Assessment and management of nonpoint source pollution based on multicriteria analysis

Wu, Bi; Zhang, Xiang; Xu, Jing; Liu, Jianfeng; Wei, Fangliang

doi:10.1007/s11356-019-05923-3

Cited by 8 publications

(5 citation statements)

References 57 publications

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“…These eight were selected from the CORINE (Coordination of Information on the Environment) land cover classification system [54], which includes 44 types. Similar numbers of land use types as in the present study (between four and six) have been used in some previous studies [11,37,55]. In those studies, land use classification integrated contextual information related to pollution potential (e.g., fertiliser inputs, pollutant or nutrient load and export) for each land use type and its management, in addition to the land use classification.…”

Section: Relative Importance Of Indicatorsmentioning

confidence: 81%

“…More detailed studies are needed on pollution related to cropland and its management, and on the effects of grassland in pollution control, in the study catchment [57]. An association between urban areas (human settlements) and higher LCI values has been found in previous land use/land cover evaluations [18][19][20], using similar indices to PNPI, such as agricultural nonpoint pollution potential index (APPI) [15,58] and multicriteria analysis [11,13,55]. On a medium working scale (covering the study catchment) of 1:50.000 spatial resolution to match Landsat imagery pixels and SRTM-DEM resolution (30 m × 30 m), the land use and LCI results (and later PNPI) showed agreement.…”

Section: Relative Importance Of Indicatorsmentioning

confidence: 98%

“…In a previous study using this approach, the LCI and RoI weighting values were changed to make these the most important factors (DI was not considered due to short distances in the study area) [37]. A widely accepted multi-attribute decision-making approach for weighting, technique for order preference by similarity to ideal solution (TOPSIS) [68], has been found to be helpful because it can reduce subjectivity errors by assuming a monotonic increase or decrease in attributes in a decision matrix [11,55]. A disadvantage of the rank sum method used in the present study is the lack of theoretical foundation and the difficulty in justifying the assigned weights, and therefore sensitivity analysis becomes particularly difficult.…”

Section: Potential Non-point Pollution Index (Pnpi)mentioning

confidence: 99%

“…Thus APPI may be an inadequate index for use in the study catchment, since most of the pollution potential is from human settlements. APPI has been used to identify a relationship between densely populated areas, high anthropogenic activities and high pollution potential [11,55].…”

Section: Potential Non-point Pollution Index (Pnpi)mentioning

confidence: 99%

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Impact of Land Use Change on Non-Point Source Pollution in a Semi-Arid Catchment under Rapid Urbanisation in Bolivia

Gossweiler

Wesström

Messing

et al. 2021

Water

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Changes in pollution pressure exerted on the Rocha River in Bolivia from diffuse sources were assessed using potential non-point pollution indexes (PNPI) for 1997 and 2017. PNPI is a simple, low-effort, time- and resource-saving method suitable for data-scarce regions, as it works at catchment level with commonly available geographical data. Land use type (obtained by Landsat imagery classification), runoff (determined by runoff coefficient characterisation) and distance to river network (calculated at perpendicular distance) were each transformed into corresponding indicators to determine their relative importance in generating pollution. Weighted sum, a multi-criteria analysis tool in the GIS environment, was used to combine indicators with weighting values. Different weighting values were assigned to each of the indicators resulting in a set of six equations. The results showed that higher PNPI values corresponded to human settlements with high population density, higher runoff values and shorter distance to river network, while lower PNPI values corresponded to semi-natural land use type, lower runoff coefficient and longer distances to river. PNPI values were positively correlated with measured nitrate and phosphate concentrations at six sub-catchment outlets. The correlation was statistical significant for phosphate in 2017. Maps were produced to identify priority source areas that are more likely to generate pollution, which is important information for future management.

