Measurement-based assessment of health burdens from long-term ozone exposure in the United States, Europe, and China

Abstract

Long-term ozone (O 3 ) exposure estimates from chemical transport models are frequently paired with exposure-response relationships from epidemiological studies to estimate associated health burdens. Impact estimates using such methods can include biases from model-derived exposure estimates. We use data solely from dense ground-based monitoring networks in the United States, Europe, and China for 2015 to estimate long-term O 3 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. Using results from the larger, extended ACS CPS-II study, 34 000 (95% CI: 24, 44 thousand), 32 000 (95% CI: 22, 41 thousand), and 200 000 (95% CI: 140, 253 thousand) premature respiratory mortalities are attributable to long-term O 3 exposure in the USA, Europe and China, respectively, in 2015. Results are approximately 32%-50% lower when using an older analysis of the ACS CPS-II cohort. Both sets of results are lower (∼20%-60%) on a region-byregion basis than analogous prior studies based solely on modeled O 3 , due in large part to the fact that the latter tends to be high biased in estimating exposure. This study highlights the utility of dense observation networks in estimating exposure to long-term O 3 exposure and provides an observational constraint on subsequent health burdens for three regions of the world. In addition, these results demonstrate how small biases in modeled results of long-term O 3 exposure can amplify estimated health impacts due to nonlinear exposure-response curves.

Ozone and heat-related mortality in Europe in 2050 significantly affected by changes in climate, population and greenhouse gas emission

“…This indicates that climate could have a larger effect on very high short-term concentrations of nearsurface ozone. Compared to the recent study by Seltzer et al (2018), we found somewhat larger effects of nearsurface ozone for Europe, as our estimates are based on all-cause mortality.…”

“…Past studies have estimated the future health effects of summertime ozone (Ebi and McGregor 2008, Sujaritpong et al 2014, Orru et al 2017 and high temperatures (Huang et al 2011, Sanderson et al 2017 globally, regionally, and locally, but very few have explored the long-term effects of ozone (Seltzer et al 2018), and ozone and temperatures simultaneously (Lee et al 2017). Moreover, there is limited knowledge about how other factors, such as decreases in ozone precursor emissions (Geels et al 2015 and changes in susceptible populations (Arbuthnott et al 2016, Petkova et al 2017 might affect projected health burdens.…”

A new method (M<sup>3</sup>Fusion v1) for combining observations and multiple model output for an improved estimate of the global surface ozone distribution

“…(2) For regions with large observational gaps, such as South America, Africa or Russia, the spatial difference between the fused product and the multi-model mean is rather featureless, because the model weighting can only adjust the overall regional mean according to a few monitoring sites and cannot address the local variations. Filling large data gaps with the intermediate multi-model composite can indeed avoid the influence of preferential sampling (Diggle et al, 2010;Shaddick and Zidek, 2014), but it is still subject to a high uncertainty due to lack of data.…”

Magnitude, Trends, and Impacts of Ambient Long-Term Ozone Exposure in the United States from 2000–2015

“…While CTMs accurately reproduce many features of atmospheric chemistry (Shindell et al, 2013;Hu et al, 2018), one important issue associated with CTM-based impact assessments is that CTMs are consistently high biased when predicting O 3 exposurerelevant concentrations (Schnell et al, 2015;Travis et al, 2016;Yan et al, 2016;Seltzer et al, 2017Guo et 40 al., 2018). Such biases can influence estimates of impacts and are often amplified by nonlinear concentration-response functions (Seltzer et al, 2018). Measurement-based methods, including area-weighted average of nearby monitors, nearest monitor, inverse distance weighting, Kriging interpolation, and multiple-linear regression under a Bayesian framework, can also be used to estimate exposure (Bell, 2006;Brauer et al, 2008;Marshall et al, 2008;Chang et al, 2010;Seltzer et al, 2018).…”

“…Such biases can influence estimates of impacts and are often amplified by nonlinear concentration-response functions (Seltzer et al, 2018). Measurement-based methods, including area-weighted average of nearby monitors, nearest monitor, inverse distance weighting, Kriging interpolation, and multiple-linear regression under a Bayesian framework, can also be used to estimate exposure (Bell, 2006;Brauer et al, 2008;Marshall et al, 2008;Chang et al, 2010;Seltzer et al, 2018). However, a notable limitation of such methods stems from the sparse spatial coverage of monitoring sites.…”