Application of Land Use Regression Model to Estimate Long-term Fine Particulate Matter Concentrations in Beijing

Dr Li, Weifeng   (Principal Investigator (PI))

12

2014-05-01

2015-04-30

35800

Application of Land Use Regression Model to Estimate Long-term Fine Particulate Matter Concentrations in Beijing

Air quality, Beijing, Land use regression, Particulate Matter, PM2.5

Urban Studies and Planning,Environmental

201311159209

Seed Fund for PI Research – Basic Research

2013

Completed

While Beijing is not alone when it comes to smoke-filled skies, this city of more than 20 million people has come to symbolize the environmental cost of China's break-neck economic growth. The air pollution reading in Beijing has frequently exceeded the scale's maximum reading this year when the government decided to publicly reveal real-time PM2.5 data for the first time. It has been a heated topic for discussion which also lead to health concerns greatly. In fact, Particulate Matter up to 2.5 micrometers in size (PM2.5) and NOx are the main air pollutants in Beijing (Zeng et al., 2010). Health studies have shown a significant association between exposure to fine particles and premature death from heart or lung disease. The sources of PM2.5 could be from burning of coal and biofuel, dust from roads, exhausted gases from vehicles and industrialization. Therefore, the first assessment of PM2.5 data in the capital of China, a symbol of smog city, may give a valuable contribution for legislative and environmental purposes. 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.