 12 1091 336 252 240 0 7 0 116 0 63 5 69 0 0 0 0 0 0 3 0 0 0
Alkalinity 12 940 243 420 169 0 14 0 0 0 27 10 44 11 0 0 0 0 0 2 0 0 0
Hardness (H#CO3) meq./L 12 932 242 420 161 0 14 0 0 0 27 11 44 11 0 0 0 0 0 2 0 0 0
Total Hardness meq. 12 932 242 420 161 0 14 0 0 0 27 11 44 11 0 0 0 0 0 2 0 0 0
Boron-B 13 434 6 36 0 0 109 0 76 0 106 14 84 0 0 0 0 0 3 0 0 0 0
Arsenic-As 13 343 6 36 0 0 93 0 58 0 70 14 63 0 0 0 0 0 3 0 0 0 0
Vanadium-V 13 304 6 36 0 0 93 0 58 0 70 14 24 0 0 0 0 0 3 0 0 0 0
Silver-Ag 13 304 6 36 0 0 93 0 58 0 70 14 24 0 0 0 0 0 3 0 0 0 0
E.coli 14 726 7 535 1 140 0 0 0 9 0 20 0 0 14 0 0 0 0 0 0 0 0
Intestinal enterococci 14 726 7 535 1 140 0 0 0 9 0 20 0 0 14 0 0 0 0 0 0 0 0
Mercury-Hg 14 321 0 0 0 0 93 0 0 0 70 57 68 15 15 0 0 0 3 0 0 0 0
Total coliforms 14 319 1 135 1 140 0 0 0 9 0 19 0 0 14 0 0 0 0 0 0 0 0
Total Xylène 14 317 0 0 0 0 90 0 0 0 70 57 68 15 15 0 0 0 0 0 0 2 0
Faecal coliforms 14 228 1 44 1 140 0 0 0 9 0 19 0 0 14 0 0 0 0 0 0 0 0
Fluoride-F 16 192 0 0 0 0 93 12 0 0 70 14 0 0 0 0 0 0 3 0 0 0 0
Silicon-Si 16 161 0 0 0 0 0 0 0 0 14 43 74 15 15 0 0 0 0 0 0 0 0
Sampling time 17 141 0 0 0 0 0 0 0 0 0 43 68 15 15 0 0 0 0 0 0 0 0
Suspended solids 17 141 0 0 0 0 0 0 0 0 0 43 68 15 15 0 0 0 0 0 0 0 0
Indeno(1.2.3.c.d)pyrène 18 299 0 0 0 0 152 0 0 0 133 14 0 0 0 0 0 0 0 0 0 0 0
Benzo(k)fluoranthène 18 299 0 0 0 0 152 0 0 0 133 14 0 0 0 0 0 0 0 0 0 0 0
Fluoranthène 18 299 0 0 0 0 152 0 0 0 133 14 0 0 0 0 0 0 0 0 0 0 0
Benzo(a)pyrène 18 299 0 0 0 0 152 0 0 0 133 14 0 0 0 0 0 0 0 0 0 0 0
Benzo(b)fluoranthène 18 299 0 0 0 0 152 0 0 0 133 14 0 0 0 0 0 0 0 0 0 0 0
Benzo(g.h.i)pérylène 18 299 0 0 0 0 152 0 0 0 133 14 0 0 0 0 0 0 0 0 0 0 0
Tétrachloroéthylène 18 248 0 0 0 0 128 0 0 0 100 20 0 0 0 0 0 0 0 0 0 0 0
Chloroform 18 174 0 0 0 0 90 0 0 0 70 14 0 0 0 0 0 0 0 0 0 0 0
1.1.1-Trichloroéthane 18 174 0 0 0 0 90 0 0 0 70 14 0 0 0 0 0 0 0 0 0 0 0
Toluène 18 174 0 0 0 0 90 0 0 0 70 14 0 0 0 0 0 0 0 0 0 0 0
Benzène 18 174 0 0 0 0 90 0 0 0 70 14 0 0 0 0 0 0 0 0 0 0 0
Dichlorométhane 18 174 0 0 0 0 90 0 0 0 70 14 0 0 0 0 0 0 0 0 0 0 0
Ethylbenzène 18 174 0 0 0 0 90 0 0 0 70 14 0 0 0 0 0 0 0 0 0 0 0
Tétrachlorométhane 18 174 0 0 0 0 90 0 0 0 70 14 0 0 0 0 0 0 0 0 0 0 0
Trichloroéthylène 18 100 0 0 0 0 52 0 0 0 40 8 0 0 0 0 0 0 0 0 0 0 0
Diatom biological index 19 104 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 0 0 0 4 0 0
Diatom poll.sens.ind. 19 104 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 0 0 0 4 0 0
Cote Macrozoobenthos 20 141 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 141 0 0 0 0 0
Macrophyte bio. Index 20 64 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 64 0 0
