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 s...

Full description

Saved in:

Bibliographic Details
Published in:	The Science of the total environment 2008-02, Vol.390 (2), p.520-529
Main Authors:	Su, Jason G., Brauer, Michael, Ainslie, Bruce, Steyn, Douw, Larson, Timothy, Buzzelli, Michael
Format:	Article
Language:	eng
Subjects:	Air Air Pollutants - chemistry Biological and medical sciences Environmental pollutants toxicology Exposure analysis Geographic Information Systems Land use regression Medical sciences Meteorological Concepts Meteorology Models, Theoretical Nitric Oxide - chemistry Nitrogen Dioxide - chemistry Regression Analysis Source area analysis Space-Time Clustering Spatio-temporal model Toxicology Weather
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
Summary:	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.
ISSN:	0048-9697 1879-1026