Climate change negatively impacts human health through heat stress and exposure to worsened air pollution, amongst other pathways. Indoor use of air conditioning can be an effective strategy to reduce heat exposure. However, increased air conditioning use increases emissions of air pollutants from power plants, in turn worsening air quality and human health impacts. We used an interdisciplinary linked model system to quantify the impacts of heat-driven adaptation through building cooling demand on air-quality-related health outcomes in a representative mid-century climate scenario. We used a modeling system that included downscaling historical and future climate data with the Weather Research and Forecasting (WRF) model, simulating building electricity demand using the Regional Building Energy Simulation System (RBESS), simulating power sector production and emissions using MyPower, simulating ambient air quality using the Community Multiscale Air Quality (CMAQ) model, and calculating the incidence of adverse health outcomes using the Environmental Benefits Mapping and Analysis Program (BenMAP). We performed simulations for a representative present-day climate scenario and 2 representative mid-century climate scenarios, with and without exacerbated power sector emissions from adaptation in building energy use. We find that by mid-century, climate change alone can increase fine particulate matter (PM_2.5) concentrations by 58.6% (2.50 μg/m³) and ozone (O₃) by 14.9% (8.06 parts per billion by volume [ppbv]) for the month of July. A larger change is found when comparing the present day to the combined impact of climate change and increased building energy use, where PM_2.5 increases 61.1% (2.60 μg/m³) and O₃ increases 15.9% (8.64 ppbv). Therefore, 3.8% of the total increase in PM_2.5 and 6.7% of the total increase in O₃ is attributable to adaptive behavior (extra air conditioning use). Health impacts assessment finds that for a mid-century climate change scenario (with adaptation), annual PM_2.5-related adult mortality increases by 13,547 deaths (14 concentration–response functions with mean incidence range of 1,320 to 26,481, approximately US$126 billion cost) and annual O₃-related adult mortality increases by 3,514 deaths (3 functions with mean incidence range of 2,175 to 4,920, approximately US$32.5 billion cost), calculated as a 3-month summer estimate based on July modeling. Air conditioning adaptation accounts for 654 (range of 87 to 1,245) of the PM_2.5-related deaths (approximately US$6 billion cost, a 4.8% increase above climate change impacts alone) and 315 (range of 198 to 438) of the O₃-related deaths (approximately US$3 billion cost, an 8.7% increase above climate change impacts alone). Limitations of this study include modeling only a single month, based on 1 model-year of future climate simulations. As a result, we do not project the future, but rather describe the potential damages from interactions arising between climate, energy use, and air quality. This study examines the contribution of future air-pollution-related health damages that are caused by the power sector through heat-driven air conditioning adaptation in buildings. Results show that without intervention, approximately 5%–9% of exacerbated air-pollution-related mortality will be due to increases in power sector emissions from heat-driven building electricity demand. This analysis highlights the need for cleaner energy sources, energy efficiency, and energy conservation to meet our growing dependence on building cooling systems and simultaneously mitigate climate change.

We evaluate nitrogen dioxide (NO₂) simulations from a widely used air quality model, the Environmental Protection Agency (EPA) Community Multiscale Air Quality (CMAQ) model, using ground‐ and satellite‐based observations. In addition to direct comparison of modeled and measured variables, we compare the response of NO₂ to meteorological conditions and the ability of the model to capture these sensitivities over the continental U.S. during winter and summer periods of 2007. This is the first study to evaluate relationships between NO₂ and meteorological variables using satellite data, the first to apply these relationships for model validation, and the first to characterize variability in sensitivities over a wide geographic and temporal scope. We find boundary layer height, wind speed, temperature, and relative humidity to be the most important variables in determining near‐surface NO₂ variability. Consistent with earlier studies on NO₂‐meteorology relationships, we find that, in general, NO₂ responds negatively to planetary boundary height, negatively to wind speed, and negatively to insolation. Unlike previous studies, we find a slight positive association between precipitation and NO₂, and we find a consistently positive average association between temperature and NO₂. CMAQ agreed with relationships observed in ground‐based data from the EPA Air Quality System and the Ozone Monitoring Instrument over most regions. However, we find that the southwest U.S. is a problem area for CMAQ, where modeled NO₂ responses to insolation, boundary layer height, and other variables are at odds with the observations.

This study evaluates formaldehyde (HCHO) over the U.S. from 2006 to 2015 by comparing ground monitor data from the Air Quality System (AQS) and a satellite retrieval from the Ozone Monitoring Instrument (OMI). Our comparison focuses on the utility of satellite data to inform patterns, trends, and processes of ground-based HCHO across the U.S. We find that cities with higher levels of biogenic volatile organic compound (BVOC) emissions, including primary HCHO, exhibit larger HCHO diurnal amplitudes in surface observations. These differences in hour-to-hour variability in surface HCHO suggests that satellite agreement with ground-based data may depend on the distribution of emission sources. On a seasonal basis, OMI exhibits the highest correlation with AQS in summer and the lowest correlation in winter. The ratios of HCHO in summer versus other seasons show pronounced seasonal variability in OMI, likely due to seasonal changes in the vertical HCHO distribution. The seasonal variability in HCHO from satellite is more pronounced than at the surface, with seasonal variability 20–100% larger in satellite than surface observations. The seasonal variability also has a latitude dependency, with more variability in higher latitude regions. OMI agrees with AQS on the interannual variability in certain periods, whereas AQS and OMI do not show a consistent decadal trend. This is possibly due to a rather large interannual variability in HCHO, which makes the small decadal drift less significant. Temperature also explains part of the interannual variabilities. Small temperature variations in the western U.S. are reflected with more quiescent HCHO interannual variability in that region. The decrease in summertime HCHO in the southeast U.S. could also be partially explained by a small and negative trend in local temperatures.