Potential Effects of Contaminants on Fraser River Sockeye Salmon

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$ All DN2_CompDate years occurring after 1990 where theDN1_CompDate field was blank were calculated to APost-1990".$ Records where both DN1_CompDate and DN2_CompDate wereblank were left out <strong>of</strong> the analysis.The Dissolve tool in ArcGIS was used to generalize the RESULTS-Openings spatialpolygons for each area <strong>of</strong> interest and the PREV_SPP1 (primary species description)field. The geoprocessing results were exported into a geodatabase, grouped by area<strong>of</strong> interest and summarized in order to determine the predominant tree species.Fraser Basin Landfill InventoryThe Fraser Basin Landfill Inventory listed in the Fraser River Action Plan: Fraser BasinLandfill Inventory (Gartner Lee Ltd. 1997; Appendix 5) lists solid waste landfills in theFraser River Basin and their associated geographic coordinates. The Micros<strong>of</strong>tAccess database developed for the 1997 report was not available. To obtain thelocation <strong>of</strong> the landfills in the Fraser River Basin, a map [Figure 2.1 <strong>of</strong> the Gartner LeeLtd. (1997) report] showing the spatial location <strong>of</strong> each landfill was scanned andimported into ESRI7 ArcMapJ. The map image was rubber-sheeted andgeo-referenced using a comparable provincial dataset (i.e., the provincial border andthe Fraser River basin). All locations were manually digitized to create the spatialrepresentation <strong>of</strong> landfill locations. Associated attribute data listed in Appendix B <strong>of</strong> theGartner Lee Ltd. (1997) report, was imported into a Micros<strong>of</strong>t Excel spreadsheet toaccompany the spatial data. Due to differences in the delineation <strong>of</strong> regions in theGartner Lee Ltd. (1997) report and areas <strong>of</strong> interest used in this contaminantassessment, a direct link between the created spatial layer and the supplementalattribute data was not available. All landfill locations were confirmed andcross-referenced to the supplementary data and each <strong>of</strong> the landfills was assigned toan area <strong>of</strong> interest. Lastly, the landfill categories [Table 2.1 <strong>of</strong> the Gartner Lee Ltd.(1997) report] were used to assist in evaluating landfill characteristics.A4.2 Treatment <strong>of</strong> Environmental DataTo support the assessment <strong>of</strong> potential effects <strong>of</strong> contaminants on Fraser Riversockeye salmon, environmental data (including surface water quality, sediment quality,and fish tissue chemistry) were obtained and compiled into multiple project databases.As data were collected from multiple government agencies, reports, and scientificliterature, data were screened to ensure they were comparable and that adequatequality assurance was performed.Surface water data were additionally screened for erroneous values and values whichwere unlikely (i.e., data were screened based on the accompanying metadata).Examples <strong>of</strong> this procedure include the screening <strong>of</strong> pH values to exclude those thatfall outside <strong>of</strong> the range <strong>of</strong> 0 to 14, and the screening <strong>of</strong> water temperature to excludevalues below 0 EC. Furthermore, an exposure point concentration was determinedA-31
when multiple samples were reported in surface water at the same station on the samesampling date and time; the maximum value for each contaminant <strong>of</strong> potential concernwas used as the exposure point concentration in all cases except for dissolved oxygen,when the minimum value was used. Total metals concentrations in surface waterdata were used in the assessment rather than dissolved metals to achieve greaterspatial and temporal coverage <strong>of</strong> the surface water quality data; implications <strong>of</strong> thisprocedure are discussed in Chapter 7.The treatment <strong>of</strong> environmental data has the potential to influence the results <strong>of</strong> theassessment. In particular, the treatment <strong>of</strong> less than detection limit data can affect theresults <strong>of</strong> the exposure assessment, the hazard evaluation, and the calculation <strong>of</strong> theWater Quality Index. A number <strong>of</strong> investigators have evaluated the implications <strong>of</strong>applying various procedures for estimating the concentrations <strong>of</strong> contaminants <strong>of</strong>potential concern from less than detection limit data (Gaskin et al. 1990; Porter andWard 1991; El-Shaawari and Esterby 1992; Clarke and Brandon 1994). While there isno consensus on which data censoring methods should be used in variousapplications, the simplest methods tend to be used most frequently, including deletion<strong>of</strong> non-detect values or substitution <strong>of</strong> a constant, such as zero, the detection limit, orone-half the detection limit (USACE 1995).To address the need for guidelines for statistical treatment <strong>of</strong> less than detection limitdata, the USACE (1995) conducted