Issue 11, 2021

Microcystin in source water: pollution characteristics and human health risk assessment

Abstract

Frequent cyanobacterial blooms in eutrophic waters produce a variety of toxins such as microcystins (MCs), which are seriously harmful to waterbodies and human health. The spatiotemporal distribution characteristics of the MC-LR concentration in drinking water sources in seven river basins in China were investigated in this study. The removal rate of MC-LR in the purification process of water treatment plants and the human health risk of MC-LR in drinking water are also discussed. The results show that the detection frequency of MC-LR in source water was 55.46% and its concentration ranged from 0.06 × 10−3 to 52 × 10−3 μg L−1 (mean of 12.47 × 10−3 μg L−1), which are both below China's drinking water quality standard for algal toxins. The MC-LR concentration in lakes and reservoirs was higher than that in rivers, and exhibited an obvious spatiotemporal variation. The mean removal rate of MC-LR varied with river basin, and was also slightly higher for the advanced water treatment process (97.46%) in comparison to that of the conventional process (96.74%). The concentration of MC-LR in 8.26% of treated water samples was higher than that of raw water, thus indicating that MC-LR may be further released during the purification process. The risk index of MC-LR in treated water samples ranged from 2.29 × 10−3 to 8.40 × 10−3 (mean of 4.73 × 10−3), which corresponded to an extremely low level of risk. However, intensive monitoring should still be carried out in some high-concentration watersheds during the summer to ensure the safety of public drinking water.

Graphical abstract: Microcystin in source water: pollution characteristics and human health risk assessment

Article information

Article type
Paper
Submitted
22 10 2020
Accepted
18 1 2021
First published
04 2 2021
This article is Open Access
Creative Commons BY-NC license

RSC Adv., 2021,11, 6415-6422

Microcystin in source water: pollution characteristics and human health risk assessment

S. Ge, X. Qiao, X. Zhao, X. Li and Y. Liu, RSC Adv., 2021, 11, 6415 DOI: 10.1039/D0RA08983D

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