UC Berkeley

Exposure to fine particulate matter (PM2.5) is a significant environmental health risk. Infrastructure systems related to energy processing and use, such as transportation and electric power systems, are major sources of primary PM2.5 and secondary PM2.5 precursor emissions in urban areas, resulting in personal exposures that lead to health impacts. There is a need for the improved design and management of infrastructure systems under environmental and human health considerations. This research has developed an analytical framework that captures the physical and chemical behavior of air pollutants emitted from urban infrastructure. The framework is employed to develop two exposure-based infrastructure optimization models used to identify infrastructure system design and management options and strategies that minimize human exposure to PM2.5.

The first model is an exposure-based traffic assignment (TA) model. It is developed with an accompanying analysis framework that is used to quantify primary and secondary PM2.5 exposure due to modeled on-road vehicle flow on a regional network at a high spatial resolution. The Chicago Metropolitan Area (CMA) transportation network is used to demonstrate the model’s decision-informing power. The model accounts for PM2.5 exposure due to emissions from light-duty vehicles, heavy-duty trucks, public transportation (bus and light-rail), and electricity generation units (EGUs) supplying electricity for electric vehicle (EV) charging and light-rail travel. The study first compares the spatially distributed exposure impacts due to traffic emissions of two TA optimization scenarios: a baseline user equilibrium with respect to travel time (UET) and a novel system optimal with respect to pollutant intake (SOI). The UET and SOI scenarios are developed through the use of (1) the TA model used for obtaining vehicle flow patterns and characteristics, including emissions, (2) a source-receptor (SR) matrix for PM2.5 developed through a reduced-complexity air quality model to quantify primary and secondary PM2.5 concentrations across the exposure domain, (3) spatial analysis for assessing exposure profiles at the census-tract level, and (4) a health impact model to quantify exposure damages. Baseline results show that transportation-sourced PM2.5 exposure damages are on the order of $3.7B – $8.3B/y. The SOI scenario yields an 8.2% total reduction in exposure damages, with the most impacted census tracts benefiting from 10% – 20% of reductions, but it leads to a 66% increase in travel time costs. Further reduction to PM2.5 exposure by the SOI is hindered by network constraints where travel demand in populous areas around the network must still be satisfied.

The model is then used to systematically quantify the mitigation potential of different transportation exposure reduction strategies. The strategies include a bi-objective optimization formulation that minimizes travel time and PM2.5 exposure concurrently, a higher penetration of alternative fuel vehicles, a higher public transportation share, particle filtration, and exposure-based truck routing. The overall reduction in exposure from these strategies is in the range of 0.8% – 40%, with the highest reductions obtained from adopting a highly-electrified vehicle fleet. However, the state of the electricity power mix dictates the magnitude of the reductions achieved from electrification. The current Illinois power mix, under high vehicle electrification scenarios, can lead to a 5.7% – 12% increase in overall exposure, mostly due to the large presence of coal-based generation. Personal exposure reduction through particle filtration in high exposure areas can also lead to considerable reductions in the near-term (up to 40%), but it does require major capital investments (up to $16B). Furthermore, using particle filtration simply masks the pollution problem without fully eliminating it. A high increase in public transportation use is required to generate significant exposure and congestion reductions due to the low baseline use of public transportation. Balancing travel time and exposure reduction goals can be achieved using a bi-objective optimization approach where policy makers can weigh each respective objective such that the highest damages of exposure are reduced without a major trade-off in travel time. Shifting flows away from high exposure impact roadways can be achieved using exposure-based road tolls. Exposure-based routing can also be applied specifically to trucks, which are a more readily controllable vehicle class from a policy standpoint due to the lower number of trips as well their commercial travel (as opposed to personal travel) status. Baseline results showed that, due to their high emissions, trucks contribute 38% of all PM2.5 intake while only comprising 11% of all trips. Thus, shifting truck trips to off-peak periods and applying exposure-based routing to them can reduce exposure (25%) and congestion (15%) concurrently. The collective adoption of some form of all strategies assessed can lead to exposure reductions upwards of 50%.

The second model is an exposure-based optimal power flow (OPF) model that accounts for PM2.5 exposure from 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 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. Baseline PM2.5 exposure damages are on the order of $1.8B – $2.0B/y. Other interventions assessed include adopting best-available EGU emission control technologies, higher renewable generation, and relocating high-polluting EGUs. Neglecting transmission constraints (such as line loading and voltage limits) and reactive power flow fails to account for 4% of exposure damages ($60M/y) and dispatch costs ($240M/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.