Income and racial disparity in household publicly available electric vehicle infrastructure accessibility.

Publicly available electric vehicle (EV) infrastructure is pivotal for the United States EV transition by 2030. Existing infrastructure lacks equitably distribution to low-income and underrepresented communities, impeding mass adoption. Our study, utilizing 2021 micro-level data from 121 million United States households, comprehensively examines income and racial disparities in EV infrastructure accessibility. Our analysis of national averages indicates that lower-income groups face less accessibility to public EV infrastructure in both urban and rural geographies. Black households experience less rural accessibility, but greater urban accessibility compared to White households conditioning on income. However, our localized analysis uncovers significant variations in accessibility gaps among counties, rural and urban settings, and dwelling types. While Black households experience greater urban accessibility nationally, a closer look at the county level reveals diminishing advantages. This study identifies areas with pronounced inequality and urgent needs for enhanced accessibility, emphasizing the necessity for tailored solutions by local governments to enhance equitable access to EV infrastructure.

Demographics and risk of isolation due to sea level rise in the United States.

Within coastal communities, sea level rise (SLR) will result in widespread intermittent flooding and long-term inundation. Inundation effects will be evident, but isolation that arises from the loss of accessibility to critical services due to inundation of transportation networks may be less obvious. We examine who is most at risk of isolation due to SLR, which can inform community adaptation plans and help ensure that existing social vulnerabilities are not exacerbated. Combining socio-demographic data with an isolation metric, we identify social and economic disparities in risk of isolation under different SLR scenarios (1-10 ft) for the coastal U.S. We show that Black and Hispanic populations face a disproportionate risk of isolation at intermediate levels of SLR (4 ft and greater). Further, census tracts with higher rates of renters and older adults consistently face higher risk of isolation. These insights point to significant inequity in the burdens associated with SLR.

Facility design and worker justice: COVID-19 transmission in meatpacking plants.

BACKGROUND: Meatpacking plants were major sources of COVID-19 outbreaks, posing unprecedented risks to employees, family members, and local communities. The effect on food availability during outbreaks was immediate and staggering: within 2 months, the price of beef increased by almost 7% with documented evidence of significant meat shortages. Meatpacking plant designs, in general, optimize on production; this design approach constrains the ability to enhance worker respiratory protection without reducing output. METHODS: Using agent-based modeling, we simulate the spread of COVID-19 within a typical meatpacking plant design under varying levels of mitigation measures, including combinations of social distancing and masking interventions. RESULTS: Simulations show an average infection rate of close to 99% with no mitigation, 99% with the policies that US companies ultimately adopted, 81% infected with the combination of surgical masks and distancing policies, and 71% infected with N95 masks and distancing. Estimated infection rates were high, reflecting the duration and exertion of the processing activities and lack of fresh airflow in an enclosed space. CONCLUSION: Our results are consistent with anecdotal findings in a recent congressional report, and are much higher than US industry has reported. Our results suggest current processing plant designs made rapid transmission of the virus during the pandemic's early days almost inevitable, and implemented worker protections during COVID-19 did not significantly affect the spread of the virus. We argue current federal policies and regulations are insufficient to ensure the health and safety of workers, creating a justice issue, and jeopardizing food availability in a future pandemic.

Spatial assignment of emissions using a new locomotive emissions model.

Estimates of fuel use and air pollutant emissions from freight rail currently rely highly on aggregate methods and largely obsolete data which offer little insight into contemporary air quality problems. Because the freight industry is for the most part privately held and data are closely guarded for competitive reasons, the challenge is to produce robust estimates using current reporting requirements, while accurately portraying the spatial nature of freight rail impacts. This research presents a new spatially resolved model for estimating air pollutant emissions (hydrocarbons, carbon monoxide, nitrogen oxides, particulate matter less than 10 µm in diameter, sulfur dioxide, and carbon dioxide) from locomotives. Emission estimates are based on track segment level data including track grade, type of train traffic (bulk, intermodal, or manifest) and the local locomotive fleet (EPA tier certification level and fuel efficiency). We model the California Class I freight rail system and compare our results to regional estimates from the California Air Resources Board and to estimates following U.S. Environmental Protection Agency guidance. We find that our results vary considerably from the other methods depending on the region or corridor analyzed. We also find large differences in fuel and emission intensity for individual rail corridors.

Near-roadway air quality: synthesizing the findings from real-world data.

Despite increasing regulatory attention and literature linking roadside air pollution to health outcomes, studies on near roadway air quality have not yet been well synthesized. We employ data collected from 1978 as reported in 41 roadside monitoring studies, encompassing more than 700 air pollutant concentration measurements, published as of June 2008. Two types of normalization, background and edge-of-road, were applied to the observed concentrations. Local regression models were specified to the concentration-distance relationship and analysis of variance was used to determine the statistical significance of trends. Using an edge-of-road normalization, almost all pollutants decay to background by 115-570 m from the edge of road; using the more standard background normalization, almost all pollutants decay to background by 160-570 m from the edge of road. Differences between the normalization methods arose due to the likely bias inherent in background normalization, since some reported background values tend to underpredict (be lower than) actual background. Changes in pollutant concentrations with increasing distance from the road fell into one of three groups: at least a 50% decrease in peak/edge-of-road concentration by 150 m, followed by consistent but gradual decay toward background (e.g., carbon monoxide, some ultrafine particulate matter number concentrations); consistent decay or change over the entire distance range (e.g., benzene, nitrogen dioxide); or no trend with distance (e.g., particulate matter mass concentrations).