Individual and Interactive Influences of Anthropogenic and Ecological Factors on Forest PM2.5 Concentrations at an Urban Scale

• Published in: Remote sensing (Basel, Switzerland) Vol. 10; no. 4; p. 521
• Main Authors: Yun, Guoliang, Zuo, Shudi, Dai, Shaoqing, Song, Xiaodong, Xu, Chengdong, Liao, Yilan, Zhao, Peiqiang, Chang, Weiyin, Chen, Qi, Li, Yaying, Tang, Jianfeng, Man, Wang, Ren, Yin
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.04.2018
• Subjects: aerosol retrieval; Anthropogenic factors; Atmospheric models; Carbon sinks; Data base management systems; Data processing; Density distribution; Dominant species; Ecological effects; Ecological monitoring; Elevation; Forest biomass; Forest management; Forests; Geographic information systems; geographical detector model; Human influences; Landsat; Landsat satellites; Management planning; Mathematical models; multiple ecological factors; multisource data fusion; Particulate emissions; Particulate matter; Particulates; Plant species; Population (statistical); Population density; Remote sensing; Satellite imagery; Spatial analysis; Spatial data; Statistical analysis; Statistical methods; Statistical models; Suburbs; Urban areas; Urban environments; Urban forests; urban scale; vegetation dust-retention
• DOI: 10.3390/rs10040521
• ISSN: 2072-4292

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.