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Your Environment. Your Health.

Assessing Computational Models for Traffic-related Pollution

John Durant, Ph.D.
Tufts University
R01ES015462, T32ES198543, P30ES005022, P30ES017885

A new study by NIEHS grantees found that computer-based models of near-roadway air pollution agree in some, but not all, meteorological and building scenarios, and that models need to be selected that align with an area’s characteristics. The new study is one of the most comprehensive to date comparing near-road models of particulate air pollution on an hourly timescale.

Exposure to ultrafine particles, a type of air pollution emitted in motor vehicle exhaust, may contribute to increased risks of respiratory and cardiovascular disease. Potential exposures to ultrafine particles are measured as particle number concentrations (PNCs). Computational models have been used to measure PNCs near roadways.

The researchers evaluated four freely available models used in research and regulatory applications. They studied levels of near-roadway air pollution in a residential neighborhood, an urban center, and near highways in and around Boston. They found differences among models in areas with complex roadway geometries and wind patterns. They also found that the most important parameters affecting the predictions were the relationships between wind direction and particle source and building locations. They also discovered that the models underpredicted, by a factor of three, the PNCs in areas less than 50 meters from the edge of the highway.

The team identified the model approach that most accurately predicted PNCs based on the type of study area. They included hybrid approaches that used multiple models. According to the authors, their methods and results are useful to hourly models of PNCs in similar urban areas near highways.

Citation: Patton AP, Milando C, Durant JL, Kumar P. 2017. Assessing the suitability of multiple dispersion and land use regression models for urban traffic-related ultrafine particles. Environ Sci Technol 51(1):384–392.