Response of non-point source pollution to landscape pattern: case study in mountain-rural region, China

Li, Wuhua; Cheng, Xiangju; Zheng, Yu; Lai, Chengguang; Sample, David J.; Zhu, Dantong; Wang, Zhaoli

doi:10.1007/s11356-020-12196-8

Cited by 15 publications

(2 citation statements)

References 60 publications

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“…ANSP is a "spillover" pollutant with a strong spatial spillover effect [42]. Existing studies on the influencing factors of ANSP are usually divided into different spatial units based on administrative boundaries, assuming that each regional unit is an independent individual, ignoring the spillover effects of ANSP between regions [43]. In contrast, the spatial econometric model assumes the existence of a spatial correlation among regions and considers the influence of surrounding areas on the study area, which can more fully reveal the effect of each influencing factor on ANSP [12].…”

Section: Introductionmentioning

confidence: 99%

Spatial Pattern Evolution and Influencing Factors on Agricultural Non-Point Source Pollution in Small Town Areas under the Background of Rapid Industrialization

Yan

Jianji

Jia-jun

et al. 2023

IJERPH

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To promote sustainable agricultural development in small town areas during rapid industrialization, it is important to study the evolution of agricultural non-point source pollution (ANSP) and its influencing factors in small town areas in the context of rapid industrialization. The non-point source inventory method was used to study the characteristics of ANSP evolution in 14 small town areas in Gongyi City from 2002 to 2019. Using the spatial Durbin model and geographical detectors, the factors influencing ANSP in small town areas were analyzed in terms of spatial spillover effects and the spatial stratified heterogeneity. The results showed a zigzagging downward trend of ANSP equivalent emissions over time. Spatially, the equivalent emissions of ANSP showed a distribution pattern of being high in the west and low in the east. There was a significant positive global spatial autocorrelation feature and there was an inverted “U-shaped” Environmental Kuznets Curve relationship between industrialization and ANSP. Affluence, population size, and cropping structure positively contributed to the reduction of ANSP. Population size, land size, and industrialization were highly influential factors affecting the spatial variation of ANSP and the interaction of these factors was bivariate or nonlinearly enhanced. This study provides a feasible reference for policymakers and managers to develop reasonable management measures to mitigate ANSP in small town areas during rapid industrialization.

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Section: Introductionmentioning

confidence: 99%

Spatial Pattern Evolution and Influencing Factors on Agricultural Non-Point Source Pollution in Small Town Areas under the Background of Rapid Industrialization

Yan

Jianji

Jia-jun

et al. 2023

IJERPH

View full text Add to dashboard Cite

show abstract

“…With the improvement of PS pollution control, NPS pollution has become more notable and difficult to control as it disperses easily, is difficult to detect, varies widely, and is hysteretic [4]. NPS pollution is primarily caused by local agricultural activities, such as the use of fertilizers and pesticides, livestock and poultry breeding, and rural household waste [5][6][7][8]. Agricultural NPS (ANPS) pollution has become a major cause of water quality degradation in many rivers [9][10][11] and identifying and controlling it has become key to improving the quality of the aquatic environment [12].…”

Section: Introductionmentioning

confidence: 99%

Effectivity and Efficiency of Best Management Practices Based on a Survey and SWAPP Model of the Xiangxi River Basin

Liu

Miao

Jiao

et al. 2021

Water

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A questionnaire survey was conducted among farmers in the Xiangxi River Basin to investigate the local livestock situation and the farmers’ understanding of and attitude towards pollution. The results showed that local farmers lacked environmental awareness and few livestock and poultry pollution treatment measures had been implemented. However, once farmers understood that livestock pollution would greatly influence their lives and interests, they would act to prevent Agricultural non–point source (ANPS) pollution. The farmers’ education level and satisfaction with the environment were the main factors affecting their awareness regarding ANPS pollution. The “Comprehensive Environmental Optimization Tool SWAT–APEX Interface” model (SWAPP) was used to simulate the reduction of ANPS by different best management practices (BMPs) and the construction cost was calculated. The results showed that compound bedding and piping systems and ponds were the most effective and economic measures for reducing ANPS pollution. Spatially, implementing BMPs in the upstream region was better for improving water quality. The nitrate reduction rate in upstream sub–basins reached 90%, which is 30% larger than that in downstream sub–basins with combined bedding and piping systems. Combining the farmers’ awareness of and engagement in livestock pollution with cost–effective BMPs can improve the BMPs’ effectivity and efficiency.

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The response of net primary productivity to climate change and its impact on hydrology in a water-limited agricultural basin

Ren

et al. 2021

Environ Sci Pollut Res

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Response of non-point source pollution to landscape pattern: case study in mountain-rural region, China

Cited by 15 publications

References 60 publications

Spatial Pattern Evolution and Influencing Factors on Agricultural Non-Point Source Pollution in Small Town Areas under the Background of Rapid Industrialization

Spatial Pattern Evolution and Influencing Factors on Agricultural Non-Point Source Pollution in Small Town Areas under the Background of Rapid Industrialization

Effectivity and Efficiency of Best Management Practices Based on a Survey and SWAPP Model of the Xiangxi River Basin

The response of net primary productivity to climate change and its impact on hydrology in a water-limited agricultural basin

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