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ANN-Based Integrated Risk Ranking Approach: A Case Study of Contaminants of Emerging Concern of Fish and Seafood in Europe

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  • Vikas Kumar

    (Environmental Engineering Laboratory, Departament d’Enginyeria Química, Universitat Rovira i Virgili, 43007 Tarragona, Spain
    IISPV, Hospital Universitari Sant Joan de Reus, Universitat Rovira i Virgili, 43201 Reus, Spain)

  • Saurav Kumar

    (Environmental Engineering Laboratory, Departament d’Enginyeria Química, Universitat Rovira i Virgili, 43007 Tarragona, Spain
    Department of Biochemistry, Deshbandhu College, University of Delhi, Delhi 110019, India)

Abstract

Seafood, one of the most important food commodities consumed worldwide, is considered a high-quality, healthy, and safe food option. However, marine ecosystems are the ultimate destination for a large group of chemicals, including contaminants of emerging concern, and seafood consumption is a major pathway of human exposure. With growing awareness of food safety and food quality, and increased demand for information on the risk of contaminants of emerging concern, there is a need to assess food safety issues related to harmful contaminants in seafood and ensure the safety of marine food resources. In this study, the risks of emerging compounds (endocrine disruptors, brominated flame retardants, pharmaceuticals and personal care products, and toxic elements) in fish and seafood were analyzed according to their PBT (persistence, bioaccumulation, toxicity) properties as well as in terms of their concentration levels in seafood. A hazard index (HI) was estimated for each compound by applying an artificial neural network (ANN) approach known as Self-Organizing-Maps. Subsequently, an integrated risk rank (IRI) was developed considering the values of HI and the concentrations of emerging compounds in seafood species gathered from the scientific literature. Current results identified HHCB, MeHg, NP, AHTN and PBDE209 as the top five highest ranked compounds present in seafood, according to the 50th percentile (mean) of the IRI. However, this ranking slightly changed when taking into account the 99th percentile of the IRI, showing toxic elements, methylmercury and inorganic arsenic, as having the highest risk. The outcome of this study identified the priority contaminants and should help in regulatory decision-making and scientific panels to design screening programs as well as to take the appropriate safety measures.

Suggested Citation

  • Vikas Kumar & Saurav Kumar, 2021. "ANN-Based Integrated Risk Ranking Approach: A Case Study of Contaminants of Emerging Concern of Fish and Seafood in Europe," IJERPH, MDPI, vol. 18(4), pages 1-17, February.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:4:p:1598-:d:495887
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    References listed on IDEAS

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    1. Martí Nadal & Vikas Kumar & Marta Schuhmacher & José L. Domingo, 2008. "Applicability of a Neuroprobabilistic Integral Risk Index for the Environmental Management of Polluted Areas: A Case Study," Risk Analysis, John Wiley & Sons, vol. 28(2), pages 271-286, April.
    2. Osnat Segev & Ariel Kushmaro & Asher Brenner, 2009. "Environmental Impact of Flame Retardants (Persistence and Biodegradability)," IJERPH, MDPI, vol. 6(2), pages 1-14, February.
    3. J. Wang & H. S. Sii & J. B. Yang & A. Pillay & D. Yu & J. Liu & E. Maistralis & A. Saajedi, 2004. "Use of Advances in Technology for Maritime Risk Assessment," Risk Analysis, John Wiley & Sons, vol. 24(4), pages 1041-1063, August.
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    Cited by:

    1. Saurav Kumar & Deepika Deepika & Vikas Kumar, 2022. "Pharmacophore Modeling Using Machine Learning for Screening the Blood–Brain Barrier Permeation of Xenobiotics," IJERPH, MDPI, vol. 19(20), pages 1-16, October.

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