Abstract
Integration of Landsat images and multisource data using spatial statistical analysis and geographical detector models can reveal the individual and interactive influences of anthropogenic activities and ecological factors on concentrations of atmospheric particulate matter less than 2.5 microns in diameter (PM2.5). This approach has been used in many studies to estimate biomass and forest disturbance patterns and to monitor carbon sinks. However, the approach has rarely been used to comprehensively analyze the individual and interactive influences of anthropogenic factors (e.g., population density, impervious surface percentage) and ecological factors (e.g., canopy density, stand age, and elevation) on PM2.5 concentrations. To do this, we used Landsat-8 images and meteorological data to retrieve quantitative data on the concentrations of particulates (PM2.5), then integrated a forest management planning inventory (FMPI), population density distribution data, meteorological data, and topographic data in a Geographic Information System database, and applied a spatial statistical analysis model to identify aggregated areas (hot spots and cold spots) of particulates in the urban area of Jinjiang city, China. A geographical detector model was used to analyze the individual and interactive influences of anthropogenic and ecological factors on PM2.5 concentrations. We found that particulate concentration hot spots are mainly distributed in urban centers and suburbs, while cold spots are mainly distributed in the suburbs and exurban region. Elevation was the dominant individual factor affecting PM2.5 concentrations, followed by dominant tree species and meteorological factors. A combination of human activities (e.g., population density, impervious surface percentage) and multiple ecological factors caused the dominant interactive effects, resulting in increased PM2.5 concentrations. Our study suggests that human activities and multiple ecological factors effect PM2.5 concentrations both individually and interactively. We conclude that in order to reveal the direct and indirect effects of human activities and multiple factors on PM2.5 concentrations in urban forests, quantification of fusion satellite data and spatial statistical methods should be conducted in urban areas.
Highlights
As industrialization and urbanization have intensified, so has the concentration of fine particulates in the atmosphere
The aggregated areas of PM2.5 identified by Getis-Ord Gi* showed that PM2.5 hot spots were concentrated in urban centers and suburbs (n = 1917 on 13 December 2014; n = 2283 on 29 December 2014; n = 1887 on 14 January 2015) and cold spots were mainly distributed in the suburbs and exurban regions (n = 1082 on 13 December 2014; n = 1212 on 29 December 2014; n = 1337 on 14 January 2015) (Figure 2)
The Getis-Ord Gi* statistics revealed that the spatial distributions of the population and PM2.5 concentrations were similar in the northwest and on the central coast, but were different in the south
Summary
As industrialization and urbanization have intensified, so has the concentration of fine particulates in the atmosphere. These particulates, known as PM2.5 (aerodynamic diameters < 2.5 μm [1]), originate from vehicle exhaust, coal-fired power plants, building construction (dust), and domestic heating (coal). Studies examining the degree to which urban forests trap particulates, combined with data from forest management planning inventories (FMPI), remote sensing imagery, population density, and impervious surface percentages, are rare. These types of environmental data could be extremely helpful for managing urban forests and improving air quality [6]. In one study of a 10 × 10 km grid in London with a tree coverage of 25%, the urban forest was estimated to remove 900 t of PM10 annually, which is the equivalent of preventing two deaths and two hospital admissions each year [10]
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