Abstract
Water quality, increasingly recognized for its significant impact on health, is garnering heightened attention. Previous studies were limited by the number of water quality indicators and the duration of analysis. This study assessed the drinking water quality and its associated health risk in suburban areas of Wuhan, a city in central China, from 2016 to 2021. We collected 368 finished water samples and 1090 tap water samples and tested these for 37 different indicators. The water quality was evaluated using the water quality index, with trends over time analyzed via the Mann–Kendall test. Furthermore, an artificial neural network model was employed for future water quality prediction. Our findings indicated that the water quality in rural Wuhan was generally good and had an improvement from 2016 to 2021. The qualification and excellent rates were 98.91% and 86.81% for finished water, and 97.89% and 78.07% for tap water, respectively. The drinking water quality was predicted to maintain satisfactory in 2022 and 2023. Additionally, principal component analysis revealed that the primary sanitary issues in the water were poor sensory properties, elevated metal contents, high levels of dissolved solids, and microbial contamination. These issues were likely attributable to domestic and industrial waste discharge and aging water pipelines. The health risks associated with the long-term consumption of this water have been steadily decreasing over the years, underscoring the effectiveness of Wuhan’s ongoing water management efforts.
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Data availability
The datasets generated and/or analyzed during the current study are not publicly available but are available from the corresponding author on reasonable request.
Abbreviations
- PCA:
-
Principal component analysis
- PCs:
-
Principal components
- FW:
-
Finished water
- TW:
-
Tap water
- WQI:
-
Water quality index
- MK analysis:
-
Mann-Kendall trend analysis
- BP-ANNM:
-
Back propagation artificial neural network model
- ANNs:
-
Artificial neural networks
- WQI1 :
-
Water quality subindex of sensory and general chemistry
- KMO:
-
Kaiser-Meyer-Olkin
- USEPA:
-
US Environmental Protection Agency
- TDS:
-
Total dissolved solids
- TH:
-
Total hardness
- OD:
-
Oxygen demand
- ABC:
-
Aerobic bacterial count
- TCG:
-
Total coliform group
- TC:
-
Trichloromethane
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Funding
This work was supported by the National Program for Support of Top-notch Young Professionals for Li Liu, Health commission of Hubei Province scientific research project (Grant No. WJ2019H308), Wuhan Municipal Health Commission scientific research project (Grant No. WY19A04, Grant No. WG18Q12), Wuhan Preventive Medicine Special Research Project (Grant No. MY19M01) and Key Prevention Project of Hubei Provincial Health Commission in 2019 (Grant No. WJ2019H303). We would like to thank all participants in this study and the Wuhan Centers for Disease Prevention and Control for their assistance in the sample collection and analysis.
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Feng Pan was in charge with investigation, data curation, resources, and methodology. Sijia Zhu was in responsible for software modification, data analysis, and original draft writing. Lv Shang took charge of validation, investigation, and project administration. Pei Wang participated in investigation and data curation. Junling Liu took part in conceptualization, supervision, and funding acquisition. Li Liu took responsibility of conceptualization, methodology, investigation funding acquisition, and project administration. All authors reviewed and edited the manuscript.
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Pan, F., Zhu, S., Shang, L. et al. Assessment of drinking water quality and health risk using water quality index and multiple computational models: a case study of Yangtze River in suburban areas of Wuhan, central China, from 2016 to 2021. Environ Sci Pollut Res 31, 22736–22758 (2024). https://doi.org/10.1007/s11356-024-32187-3
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DOI: https://doi.org/10.1007/s11356-024-32187-3