Coupling ITO3dE model and GIS for spatiotemporal evolution analysis of agricultural non-point source pollution risks in Chongqing in China

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.

Evolutionary Trend Analysis of Agricultural Non-Point Source Pollution Load in Chongqing Based on Land Use Simulation

“…As a result, the risk of ANPSP in the region is relatively high. According to publicly available data from Chongqing in 2020, the application intensity of fertilizer per hectare of cultivated land area was 266.36 kg/hm 2 , which exceeds the internationally recognized safe upper limit of fertilizer application of 225 kg/hm 2 [25].…”

Evolving Trends and Influencing Factors of the Rural Green Development Level in Chongqing

“…For convenience of description, the study areas were divided into "one area and two groups (the main urban area, southeast area, and northeast area)". The main urban area includes 21 districts and counties (i.e., Changshou, Fuling, Nanchuan, and all districts and counties located to the west of Nanchuan), the southeast area includes 6 districts and counties (i.e., Wulong, Pengshui, Shizhu and all areas located to the south of Shizhu), and the northeast area includes 11 districts and counties (i.e., Dianjiang, Fengdu, Zhong, Liangping, Wanzhou, and areas located to the north of Wanzhou) [20]. In 2021, the GDP of Chongqing was CNY 2.789 trillion, an increase of 8.3% from the previous year.…”

Impact of Future Development Scenario Selection on Landscape Ecological Risk in the Chengdu-Chongqing Economic Zone

“…The Getis-Ord Gi* analysis is widely used in crime analysis, epidemiology, and economic geography to identify spatial gathering of high values (hot spots) and low values (cold spots) with statistical significance [27]. In a Getis-Ord Gi* analysis, the z score, p values, and confidence intervals (Gi_Bin) are employed to create a new output class for each element in the input element class.…”