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Improving the roadside environment through integrating air quality and traffic-related data.
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  • Abstract:
    Urban arterial corridors are landscapes that give rise to short and long-term

    exposures to transportation-related pollution. With high traffic volumes, congestion, and

    a wide mix of road users and land uses at the road edge, urban arterial environments are

    important targets for improved exposure assessment to traffic-related pollution. Applying

    transportation management strategies to reduce emissions along arterial corridors could

    be enhanced if the ability to quantify and evaluate such actions was improved. However,

    arterial roadsides are under-sampled in terms of air pollution measurements in the United

    States and using observational data to assess such effects has many challenges such as

    lack of control sites for comparisons and temporal autocorrelation. The availability of

    traffic-related data is also typically limited in air monitoring and health studies. The work

    presented here uses unique long-term roadside air quality monitoring collected at the

    intersection of an urban arterial in Portland, OR to characterize the roadside atmospheric

    environment. This air quality dataset is then integrated with traffic-related data to assess

    various methods for improving exposure assessment and the roadside environment.

    Roadside nitric oxide (NO), nitrogen dioxide (NO2), and particle number

    concentration (PNC) measurements all demonstrated a relationship with local traffic

    volumes. Seasonal and diurnal characterizations show that roadside PM2.5 (mass)

    measurements do not have a relationship with local traffic volumes, providing evidence

    that PM2.5 mass is more tied to regional sources and meteorological conditions. The

    relationship of roadside NO and NO2 with traffic volumes was assessed over short and

    long-term aggregations to assess the reliability of a commonly employed method of using traffic volumes as a proxy for traffic-related exposure. This method was shown to be

    insufficient for shorter-time scales. Comparisons with annual aggregations validate the

    use of traffic volumes to estimate annual exposure concentrations, demonstrating this

    method can capture chronic but not acute exposure. As epidemiology and exposure

    assessment aims to target health impacts and pollutant levels encountered by pedestrians,

    cyclists, and those waiting for transit, these results show when traffic volumes alone can

    be a reliable proxy for exposure and when this approach is not warranted. Next, it is demonstrated that a change in traffic flow and change in emissions can

    be measured through roadside pollutant concentrations suggesting roadside pollution can

    be affected by traffic signal timing. The effect of a reduced maximum traffic signal cycle

    length on measurements of degree of saturation (DS), NO, and NO2 were evaluated for

    the peak traffic periods in two case studies at the study intersection. In order to reduce

    bias from covariates and assess the effect due to the change in cycle length only, a

    matched sampling method based on propensity scores was used to compare treatment

    periods (reduced cycle length) with control periods (no change in cycle length).

    Significant increases in DS values of 2-8% were found along with significant increases of

    5-8ppb NO and 4-5ppb NO2 across three peak periods in both case studies. Without

    matched sampling to address the challenges of observational data, the small DS and NOx

    changes for the study intersection would have been masked and matched sampling is

    shown to be a helpful tool for future urban air quality empirical investigations.

    Dispersion modeling evaluations showed the California Line Source Dispersion

    Model with Queuing and Hotspot Calculations (CAL3QHCR), an approved regulatory model to assess the impacts of transportation projects on PM2.5, performed both poor and

    well when predictions were compared with PM2.5 observations depending on season.

    Varying levels of detail in emissions, traffic signal, and traffic volume data for model

    inputs, assessed using three model scenarios, did not affect model performance for the

    study intersection. Model performance is heavily dependent on background

    concentrations and meteorology. It was also demonstrated that CAL3QHC can be used in

    combination with roadside PNC measurements to back calculate PNC emission factors

    for a mixed fleet and major arterial roadway in the U.S. The integration of roadside air quality and traffic-related data made it possible to

    perform unique empirical evaluations of exposure assessment methods and dispersion

    modeling methods for roadside environments. This data integration was used for the

    assessment of the relationship between roadside pollutants and a change in a traffic signal

    setting, a commonly employed method for transportation management and emissions

    mitigation, but rarely evaluated outside of simulation and emissions modeling. Results

    and methods derived from this work are being used to implement a second roadside air

    quality station, to design a city-wide integrated network of air quality, meteorological,

    and traffic data including additional spatially resolved measurements with feedback loops

    for improved data quality and data usefulness. Results and methods are also being used to

    design future evaluations of transportation projects such as freight priority signaling,

    improved transit signal priority, and to understand the air quality impacts of changes in

    fleet composition such as an increase in electric vehicles.

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