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Nitrogen Dioxide Concentrations in Neighborhoods Adjacent to a Commercial Airport: A land use Regression Modeling Study
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Nitrogen Dioxide Concentrations in Neighborhoods Adjacent to a Commercial Airport: A land use Regression Modeling Study

Nitrogen Dioxide Concentrations in Neighborhoods Adjacent to a Commercial Airport: A land use Regression Modeling Study
News

Nitrogen Dioxide Concentrations in Neighborhoods Adjacent to a Commercial Airport: A land use Regression Modeling Study

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The work reported within this article demonstrates that there is a clear local variation in NO2 in the neighbourhoods that surround an urban airport. Utilising LUR modelling the authors were also able to demonstrate a strong influence of local traffic, except the smallest roads that predominate in residential areas, as well as proximity to the airport terminal.

Abstract

Background:
There is growing concern in communities surrounding airports regarding the contribution of various emission sources (such as aircraft and ground support equipment) to nearby ambient concentrations. We used extensive monitoring of nitrogen dioxide (NO2) in neighborhoods surrounding T.F. Green Airport in Warwick, RI, and land-use regression (LUR) modeling techniques to determine the impact of proximity to the airport and local traffic on these concentrations.

Methods: Palmes diffusion tube samplers were deployed along the airport’s fence line and within surrounding neighborhoods for one to two weeks. In total, 644 measurements were collected over three sampling campaigns (October 2007, March 2008 and June 2008) and each sampling location was geocoded. GIS-based variables were created as proxies for local traffic and airport activity. A forward stepwise regression methodology was employed to create general linear models (GLMs) of NO2 variability near the airport. The effect of local meteorology on associations with GIS-based variables was also explored.

Results: Higher concentrations of NO2 were seen near the airport terminal, entrance roads to the terminal, and near major roads, with qualitatively consistent spatial patterns between seasons. In our final multivariate model (R2 = 0.32), the local influences of highways and arterial/collector roads were statistically significant, as were local traffic density and distance to the airport terminal (all p < 0.001). Local meteorology did not significantly affect associations with principal GIS variables, and the regression model structure was robust to various model-building approaches.

Conclusion: Our study has shown that there are clear local variations in NO2 in the neighborhoods that surround an urban airport, which are spatially consistent across seasons. LUR modeling demonstrated a strong influence of local traffic, except the smallest roads that predominate in residential areas, as well as proximity to the airport terminal.  

The article is published online within the journal Environmental Health and is free to access.
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