A document by Maxime Visa. Click on the document to view its contents.

The U.S. and much of the world sit on the cusp of an electrification revolution – a moment driven largely by the need to reduce the emission of greenhouse gases to limit the impacts of anthropogenic climate change. The electrify everything movement aims to transition combustion-powered sectors into technologies powered solely by electricity, with the idea that the electric grid – currently comprised of a mix of combustion, renewable, and nuclear generation units – will become cleaner and greener over time. Studies indicate that electrifying high-efficiency devices, appliances, and vehicles will reduce greenhouse gas emissions regardless of the grid’s composition, however ancillary air quality benefits and tradeoffs remain poorly resolved, particularly at impact- and equity-relevant scales. Here, I use a fine-scale CONUS-wide climate and air quality co-benefit and tradeoff analysis framework (i.e., 4 km2 SMOKE-CMAQ-WRF simulations) to assess sustainable climate solutions. Analyses utilize emission scenarios that account for increased grid demand and uncertainties in grid evolution, simulate the interaction of meteorological and chemical processes, characterize changes in greenhouse gases and air pollutants, and assess economic, social, and public health consequences of sustainable transitions over two key, yet methodologically disparate, residential/commercial sectors: transportation: via the replacement of internal combustion vehicles with electric vehicles and lighting: via the replacement of low efficiency bulbs with high-efficiency LEDs.

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 Climate Data Center and EPA 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, i.e., 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 NO2 and O3 are highly correlated with observations (r > 0.6) while PM2.5 is less so (r = 0.4). Similarly, hourly simulated NO2 and PM2.5 have low biases (<10%), whereas O3 biases are larger (<30%). Simulated spatial pollutant patterns show distinct urban-rural footprints, with urban NO2 and PM2.5 20-60% higher than rural, and urban O3 6% lower. We use our 1.3 km simulations to resolve high-pollution areas within individual urban neighborhoods and characterize changes in O3 regimes across tight spatial gradients. Our findings demonstrate both the benefits and limitations of high-resolution simulations, particularly over urban settings.