Abstract
Abstract. Long-term exposure to particulate matter (PM) with aerodynamic diameters < 10 (PM10) and 2.5 µm (PM2.5) has negative effects on human health. Although station-based PM monitoring has been conducted around the world, it is still challenging to provide spatially continuous PM information for vast areas at high spatial resolution. Satellite-derived aerosol information such as aerosol optical depth (AOD) has been frequently used to investigate ground-level PM concentrations. In this study, we combined multiple satellite-derived products including AOD with model-based meteorological parameters (i.e., dew-point temperature, wind speed, surface pressure, planetary boundary layer height, and relative humidity) and emission parameters (i.e., NO, NH3, SO2, primary organic aerosol (POA), and HCHO) to estimate surface PM concentrations over South Korea. Random forest (RF) machine learning was used to estimate both PM10 and PM2.5 concentrations with a total of 32 parameters for 2015–2016. The results show that the RF-based models produced good performance resulting in R2 values of 0.78 and 0.73 and root mean square errors (RMSEs) of 17.08 and 8.25 µg m−3 for PM10 and PM2.5, respectively. In particular, the proposed models successfully estimated high PM concentrations. AOD was identified as the most significant for estimating ground-level PM concentrations, followed by wind speed, solar radiation, and dew-point temperature. The use of aerosol information derived from a geostationary satellite sensor (i.e., Geostationary Ocean Color Imager, GOCI) resulted in slightly higher accuracy for estimating PM concentrations than that from a polar-orbiting sensor system (i.e., the Moderate Resolution Imaging Spectroradiometer, MODIS). The proposed RF models yielded better performance than the process-based approaches, particularly in improving on the underestimation of the process-based models (i.e., GEOS-Chem and the Community Multiscale Air Quality Modeling System, CMAQ).
Highlights
Epidemiological studies have consistently shown that negative human health effects including premature mortality can be caused by long-term exposure to atmospheric aerosols and particles, especially PM10 and PM2.5 (particulate matter (PM) with an aerodynamic diameter of less than 10 and 2.5 μm, respectively)
The objectives of this study are to (1) estimate ground-level PM10 and PM2.5 concentrations based on Geostationary Ocean Color Imager (GOCI) aerosol products and meteorological and emission model output data using Random forest (RF); (2) validate the estimated PM concentrations using in situ observation data; (3) compare the results to those when Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol products were used instead of GOCI products; and (4) evaluate the proposed remote-sensing-based models in comparison with the results from physical models such as GEOS-Chem and the Community Multiscale Air Quality Modeling System (CMAQ)
mean bias (MB) and mean error (ME) confirmed that the balanced samples improved the models estimating ground-level PM concentrations (Table 3; Fig. 3)
Summary
Epidemiological studies have consistently shown that negative human health effects including premature mortality can be caused by long-term exposure to atmospheric aerosols and particles, especially PM10 and PM2.5 (particulate matter (PM) with an aerodynamic diameter of less than 10 and 2.5 μm, respectively) The monitoring and assessment of exposure to PM10 and PM2.5 are crucial for effective management of public health risks. East Asia has been significantly industrialized and urbanized through its rapid economic growth. The industrialization and urbanization have resulted in adverse effects on air quality in this region and in neighboring countries (Koo et al, 2012)
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