Modeling groundwater nitrate concentrations using spatial and non-spatial regression models in a semi-arid environment
Nitrate nitrogen (NO3−-N) from agricultural activities and in industrial wastewater has become the main source of groundwater pollution, which has raised widespread concerns, particularly in arid and semi-arid river basins with little water that meets relevant standards. This study aimed to investig...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-09-01
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| Series: | Water Science and Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1674237022000400 |
| _version_ | 1811216982317465600 |
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| author | Azadeh Atabati Hamed Adab Ghasem Zolfaghari Mahdi Nasrabadi |
| author_facet | Azadeh Atabati Hamed Adab Ghasem Zolfaghari Mahdi Nasrabadi |
| author_sort | Azadeh Atabati |
| collection | DOAJ |
| description | Nitrate nitrogen (NO3−-N) from agricultural activities and in industrial wastewater has become the main source of groundwater pollution, which has raised widespread concerns, particularly in arid and semi-arid river basins with little water that meets relevant standards. This study aimed to investigate the performance of spatial and non-spatial regression models in modeling nitrate pollution in a semi-intensive farming region of Iran. To perform the modeling of the groundwater's NO3−-N concentration, both natural and anthropogenic factors affecting groundwater NO3−-N were selected. The results of Moran's I test showed that groundwater nitrate concentration had a significant spatial dependence on the density of wells, distance from streams, total annual precipitation, and distance from roads in the study area. This study provided a way to estimate nitrate pollution using both natural and anthropogenic factors in arid and semi-arid areas where only a few factors are available. Spatial regression methods with spatial correlation structures are effective tools to support spatial decision-making in water pollution control. |
| first_indexed | 2024-04-12T06:47:33Z |
| format | Article |
| id | doaj.art-f0b18d5fe1404ad69c7c5a8ab0486326 |
| institution | Directory Open Access Journal |
| issn | 1674-2370 |
| language | English |
| last_indexed | 2024-04-12T06:47:33Z |
| publishDate | 2022-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Water Science and Engineering |
| spelling | doaj.art-f0b18d5fe1404ad69c7c5a8ab04863262022-12-22T03:43:31ZengElsevierWater Science and Engineering1674-23702022-09-01153218227Modeling groundwater nitrate concentrations using spatial and non-spatial regression models in a semi-arid environmentAzadeh Atabati0Hamed Adab1Ghasem Zolfaghari2Mahdi Nasrabadi3Department of Environmental Sciences and Engineering, Faculty of Geography and Environmental Sciences, Hakim Sabzevari University, Sabzevar, Khorasan Razavi 9617976487, Iran; Corresponding author.Department of Remote Sensing and Geographic Information System, Faculty of Geography and Environmental Sciences, Hakim Sabzevari University, Sabzevar, Khorasan Razavi 9617976487, IranDepartment of Environmental Sciences and Engineering, Faculty of Geography and Environmental Sciences, Hakim Sabzevari University, Sabzevar, Khorasan Razavi 9617976487, IranDepartment of Environmental Sciences and Engineering, Faculty of Geography and Environmental Sciences, Hakim Sabzevari University, Sabzevar, Khorasan Razavi 9617976487, IranNitrate nitrogen (NO3−-N) from agricultural activities and in industrial wastewater has become the main source of groundwater pollution, which has raised widespread concerns, particularly in arid and semi-arid river basins with little water that meets relevant standards. This study aimed to investigate the performance of spatial and non-spatial regression models in modeling nitrate pollution in a semi-intensive farming region of Iran. To perform the modeling of the groundwater's NO3−-N concentration, both natural and anthropogenic factors affecting groundwater NO3−-N were selected. The results of Moran's I test showed that groundwater nitrate concentration had a significant spatial dependence on the density of wells, distance from streams, total annual precipitation, and distance from roads in the study area. This study provided a way to estimate nitrate pollution using both natural and anthropogenic factors in arid and semi-arid areas where only a few factors are available. Spatial regression methods with spatial correlation structures are effective tools to support spatial decision-making in water pollution control.http://www.sciencedirect.com/science/article/pii/S1674237022000400GroundwaterNitrateNatural and anthropogenic factorsSpatial autoregression modelsSpatial autocorrelation |
| spellingShingle | Azadeh Atabati Hamed Adab Ghasem Zolfaghari Mahdi Nasrabadi Modeling groundwater nitrate concentrations using spatial and non-spatial regression models in a semi-arid environment Water Science and Engineering Groundwater Nitrate Natural and anthropogenic factors Spatial autoregression models Spatial autocorrelation |
| title | Modeling groundwater nitrate concentrations using spatial and non-spatial regression models in a semi-arid environment |
| title_full | Modeling groundwater nitrate concentrations using spatial and non-spatial regression models in a semi-arid environment |
| title_fullStr | Modeling groundwater nitrate concentrations using spatial and non-spatial regression models in a semi-arid environment |
| title_full_unstemmed | Modeling groundwater nitrate concentrations using spatial and non-spatial regression models in a semi-arid environment |
| title_short | Modeling groundwater nitrate concentrations using spatial and non-spatial regression models in a semi-arid environment |
| title_sort | modeling groundwater nitrate concentrations using spatial and non spatial regression models in a semi arid environment |
| topic | Groundwater Nitrate Natural and anthropogenic factors Spatial autoregression models Spatial autocorrelation |
| url | http://www.sciencedirect.com/science/article/pii/S1674237022000400 |
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