Integrated Modeling Of Air Quality And Health Impacts Of A Freight Transportation Corridor

With concerns about environmental issues, transportation studies have extensively evaluated emissions impacts associated with traffic operational strategies and transportation policies. However, the impact studies mainly relied on emissions impacts with a demand forecasting model. The planning model cannot capture individual vehicles’ interactions (i.e., lane changes or stop-and-go situation) or detailed traffic operations such as traffic signals. These limitations lead to under- or over-estimated emissions while evaluating several policies. Even though many studies utilized microscopic traffic models to better estimate emissions, the studies have not considered further steps such as air quality estimation and health impact studies.

This research develops an integrated framework for evaluating air quality and health impacts of transportation corridors using microscopic traffic model, micro-scale emissions model, non-steady state dispersion model, and health impact model. The main advantage of this approach is to better estimate air quality and health impacts from vehicle interactions and detailed traffic management strategies.

As a case study, we evaluate air quality and health impacts of several scenarios associated with major transportation corridors accessing the San Pedro Bay Ports (SPBP) complex, California. The corridors consist of 20 miles-long major freight freeways and arterials, as well as a line-haul rail along the Alameda corridor and several rail yards associated with the SPBP complex. For the scenarios, we consider a clean truck program, cleaner locomotives, and modal shifts compared to the 2005 baseline. All scenarios performed with the integrated framework have provided larger improvements of air quality and health impacts associated with transportation corridors than conventional frameworks using transportation planning models. However, the difference in air quality and health impacts from modal shift scenarios between clean
trucks and locomotives are minor.

As explanatory research, pollution response surface models are developed. The main feature of the pollution response surface model is to avoid the high computational cost of the microscopic traffic model, which makes it difficult to estimate traffic for multiple days needed for evaluating emissions and health impacts. A conceptual framework forestimating pollution response surface models is proposed. Using a toy network, response surfaces of NOX and PM are estimated.