Environmental Justice and Systems Analysis for Air Quality Planning in the Port of Oakland in California.

Many frontline communities experience adverse health impacts from living in proximity to high-polluting industrial sources. Securing environmental justice requires, in part, a comprehensive set of quantitative indicators. We incorporate environmental justice and life-cycle thinking into air quality planning to assess fine particulate matter (PM2.5) exposure and monetized damages from operating and maintaining the Port of Oakland, a major multimodal marine port located in the historically marginalized West Oakland community in the San Francisco Bay Area. The exposure domain for the assessment is the entire San Francisco Bay Area, a home to more than 7.5 million people. Of the more than 14 sources included in the emissions inventory, emissions from large container ships, or ocean-going vessels (OGVs), dominate the PM2.5 intake, and supply chain sources (material production and delivery, fuel production) represent between 3.5% and 7.5% of annual intake. Exposure damages, which model the costs from excess mortalities resulting from exposure from the study's emission sources, range from USD 100 to 270 million per annum. Variations in damages are due to the use of different concentration-response relationships, hazard ratios, and Port resurfacing area assumptions. Racial and income-based exposure disparities are stark. The Black population and people within the lowest income quintile are 2.2 and 1.9 times more disproportionately exposed, respectively, to the Port's pollution sources relative to the general population. Mitigation efforts focused on electrifying in-port trucking operations yield modest reductions (3.5%) compared to strategies that prioritize emission reductions from OGVs and commercial harbor craft operations (8.7-55%). Our recommendations emphasize that a systems-based approach is critical for identifying all relevant emission sources and mitigation strategies for improving equity in civil infrastructure systems.

Exploring Regional Fine Particulate Matter (PM2.5) Exposure Reduction Pathways Using an Optimal Power Flow Model: The Case of the Illinois Power Grid.

This work develops an exposure-based optimal power flow model (OPF) that accounts for fine particulate matter (PM2.5) exposure from electricity generation unit (EGU) emissions. Advancing health-based dispatch models to an OPF with transmission constraints and reactive power flow is an essential development given its utility for short- and long-term planning by system operators. The model enables the assessment of the exposure mitigation potential and the feasibility of intervention strategies while still prioritizing system costs and network stability. A representation of the Illinois power grid is developed to demonstrate how the model can inform decision making. Three scenarios minimizing dispatch costs and/or exposure damages are simulated. Other interventions assessed include adopting best-available EGU emission control technologies, having higher renewable generation, and relocating high-polluting EGUs. Neglecting transmission constraints fails to account for 4% of exposure damages ($60 M/y) and dispatch costs ($240 M/y). Accounting for exposure in the OPF reduces damages by 70%, a reduction on the order of that achieved by high renewable integration. About 80% of all exposure is attributed to EGUs fulfilling only 25% of electricity demand. Siting these EGUs in low-exposure zones avoids 43% of all exposure. Operation and cost advantages inherent to each strategy beyond exposure reduction suggest their collective adoption for maximum benefits.

Exploring Regional Reduction Pathways for Human Exposure to Fine Particulate Matter (PM2.5) Using a Traffic Assignment Model.

An exposure-based traffic assignment (TA) model is used to quantify primary and secondary fine particulate matter (PM2.5) exposure from on-road vehicle flow on the Chicago Metropolitan Area regional network. PM2.5 exposure due to emissions from light-duty vehicles, heavy-duty trucks, public transportation, and electricity generation for electric vehicle charging and light-rail transportation is considered. The model uses travel demand data disaggregated by time-of-day period and vehicle user class to compare the exposure impacts of two TA optimization scenarios: a baseline user equilibrium with respect to travel time (UET) and a system optimal with respect to pollutant intake (SOI). Estimated baseline PM2.5 exposure damages are $3.7B-$8.3B/year. The SOI uses exposure-based vehicle rerouting to reduce total damages by 8.2%, with high-impacted populations benefiting from 10% to 20% reductions. However, the SOI's rerouting principle leads to a 66% increase in travel time. The model is then used to quantify the mitigation potential of different exposure reduction strategies, including a bi-objective optimization formulation that minimizes travel time and PM2.5 exposure concurrently, adoption of a cleaner vehicle fleet, higher public transportation use, particle filtration, and exposure-based truck routing. Exposure reductions range between 1% and 40%, but collective adoption of all strategies would lead to reductions upward of 50%.