a simulation study to assess the performance <strong>of</strong>ten methods for censoring data. The results <strong>of</strong> that investigation indicated that nosingle data censoring methods works best in all situations. Accordingly, USACE(1995) recommended a variety <strong>of</strong> methods depending on the proportion <strong>of</strong> the data thatrequires censoring, the distribution and variance <strong>of</strong> the data, and the type <strong>of</strong> datatransformation. For data sets for which a low to moderate proportion <strong>of</strong> the datarequire censoring, substitution <strong>of</strong> the detection limit is generally the preferred methods(i.e., to optimize statistical power and control type I error rates). However, as theproportion <strong>of</strong> the data that requires censoring and the coefficient <strong>of</strong> variation <strong>of</strong> the dataincreases, statistical power is better maintained by substituting one-half the detectionlimit for the less than detection limit data, particularly for log-normally distributed andtransformed data. Substitution <strong>of</strong> zero or other constants was also recommended fora variety <strong>of</strong> circumstances. Overall, it was concluded that simple substitution methodswork best to maintain power and control error rates in statistical comparisons <strong>of</strong>chemical concentration data (USACE 1995).In this analysis, decisions regarding the treatment <strong>of</strong> less than detection limit data weretaken by considering the recommendations that have emerged from previousinvestigations in the context <strong>of</strong> their potential effects on the results <strong>of</strong> this assessment.Including all <strong>of</strong> the surface water, sediment, and fish-tissue chemistry data that werecompiled in the project databases, more than 30% <strong>of</strong> the data required censoring priorto data analysis. To minimize the potential effects <strong>of</strong> the less than detection limit dataon the results <strong>of</strong> the analysis, none <strong>of</strong> the less than detection limit data for which thedetection limits were greater than the corresponding toxicity screening value or toxicitythresholds for surface-water, sediment, or fish- tissue chemistry were used in theA-32
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February 2011technical report 2<str
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thresholds), which represent lowest
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Table of ContentsE
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5.5 Summary of the
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List of TablesTabl
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Table 4.17Table 4.18Table 4.19Table
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Table 4.47Table 4.48Table 4.49Table
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Table 6.4Table 8.1Compounds for Nat
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Figure 3.17 Map of
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Figure 5.20 Late Stuart Sockeye Sal
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List of Acronyms2,
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AcknowledgementsThe authors would l
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To assist it in fulfilling this man
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• Development of
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Chapter 2 Geographic and Temporal S
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life history of th
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• Pitt River Area of</str
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Chapter 3 Inventory of</str
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and Paper Mills - Canfor Pulp Limit
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3.1.1.2 Sawmills, Plywood Mills and
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(ENKON Environmental Ltd. 2001). Co
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Effluent toxicity was also included
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2011 for more information on the lo
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facilities identified in Table 3.11
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Inc.), poultry processing facilitie
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• Polycyclic aromatic hydrocarbon
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3.1.1.11 Municipal Wastewater Treat
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• Personal care products (fragran
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with oxytetracycline, ormetoprim, a
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In summary, the contaminants that c
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insecticides, such as phorate, terb
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3.1.2.4 Runoff fro
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the substances most amenable to lon
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• Plastic-related compounds (i.e.
