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Patterns of Co-contamination in Freshwater and Marine Fish of the Northeastern USA

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Abstract

Many bioaccumulative and toxic contaminants are known to co-occur in fish tissue, yet this covariance has not been explicitly incorporated into model-based risk assessments that inform fish consumption advisories. We utilize available U.S. EPA datasets to statistically model the covariance among contaminant concentrations in fish tissue and the dependence of this covariance on waterbody and watershed conditions. We find that most contaminants positively covary, whether fish were collected in rivers, lakes, or coastal waters. Mercury in lakes and mercury, PFCs, and heptachlor in rivers covary negatively with the other contaminants. While much of the variance and covariance in contaminants can be statistically related to fish characteristics and watershed and waterbody conditions, a large amount remains in model residuals. This implies that single contaminant models, even if highly precise, can misestimate total health risk by neglecting the substantial covariance with other contaminants that is left unmodelled.

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Data Availability

Data analyzed in this study can be found at the following sources. Additional data taken from multiple publications is described in the appendices.

USEPA. (2010). National Coastal Assessment. https://archive.epa.gov/emap/archive-emap/web/html/index-149.html.

USEPA. (2016). National Rivers and Streams Assessment 2008-2009: A Collaborative Survey.https://www.epa.gov/national-aquatic-resource-surveys/national-rivers-and-streams-assessment-2008-2009-report.

USEPA. (1999). EMAP Surface Waters Lake Database. https://archive.epa.gov/emap/archive-emap/web/html/index.html.

Dewitz, J., 2019, National Land Cover Database (NLCD) 2016 Products (ver. 2.0, July 2020): U.S. Geological Survey data release, https://doi.org/10.5066/P96HHBIE.

References

  1. Chase, M. E., et al. (2001). Gulfwatch: Monitoring spatial and temporal patterns of trace metal and organic contaminants in the Gulf of Maine (1991–1997) with the blue mussel. Mytilus edulis L. Marine Pollution Bulletin, 42(6), 490–504. https://doi.org/10.1016/S0025-326X(00)00193-4

    Article  Google Scholar 

  2. Driscoll, C. T., et al. (2007). Mercury contamination in forest and freshwater ecosystems in the northeastern United States. BioScience, 57, 17–28. https://doi.org/10.1641/B570106

    Article  Google Scholar 

  3. Phillips, P. J., et al. (2010). Composition, distribution, and potential toxicity of organochlorine mixtures in bed sediments of streams. Science of The Total Environment, 408, 594–606. https://doi.org/10.1016/j.scitotenv.2009.09.052

    Article  CAS  Google Scholar 

  4. Stahl, L. L., et al. (2009). Contaminants in fish tissue from US lakes and reservoirs: A national probabilistic study. Environmental monitoring and assessment, 150, 3–19. https://doi.org/10.1007/s10661-008-0669-8

    Article  CAS  Google Scholar 

  5. Chen, C. Y., et al. (2005). Patterns of Hg bioaccumulation and transfer in aquatic food webs across multi-lake studies in the northeast US. Ecotoxicology, 14, 135–147. https://doi.org/10.1007/s10646-004-6265-y

    Article  CAS  Google Scholar 

  6. Dorea, J. G. (2006). Fish meal in animal feed and human exposure to persistent bioaccumulative and toxic substances. Journal of food protection, 69, 2777–2785. https://doi.org/10.4315/0362-028x-69.11.2777

    Article  CAS  Google Scholar 

  7. Bruggeman, W. A., Opperhuizen, A., Wijbenga, A., & Hutzinger, O. (1984). Bioaccumulation of super-lipophilic chemicals in fish. Toxicological & Environmental Chemistry, 7, 173–189. https://doi.org/10.1080/02772248409357024

    Article  CAS  Google Scholar 

  8. Petersen, G. I., & Kristensen, P. (1998). Bioaccumulation of lipophilic substances in fish early life stages. Environmental Toxicology and Chemistry, 17, 1385–1395. https://doi.org/10.1002/etc.5620170724

    Article  CAS  Google Scholar 

  9. Dórea, J. G. (2008). Persistent, bioaccumulative and toxic substances in fish: Human health considerations. Science of The Total Environment, 400, 93–114. https://doi.org/10.1016/j.scitotenv.2008.06.017

    Article  CAS  Google Scholar 

  10. USEPA. (2014).  Estimated fish consumption rates for the U.S. population and selected subpopulations (NHANES 2003–2010). Washington, DC.

