Analysis of Spatial and Temporal Variability of PM10 Concentrations Using MODIS Aerosol Optical Thickness in the Pearl River Delta Region, China

Abstract

Characterizing spatial and temporal variations of PM pollution is critical for a thorough understanding of its formation, transport and accumulation in the atmosphere. In this study, Aerosol Optical Thickness (AOT) data retrieved from a Moderate Resolution Imaging Spectroradiometer (MODIS) were used to investigate the spatial and temporal variations of PM 10 (particles with aerodynamic diameters of less than 10 μm) pollution in the Pearl River Delta (PRD) region. Seasonal linear regression models between 1-km retrieved MODIS AOT data and ground PM 10 measurements were developed for the PRD region with meteorological corrections, and were subjected to a validation against observations from the regional air monitoring network in this region from 2006 to 2008, with an overall error of less than 50%. Consistent with ground observations, the estimated PM 10 concentrations from the regression models appeared to be highest in winter, lower in autumn and spring, and lowest in summer. A high PM 10 concentration band was detected over the inner part of the PRD region, where heavy industries and dense populations are located. The shape and concentration levels of this band exhibit significant seasonal variations, which shift with synoptic wind direction, indicating different source regions and their contributions to the PM 10 pollution in the PRD region. Several discrete "hot spots" were found in the southwest of the PRD region during spring and other seasons, where no ground measurements are available. The reasons for the formation of these hot spots are unclear, and further investigations are needed. Despite the limitations of this work, the results demonstrate the effectiveness of retrieving remote sensing data for characterizing regional aerosol pollution, together with ground measurements. The combination of satellite data and ground monitoring presented in this work can help in better understanding the sources, formation mechanisms and transport process of particulate matters on a regional scale.

Retrieval of High-Resolution Atmospheric Particulate Matter Concentrations from Satellite-Based Aerosol Optical Thickness over the Pearl River Delta Area, China

“…Aerosol optical thickness (AOT), derived from satellite observations, is a dimensionless measure of aerosol abundance and atmospheric extinction of radiance over the entire air column. Previous researches have revealed a strong positive relationship between satellite-based measurements of AOT and ground-based measurements of particulate matters smaller than 2.5 μm and 10 μm in aerodynamic diameters (PM2.5 and PM10, respectively) [15][16][17][18][19][20][21][22][23][24][25]. A summary of linear AOT/PM relationships, intercepts and correlation coefficients in several previous studies is shown in Table 1.…”

A Novel Robust Method for Solving CMB Receptor Model Based on Enhanced Sampling Monte Carlo Simulation

“…Atmospheric particulate matter (PM10 and PM2.5, with diameters less than 10 µm and 2.5 µm) is a mixture of solid or liquid particles suspended in the air, and is an important air pollutant in urban environments [1][2][3]. Epidemiological studies have shown that PM2.5/PM10 and an increase in respiratory symptoms, lung cancer mortality, and cardiovascular disease are closely related [4][5][6][7][8][9][10].…”

Evaluating Spatial and Temporal Variations of Aerosol Optical Depth and Biomass Burning over Southeast Asia Based on Satellite Data Products

“…Satellite sensors characterize key aerosol properties, such as the aerosol optical thickness or depth (AOD) and the aerosol effective radius, by measuring light extinction in an atmospheric column. Specifically, AOD data produced from the Moderate resolution Imaging Spectroradiometer (MODIS, on board the NASA platform Terra and Aqua) has been widely applied to examine the seasonal and regional variability of trace gases and aerosol particles (Alpert et al, 2012;Indira et al, 2013;Lalitaporn et al, 2013;Zheng et al, 2013;Mehta, 2015), and to evaluate its relation to air pollution (Zhu et al, 2011) or significant emission sources, such as burning activities (Koren et al, 2007;Bevan et al, 2009;Vadrevu et al, 2011). In view of the important impacts on global climate and regional air quality, the scientific community has developed a number of operational fire products based on the detection of thermal anomalies (i.e., active fires, abbreviated to AF) and land surface changes (i.e., burned areas, abbreviated to BA).…”