title: Decision-tree-model identification of nitrate pollution activities in groundwater: A combination of a dual isotope approach and chemical ions.
abstract: To develop management practices for agricultural crops to protect against NO3(-) contamination in groundwater, dominant pollution activities require reliable classification. In this study, we (1) classified potential NO3(-) pollution activities via an unsupervised learning algorithm based on δ(15)N- and δ(18)O-NO3(-) and physico-chemical properties of groundwater at 55 sampling locations; and (2) determined which water quality parameters could be used to identify the sources of NO3(-) contamination via a decision tree model. When a combination of δ(15)N-, δ(18)O-NO3(-) and physico-chemical properties of groundwater was used as an input for the k-means clustering algorithm, it allowed for a reliable clustering of the 55 sampling locations into 4 corresponding agricultural activities: well irrigated agriculture (28 sampling locations), sewage irrigated agriculture (16 sampling locations), a combination of sewage irrigated agriculture, farm and industry (5 sampling locations) and a combination of well irrigated agriculture and farm (6 sampling locations). A decision tree model with 97.5% classification success was developed based on SO4(2-) and Cl(-) variables. The NO3(-) and the δ(15)N- and δ(18)O-NO3(-) variables demonstrated limitation in developing a decision tree model as multiple N sources and fractionation processes both resulted in difficulties of discriminating NO3(-) concentrations and isotopic values. Although only the SO4(2-) and Cl(-) were selected as important discriminating variables, concentration data alone could not identify the specific NO3(-) sources responsible for groundwater contamination. This is a result of comprehensive analysis. To further reduce NO3(-) contamination, an integrated approach should be set-up by combining N and O isotopes of NO3(-) with land-uses and physico-chemical properties, especially in areas with complex agricultural activities.
authors.affiliation: Tianjin Key Laboratory of Water Resources and Environment, Tianjin Normal University, Tianjin 300387, China.
authors.forename: Dongmei
authors.initials: D
authors.lastname: Xue
authors.affiliation: College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, China.
authors.forename: Fengmei
authors.initials: F
authors.lastname: Pang
authors.affiliation: College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, China. Electronic address: mengfq@cau.edu.cn.
authors.forename: Fanqiao
authors.initials: F
authors.lastname: Meng
authors.affiliation: Tianjin Key Laboratory of Water Resources and Environment, Tianjin Normal University, Tianjin 300387, China.
authors.forename: Zhongliang
authors.initials: Z
authors.lastname: Wang
authors.affiliation: College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, China.
authors.forename: Wenliang
authors.initials: W
authors.lastname: Wu
mesh_terms: D057914:Agricultural Irrigation / Q000379:methods; D000383:Agriculture; D000465:Algorithms; D002712:Chlorides / Q000032:analysis; D018556:Crops, Agricultural; D003661:Decision Support Techniques; D004784:Environmental Monitoring / Q000379:methods*; D060587:Groundwater / Q000737:chemistry; D009566:Nitrates / Q000032:analysis*; D009587:Nitrogen Isotopes / Q000032:analysis; D010103:Oxygen Isotopes / Q000032:analysis; D012722:Sewage / Q000032:analysis; D013431:Sulfates / Q000032:analysis; D014874:Water Pollutants, Chemical / Q000032:analysis*; D014876:Water Pollution / Q000032:analysis / Q000517:prevention & control*
chemical_list: D002712:Chlorides; D009566:Nitrates; D009587:Nitrogen Isotopes; D010103:Oxygen Isotopes; D012722:Sewage; D013431:Sulfates; D014874:Water Pollutants, Chemical
keywords: Decision tree model; Groundwater; NO(3)(−) pollution; NO(3)(−) source; k-means clustering; δ(15)N- and δ(18)O-NO(3)(−)