DOI QR코드

DOI QR Code

Development of Human Exposure and Risk Assessment System for Chemicals in Fish and Fishery Products

수산생물 중 유해물질의 인체 노출 및 위해평가 시스템 개발

  • Lee, Jaewon (ICT Convergence Research Center, CHEM.I.Net, Co., Ltd.) ;
  • Lee, Seungwoo (ICT Convergence Research Center, CHEM.I.Net, Co., Ltd.) ;
  • Choi, Minkyu (South Sea Fisheries Research Institute, National Institute of Fisheries Science) ;
  • Lee, Hunjoo (ICT Convergence Research Center, CHEM.I.Net, Co., Ltd.)
  • 이재원 (켐아이넷(주) ICT융합연구소) ;
  • 이승우 (켐아이넷(주) ICT융합연구소) ;
  • 최민규 (해양수산부 국립수산과학원) ;
  • 이헌주 (켐아이넷(주) ICT융합연구소)
  • Received : 2021.08.24
  • Accepted : 2021.09.23
  • Published : 2021.10.31

Abstract

Background: Fish and fishery products (FFPs) unintentionally contaminated with various environmental pollutants are major exposure pathways for humans. To protect human health from the consumption of contaminated FFPs, it is essential to develop a systematic tool for evaluating exposure and risks. Objectives: To regularly, accurately, and quickly evaluate adverse health outcomes due to FFPs contamination, we developed an automated dietary exposure and risk assessment system called HERA (the Human Exposure and Risk Assessment system for chemicals in FFPs). The aim of this study was to develop an overall architecture design and demonstrate the major features of the HERA system. Methods: For the HERA system, the architecture framework consisted of multi-layer stacks from infrastructure to fish exposure and risk assessment layers. To compile different contamination levels and types of seafood consumption datasets, the data models were designed for the classification codes of FFP items, contaminants, and health-based guidance values (HBGVs). A systematic data pipeline for summarizing exposure factors was constructed through down-scaling and preprocessing the 24-hour dietary recalls raw dataset from the Korea National Health and Nutrition Examination Survey (KNAHES). Results: According to the designed data models for the classification codes, we standardized 167 seafood items and 2,741 contaminants. Subsequently, we implemented two major functional workflows: 1) preparation and 2) main process. The HERA system was developed to enable risk assessors to accumulate the concentration databases sustainably and estimate exposure levels for several populations linked to seafood consumption data in KNAHES in a user-friendly manner and in a local PC environment. Conclusions: The HERA system will support policy-makers in making risk management decisions based on a nation-wide risk assessment for FFPs.

Keywords

Acknowledgement

본 연구는 2019년도 국립수산과학원 수산과학연구사업(R2019051)의 지원으로 수행된 연구입니다.

