The spatiotemporal relationship between PM<sub>2.5</sub> and AOD in China: Influencing factors and Implications for satellite PM<sub>2.5</sub> estimations by MAIAC AOD

Abstract

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.

Changing PM2.5 and related meteorology over India from 1950–2014: a new perspective from a chemistry-climate model ensemble

“…However, given the magnitude of SOA concentrations relative to the total PM 2.5 sum, we do not expect SOA seasonality to substantially influence the alignment of the component sum with observations. Although we do not expect AOD (column light extinction by all aerosols, both fine and coarse) and surface PM 2.5 seasonal cycles to match since their relationship varies with aerosol composition and meteorology [56,57], AOD retrieved from satellite offers another observational test of the CESM2-WACCM6 representation of aerosols. The second row of figure 1 shows average monthly AOD from 2003 to 2014 in the model (red line for ensemble average and shading for the range) and from the MODIS instruments aboard the Aqua (black line, morning overpass) and Terra (green line, afternoon overpass) satellites.…”

A Pan-European, High-Resolution, Daily Total, Fine-Mode and Coarse-Mode Aerosol Optical Depth dataset based on Quantile Machine Learning

“…Over 85% of satellite AOD measurements are missing globally (Kahn et al, 2009;Chen et al, 2019b), mainly due to cloudiness, surface reflectivity and low sun angle at high-latitudes (Wei et al, 2018;Gupta et al, 2016;Chen et al, 2019b). Secondly, the accuracy of satellite AOD is still subject to various factors, like instrument calibration, cloud contamination, and climate or geographic conditions (He et al, 2021). These uncertainties may lead that the correlation between satellite AOD and PM levels varies a lot in different locations, and the correlation in some regions is always lower.…”