QUANTIFYING THE RELATIONSHIP BETWEEN NATURAL AND SOCIOECONOMIC FACTORS AND WITH FINE PARTICULATE MATTER (PM2.5) POLLUTION BY INTEGRATING REMOTE SENSING AND GEOSPATIAL BIG DATA

Abstract. PM_2.5 pollution is an environmental issue results from various natural and socioeconomic factors, frequently witnessed in the spring and winter across mainland China. However, the dominant influence of natural and socioeconomic factors within a city on PM_2.5 is not extensively studied yet. In this study, the Random Forest Regression (RFR) is utilized to quantify the relationships between PM_2.5 and potential factors within Wuhan city on a typical day turn from winter to spring. Technically, the 24-hour average PM_2.5 concentration in downtown area on February 17th 2017 are collected at 9 sites. In the meantime, we retrieve simultaneous aerosol depth optical depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS). The ground measured PM_2.5 and AOD are coupled for the retrieval of near-surface PM_2.5 concentration by Spatial-temporal CoKriging (STCK) with Normalized Vegetation Index (NDVI), Modified Normalized Water Index (MNDWI), Normalized Building Index (NDBI) from Landsat-8 and DEM from Shuttle Radar Topography Mission (SRTM). As the geospatial big data booms, the Internet-collected volunteered geographic information (VGI), representing the urban form and function, are integrating for the regression to obtain the spatial variables importance measures (VIMs) by RFR both in centre and sub-urban region of Wuhan. The results reveal that terrain characteristics and the density of industrial enterprises have obvious relationships with the accumulation of PM_2.5 while the density of roads also contributes to this.