A satellite-derived, ground-measurement-independent monthly PM2.5 mass concentration dataset over China during 2000–2015. Issue 4 (2nd October 2022)

• Record Type: Journal Article
• Authors: Zhang, Ying; Li, Zhengqiang; Wei, Yuanyuan; Peng, Zongren
• Is Part Of: Big earth data. Volume 6:Issue 4(2022)
• Page Start: 633
• Page End: 649
• Publication Date: 2022-10-02
• Subjects: PM2.5 -- data set -- satellite-derived -- China -- monthly; Earth sciences -- Periodicals; Earth sciences -- Research -- Periodicals; Geographic information systems Periodicals; 550
• DOI: 10.1080/20964471.2021.1918908
• Languages: English
• ISSNs: 2096-4471

Abstract

ABSTRACT: Following the accelerated development of urbanization and industrialization, atmospheric particulate matter has become a significant threat to public health globally. Environmental health studies usually use the mass concentration of fine particles (PM2.5 ) as a base data to predict the health risks of particulate exposure. However, PM2.5 data from ground monitoring stations in China has not been provided until January 2013 by the Ministry of Environmental Protection of China. Hence, an alternative dataset of PM2.5 spatiotemporal distributions extending to years earlier than 2013 is urgently needed, which is of great significance to atmospheric environment assessment and pollution prevention and control. Atmospheric aerosol products by the moderate-resolution imaging spectroradiometer (MODIS) have been released since 2000, which provides the possibility to reconstruct historical PM2.5 . However, most current methods do not have the ability to estimate PM2.5 mass concentration independently of ground observations. The PM2.5 mass concentration data set produced by PM2.5 remote sensing (PMRS) model based on physical processes does not depend on the ground observations, and also is not affected by the uncertainty of model emission sources or the completeness of chemical reaction mechanism. These ensure that the point-by-point validation for PM2.5 mass concentration data is more convincing, and the dataset can also be further used for model assimilation and artificial intelligence training to improve their predictions. In this study, we calculate the monthly PM2.5 mass concentration near the ground over land of China using aerosol inversion products (aerosol optical depth and fine-mode fraction) of MODIS and meteorological data (boundary layer height & relative humidity) provided by the Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2) data set. The results show that, in China, 6 pollution centers mainly concentrated in the central and eastern regions. The highest PM2.5 mass concentration occurred in winter, whereas the pollution range was larger in summer. There are 63.4% of validation sites with biases within ±20 μg m −3, and the expected error is as ±(15 μg m −3 + 30%) enveloped by the monthly mean PM2.5 mass concentrations. The monthly PM2.5 is stored as NETCDF format, with a spatial resolution of 1°×1°. The published data is available in http://www.dx.doi.org/10.11922/sciencedb.j00076.00061 .