Source Apportionment and Potential Health Risks of Trace Metals in a Contaminated Urban River in New York/New Jersey Harbor System

  • Md Shahnul Islam
  • , Sana Mirza
  • , Huan Feng
  • , Tapos Kumar Chakraborty
  • , Yu Qian
  • , Shinjae Yoo

Research output: Contribution to journalArticlepeer-review

Abstract

The Lower Passaic River (LPR), located within the New York/New Jersey Harbor Estuarine System, has experienced long-term industrial activities, resulting in elevated concentrations of trace metals in sediment and water. This study aims to assess the bioaccumulation behavior, potential human health risks, and sources of copper (Cu), lead (Pb), and mercury (Hg) in the LPR. Trace metal concentrations were measured in water, sediment, and seven edible aquatic species. Data were analyzed using statistical approaches, and evaluated by bioaccumulation factors (BAFs) and human health risk assessments based on U.S. Environmental Protection Agency (USEPA) guidelines. Results showed that Hg exhibited the highest bioaccumulation potential among the studied metals, except for Cu in Callinectes sapidus. Non-carcinogenic risks from the consumption of aquatic species followed the order Cu > Hg > Pb, with total target hazard quotient (TTHQ) values below 1, suggesting the non-carcinogenic health risk is negligible for adults and for most species in children, except C. sapidus and Morone americana. Carcinogenic risks for all species were within the acceptable threshold (Target Risk < 1 × 10−4). Sensitivity analysis indicated that body weight and exposure duration primarily influenced children’s carcinogenic risk, whereas trace metal concentrations were more significant for adults. Overall, this study provides insight into contaminant dynamics and health implications in a legacy-contaminated urban river system.

Original languageEnglish
Article number3254
JournalWater (Switzerland)
Volume17
Issue number22
DOIs
StatePublished - Nov 2025

Keywords

  • bioaccumulation
  • environmental contamination
  • Lower Passaic River
  • machine learning
  • public health
  • trace metals

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