Hardness (H#CO3) mg/L 20 34 0 0 0 0 0 0 0 0 0 0 34 0 0 0 0 0 0 0 0 0 0
TOC 20 32 0 0 0 0 0 0 0 0 0 0 32 0 0 0 0 0 0 0 0 0 0
Dibenzo(a.h)anthracène 20 28 0 0 0 0 0 0 0 0 28 0 0 0 0 0 0 0 0 0 0 0 0
Fluorène 20 28 0 0 0 0 0 0 0 0 28 0 0 0 0 0 0 0 0 0 0 0 0
Acenaphtène 20 28 0 0 0 0 0 0 0 0 28 0 0 0 0 0 0 0 0 0 0 0 0
Anthracène 20 28 0 0 0 0 0 0 0 0 28 0 0 0 0 0 0 0 0 0 0 0 0
Benzo(a)anthracène 20 28 0 0 0 0 0 0 0 0 28 0 0 0 0 0 0 0 0 0 0 0 0
Chrysène 20 28 0 0 0 0 0 0 0 0 28 0 0 0 0 0 0 0 0 0 0 0 0
Phenantrène 20 28 0 0 0 0 0 0 0 0 28 0 0 0 0 0 0 0 0 0 0 0 0
Pyrène 20 28 0 0 0 0 0 0 0 0 28 0 0 0 0 0 0 0 0 0 0 0 0
HCO3 20 17 0 0 0 0 0 0 0 0 0 0 17 0 0 0 0 0 0 0 0 0 0
Discharge 20 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0
Free cyanide-CN 20 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0
COD 20 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0
25987
19730
16668
6330
5380
5207
5132
4917
4826
4330
3458
671
566
234
200
141
120
93
72
81
22
76
16
Monitoring data assessment report M 3 103/109

Appendix VIII. Numbers of results for analytes in simplified Luxembourg Water
Quality sampling themes
site Surface water monitoring
zeros 34
n
Par_code zeros
Cond. 1 3069 803 725 588 286 350 216 101 0
NH4 1 3066 803 725 588 286 350 216 98 0
NO2 1 3066 803 725 588 286 350 216 98 0
pH 1 3066 803 725 588 286 350 216 98 0
Chloride-Cl 1 3064 801 725 588 286 350 216 98 0
Nitrate-NO3 1 3064 801 725 588 286 350 216 98 0
Sulfate-SO4 1 3064 801 725 588 286 350 216 98 0
P_tot 1 3063 802 725 588 284 350 216 98 0
Temp °C 1 3063 802 725 588 284 350 216 98 0
BOD_5 1 3060 799 725 588 284 350 216 98 0
DO 1 3051 799 716 588 284 350 216 98 0
DO(%sat) 1 3051 799 716 588 284 350 216 98 0
Potassium-K 2 2807 803 725 588 272 321 0 98 0
Sodium-Na 2 2807 803 725 588 272 321 0 98 0
Hardness (H#CO3) °Fr 2 2805 801 725 588 272 321 0 98 0
PO4-P 2 2802 800 725 588 270 321 0 98 0
Cote NH4 (IPO) 2 2717 799 722 588 205 321 0 82 0
Organic Pollution Index 2 2710 800 716 588 203 321 0 82 0
Cote BOD-5 (IPO) 2 2709 799 716 588 203 321 0 82 0
Cote NO2 (IPO) 2 2709 799 716 588 203 321 0 82 0
Cote o-PO4 (IPO) 2 2709 799 716 588 203 321 0 82 0
Magnesium-Mg 2 2644 801 677 588 223 257 0 98 0
Calcium-Ca 2 2635 801 677 588 223 248 0 98 0
Total hardness 2 2545 801 596 588 235 227 0 98 0
Iron-Fe 2 1583 578 0 99 211 238 320 137 0
Charge balance 3 1480 540 325 420 113 0 0 82 0
Sum anions 3 1480 540 325 420 113 0 0 82 0
Sum cations 3 1480 540 325 420 113 0 0 82 0
Manganèse-Mn 2 1474 570 0 96 113 238 320 137 0
Chlorophylle-a 1 1312 16 590 5 222 350 32 97 0
Pheopigments 1 1312 16 590 5 222 350 32 97 0