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Section: Relative Importance Of Indicatorsmentioning

confidence: 81%

Section: Relative Importance Of Indicatorsmentioning

confidence: 98%

Section: Potential Non-point Pollution Index (Pnpi)mentioning

confidence: 99%

Section: Potential Non-point Pollution Index (Pnpi)mentioning

confidence: 99%

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Impact of Land Use Change on Non-Point Source Pollution in a Semi-Arid Catchment under Rapid Urbanisation in Bolivia

Gossweiler

Wesström

Messing

et al. 2021

Water

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“…By using different methods, a range of studies have investigated the interaction between LULC and water quality in different parts of the world (Mehdi et al 2015;de Mello et al 2018;Zhang et al 2020), which shows the concerns of individuals on the issues of water resource deterioration under the face of human activities. These studies can broadly be categorized into those utilized: (1) the export coefficient model (Wang et al 2015;Wu et al 2019), a statistical model that focuses on the generation of the pollution export coefficient for different land use types. However, it does not consider the underlying surface conditions (e.g., land use change, vegetation, etc.…”

Section: Introductionmentioning

confidence: 99%

Evaluating long-term impacts of land use/land cover changes on pollution loads at a catchment scale

Zena,

Demissie,

Feyessa

2024

Water Science &Amp; Technology

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Evaluating how pollutant loads react to changes in land use/land cover (LULC) is a challenging task due to the intricate relationships among the many elements within a watershed. However, the difficulty in connecting LULC change and nonpoint source (NPS) pollution loads to streams may be lessened by combining hydrological modeling with geospatial tools and multivariate statistics. The objective of this study was to investigate the long-term effects of LULC change on NPS pollution loads in a highly human-dominated catchment, in central Ethiopia. In the study, hydrologic modeling was used to estimate the NPS parameters from multispectral Landsat images, and multivariate statistical techniques were then used to extract major LULC types that explain the variances of NPS loads between 1981 and 2020. The results demonstrated that there were human-induced LULC changes in the area, as the built-up and agricultural landscapes are rising (186.4% and 5.8%, respectively), and shrub and forest lands are decreasing (67.1% and 41%, respectively). As a result of these changes, the concentrations of nitrate (NO3), total P, total N, organic N, and organic P loads were increased by 69.41, 19.83, 18.45, 18.88, and 24.05%, respectively. Reductions in natural vegetation, as well as agriculture intensification, are the major contributors to the NPS pollutant losses to surface water sources. The result also revealed that pollution nutrients are strongly related to deforestation and agricultural land expansion. Proper adaptation strategies should be implemented to minimize the negative impact of LULC changes in the area.

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Effects of Planting Field on Groundwater and Surface Water Pollution in China

Opoku‐Kwanowaa

Furaha

et al. 2020

CLEAN Soil Air Water

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Water forms an essential resource for life on earth because all living things on earth depend on water for life activities. However, with the increase in the human population, which is coupled with intense urbanization and agricultural activities, global water pollution has increased over the past decades. In China, agricultural activities that occure mainly in the planting fields have been listed as the main source of surface water and groundwater pollution. This review focuses on the major factors that influence pollution from planting fields in China mainly as a result of farming activities such as flood irrigation, excessive application of fertilizers and pesticides, and poor management practices. At present, good results are achieved by adopting soil fertilization test formula, biodegradable pesticides, proper irrigation, and agroforestry interventions. In the future, pollution from planting fields as a non‐point source of water pollution can be improved and resolved by perfect nutrient management, best management practices, organic amendments, restoring water environment, and intelligent assessment management.

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Assessment and management of nonpoint source pollution based on multicriteria analysis

Cited by 8 publications

References 57 publications

Impact of Land Use Change on Non-Point Source Pollution in a Semi-Arid Catchment under Rapid Urbanisation in Bolivia

Impact of Land Use Change on Non-Point Source Pollution in a Semi-Arid Catchment under Rapid Urbanisation in Bolivia

Evaluating long-term impacts of land use/land cover changes on pollution loads at a catchment scale

Effects of Planting Field on Groundwater and Surface Water Pollution in China

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