Human health is adversely affected by potentially toxic elements (PTEs) in the topsoil, entering the bodies via inhalation, ingestion, and dermal contact. To visualize human health risks, we investigated five PTEs (Cd, As, Pb, Hg, and Cr) in 72 farmland topsoil samples from a town in Chongqing City, southwest China. Based on the human health risk assessment model, sequential indicator simulation (SIS) and the positive matrix factorization model (PMF) were used to construct the spatial health risks and to analyze the sources of PTEs; finally, health risks were combined with the source by ArcGIS. Based on our results, the use of SIS is feasible for the prediction of the spatial distribution of PTEs. Among the risks, the non-cancer risk of As for children most likely exceeded the accepted level in some areas, making As a priority pollutant. Although the health risks of soil Cd were acceptable in the region, the spatial probability distribution of Cd> 0.3 mg/kg represents a threat as Cd enters the human food chain. Even if the industrial discharge was the lowest individual contributor (29.33%), due to the impact of industrial discharge, the total non-cancer risk with a high probability (>0.85) for children still exceeded the accepted level in the northwestern area, which should be regarded as the priority pollution source. The combined method was useful to reduce efforts in environmental management, thus providing a basis for soil remediation and pollution source control.

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.