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

doi:10.3969/j.issn.1673-6141.2025.03.007

Journal of Atmospheric and Environmental Optics ›› 2025, Vol. 20 ›› Issue (3): 325-337.doi: 10.3969/j.issn.1673-6141.2025.03.007

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 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

Abstract

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

WANG Xiong , HU Jianbo , MA Pengfei , XIE Yuan , FAN Chuncan , BI Decang , ZHU Xiaopeng , DAI Guangyao , BU Lingbing , LIU Jiqiao , CHEN Weibiao , . Research on estimating PM2.5 concentration in China region based on spaceborne hyperspectral lidar[J]. Journal of Atmospheric and Environmental Optics, 2025, 20(3): 325-337.

References

参考文献：
[1] Dai H X, Song W M. Health effects of atmospheric PM2.5[J]. Foreign Medical Sciences(Section of Hygiene), 2001, (5): 299-303
戴海夏, 宋伟民. 大气PM2.5的健康影响[J]. 国外医学(卫生学分册), 2001, (5): 299-303
[2] Mao S N, Ma Y M, Cai Y Q, et al. Current Status and Progress of PM2.5 Detection Standards and Traceability Methods (continued) [J]. China Metrology, 2013, (4): 22-24
毛朔南, 马宇明, 蔡冶强,等. PM2.5检测标准及量值溯源方法现状及进展(续) [J].中国计量, 2013, (4): 22-24
[3] Wang J, Christopher S. A. Intercomparison between satellite‐derived aerosol optical thickness and PM2.5 mass: Implications for air quality studies[J]. Geophysical Research Letters, 2003, 30(21): 2095
[4] Liu Y, Sarnat J A., Kilaru A, et al. Estimating ground-level PM2.5 in the eastern united states using satellite remote sensing[J]. Environmental Science & Technology, 2005, 39(9): 3269-3278
[5] Van D A, Martin R V, Park R J. Estimating ground‐level PM2.5 using aerosol optical depth determined from satellite remote sensing[J]. Journal of Geophysical Research: Atmospheres, 2006, 111(D21): D21201
[6] Wang Z F, Chen L F, Tao J, et a. Satellite-based estimation of regional particulate matter (PM) in Beijing using vertical-and-RH correcting method[J]. Remote Sensing of Environment, 2010, 114: 50-63
[7] Tian J, Chen D. A semi-empirical model for predicting hourly ground-level fine particulate matter (PM2.5) concentration in southern Ontario from satellite remote sensing and ground-based meteorological measurements[J]. Remote Sensing of Environment, 2010, 114: 221-229
[8] Wu Y, Guo J, Zhang X, et al. Synergy of satellite and ground based observations in estimation of particulate matter in eastern China[J]. Science of the Total Environment, 2012, 433: 20-30
[9] Hu X F, Waller L A, Lyapustin A, et al. Estimating ground-level PM2.5 concentrations in the southeastern united states using MAIAC AOD retrievals and a two-stage model[J]. Remote Sensing of Environment, 2014, 140: 220-232
[10] Ma Z, Hu X, Huang L. Estimating ground-level PM2.5 in China using satellite remote sensing[J]. Environmental Science & Technology, 2014, 48: 7436-7444
[11] Chen X L, Li H, Zhang S T, et al. High spatial resolution PM2.5 retrieval using MODIS and ground observation station data based on ensemble random forest[J]. IEEE Access, 2019, 7: 44416-44430
[12] Xue T, Zheng Y X, Tong D, et al. Spatiotemporal continuous estimates of PM2.5 concentrations in China, 2000–2016: A machine learning method with inputs from satellites, chemical transport model, and ground observations[J]. Environment International, 2019, 123: 345-357
[13] Li J M, Jin M J, Li H L. Exploring spatial influence of remotely sensed PM2.5 concentration using a developed deep convolutional neural network model[J]. International Journal of Environmental Research and Public Health, 2019, 16(3): 454
[14] Yang L, Xu H, Jin Z. Estimating ground-level PM2.5 over a coastal region of China using satellite AOD and a combined model[J]. Journal of Cleaner Production, 2019, 227: 472-482
[15] Toth T D, Zhang J, Reid J S, et al. A bulk-mass-modeling-based method for retrieving particulate matter pollution using CALIOP observations[J]. Atmospheric Measurement Techniques, 2019, 12: 1739-1754
[16] Lakshmi N B, E. Resmi A, Padmalal D. Assessment of PM2.5 using satellite lidar observations: Effect of bio-mass burning emissions over India[J]. Science of Total Environment, 2022, 833: 155215
[17] Ma X J, Huang Z W, Qi S Q, et al. Ten-year global particulate mass concentration derived from space-borne CALIPSO lidar observations[J]. Science of Total Environment, 2020, 721: 137699
[18] Chu D A, Tsai T C, Chen J P. Interpreting aerosol lidar profiles to better estimate surface PM2.5 for columnar AOD measurements[J]. Atmospheric Environment, 2013, 79: 172-187
[19] Wang Z F, Chen L F, Tao J H, et al. An empirical method of RH correction for satellite estimation of ground-level PM concentrations[J]. Atmospheric Environment, 2014, 95: 71-81
[20] Zhu H, Wang Y Q, Tao J H, et al. Analysis of aerosol hygroscopic growth based on meteorological elements: A case study of Zhejiang Province[J].Climatic and Environmental Research,2019, 24(2): 186-198
祝好, 王永前, 陶金花,等. 基于气象要素的气溶胶吸湿增长分析: 以浙江省为例[J]. 气候与环境研究, 2019, 24(2): 186-198

