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

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kang-wen Zhu ◽  
Zhi-min Yang ◽  
Lei Huang ◽  
Yu-cheng Chen ◽  
Sheng Zhang ◽  
...  
Keyword(s):  
High Risk ◽  
Point Source ◽  
Kernel Density ◽  
Point Source Pollution ◽  
Spatiotemporal Evolution ◽  
Matrix Kernel ◽  
Medium Risk ◽  
Density Analysis ◽  
Kernel Density Analysis ◽  
Non Point Source Pollution

AbstractTo 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.

scholarly journals Spatiotemporal evolution analysis of agricultural non-point source pollution risks in Chongqing, China, based on the ITO3dE model and GIS

2020 ◽  
Author(s):  
Kang-wen ZHU ◽  
Zhi-min YANG ◽  
Lei HUANG ◽  
Yu-cheng CHEN ◽  
Sheng ZHANG ◽  
...  
Keyword(s):  
High Risk ◽  
Point Source ◽  
Kernel Density ◽  
Point Source Pollution ◽  
Spatiotemporal Evolution ◽  
Matrix Kernel ◽  
Medium Risk ◽  
Density Analysis ◽  
Kernel Density Analysis ◽  
Non Point Source Pollution

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 1,110, 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.

Study on the Risk Pattern of Non-Point Source Pollution Using GIS Technology in the Dianchi Lake Watershed

2011 ◽  
Vol 356-360 ◽  
pp. 771-776
Author(s):  
Cheng Wu ◽  
Guo Chun Deng ◽  
Yan Li ◽  
Zhi Ying Li ◽  
Shu Hua Yang
Keyword(s):  
High Risk ◽  
Point Source ◽  
Evaluation System ◽  
Pollutant Removal ◽  
Assessment System ◽  
Dianchi Lake ◽  
Lake Basin ◽  
Point Source Pollution ◽  
Risk Pattern ◽  
Non Point Source Pollution

A rapid quantitative risk evaluation system of non-point source pollution (NPSP), based on comprehensive consideration of various factors such as topographic features, land use construction, annual mean precipitation, soil erosion characteristics and pollutant removal cost, was constructed using an Spatial Analysis Module of GIS on watershed scale. We investigated the risk pattern of NPSP in the Dianchi Lake Watershed using the rapid risk assessment system. The results indicated that NPSP risk pattern showed the arc or normal distribution trend in the Dianchi Lake Basin, namely the medium risk area of NPSP is the largest (about 1311 km2). Moreover, the spatial difference of high risk NPSP pattern is remarkable: the high risk region for NPSP was primarily in the 10 km range of the southern and eastern parts and the 5 km range of western part around the Dianchi Lake.

Evaluation on Risk of Non-Point Source Pollution in Dayanghe Watershed Based on GIS

2012 ◽  
Vol 209-211 ◽  
pp. 2023-2026
Author(s):  
An Ning Suo ◽  
Hua Ru Wang ◽  
Yuan Bin Fu
Keyword(s):  
High Risk ◽  
Point Source ◽  
Evaluation System ◽  
Steep Slope ◽  
Nutrient Loss ◽  
Point Source Pollution ◽  
Risk Area ◽  
High Risk Area ◽  
Risk Evaluation System ◽  
Non Point Source Pollution

Four indices which include surface runoff, soil erosion, agricultural nutrient loss, human and animal feces were selected and method to evaluate risk of non-point source pollution in watershed was constructed based on GIS. As a case, non-point source pollution in Dayanghe was evaluated. Results showed that very high risk area of non-point source pollution accounted for 3.41% area of the watershed, mainly located in farmland with steep slope along upper valley of the watershed. High risk area of non-point source pollution was located in farmland and human settlement placecs, accounted for 16.40% area of the watershed. Areas with low risk of non-point source pollution was riverbeds, shrub and grassland in eastern and western hilly with steep slope. Areas with lower risk of non-point source pollution located in middle of the watershed and accounted for 60.55% area. GIS-based risk evaluation system of non-point source pollution can reflect real map of pollution in the Dayanghe watershed and give implication for protection plan.

scholarly journals Spatial-Temporal Change of Land Use and Its Impact on Water Quality of East-Liao River Basin from 2000 to 2020

Water ◽  
2021 ◽  
Vol 13 (14) ◽  
pp. 1955
Author(s):  
Mingxi Zhang ◽  
Guangzhi Rong ◽  
Aru Han ◽  
Dao Riao ◽  
Xingpeng Liu ◽  
...  
Keyword(s):  
Water Quality ◽  
Land Use ◽  
Land Use Change ◽  
Point Source ◽  
Water Body ◽  
Forest Land ◽  
Point Source Pollution ◽  
Construction Land ◽  
Unused Land ◽  
Non Point Source Pollution

Land use change is an important driving force factor affecting the river water environment and directly affecting water quality. To analyze the impact of land use change on water quality change, this study first analyzed the land use change index of the study area. Then, the study area was divided into three subzones based on surface runoff. The relationship between the characteristics of land use change and the water quality grade was obtained by grey correlation analysis. The results showed that the land use types changed significantly in the study area since 2000, and water body and forest land were the two land types with the most significant changes. The transfer rate is cultivated field > forest land > construction land > grassland > unused land > water body. The entropy value of land use information is represented as Area I > Area III > Area II. The shift range of gravity center is forest land > grassland > water body > unused land > construction land > cultivated field. There is a strong correlation between land use change index and water quality, which can be improved and managed by changing the land use type. It is necessary to establish ecological protection areas or functional areas in Area I, artificial lawns or plantations shall be built in the river around the water body to intercept pollutants from non-point source pollution in Area II, and scientific and rational farming in the lower reaches of rivers can reduce non-point source pollution caused by farming.

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