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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 NO2 hotspots 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-Ord G_i^*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 discrepanciesmore »in data products and can help regulators evaluate confidence in policy-relevant insights.

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 PM_2.5(−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 ourmore »domain.

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 PM_2.5is less so (r = 0.4). Similarly, hourly simulated NO₂and PM_2.5have low biases (<10%), whereas O₃biases are larger (>30%). Simulated spatial pollutant patterns show distinct urban‐rural footprints, with urban NO₂and PM_2.520%–60% higher than rural, and urban O₃6% lower. We use our 1.3 kmmore »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.