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<p>Long-term exposure to ambient ozone (O3) is associated with a variety of impacts,
including adverse health effects in humans (U.S. EPA 2013) and reduced yields in commercial
crops (Chameides et al., 1994; Mauzerall and Wang, 2001). Due to such impacts, efforts
have been undertaken in recent decades to reduce ground-level O3 through public policy
regulating the emission of anthropogenic precursor emissions, such as nitrogen oxides
(NOx) and volatile organic compounds (VOCs). These efforts have been widely successful
in reducing peak concentrations (Simon et al., 2015; Lefohn et al., 2017; Fleming
et al., 2018), but impacts related to both human-health and crop yields nonetheless
persist (Cohen et al., 2017; Seltzer et al., 2018; Zhang et al., 2018; Shindell et
al., 2019). Of particular importance, the maginute and trend of impacts reported in
the literature often feature substantial differences (Seltzer et al., 2018; Zhang
et al., 2018; Stanaway et al., 2018). As climate change is anticipated to exacerbate
O3 pollution (Leibensperger et al., 2008; Jacob and Winner 2009; Nolte et al., 2018)
and the emission of O3 precursors are projected to vary dramatically in both direction
and region over the coming decades (Rao et al., 2017), there is a growing need to
better constrain the magnitude and trends of, as well as illuminate the reason for
the persistent differences in, impacts attributable to long-term O3 exposure. Here,
I used a variety of modeling methods to explore the strengths and weaknesses of standard
methods that are frequently used to simulate impact metrics related to air quality,
generate a measurement-based estimate of the magnitude of O3 exposure and subsequent
impacts within several populous regions of the world, and use machine learning to
predict the trends in O3 exposure and subsequent impacts within the United States
over an extended period. </p><p>First, I use the NASA GISS ModelE2 and GEOS-Chem models,
each setup in a number of configurations, to simulate the near-present chemistry of
the atmosphere and predict a number of impact metrics. Results featured minor differences
due to the model resolution, whereas model, meteorology, and emissions inventory each
drove large variances. Surface metrics related to O3 were consistently high biased
and capturing the change in O3 metrics over time proved difficult, demonstrating the
need to evaluate particular modeling frameworks before O3 impacts are quantified.
Oftentimes, the configuration that captured the change of a metric best over time
differed from the configuration that captured the magnitude of the same metric best,
illustrating the difficulty in skillfully simulating and evaluating predicted impacts.
</p><p>Then, I use data solely from dense ground-based monitoring networks in the
United States, Europe, and China for 2015 to estimate long-term O3 exposure and calculate
premature respiratory mortality using exposure-response relationships derived from
two separate analyses of the American Cancer Society Cancer Prevention Study-II (ACS
CPS-II) cohort. Results show that estimated impacts were quite different when using
the two cohort analyses, with the analysis using the older ACS CPS-II cohort yielding
approximately 32%–50% lower health impacts. In addition, both sets of results are
lower (∼20%–60%) on a region-by-region basis than analogous prior studies based solely
on CTM predicted O3, due in large part to the fact that the latter tends to be high
biased in estimating exposure. I also demonstrate how small biases in modeled results
of long-term O3 exposure can amplify health impacts due to nonlinear exposure-response
relationships. </p><p>Finally, I develop and apply artificial neural networks to empirically
model long-term O3 exposure over the continental United States from 2000-2015, generating
a 16-year measurement-based assessment of impacts on human-health and crop yields.
I again find that the impacts are quite different when using the two ACS CPS-II cohort
studies, but I notably also report that the results differ in their trends over the
study period due to the seasons included in each averaging metric. When using the
older averaging metric and concentration-response function, there was a ~18% decrease
in normalized human-health impacts. In contrast, there was little change in the newer
averaging metric between 2000-2015, which resulted in a ~5% increase in normalized
human-health impacts. In both cases, an aging population structure played a substantial
role in modulating these trends. All agriculture-weighted crop-loss metrics indicate
yield improvements over this period, with reductions in the estimated national relative
yield loss ranging from 1.7-1.9 % for maize, 5.1-7.1% for soybeans, and 2.7% for wheat.
Overall, the results from this study illustrate how different conclusions regarding
historical impacts can be made through the use of varying metrics.</p>
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