BackgroundClimate 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.Methods and findingsWe 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 (PM2.5) 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 PM2.5 increases 61.1% (2.60 μg/m3) and O3 increases 15.9% (8.64 ppbv). Therefore, 3.8% of the total increase in PM2.5 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 PM2.5-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 PM2.5-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.ConclusionsThis study exa...

Past studies have established strong connections between meteorology and air quality, via chemistry, transport, and natural emissions. A less understood linkage between weather and air quality is the temperature-dependence of emissions from electricity generating units (EGUs), associated with high electricity demand to support building cooling on hot days. This study quantifies the relationship between ambient surface temperatures and EGU air emissions (CO, SO, and NO) using historical data. We find that EGUs in the Eastern U.S. region from 2007 to 2012 exhibited a 3.87% ± 0.41% increase in electricity generation per °C increase during summer months. This is associated with a 3.35%/°C ± 0.50%/°C increase in SO emissions, a 3.60%/°C ± 0.49%/°C increase in NO emissions, and a 3.32%/°C ± 0.36%/°C increase in CO emissions. Sensitivities vary by year and by pollutant, with SO both the highest sensitivity (5.04% in 2012) and lowest sensitivity (2.19% in 2007) in terms of a regional average. Texas displays 2007-2012 sensitivities of 2.34%/°C ± 0.28%/°C for generation, 0.91%/°C ± 0.25%/°C for SO emissions, 2.15%/°C ± 0.29%/°C for NO emissions, and 1.78%/°C ± 0.22%/°C for CO emissions. These results suggest demand-side and supply side technological improvements and fuel choice could play an important role in cost-effective reduction of carbon emissions and air pollution.

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.

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[1] To assess climate change impacts on hydrology, conservation biology, and air quality, impact studies typically require future climate data with spatial resolution high enough to resolve urban-rural gradients, complex topography, and sub-synoptic atmospheric phenomena. We present here an approach to dynamical downscaling using analysis nudging, where the entire domain is constrained to coarser-resolution parent data. Here meteorology from the North American Regional Reanalysis and the North American Regional Climate Change Assessment Program data archive are used as parent data and downscaled with the Advanced Research version of the Weather Research and Forecasting model to a 12 km  12 km horizontal resolution over the Eastern U.S. Our results show when analysis nudging is applied to all variables at all levels, mean fractional errors relative to parent data are less than 2% for maximum 2 m temperatures, less than 15% for minimum 2 m temperatures, and less than 18% for10 m wind speeds. However, the skill of representing fields that are not nudged, such as boundary layer height and precipitation, is less clear. Our results indicate that though nudging can be a useful tool for consistent, comparable studies of downscaling climate for regional and local impacts, which variables are nudged and at what levels should be carefully considered based on the climate impact(s) of study.Citation: Harkey, M., and T. Holloway (2013), Constrained dynamical downscaling for assessment of climate impacts,