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Identification and Assessment of Potential Water Quality Impact Factors for Drinking-Water Reservoirs

Various reservoirs have been serving as the most important drinking water sources in Zhejiang Province, China, due to the uneven distribution of precipitation and severe river pollution. Unfortunately, rapid urbanization and industrialization have been continuously challenging the water quality of t...

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Autores principales: Gu, Qing, Deng, Jinsong, Wang, Ke, Lin, Yi, Li, Jun, Gan, Muye, Ma, Ligang, Hong, Yang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4078566/
https://www.ncbi.nlm.nih.gov/pubmed/24919129
http://dx.doi.org/10.3390/ijerph110606069
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author Gu, Qing
Deng, Jinsong
Wang, Ke
Lin, Yi
Li, Jun
Gan, Muye
Ma, Ligang
Hong, Yang
author_facet Gu, Qing
Deng, Jinsong
Wang, Ke
Lin, Yi
Li, Jun
Gan, Muye
Ma, Ligang
Hong, Yang
author_sort Gu, Qing
collection PubMed
description Various reservoirs have been serving as the most important drinking water sources in Zhejiang Province, China, due to the uneven distribution of precipitation and severe river pollution. Unfortunately, rapid urbanization and industrialization have been continuously challenging the water quality of the drinking-water reservoirs. The identification and assessment of potential impacts is indispensable in water resource management and protection. This study investigates the drinking water reservoirs in Zhejiang Province to better understand the potential impact on water quality. Altogether seventy-three typical drinking reservoirs in Zhejiang Province encompassing various water storage levels were selected and evaluated. Using fifty-two reservoirs as training samples, the classification and regression tree (CART) method and sixteen comprehensive variables, including six sub-sets (land use, population, socio-economy, geographical features, inherent characteristics, and climate), were adopted to establish a decision-making model for identifying and assessing their potential impacts on drinking-water quality. The water quality class of the remaining twenty-one reservoirs was then predicted and tested based on the decision-making model, resulting in a water quality class attribution accuracy of 81.0%. Based on the decision rules and quantitative importance of the independent variables, industrial emissions was identified as the most important factor influencing the water quality of reservoirs; land use and human habitation also had a substantial impact on water quality. The results of this study provide insights into the factors impacting the water quality of reservoirs as well as basic information for protecting reservoir water resources.
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spelling pubmed-40785662014-07-02 Identification and Assessment of Potential Water Quality Impact Factors for Drinking-Water Reservoirs Gu, Qing Deng, Jinsong Wang, Ke Lin, Yi Li, Jun Gan, Muye Ma, Ligang Hong, Yang Int J Environ Res Public Health Article Various reservoirs have been serving as the most important drinking water sources in Zhejiang Province, China, due to the uneven distribution of precipitation and severe river pollution. Unfortunately, rapid urbanization and industrialization have been continuously challenging the water quality of the drinking-water reservoirs. The identification and assessment of potential impacts is indispensable in water resource management and protection. This study investigates the drinking water reservoirs in Zhejiang Province to better understand the potential impact on water quality. Altogether seventy-three typical drinking reservoirs in Zhejiang Province encompassing various water storage levels were selected and evaluated. Using fifty-two reservoirs as training samples, the classification and regression tree (CART) method and sixteen comprehensive variables, including six sub-sets (land use, population, socio-economy, geographical features, inherent characteristics, and climate), were adopted to establish a decision-making model for identifying and assessing their potential impacts on drinking-water quality. The water quality class of the remaining twenty-one reservoirs was then predicted and tested based on the decision-making model, resulting in a water quality class attribution accuracy of 81.0%. Based on the decision rules and quantitative importance of the independent variables, industrial emissions was identified as the most important factor influencing the water quality of reservoirs; land use and human habitation also had a substantial impact on water quality. The results of this study provide insights into the factors impacting the water quality of reservoirs as well as basic information for protecting reservoir water resources. MDPI 2014-06-10 2014-06 /pmc/articles/PMC4078566/ /pubmed/24919129 http://dx.doi.org/10.3390/ijerph110606069 Text en © 2014 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
spellingShingle Article
Gu, Qing
Deng, Jinsong
Wang, Ke
Lin, Yi
Li, Jun
Gan, Muye
Ma, Ligang
Hong, Yang
Identification and Assessment of Potential Water Quality Impact Factors for Drinking-Water Reservoirs
title Identification and Assessment of Potential Water Quality Impact Factors for Drinking-Water Reservoirs
title_full Identification and Assessment of Potential Water Quality Impact Factors for Drinking-Water Reservoirs
title_fullStr Identification and Assessment of Potential Water Quality Impact Factors for Drinking-Water Reservoirs
title_full_unstemmed Identification and Assessment of Potential Water Quality Impact Factors for Drinking-Water Reservoirs
title_short Identification and Assessment of Potential Water Quality Impact Factors for Drinking-Water Reservoirs
title_sort identification and assessment of potential water quality impact factors for drinking-water reservoirs
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4078566/
https://www.ncbi.nlm.nih.gov/pubmed/24919129
http://dx.doi.org/10.3390/ijerph110606069
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