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

Qing Gu, Jinsong Deng, Ke Wang, Yi Lin, Jun Li, Muye Gan, Ligang Ma and Yang Hong
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Qing Gu: Institute of Applied Remote Sensing & Information Technology, Zhejiang University, Hangzhou 310058, Zhejiang, China
Jinsong Deng: Institute of Applied Remote Sensing & Information Technology, Zhejiang University, Hangzhou 310058, Zhejiang, China
Ke Wang: Institute of Applied Remote Sensing & Information Technology, Zhejiang University, Hangzhou 310058, Zhejiang, China
Yi Lin: Institute of Applied Remote Sensing & Information Technology, Zhejiang University, Hangzhou 310058, Zhejiang, China
Jun Li: Institute of Applied Remote Sensing & Information Technology, Zhejiang University, Hangzhou 310058, Zhejiang, China
Muye Gan: Institute of Applied Remote Sensing & Information Technology, Zhejiang University, Hangzhou 310058, Zhejiang, China
Ligang Ma: Institute of Applied Remote Sensing & Information Technology, Zhejiang University, Hangzhou 310058, Zhejiang, China
Yang Hong: School of Civil Engineering and Environmental Sciences and School of Meteorology, University of Oklahoma, Norman, OK 73019, USA

IJERPH, 2014, vol. 11, issue 6, 1-16

Abstract: 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.

Keywords: drinking water; reservoir; water quality; potential impact; CART (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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