Estimation of Ultrahigh Resolution PM2.5 Mass Concentrations Based on Mie Scattering Theory by Using Landsat8 OLI Images over Pearl River Delta

Abstract

The aerosol optical depth (AOD), retrieved by satellites, has been widely used to estimate ground-level PM2.5 mass concentrations, due to its advantage of large-scale spatial continuity. However, it is difficult to obtain urban-scale pollution patterns from the coarse resolution retrieval results (e.g., 1 km, 3 km, or 10 km) at present, and little research has been conducted on PM2.5 mass concentration retrieval from high resolution remote sensing data. In this study, a physical model is proposed based on Mie scattering theory to evaluate the PM2.5 mass concentrations by using Landsat8 Operational Land Imager (OLI) images. First, the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) model (which can simulate the transmission process of solar radiation in the Earth-atmosphere system and calculate the radiance at the top of the atmosphere) is used to build a lookup table to retrieve the AOD of the coast and blue bands based on the improved deep blue (DB) method. Then, the Angstrom formula is used to obtain the AOD of the green and red bands. Second, the dry near-surface AOD of four bands (coast, blue, green, red) is obtained through vertical correction and humidity correction. Third, aerosol particles are divided into four types based on the standard radiation atmosphere (SRA) model, and the optical properties of different aerosol types are analyzed to derive the volume distribution of aerosol particles. Finally, the relationship between the dry near-surface AOD of each band and the volume distribution of four aerosol particles is correlated, based on Mie scattering theory, and a physical model is established between the AOD and PM2.5 mass concentrations. Then, the distribution of PM2.5 mass concentrations is obtained. The retrieval results show that the distribution of AOD and PM2.5 at the urban scale in detail. The AOD results show that a reasonable relationship with a correlation coefficient (R2) of 0.66 and root mean square error (RMSE) of 0.1037 between Landsat8 OLI AOD and MODO4 DB AOD at 550 nm. The PM2.5 retrieval results are compared with the PM2.5 values measured by ground monitoring stations. The RMSEs for a certain day in different years, including 2017, 2018, 2019, and 2020, are 11.9470 μg/m³, 11.9787 μg/m³, 7.4217 μg/m³, and 5.4723 μg/m³, respectively. The total RMSE is 10.0224 μg/m³. The ultrahigh resolution PM2.5 results can provide pollution details at the urban scale and support better decisions on urban atmospheric environmental governance.

PM2.5 Modeling and Historical Reconstruction over the Continental USA Utilizing GOES-16 AOD

“…Tree-based methods, deep networks, and the combination of traditional machine learning methods with ancillary data were found to be effective methods in PM 2.5 modeling [22,29,[31][32][33][34][35]. Statistical methods and physical or chemical theories based methods were also found capable in PM 2.5 modeling, such as the urban fine scale PM 2.5 estimation by using Landsat 8 images [36], Gaussian processes modeling in a Bayesian hierarchical setting [10], long-term estimation using remote sensing products and chemical transport models [37], and the estimating of PM 2.5 using a hybrid method that combines multiple sub-models [38].…”