PM2.5 concentration modeling and mapping in the urban areas

Abstract

The aim of our study was to compare and evaluate four models by which the ground-level PM2.5 concentrations in the urban area are predicted. These are ordinary kriging (OK), universal kriging (UK), inverse distance weighting (IDW), and multi-layer perceptron (MLP). Root mean square error (RMSE) and standard deviation (SD) were used to select semi-variogram and the transformation type of renormalized data in kriging models. The spherical semi-variogram, Box-Cox with the power parameter equal to one, and first order trend of removal were used to run OK and UK models. In the IDW method, the weighing was conducted according to the distance between points and the measurement station. The trial and error method along with a coefficient of determination (R2) and the lowest root mean square error (RMSE) values were used to obtain the optimum model in MLP approach. Regarding the mapping, the nearest area to main roads showed generally the highest annual average concentrations of PM2.5. Based on the RMSE and R2 of models, the order of fitted models of PM2.5 concentrations in the air from the best to worst was as follows: MLP > OK > UK > IDW.