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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 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.

The global increase of urban impervious land cover poses a significant threat to the integrity of river ecosystems. Hence, it is critical to assess the efficiency of green roofs (GR) to mitigate the negative impacts of urbanization on river ecosystems, such as thermal surges and pollutants. In this study, we evaluated the ecohydrological behaviour of two fully established GR under differing management regimes at the Chicago Botanical Gardens from July to September 2019. The drainage outflow from a non‐vegetated roof, a managed GR (perennial native and non‐native plants) and an unmanaged GR (perennial natural prairie vegetation) were monitored, and thermal dynamics, dissolved organic matter (DOM) composition and nitrate concentration assessed. The managed GR runoff had a lower DOC concentration and less humic‐like DOM signal (SUVA₂₅₄) compared to the unmanaged GR. In contrast, lower concentrations of nitrate and more recalcitrant DOM (less protein‐like compounds relative to humic‐like compounds) were associated with the unmanaged GR. The unmanaged GR also displayed a greater capacity to reduce thermal surges associated with storm events. Our study provides new information on the implications of GR management for water quality with particular relevance to the urban stream syndrome. Further, the impacts of GR management on the mitigation of thermal surges and DOM composition can help to improve future GR design, as these ecohydrological responses have been largely overlooked to date. Our findings can support future urban planning, particularly for scenarios where green infrastructures are used to mitigate the impacts of climate change on urban river ecosystems.

Electric vehicle (EV) adoption promises potential air pollutant and greenhouse gas (GHG) reduction co‐benefits. As such, China has aggressively incentivized EV adoption, however much remains unknown with regard to EVs’ mitigation potential, including optimal vehicle type prioritization, power generation contingencies, effects of Clean Air regulations, and the ability of EVs to reduce acute impacts of extreme air quality events. Here, we present a suite of scenarios with a chemistry transport model that assess the potential co‐benefits of EVs during an extreme winter air quality event. We find that regardless of power generation source, heavy‐duty vehicle (HDV) electrification consistently improves air quality in terms of NO₂and fine particulate matter (PM_2.5), potentially avoiding 562 deaths due to acute pollutant exposure during the infamous January 2013 pollution episode (∼1% of total premature mortality). However, HDV electrification does not reduce GHG emissions without enhanced emission‐free electricity generation. In contrast, due to differing emission profiles, light‐duty vehicle (LDV) electrification in China consistently reduces GHG emissions (∼2 Mt CO₂), but results in fewer air quality and human health improvements (145 avoided deaths). The calculated economic impacts for human health endpoints and CO₂reductions for LDV electrification are nearly double those of HDV electrification in present‐day (155M vs. 87M US$), but are within ∼25% when enhanced emission‐free generation is used to power them. Overall, we find only a modest benefit for EVs to ameliorate severe wintertime pollution events, and that continued emission reductions in the power generation sector will have the greatest human health and economic benefits.

Vehicle electrification is a common climate change mitigation strategy, with policymakers invoking co‐beneficial reductions in carbon dioxide (CO₂) and air pollutant emissions. However, while previous studies of U.S. electric vehicle (EV) adoption consistently predict CO₂mitigation benefits, air quality outcomes are equivocal and depend on policies assessed and experimental parameters. We analyze climate and health co‐benefits and trade‐offs of six U.S. EV adoption scenarios: 25% or 75% replacement of conventional internal combustion engine vehicles, each under three different EV‐charging energy generation scenarios. We transfer emissions from tailpipe to power generation plant, simulate interactions of atmospheric chemistry and meteorology using the GFDL‐AM4 chemistry climate model, and assess health consequences and uncertainties using the U.S. Environmental Protection Agency Benefits Mapping Analysis Program Community Edition (BenMAP‐CE). We find that 25% U.S. EV adoption, with added energy demand sourced from the present‐day electric grid, annually results in a ~242 M ton reduction in CO₂emissions, 437 deaths avoided due to PM_2.5reductions (95% CI: 295, 578), and 98 deaths avoided due to lesser ozone formation (95% CI: 33, 162). Despite some regions experiencing adverse health outcomes, ~$16.8B in damages avoided are predicted. Peak CO₂reductions and health benefits occur with 75% EV adoption and increased emission‐free energy sources (~$70B in damages avoided). When charging‐electricity from aggressive EV adoption is combustion‐only, adverse health outcomes increase substantially, highlighting the importance of low‐to‐zero emission power generation for greater realization of health co‐benefits. Our results provide a more nuanced understanding of the transportation sector's climate change mitigation‐health impact relationship.

High‐impact poor air quality events, such as Beijing's so‐called “Airpocalypse” in January 2013, demonstrate that short‐lived poor air quality events can have significant effects on health and economic vitality. Poor air quality events result from the combination of the emission of pollutants and meteorological conditions favorable to their accumulation, which include limited scavenging, dispersion, and ventilation. The unprecedented nature of events such as the 2013 Airpocalypse, in conjunction with our nonstationary climate, motivate an assessment of whether climate change has altered the meteorological conditions conducive to poor winter air quality in Beijing. Using three indices designed to quantify the meteorological conditions that support poor air quality and drawing on the attribution methods of Diffenbaugh et al. (2017,https://doi.org/10.1073/pnas.1618082114), we assess (i) the contribution of observed trends to the magnitude of events, (ii) the contribution of observed trends to the probability of events, (iii) the return interval of events in the observational record, preindustrial model‐simulated climate and historical model‐simulated climate, (iv) the probability of the observed trend in the preindustrial and historical model‐simulated climates, and (v) the relative influences of anthropogenic forcing and natural variability on the observed trend. We find that anthropogenic influence has had a small effect on the probability of the January 2013 event in all three indices but has increased the probability of a long‐term positive trend in two out of three indices. This work provides a framework for both further understanding the role of climate change in air quality and expanding the scope of event attribution.