TY - JOUR
T1 - Nitrogen dioxide concentrations in neighborhoods adjacent to a commercial airport
T2 - A land use regression modeling study
AU - Adamkiewicz, Gary
AU - Hsu, Hsiao Hsien
AU - Vallarino, Jose
AU - Melly, Steven J.
AU - Spengler, John D.
AU - Levy, Jonathan I.
N1 - Funding Information:
This study was sponsored by the Federal Aviation Administration (FAA) through the Partnership for AiR Transportation Noise and Emissions Reduction (PARTNER) under Cooperative Agreement No. 07-C-NE-HU. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the FAA. We thank Roger Wayson, John MacDonald, George Noel, and Gregg Fleming for their contributions to meteorological sampling and analysis, and we thank Beatriz Vinas, Ceren Barlas, Melissa Ekstrand, Janette Heung and Coco Joly for their assistance in the field and laboratory.
PY - 2010
Y1 - 2010
N2 - 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 NO2variability near the airport. The effect of local meteorology on associations with GIS-based variables was also explored. Results. Higher concentrations of NO 2were 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 NO2in 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.
AB - 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 NO2variability near the airport. The effect of local meteorology on associations with GIS-based variables was also explored. Results. Higher concentrations of NO 2were 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 NO2in 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.
UR - http://www.scopus.com/inward/record.url?scp=78349292656&partnerID=8YFLogxK
U2 - 10.1186/1476-069X-9-73
DO - 10.1186/1476-069X-9-73
M3 - Article
C2 - 21083910
AN - SCOPUS:78349292656
SN - 1476-069X
VL - 9
JO - Environmental Health: A Global Access Science Source
JF - Environmental Health: A Global Access Science Source
IS - 1
M1 - 73
ER -