Advertisement

Does a sum of toxic units exceeding 1 imply adverse impacts on macroinvertebrate assemblages? A field study in a northern Japanese river receiving treated mine discharge

  • Yuichi IwasakiEmail author
  • Megumi Fujisawa
  • Tagiru Ogino
  • Hiroyuki Mano
  • Naohide Shinohara
  • Shigeki Masunaga
  • Masashi Kamo
Article

Abstract

In ecological risk assessment, sum-of-toxic-unit approaches based on measured water quality factors such as trace metals are used to infer ecological impacts in the environment. However, it is uncertain whether the use of such approaches yields accurate risk predictions. To address this issue, we investigated and compared (1) water quality, including trace metals, and (2) benthic macroinvertebrate communities in a northern Japanese river receiving treated discharge from an abandoned mine and in a nearby reference river. As a sum-of-toxic-unit approach, we employed a cumulative criterion unit (CCU), namely, the sum of the ratios of the dissolved concentrations of a metal (Cu, Zn, Cd, or Pb) divided by the US Environmental Protection Agency hardness-adjusted environmental water quality criterion for that metal. Compared with the reference sites, at the metal-contaminated sites, the richness, abundance, and structure of macroinvertebrate communities were little affected, with CCUs of 1.7 to 7.4, suggesting that CCU values exceeding 1 do not always indicate marked adverse impacts on these metrics. Further study is still required to derive a more compelling conclusion on the generally applicable relationships between CCUs and ecological impacts on river invertebrates. This would lead to better ecological risk assessments based on sum-of-toxic-unit approaches.

Keywords

Heavy metals Aquatic insects Invertebrates Species richness Streams Ecological impacts 

Notes

Acknowledgments

We are grateful to Susumu Norota and Kazuto Ohmori of Hokkaido Research Organization for their help in study site selection, and Shosaku Kashiwada and Daiki Kitamura of Toyo University for their help in metal analysis. The paper does not necessarily reflect the policies or views of any government agencies. Useful comments by anonymous reviewers are greatly appreciated.

Funding information

Preparation of this manuscript was supported partly by the Environment Research and Technology Development Fund (5RF-1801) of the Environmental Restoration and Conservation Agency of Japan.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

10661_2019_8047_MOESM1_ESM.docx (509 kb)
ESM 1 (DOCX 508 kb)
10661_2019_8047_MOESM2_ESM.xlsx (28 kb)
ESM 2 (XLSX 27 kb)

