A review of land-use regression models to assess spatial variation of outdoor air pollution

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• 作者：Gerard Hoek ; Rob Beelen ; Kees de Hoogh ; Danielle Vienneau ; John Gulliver ; Paul Fischer ; David Briggs
• 关键词：Land use regression ; Spatial variation ; NO₂ ; Particulate matter ; Air pollution
• 刊名：Atmospheric Environment
• 出版年：2008
• 出版时间：October 2008
• 年：2008
• 卷：42
• 期：33
• 页码：7561-7578
• 全文大小：298 K

文摘

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. Current approaches for assessing intra-urban air pollution contrasts include the use of exposure indicator variables, interpolation methods, dispersion models and land-use regression (LUR) models. LUR models have been increasingly used in the past few years. This paper provides a critical review of the different components of LUR models.

We identified 25 land-use regression studies. Land-use regression combines monitoring of air pollution at typically 20–100 locations, spread over the study area, and development of stochastic models using predictor variables usually obtained through geographic information systems (GIS). Monitoring is usually temporally limited: one to four surveys of typically one or two weeks duration. Significant predictor variables include various traffic representations, population density, land use, physical geography (e.g. altitude) and climate.

Land-use regression methods have generally been applied successfully to model annual mean concentrations of NO₂, NO_x, PM_2.5, the soot content of PM_2.5 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.

Further developments of the land-use regression method include more focus on developing models that can be transferred to other areas, inclusion of additional predictor variables such as wind direction or emission data and further exploration of focalsum methods. Models that include a spatial and a temporal component are of interest for (e.g. birth cohort) studies that need exposure variables on a finer temporal scale. There is a strong need for validation of LUR models with personal exposure monitoring.