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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.

This paper presented the levels of PM2.5 and PM10 in different stations at the city of Sabzevar, Iran. Furthermore, this study was an attempt to evaluate spatial interpolation methods for determining the PM2.5 and PM10 concentrations in the city of Sabzevar. Particulate matters were measured by Haz-Dust EPAM at 48 stations. Then, four interpolating models, including Radial Basis Functions (RBF), Inverse Distance Weighting (IDW), Ordinary Kriging (OK), and Universal Kriging (UK) were used to investigate the status of air pollution in the city. Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) were employed to compare the four models. The results showed that the PM2.5 concentrations in the stations were between 10 and 500 μg/m3. Furthermore, the PM10 concentrations for all of 48 stations ranged from 20 to 1500 μg/m3. The concentrations obtained for the period of nine months were greater than the standard limits. There was difference in the values of MAPE, RMSE, MBE, and MAE. The results indicated that the MAPE in IDW method was lower than other methods: (41.05 for PM2.5 and 25.89 for PM10). The best interpolation method for the particulate matter (PM2.5 and PM10) seemed to be IDW method. • The PM10 and PM2.5 concentration measurements were performed in the period of warm and risky in terms of particulate matter at 2016. • Concentrations of PM2.5 and PM10 were measured by a monitoring device, environmental dust model Haz-Dust EPAM 5000. • Interpolation is used to convert data from observation points to continuous fields to compare spatial patterns sampled by these measurements with spatial patterns of other spatial entities.