References

  1. Anderson, M. J. (2001). A new method for non-parametric multivariate analysis of variance. Austral Ecology, 26(1), 32–46.  https://doi.org/10.1046/j.1442-9993.2001.01070.x.CrossRefGoogle Scholar
  2. Chapman, P. M. (2018). Environmental quality benchmarks—the good, the bad, and the ugly. Environmental Science and Pollution Research, 25(4), 3043–3046.  https://doi.org/10.1007/s11356-016-7924-2.CrossRefGoogle Scholar
  3. Clements, W. H., Carlisle, D. M., Lazorchak, J. M., & Johnson, P. C. (2000). Heavy metals structure benthic communities in Colorado mountain streams. Ecological Applications, 10(2), 626–638.CrossRefGoogle Scholar
  4. Environment Canada. (2012). Metal mining technical guidance for environmental effects monitoring. Gatineau: Environment Canada.Google Scholar
  5. Heino, J. (2010). Are indicator groups and cross-taxon congruence useful for predicting biodiversity in aquatic ecosystems? Ecological Indicators, 10(2), 112–117.  https://doi.org/10.1016/j.ecolind.2009.04.013.CrossRefGoogle Scholar
  6. Hirst, H., Jüttner, I., & Ormerod, S. J. (2002). Comparing the responses of diatoms and macroinvertebrates to metals in upland streams of Wales and Cornwall. Freshwater Biology, 47(9), 1752–1765.CrossRefGoogle Scholar
  7. Hokkaido Prefecture (2017). Report on work, survey, and analysis for preventing abandoned mine pollution in the fiscal year of 2016.Google Scholar
  8. Iwasaki, Y., & Gauthier, P. (2016). Concentration addition and response addition to analyze mixture toxicity: Is it worth testing? Environmental Toxicology and Chemistry, 35(3), 526–527.  https://doi.org/10.1002/etc.3263.CrossRefGoogle Scholar
  9. Iwasaki, Y., Kagaya, T., & Matsuda, H. (2018a). Comparing macroinvertebrate assemblages at organic-contaminated river sites with different zinc concentrations: Metal-sensitive taxa may already be absent. Environmental Pollution, 241, 272–278.  https://doi.org/10.1016/j.envpol.2018.05.041.CrossRefGoogle Scholar
  10. Iwasaki, Y., Kagaya, T., Miyamoto, K., & Matsuda, H. (2009). Effects of heavy metals on riverine benthic macroinvertebrate assemblages with reference to potential food availability for drift-feeding fishes. Environmental Toxicology and Chemistry, 28(2), 354–363.CrossRefGoogle Scholar
  11. Iwasaki, Y., Kagaya, T., Miyamoto, K., & Matsuda, H. (2012). Responses of riverine macroinvertebrates to zinc in natural streams: Implications for the Japanese water quality standard. Water, Air, and Soil Pollution, 223(1), 145–158.CrossRefGoogle Scholar
  12. Iwasaki, Y., Kagaya, T., Miyamoto, K., Matsuda, H., & Sakakibara, M. (2011). Effect of zinc on diversity of riverine benthic macroinvertebrates: Estimation of safe concentrations from field data. Environmental Toxicology and Chemistry, 30(10), 2237–2243.CrossRefGoogle Scholar
  13. Iwasaki, Y., & Ormerod, S. J. (2012). Estimating safe concentrations of trace metals from inter-continental field data on river macroinvertebrates. Environmental Pollution, 166, 182–186.CrossRefGoogle Scholar
  14. Iwasaki, Y., Schmidt, T. S., & Clements, W. H. (2018b). Quantifying differences in responses of aquatic insects to trace metal exposure in field studies and short-term stream mesocosm experiments. Environmental Science & Technology, 52(7), 4378–4384.  https://doi.org/10.1021/acs.est.7b06628.CrossRefGoogle Scholar
  15. Jones, J. I., Murphy, J. F., Collins, A. L., Spencer, K. L., Rainbow, P. S., Arnold, A., Pretty, J. L., Moorhouse, A. M. L., Aguilera, V., Edwards, P., Parsonage, F., Potter, H., & Whitehouse, P. (2019). The impact of metal-rich sediments derived from mining on freshwater stream life. Reviews of Environmental Contamination and Toxicology, 247, 1–79.  https://doi.org/10.1007/398_2018_21.CrossRefGoogle Scholar
  16. Luoma, S. N., & Rainbow, P. S. (2008). Metal contamination in aquatic environments. Cambridge: Cambridge University Press.Google Scholar
  17. Manly, B. F. J., & Navarro Alberto, J. A. (2016). Multivariate statistical methods: A primer (4th ed.). Boca Raton: CRC press.Google Scholar
  18. Merrington, G., An, Y.-J., Grist, E. P. M., Jeong, S.-W., Rattikansukha, C., Roe, S., et al. (2014). Water quality guidelines for chemicals: Learning lessons to deliver meaningful environmental metrics. Environmental Science and Pollution Research, 21(1), 6–16.  https://doi.org/10.1007/s11356-013-1732-8.CrossRefGoogle Scholar
  19. Naito, W., Kamo, M., Tsushima, K., & Iwasaki, Y. (2010). Exposure and risk assessment of zinc in Japanese surface waters. Science of the Total Environment, 408(20), 4271–4284.CrossRefGoogle Scholar
  20. Nriagu, J. O., & Pacyna, J. M. (1988). Quantitative assessment of worldwide contamination of air, water and soils by trace-metals. Nature, 333(6169), 134–139.CrossRefGoogle Scholar
  21. R Core Team (2018). R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing.Google Scholar
  22. Schmidt, T. S., Clements, W. H., Mitchell, K. A., Church, S. E., Wanty, R. B., Fey, D. L., Verplanck, P. L., & San Juan, C. A. (2010). Development of a new toxic-unit model for the bioassessment of metals in streams. Environmental Toxicology and Chemistry, 29(11), 2432–2442.CrossRefGoogle Scholar
  23. Schmidt, T. S., Clements, W. H., Wanty, R. B., Verplanck, P. L., Church, S. E., San Juan, C. A., Fey, D. L., Rockwell, B. W., DeWitt, E., & Klein, T. L. (2012). Geologic processes influence the effects of mining on aquatic ecosystems. Ecological Applications, 22(3), 870–879.CrossRefGoogle Scholar
  24. U. S. Environmental Protection Agency (1994). Method 200.8: Determination of trace elements in waters and wastes by inductively coupled plasma-mass spectrometry, Revison 5.4. Cincinnati, OH.Google Scholar
  25. U. S. Environmental Protection Agency (2002). National Recommended Water Quality Criteria: EPA822-R-02-047. Washington.Google Scholar
  26. U. S. Environmental Protection Agency (2007). Aquatic life ambient freshwater quality criteria—Copper 2007 revision, EPA-822-F-07-001. WashingtonGoogle Scholar
  27. Welch, B. L. (1947). The generalization of student's' problem when several different population variances are involved. Biometrika, 34(1/2), 28–35.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Research Institute of Science for Safety and SustainabilityNational Institute of Advanced Industrial Science and TechnologyIbarakiJapan
  2. 2.College of Engineering ScienceYokohama National UniversityYokohamaJapan
  3. 3.Faculty of Environment and Information SciencesYokohama National UniversityYokohamaJapan
  4. 4.Geological Survey of HokkaidoHokkaido Research OrganizationSapporoJapan

Personalised recommendations