Mapping Yearly Fine Resolution Global Surface Ozone Through the Bayesian Maximum Entropy Data Fusion of Observations and Model Output for 1990–2017

Details

• Personal Author:
  Becker JS ; Brauer M ; Chang K-L ; Cleland SE ; Collins EL ; Cooper OR ; DeLang MN ; Deushi M ; Josse B ; Keller CA ; Lamarque J-F ; Lin M ; Liu J ; Lu X ; Marécal V ; Schröder S ; Schultz MG ; Serre ML ; Strode SA ; Sudo K ; Tilmes S ; West JJ ; Zhang L
• Description:
  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 degrees resolution using BME, we add fine spatial detail from an additional model, yielding estimates at 0.1 degrees resolution. Observed ozone is predicted more accurately (R2 = 0.81 at the test point, 0.63 at 0.1 degrees, and 0.62 at 0.5 degrees) than the multimodel mean (R2 = 0.28 at 0.5 degrees). 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. [Description provided by NIOSH]
• Subjects:
  Air Pollution Climate Change Occupational Health Ozone Particulate Matter Respiratory System Safety
• Keywords:
  Air Pollution Sampling Climate Change Climatic Conditions Environmental Health Monitoring Particulates Respiratory Irritants
• ISSN:
  0013-936X
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Journal Article
• Place as Subject:
  Colorado ; Maryland ; New Jersey ; North Carolina ; OSHA Region 10 ; OSHA Region 2 ; OSHA Region 3 ; OSHA Region 4 ; OSHA Region 8 ; Pennsylvania ; Washington
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Volume:
  55
• Issue:
  8
• NIOSHTIC Number:
  nn:20068124
• Citation:
  Environ Sci Technol 2021 Apr; 55(8):4389-4398
• Contact Point Address:
  J. Jason West, Department of Environmental Sciences and Engineering, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
• Email:
  jasonwest@unc.edu
• CAS Registry Number:
  Ozone (CAS RN 10028-15-6)
• Federal Fiscal Year:
  2021
• Performing Organization:
  University of North Carolina, Chapel Hill
• Peer Reviewed:
  True
• Start Date:
  20050701
• Source Full Name:
  Environmental Science and Technology
• End Date:
  20270630
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:4324667d723fb03dd4fc28c58d2fbb249bcb03fbf4c11c83bb83673001a9e4035a3ee9f08930d1cb326be1db314fde6b4acb0ce1f2a7aed95b751cefa6be4b00
• Download URL:
  https://stacks.cdc.gov/view/cdc/230339/cdc_230339_DS1.pdf
• File Type:
  [PDF - 4.43 MB ]