Daniel Horton (@danethan.bsky.social)

Out now in @acs.org's ES&T: Intercomparison of Modeled Urban-Scale Vehicle NOx & PM2.5 Emissions–Implications for Equity Assessments. Wherein @victorialangwx.bsky.social assembled 4 emission modeling groups to assess the consistency of hi-res transport emission estimates. 🧪 doi.org/10.1021/acs....

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Intercomparison of Modeled Urban-Scale Vehicle NOx and PM2.5 Emissions–Implications for Equity Assessments Accurate characterization of emissions is essential for understanding spatiotemporal variations of air pollutants and their societal impacts, including population exposure, health outcomes, and environmental justice implications. Characterizing emissions from the transportation sector is challenging due to uncertainties in emission-producing processes and in fleet composition and activity–factors that lead to differences across modeled vehicle emissions data sets. Here, we compare four data sets─Fuel-Inventory Vehicle Emissions, Neighborhood Emission Mapping Operation, Lake Michigan Air Director Consortium-Northwestern University, and University of Vermont─over the Greater Chicago region at three shared spatial resolutions (1.0, 1.3, and 4 km2). While domain-level data set agreement is strongest at the coarsest resolution, at finer resolutions we find notable inconsistencies, particularly at local scales. At 1 km2, simulated domain total NOx emissions across the four data sets differ up to 82% (∼32–58 k tons/year), while grid cell maximum PM2.5 emissions vary up to 272% (∼1.5–5.5 tons/km2/year). Intercompared emissions data sets share similar inputs; however, divergent outcomes arise from differences in emission factors, simulated vehicle processes, and characterization of traffic data. While domain-level emission burdens among racial/ethnic subgroups are generally ranked similarly across data sets, the magnitude of relative disparities can vary up to 11%–a potentially consequential factor to consider in downstream impact analyses. https://doi.org/10.1021/acs.est.4c09777