Develop a Performance Metric to Quantify the Inhalation of Traffic-Related Air Pollutants at both Mesoscale and Macroscale

Performance metrics to quantify traffic-related air pollutants and exposure disparities are critical for identifying disadvantaged population and developing clean air policies. Currently, several screening tools are available. However, they do not apportion traffic-related emissions nor do they reflect how much of the emissions actually reach and are inhaled by localized population. For example, when comparing two communities in Port of Long Beach area and Inland California, they may have similar level of traffic-related PM emissions in terms of kilogram per day. However, depending on the local micrometeorology, location of homes, schools, workplace, number of populations by age groups, the level of exposure could be far apart between the two communities. This study proposes to develop such a performance metric to quantify the inhalation of traffic-related air pollutants. The method will: (1) if traffic state not readily available, the study team will apply estimation methods to estimate traffic volume and fleet composition on freeways and arterials; (2) apply emission models or leverage measurement results to estimate emissions based on traffic states; (3) utilize location and activity information to model sensitive population’s distribution; (4) apply dispersion models to estimate pollutant concentration at selected receptors for a given time span; (5) assess the inhalation of traffic-related pollutants based on various factors, for example, age group characteristics, indoor filtration ratios, etc. The final metric can be aggregated or disaggregated at user-defined dimension. The team plans to apply two disadvantaged communities in Southern California as case studies.