Seasonal variability, predictive modeling and health risks of N-nitrosamines in drinking water of Shanghai

Science of The Total Environment(2023)

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摘要
The prevalence of carcinogenic N-nitrosamines in drinking water is of significant concern. In the present study, eight N-nitrosamines from three representative drinking water treatment plants (DWTPs) in Shanghai, China were monitored for an entire year to evaluate their seasonal variability, probabilistic cancer risk and the resulting disease burden. The possibility of employing routinely monitored water quality parameters as predictors of N-nitrosamines was also examined. The results showed that the Taipu River-fed reservoir suffered more serious N-nitrosamine contamination than the Yangtze River-fed reservoirs. Winter witnessed higher levels of N-nitrosamines in both source and finished water. N-nitrosamine concentrations increased fromsourcewater to finished water in autumn orwinter, but no spatial variations were observed in summer. The total lifetime cancer risk (LCR) posed by N-nitrosamines in finished water was within the acceptable range (1.00 x 10(-6) to 1.00 x 10(-4)), with N-nitrosodimethylamine (NDMA) and Nnitrosodiethylamine (NDEA) being the main contributors. Winter and autumn were found to have higher total LCR values. The average individual disability-adjusted life years (DALYs) lost was 4.43x10(-6) per person-year (ppy), exceeding the reference risk level (1.00 x 10(-6) ppy). Liver cancer accounted for 97.1 % of the total disease burden, while bladder and esophagus cancers made a little contribution (2.9 %). A multiple regression model was developed to estimate the total N-nitrosamines in finished water as a function of water quality parameters, and the R-2 value was 0.735. This study not only provides fundamental data for public health policy development, but also reveals the necessity to incorporate a seasonal control strategy in DWTPs to minimize the associated health risks induced by N-nitrosamines.
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关键词
N-nitrosamine,Drinking water,Seasonal variation,Risk assessment,Regression analysis
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