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US background (US-B) ozone (O 3 ) is the O 3 that would be present in the absence of US anthropogenic (US-A) emissions. US-B O 3 varies by location and season and can make up a large, sometimes dominant, portion of total O 3 . Typically, US-B O 3 is quantified using a chemical transport model (CTM) though results are uncertain due to potential errors in model process descriptions and inputs, and there are significant differences in various model estimates of US-B O 3 . We develop and apply a method to fuse observed O 3 with US-B O 3 simulated by a regional CTM (CMAQ). We apportion the model bias as a function of space and time to US-B and US-A O 3 . Trends in O 3 bias are explored across different simulation years and varying model scales. We found that the CTM US-B O 3 estimate was typically biased low in spring and high in fall across years (2016-2017) and model scales. US-A O 3 was biased high on average, with bias increasing for coarser resolution simulations. With the application of our data fusion bias adjustment method, we estimate a 28% improvement in the agreement of adjusted US-B O 3 . Across the four estimates, we found annual mean CTM-simulated US-B O 3 ranging from 30 to 37 ppb with the spring mean ranging from 32 to 39 ppb. After applying the bias adjustment, we found annual mean US-B O 3 ranging from 32 to 33 ppb with the spring mean ranging from 37 to 39 ppb.

United States (US) background ozone (O3) is the counterfactual O3 that would exist with zero US anthropogenic emissions. Estimates of US background O3 typically come from chemical transport models (CTMs), but different models vary in their estimates of both background and total O3. Here, a measurement–model data fusion approach is used to estimate CTM biases in US anthropogenic O3 and multiple US background O3 sources, including natural emissions, long-range international emissions, short-range international emissions from Canada and Mexico, and stratospheric O3. Spatially and temporally varying bias correction factors adjust each simulated O3 component so that the sum of the adjusted components evaluates better against observations compared to unadjusted estimates. The estimated correction factors suggest a seasonally consistent positive bias in US anthropogenic O3 in the eastern US, with the bias becoming higher with coarser model resolution and with higher simulated total O3, though the bias does not increase much with higher observed O3. Summer average US anthropogenic O3 in the eastern US was estimated to be biased high by 2, 7, and 11 ppb (11 %, 32 %, and 49 %) for one set of simulations at 12, 36, and 108 km resolutions and 1 and 6 ppb (10 % and 37 %) for another set of simulations at 12 and 108 km resolutions. Correlation among different US background O3 components can increase the uncertainty in the estimation of the source-specific adjustment factors. Despite this, results indicate a negative bias in modeled estimates of the impact of stratospheric O3 at the surface, with a western US spring average bias of - 3.5 ppb (- 25 %) estimated based on a stratospheric O3 tracer. This type of data fusion approach can be extended to include data from multiple models to leverage the strengths of different data sources while reducing uncertainty in the US background ozone estimates. [ABSTRACT FROM AUTHOR]

United States (US) background ozone (O3) is the counterfactual O3 that would exist with zero US anthropogenic emissions. Estimates of US background O3 typically come from chemical transport models (CTMs), but different models vary in their estimates of both background and total O3. Here, a measurement-model data fusion approach is used to estimate CTM biases in US anthropogenic O3 and multiple US background O3 sources, including natural emissions, long-range international emissions, short-range international emissions from Canada and Mexico, and stratospheric O3. Spatially and temporally varying bias correction factors adjust each simulated O3 component so that the sum of the adjusted components evaluates better against observations compared to unadjusted estimates. The estimated correction factors suggest a seasonally consistent positive bias in US anthropogenic O3 in the eastern US, with the bias becoming higher with coarser model resolution and with higher simulated total O3 though the bias does not increase much with higher observed O3. Correlation among different US background O3 components can increase the uncertainty in the estimation of the source-specific adjustment factors. Despite this, results indicate that there may be a negative bias in modeled estimates of the impact of stratospheric O3 at the surface. This type of data fusion approach can be extended to include data from multiple models to leverage the strengths of different data sources while reducing uncertainty in the US background ozone estimates. [ABSTRACT FROM AUTHOR]

The transition of onroad vehicles from internal combustion engine vehicles to electric vehicles (EVs) has been promoted in the United States at federal and state levels to reduce emissions of conventional air pollutants and CO 2. One state level effort is a plan from California to mandate that all new passenger vehicle sales are EVs by 2035. Using the Community Multiscale Air Quality (CMAQ) model, we analyze the potential air quality impacts of having varying amounts of EVs in the California fleet for a historical (2016) and a future (2028) year, with emphasis on ozone (O 3) and fine particulate matter (PM 2.5). We find that for both PM 2.5 and O 3 , reductions in concentrations scale approximately linearly with increasing EV fleet turnover. Improvements in air quality are widespread throughout the state, with the largest improvements occurring near Los Angeles and throughout the Central Valley. With full electrification of onroad vehicle fleets, the statewide population-weighted annual average PM 2.5 concentration is reduced by about 0.5 μg/m3 for both 2016 and 2028. Reductions in state population-weighted average fourth highest MDA8 O 3 are 6.6 ppb and 4.3 ppb for 2016 and 2028. The results indicate that there are air quality benefits, and thus health benefits, to be gained from the transition to EVs and that these benefits do not diminish with increasing rates of EV adoption. [Display omitted] • Vehicle electrification results in significant reductions in PM 2.5 and ozone. • Air quality improvements are largest in Los Angeles and the Central Valley. • Reductions in PM 2.5 and ozone scale approximately linearly with electrification. • Reductions in population weighted average PM 2.5 exceed unweighted average. • Brake wear, tire wear, and road dust are still significant sources of emissions. [ABSTRACT FROM AUTHOR]