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@ARTICLE{DeLang:904565,
      author       = {DeLang, Marissa N. and Becker, Jacob S. and Chang, Kai-Lan
                      and Serre, Marc L. and Cooper, Owen R. and Schultz, Martin
                      and Schröder, Sabine and Lu, Xiao and Zhang, Lin and
                      Deushi, Makoto and Josse, Beatrice and Keller, Christoph A.
                      and Lamarque, Jean-François and Lin, Meiyun and Liu, Junhua
                      and Marécal, Virginie and Strode, Sarah A. and Sudo, Kengo
                      and Tilmes, Simone and Zhang, Li and Cleland, Stephanie E.
                      and Collins, Elyssa L. and Brauer, Michael and West, J.
                      Jason},
      title        = {{M}apping {Y}early {F}ine {R}esolution {G}lobal {S}urface
                      {O}zone through the {B}ayesian {M}aximum {E}ntropy {D}ata
                      {F}usion of {O}bservations and {M}odel {O}utput for
                      1990–2017},
      journal      = {Environmental science $\&$ technology},
      volume       = {55},
      number       = {8},
      issn         = {0013-936X},
      address      = {Columbus, Ohio},
      publisher    = {American Chemical Society},
      reportid     = {FZJ-2021-06135},
      pages        = {4389 - 4398},
      year         = {2021},
      abstract     = {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.},
      cin          = {JSC},
      ddc          = {333.7},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / Earth System Data
                      Exploration (ESDE)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(DE-Juel-1)ESDE},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:33682412},
      UT           = {WOS:000643546400020},
      doi          = {10.1021/acs.est.0c07742},
      url          = {https://juser.fz-juelich.de/record/904565},
}