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Prioritising local action for water quality improvement using citizen science; a study across three major metropolitan areas of China

Thornhill, I, Ho, J.G, Zhang, Y, Li, H, Ho, K.C, Miguel-Chinchilla, L and Loiselle, S.A (2017) 'Prioritising local action for water quality improvement using citizen science; a study across three major metropolitan areas of China.' Science of the Total Environment, 584. pp. 1268-1281. ISSN 0048-9697

Abstract

Streams in urban areas are prone to degradation. While urbanization-induced poor water quality is a widely observed and well documented phenomenon, the mechanism to pinpoint local drivers of urban stream degradation, and their relative influence on water quality, is still lacking. Utilizing data from the citizen science project FreshWater Watch, we use a machine learning approach to identify key indicators, potential drivers, and potential controls to water quality across the metropolitan areas of Shanghai, Guangzhou and Hong Kong. Partial dependencies were examined to establish the direction of relationships between predictors and water quality. A random forest classification model indicated that predictors of stream water colour (drivers related to artificial land coverage and agricultural land use coverage) and potential controls related to the presence of bankside vegetation were found to be important in identifying basins with degraded water quality conditions, based on individual measurements of turbidity and nutrient (N-NO3 and P-PO4) concentrations.

Item Type: Article
Keywords: machine learning, citizen science, water quality, urbanization, eutrophication, turbidity
Subjects: G Geography. Anthropology. Recreation > GE Environmental Sciences
H Social Sciences > H Social Sciences (General)
Q Science > Q Science (General)
Divisions: College of Liberal Arts
Date Deposited: 05 Dec 2017 17:11
Last Modified: 05 Dec 2017 17:11
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