Abstract
Satellite-derived aerosol optical depth (AOD) is widely used to estimate surface PM2.5 concentrations. Most AOD products have relatively low spatial resolutions (i.e., ≥1 km). Consequently, insufficient research exists on the relationship between high-resolution (i.e., <1 km) AOD and PM2.5 concentrations. Taking Shenzhen City, China as the study area, we derived AOD at the 16-m spatial resolution for the period 2015–2017 based on Gaofen-1 (GF-1) satellite images and the Dark Target (DT) algorithm. Then, we extracted AOD at spatial scales ranging from 40 m to 5000 m and applied vertical and humidity corrections. We analyzed the correlation between AOD and PM2.5 concentrations, and the impacts of AOD correction and spatial scale on the correlation. It was found that the DT-derived GF-1 AOD at different spatial scales had statistically significant correlations with surface PM2.5 concentrations, and the AOD corrections strengthened the correlations. The correlation coefficients (R) between AOD at different spatial scales and PM2.5 concentrations were 0.234–0.329 and 0.340–0.423 before and after AOD corrections, respectively. In spring, summer, autumn, and winter, PM2.5 concentrations had the best correlations with humidity-corrected AOD, uncorrected AOD, vertical and humidity-corrected AOD, and uncorrected AOD, respectively, indicating a distinct seasonal variation of the aerosol characteristics. At spatial scales of 1–5 km, AOD at finer spatial scales generally had higher correlations with PM2.5 concentrations. However, at spatial scales <1 km, the correlations fluctuated irregularly, which could be attributed to scale mismatches between AOD and PM2.5 measurements. Thus, 1 km appears to be the optimum spatial scale for DT-derived AOD to maximize the correlation with PM2.5 concentrations. It is also recommended to aggregate very high-resolution DT-derived AOD to an appropriate medium resolution (e.g., 1 km) before matching them with in situ PM2.5 measurements in regional air pollution studies.
Highlights
Fine particulate matter, or particulate matter with an aerodynamic equivalent diameter less than 2.5 μm (PM2.5), is a key air pollutant
The study mainly focused on the following two scientific questions: (1) How well correlated were the GF-1 aerosol optical depth (AOD) retrieved by the Dark Target (DT) algorithm and the in situ measured PM2.5 concentrations in Shenzhen City, and which correction
To better reveal the distribution of the data, kurtosis and skewness were computed in addition to the mean and standard deviation (S.D.)
Summary
Particulate matter with an aerodynamic equivalent diameter less than 2.5 μm (PM2.5), is a key air pollutant. The spatial distribution of the stations is nonuniform, and the spatial coverage is limited To address this limitation, aerosol optical depth (AOD) derived from satellite remote sensing is widely used to estimate regional surface PM2.5 concentrations and atmospheric pollution conditions [14,15]. Studies on the relationship between finer-resolution AOD data and PM2.5 concentrations can be beneficial for analyzing the relationship between sub-kilometer urban landscape pattern and air pollution [38], locating main PM2.5 emission sources [39,40], and building microscale PM2.5 estimation models in urban areas [41]. The study mainly focused on the following two scientific questions: (1) How well correlated were the GF-1 AOD retrieved by the DT algorithm and the in situ measured PM2.5 concentrations in Shenzhen City, and which correction.
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