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

大气与环境光学学报 ›› 2022, Vol. 17 ›› Issue (6): 613-629.

• “新型卫星载荷大气遥感及应用” 专辑 • 上一篇下一篇

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

戴刘新1;2, 张莹1∗, 李正强1, 漏嗣佳3

1 中国科学院空天信息创新研究院国家环境保护卫星遥感重点实验室, 北京100101; 2 中国科学院大学资源与环境学院, 北京100049; 3 南京大学大气科学学院大气与地球系统科学国际合作联合实验室, 江苏南京210023

收稿日期:2021-12-31 修回日期:2022-02-18 出版日期:2022-11-28 发布日期:2022-12-14
通讯作者: E-mail: zhang ying@aircas.ac.cn E-mail:zhangying02@radi.ac.cn
作者简介:戴刘新(1998 - ), 女, 河北石家庄人, 硕士研究生, 主要从事大气颗粒物遥感研究。E-mail: dailx142@qq.com
基金资助:
Supported by National Natural Science Foundation of China (国家自然科学基金, 41925019, 42101365, 42075095), Key Project of Strategic International Scientific and Technological Innovation Cooperation of the Ministry of Science and Technology (科技部战略性国际科技创新合作重点专项, 2016YFE0201400)

Comparison and historical trend analysis of satellite remote sensing datasets of near-surface PM2.5 mass concentration in China

DAI Liuxin1;2, ZHANG Ying1∗, LI Zhengqiang1, LOU Sijia3

1 State Environmental Protection Key Laboratory of Satellite Remote Sensing, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China; 2 College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China; 3 The Joint International Research Laboratory of Atmospheric and Earth System Sciences, School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China

Received:2021-12-31 Revised:2022-02-18 Published:2022-11-28 Online:2022-12-14

摘要/Abstract

摘要： 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。

关键词: PM2:5, 卫星遥感, 标准差椭圆, 空间自相关

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

Key words: PM2:5, satellite remote sensing, standard deviation ellipse, spatial autocorrelation

中图分类号:

P407

戴刘新, 张莹∗, 李正强, 漏嗣佳. 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[J]. 大气与环境光学学报, 2022, 17(6): 613-629.

DAI Liuxin, ZHANG Ying∗, LI Zhengqiang, LOU Sijia. Comparison and historical trend analysis of satellite remote sensing datasets of near-surface PM2.5 mass concentration in China[J]. Journal of Atmospheric and Environmental Optics, 2022, 17(6): 613-629.

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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

Comparison and historical trend analysis of satellite remote sensing datasets of near-surface PM2.5 mass concentration in China

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