Air Quality Modeling in Support of the Near-Road Exposures and Effects of Urban Air Pollutants Study (NEXUS)

Abstract

A major challenge in traffic-related air pollution exposure studies is the lack of information regarding pollutant exposure characterization. Air quality modeling can provide spatially and temporally varying exposure estimates for examining relationships between traffic-related air pollutants and adverse health outcomes. A hybrid air quality modeling approach was used to estimate exposure to traffic-related air pollutants in support of the Near-Road Exposures and Effects of Urban Air Pollutants Study (NEXUS) conducted in Detroit (Michigan, USA). Model-based exposure metrics, associated with local variations of emissions and meteorology, were estimated using a combination of the American Meteorological Society/Environmental Protection Agency Regulatory Model (AERMOD) and Research LINE-source dispersion model for near-surface releases (RLINE) dispersion models, local emission source information from the National Emissions Inventory, detailed road network locations and traffic activity, and meteorological data from the Detroit City Airport. The regional background contribution was estimated using a combination of the Community Multi-scale Air Quality (CMAQ) and the Space-Time Ordinary Kriging (STOK) models. To capture the near-road pollutant gradients, refined “mini-grids” of model receptors were placed around participant homes. Exposure metrics for CO, NOx, PM2.5 and its components (elemental and organic carbon) were predicted at each home location for multiple time periods including daily and rush hours. The exposure metrics were evaluated for their ability to characterize the spatial and temporal variations of multiple ambient air pollutants compared to measurements across the study area.

Finely Resolved On‐Road PM2.5 and Estimated Premature Mortality in Central North Carolina

“…We refined a previously developed novel hybrid approach that now combines dispersion modeling (R‐LINE), CMAQ, and space–time ordinary kriging (STOK) technique to model primary and secondary on‐road PM 2.5 as well as total PM 2.5 at Census block centroids (∼105,000 Census blocks). The detail of the R‐LINE and STOK hybrid framework is described elsewhere . We further improved the approach to capture secondary on‐road PM 2.5 by utilizing CMAQ predictions (see Section ).…”

“…The detail of the R-LINE and STOK hybrid framework is described elsewhere. (28,29) We further improved the approach to capture secondary on-road PM 2.5 by utilizing CMAQ predictions (see Section 2.3.2).…”

“…In a dispersion model, the emission source is allocated in a much finer spatial resolution more realistically retaining the shape and physical characteristics of the emitted plume. For example, dispersion models can model roadways as adjacent area sources or line sources and thus the sharp concentration gradients from roadways can be captured with a higher spatial resolution . Modeling TRAPs with dispersion models can still be challenging due to the requirement to characterize a temporally refined emission for each roadway.…”

“…For example, dispersion models can model roadways as adjacent area sources (22)(23)(24) or line sources (25)(26)(27) and thus the sharp concentration gradients from roadways can be captured with a higher spatial resolution. (28,29) Modeling TRAPs with dispersion models can still be challenging due to the requirement to characterize a temporally refined emission for each roadway. This link-based emission can be obtained with a "bottom-up" approach, (24,30,31) where detailed information including geometry of the road network, traffic activity, temporal allocation factor, (32) emission factors of pollutants, and meteorology is required.…”

A Hybrid Fuzzy Inference System Based on Dispersion Model for Quantitative Environmental Health Impact Assessment of Urban Transportation Planning

“…This dispersion model was used in numerous EHIAs for modeling the pollution caused by a specific source of emission [59][60][61][62][63][64][65].…”