title: Estimating PM2.5 Concentrations in Xi'an City Using a Generalized Additive Model with Multi-Source Monitoring Data.
abstract: Particulate matter with an aerodynamic diameter <2.5 μm (PM2.5) 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 PM2.5 in China. In 2013, in total, there were 191 days in Xi'an City on which PM2.5 concentrations were greater than 100 μg/m3. Recently, a few studies have explored the potential causes of high PM2.5 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 PM2.5 concentrations and other pollutants, including CO, NO2, SO2, and O3, 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 PM2.5 concentrations. This model contains linear functions of SO2 and CO, univariate smoothing non-linear functions of NO2, O3, AOT and temperature, and bivariate smoothing non-linear functions of location and wind variables. The model can explain 69.50% of PM2.5 concentrations, with R2 = 0.691, which improves the result of a stepwise linear regression (R2 = 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, SO2, NO2, and O3 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 PM2.5. Temperature, location, and wind variables also non-linearly related with PM2.5.
authors.affiliation: School of Land Science and Technology, China University of Geosciences, Beijing, China.
authors.forename: Yong-Ze
authors.initials: YZ
authors.lastname: Song
authors.affiliation: School of Land Science and Technology, China University of Geosciences, Beijing, China.
authors.forename: Hong-Lei
authors.initials: HL
authors.lastname: Yang
authors.affiliation: School of Land Science and Technology, China University of Geosciences, Beijing, China.
authors.forename: Jun-Huan
authors.initials: JH
authors.lastname: Peng
authors.affiliation: Department of Geological Engineering, Qinghai University, Xining, Qinghai Province, China.
authors.forename: Yi-Rong
authors.initials: YR
authors.lastname: Song
authors.affiliation: School of Water Resources and Environment, China University of Geosciences, Beijing, China.
authors.forename: Qian
authors.initials: Q
authors.lastname: Sun
authors.affiliation: School of Geophysics and Information Technology, China University of Geosciences, Beijing, China.
authors.forename: Yuan
authors.initials: Y
authors.lastname: Li
mesh_terms: D000336:Aerosols / Q000032:analysis; D000393:Air Pollutants / Q000032:analysis*; D000397:Air Pollution / Q000032:analysis*; D002248:Carbon Monoxide / Q000737:chemistry; D002681:China; D002947:Cities; D004784:Environmental Monitoring / Q000379:methods; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods; D008962:Models, Theoretical; D009585:Nitrogen Dioxide / Q000737:chemistry; D010126:Ozone / Q000737:chemistry; D052638:Particulate Matter / Q000032:analysis*; D013458:Sulfur Dioxide / Q000737:chemistry; D001335:Vehicle Emissions / Q000032:analysis
chemical_list: D000336:Aerosols; D000393:Air Pollutants; D052638:Particulate Matter; D001335:Vehicle Emissions; D013458:Sulfur Dioxide; D010126:Ozone; D002248:Carbon Monoxide; D009585:Nitrogen Dioxide