A model supported by GIS for locating and quantifying PM_{2.5} emission originated from residential wood burning

Article type: Research Article

Authors: ZHOU, De-min^; | RADKE, John | TIAN, Yong-qian | XU, Jian-chun | MU, Lan

Affiliations: Northeast Institute of Geography and Agricultural Ecology, Chinese Academy Sciences, Changchun 130012, China | Geographical Information Science Center, 102 Wheeler Hall, University of California, Berkeley, CA 94720, USA | Center for Assessment and Monitoring of Forest and Environmental Resources, 151 Hilgard Hall, University of California, Berkeley, CA 94720, USA

Note: [] Corresponding author. E-mail: [email protected]

Abstract: A research method was presented for spatially quantifying and allocating the potential activity of a fine particle matter emission (PM_{2.5}), which originated from residential wood burning (RWB) in this study. Demographic, hypsographic, climatic and topographic data were compiled and processed within a geographic information system (GIS), and as independent variables put into a linear regression model for describing spatial distribution of the potential activity of residential wood burning as primary heating source. In order to improve the estimation, the classifications of urban, suburban and rural were redefined to meet the specifications of this application. Also, several definitions of forest accessibility were tested for estimation. The results suggested that the potential activity of RWB was mostly determined by elevation of a location, forest accessibility, urban/non-urban position, climatic conditions and several demographic variables. The linear regression model could explain approximately 86% of the variation of surveyed potential activity of RWB. The analysis results were validated by employing survey data collected mainly from a WebGIS based phone interview over the study area in central California. Based on lots free public GIS data, the model provided an easy and ideal tool for geographic researchers, environmental planners and administrators to understand where and how much PM_{2.5} emission from RWB was contributed to air quality. With this knowledge they could identify regions of concern, and better plan mitigation strategies to improve air quality. Furthermore, it allows for future adjustment on some parameters as the spatial analysis method is implemented in the different regions or various eco-social models.

Keywords: residential wood burning (RWB), PM[TeX:] _{2.5}, demographical characteristies, Geographic information system (GIS), stepwise linear regression

Journal: Journal of Environmental Sciences, vol. 17, no. 5, pp. 861-865, 2005