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| 001 | 904565 | ||
| 005 | 20230127125339.0 | ||
| 024 | 7 | _ | |a 10.1021/acs.est.0c07742 |2 doi |
| 024 | 7 | _ | |a 0013-936X |2 ISSN |
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| 037 | _ | _ | |a FZJ-2021-06135 |
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| 100 | 1 | _ | |a DeLang, Marissa N. |0 P:(DE-HGF)0 |b 0 |
| 245 | _ | _ | |a Mapping Yearly Fine Resolution Global Surface Ozone through the Bayesian Maximum Entropy Data Fusion of Observations and Model Output for 1990–2017 |
| 260 | _ | _ | |a Columbus, Ohio |c 2021 |b American Chemical Society |
| 336 | 7 | _ | |a article |2 DRIVER |
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| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 520 | _ | _ | |a Estimates of ground-level ozone concentrations are necessary to determine the human health burden of ozone. To support the Global Burden of Disease Study, we produce yearly fine resolution global surface ozone estimates from 1990 to 2017 through a data fusion of observations and models. As ozone observations are sparse in many populated regions, we use a novel combination of the M3Fusion and Bayesian Maximum Entropy (BME) methods. With M3Fusion, we create a multimodel composite by bias-correcting and weighting nine global atmospheric chemistry models based on their ability to predict observations (8834 sites globally) in each region and year. BME is then used to integrate observations, such that estimates match observations at each monitoring site with the observational influence decreasing smoothly across space and time until the output matches the multimodel composite. After estimating at 0.5° resolution using BME, we add fine spatial detail from an additional model, yielding estimates at 0.1° resolution. Observed ozone is predicted more accurately (R2 = 0.81 at the test point, 0.63 at 0.1°, and 0.62 at 0.5°) than the multimodel mean (R2 = 0.28 at 0.5°). Global ozone exposure is estimated to be increasing, driven by highly populated regions of Asia and Africa, despite decreases in the United States and Russia. |
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| 700 | 1 | _ | |a Becker, Jacob S. |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Chang, Kai-Lan |0 P:(DE-HGF)0 |b 2 |
| 700 | 1 | _ | |a Serre, Marc L. |0 P:(DE-HGF)0 |b 3 |
| 700 | 1 | _ | |a Cooper, Owen R. |0 P:(DE-HGF)0 |b 4 |
| 700 | 1 | _ | |a Schultz, Martin |0 P:(DE-Juel1)6952 |b 5 |
| 700 | 1 | _ | |a Schröder, Sabine |0 P:(DE-Juel1)16212 |b 6 |
| 700 | 1 | _ | |a Lu, Xiao |0 P:(DE-HGF)0 |b 7 |
| 700 | 1 | _ | |a Zhang, Lin |0 0000-0003-2383-8431 |b 8 |
| 700 | 1 | _ | |a Deushi, Makoto |0 P:(DE-HGF)0 |b 9 |
| 700 | 1 | _ | |a Josse, Beatrice |0 P:(DE-HGF)0 |b 10 |
| 700 | 1 | _ | |a Keller, Christoph A. |0 P:(DE-HGF)0 |b 11 |
| 700 | 1 | _ | |a Lamarque, Jean-François |0 P:(DE-HGF)0 |b 12 |
| 700 | 1 | _ | |a Lin, Meiyun |0 P:(DE-HGF)0 |b 13 |
| 700 | 1 | _ | |a Liu, Junhua |0 P:(DE-HGF)0 |b 14 |
| 700 | 1 | _ | |a Marécal, Virginie |0 P:(DE-HGF)0 |b 15 |
| 700 | 1 | _ | |a Strode, Sarah A. |0 P:(DE-HGF)0 |b 16 |
| 700 | 1 | _ | |a Sudo, Kengo |0 P:(DE-HGF)0 |b 17 |
| 700 | 1 | _ | |a Tilmes, Simone |0 P:(DE-HGF)0 |b 18 |
| 700 | 1 | _ | |a Zhang, Li |0 P:(DE-HGF)0 |b 19 |
| 700 | 1 | _ | |a Cleland, Stephanie E. |0 P:(DE-HGF)0 |b 20 |
| 700 | 1 | _ | |a Collins, Elyssa L. |0 P:(DE-HGF)0 |b 21 |
| 700 | 1 | _ | |a Brauer, Michael |0 0000-0002-9103-9343 |b 22 |
| 700 | 1 | _ | |a West, J. Jason |0 0000-0001-5652-4987 |b 23 |e Corresponding author |
| 773 | _ | _ | |a 10.1021/acs.est.0c07742 |g Vol. 55, no. 8, p. 4389 - 4398 |0 PERI:(DE-600)1465132-4 |n 8 |p 4389 - 4398 |t Environmental science & technology |v 55 |y 2021 |x 0013-936X |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/904565/files/acs.est.0c07742.pdf |
| 856 | 4 | _ | |y Published on 2021-03-08. Available in OpenAccess from 2022-03-08. |u https://juser.fz-juelich.de/record/904565/files/Preprint.pdf |
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