Song Yong-Ze, Yang Hong-Lei, Peng Jun-Huan, Song Yi-Rong, Sun Qian, Li Yuan
School of Land Science and Technology, China University of Geosciences, Beijing, China.
Department of Geological Engineering, Qinghai University, Xining, Qinghai Province, China.
PLoS One. 2015 Nov 5;10(11):e0142149. doi: 10.1371/journal.pone.0142149. eCollection 2015.
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.
空气动力学直径小于2.5微米的颗粒物（PM2.5）是一个严重的环境问题，对人类健康有负面影响。西安市有650万人口，是中国PM2.5浓度最高的城市之一。2013年，西安市共有191天的PM2.5浓度大于100微克/立方米。最近，一些研究利用中分辨率成像光谱仪（MODIS）气溶胶光学厚度（AOT）产品等遥感数据，探索了PM2.5高浓度的潜在原因。线性回归是一种常用的方法，用于找出PM2.5浓度与其他污染物（包括一氧化碳、二氧化氮、二氧化硫和臭氧）之间的统计关系，这些污染物可指示排放源。然而，这些变量之间的关系通常是复杂的非线性关系。因此，采用广义相加模型（GAM）来估计潜在变量与PM2.5浓度之间的统计关系。该模型包含二氧化硫和一氧化碳的线性函数、二氧化氮、臭氧、气溶胶光学厚度和温度的单变量平滑非线性函数，以及位置和风变量的双变量平滑非线性函数。该模型可以解释69.50%的PM2.5浓度，决定系数R2 = 0.691，比逐步线性回归的结果（R2 = 0.582）提高了18.73%。两个最显著的变量，一氧化碳浓度和气溶胶光学厚度，分别占偏差的20.65%和19.54%，而其他三种气相浓度，二氧化硫、二氧化氮和臭氧占总偏差的10.88%。这些结果表明，在西安市，交通和其他工业排放是PM2.5的主要来源。温度、位置和风变量也与PM2.5呈非线性关系。
