Measurement-based assessment of health burdens from long-term ozone exposure in the United States, Europe, and China
Bibliographic Data
| ID | 15550840 |
|---|---|
| Authors | Karl M Seltzer (0000-0002-2175-5678, Duke University, corresponding author), Drew Shindell (0000-0003-1552-4715, Duke University), Drew T Shindell, Christopher S Malley (0000-0001-5897-9977, Stockholm Environment Institute) |
| Year | 2018 |
| Volume | 13 |
| Issue | 10 |
| Pages | 104018-104018 |
| Publication date | 2018-10-10 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Environmental Research Letters (JOURNAL) |
| Journal identifiers | ISSN: 1748-9326 • E-ISSN: 1748-9326 |
| Publisher | IOP Publishing (PUBLISHER • GB) |
| DOI | 10.1088/1748-9326/aae29d |
| OpenAlex | W2896499985 |
| Language | EN |
| Citations received | 5 |
| References cited | 36 |
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-by-region 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
China · Cohort · Cohort study · Environmental health · Exposure assessment · Geography · Meteorology · Ozone · Term (time · Air Quality and Health Impacts · Atmospheric chemistry and aerosols · Climate Change and Health Impacts · Environmental Science · Medicine · Demography · Epidemiology
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| Unique citing works | 5 |
|---|---|
| Citations per year | 0,71 |
| Citation span | 2019 - 2026 (8) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 5 |