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

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles 0

Metrics

Comments

[1]	CHEN Shanlong , LI Yi , NIU Dan , HU Yiwen , ZANG Zengliang . PM2.5 prediction in East China based on improved Seq2Seq model [J]. Journal of Atmospheric and Environmental Optics, 2025, 20(1): 82-94.
[2]	FANG Wensheng , CAO Nianwen , SHI Xinchi. Analysis of a synergistic pollution process of PM2.5 and ozone in Beijing-Tianjin-Hebei Region [J]. Journal of Atmospheric and Environmental Optics, 2024, 19(3): 342-356.
[3]	WANG Lijun , ZHOU Yu , WAN Lijuan , CHENG Liangliang. Application of the Bayesian-based big data model in the analysis of the source of air pollution [J]. Journal of Atmospheric and Environmental Optics, 2023, 18(3): 227-234.
[4]	CAO Yuan , GONG Mingyan , SHEN Fei , MA Jinji , YANG Guang , LIN Xiwen , . Estimation of PM2.5 concentration and analysis of influencing factors in China [J]. Journal of Atmospheric and Environmental Optics, 2023, 18(3): 245-257.
[5]	SUN Erchang , MA Jinji , WU Wenhan , YANG Guang , GUO Jinyu , . Improvement of PM2.5 predictions via variational assimilation of Himawari-8 satellite AOD product [J]. Journal of Atmospheric and Environmental Optics, 2023, 18(1): 59-72.
[6]	CHEN Yukuan , , WANG Shuo , XU Xuezhe , ZHAO Weixiong , GAI Yanbo , FANG Bo , ZHANG Weijun , ∗. Variation characteristics of aerosol extinction coefficient in Shouxian, Anhui Province [J]. Journal of Atmospheric and Environmental Optics, 2022, 17(5): 533-541.
[7]	YANG Mingliang, ZHU Zongjiu. Simulation analysis of spatial distribution of PM2.5 concentration based on LUR model [J]. Journal of Atmospheric and Environmental Optics, 2022, 17(3): 347-359.
[8]	PENG Liping, HUANG Yi∗, ZHENG Kaili. Response of Atmospheric PM2.5 Concentration to Tourist Activities in Destinations Under Different Tourism Activity Intensity [J]. Journal of Atmospheric and Environmental Optics, 2021, 16(6): 471-482.
[9]	JIA Hongliang, LUO Jun, XIAO Dongsheng∗. Temporal and Spatial Distribution Characteristics of PM2.5 in Chengdu Area Based on Remote Sensing Data and GWR Model [J]. Journal of Atmospheric and Environmental Optics, 2021, 16(6): 529-540.
[10]	CHEN Jie, LI Zhengqiang∗, CHANG Wenyuan∗, ZHANG Ying, WEI Yuanyuan, XIE Yisong, GE Bangyu, ZHANG Chi, . Impact Evaluation of Aerosol Variational Assimilation Based on Improved GSI System on WRF-Chem PM2.5 Analysis and Forecast [J]. Journal of Atmospheric and Environmental Optics, 2020, 15(5): 321-333.
[11]	SHI Yiqiang, CHEN Jiongfeng, WANG Jian, HUANG Baoyan, WU Jun, . Spatial and Temporal Characteristics and Correlation of MODIS Aerosol Optical Depth and PM2.5 Concentration Over Xiamen City [J]. Journal of Atmospheric and Environmental Optics, 2020, 15(5): 334-346.
[12]	ZHENG Kaili, HUANG Yi∗, YAO Xiaoyun, HU Xiaoyan. Correlation Between PM2.5, NO2 and Tourism Activities, Weather Factors in Zhangjiajie City [J]. Journal of Atmospheric and Environmental Optics, 2020, 15(5): 347-356.
[13]	. Pollution Characteristics of Secondary Water-Soluble Inorganic Ions in PM2.5 and Their Gaseous Precursors in Xiangtan, China [J]. Journal of Atmospheric and Environmental Optics, 2020, 15(4): 296-304.
[14]	. Investigation on Discrimination Characteristics of Atmospheric and Oceanic High-Spectral-Resolution Lidar [J]. Journal of Atmospheric and Environmental Optics, 2020, 15(1): 48-54.
[15]	PAN Nan, LU Xin-Wei. Pollution Characteristic of PAHs in Atmospheric Particles During the 2016 Spring Festival in Xi’an [J]. Journal of Atmospheric and Environmental Optics, 2018, 13(3): 208-217.