Drinking water quality & health risk assessment of secondary water supply systems in residential neighborhoods

doi:10.1007/s11783-024-1778-5

Front. Environ. Sci. Eng.

2024, Vol. 18

Issue (2) : 18 https://doi.org/10.1007/s11783-024-1778-5

RESEARCH ARTICLE

Drinking water quality & health risk assessment of secondary water supply systems in residential neighborhoods

Yating Wei¹, Dong Hu², Chengsong Ye¹, Heng Zhang¹, Haoran Li¹, Xin Yu¹(

)

¹. College of the Environment & Ecology, Xiamen University, Xiamen 361102, China
². School of Public Health, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan 250117, China

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Abstract

● Most water samples had excellent quality and negligible or acceptable health risks.

● One summer sample’ quality was extremely poor due to the total bacterial count.

● Samples showed higher carcinogenic risk (7.63×10⁻⁵ ± 3.29×10⁻⁶) for children_0–5.

● Arsenic was the major substance contributing to carcinogenic risk.

● Summer samples’ poor water quality and higher health risks deserve attention.

Secondary water supply systems (SWSSs) are important components of the water supply infrastructure that ensure residents’ drinking water safety. SWSSs are characterized by long detention time, warm temperature, and unreasonable management, which may trigger the deterioration of water quality and increase risks. In this study, drinking water quality index (DWQI) and health risk assessment (HRA) were selected and modified to quantitatively assess the water quality and health risks of SWSSs in residential neighborhoods. In total, 121 seasonal water samples were selected. It was observed that the water quality was excellent with the DWQI of 0.14 ± 0.04, excluding one sample, which was extremely poor owing to its excessive total bacterial count. The HRA results revealed that the health risks were low: negligible non-carcinogenic risk for any population; negligible and acceptable carcinogenic risk for children aged 6–17 and adults. However, samples revealed higher carcinogenic risk (7.63 × 10⁻⁵ ± 3.29 × 10⁻⁶) for children aged 0–5, and arsenic was the major substance. Summer samples had poor water quality and higher health risks, which called for attention. To further investigate the water quality and health risks of SWSSs, monthly sampling was conducted during summer. All 24 water samples were qualified in Chinese standard (GB 5749-2022) and characterized as excellent quality. Their HRA results were consistent with the seasonal samples’ and the health risks were mainly concentrated in May. Overall, our study provides a suitable framework for water quality security, advice for managers, and references for administrators in other cities.

Keywords Drinking water quality Water quality index Health risk assessment Secondary water supply systems Heavy metals

Corresponding Author(s): Xin Yu

Issue Date: 18 October 2023

Cite this article:

Yating Wei,Dong Hu,Chengsong Ye, et al. Drinking water quality & health risk assessment of secondary water supply systems in residential neighborhoods[J]. Front. Environ. Sci. Eng., 2024, 18(2): 18.

URL:

https://academic.hep.com.cn/fese/EN/10.1007/s11783-024-1778-5
https://academic.hep.com.cn/fese/EN/Y2024/V18/I2/18

Fig.1 Boxplots of water quality assessment results of seasonal water samples. (a) Drinking water quality index (DWQI), (b) sensory and chemical indices (WQI₁), (c) organic pollution indices (WQI₂), (d) carcinogen indices (WQI₃), (e) generally toxic indices (WQI₄), (f) enteric infection indices (WQI₅). Groups of boxplots without common letters (a–d) were significantly different (p < 0.05, N = 110). The scatter dot plot displayed all the data (N = 121), where the culled data was marked with the solid dots.

Fig.2 Boxplots of seasonal samples’ carcinogenic risk assessment results. Boxplots of population groups (a) depict carcinogenic risks of all samples for all population groups (N = 363) and other classifications’ boxplots (b–d) depict carcinogenic risks for children_0–5 alone (N = 121). In the boxplots of material contribution (e), several outliers were removed and some suitable data (75 samples, N = 225) were left. Boxplots displayed all the data, and their whiskers were 10–90 percentile, where groups without common letters (a–f) were significantly different (p < 0.05).

Tab.1 The results of HRA and classification of seasonal samples

Fig.3 Boxplots of seasonal samples’ non-carcinogenic risk assessment results. Boxplots of population groups (a) reveal non-carcinogenic risks of all samples for all population groups (N = 363) and other classifications’ boxplots (b–d) depict non-carcinogenic risks for children_0–5 alone (N = 121). In the boxplots of material contribution (e), several outliers were removed and some suitable data (72 samples, N = 300) were left. Boxplots display all the data, and their whiskers were 10–90 percentile, where groups without common letters (a–d) were significantly different (p < 0.05).

Fig.4 Bar graphs of water quality assessment results of monthly summer samples. (a) Drinking water quality index (DWQI), (b) sensory and chemical indices (WQI₁), (c) organic pollution indices (WQI₂), (d) carcinogen indices (WQI₃), (e) generally toxic indices (WQI₄), (f) enteric infection indices (WQI₅). Groups without common letters (a–c) were significantly different (p < 0.05, N = 24).

Tab.2 The results of HRA and classification of monthly summer samples

Fig.5 Stacked bar graphs of carcinogenic risk assessment results of monthly summer samples. (a) Total carcinogenic risk (TCR), (b) carcinogenic risk through oral ingestion (CR_oral), (c) carcinogenic risk through dermal exposure (CR_dermal). Groups without common letters (a and b) were significantly different (p < 0.05, N = 24) in the additive values of carcinogenic risks contributed by all substances.

Fig.6 Stacked bar graphs of non-carcinogenic risk assessment results of monthly summer samples. (a) Hazard index (HI), (b) hazard quotient through oral ingestion (HQ_oral), (c) hazard quotient through dermal exposure (HQ_dermal). Groups without common letters (a–c) were significantly different (p < 0.05, N = 24) in the additive values of carcinogenic risks contributed by all substances.

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