Abstract
Satellite remote sensing offers an effective approach to estimate indicators of air quality on a large scale. It is critically significant for air quality monitoring in areas experiencing rapid urbanization and consequently severe air pollution, like the Pearl River Delta (PRD) in China. This paper starts with examining ground observations of particulate matter (PM) and the relationship between PM10 (particles smaller than 10 μm) and aerosol optical thickness (AOT) by analyzing observations on the sampling sites in the PRD. A linear regression (R2 = 0.51) is carried out using MODIS-derived 500 m-resolution AOT and PM10 concentration from monitoring stations. Data of atmospheric boundary layer (ABL) height and relative humidity are used to make vertical and humidity corrections on AOT. Results after correction show higher correlations (R2 = 0.55) between extinction coefficient and PM10. However, coarse spatial resolution of meteorological data affects the smoothness of retrieved maps, which suggests high-resolution and accurate meteorological data are critical to increase retrieval accuracy of PM. Finally, the model provides the spatial distribution maps of instantaneous and yearly average PM10 over the PRD. It is proved that observed PM10 is more relevant to yearly mean AOT than instantaneous values.
Highlights
Particulate matters (PM), or aerosols, have direct and indirect radiative forcing effects on climate systems [1,2], and reduce visibility and induce respiratory diseases affecting air quality and human health [3]
Monitoring air quality at high spatial-temporal resolution is of great significance, especially for rapidly growing megacities which are facing severe air pollution owing to industrial development and population expansion
We explored the potential of retrieving the spatial distribution of particulate matters both daily and yearly from Moderate-resolution imaging spectroradiometer (MODIS)-derived aerosol optical thickness (AOT)
Summary
Particulate matters (PM), or aerosols, have direct and indirect radiative forcing effects on climate systems [1,2], and reduce visibility and induce respiratory diseases affecting air quality and human health [3]. Though well-calibrated, are spatially-limited and inadequate to evaluate time-space dynamics of air pollution and effects on human health. Delta (PRD) (Figure 1) is an area that boasts the fastest economic growth and population increase in the world. It has become an area of China which is facing the severest air pollution because of rapid industrial development, vegetation reduction, and heavy traffic pressure. By combining ground measurements and remote sensing observations, estimations of AOT-based PM concentrations, especially with high spatial resolution data, could offer spatially continuous mapping with high accuracy, which is useful for regional air quality monitoring
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