References

  1. Duran A, Tuzen M, Soylak M. Assessment of trace metal concentrations in muscle tissue of certain commercially available fish species from Kayseri, Turkey. Environ Monit Assess. 2014; 186(7): 4619-4628. https://doi.org/10.1007/s10661-014-3724-7
  2. Djedjibegovic J, Marjanovic A, Tahirovic D, Caklovica K, Turalic A, Lugusic A, et al. Heavy metals in commercial fish and seafood products and risk assessment in adult population in Bosnia and Herzegovina. Sci Rep. 2020; 10(1): 13238. https://doi.org/10.1038/s41598-020-70205-9
  3. Olmedo P, Pla A, Hernandez AF, Barbier F, Ayouni L, Gil F. Determination of toxic elements (mercury, cadmium, lead, tin and arsenic) in fish and shellfish samples. Risk assessment for the consumers. Environ Int. 2013; 59: 63-72. https://doi.org/10.1016/j.envint.2013.05.005
  4. Ferrante M, Zanghi G, Cristaldi A, Copat C, Grasso A, Fiore M, et al. PAHs in seafood from the Mediterranean Sea: an exposure risk assessment. Food Chem Toxicol. 2018; 115: 385-390. https://doi.org/10.1016/j.fct.2018.03.024
  5. Jeong JY, Choi CW, Ryeom TK, Cho KH, Park SR, Shin HS, et al. Analysis and risk assessment of polycyclic aromatic hydrocarbons (PAHs) in seafood from oil contaminated bay. Anal Sci Technol. 2010; 23(2): 187-195. https://doi.org/10.5806/AST.2010.23.2.187
  6. Lee SG, Kang EH, Kim AH, Choi SH, Hong DH, Karaulova EP, et al. Concentrations and risk assessment of heavy metal in shellfish and crustacean collected from Vladivostok Area in Russia. Korean J Fish Aquat Sci. 2019; 52(5): 452-460.
  7. Im R, Youm HC, Kim DW, Bae HS, Ahn SJ, Ryu DY, et al. Dietary exposure assessment of arsenic in Korean adults. Environ Health Toxicol. 2010; 25(4): 307-314.
  8. World Health Organization. Principles and Methods for the Risk Assessment of Chemicals in Food. Geneva: World Health Organization; 2009. p.6-11~6-12.
  9. Kwon N, Suh J, Lee H. Data cleaning and integration of multi-year dietary survey in the Korea National Health and Nutrition Examination Survey (KNHANES) using database normalization theory. J Environ Health Sci. 2017; 43(4): 298-306. https://doi.org/10.5668/JEHS.2017.43.4.298
  10. Yoon H, Seo J, Kim T, Kim J, Jo A, Lee B, et al. Development of Korean exposure factors for children in Korea. J Environ Health Sci. 2017; 43(3): 167-175. https://doi.org/10.5668/JEHS.2017.43.3.167
  11. Xue J, Zartarian VG, Liu SV, Geller AM. Methyl mercury exposure from fish consumption in vulnerable racial/ethnic populations: probabilistic SHEDS-Dietary model analyses using 1999-2006 NHANES and 1990-2002 TDS data. Sci Total Environ. 2012; 414: 373-379. https://doi.org/10.1016/j.scitotenv.2011.10.010
  12. Lee H, Lee K, Park JY, Min SG. Korean Ministry of Environment's web-based visual consumer product exposure and risk assessment system (COPER). Environ Sci Pollut Res Int. 2017; 24(14): 13142-13148. https://doi.org/10.1007/s11356-017-8965-x
  13. Boon PE, Cunningham J, Moy GG, Ormerod D, Peterson BJ, Reuss R. Automated programs for calculating dietary exposure. In: Moy GG, Vannoort RW. editors. Total Diet Studies. New York: Springer; 2013. p.445-452.
  14. Guo Z. Development of a Windows-based indoor air quality simulation software package. Environ Model Softw. 2000; 15(4): 403-410. https://doi.org/10.1016/S1364-8152(00)00020-7
  15. Fielding RT, Taylor RN. Principled design of the modern Web architecture. Paper presented at: Proceedings of the 2000 International Conference on Software Engineering. ICSE 2000 the New Millennium; 2000 June 9; Limerick, Ireland. New York: ACM Transactions on Internet Technology, 2002. p. 115-150.
  16. Kang HS, Kwon NJ, Jeong J, Lee K, Lee H. Web-based Korean maximum residue limit evaluation tools: an applied example of maximum residue limit evaluation for trichlorfon in fishery products. Environ Sci Pollut Res Int. 2019; 26(7): 7284-7299. https://doi.org/10.1007/s11356-019-04314-y
  17. Xue J, Zartarian V, Wang SW, Liu SV, Georgopoulos P. Probabilistic modeling of dietary arsenic exposure and dose and evaluation with 2003-2004 NHANES data. Environ Health Perspect. 2010; 118(3): 345-350. https://doi.org/10.1289/ehp.0901205
  18. European Food Safety Authority (EFSA), Brancato A, Brocca D, Ferreira L, Greco L, Jarrah S, et al. Use of EFSA pesticide residue intake model (EFSA PRIMo revision 3). EFSA J. 2018; 16(1): e05147.
  19. DiNovi M. International Peer Review of FSANZ Dietary Modelling Team Practices and Procedures. Majura: Food Standards Australia & New Zealand; 2007. p.4-10.
  20. Chen Y, Dennis SB, Hartnett E, Paoli G, Pouillot R, Ruthman T, et al. FDA-iRISK--a comparative risk assessment system for evaluating and ranking food-hazard pairs: case studies on microbial hazards. J Food Prot. 2013; 76(3): 376-385. https://doi.org/10.4315/0362-028X.JFP-12-372
  21. van der Voet H, de Boer WJ, Kruisselbrink JW, Goedhart PW, van der Heijden GW, Kennedy MC, et al. The MCRA model for probabilistic single-compound and cumulative risk assessment of pesticides. Food Chem Toxicol. 2015; 79: 5-12. https://doi.org/10.1016/j.fct.2014.10.014
  22. Jeong DI, Kang HS, Hwang MS, Hwang IG, Min SG, Lee H. The trend of monitoring database and risk assessment systems for food chemical in national and regional levels. Safe Food. 2015; 10(3): 3-11.
  23. Kim JA, Jo IS, Shin Y, Jang JI, Kim SJ, Jung JH, et al. Analysis and risk assessment on arsenic, chrome, and nickel in dried marine products. J Food Hyg Saf. 2021; 36(2): 135-140. https://doi.org/10.13103/JFHS.2021.36.2.135
  24. Duan Y, Edwards JS, Xu MX. Web-based expert systems: benefits and challenges. Inf Manag. 2005; 42(6): 799-811. https://doi.org/10.1016/j.im.2004.08.005
  25. Vilavert L, Borrell F, Nadal M, Jacobs S, Minnens F, Verbeke W, et al. Health risk/benefit information for consumers of fish and shellfish: FishChoice, a new online tool. Food Chem Toxicol. 2017; 104: 79-84. https://doi.org/10.1016/j.fct.2017.02.004
  26. Domingo JL. Nutrients and chemical pollutants in fish and shellfish. Balancing health benefits and risks of regular fish consumption. Crit Rev Food Sci Nutr. 2016; 56(6): 979-988. https://doi.org/10.1080/10408398.2012.742985
  27. Domingo JL, Bocio A, Falco G, Llobet JM. Benefits and risks of fish consumption Part I. A quantitative analysis of the intake of omega-3 fatty acids and chemical contaminants. Toxicology. 2007; 230(2-3): 219-226. https://doi.org/10.1016/j.tox.2006.11.054
  28. Domingo JL, Bocio A, Marti-Cid R, Llobet JM. Benefits and risks of fish consumption Part II. RIBEPEIX, a computer program to optimize the balance between the intake of omega-3 fatty acids and chemical contaminants. Toxicology. 2007; 230(2-3): 227-233. https://doi.org/10.1016/j.tox.2006.11.059
  29. Domingo JL. Omega-3 fatty acids and the benefits of fish consumption: is all that glitters gold? Environ Int. 2007; 33(7): 993-998. https://doi.org/10.1016/j.envint.2007.05.001