  11. Carpenter, D. O., et al. (1998). Human health and chemical mixtures: An overview. Environmental Health Perspectives, 106, 1263–1270. https://doi.org/10.1289/ehp.98106s61263

    Article  CAS  Google Scholar 

  12. Yang, R. S. H., Hong, H. L., & Boorman, G. A. (1989). Toxicology of chemical mixtures: Experimental approaches, underlying concepts, and some results. Toxicology Letters, 49, 183–197. https://doi.org/10.1016/0378-4274(89)90032-5

    Article  CAS  Google Scholar 

  13. Monosson, E. (2005). Chemical mixtures: Considering the evolution of toxicology and chemical assessment. Environmental Health Perspectives, 113, 383–390. https://doi.org/10.1289/ehp.6987

    Article  CAS  Google Scholar 

  14. Spurgeon, D. J., et al. (2010). Systems toxicology approaches for understanding the joint effects of environmental chemical mixtures. Science of The Total Environment, 408, 3725–3734. https://doi.org/10.1016/j.scitotenv.2010.02.038

    Article  CAS  Google Scholar 

  15. ASTDR. (2018). Interaction profiles for toxic substances.

  16. USEPA. (2000). Guidance for assessing chemical contaminant data for use in fish advisories. Washington, DC. O.o. Water, Editor.

  17. Gewurtz, S. B., et al. (2008). Spatial distributions of legacy contaminants in sediments of Lakes Huron and Superior. Journal of Great Lakes Research, 34, 153–168. https://doi.org/10.3394/0380-1330(2008)34[153:SDOLCI]2.0.CO;2

    Article  CAS  Google Scholar 

  18. Nicklisch, S. C., et al. (2017). Geographic differences in persistent organic pollutant levels of yellowfin tuna. Environmental Health Perspectives, 67014, 1. https://doi.org/10.1289/ehp518

    Article  CAS  Google Scholar 

  19. Nowell, L. H., et al. (2013). Contaminants in stream sediments from seven United States metropolitan areas: Part I: Distribution in relation to urbanization. Archives of Environmental Contamination and Toxicology, 64, 32–51. https://doi.org/10.1007/s00244-012-9813-0

    Article  CAS  Google Scholar 

  20. De Vault, D. S., et al. (1996). Contaminant trends in lake trout and walleye from the Laurentian Great Lakes. Journal of Great Lakes Research, 22, 884–895. https://doi.org/10.1016/S0380-1330(96)71009-2

    Article  Google Scholar 

  21. Gandhi, N., et al. (2017).  Are fish consumption advisories for the Great Lakes adequately protective from chemical mixture? Environmental Health Perspectives.

  22. USEPA. (2010). National coastal assessment. https://archive.epa.gov/emap/archive-emap/web/html/index-149.html

  23. USEPA. (2016). National rivers and streams assessment 2008–2009: A collaborative survey, Development, Editor. O.o.W.a.O.o.R.a. https://www.epa.gov/national-aquatic-resource-surveys/national-rivers-and-streams-assessment-2008-2009-report

  24. USEPA. (1999). EMAP surface waters lake database. https://archive.epa.gov/emap/archive-emap/web/html/index.html

  25. Clark, J. S., et al. (2017). Generalized joint attribute modeling for biodiversity analysis: Median-zero, multivariate, multifarious data. Ecological Monographs, 87, 34–56. https://doi.org/10.1002/ecm.1241

    Article  Google Scholar 

  26. Helsel, D. R. (1990). Less than obvious - Statistical treatment of data below the detection limit. Environmental Science & Technology, 24, 1766–1774. https://doi.org/10.1021/es00082a001

