中国近地面PM2.5 质量浓度卫星遥感数据集比较及历史趋势分析

随着对大气污染问题的日益重视, 监测大气颗粒物质量浓度也成为了热门研究领域。针对现行两种流行算法(基于模式模拟和基于机器学习) 产生的近地面PM2:5 质量浓度科学数据集进行了比较分析, 利用城市年均PM2:5 监测数据定量评估两数据集的不确定性, 并通过空间自相关分析对两数据集的空间合理性进行了评价。同时, 还利用标准差椭圆分析研究了2000–2018 年间主要污染区域(北京、天津、河北、河南、山西、山东等地) PM2:5 的时空演变趋势。结果表明, 基于机器学习算法产生的数据集(CHAP) 具有较高的精度, 适用于区域性空气质量研究; 而基于模式模拟算法产生的数据集(vanDonkelaarA) 具有合理的空间分布, 更适合于大尺度、长时间的污染趋势分析。由标准差椭圆分析发现, 2000–2018 年研究区域标准差椭圆中心的位置整体向东北方向移动; 2013 年前PM2:5 分布范围及年均值在波动中呈现整体上升的趋势, 随后显著下降, 造成PM2:5 浓度下降的主要因素是有效管控措施的实施。研究结果为中国区域的细颗粒物污染研究的数据集选取提供了参考依据, 为大气细颗粒物污染的防控提供科学支撑。

Comparison and historical trend analysis of satellite remotesensing datasets of near-surface PM2.5 massconcentration in China

With the increasing attention to air pollution, monitoring atmospheric particulate mass concentration hasbecome a hot research field. In this paper, the scientific data sets of near-surface PM2:5 mass concentration generatedby two popular algorithms (model simulation and machine learning) are compared, quantitatively evaluated theuncertainty of the two data sets by using urban annual average PM2:5 monitoring data, and the spatial rationality ofthe two data sets through spatial autocorrelation analysis. Meanwhile, the spatial-temporal evolution trend of PM2:5in major pollution areas (Beijing, Tianjin, Hebei, Henan, Shanxi and Shandong) from 2000 to 2018 was also studiedby using standard deviation ellipse analysis. The results show that the data set (CHAP) based on machine learningalgorithm has high precision and is suitable for regional air quality research, and the data set (vanDonkelaarA)generated by the model simulation algorithm has a reasonable spatial distribution and is more suitable for largescaleand long-term pollution trend analysis. According to the analysis of standard deviation ellipse, the center ofstandard deviation ellipse in the study area moved to the northeast from 2000 to 2018. Before 2013, the distributionrange and annual mean value of PM2:5 showed an overall trend of increase, and then decreased significantly. It isshown that the main factor contributing to the decrease in PM2:5 concentration is the implementation of effectivecontrol measures. The result provides a reference for the selection of fine particulate matter pollution research datasets in China, and also provides scientific support for the prevention and control of atmospheric fine particulatematter pollution.