Water treatment

H 0 : H a : Treatment levels working in parallel in the MSF pilot system do not present statistically significant differences in the mean removal efficiencies. This means that τ 1 =τ 2 =τ 3 , in the case of filtration rates in DyGF stage, and τ 1 =τ 2 =τ 3 =τ 4 =τ 5 , in the case of the CGF lines or the SSF units working in series with the CGF lines. τi ≠ τk, for any i ≠ k The process to accept or reject the null hypothesis (H 0 ), with an established level of significance, involves the F-Test, after the British statistician, Sir Ronald A. Fisher, who developed the method called analysis of variance (ANOVA) on which the F-test rests (Fisher, 1947, quoted by Mesa, 1999; Rowntree, 1981). ANOVA allows knowing if the observed values divided in three or more groups might all belong to the same population, regardless of group, or whether the observations in at least one of the groups seem to come from a different population. The answer is obtained by comparing the variability of the values within groups with the variability of the values between groups. If the null hypothesis is false (and there is a real difference in population means), the between groups estimate of variance should be larger than the within groups estimate. If the variance ratio or F-ratio – dividing the between groups estimate by the within group estimate – is greater than 1 it is necessary to check if the difference is large enough to be confident that it is attributable not simply to random, sampling variation. In other words, it is necessary to check if the difference between the groups is a reliable one that could be repeated in similar tests. To do this the calculated F-ratio is checked against the F-distribution, which is a family of curves varying according to the size and the number of groups being compared. In general, the smaller or the fewer the groups from the experimental work, the bigger must the calculated F-ratio be in order to obtain significance (Rowntree, 1981). To apply ANOVA technique with the statistical model represented by equation 3.1 the total variance in the experiment is distributed among the variance due to treatment levels (between), blocks (between), and experimental error (within). This is represented by equation 3.2. Table 3.5 is prepared with the results obtained after applying this equation. Annex 4 includes an example of F-test application. Sum of squares (SS) total = SS Treatment + SS Blocks + SS Error b 2 2 2 ∑ ∑( yij − y) = b∑( yi − y) + t∑ ( y j − y) + ∑ ∑ j= 1 t t i= 1 i= 1 j= 1 j= 1 in which y = b ∑ j= 1 t ∑ i= 1 yij ; tb yi = b ∑ j= 1 b y b ij ; yj = t ∑ i= 1 t y ij b t i= 1 ( yij − yi − y j + y) 2 (3.2) The F-ratio calculated with the experimental data is identified as F c in table 3.5. H 0 is rejected when F c > F (α, t-1, (t-1)(b-1)) , where F (α, t-1, (t-1)(b-1)) is the F distribution with t-1 degrees of freedom for the numerator, (t-1)(b-1) degrees of freedom for the denominator and α is the significance level of the test. 87

Table 3.5 Analysis of Variance (ANOVA) to the randomised block design Source of Variation Degrees of freedom Sum of Squares (SS) Mean Square (MS) F c (1) Treatment (between) t-1 SSTreat. SSTreat/(t-1) = MSTreat MSTreat/MSE Blocks (between) b-1 SSBlocks SSBlocks/(b-1) = MSBlocks MSBlocks/MSE Error (within) (t-1)(b-1) SSE SSE/((t-1)(b-1)) = MSE Total tb-1 SST If H 0 is rejected other techniques can be used to compare sample means of all possible pairs of treatment levels included in each main treatment stage of the MSF pilot system. Tukey test will be used if all treatment levels have the same number of observed values and Bonferroni methodology if the number of observed values are different (Mendenhall, 1997). Tukey test is employed to make all possible comparisons of means based on the Minimum Significant Difference (MSD) calculated as follows (Reyes, 1980) MSD q , t, n1) = ( α ∗ ( MSE b ) In which q (α, t, n1) is a critical value of Studentized Range; α is the significance level; t is the number of means being compared; n1 = (t – 1)(b – 1), the degrees of freedom of MSE (Mean Square of error); and b is the data number for each treatment level. The difference between each pair of means (D) is calculated as D = ⎺Y i – ⎺Y k for i ≠ k. The decision rules are as follows: If D > MSD, the two means are statistically different (⎺Y i ≠⎺Y k ), otherwise they are not statistically different (⎺Y i = ⎺Y k ). Bonferroni test is based on calculating a B ij value as follows (Mendenhall, 1997) (3.3) B ( MSE) ∗ ( 1 1 ) i k ij = ( α1 / 2) ∗ + t b b (3.4) In which, t (α1/2) is a critical value of t –Student distribution; α 1 = α/(p(p-1)/2) being α the significance level; p is the number of means to be compared; MSE mean square of within; b i and b k the number of data used to calculate the means i and k respectively. The difference between each pair of means (D) is calculated as D = ⎺Y i – ⎺Y k for i ≠ k. The decision rules are as follows: If D > B ij , the two means are statistically different (⎺Y i ≠ ⎺Y k ), otherwise they are not statistically different (⎺Y i = ⎺Y k ). Statistical analyses were accomplished using the commercial software SPSS (Statistical Package for the Social Sciences) version. 8.0 for Windows 95 or 98. 88

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Development and Evaluation of Multi

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ACKNOWLEDGEMENTS To my supervisor,

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ABBREVIATIONS ABNT: Acuavalle: ACV:

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SOCs: Synthetic Organic Chemicals S

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u c V V f Vs uniformity coefficient

