Abstract
To determine the risk state distribution, risk level, and risk evolution situation of agricultural non-point source pollution (AGNPS), we built an ‘Input-Translate-Output’ three-dimensional evaluation (ITO3dE) model that involved 12 factors under the support of GIS and analyzed the spatiotemporal evolution characteristics of AGNPS risks from 2005 to 2015 in Chongqing by using GIS space matrix, kernel density analysis, and Getis-Ord Gi* analysis. Land use changes during the 10 years had a certain influence on the AGNPS risk. The risk values in 2005, 2010, and 2015 were in the ranges of 0.40–2.28, 0.41–2.57, and 0.41–2.28, respectively, with the main distribution regions being the western regions of Chongqing (Dazu, Jiangjin, etc.) and other counties such as Dianjiang, Liangping, Kaizhou, Wanzhou, and Zhongxian. The spatiotemporal transition matrix could well exhibit the risk transition situation, and the risks generally showed no changes over time. The proportions of ‘no-risk no-change’, ‘low-risk no-change’, and ‘medium-risk no-change’ were 10.86%, 33.42%, and 17.25%, respectively, accounting for 61.53% of the coverage area of Chongqing. The proportions of risk increase, risk decline, and risk fluctuation were 13.45%, 17.66%, and 7.36%, respectively. Kernel density analysis was suitable to explore high-risk gathering areas. The peak values of kernel density in the three periods were around 1110, suggesting that the maximum gathering degree of medium-risk pattern spots basically showed no changes, but the spatial positions of high-risk gathering areas somehow changed. Getis-Ord Gi* analysis was suitable to explore the relationships between hot and cold spots. Counties with high pollution risks were Yongchuan, Shapingba, Dianjiang, Liangping, northwestern Fengdu, and Zhongxian, while counties with low risks were Chengkou, Wuxi, Wushan, Pengshui, and Rongchang. High-value hot spot zones gradually dominated in the northeast of Chongqing, while low-value cold spot zones gradually dominated in the Midwest. Our results provide a scientific base for the development of strategies to prevent and control AGNPS in Chongqing.
Highlights
Agricultural non-point source pollution (AGNPS) refers to water pollution caused by nitrogen and phosphorus, pesticides, and other contaminants through farmland water runoff and p ercolation[1]
It is of great importance to understand the spatiotemporal evolution situation of agricultural nonpoint source pollution (AGNPS) risks in districts and counties of Chongqing
This research adopts the method of combining multi factor index and GIS spatial analysis, which was more advanced in AGNPS risk analysis at present, and could enrich the research results in this field
Summary
Agricultural non-point source pollution (AGNPS) refers to water pollution caused by nitrogen and phosphorus, pesticides, and other contaminants through farmland water runoff and p ercolation[1]. There were 12 factors involved in the ITO3dE model, including input dimension (fertilizer use intensity, pesticide use intensity, livestock intensity), translate dimension (erosion caused by rainfall, slope length and gradient, soil erodibility, sloping field, distance from water area), and output dimension (water quality, water capacity, water network density, degree of paddy field retention), selected by expert consultation and system analytic hierarchy processes. On this basis, the weights of these factors were determined through multiple assignment by combination with the Delphi method[18]. To reflect the characteristics of each factor in the large-scale spatialization process, it was assumed that the factors fertilizer use intensity and pesticide use intensity were evenly dispersed in the farmland area, that the livestock intensity factor was evenly dispersed in the suitable breeding area, and that the paddy field retention factor was evenly dispersed in the paddy field area
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