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were used in the effects and exposu
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warrants further assessment to dete
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• Data collected for migration co
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Hazard quotients were calculated fo
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Many other substances have the pote
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Chapter 5 Evaluation of</st
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For surface water and sediment, the
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3. For hardness-dependent water qua
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• 2,3,7,8-TCDD toxic equivalents
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concern by area of
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0.05 mg/L; Glew and Hames 1971). As
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least time rearing in freshwater ha
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The results of thi
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Interest and in the South Thompson
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• Polychlorinated dibenzo-p-dioxi
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glycoproteins, polypeptides, peptid
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In-use herbicides in the Fraser Riv
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and home wood heating; Johannessen
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Pulp and paper mills, saw mills, an
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salinity tolerance were significant
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and gonadosomatic index scores were
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the lowest observed effect concentr
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Spitsbergen et al. (1991) reported
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Chakravorty et al. (1992) observed
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observed in saltwater gobies (Chasm
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(See Chapters 4 and 5 for further i
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emoval efficiency of</stron
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point and non-point source discharg
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Location of NatalS
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Nevertheless, it is possible that e
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concern relative to human or enviro
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these substances to aquatic ecosyst
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• Alkylphenol ethoxylates (APEOs;
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chemicals of poten
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productivity data, it is apparent t
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• Sufficiency - For a cause and e
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infection by various disease agents
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magnitude, and spatial extent <stro
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potential concern were documented a
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pathways and the intensity and exte
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Chilko River, Quesnel River, Nechak
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• The majority of</strong
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7.4.6 Information on Interactive <s
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• Development of
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8.4 Preliminary Evaluation
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8.5 Evaluation of
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the low returns of
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8.9 RecommendationsThis evaluation
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Chapter 9 References CitedAdams, R.
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Braune, B., D. Muir, B. DeMarch, M.
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EIP Associates. 1997. Polychlorinat
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Gallaugher, P. and L. Wood (Eds). P
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Hinch, S.G. and E.G. Martins. 2011.
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Kemble, N.E., D.K. Hardesty, C.G. I
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MacLatchy, D., L. Peters, J. Nickle
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Milston, R.H., M.S. Fitzpatrick, A.
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Peterman, R.M., D. Marmorek, B. Bec
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Smith, D.B., R.A. Zielinski, H.E. T
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USACE (U.S. Army Corps of</
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Walker, M.K., J.M. Spitsbergen, J.R
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Table 2.1. Overview of</str
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Table 2.2. Sampling sites for Early
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Table 2.3. Sampling sites for summe
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Table 2.4. Sampling sites for river
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Table 3.1. Listing of</stro
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Table 3.2. Chemicals of</st
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Table 3.8. Major pipelines transpor
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Table 3.10. Locations of</s
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Table 3.11. Listing of</str
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Table 3.16. List of</strong
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Table 3.17. List of</strong
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Table 3.18. Location of</st
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Table 3.20. Summary of</str
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Table 3.23. Estimated annual contam
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Table 3.28. Inventory of</s
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Table 4.1. Selected toxicity screen
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Table 4.2. Selected toxicity screen
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Table 4.3. Summary of</stro
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Table 4.19. Evaluation of</
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Table 4.32. Frequency of</s
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Table 4.53. Identification
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Table 5.1. Selected toxicity refere
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Table 5.2. Selected toxicity refere
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Table 5.3. Exposure point concentra
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Table 5.9. Hazard quotients <strong
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Table 5.11. Hazard quotients <stron
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Table 5.19. Hazard quotients <stron
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Table 5.23. Mean concentrations <st
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Table 6.2. Effects
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Table 6.3. Field studies of
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Table 6.4. Compounds for National r
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Table 8.1. Inventory of</st
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Figures
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Figure 5.1. Water Quality Index sco
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Figure 5.3. Fraser River Sockeye Sa
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Page 529 and 530: Figure 5.7. Weaver Sockeye Salmon P
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Page 533 and 534: Figure 5.11. Portage Sockeye Salmon
Page 535 and 536: Figure 5.13. Fennell Sockeye Salmon
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Page 541 and 542: Figure 5.19. Early Stuart Sockeye S
Page 543 and 544: Figure 5.21. Stellako Sockeye Salmo
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Page 547 and 548: Appendices
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Page 555 and 556: WeaknessesThere is a lot of
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Page 589 and 590: deBruyn, A.M., H. deBruyn, M.G. Iko
Page 591 and 592: Appendix 5. Data description and so
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Potential Effects of Contaminants on Fraser River Sockeye Salmon

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