The observation‐based relationships between PM_2.5 and AOD over China

Abstract

This is the first investigation of the generalized linear regressions of PM 2.5 and aerosol optical depth (AOD) with the Campaign on atmospheric Aerosol Research-China network over the large high-concentration aerosol region during the period from 2012 to 2013. The map of the PM 2.5 and AOD levels showed large spatial differences in the aerosol concentrations and aerosol optical properties over China. The ranges of the annual mean PM 2.5 and AOD were 10-117 μg/m 3 and 0.12-1.11 from the clean regions to seriously polluted regions, from the almost "arctic" and the Tibetan Plateau to tropical environments. There were significant spatial agreements and correlations between the PM 2.5 and AOD. However, the linear regression functions (PM 2.5 = A*AOD + B) exhibited large differences in different regions and seasons. The slopes (A) were from 13 to 90, the intercepts (B) were from 0.8 to 33.3, and the correlation coefficients (R 2 ) ranged from 0.06 to 0.75. The slopes (A) were much higher in the north (41-99) than in the south (13-64) because the extinction efficiency of hygroscopic aerosol was rapidly increasing with the increasing humidity from the dry north to the humid south. Meanwhile, the intercepts (B) were generally lower, and the correlation coefficients (R 2 ) were much higher in the dry north than in the humid south. There was high consistency of AOD versus PM 2.5 for all sites in three ranges of the atmospheric column precipitable water vapor (PWV). The segmented linear regression functions were y = 84.66x + 9.85 (PWV < 1.0), y = 69.47x + 11.87 (1.0 < PWV < 2.5), and y = 52.37x + 8.59 (PWV > 2.5). The correlation coefficients (R 2 ) were high from 0.64 to 0.70 across China. Key Points:• This is the first investigation of the relationship between PM2.5 and AOD with a network in China • The map of the PM2.5 and AOD levels showed large spatial differences over China • The segmented linear regression functions applied across China in three PWV rangesCorrespondence to:

Impact and Suggestion of Column-to-Surface Vertical Correction Scheme on the Relationship between Satellite AOD and Ground-Level PM2.5 in China

“…In addition, similar Pearson correlation coefficients (R) of approximately 0.40 were observed in Central China, and the analogous values of the slopes and intercepts were approximately 32.9 and 45.9, respectively, which attributes to similarities in aerosol components and analogous weather conditions. In summary, the relationship between the original satellite-derived AOD and ground-measured PM2.5 varied significantly throughout China, based on multifarious ecosystems [29]. …”

“…Nevertheless, such research has not been conducted in large areas in China. Since there exist variable terrain conditions from the Tibetan plateau and northwest deserts to the eastern plains [29], and the complex climate controlled by seasonal monsoon system and multi-directional jet stream in China [30], combined with multiple aerosol sources from anthropogenic emissions and natural dust aerosol, aerosol concentrations vary dramatically in both horizontal and vertical dimensions [31,32]. Methods for conducting vertical correction on the satellite column AOD for better correspondence with ground-level PM 2.5 is an urgent issue to solve.…”

Hourly PM2.5 Estimates from a Geostationary Satellite Based on an Ensemble Learning Algorithm and Their Spatiotemporal Patterns over Central East China

“…About 87% and 70% of the biases fall in the range of −20 to 20 µg m −3 and −10 to 10 µg m −3 , respectively, with a mean value of 0.4 µg m −3 ( Figure S3). Many potential factors influence the relationship between PM 2.5 and AOD and thus the model performance, including the number of samples, aerosol chemical composition, aerosol particle size, and weather conditions [42]. The aerosol composition influences the aerosol swelling, which increases the AOD but does not influence the PM 2.5 concentration.…”

“…The CV R 2 ranges from 0.79 to 0.89 with relatively high values between 1300 and 1500 LT and low values in the early morning. As previously discussed, many factors influence the model performance, including the number of samples, aerosol chemical composition, aerosol particle size, and weather conditions [42]. The larger solar zenith angle in the early morning may reduce the accuracy of Himawari-8 aerosol retrievals, which possibly weakens the model performance.…”

Investigating the Transport Mechanism of PM2.5 Pollution during January 2014 in Wuhan, Central China

“…In China, rapid industrialization and urbanization have given rise to frequent and persistent haze pollution across pop-ulated and prosperous regions (Xin et al, 2016). The haze pollution found in China is the result of several factors such as industrial and urban pollutant sources, diverse topographies and weather patterns (Wang et al, 2018b), and complex regional transport of pollutants (Wang et al, 2018a;Chen et al, 2019).…”