Volume 15 Issue 3
Aug.  2022
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Article Navigation >  Water Science and Engineering  > 2022  >  15(3): 218-227
Azadeh Atabati, Hamed Adab, Ghasem Zolfaghari, Mahdi Nasrabadi. 2022: Modeling groundwater nitrate concentrations using spatial and non-spatial regression models in a semi-arid environment. Water Science and Engineering, 15(3): 218-227. doi: 10.1016/j.wse.2022.05.002
Citation: Azadeh Atabati, Hamed Adab, Ghasem Zolfaghari, Mahdi Nasrabadi. 2022: Modeling groundwater nitrate concentrations using spatial and non-spatial regression models in a semi-arid environment. Water Science and Engineering, 15(3): 218-227. doi: 10.1016/j.wse.2022.05.002
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Modeling groundwater nitrate concentrations using spatial and non-spatial regression models in a semi-arid environment

doi: 10.1016/j.wse.2022.05.002

  • a. Department of Environmental Sciences and Engineering, Faculty of Geography and Environmental Sciences, Hakim Sabzevari University, Sabzevar, Khorasan Razavi 9617976487, Iran;

  • b. Department of Remote Sensing and Geographic Information System, Faculty of Geography and Environmental Sciences, Hakim Sabzevari University, Sabzevar, Khorasan Razavi 9617976487, Iran

  • Received Date: 2021-08-02
  • Accepted Date: 2022-01-07
  • Rev Recd Date: 2022-01-07
    • Available Online: 2022-08-24
    Abstract
  • 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.

     

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