Abstract
Traffic emissions significantly impact near-road air quality and public health. This research applies a Bayesian modeling framework to investigate these impacts using high-resolution traffic and air pollutant data from an urban corridor in Columbia, South Carolina. Despite a data collection period truncated by the COVID-19 lockdown, the Bayesian approach successfully identified significant predictors and quantified model uncertainty. Employing Bayesian Model Selection and Averaging enhanced prediction accuracy and evaluated model uncertainty. Findings indicate that higher temperatures and increased moisture levels elevate particulate matter (PM1.0, PM2.5, PM10) concentrations, while traffic speed significantly affects nitrogen dioxide (NO2) levels. Specifically, higher average traffic speeds (indicative of smoother flow) correspond to lower NO2 concentrations, suggesting that less congested conditions reduce NO2 emissions. This study highlights the robustness of Bayesian methods for generating reliable air quality insights even under data-constrained conditions. The findings underscore the importance of traffic flow management (e.g., reducing congestion) for mitigating near-road NO2 exposure and provide a basis for developing targeted public health strategies.
| Original language | English |
|---|---|
| Article number | 100328 |
| Journal | Atmospheric Environment: X |
| Volume | 26 |
| DOIs | |
| State | Published - Apr 2025 |
Funding
For particulate matter, the moderate-to-high inclusion probability for 'Truck' counts (Table 2), despite low probabilities for other vehicle types, supports the expectation that heavy-duty vehicles are significant contributors to primary PM emissions per vehicle. The lack of a strong signal from 'Car' counts, as discussed previously regarding technological improvements and fleet changes (“EPA's 2020 National Emissions Inventory and Trends Report,” 2023; Wang et al., 2023), suggests that fluctuations in passenger car volume added little marginal explanatory power for PM beyond background levels and meteorological effects in this context.Despite limitations, the study offers practical insights. The robust finding linking higher traffic speeds (smoother flow) to lower near-road NO2 strongly supports traffic management strategies like optimized signal timing or congestion pricing as potential air quality improvement measures. The identified importance of meteorology for PM highlights potential for exposure warnings or adaptive traffic controls during high-risk weather conditions. The clear (though statistically weaker than meteorology) signal for trucks impacting PM supports policies targeting heavy-duty vehicle emissions or route restrictions.This study demonstrated the effectiveness of a Bayesian modeling framework, incorporating Bayesian Model Averaging, for analyzing near-road air quality using high-resolution traffic and pollutant data collected under the constrained conditions of the COVID-19 lockdown. The framework successfully identified key predictor-pollutant relationships and quantified uncertainty despite the limited dataset. Results showed that meteorological factors, especially temperature and humidity, significantly influenced particulate matter, while average traffic speed emerged as the dominant traffic-related determinant of NO2 levels. The inverse relationship between speed and NO2 concentrations suggests that congestion mitigation can yield air quality benefits. Although heavy vehicle counts were not significant for NO2 after adjusting for speed and weather, their impact on PM supports diesel emission reduction strategies.This study is based upon work supported by the USDOT Connected Multimodal Mobility University Transportation Center (C2M2) (Tier 1 University Transportation Center) headquartered at Clemson University, Clemson, South Carolina, USA. This manuscript has also been authored in part by UT-Battelle LLC, under contract DE-AC-05-00OR22725 with the US Department of Energy (DOE). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the USDOT Center for Connected Multimodal Mobility (C2M2), and the U.S. Government assumes no liability for the contents or use thereof. This study is based upon work supported by the USDOT Connected Multimodal Mobility University Transportation Center (C2M2) (Tier 1 University Transportation Center ) headquartered at Clemson University , Clemson , South Carolina, USA . This manuscript has also been authored in part by UT-Battelle LLC, under contract DE-AC-05-00OR22725 with the US Department of Energy (DOE). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the USDOT Center for Connected Multimodal Mobility (C2M2), and the U.S. Government assumes no liability for the contents or use thereof.
Keywords
- Bayesian modeling
- Near-road traffic management
- Predictive models
- Roadway air quality
- Traffic-related air pollution