Life is a highway, but what are the emissions along it? It turns out, that answer could change depending on which dataset one uses.
A new study led by Victoria Lang, a Ph.D. candidate in the Department of Earth, Environmental, and Planetary Sciences at Northwestern University, sheds light on critical disparities in urban-scale vehicle emissions data and their implications for people in metropolitan Chicago. The study, “Intercomparison of Modeled Urban-Scale Vehicle NOx and PM2.5 Emissions–Implications for Equity Assessments,” was published this February in Environmental Science & Technology.
Lang, working under the guidance of senior author Daniel Horton, an associate professor in the Department of Earth, Environmental, and Planetary Sciences, and co-workers analyzed four high-resolution vehicle emission datasets over the Greater Chicago region. These datasets are crucial for urban planning, quantifying air pollution and greenhouse gas emissions from the transportation sector, and evaluating the effectiveness of emission control policies, particularly when used in air quality models that simulate primary and secondary pollutants from transportation sources.
Exposure to traffic-related air pollution is associated with a myriad of adverse health outcomes, including respiratory and cardiovascular diseases. Across the country, the populations living closest to major roadways are largely non-white, raising concerns about the equitable distribution of air pollution burdens.
“Typically, for regulatory purposes, these datasets are used at coarse resolutions, capturing county-level changes in emissions,” Lang explains. “But as concerns about economic and public health impacts grow, datasets are being provided at increasingly fine resolutions.”
The study finds that while vehicle emission datasets show strong agreement at coarse resolutions, disparities emerge when emissions are modeled at a high-resolution scale (1 km2). The researchers observed significant differences in total emissions estimates and localized grid cell maximums. Specifically, simulated nitrogen oxides (NOx) emissions across the four datasets varied by up to 82% (approximately 32–58 kilotons per year), while grid cell maximum particulate matter (PM2.5) emissions diverged by as much as 272% (approximately 1.5–5.5 tons/km² annually).
These inconsistencies have significant implications for environmental demographic assessments. When the datasets were used to analyze the demographic composition of populations affected by traffic-related emissions near their residences, the study confirmed that non-white populations disproportionately experience higher exposure levels. However, the magnitude of this racial disparity varied depending on the method used to develop the dataset.
“The methods used to create these datasets can likely influence the outcomes of downstream impact analyses,” Lang states. “We recommend that researchers and policymakers carefully consider these methodological differences when conducting exposure assessments and policy evaluations.”
The findings underscore the importance of reducing uncertainty within these neighborhood-scale emission datasets, particularly for use in air quality and public health assessments of vulnerable communities. So, life may be a highway but ensuring that everyone breathes clean air along the way starts with getting vehicle emission estimates right.
This project was supported by the National Science Foundation and the Environmental Defense Fund.
Simulated nitrogen oxides (NOx) emissions across the four datasets varied by up to 82% (approximately 32–58 kilotons per year), while grid cell maximum particulate matter (PM2.5) emissions diverged by as much as 272% (approximately 1.5–5.5 tons/km² annually).