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TABLE OF CONTENTS 1. INTRODUCTION 1

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4 MULTISTAGE FILTRATION EXPERIENCIE

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1 INTRODUCTION Water is essential f

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Table 1.2 Access to WS&S in Colombi

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Table 1.5 Safe drinking water cover

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1.2 Multiple Barriers Strategy and

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2 OVERCOMING THE LIMITATIONS OF SLO

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adjustment, are among the technolog

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On January 14, 1829, Simpson’s on

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With increasing life expectancy, en

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Table 2.2 Treatments steps recommen

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In table 2.3, WHO guideline values

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2.5 The Slow Sand Filtration Proces

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When the particles are very close t

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in which p 0 is the clean media por

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Yao et al (1971) related the remova

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compensate for the increase in the

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can be applied, but intermittent op

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Table 2.4 Comparison of design crit

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Although accepted as indirect indic

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- Page 53 and 54: Figure 2.9 Flow diagram of the wate
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- Page 57 and 58: Headloss and flow control. Final he
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- Page 61 and 62: Operation and maintenance (O & M).
- Page 63 and 64: in parallel (Galvis, 1983; Smet et
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- Page 69 and 70: Table 2.9 Data about three experien
- Page 71 and 72: Some points of discussion about HGF
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- Page 89 and 90: l Figure 3.7 Plan view of Cinara's
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- Page 93: Table 3.1. Design parameters, grave
- Page 96 and 97: Figure 3.9. Piezometer distribution
- Page 98 and 99: were used to collect samples for DO
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- Page 104 and 105: 3.2 Results and Specific Discussion
- Page 106 and 107: 3.2.2 Dynamic gravel filtration (Dy
- Page 108 and 109: Mean faecal coliform removal effici
- Page 110 and 111: Table 3.10 Comparative analysis of
- Page 112 and 113: DyGF-A had flow reductions in the r
- Page 114 and 115: The experimental data used to produ
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- Page 118 and 119: ates (figure 3.17 B). However, at t
- Page 120 and 121: Longer “initial-ripening” perio
- Page 122 and 123: Table 3.17. Descriptive statistics
- Page 124: 100 Filtration rate = 0.3 mh -1 100
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- Page 130 and 131: nature of the organic matter and th
- Page 132 and 133: Table 3.24 Comparative analyses of
- Page 134 and 135: 3.2.4.3. Filtration run lengths and
- Page 136 and 137: deep bed filter (data not included
- Page 138 and 139: and operational considerations Pard
- Page 140 and 141: than in sand samples from other SSF
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- Page 144 and 145: for HGFS and from 3 to 5 for HGF. T
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community based organisations and l

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systems. All these systems were fed

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Parts of the suburban settlements o

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Figure 4.2. Layout of Retiro MSF pl

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Traditionally, in the WS&S of Colom

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Photo 4.10. Partial cleaning activi

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Figure 4.3 Location of full-scale M

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4.4.1.3 Main characteristics of mul

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Figure 4.4 Layout of Restrepo MSF p

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Figure 4.6 Layout of Javeriana MSF

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Figure 4.9 Layout of Cañasgordas M

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Figure 4.11. Layout of Ceylan MSF p

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Table 4.4 Descriptive statistics fo

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Water sources in the coffee region

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Filterability results seem to under

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Table 4.8 Mean removal efficiencies

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The length of this ripening period

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in Peru (Pardon, 1989) and Colombia

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Photo 4.24 Drainage facilities in u

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the Cauca Valley. This is not the c

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Pardon (1989) reports similar evide

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5. COST OF MULTI-STAGE FILTRATION P

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ecame formally established as WS en

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Models for assessing construction q

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MSF system can then be calculated o

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5.7 Cost Model for the Cali Area an

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Table 5.8. Annual labour costs due

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5.8 General Discussion The followin

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systems. The differences between MS

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guideline for colour is < 15 PCU (W

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Table 6.1. Individual (at each trea

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Table 6.3. Individual (at each trea

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As shown in tables 6.1 and 6.3, col

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UGFL 0.45 UGFS 0.45 (32;51;85) (44;

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Table 6.4. An example of identifica

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MSF technology showed great flexibi

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In harmony with the new development

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epresents the risk the community ha

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The selection of MSF alternatives i

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scouring and transporting away prev

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REFERENCES ABNT, (1989) NB-592 Proj

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Craun, G.F., Bull, R.J., Clark, R.M

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Drinking Water Disinfection, ed. by

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Huisman, L. (1989) Plain Sedimentat

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Mendenhall, W. and Sincich, T. (199

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Ridley, J.E. (1967) Experience in t

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Visscher, J.T. and Galvis, G. (1992

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ANNEXES Annex 1: Accessories for Mu

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aw water. The red colour is used fo

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Annex 2: Design of Manifolds Manifo

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+ q 2 Q1 (1.2 qn + qn) (2.2 qn) = =

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R 1 = (total orifice area / lateral

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0.30 0.25 0.20 0.15 0.10 0.05 0.00

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Table A.4-2 General notation for th

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Box A4-3. Sum of Square Error (SSE)

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Annex 5: Residence times in coarse

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Table A5-1 Percentage of incoming w

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Annex 6 Number and Type of Valves N

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Table A7-1. Descriptive statistics

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Tables A7-3 Removal efficiencies of

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Tables A7-5 Removal efficiencies of

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Construction quantities of DyGF com

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Net present value (US$) of MSF and