Cote BOD-5 (Indice biochim.) 3 1250 260 407 168 94 321 0 0 0
Cote Sat O2 (Indice biochim.) 3 1250 260 407 168 94 321 0 0 0
Indice biochim. 3 1250 260 407 168 94 321 0 0 0
Cote NH4 (Indice biochim.) 3 1242 260 401 168 92 321 0 0 0
Cadmium-Cd 2 1167 326 0 96 240 153 245 107 0
Chromium-Cr 2 1167 326 0 96 240 153 245 107 0
Copper-Cu 2 1167 326 0 96 240 153 245 107 0
Lead-Pb 2 1167 326 0 96 240 153 245 107 0
Nickel-Ni 2 1167 326 0 96 240 153 245 107 0
Zinc-Zn 2 1167 326 0 96 240 153 245 107 0
Turbidity 2 1091 336 243 252 75 0 116 69 0
Alkalinity 3 940 243 171 420 51 0 0 55 0
Hardness (H#CO3) meq./L 3 932 242 163 420 52 0 0 55 0
Total Hardness meq. 3 932 242 163 420 52 0 0 55 0
E.coli 2 726 7 10 535 20 140 0 14 0
Intestinal enterococci 2 726 7 10 535 20 140 0 14 0
Boron-B 3 434 9 0 36 229 0 76 84 0
Arsenic-As 3 343 9 0 36 177 0 58 63 0
Mercury-Hg 5 321 3 0 0 220 0 0 98 0
Total coliforms 2 319 1 10 135 19 140 0 14 0
Total Xylène 5 317 2 0 0 217 0 0 98 0
Silver-Ag 3 304 9 0 36 177 0 58 24 0
Vanadium-V 3 304 9 0 36 177 0 58 24 0
Benzo(a)pyrène 7 299 0 0 0 299 0 0 0 0
Benzo(b)fluoranthène 7 299 0 0 0 299 0 0 0 0
Benzo(g.h.i)pérylène 7 299 0 0 0 299 0 0 0 0
Benzo(k)fluoranthène 7 299 0 0 0 299 0 0 0 0
Fluoranthène 7 299 0 0 0 299 0 0 0 0
Indeno(1.2.3.c.d)pyrène 7 299 0 0 0 299 0 0 0 0
Tétrachloroéthylène 7 248 0 0 0 248 0 0 0 0
Faecal coliforms 2 228 1 10 44 19 140 0 14 0
Fluoride-F 5 192 3 0 0 177 12 0 0 0
1.1.1-Trichloroéthane 7 174 0 0 0 174 0 0 0 0
Benzène 7 174 0 0 0 174 0 0 0 0
Chloroform 7 174 0 0 0 174 0 0 0 0
Dichlorométhane 7 174 0 0 0 174 0 0 0 0
Ethylbenzène 7 174 0 0 0 174 0 0 0 0
Tétrachlorométhane 7 174 0 0 0 174 0 0 0 0
Toluène 7 174 0 0 0 174 0 0 0 0
Silicon-Si 6 161 0 0 0 57 0 0 104 0
Cote Macrozoobenthos 7 141 0 0 0 0 0 0 0 141
Sampling time 6 141 0 0 0 43 0 0 98 0
Suspended solids 6 141 0 0 0 43 0 0 98 0
Diatom biological index 7 104 0 0 0 0 0 0 0 104
Diatom poll.sens.ind. 7 104 0 0 0 0 0 0 0 104
Trichloroéthylène 7 100 0 0 0 100 0 0 0 0
Macrophyte bio. Index 7 64 0 0 0 0 0 0 0 64
Hardness (H#CO3) mg/L 7 34 0 0 0 0 0 0 34 0
TOC 7 32 0 0 0 0 0 0 32 0
Acenaphtène 7 28 0 0 0 28 0 0 0 0
Anthracène 7 28 0 0 0 28 0 0 0 0
Benzo(a)anthracène 7 28 0 0 0 28 0 0 0 0
Chrysène 7 28 0 0 0 28 0 0 0 0
Dibenzo(a.h)anthracène 7 28 0 0 0 28 0 0 0 0
Fluorène 7 28 0 0 0 28 0 0 0 0
Phenantrène 7 28 0 0 0 28 0 0 0 0
Pyrène 7 28 0 0 0 28 0 0 0 0
HCO3 7 17 0 0 0 0 0 0 17 0
Discharge 7 3 0 3 0 0 0 0 0 0
COD
Free cyanide-CN
7
7
2
2
2
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
26145
Eutrophication
50
21678
Bathing waters monitoring
39
19730
Priority substances
10
14536
Full monitoring etc..
49
11771
Fisheries monitoring
65
5132
Moselle_Saar
35
4695
Biological surveys
88
413
Monitoring data assessment report M 3 104/109

Appendix IX. Numbers of samples for Luxembourg sites, years and Water Quality
sampling themes
Bio survey
Location name Site code
Ettelbruck L100011A21 bio Q 120 22 32 27 30 9 10 4 2 5 0 0 0 0 0 0 7 22 10 6 2 0 4 4 4 1 4 0 0 0 0 1 2 2 2 1 0 0 9 13 5
amont Wasserbillig L112010A24 bio 115 39 19 26 24 7 10 8 5 7 3 0 5 12 10 2 8 6 6 6 2 0 0 0 1 0 7 0 0 0 0 0 0 0 0 0 14 0 3 0 0
Kautenbach. aval embouchure Clerve L110030A11 Q 108 20 31 23 26 8 10 4 2 4 0 0 0 0 0 0 7 24 9 6 2 0 1 1 1 0 2 0 0 0 0 1 1 2 2 1 0 1 9 13 5
grousse Brill L300030A06 bio 84 19 15 18 25 7 10 8 4 4 0 0 0 0 0 0 7 5 6 6 2 0 2 1 2 0 2 0 0 0 0 0 0 0 0 0 0 0 7 13 5
Esch/Alzette frontière L100011A01 80 21 18 17 18 6 11 8 7 7 3 0 0 0 0 0 7 5 6 6 2 0 5 4 5 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Hespérange L100011A09 Q 79 20 18 17 18 6 10 8 7 7 3 0 0 0 0 0 7 5 6 6 2 0 5 4 5 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
aval Colmar-Berg L106030A12 bio 76 20 17 16 17 6 10 8 7 7 3 0 0 0 0 0 7 6 6 6 2 0 3 3 4 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Martelange L112010A01 Q 71 16 17 17 15 6 10 12 12 11 5 0 5 5 3 1 1 0 0 0 0 0 0 0 1 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Liefrange L112010A04 Q 71 16 17 17 15 6 10 12 12 11 5 0 5 5 3 1 1 0 0 0 0 0 0 0 1 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Grundhof L144030A09 bio 65 14 15 15 15 6 11 12 12 11 5 0 0 0 0 0 1 0 0 0 0 0 1 1 2 0 1 0 0 0 0 1 2 2 2 1 0 0 0 0 0
amont Fielsmillen L202030A12 bio 65 14 15 15 15 6 11 12 12 11 5 0 0 0 0 0 1 0 0 0 0 0 1 1 2 0 1 0 0 0 0 1 2 2 2 1 0 0 0 0 0
Rittefenn L110040A03 62 14 15 14 14 5 10 12 12 11 5 0 0 0 0 0 1 0 0 0 0 0 3 2 3 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Steinfort L105030A04 56 12 13 13 13 5 11 12 12 11 5 0 0 0 0 0 1 0 0 0 0 0 1 1 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Reckange L105030A12 bio Q 56 12 13 13 13 5 11 12 12 11 5 0 0 0 0 0 1 0 0 0 0 0 1 1 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Wilspull L112010A11 bio Q 48 11 11 12 12 2 0 0 0 1 0 0 11 12 11 2 1 0 0 0 0 0 0 0 0 0 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Bourscheid moulin L112010A09 bio Q 47 11 11 12 11 2 0 0 0 0 0 0 11 12 11 2 1 0 0 0 0 0 0 0 0 0 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0
aval Michelau L112010A10 Q 47 11 11 12 11 2 0 0 0 0 0 0 11 12 11 2 1 0 0 0 0 0 0 0 0 0 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0
amont confluent Alzette à Mersch L104030A11 Q 45 12 2 13 13 5 11 1 12 11 5 0 0 0 0 0 1 0 0 0 0 0 1 1 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Steinsel-Heisdorf L100011A15 Q 43 13 1 12 12 5 9 1 12 11 5 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
aval Vianden L122020A07 bio Q 43 7 7 13 12 4 1 0 0 0 0 0 5 11 9 3 1 0 0 1 0 0 0 0 0 0 4 0 0 0 0 1 2 2 2 1 0 0 0 0 0
Dirbach L112010A07 41 11 5 12 11 2 0 0 0 0 0 0 5 12 11 2 1 0 0 0 0 0 0 0 0 0 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Born L112010A23 bio Q 40 11 5 12 10 2 0 0 0 0 0 0 5 12 10 2 1 0 0 0 0 0 0 0 0 0 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Moulin de Bigonville L112010A02 Q 40 3 7 14 13 3 0 0 0 0 0 0 5 12 11 2 0 1 0 0 0 0 0 0 0 0 2 0 0 0 0 1 1 2 2 1 0 0 0 0 0
Rosport L112010A22 Q 38 9 5 12 10 2 0 0 0 0 0 0 5 12 10 2 1 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ingeldorf L112010A12 bio Q 29 7 7 8 5 2 0 0 0 0 0 0 5 6 3 1 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 1 2 2 2 1 0 0 0 0 0
Bettendorf L112010A15 Q 29 7 7 8 5 2 0 0 0 0 0 0 5 6 3 1 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 1 2 2 2 1 0 0 0 0 0
pont Misère L112010A03 Q 28 7 7 7 5 2 0 0 0 0 0 0 5 5 3 1 0 1 0 0 0 0 0 0 0 0 6 0 0 0 0 1 1 2 2 1 0 0 0 0 0
Remerschen - vir beim Steg L299010A02 28 5 5 11 5 2 0 0 0 0 0 0 5 11 5 2 1 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Remerschen - lënks beim Steg L299010A03 28 5 5 11 5 2 0 0 0 0 0 0 5 11 5 2 1 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0
hannen am Eck L122030A02 26 8 5 6 5 2 1 0 0 0 0 0 5 6 5 2 2 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Mersch L100011A17 Q 25 5 6 6 6 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 4 4 1 4 0 0 0 0 1 2 2 2 1 0 0 0 0 0
Remerschen - riets beim Steg L299010A01 25 2 5 11 5 2 0 0 0 0 0 0 5 11 5 2 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0
vir beim Steg L122030A01 23 5 5 6 5 2 1 0 0 0 0 0 5 6 5 2 1 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
route d'Oberpallen L106030A01 bio 22 5 5 5 5 2 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 3 3 3 1 4 0 0 0 0 1 1 2 2 1 0 0 0 0 0
Reisdorf L112010A17 bio 21 6 5 6 3 1 0 0 0 0 0 0 5 6 3 1 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Camping Heiderscheidergrund L112010A05 bio Q 20 5 5 6 3 1 0 0 0 0 0 0 5 6 3 1 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Dillingen L112010A18 bio 20 5 5 6 3 1 0 0 0 0 0 0 5 6 3 1 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0
aval step Clervaux L110040A04 bio 19 5 5 4 4 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 3 2 2 0 4 0 0 0 0 1 1 2 2 1 0 0 0 0 0
Kautenbach L110040A08 bio 19 5 5 4 4 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 3 2 2 0 4 0 0 0 0 1 1 2 2 1 0 0 0 0 0
Weilerbach L112010A19 Q 19 5 5 5 3 1 0 0 0 0 0 0 5 5 3 1 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0
aval Echternach L112010A21 bio Q 19 5 5 5 3 1 0 0 0 0 0 0 5 5 3 1 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Bissen L106030A11 Q 18 3 3 5 5 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 3 3 1 3 0 0 0 0 0 0 2 2 1 0 0 0 0 0
entre Ouren et Rodershausen L122020A04 bio Q 18 5 6 3 3 1 1 0 0 0 0 0 6 3 3 1 1 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
amont step Esch/Schifflange L100011A03 bio 17 4 4 4 4 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 4 4 4 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
aval Bettembourg L100011A07 Q 17 4 4 4 4 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 4 4 4 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
amont step Beggen L100011A13 Q 17 4 4 4 4 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 4 4 1 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Lintgen-Gosseldange L100011A16 17 4 4 4 4 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 4 4 1 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0
aval Essingen L100011A19 17 4 4 4 4 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 4 4 1 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0
aval Everlange (amont step) L106030A06 bio 17 5 0 5 5 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 3 1 4 0 0 0 0 1 0 2 2 1 0 0 0 0 0
amont Bissen L106030A10 Q 17 5 5 3 3 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 3 3 3 1 4 0 0 0 0 1 1 0 0 0 0 0 0 0 0
Ouren L122020A01 bio 17 5 5 3 3 1 1 0 0 0 0 0 5 3 3 1 1 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Tintesmühle L122020A02 bio Q 17 5 5 3 3 1 1 0 0 0 0 0 5 3 3 1 1 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Rodershausen L122020A03 Q 17 5 5 3 3 1 1 0 0 0 0 0 5 3 3 1 1 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Untereisenbach L122020A05 Q 17 5 5 3 3 1 1 0 0 0 0 0 5 3 3 1 1 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Stolzembourg L122020A06 Q 17 5 5 3 3 1 1 0 0 0 0 0 5 3 3 1 1 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Monitoring data assessment report M 3 105/109
Flow
Total samps
2005
2006
2007
2008
2009
2005
2006
2007
2008
2009
2005
2006
2007
2008
2009
2005
2006
2007
2008
2009
2005
2006
2007
2008
2009
2005
2006
2007
2008
2009
2005
2006
2007
2008
2009
2005
2006
2007
2008
2009
All programs Eutrophication Bathing waters monitoring
Priority substances Surface water monitoringFull monitoring etc.. Fisheries monitoring Moselle_Saar

Noertzange L100011A04 bio 16 3 4 4 4 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 4 4 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Fennange L100011A05 bio Q 16 3 4 4 4 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 4 4 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Roeser L100011A08 bio Q 16 3 4 4 4 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 4 4 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
amont step Bonnevoie L100011A11 Q 16 3 4 4 4 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 4 4 4 1 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Pulvermühle L100011A12 Q 16 3 4 4 4 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 4 4 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Beringen L100011A18 bio Q 16 3 4 4 4 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 4 4 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Colmar-Berg L100011A20 16 3 4 4 4 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 4 4 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Gosseldange L104030A10 bio Q 15 1 12 1 1 0 1 11 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Thillsmillen L104030A06 bio 13 3 3 3 3 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 1 0 1 0 0 0 0 1 1 2 2 1 0 0 0 0 0
Baafeltsbréck (aval Hobscheid) L105030A08 13 3 3 3 3 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 1 0 1 0 0 0 0 1 1 2 2 1 0 0 0 0 0
Rédange (amont step) L106030A03 13 3 3 3 3 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 3 3 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Useldange L106030A08 bio 13 3 3 3 3 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 3 3 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Boevange L106030A09 13 3 3 3 3 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 3 3 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
amont Warken L107030A09 bio Q 13 3 3 3 3 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 1 0 1 0 0 0 0 1 1 2 2 1 0 0 0 0 0
pont Weidingen L110030A07 13 3 3 3 3 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 1 0 2 0 0 0 0 1 1 2 2 1 0 0 0 0 0
Kautenbach. amont embouchure Clerve L110030A12 bio Q 13 3 3 3 3 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 1 0 2 0 0 0 0 1 1 2 2 1 0 0 0 0 0
pont vers Schiltzberg (Koedange) L141030A05 bio 13 3 3 3 3 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 1 0 0 0 0 1 2 2 2 1 0 0 0 0 0
Reisdorf L141030A13 bio 13 3 3 3 3 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 1 0 0 0 0 1 2 2 2 1 0 0 0 0 0
Blumenthal L144030A05 bio 13 3 3 3 3 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 1 0 0 0 0 1 2 2 2 1 0 0 0 0 0
Syren-Moutfort L202030A02 bio 13 3 3 3 3 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 1 0 0 0 0 1 2 2 2 1 0 0 0 0 0
Walferdange L100011A14 bio Q 12 3 0 4 4 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 4 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
aval Grümmelscheid L110030A03 bio 12 2 3 3 3 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 1 0 1 0 0 0 0 1 1 2 2 1 0 0 0 0 0
Seltz L140030A06 bio 11 3 2 2 3 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 1 2 2 2 1 0 0 0 0 0
amont Troisvierges L110040A01 bio 10 3 3 2 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 2 2 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Camping Maulusmillen L110040A05 10 3 3 2 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 2 2 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Wilwerwiltz L110040A07 10 3 3 2 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 2 2 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Goebelsmühle L112010A08 9 2 2 2 2 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 2 2 1 0 0 0 0 0
Obercorn. rue des champs L300030A01 bio 8 2 2 2 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 2 2 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Biff (amont step) L300030A03 bio 8 2 2 2 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 2 2 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Linger L300030A04 8 2 2 2 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 2 2 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Pétange (amont step) L300030A05 8 2 2 2 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 2 2 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0
aval Lasauvage. près du terrain de foot L300032A01 7 4 1 1 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 1 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Hesperange L101530A01 5 0 1 1 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Hagen L105031A01 bio 5 0 1 1 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Diekirch L112010A13 bio 5 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0
amont Altwies L200030A06 bio 5 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
amont Mondorf L200030A07 5 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Emerange L200030A11 bio 5 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Schengen L299001A01 5 0 1 1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Grevenmacher L299001A03 5 1 1 1 2 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
près de l'embouchure à Linger L300031A02 5 2 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0
amont Sprinkange L101030A01 4 1 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
aval Bettange L101030A03 bio 4 1 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
aval Reckange L101030A05 4 1 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Wickrange L101030A07 4 1 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Bergem L101030A09 bio 4 1 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Noertzange L101030A10 bio 4 1 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Rumelange frontière L102030A01 bio 4 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
pont à Kayl L102030A03 4 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
amont confluent Alzette. Noertzange L102030A04 bio 4 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Dudelange frontière L103030A01 bio 4 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
aval étang Arbed L103030A03 4 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
amont Giebel L103030A05 4 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
entrée souterrain amont Bettembourg L103030A06 4 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
sortie souterrain aval Bettembourg L103030A07 bio 4 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Monitoring data assessment report M 3 106/109

Monitoring data assessment report M 3 107/109
Garnich L104030A01 bio 4 1 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Holzem L104030A02 4 1 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
aval step Mamer L104030A05 4 1 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
aval Kopstal L104030A08 4 1 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
aval Clemency L105030A01 bio 4 1 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
pont Grass L105030A02 4 1 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
pont Hagen L105030A03 4 1 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Eischen L105030A06 bio 4 1 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
aval Septfontaines L105030A09 4 1 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Dondelange L105030A10 bio 4 1 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Nidderpallenermillen L106036A01 bio 4 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Mertzig L107030A04 4 1 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Oberfeulen L107030A05 bio 4 1 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
aval Welscheid L107030A08 Q 4 1 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Park & Ride Hollerich L108030A01 4 1 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Parc Minigolf L108030A03 4 1 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
station de Schimpach L110030A01 4 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Schleif L110030A02 bio 4 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
pont Niederwiltz L110030A06 4 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
pont aval Tutschenmillen L110030A08 bio 4 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
aval SIDA. aval Himmelbach L110030A09 4 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Heiderscheidergrund L112012A01 bio 4 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Bavigne L112015A01 4 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
aval Brandenbourg L140030A03 bio 4 1 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
aval Bastendorf L140030A05 bio 4 1 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Tandel L140031A01 bio 4 1 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Eisenborn L141030A02 bio 4 1 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Imbringen L141030A03 4 1 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Supp L141030A06 4 1 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
amont Larochette L141030A08 4 1 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
amont Medernach L141030A10 bio 4 1 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Hessemillen (=Rte vers Eppeldorf) L141030A12 4 1 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Gonderange L144030A02 4 1 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
aval Junglinster L144030A04 4 1 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
aval Blumenthal L144030A06 4 1 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
aval Aspelt L200030A02 4 1 0 0 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
amont Ehnen L201030A06 bio 4 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
amont Syren L202030A01 bio 4 1 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Schrassig L202030A03 4 1 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Mensdorf (aval SIAS) L202030A06 4 1 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Olingen L202030A08 bio 4 1 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
am. Manternach (av.step) L202030A11 bio 4 1 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
rond-point Foetz. Dumontshaff L100930A01 3 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Felleschmillen (Eischen) L105032A01 3 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Rédange. Schummeschmillen L106030A04 3 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Betzemillen. aval Boevange L106031A01 3 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Boevange/Attert L106032A01 3 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Useldange L106033A01 bio 3 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Roudbach L106035A01 3 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
amont Grosbous L107030A02 3 1 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Cessange L108031A01 3 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Laangert. ennert Helfent L108032A01 3 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Merkholtz- Halte L110030A10 3 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Sak L110032A01 bio 3 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
amont Merkholz L110033A01 bio 3 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
aval Schimpach L110034A01 3 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
aval Oberwampach L110035A01 3 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Basbellain L110040A02 3 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
aval Drauffelt L110040A06 3 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
amont Lellingen L110041A01 3 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Drauffelt L110042A01 3 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Sassel L110044A04 3 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Schlinder L112011A01 3 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Dell L140030A01 3 1 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
aval camping Bleesbréck L140030A07 3 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Mëllerdall L144032A01 bio 3 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Echternach. dir. Chapelle L146030A01 3 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
aval Girst L147030A01 3 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
amont Ehnen L201031A01 bio 3 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Munsbach L202030A05 3 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Fausermillen L202031A02 bio 3 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Manternach L202032A01 bio 3 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Wecker L202036A01 3 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Remich L299001A02 3 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Wasserbillig L299001A04 3 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Mamer L104031A01 2 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Thillsmillen L104032A01 2 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Lintgen L104530A01 2 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
amont Rédange L106037A01 2 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
route d'Oberpallen L106038A01 bio 2 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Holtz (route de Perlé) L106039A01 2 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Niederpallen L106040A01 2 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Niederfeulen L107031A01 2 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Oberfeulen L107032A01 2 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
amont Mertzig L107033A01 2 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
près de Hanff L108030A02 2 1 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Hensenal. aval Eschweiler L110031A01 2 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Brillsbësch/Conzefenn L110043A01 2 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
amont Trëtterbaach L110045A01 2 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ueschdrefermillen L112014A01 2 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
amont Surré L112016A01 bio 2 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
pt. aval Surré (Bauschelt) L112016A02 2 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
amont Schiebech. LBN 58 L122022A01 2 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Gilsdorf L140033A01 2 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Château Grondhaff L144031A02 2 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Frisange L200030A01 2 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
amont Aspelt L200031A01 2 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
amont Ahn (aval step) L201032A01 bio 2 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
aval Dreiborn - option 2 L201033A01 2 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Biwer L202033A01 bio 2 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Boudlerbach L202035A01 2 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
amont Olingen L202037A01 2 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Bauerebech. bei Roodt/Syr L202038A01 2 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
pont aval Bous L203030A01 bio 2 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Stadtbredimus L203030A02 2 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Herdermillen L203031A01 2 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Echternach. Brill L145030A01 2 0 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
?? L110043A02 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
amont Grondmillen L112013A01 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Baerburenerkapp. LB 180 L112017A01 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Landscheid L140032A01 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ferme Schorlemer - Camping Leiser L144030A 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ernster L144030A01 bio 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
aval Müllerthal L144030A08 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Haller L144031A01 bio 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
aval Wollefsmillen L148030A02 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Oenneschtmillen L201030A04 bio 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Aval Ehnen L201031A07 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Appendix X. Limits of detection for substances in Luxembourg Water Quality Samples
Table A8.1 Limits of detection (units µg/l) with methodological references for measured
polynuclear aromatic hydrocarbons in Luxembourg Gestion de l’eau water quality samples.
SoP-305-HMW_PAH Raw Filtered
Fluoranthène 1.0 0.002 L1606-FLUORA
Pyrene 1.0 0.002 L1607-PYRE
Benzo(a)anthracène 1.0 0.002 L1608-BENANTH
Chrysène 1.0 0.002 L1609-CHRY
Benzo(b)fluoranthène 1.0 0.002 L1611-BENBFLUOR
Benzo(k)fluoranthène 1.0 0.002 L1612-BENKFLUOR
Benzo(a)pyrène 1.0 0.002 L1613-BENPYT
Dibenzo(a,h)anthracène 1.0 0.004 L1614-DBENANT
Benzo(g,h,i)pérylène 1.0 0.002 / 0.004 L1615-BENPER
Indeno(1,2,3,c,d)pyrène 1.0 0.002 / 0.004 L1616-INDENO
SoP-305-LMW_PAH
Acenaphtène - 0.002 L1602-ACNAP
Anthracène - 0.002 L1605-ANTHR
Fluorène - 0.002 L1603-FLUORENE
Phenantrène - 0.010 L1604-PHENAN
Table A8.2 Limits of detection (units µg/l) with methodological references for solvents and
degreasants in Luxembourg Gestion de l’eau water quality samples.
SoP-303 DL April 05 to April 06 Analysis reference DL May-Jun 06 and July
06 onwards
Analysis reference
1,1,1-Trichloroéthane 10 L1306-111TCLET 5 / 1 L2120-111TCLET
Benzène 10 L1501-BEN 1 L2130-BEN
Chloroforme 10 L1305-TCLME 1 L2115-TCLME
Dichlorométhane 10 L1303-DCLME 5 / 1 L2110-DCLME
Ethylbenzène 10 L1506-ETBEN 1 L2165-ETBEN
Tétrachloroéthylène 10 L1317-TETRACLET 1 L2155-TETRACLET
Tétrachlorométhane 10 L1307-TCLME 1 L2125-TCLME
Toluène 10 L1502-TOL 5 / 1 L2150-TOL
Trichloroéthylène 10 L1308-TCLET 1 L2140-TCLET
Xylènes totaux 10 L1505-XYLTOT 3 / 1 L2176-XYLTOT
Monitoring data assessment report M 3 108/109

Table A8.3 Limits of detection (units mg/l) with methodological references for metals in water
quality samples from Luxembourg.
Element n, records n % for element n < d.l. % < L.o.D L.o.D
Ag-MS 214 70,4 207 96,7 0.005
Ag-OES 90 29,6 90 100,0 0.005
As-FIAS 179 52,2 63 35,2 0.001
As-MS 164 47,8 132 80,5 0.01
B-209 17 3,9 1 5,9 0.05
B-223 66 15,2 16 24,2 0.05
B-MS 344 79,3 132 38,4 0.01
B-OES 7 1,6 4 57,1 0.10
Cd-MS 157 13,5 155 98,7 0.0005
Cd-GFAAS 1010 86,5 1000 99,0 0.0001
Cr-OES 515 44,2 508 98,6 0.01
Cr-MS 651 55,8 647 99,4 0.005
Cu-GFAAS 32 2,7 10 31,3 0.002
Cu-OES 484 41,5 472 97,5 0.01
Cu-MS 651 55,8 644 98,9 0.01
Fe-MS 931 58,8 0 0,0 0.01
Fe-OES 652 41,2 11 1,7 0.01
Mn-MS 836 56,7 3 0,4 0.01
Mn-OES 638 43,3 21 3,3 0.005
Hg-FIMS 321 100 311 96,9 0.0005
Ni-MS 651 55,8 125 19,2 0.002
Ni-OES 515 44,2 504 97,9 0.005
Pb-MS 651 55,8 361 55,5 0.001
Pb-OES 515 44,2 513 99,6 0.005
V-MS 214 70,4 210 98,1 0.01
V-OES 90 29,6 90 100,0 0.1
Zn-MS 651 55,8 409 62,8 0.01
Zn-OES 516 44,2 420 81,4 0.01/0.005
Monitoring data assessment report M 3 109/109