Abstract:

The analysis of the driving causes of PM_2.5 is of great importance to the prevention and control of air pollution. Using the PM_2.5 ground monitoring data from 2015 to 2018, combined with geographic detectors and geographically weighted ridge regression methods, the key driving factors of PM_2.5 and their spatiotemporal heterogeneity in 282 cities in China were investigated. The results indicate that meteorological parameters and socioeconomic activities can better explain the spatiotemporal heterogeneity of PM_2.5 distribution. The R² of the proposed model for each year (2015?2018) are 0.698, 0.724, 0.656, and 0.712 and AICc of the model for the four years' data are 1317.533, 1234.400, 1256.107, 1110.740, respectively. Based on these indicators, the proposed model has better fitting results than the global regression model and the geographically weighted regression model. In addition, the model-fitting results show that temperature, specific humidity, GDP, annual average population, and the number of industrial enterprises are the main driving factors of PM_2.5. These factors have either a positive or a negative effect on PM_2.5 and their regression coefficients have obvious spatiotemporal heterogeneity.

Key words: PM_2.5, influence factors, geographical detector, geographically weighted ridge regression, spatiotemporal heterogeneity

摘要：

开展PM_2.5的驱动成因分析,对大气污染防治具有重要意义。利用2015—2018年PM_2.5地面监测数据,结合地理探测器和地理加权岭回归方法,探测了全国282个城市PM_2.5空间分异的关键驱动因素,分析了各关键驱动因素对PM_2.5影响的时空异质性。结果表明,气象参数和社会经济活动可更好地解释PM_2.5呈现的空间分异性;在2015—2018年间,所建地理加权岭回归模型的R²分别为0.698、0.724、0.656和0.712,AICc分别为1317.533、1234.400、1256.107和1110.740,2种指标均优于全局回归模型和地理加权回归模型,说明地理加权岭回归模型可更好地解释PM_2.5产生空间分异的关键影响机制;模型拟合结果进一步显示,气温、比湿度、地区生产总值、年平均人口和工业企业数是引起PM_2.5空间分异的关键驱动因素,各因素的影响既存在正向效应也存在负向效应,其对应的回归系数具有明显的时空异质性。

关键词: PM_2.5, 影响因素, 地理探测器, 地理加权岭回归, 时空异质性