Use of Mobile and Passive Badge Air Monitoring Data for NOx and Ozone Air Pollution Spatial Exposure Prediction Models

Details

• Personal Author:
  Austin E ; Gould TR ; Hartin K ; Larson TV ; Riley EA ; Sampson PD ; Sasakura M ; Schaal L ; Simpson CD ; Vedal S ; Xiu G ; Xu W ; Yost MG
• Description:
  Air pollution exposure prediction models can make use of many types of air monitoring data. Fixed location passive samples typically measure concentrations averaged over several days to weeks. Mobile monitoring data can generate near continuous concentration measurements. It is not known whether mobile monitoring data are suitable for generating well-performing exposure prediction models or how they compare with other types of monitoring data in generating exposure models. Measurements from fixed site passive samplers and mobile monitoring platform were made over a 2-week period in Baltimore in the summer and winter months in 2012. Performance of exposure prediction models for long-term nitrogen oxides (NOX) and ozone (O3) concentrations were compared using a state-of-the-art approach for model development based on land use regression (LUR) and geostatistical smoothing. Model performance was evaluated using leave-one-out cross-validation (LOOCV). Models performed well using the mobile peak traffic monitoring data for both NOX and O3, with LOOCV R2s of 0.70 and 0.71, respectively, in the summer, and 0.90 and 0.58, respectively, in the winter. Models using 2-week passive samples for NOX had LOOCV R2s of 0.60 and 0.65 in the summer and winter months, respectively. The passive badge sampling data were not adequate for developing models for O3. Mobile air monitoring data can be used to successfully build well-performing LUR exposure prediction models for NOX and O3 and are a better source of data for these models than 2-week passive badge data. [Description provided by NIOSH]
• Subjects:
  Air Pollution Environmental Monitoring Epidemiology Occupational Health Particulate Matter Safety
• