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  1. Free, publicly-accessible full text available May 1, 2025
  2. Abstract

    Heavy-duty vehicles (HDVs) disproportionately contribute to the creation of air pollutants and emission of greenhouse gases—with marginalized populations unequally burdened by the impacts of each. Shifting to non-emitting technologies, such as electric HDVs (eHDVs), is underway; however, the associated air quality and health implications have not been resolved at equity-relevant scales. Here we use a neighbourhood-scale (~1 km) air quality model to evaluate air pollution, public health and equity implications of a 30% transition of predominantly diesel HDVs to eHDVs over the region surrounding North America’s largest freight hub, Chicago, IL. We find decreases in nitrogen dioxide (NO2) and fine particulate matter (PM2.5) concentrations but ozone (O3) increases, particularly in urban settings. Over our simulation domain NO2and PM2.5reductions translate to ~590 (95% confidence interval (CI) 150–900) and ~70 (95% CI 20–110) avoided premature deaths per year, respectively, while O3increases add ~50 (95% CI 30–110) deaths per year. The largest pollutant and health benefits simulated are within communities with higher proportions of Black and Hispanic/Latino residents, highlighting the potential for eHDVs to reduce disproportionate and unjust air pollution and associated air-pollution attributable health burdens within historically marginalized populations.

     
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    Free, publicly-accessible full text available December 1, 2024
  3. Abstract

    High-resolution air quality data products have the potential to help quantify inequitable environmental exposures over space and across time by enabling the identification of hotspots, or areas that consistently experience elevated pollution levels relative to their surroundings. However, when different high-resolution data products identify different hotspots, the spatial sparsity of ‘gold-standard’ regulatory observations leaves researchers, regulators, and concerned citizens without a means to differentiate signal from noise. This study compares NO2hotspots detected within the city of Chicago, IL, USA using three distinct high-resolution (1.3 km) air quality products: (1) an interpolated product from Microsoft Research’s Project Eclipse—a dense network of over 100 low-cost sensors; (2) a two-way coupled WRF-CMAQ simulation; and (3) a down-sampled product using TropOMI satellite instrument observations. We use the Getis-OrdGi*statistic to identify hotspots of NO2and stratify results into high-, medium-, and low-agreement hotspots, including one consensus hotspot detected in all three datasets. Interrogating medium- and low-agreement hotspots offers insights into dataset discrepancies, such as sensor placement and model physics considerations, data retrieval caveats, and the potential for missing emission inventories. When treated as complements rather than substitutes, our work demonstrates that novel air quality products can enable researchers to address discrepancies in data products and can help regulators evaluate confidence in policy-relevant insights.

     
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  4. Abstract

    Electric vehicles (EVs) constitute just a fraction of the current U.S. transportation fleet; however, EV market share is surging. EV adoption reduces on-road transportation greenhouse gas emissions by decoupling transportation services from petroleum, but impacts on air quality and public health depend on the nature and location of vehicle usage and electricity generation. Here, we use a regulatory-grade chemical transport model and a vehicle-to-electricity generation unit electricity assignment algorithm to characterize neighborhood-scale (∼1 km) air quality and public health benefits and tradeoffs associated with a multi-modal EV transition. We focus on a Chicago-centric regional domain wherein 30% of the on-road transportation fleet is instantaneously electrified and changes in on-road, refueling, and power plant emissions are considered. We find decreases in annual population-weighted domain mean NO2(−11.83%) and PM2.5(−2.46%) with concentration reductions of up to −5.1 ppb and −0.98µg m−3in urban cores. Conversely, annual population-weighted domain mean maximum daily 8 h average ozone (MDA8O3) concentrations increase +0.64%, with notable intra-urban changes of up to +2.3 ppb. Despite mixed pollutant concentration outcomes, we find overall positive public health outcomes, largely driven by NO2concentration reductions that result in outsized mortality rate reductions for people of color, particularly for the Black populations within our domain.

     
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  5. Abstract

    Accurate soil moisture and streamflow data are an aspirational need of many hydrologically relevant fields. Model simulated soil moisture and streamflow hold promise but models require validation prior to application. Calibration methods are commonly used to improve model fidelity but misrepresentation of the true dynamics remains a challenge. In this study, we leverage soil parameter estimates from the Soil Survey Geographic (SSURGO) database and the probability mapping of SSURGO (POLARIS) to improve the representation of hydrologic processes in the Weather Research and Forecasting Hydrological modeling system (WRF‐Hydro) over a central California domain. Our results show WRF‐Hydro soil moisture exhibits increased correlation coefficients (r), reduced biases, and increased Kling‐Gupta Efficiencies (KGEs) across seven in situ soil moisture observing stations after updating the model's soil parameters according to POLARIS. Compared to four well‐established soil moisture data sets including Soil Moisture Active Passive data and three Phase 2 North American Land Data Assimilation System land surface models, our POLARIS‐adjusted WRF‐Hydro simulations produce the highest mean KGE (0.69) across the seven stations. More importantly, WRF‐Hydro streamflow fidelity also increases, especially in the case where the model domain is set up with SSURGO‐informed total soil thickness. The magnitude and timing of peak flow events are better captured,rincreases across nine United States Geological Survey stream gages, and the mean KGE across seven of the nine gages increases from 0.12 to 0.66. Our pre‐calibration parameter estimate approach, which is transferable to other spatially distributed hydrological models, can substantially improve a model's performance, helping reduce calibration efforts and computational costs.

     
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  6. Abstract. In steep wildfire-burned terrains, intense rainfall can produce large runoff that can trigger highly destructive debris flows. However, the abilityto accurately characterize and forecast debris flow susceptibility in burned terrains using physics-based tools remains limited. Here, we augmentthe Weather Research and Forecasting Hydrological modeling system (WRF-Hydro) to simulate both overland and channelized flows and assess postfiredebris flow susceptibility over a regional domain. We perform hindcast simulations using high-resolution weather-radar-derived precipitation andreanalysis data to drive non-burned baseline and burn scar sensitivity experiments. Our simulations focus on January 2021 when an atmospheric rivertriggered numerous debris flows within a wildfire burn scar in Big Sur – one of which destroyed California's famous Highway 1. Compared to thebaseline, our burn scar simulation yields dramatic increases in total and peak discharge and shorter lags between rainfall onset and peakdischarge, consistent with streamflow observations at nearby US Geological Survey (USGS) streamflow gage sites. For the 404 catchments located inthe simulated burn scar area, median catchment-area-normalized peak discharge increases by ∼ 450 % compared to the baseline. Catchmentswith anomalously high catchment-area-normalized peak discharge correspond well with post-event field-based and remotely sensed debris flowobservations. We suggest that our regional postfire debris flow susceptibility analysis demonstrates WRF-Hydro as a compelling new physics-basedtool whose utility could be further extended via coupling to sediment erosion and transport models and/or ensemble-based operational weatherforecasts. Given the high-fidelity performance of our augmented version of WRF-Hydro, as well as its potential usage in probabilistic hazardforecasts, we argue for its continued development and application in postfire hydrologic and natural hazard assessments. 
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  7. Abstract

    The southern Lake Michigan region of the United States, home to Chicago, Milwaukee, and other densely populated Midwestern cities, frequently experiences high pollutant episodes with unevenly distributed exposure and health burdens. Using the two‐way coupled Weather Research Forecast and Community Multiscale Air Quality Model (WRF‐CMAQ), we investigate criteria pollutants over a southern Lake Michigan domain using 1.3 and 4 km resolution hindcast simulations. We assess WRF‐CMAQ's performance using data from the National Climatic Data Center and Environmental Protection Agency Air Quality System. Our 1.3 km simulation slightly improves on the 4 km simulation's meteorological and chemical performance while also resolving key details in areas of high exposure and impact, that is, urban environments. At 1.3 km, we find that most air quality‐relevant meteorological components of WRF‐CMAQ perform at or above community benchmarks. WRF‐CMAQ's chemical performance also largely meets community standards, with substantial nuance depending on the performance metric and component assessed. For example, hourly simulated NO2and O3are highly correlated with observations (r > 0.6) while PM2.5is less so (r = 0.4). Similarly, hourly simulated NO2and PM2.5have low biases (<10%), whereas O3biases are larger (>30%). Simulated spatial pollutant patterns show distinct urban‐rural footprints, with urban NO2and PM2.520%–60% higher than rural, and urban O36% lower. We use our 1.3 km simulations to resolve high‐pollution areas within individual urban neighborhoods and characterize seasonal changes in O3regimes across tight spatial gradients. Our findings demonstrate both the benefits and limitations of high‐resolution simulations, particularly over urban settings.

     
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  8. Abstract

    Storylines of atmospheric circulation change, or physically self-consistent narratives of plausible future events, have recently been proposed as a non-probabilistic means to represent uncertainties in climate change projections. Here, we apply the storyline approach to 21st century projections of summer air stagnation over Europe and the United States. We use a Climate Model Intercomparison Project Phase 6 (CMIP6) ensemble to generate stagnation storylines based on the forced response of three remote drivers of the Northern Hemisphere mid-latitude atmospheric circulation: North Atlantic warming, North Pacific warming, and tropical versus Arctic warming. Under a high radiative forcing scenario (SSP5-8.5), models consistently project increases in stagnation over Europe and the U.S., but the magnitude and spatial distribution of changes vary substantially across CMIP6 ensemble members, suggesting that future projections are not well-constrained when using the ensemble mean alone. We find that the diversity of projected stagnation changes depends on the forced response of remote drivers in individual models. This is especially true in Europe, where differences of ∼2 summer stagnant days per degree of global warming are found amongst the different storyline combinations. For example, the greatest projected increase in stagnation for most European regions leads to the smallest increase in stagnation for southwestern Europe; i.e. limited North Atlantic warming combined with near-equitable tropical and Arctic warming. In the U.S., only the atmosphere over the northern Rocky Mountain states demonstrates comparable stagnation projection uncertainty, due to opposite influences of remote drivers on the meteorological conditions that lead to stagnation.

     
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