Abstract
Abstract. PM2.5 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 PM2.5 is not extensively studied yet. In this study, the Random Forest Regression (RFR) is utilized to quantify the relationships between PM2.5 and potential factors within Wuhan city on a typical day turn from winter to spring. Technically, the 24-hour average PM2.5 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 PM2.5 and AOD are coupled for the retrieval of near-surface PM2.5 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 PM2.5 while the density of roads also contributes to this.
Highlights
The harm of fine particulate matter (PM2.5) to public health has drawn across China in recent years (Ma et al, 2016; Wang et al, 2015)
In order to obtain seam-less and continuous PM2.5 concentration at surface-level, many previous studues utilize the relationship between satellite observed atmospheric optical depth (AOD) and ground-based PM2.5 observations (Guo et al, 2014; Lin et al, 2015)
These methods retrieve surface-level PM2.5 concentration using AOD-PM2.5 relationship can be classified into two categories: empirically and semi-empirically observation based methods (Lin et al, 2015)
Summary
The harm of fine particulate matter (PM2.5) to public health has drawn across China in recent years (Ma et al, 2016; Wang et al, 2015). Socioeconomic factors are directly related to the emergence and spatial-temporal variations of PM2.5 and natural elements have influence on the diffusion and accumulation of PM2.5 (Fanizza et al, 2018; Kioumourtzoglou et al, 2016). In order to obtain seam-less and continuous PM2.5 concentration at surface-level, many previous studues utilize the relationship between satellite observed atmospheric optical depth (AOD) and ground-based PM2.5 observations (Guo et al, 2014; Lin et al, 2015). The empirically observation based methods rely on the statistical regressions of AOD-PM2.5 relationships. Conventional statistical and monitoring methods are not qualified enough to meet the rigorous requirements of data volume and temporal resolution for implementing spatial-temporal pattern analysis of socioeconomic activities within cities. Previous studies show that POI and PTM are related to
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