An innovative land use regression model incorporating meteorology for exposure analysis.
The advent of spatial analysis and geographic information systems (GIS) has led to studies of chronic exposure and health effects based on the rationale that intra-urban variations in ambient air pollution concentrations are as great as inter-urban differences. Such studies typically rely on local spatial covariates (e.g., traffic, land use type) derived from circular areas (buffers) to predict concentrations/exposures at receptor sites, as a means of averaging the annual net effect of meteorological influences (i.e., wind speed, wind direction and insolation). This is the approach taken in the now popular land use regression (LUR) method. However spatial studies of chronic exposures and temporal studies of acute exposures have not been adequately integrated. This paper presents an innovative LUR method implemented in a GIS environment that reflects both temporal and spatial variability and considers the role of meteorology. The new source area LUR integrates wind speed, wind direction and cloud cover/insolation to estimate hourly nitric oxide (NO) and nitrogen dioxide (NO(2)) concentrations from land use types (i.e., road network, commercial land use) and these concentrations are then used as covariates to regress against NO and NO(2) measurements at various receptor sites across the Vancouver region and compared directly with estimates from a regular LUR. The results show that, when variability in seasonal concentration measurements is present, the source area LUR or SA-LUR model is a better option for concentration estimation.
Su
Jason G
JG
Department of Geography, University of British Columbia, Vancouver, British Columbia, Canada V6T 1Z2.
Brauer
Michael
M
Ainslie
Bruce
B
Steyn
Douw
D
Larson
Timothy
T
Buzzelli
Michael
M
D000393:Air Pollutants / Q000737:chemistry*; D040362:Geographic Information Systems; D008685:Meteorological Concepts; D008962:Models, Theoretical; D009569:Nitric Oxide / Q000737:chemistry*; D009585:Nitrogen Dioxide / Q000737:chemistry*; D012044:Regression Analysis; D013029:Space-Time Clustering; D014887:Weather
D000393:Air Pollutants; D009569:Nitric Oxide; D009585:Nitrogen Dioxide