4.7 Article

Spatial variations of human health risk associated with exposure to chlorination by-products occurring in drinking water

Journal

JOURNAL OF ENVIRONMENTAL MANAGEMENT
Volume 92, Issue 3, Pages 892-901

Publisher

ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.jenvman.2010.10.056

Keywords

Drinking water distribution system; Chlorination by-products; Human health risk assessment; Multi-pathway exposure; Spatial variability; Monte Carlo simulations

Funding

  1. Canadian Institute of Health Research (CIHR)
  2. Drinking Water Research Chair of Universite Laval (Quebec City, Canada)
  3. NSERC
  4. Cities of Quebec and Levis
  5. ITF-Labs-Avensys
  6. Dessau

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During disinfection, chlorine reacts with organic matter present in drinking water and forms various undesirable chlorinated by-products (CBPs). This paper describes a study of the spatial variability of human health risk (i.e., cancer effects) from CBP exposure through drinking water in a specific region. The region under study involves nine drinking water distribution systems divided into several zones based on their characteristics. The spatial distribution of cancer risk (CR) was estimated using two years of data (2006-2008) on various CBP species. In this analysis, trihalomethanes (THMs) and haloacetic acids (HAAs) served as surrogates for CBPs. Three possible routes of exposure (i.e., via ingestion, inhalation and dermal contact) were considered for each selected compound. The cancer risk assessment involved estimating a unit risk (R-T) in each zone of the selected distribution systems. A probabilistic analysis based on Monte Carlo simulations was employed. Risk assessment results showed that cancer risk varied between systems, but also within individual systems. As a result, the population of the same region was not exposed to the same risk associated with CBPs in drinking water. Unacceptable levels (i.e., R-T > 10(-4)) for the estimated CR were determined for several zones in the studied region. This study demonstrates that a spatial-based analysis performed to represent the spatial distribution of risk estimates can be helpful in identifying suitable risk management strategies. Suggestions for improving the risk analysis procedure are also presented. (C) 2010 Elsevier Ltd. All rights reserved.

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