High-Resolution Modeling of Black Carbon in West Oakland

Concentrations of directly-emitted air pollutants vary on fine temporal and spatial scales, depending on meteorological conditions and proximity to pollution sources. Accurate knowledge of pollution concentrations is needed to understand exposures to pollutants and protect vulnerable communities from the adverse health impacts of poor air quality. Measurements of pollutant concentrations are typically only recorded at a few locations within an urban area, which fail to capture elevated concentrations near pollutant sources. Recently, networks of low-cost sensors have been deployed to better capture the spatial variation in concentrations across urban areas, but these have been limited in duration and can be labor-intensive to maintain. Modeling air quality at high spatial resolution presents an opportunity to better understand the variation in pollutant concentrations across various spatial and temporal scales, but involves its own set of challenges.

At spatial resolutions fine enough to resolve near roadway variations in pollutant concentrations, the model grid is able to resolve some of the turbulent eddies in the atmosphere, so the Reynolds averaged turbulence closures typically used in air quality models are no longer valid. Additionally, information on pollutant emissions is often not available at this fine resolution, and increasing the model resolution without increasing the resolution of the emissions inputs will therefore not improve predictions. The Weather Research and Forecasting model (WRF) has the ability to perform large-eddy simulation (LES) at fine spatial resolutions, as well as the ability to model up to eight passive scalar species that can be used to track pollutants. In this study the WRF model was used to model concentrations of black carbon during the summer of 2017 in West Oakland, a community in the San Francisco Bay Area located in close proximity to the Port of Oakland and surrounded by major freeways. Emissions of black carbon were mapped based on traffic counts and local emissions inventories and gridded to various spatial resolutions from 4.05km to 150m. WRF was run in a nested configuration with three domains, with spatial resolutions of 4.05 km, 1.35 km, and 150 m, with LES turbulence closures on the finest domain. Modeled concentrations were compared to hourly measurements from a network of 100 sensors (Caubel et al., ES&T 2019).

The goal of this study is to assess the ability of the WRF model to replicate measured concentrations of black carbon at fine spatial and temporal scales. Eight different sources of black carbon are tracked separately in the model and the relative impact of each source is quantified. Modeling at multiple spatial resolutions also enables analysis of the effect of increases in spatial resolution and different turbulence parameterizations on the accuracy of predicted concentrations.