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

Abstract

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 (PM 2.5 ). 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 PM 2.5 concentrations. To do this, we used Landsat-8 images and meteorological data to retrieve quantitative data on the concentrations of particulates (PM 2.5 ), 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 PM 2.5 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 PM 2.5 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 PM 2.5 concentrations. Our study suggests that human activities and multiple ecological factors effect PM 2.5 concentrations both individually and interactively. We conclude that in order to reveal the direct and indirect effects of human activities and multiple factors on PM 2.5 concentrations in urban forests, quantification of fusion satellite data and spatial statistical methods should be conducted in urban areas.

PM2.5 Spatiotemporal Evolution and Drivers in the Yangtze River Delta between 2005 and 2015

“…Burgeoning industrialization and urbanization have brought sharp rises in industrial exhaust, construction dust and automobile exhaust. Fine-particulate air pollution has manifold effects on people's daily lives and health and on ecosystems, national heritage and the global atmosphere [1][2][3][4]. Air pollution causes an estimated 2.0-4.0 million premature deaths per year [5,6].…”

Integration of Remote Sensing and Social Sensing Data in a Deep Learning Framework for Hourly Urban PM2.5 Mapping

“…It is worth noting that there are many factors that are related to PM 2.5 , including meteorological influences, atmospheric boundary layer height, land use types, urban form, traffic conditions, human activities, and so on [34,35,36,37,38,39]. To mine the complex relationships between the various influencing factors and PM 2.5 , machine learning methods [11,13,14,15,16,17,18,19] have been widely used, especially the deep learning methods [27,40].…”

The Spatiotemporal Pattern of the Aerosol Optical Depth (AOD) on the Canopies of Various Forest Types in the Exurban National Park: A Case in Ningbo City, Eastern China

“…On the other hand, a wide range of meteorological digital model simulations do not reflect the characteristics of vegetation properties inside the forest park. Furthermore, the AOD spatial resolution of existing satellite remote sensing products are 500 m or coarser, making it difficult to fully describe forest property details [25]. e ground forest management planning inventory (FMPI) data required the remote sensing images with high spatial resolution, such as 30 m, to support the mechanism analysis.…”