Application of Land Use Regression Model to Estimate Long-term Fine Particulate Matter Concentrations in Beijing
Mr Li, Weifeng (Principal investigator)
Particulate Matter, PM2.5, Land use regression, Air quality, Beijing
Urban Studies and Planning,Environmental
Block Grant Earmarked for Research (104)
HKU Project Code
Seed Fund for Basic Research
Land Use Regression (LUR) Model was developed by Briggs et al., (1997). LUR models have been increasingly used in the past few years. It is derived from the GIS and to include land use factor and develop the measurement of air pollutants in the atmosphere. It is a model that can be used to estimate the concentration of air pollutants. Land-use regression methods have generally been applied successfully to model annual mean concentrations of NO2, NOx, PM2.5, the soot content of PM2.5 and VOCs in different settings, including European and North-American cities. The performance of the method in urban areas is typically better or equivalent to geo-statistical methods, such as kriging, and dispersion models. Studies on the health effects of long-term average exposure to outdoor air pollution have played an important role in recent health impact assessments. Exposure assessment for epidemiological studies of long-term exposure to ambient air pollution remains a difficult challenge because of substantial small-scale spatial variation. LUR model might provide new insights for assessing the long-term exposure to fine particles. This research aims to fill the gap by applying the LUR model to assess the average exposure to fine particles in Beijing, and to understand the impacts from natural vegetation, land use and transport factors. It makes use of the data collected from 35 air quality monitoring stations in Beijing City between March and November 2013 (with a possibility to extend to March 2014). Combined with the road networks, demographics, distribution of catering services and land use, the LUR model will be built to analyze the contributions of those factors to the concentrations of PM2.5.