AAAR 32nd Annual Conference
September 30 - October 4, 2013
Oregon Convention Center
Portland, Oregon, USA
Abstract View
Characterizing Urban Roadside Environments through Long-Term Monitoring: Particle Mass, NOx, Traffic and Signal Phasing
CHRISTINE M. KENDRICK, Linda A. George, Portland State University
Abstract Number: 435 Working Group: Urban Aerosols
Abstract Roadside monitoring of nitrogen dioxide (NO$_2) will begin in the United States over the next several years due to recent changes in the Clean Air Act. Urban roadside environments also have elevated particle mass, number concentrations and nitric oxide (NO) from traffic emissions. Daily commuter populations experience frequent short-term exposures of increased pollution as drivers, vehicle occupants, bicyclists, and pedestrians. Populations living, working, and attending school in close proximity to roadways experience long-term exposures. This presentation describes the unique opportunity of establishing a continuous, long-term roadside monitoring station at an intersection on SE Powell Boulevard in Portland, Oregon beginning in fall 2012. The station measures PM$_(10), PM$_(2.5), NOx and meteorological variables. Through a partnership with the City of Portland, traffic volumes and timing of adaptive signal phases are also collected. SE Powell is a major, urban arterial corridor with a high compositional mix of traffic (buses, freight, cars, pedestrians). The intersecting road, SE 26th, also includes bicycles. While higher average PM$_(10) and PM$_(2.5) concentrations were expected for winter compared to fall due to lower mixing height and other meteorological conditions, data collected to date shows the upper ranges of PM$_(10) and PM$_(2.5) to be three times higher in winter than fall, even at 10 and 15 minute aggregations. The continuous monitoring is showing the type of frequent, elevated, short-term exposure concentrations that exist at a roadside while also providing a starting point to document long-term, seasonal trends in the Pacific Northwest. Preliminary modeling shows some expected patterns such as PM$_(10) and PM$_(2.5) inversely correlated with wind speed. However, low wind speed days show particulate mass concentrations to follow diurnal traffic patterns and correlations with NO and NO$_2. Identification of such conditions alongside measured roadside concentrations help to piece out the meteorological and traffic contributions to the roadside environment.