大气与环境光学学报 ›› 2025, Vol. 20 ›› Issue (3): 325-337.doi: 10.3969/j.issn.1673-6141.2025.03.007

• “激光雷达新技术及其在大气环境中的应用”专辑 • 上一篇    

基于星载高光谱激光雷达估计中国区域PM2.5浓度的研究

王雄 1,2,3, 胡建波 1,2,4, 马鹏飞 5, 谢缘 1,2,3,4, 樊纯璨 1,2,3, 毕德仓 1,2,3,4, 竹孝鹏 1,2,3,4, 戴光耀 6, 卜令兵 7, 刘继桥 1,2,3,4*, 陈卫标 1,2,3,4*   

  1. 1 中国科学院上海光学精密机械研究所空天激光技术与系统部, 上海 201800; 2 中国科学院上海光学精密机械研究所王之江激光创新中心, 上海 201800; 3 中国科学院上海光学精密机械研究所空间激光信息传输与探测技术重点实验室, 上海 201800; 4 中国科学院大学材料与光电研究中心, 北京 100049; 5 生态环境部卫星环境应用中心, 北京 100094; 6 中国海洋大学信息科学与工程学部海洋技术学院, 山东 青岛 266100; 7 南京信息工程大学大气物理学院, 江苏 南京 210044
  • 收稿日期:2025-03-20 修回日期:2025-04-25 出版日期:2025-05-28 发布日期:2025-05-26
  • 通讯作者: E-mail: liujiqiao@siom.ac.cn; wbchen@siom.ac.cn E-mail:wangxiong@siom.ac.cn
  • 作者简介:王雄 (1998- ), 江西瑞昌人, 硕士, 助理工程师, 主要从事激光雷达气溶胶探测方面的研究。E-mail: wangxiong@siom.ac.cn
  • 基金资助:
    中国科学院国际伙伴计划项目 (18123KYSB20210013), 上海市"科技创新行动计划"科技支撑碳达峰碳中和专项 (22dz208700)

Research on estimating PM2.5 concentration in China region based on spaceborne hyperspectral lidar

WANG Xiong 1,2,3, HU Jianbo 1,2,4, MA Pengfei 5, XIE Yuan 1,2,3,4, FAN Chuncan 1,2,3, BI Decang 1,2,3,4, ZHU Xiaopeng 1,2,3,4, DAI Guangyao 6, BU Lingbing 7, LIU Jiqiao 1,2,3,4*, CHEN Weibiao 1,2,3,4*   

  1. 1 Aerospace Laser Technology and System Department, Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai 201800, China; 2 Wang Zhijiang Innovation Center for Laser, Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai 201800, China; 3 Key Laboratory of Space Laser Communication and Detection Technology, Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai 201800, China; 4 Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing 100049, China; 5 Satellite Application Center for Ecology and Environment, Beijing 100094, China; 6 Institute for Advanced Ocean Study, College of Information Science and Engineering, Ocean Remote Sensing Institute, Ocean University of China, Qingdao 266100, China; 7 School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing 210044, China
  • Received:2025-03-20 Revised:2025-04-25 Online:2025-05-28 Published:2025-05-26

摘要: 为研究中国区域的PM2.5质量浓度分布, 监测中国大气环境变化, 基于大气环境监测 (DQ-1) 卫星大气探测激 光雷达 (ACDL) 的气溶胶数据和中国区域PM2.5地基观测数据, 首先通过垂直订正和湿度订正提高雷达气溶胶光学厚 度 (AOD) 和地基站点PM2.5观测数据的相关性, 进而使用半经验法估计了中国区域的2022 年6 月―2023 年5 月按季度 的PM2.5质量浓度季节变化。研究结果表明在2022 年6 月―2023 年5 月时间内中国大陆区域内夏季PM2.5质量浓度最 低, 冬季最高, 在新疆地区由于沙漠的存在, 全年PM2.5 质量浓度相对较高。全国大部分地区PM2.5 质量浓度在 30 μg/m3 左右, 遥感估计PM2.5 年均值与站点数据的相关性优于0.71。本研究对于丰富区域PM2.5 反演方式和扩展 ACDL数据应用具有一定意义。

关键词: PM2.5, 高光谱激光雷达, 半经验法, 垂直订正, 湿度订正

Abstract: In order to study the distribution of PM2.5 mass concentration in China and monitor the changes of atmospheric environment in China, based on the Aerosol and Carbon dioxide Detection Lidar (ACDL) aerosol data of atmospheric environment monitoring satellites (DQ-1) and the ground-based PM2.5 observation data of China, the correlation between aerosol optical depth (AOD) and PM2.5 data was improved through vertical correction and humidity correction, and the seasonal variation of PM2.5 mass concentration changes in China from June 2022 to May 2023 was estimated using the semi-empirical method. The results show that during the period from June 2022 to May 2023, the PM2.5 mass concentration in China is the lowest in summer and the highest in winter. Due to the existence of desert in Xinjiang, the annual PM2.5 mass concentration is relatively high. The PM2.5 mass concentration in most parts of China is about 30 μg/m3, and the correlation between remote sensing estimation of PM2.5 annual mean and station data is better than 0.71. This study is of certain significance for enriching regional PM2.5 inversion methods and expanding the application of ACDL atmospheric data.

Key words: PM2.5, high-spectral-resolution lidar, semi-empirical method, vertical corrections, humidity corrections