Preprints
https://doi.org/10.5194/acp-2021-143
https://doi.org/10.5194/acp-2021-143

  18 Mar 2021

18 Mar 2021

Review status: this preprint is currently under review for the journal ACP.

The spatiotemporal relationship between PM2.5 and AOD in China: Influencing factors and Implications for satellite PM2.5 estimations by MAIAC AOD

Qingqing He1,2, Mengya Wang3, and Steve Hung Lam Yim3,4,1 Qingqing He et al.
  • 1Institute of Environment, Energy and Sustainability, The Chinese University of Hong Kong, Hong Kong, China
  • 2School of Resource and Environmental Engineering, Wuhan University of Technology, Wuhan, 430070, China
  • 3Department of Geography and Resource Management, The Chinese University of Hong Kong, Hong Kong, China
  • 4Stanley Ho Big Data Decision Analytics Research Centre, The Chinese University of Hong Kong, Hong Kong, China

Abstract. Satellite aerosol retrievals have been a popular alternative to monitoring surface PM2.5 concentration due to its extensive spatial and temporal coverage. Satellite-derived PM2.5 estimation strongly relies on an accurate representation of the relationship between ground PM2.5 and satellite aerosol optical depth (AOD). Due to the limitation of satellite AOD data, most studies examined the relationship at a coarse-resolution (i.e., ≥ 10 km) scale; more effort is still needed to better understand the relationship between in-situ PM2.5 and AOD at finer spatial scales. While PM2.5 and AOD could have obvious temporal variations, few studies have examined the diurnal variation in their relationship. Considerable uncertainty therefore still exists in satellite-derived PM2.5 estimation due to these research gaps. Taking advantage of the newly released fine-spatial-resolution satellite AOD data derived by the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm and real-time ground aerosol and PM2.5 measurements, this study explicitly explored the relationship between PM2.5 and AOD and its plausible impact factors including meteorological parameters and topography in mainland China during 2019, at various spatial and temporal scales. Coefficient of variation, Pearson correlation coefficient and slope of linear regression model were used. Spatially, stronger correlations mainly occurred in northern and eastern China and linear slope in northern inland regions was on average larger than those in other areas. Temporally, the PM2.5-AOD correlation peaked in the noon and afternoon and reached the maximum in winter. Simultaneously considering relative humidity (RH) and planetary boundary layer height (PBLH) in the relationship can improve the correlation but the effect of RH and PBLH on the correlation varied spatially and temporally, both in strength and direction. In addition, the largest correlation occurred at 400–600 m primarily in basin terrain such as Sichuan Basin, Shanxi-Shaanxi Basins and Junggar Basin. MAIAC 1-km AOD can better represent the ground-level fine particulate matter in most domains with exceptions such as in very high terrain i.e. Tibetan Plateau and north-central China i.e. Qinghai and Gansu. Findings in this study have useful implications for satellite-based PM2.5 monitoring and will further inform the understanding of the aerosol variation and PM2.5 pollution status in mainland China.

Qingqing He et al.

Status: open (until 15 May 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on acp-2021-143', Anonymous Referee #1, 07 Apr 2021 reply

Qingqing He et al.

Qingqing He et al.

Viewed

Total article views: 190 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
135 52 3 190 15 1 3
  • HTML: 135
  • PDF: 52
  • XML: 3
  • Total: 190
  • Supplement: 15
  • BibTeX: 1
  • EndNote: 3
Views and downloads (calculated since 18 Mar 2021)
Cumulative views and downloads (calculated since 18 Mar 2021)

Viewed (geographical distribution)

Total article views: 266 (including HTML, PDF, and XML) Thereof 266 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 15 Apr 2021
Download
Altmetrics