Fifteen Years of Community Exposure to Heavy-Duty Emissions: Capturing Disparities over Space and Time

Traffic-related air pollution backcasting using convolutional neural network and long short-term memory approach

Air pollution backcasting, especially nitrogen dioxide (NO2), is crucial in epidemiological studies, thus enabling the reconstruction of historical exposure levels for assessing long-term health effects. Changes in NO2 concentrations in urban areas are typically influenced by vehicle composition, technology, and traffic volumes. However, the observed NO2 levels at a monitoring site also reflect contributions from other sources, such as industrial and regional backgrounds. This study proposes a model that captures the spatial variability of NO2 concentrations, incorporating temporal trends through traffic-related predictors like nitrogen oxides (NOx) emissions and Annual Average Daily Traffic (AADT). Our approach integrates a Convolutional Neural Network (CNN) for spatial variation and Long Short-Term Memory (LSTM) for long-term temporal dynamics, yielding optimal spatiotemporal predictions for NO2 levels across the City of Toronto, Canada. The model, trained with NO2 measurements collected via the Urban Scanner mobile platform in 2020 and 2021, utilizes a Traffic Emission Prediction scheme (TEPs) to develop NOx and AADT inventories, serving as input to the LSTM model. Our proposed approach successfully estimates traffic-related NO2 levels across Toronto from 2006 to 2020. By intersecting the backcasted levels with census data, we noted that despite an overall decrease in NO2 levels between 2006 and 2020, disparities in exposure grew as more marginalized communities faced environmental injustice.

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.

Real-World Particle Emissions from a Modern Heavy-Duty Diesel Vehicle during Normal Operation and DPF Regeneration Events: Impacts on Disadvantaged Communities

We assessed the real-world particulate emissions of a goods movement diesel vehicle, with an emphasis on total particle number and solid particle number emissions at different cutoff sizes. The vehicle was tested on routes in the South Coast Air Basin (SCAB) of California, representative of typical goods movement operation between the ports to warehouses and logistic centers with a mixture of urban and highway driving, as well as elevation change. We evaluated emissions during normal vehicle operation and diesel particulate filter (DPF) active regeneration events. Results revealed small variations in particle emissions between the routes, with particles below 23 nm and even 10 nm being abundant in the exhaust. Both total and solid particle number emissions were about 3 to 246 times higher during DPF regeneration compared to normal vehicle operation, with higher fractions of sub-10 nm solid particles. We showed that typical daily routes for goods movement operation in SCAB, especially the more urban routes, mostly occurred within disadvantaged communities, with minority populations and high indices for poverty, unemployment, and poor education. Our results indicated the vehicles spent a higher fraction of their total time within these areas at low speed and idling conditions, resulting in disproportionately higher exposures to ultrafine particles.

Capturing Exposure Disparities with Chemical Transport Models: Evaluating the Suitability of Downscaling Using Land Use Regression

High resolution exposure surfaces are essential to capture disparities in exposure to traffic-related air pollution in urban areas. In this study, we develop an approach to downscale Chemical Transport Model (CTM) simulations to a hyperlocal level (∼100m) in the Greater Toronto Area (GTA) under three scenarios where emissions from cars, trucks and buses are zeroed out, thus capturing the burden of each transportation mode. This proposed approach statistically fuses CTMs with Land-Use Regression using machine learning techniques. With this proposed downscaling approach, changes in air pollutant concentrations under different scenarios are appropriately captured by downscaling factors that are trained to reflect the spatial distribution of emission reductions. Our validation analysis shows that high-resolution models resulted in better performance than coarse models when compared with observations at reference stations. We used this downscaling approach to assess disparities in exposure to nitrogen dioxide (NO2) for populations composed of renters, low-income households, recent immigrants, and visible minorities. Individuals in all four categories were disproportionately exposed to the burden of cars, trucks, and buses. We conducted this analysis at spatial resolutions of 12, 4, 1 km, and 100 m and observed that disparities were significantly underestimated when using coarse spatial resolutions. This reinforces the need for high-spatial resolution exposure surfaces for environmental justice analyses.

