Spatial analysis and risk assessment of urban BTEX compounds in Urmia, Iran.

Land Use Regression models (LUR) are the most common tools to estimate intra-urban air pollutant exposure in epidemiological studies. However, number of available and published models in developing and middle up income countries is still scarce. Here, we developed seasonal and overall LUR models for the spatial distribution of benzene, toluene, ethylbenzene and xylene (BTEX) based on 20 monitoring stations and 166 potentially predictive variables (PPVs) in Urmia, Iran. Carcinogenic and non-carcinogenic risks of exposure to BTEX and its sensitivity analysis were assessed using a probabilistic approach. The mean and standard deviation (in brackets) of overall benzene, toluene, ethylbenzene and xylene were 12.83 (16.19), 27.03 (32.00), 4.72 (4.15) and 27.35 (29.36) µg/m3, respectively. In all models the R2 value of LUR models of benzene, toluene, ethylbenzene, xylene and total BTEX ranged from 0.66 to 0.85, 0.61, 0.88, 0.72 to 0.94, 0.75 to 0.84 and 0.67 to 0.93. The root mean square error (RMSE) for leave-one-out cross-validations (LOOCV) for benzene, toluene, ethylbenzene and xylene ranged from 7.48 to 10.31, 23.0 to 30.0, 3.40 to 6.90, 16.27 to 24.49, 36.10-50.0 µg/m3, respectively. The estimated lifetime carcinogenic risk (LTCR) indicated that ambient concentration of benzene is at a risk level for Urmia inhabitants (LTCR >10-6). Sensitivity analysis for LTCR model indicated that concentration of benzene (C) was the most effective variable in increasing the carcinogenic risk (correlation coefficient ranged from 0.97 to 0.98 for all models).

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 PM1, PM2.5 and PM10 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 PM1, PM2.5 and PM10 were 36.46 (8.56), 39.62 (10.55) and 51.99 (16.25) µg/m3, respectively. The R2 values and root mean square error for leave-one-out cross validations (RMSE for LOOCV) of PM1 models ranged from 0.23 to 0.79 and 3.43-22.5, respectively. Further, R2 and RMSE for LOOCV of PM2.5 models ranged from 0.56 to 0.93 and 3.66-28.3, respectively. For PM10 models the R2 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.

Intra-urban variation of ultrafine particles as evaluated by process related land use and pollutant driven regression modelling.

The microscale intra-urban variation of ultrafine particle concentrations (UFP, diameter Dp<100 nm) and particle number size distributions was studied by two statistical regression approaches. The models were applied to a 1 km2 study area in Braunschweig, Germany. A land use regression model (LUR) using different urban morphology parameters as input is compared to a multiple regression type model driven by pollutant and meteorological parameters (PDR). While the LUR model was trained with UFP concentration the PDR model was trained with measured particle number size distribution data. The UFP concentration was then calculated from the modelled size distributions. Both statistical approaches include explanatory variables that try to address the 'process chain' of particle emission, dilution and deposition. LUR explained 74% and 85% of the variance of UFP for the full data set with a root mean square error (RMSE) of 668 cm(-3) and 1639 cm(-3) in summer and winter, respectively. PDR explained 56% and 74% of the variance with RMSE of 4066 cm(-3) and 6030 cm(-3) in summer and winter, respectively. Both models are capable to depict the spatial variation of UFP across the study area and in different outdoor microenvironments. The deviation from measured UFP concentrations is smaller in the LUR model than in PDR. The PDR model is well suited to predict urban particle number size distributions from the explanatory variables (total particle number concentration, black carbon and wind speed). The urban morphology parameters in the LUR model are able to resolve size dependent concentration variations but not as adequately as PDR.