title: Prioritising local action for water quality improvement using citizen science; a study across three major metropolitan areas of China.
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-NO3and P-PO4) concentrations.
authors.affiliation: Earthwatch Institute (Europe), Mayfield House, 256 Banbury Road, Summertown, Oxford, OX2 7DE, United Kingdom. Electronic address: ithornhill@earthwatch.org.uk.
authors.forename: Ian
authors.initials: I
authors.lastname: Thornhill
authors.affiliation: Earthwatch Institute (Hong Kong), Room 1402 Breakthrough Centre, 191 Woosung Street, Jordan, Kowloon, Hong Kong Special Administrative Region.
authors.forename: Jonathan G
authors.initials: JG
authors.lastname: Ho
authors.affiliation: State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, China.
authors.forename: Yuchao
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: College of Natural Resources and Environment, South China Agricultural University, Guangzhou, China.
authors.forename: Huashou
authors.initials: H
authors.lastname: Li
authors.affiliation: Open University of Hong Kong, Hong Kong Special Administrative Region.
authors.forename: Kin Chung
authors.initials: KC
authors.lastname: Ho
authors.affiliation: Earthwatch Institute (Europe), Mayfield House, 256 Banbury Road, Summertown, Oxford, OX2 7DE, United Kingdom.
authors.forename: Leticia
authors.initials: L
authors.lastname: Miguel-Chinchilla
authors.affiliation: Earthwatch Institute (Europe), Mayfield House, 256 Banbury Road, Summertown, Oxford, OX2 7DE, United Kingdom.
authors.forename: Steven A
authors.initials: SA
authors.lastname: Loiselle
mesh_terms: D002681:China; D002947:Cities; D003256:Community Participation; D004784:Environmental Monitoring / Q000379:methods*; D006723:Hong Kong; D058996:Quality Improvement; D045483:Rivers; D014867:Water; D060753:Water Quality
chemical_list: D014867:Water
keywords: Citizen science; Eutrophication; Machine learning; Turbidity; Urbanization; Water quality