Societal Co-benefits of Zero-Emission Vehicles in the Freight Industry

This study was set in the Greater Toronto and Hamilton Area (GTHA), where commercial vehicle movements were assigned across the road network. Implications for greenhouse gas (GHG) emissions, air quality, and health were examined through an environmental justice lens. Electrification of light-, medium-, and heavy-duty trucks was assessed to identify scenarios associated with the highest benefits for the most disadvantaged communities. Using spatially and temporally resolved commercial vehicle movements and a chemical transport model, changes in air pollutant concentrations under electric truck scenarios were estimated at 1-km2 resolution. Heavy-duty truck electrification reduces ambient black carbon and nitrogen dioxide on average by 10 and 14%, respectively, and GHG emissions by 10.5%. It achieves the highest reduction in premature mortality attributable to fine particulate matter chronic exposure (around 200 cases per year) compared with light- and medium-duty electrification (less than 150 cases each). The burden of all traffic in the GTHA was estimated to be around 600 cases per year. The benefits of electrification accrue primarily in neighborhoods with a high social disadvantage, measured by the Ontario Marginalization Indices, narrowing the disparity of exposure to traffic-related air pollution. Benefits related to heavy-duty truck electrification reflect the adverse impacts of diesel-fueled freight and highlight the co-benefits achieved by electrifying this sector.

Exploring the triple burden of social disadvantage, mobility poverty, and exposure to traffic-related air pollution

Understanding the relationships between ultrafine particle (UFP) exposure, socioeconomic status (SES), and sustainable transportation accessibility in Toronto, Canada is crucial for promoting public health, addressing environmental justice, and ensuring transportation equity. We conducted a large-scale mobile measurement campaign and employed a gradient boost model to generate exposure surfaces using land use, built environment, and meteorological conditions. The Ontario Marginalization Index was used to quantify various indicators of social disadvantage for Toronto's neighborhoods. Our findings reveal that people in socioeconomically disadvantaged areas experience elevated UFP exposures. We highlight significant disparities in accessing sustainable transportation, particularly in areas with higher ethnic concentrations. When factoring in daily mobility, UFP exposure disparities in disadvantaged populations are further exacerbated. Furthermore, individuals who do not generate emissions themselves are consistently exposed to higher UFPs, with active transportation users experiencing the highest UFP exposures both at home and at activity locations. Finally, we proposed a novel index, the Community Prioritization Index (CPI), incorporating three indicators, including air quality, social disadvantage, and sustainable transportation. This index identifies neighborhoods experiencing a triple burden, often situated near major infrastructure hubs with high diesel truck activity and lacking greenspace, marking them as high-priority areas for policy action and targeted interventions.

In-use NOx and black carbon emissions from heavy-duty freight diesel vehicles and near-zero emissions natural gas vehicles in California's San Joaquin Air Basin

This study assessed the real-world nitrogen oxide (NOx) and black carbon emissions from six goods movement heavy-duty diesel and compressed natural gas (CNG) vehicles operating in California's San Joaquin Valley and Sacramento regions. The diesel vehicles were all equipped with diesel oxidation catalysts (DOCs) and diesel particulate filters (DPFs), while two diesel vehicles were also equipped with selective catalytic reduction (SCR). All CNG vehicles were equipped with three-way catalysts and fitted with stoichiometric engines meeting the optional ultra-low NOx standard of 0.02 g/bhp-hr. Emissions measurements were conducted with a portable emissions measurement systems (PEMS) during typical goods movement vehicle operation. Black carbon emissions were about 3-7 times higher for the CNG vehicles than those of the DPF-equipped diesel vehicles. NOx emissions for the CNG vehicles were found at or below the optional NOx standard and on average 35 times lower NOx than those of the diesel vehicles. Diesel vehicle NOx hotspots were identified in urban areas and intersections with frequent stop-and-go driving events, whereas the CNG vehicles showed uniform NOx emissions rates along the route. The dispersion modeling results showed elevated NOx and PM emissions exposures to receptors in close proximity to the highway. Our findings suggest that real-time emissions measurements at the tailpipe provide more accurate population exposure assessments near freight corridors compared to utilizing trip-averaged emissions rates values in dispersion models. Under the present test conditions, >70 % of black carbon and NOx were emitted within disadvantaged communities, characterized by low-income minority populations.