Implications of nonstationary effect on geographically weighted total least squares regression for PM2.5 estimation

Abstract

Land use regression (LUR) models are used for high‐resolution air pollution assessment. These models use independent parameters based on an assumption that these parameters are accu-rate and invariable; however, they are observational parameters derived from measurements or modeling. Therefore, the parameters are commonly inaccurate, with nonstationary effects and variable characteristics. In this study, we propose a geographically weighted total least squares regression (GWTLSR) to model air pollution under various traffic, land use, and meteorological parame-ters. To improve performance, the proposed model considers the dependent and independent variables as observational parameters. The GWTLSR applies weighted total least squares in order to take into account the variable characteristics and inaccuracies of observational parameters. Moreo-ver, the proposed model considers the nonstationary effects of parameters through geographically weighted regression (GWR). We examine the proposed model’s capabilities for predicting daily PM2.5 concentration in Isfahan, Iran. Isfahan is a city with severe air pollution that suffers from insufficient data for modeling air pollution with conventional LUR techniques. The advantages of the model features, including consideration of the variable characteristics and inaccuracies of pre-dictors, are precisely evaluated by comparing the GWTLSR model with ordinary least squares (OLS) and GWR models. The R2 values estimated by the GWTLSR model during the spring and autumn are 0.84 and 0.91, respectively. The corresponding average R2 values estimated by the OLS model during the spring and autumn are 0.74 and 0.69, respectively, and the R2 values estimated by the GWR model are 0.76 and 0.70, respectively. The results demonstrate that the proposed functional model efficiently described the physical nature of the relationships among air pollutants and independent variables.