    Article  CAS  Google Scholar 

  27. Shumway, R. H., Azari, R. S., & Kayhanian, M. (2002). Statistical approaches to estimating mean water quality concentrations with detection limits. Environmental Science & Technology, 36, 3345–3353 https://doi.org/10.1021/es0111129

  28. Antweiler, R.C. & Taylor, H. E. (2008). Evaluation of statistical treatments of left-censored environmental data using coincident uncensored data sets: I. Summary statistics. Environmental Science & Technology, 42, 3732–3738 https://doi.org/10.1021/es071301c

  29. Shanley, J. B., et al. (2012). MERGANSER: An empirical model to predict fish and loon mercury in New England lakes. Environmental Science & Technology, 46, 4641–4648. https://doi.org/10.1021/es300581p

    Article  CAS  Google Scholar 

  30. Sunderland, E.M., et al. (2009). Mercury sources, distribution, and bioavailability in the North Pacific Ocean: Insights from data and models. 23(2).

  31. Stahl, L. L., et al. (2014). Perfluorinated compounds in fish from U.S. urban rivers and the Great Lakes. Science of The Total Environment, 499, 185–195 https://doi.org/10.1016/j.scitotenv.2014.07.126

  32. Spiegelhalter, D. J., et al. (2014). The deviance information criterion: 12 years on. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 76, 485–493. https://doi.org/10.1111/rssb.12062

    Article  Google Scholar 

  33. Gelman, A., Hwang, J., & Vehtari, A. (2014). Understanding predictive information criteria for Bayesian models. Statistics and Computing, 24, 997–1016 https://doi.org/10.1007/s11222-013-9416-2

  34. Benjamin, D. J., et al. (2018). Redefine statistical significance. Nature human behaviour, 2, 6–10. https://doi.org/10.1038/s41562-017-0189-z

    Article  Google Scholar 

  35. Visha, A., et al. (2015). A Bayesian assessment of the mercury and PCB temporal trends in lake trout (Salvelinus namaycush) and walleye (Sander vitreus) from lake Ontario, Ontario. Canada. Ecotoxicology and Environmental Safety, 117, 174–186. https://doi.org/10.1016/j.ecoenv.2015.03.022

    Article  CAS  Google Scholar 

  36. Lange, T. R., Royals, H. E., & Connor, L. L. (1993). Influence of water chemistry on mercury concentration in largemouth bass from Florida lakes. Transactions of the American Fisheries Society, 122, 74–84 https://doi.org/10.1577/1548-8659(1993)122%3C0074:IOWCOM%3E2.3.CO;2

  37. Miskimmin, B. M., Rudd, J. W., & Kelly, C. A. (1992). Influence of dissolved organic carbon, pH, and microbial respiration rates on mercury methylation and demethylation in lake water. Canadian Journal of Fisheries and Aquatic Sciences, 49(1), 17–22.

    Article  CAS  Google Scholar 

  38. Ravichandran, M. (2004). Interactions between mercury and dissolved organic matter––A review. Chemosphere, 55, 319–331. https://doi.org/10.1016/j.chemosphere.2003.11.011

    Article  CAS  Google Scholar 

  39. Chapman. (2017). A. New Hampshire Department of Environmental Services biomonitoring program.

  40. Tsui, M. T. K. & Finlay, J. C. (2011). Influence of dissolved organic carbon on methylmercury bioavailability across Minnesota stream ecosystems. Environmental Science & Technology, 45, 5981–5987 https://doi.org/10.1021/es200332f

  41. Broadley, H. J., et al. (2019). Factors affecting MeHg bioaccumulation in stream biota: The role of dissolved organic carbon and diet. Ecotoxicology, 28, 949–963. https://doi.org/10.1007/s10646-019-02086-2

    Article  CAS  Google Scholar 

  42. Gourlay, C., et al. (2003). Effect of dissolved organic matter of various origins and biodegradabilities on the bioaccumulation of polycyclic aromatic hydrocarbons in Daphnia magna. Environmental Toxicology and Chemistry: An International Journal, 22, 1288–1294. https://doi.org/10.1002/etc.5620220615

