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Particulate matter with an aerodynamic diameter <2.5 μm (PM 2.5 ) represents a severe environmental problem and is of negative impact on human health. Xi'an City, with a population of 6.5 million, is among the highest concentrations of PM 2.5  in China. In 2013, in total, there were 191 days in Xi’an City on which PM 2.5  concentrations were greater than 100 μg/m 3 . Recently, a few studies have explored the potential causes of high PM 2.5  concentration using remote sensing data such as the MODIS aerosol optical thickness (AOT) product. Linear regression is a commonly used method to find statistical relationships among PM 2.5  concentrations and other pollutants, including CO, NO 2 , SO 2 , and O 3 , which can be indicative of emission sources. The relationships of these variables, however, are usually complicated and non-linear. Therefore, a generalized additive model (GAM) is used to estimate the statistical relationships between potential variables and PM 2.5  concentrations. This model contains linear functions of SO 2  and CO, univariate smoothing non-linear functions of NO 2 , O 3 , AOT and temperature, and bivariate smoothing non-linear functions of location and wind variables. The model can explain 69.50% of PM 2.5  concentrations, with R 2  = 0.691, which improves the result of a stepwise linear regression (R 2  = 0.582) by 18.73%. The two most significant variables, CO concentration and AOT, represent 20.65% and 19.54% of the deviance, respectively, while the three other gas-phase concentrations, SO 2 , NO 2 , and O 3  account for 10.88% of the total deviance. These results show that in Xi'an City, the traffic and other industrial emissions are the primary source of PM 2.5 . Temperature, location, and wind variables also non-linearly related with PM 2.5 .

Estimating PM2.5 Concentrations in Xi'an City Using a Generalized Additive Model with Multi-Source Monitoring Data