Estimate annual and seasonal PM1, PM2.5 and PM10 concentrations using land use regression model

Exposure to ambient particulate matter (PM) can increase mortality and morbidity in urban area. In this study, annual and seasonal spatial pattern of PM 1 , PM 2.5 and PM 10 pollutants were assessed using land use regression (LUR) models in Sabzevar, Iran. The studied pollutants were measured at 26 monitoring stations of different microenvironments in the study area. Sampling was conducted during four campaigns from April 2017 to February 2018. LUR models were developed based on 104 potentially predictive variables (PPVs) subdivided in six categories and 22 sub-categories. The annual mean (standard deviation) of PM 1 , PM 2.5 and PM 10 were 36.46 (8.56), 39.62 (10.55) and 51.99 (16.25) μg/m 3 , respectively. The R 2 values and root mean square error for leave-one-out cross validations (RMSE for LOOCV) of PM 1 models ranged from 0.23 to 0.79 and 3.43–22.5, respectively. Further, R 2 and RMSE for LOOCV of PM 2.5 models ranged from 0.56 to 0.93 and 3.66–28.3, respectively. For PM 10 models the R 2 ranged from 0.31 to 0.82 and the RMSE for LOOCV ranged from 9.16 to 33.9. The generated models can be useful for population based epidemiologic studies and to estimate these pollutants in different parts of the study area for scientific decision making .