    Article  CAS  Google Scholar 

  43. Bejarano, A. C., Decho, A. W., & Thomas Chandler, G. (2005). The role of various dissolved organic matter forms on chlorpyrifos bioavailability to the estuarine bivalve Mercenaria mercenaria. Marine Environmental Research, 60, 111–130 https://doi.org/10.1016/j.marenvres.2004.10.001

  44. Akkanen, J., et al. (2001). Bioavailability of atrazine, pyrene and benzo[a]pyrene in European river waters. Chemosphere, 45, 453–462. https://doi.org/10.1016/S0045-6535(01)00038-8

    Article  CAS  Google Scholar 

  45. Haitzer, M., et al. (2001). No enhancement in bioconcentration of organic contaminants by low levels of DOM. Chemosphere, 44, 165–171. https://doi.org/10.1016/S0045-6535(00)00269-1

    Article  CAS  Google Scholar 

  46. Mahmood, M., Bhavsar, S. P., & G. B. (2013). Arhonditsis Fish contamination in Lake Erie: An examination of temporal trends of organochlorine contaminants and a Bayesian approach to consumption advisories. Ecological informatics, 18, 131–148 https://doi.org/10.1016/j.ecoinf.2013.08.001

  47. Visha, A., et al. (2016). Guiding fish consumption advisories for Lake Ontario: A Bayesian hierarchical approach. Journal of Great Lakes Research, 42(1), 70–82.

    Article  CAS  Google Scholar 

  48. Craig, P., & Moreton, P. (1986). Total mercury, methyl mercury and sulphide levels in British estuarine sediments—III. Water Research, 20, 1111–1118 https://doi.org/10.1016/0043-1354(81)90180-9

  49. Hope, B. K., et al. (2007). Environmental management with knowledge of uncertainty: A methylmercury case study. Integrated Environmental Assessment and Management: An International Journal, 3, 144–149. https://doi.org/10.1897/1551-3793(2007)3[144:EMWKOU]2.0.CO;2

    Article  CAS  Google Scholar 

  50. Cohen, J., et al. (2005). A quantitative risk–benefit analysis of changes in population fish consumption. American Journal of Preventive Medicine, 29, 325–325. https://doi.org/10.1016/j.amepre.2005.07.003

    Article  Google Scholar 

  51. Rheinberger, C. M., & Hammitt, J. K. (2012). Risk trade-offs in fish consumption: A public health perspective. Environmental Science & Technology, 46, 12337–12346 https://doi.org/10.1021/es302652m

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Acknowledgements

The authors would like to thank Jim Clark, the creator of GJAM, for his support in model trouble shooting and aiding in the interpretation of the model results.

Funding

Funding to support this work was provided by the National Institute of Environmental Health Sciences grant number P42 ES007373-S1 awarded to Dr. Celia Y. Chen.

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Authors and Affiliations

  1. Department of Civil and Environmental Engineering, Duke University, 121 Hudson Hall, Box 90287, Durham, NC, 27708, USA

    Kimberly Bourne & Mark E. Borsuk

  2. Program in Ecology, Evolution, and Conservation Biology, University of Illinois at Urbana-Champaign, 505 South Goodwin Ave, Urbana, IL, 61801, USA

    Amanda N. Curtis

  3. Department of Geography, Dartmouth College, 42 College St, Hanover, NH, 03755, USA

    Jonathan Chipman

  4. Department of Biological Sciences, Dartmouth College, 78 College Street, Hanover, NH, 03755, USA

    Celia Y. Chen

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  1. Kimberly Bourne
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Contributions

K.B. performed the analysis and created the figures. A.C., J.C., and K.B. collected the data. M.E.B. and K.B. wrote the manuscript. All authors reviewed the manuscript.

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Correspondence to Kimberly Bourne.

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Bourne, K., Curtis, A.N., Chipman, J. et al. Patterns of Co-contamination in Freshwater and Marine Fish of the Northeastern USA. Environ Model Assess 28, 1127–1137 (2023). https://doi.org/10.1007/s10666-023-09912-2

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