RESUMEN
An Environmental Justice (EJ) analysis was carried out using full Chemical Transport Models (CTMs) over Los Angeles, California, to determine how the combination of domain size and spatial resolution affects predicted air pollution disparities in present day and future simulations when data support from measurements is not available. One set of simulations used the Weather Research and Forecasting (WRF) model coupled with Chemistry (WRF/Chem) with spatial resolution ranging from 250 m to 36 km, comparable to census tract sizes, over domains ranging in size from 320 km2 to 10,000 km2. A second set of simulations used the UCD/CIT CTM with spatial resolution ranging from 4 km to 24 km over domains ranging in size from 98,000 km2 to 1,000,000 km2. Overall WRF/Chem model accuracy improved approximately 9% as spatial resolution increased from 4 km to 250 m in present-day simulations, with similar results expected for future simulations. Exposure disparity results are consistent with previous findings: the average Non-Hispanic White person in the study domain experiences PM2.5 mass concentrations 6-14% lower than the average resident, while the average Black and African American person experiences PM2.5 mass concentrations that are 3-22% higher than the average resident. Predicted exposure disparities were a function of the model configuration. Increasing the spatial resolution finer than approximately 1 km produced diminishing returns because the increased spatial resolution came at the expense of reduced domain size in order to maintain reasonable computational burden. Increasing domain size to capture regional trends, such as wealthier populations living in coastal areas, identified larger exposure disparities but the benefits were limited. CTM configurations that use spatial resolution/domain size of 1 km/103 km2 and 4 km/104 km2 over Los Angeles can detect a 0.5 µg m-3 exposure difference with statistical power greater than 90%. These configurations represent a balanced approach between statistical power, sensitivity across socio-economic groups, and computational burden when predicting current and future air pollution exposure disparities in Los Angeles.
RESUMEN
Exposure to diesel-related air pollution, which includes black carbon (BC) as a major component of the particulate matter emitted in engine exhaust, is a known human health hazard. The resulting health burden falls heavily on vulnerable communities located close to major sources including highways, rail yards, and ports. Determination of source contributions to the overall pollution burden is challenging due to collinearity in the exhaust composition profiles for relevant sources including heavy-duty diesel trucks, railroad locomotives, cargo-handling equipment, and marine engines. Additionally, the impact of each source depends not just on the magnitude of emissions but on its location relative to receptors as well as on meteorology. We modeled source-resolved BC concentrations in West Oakland, California, at a high (150 m) spatial resolution using the Weather Research and Forecasting model. The ability of the model to predict hourly and 24 h average BC concentrations is evaluated for a 100-day period in summer 2017 when BC was measured at 100 sites within the community. We find that a community monitoring site is representative of population-weighted average BC exposure in the community. Major contributing sources to BC in West Oakland include on-road diesel trucks (44 ± 5%) and three off-road diesel sources: ocean-going vessels (19 ± 1%), railroad locomotives (16 ± 2%), and harbor craft such as tugboats and ferries (11 ± 1%).
