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 PM10 (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 PM10 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 PM10 concentrations from the regression models appeared to be highest in winter, lower in autumn and spring, and lowest in summer. A high PM10 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 PM10 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.
Highlights
Particulate matter (PM) or aerosol is an important air pollutant, which is associated with adverse human health effects (Pope III et al, 2000; Sacks et al, 2011), deterioration in visibility (Cheung et al, 2005) and uncertain impacts on climate change (IPCC, 2007)
Seasonal linear regression models between 1-km retrieved Moderate Resolution Imaging Spectroradiometer (MODIS) Aerosol Optical Thickness (AOT) data and ground PM10 measurements were developed for the Pearl River Delta (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%
In the PRD region, seasonal variations were found for these species at different sites (Yue et al, 2010; Peng et al, 2011; Huang et al, 2012), these (a) Spring (b) Summer (c) Fall (d) Winter variations might lead to different light extinction coefficients of PM at different seasons and locations, which will further influence the image received by MODIS. Given that these validation sites were not included in the development of the regression models, the AOT-PM model established in this study showed an operational capability to predict the groundlevel PM10 concentrations in terms of the magnitude
Summary
Particulate matter (PM) or aerosol is an important air pollutant, which is associated with adverse human health effects (Pope III et al, 2000; Sacks et al, 2011), deterioration in visibility (Cheung et al, 2005) and uncertain impacts on climate change (IPCC, 2007). Liu et al (2005) estimated ground-level PM2.5 in the eastern United States based upon satellite remote sensing data using an empirical regression model with adjustments by meteorological conditions. Van Donkelaar et al (2010) applied satellite-based aerosol optical depth to estimate global long-term average PM2.5 concentrations They found that the World Health Organization Air Quality PM2.5 Interim Target-1 (35 μg/m3 annual average) was exceeded over central and eastern Asia for 30% and for 50% of the population. The findings of these studies illustrate the strong potential of satellite remote sensing used in ambient air quality monitoring as a supplemental approach to ground networks and air quality modeling
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