title: Evaluation of agricultural nonpoint source pollution potential risk over China with a Transformed-Agricultural Nonpoint Pollution Potential Index method.
abstract: Agricultural nonpoint source (NPS) pollution has been the most important threat to water environment quality. Understanding the spatial distribution of NPS pollution potential risk is important for taking effective measures to control and reduce NPS pollution. A Transformed-Agricultural Nonpoint Pollution Potential Index (T-APPI) model was constructed for evaluating the national NPS pollution potential risk in this study; it was also combined with remote sensing and geographic information system techniques for evaluation on the large scale and at 1 km2 spatial resolution. This model considers many factors contributing to the NPS pollution as the original APPI model, summarized as four indicators of the runoff, sediment production, chemical use and the people and animal load. These four indicators were analysed in detail at 1 km2 spatial resolution throughout China. The T-APPI model distinguished the four indicators into pollution source factors and transport process factors; it also took their relationship into consideration. The studied results showed that T-APPI is a credible and convenient method for NPS pollution potential risk evaluation. The results also indicated that the highest NPS pollution potential risk is distributed in the middle-southern Jiangsu province. Several other regions, including the North China Plain, Chengdu Basin Plain, Jianghan Plain, cultivated lands in Guangdong and Guangxi provinces, also showed serious NPS pollution potential. This study can provide a scientific reference for predicting the future NPS pollution risk throughout China and may be helpful for taking reasonable and effective measures for preventing and controlling NPS pollution.
authors.forename: Fei
authors.initials: F
authors.lastname: Yang
authors.forename: Zhencheng
authors.initials: Z
authors.lastname: Xu
authors.forename: Yunqiang
authors.initials: Y
authors.lastname: Zhu
authors.forename: Chansheng
authors.initials: C
authors.lastname: He
authors.forename: Genyi
authors.initials: G
authors.lastname: Wu
authors.forename: Jin Rong
authors.initials: JR
authors.lastname: Qiu
authors.forename: Qiang
authors.initials: Q
authors.lastname: Fu
authors.forename: Qingsong
authors.initials: Q
authors.lastname: Liu
mesh_terms: D000383:Agriculture / Q000706:statistics & numerical data*; D002681:China; D003627:Data Interpretation, Statistical; D004777:Environment; D004784:Environmental Monitoring / Q000379:methods*; D040362:Geographic Information Systems; D058998:Remote Sensing Technology / Q000379:methods*; D018570:Risk Assessment / Q000379:methods*; D062211:Spatio-Temporal Analysis; D014876:Water Pollution / Q000032:analysis / Q000706:statistics & numerical data*; D060753:Water Quality