Real-Time Estimation of Pollution Emissions and Dispersion from Highway Traffic

Traffic-related air pollution is a serious problem with significant health impacts in both urban and suburban environments. Despite an increased realization of the negative impacts of air pollution, assessing individuals’ exposure to traffic-related air pollution remains a challenge. Obtaining high resolution estimates are difficult due to the spatial and temporal variability of emissions, the dependence on local atmospheric conditions and the lack of monitoring infrastructure. This presents a significant hurdle to identifying pollution concentration hot spots and understanding the emission sources responsible for these hot spots, which in turn makes it difficult to reduce the uncertainty of health risk estimates for communities and to develop policies that mitigate these risks. We present a novel air pollution estimation method that models the highway traffic state, highway traffic-induced air pollution emissions and pollution dispersion, and describe a prototype implementation for the San Francisco Bay Area. Our model is based on the availability of real-time traffic estimates on highways, which we obtain using a traffic dynamics model and an estimation algorithm that augments real-time data from both fixed sensors and probe vehicles. These traffic estimates combined with local weather conditions are used as inputs to an emission model that estimates pollutant levels for multiple gases and particulates in real-time. Finally, a dispersion model is used to assess the spread of these pollutants away from the highway source. Maps generated using the output of the dispersion model allow users to easily analyze the evolution of individual pollutants over time, and provides transportation engineers and public health officials with valuable information that can be used to minimize health risks.