Ten-year global particulate mass concentration derived from space-borne CALIPSO lidar observations

Xiaojun Ma; Zhongwei Huang; Siqi Qi; Jianping Huang; Shuang Zhang; Qingqing Dong; Xin Wang

doi:10.1016/j.scitotenv.2020.137699

Ten-year global particulate mass concentration derived from space-borne CALIPSO lidar observations

Sci Total Environ. 2020 Jun 15:721:137699. doi: 10.1016/j.scitotenv.2020.137699. Epub 2020 Mar 5.

Authors

Xiaojun Ma¹, Zhongwei Huang², Siqi Qi¹, Jianping Huang³, Shuang Zhang¹, Qingqing Dong¹, Xin Wang¹

Affiliations

¹ Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China.
² Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China; Collaborative Innovation Center for West Ecological Safety (CIWES), Lanzhou University, Lanzhou 730000, China. Electronic address: huangzhongwei@lzu.edu.cn.
³ Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China; Collaborative Innovation Center for West Ecological Safety (CIWES), Lanzhou University, Lanzhou 730000, China.

PMID: 32179344
DOI: 10.1016/j.scitotenv.2020.137699

Abstract

Passive remote sensing has been widely used in recent decades to obtain global particulate matter (PM) mass concentration at daytime and under cloud-free condition. In this study, a retrieval method was developed for providing PM (PM₁₀ and PM_2.5) mass concentration both at daytime and nighttime using the latest data version (V4.10) from space-borne Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) lidar measurements. The advantage of the method is that PM₁₀ & PM_2.5 mass concentrations were obtained for seven aerosol types respectively base on active remote sensing observation at daytime and nighttime, even under cloudy condition. The results show that satellite-based PM mass concentrations are in good agreement with in-situ observations from 1602 ground monitoring sites throughout the world. Moreover, global distributions of PM₁₀ and PM_2.5 mass concentration during 2007-2016 were investigated, showing that for Beijing the annual mean PM_2.5 mass concentration at nighttime is 11.31% less than those at daytime, however for London is 36.62%. It is suggested that diurnal variations in PM_2.5 mass concentration are closely related to human activities. This work provides a reliable high-resolution database for long-term particulate mass concentrations on the global scale, which is of importance to evaluate aerosol impacts on climate, environment as well as ecosystem.

Keywords: Active remote sensing; Aerosol; CALIPSO; Diurnal variations; Lidar; Mass concentration.