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Using mobile monitoring to characterize roadway and aircraft contributions to ultrafine particle concentrations near a mid-sized airport
Abstract Ultrafine particles (UFP) have complex spatial and temporal patterns that can be difficult to characterize, especially in areas with multiple source types. In this study, we utilized mobile monitoring and statistical modeling techniques to determine the contributions of both roadways and aircraft to spatial and temporal patterns of UFP in the communities surrounding an airport. A mobile monitoring campaign was conducted in five residential areas surrounding T.F. Green International Airport (Warwick, RI, USA) for one week in both spring and summer of 2008. Monitoring equipment and geographical positioning system (GPS) instruments were carried following scripted walking routes created to provide broad spatial coverage while recognizing the complexities of simultaneous spatial and temporal heterogeneity. Autoregressive integrated moving average models (ARIMA) were used to predict UFP concentrations as a function of distance from roadway, landing and take-off (LTO) activity, and meteorology. We found that distance to the nearest Class 2 roadway (highways and connector roads) was inversely associated with UFP concentrations in all neighborhoods. Departures and arrivals on a major runway had a significant influence on UFP concentrations in a neighborhood proximate to the end of the runway, with a limited influence elsewhere. Spatial patterns of regression model residuals indicate that spatial heterogeneity was partially explained by traffic and LTO terms, but with evidence that other factors may be contributing to elevated UFP close to the airport grounds. Regression model estimates indicate that mean traffic contributions exceed mean LTO contributions, but LTO activity can dominate the contribution during some minutes. Our combination of monitoring and statistical modeling techniques demonstrated contributions from major surrounding runways and LTO activity to UFP concentrations near a mid-sized airport, providing a methodology for source attribution within a community with multiple distinct sources.
Highlights We used mobile monitoring to estimate source contributions to UFP near an airport. Autoregressive integrated moving average models included aircraft and traffic terms. UFP was elevated near major roadways, near the airport, and during LTO activity. Our methods provide insight in settings where emissions vary in space and time.
Using mobile monitoring to characterize roadway and aircraft contributions to ultrafine particle concentrations near a mid-sized airport
Abstract Ultrafine particles (UFP) have complex spatial and temporal patterns that can be difficult to characterize, especially in areas with multiple source types. In this study, we utilized mobile monitoring and statistical modeling techniques to determine the contributions of both roadways and aircraft to spatial and temporal patterns of UFP in the communities surrounding an airport. A mobile monitoring campaign was conducted in five residential areas surrounding T.F. Green International Airport (Warwick, RI, USA) for one week in both spring and summer of 2008. Monitoring equipment and geographical positioning system (GPS) instruments were carried following scripted walking routes created to provide broad spatial coverage while recognizing the complexities of simultaneous spatial and temporal heterogeneity. Autoregressive integrated moving average models (ARIMA) were used to predict UFP concentrations as a function of distance from roadway, landing and take-off (LTO) activity, and meteorology. We found that distance to the nearest Class 2 roadway (highways and connector roads) was inversely associated with UFP concentrations in all neighborhoods. Departures and arrivals on a major runway had a significant influence on UFP concentrations in a neighborhood proximate to the end of the runway, with a limited influence elsewhere. Spatial patterns of regression model residuals indicate that spatial heterogeneity was partially explained by traffic and LTO terms, but with evidence that other factors may be contributing to elevated UFP close to the airport grounds. Regression model estimates indicate that mean traffic contributions exceed mean LTO contributions, but LTO activity can dominate the contribution during some minutes. Our combination of monitoring and statistical modeling techniques demonstrated contributions from major surrounding runways and LTO activity to UFP concentrations near a mid-sized airport, providing a methodology for source attribution within a community with multiple distinct sources.
Highlights We used mobile monitoring to estimate source contributions to UFP near an airport. Autoregressive integrated moving average models included aircraft and traffic terms. UFP was elevated near major roadways, near the airport, and during LTO activity. Our methods provide insight in settings where emissions vary in space and time.
Using mobile monitoring to characterize roadway and aircraft contributions to ultrafine particle concentrations near a mid-sized airport
Hsu, Hsiao-Hsien (author) / Adamkiewicz, Gary (author) / Houseman, E. Andres (author) / Spengler, John D. (author) / Levy, Jonathan I. (author)
Atmospheric Environment ; 89 ; 688-695
2014-02-11
8 pages
Article (Journal)
Electronic Resource
English
Air quality , Aircraft , Ground measurements , Regression , Source attribution , Ultrafine particulate matter , ARIMA , autoregressive integrated moving average , GPS , global positioning systems , LTO , landing and take-off , PAH , polycyclic aromatic hydrocarbon , PM<inf>2.5</inf> , fine particulate matter , PVD , T.F. Green International Airport , UFP , ultrafine particles , WCPC , water-based condensation particle counter
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