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

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

The importance of long-range and local emission sources for mitigating the potential health impact of airborne particulate matter in Thailand

The negative health impacts of particulate matter (PM) air pollution are associated with long-term exposure, most commonly quantified by the annual average PM10 or PM2.5 concentrations. The Thai government has set air quality standards to protect public health based on these. This study explores the relative importance of local to regional emission sources in determining annual average of PM concentrations across Thailand using both measurement and modelling approaches. Firstly, a chemical climatology approach is used to explore the contribution of biomass burning episodes to the annual average PM10 concentrations between 2011 and 2015. In Northern Thailand, biomass-burning events result in short-term peak PM10 concentrations that influence annual PM10 concentrations and lead to exceedance of standards. The highest hourly PM10 concentrations occurred predominantly in March contributing 15-20% to the annual mean. In contrast, in Southern Thailand results show that biomass burning events can result in elevated hourly PM10 concentrations with a very small effect on annual PM10 concentrations (<5%). Secondly, different types of location in Bangkok and central Thailand were analysed to understand how these contribute to PM concentrations. There was greater variation in annual average PM10 concentrations at Bangkok roadside sites (26 to 63 µg m-3) compared to between at general sites in Bangkok (24 to 48 µg m-3). At sites exceeding the Thai national standard of 50 µg m-3, large local emission sources are important in causing exceedance of the annual PM10 standard. Lastly, to understand how future emissions will influence PM2.5 concentrations and human health, the study develops an emission inventory of all relevant pollutants for 2010 and future scenarios to estimate how these emissions will change up to 2030. The findings show that the expected increases in annual PM2.5 concentrations can be avoided if current government plans are fully implemented, but additional actions are needed as well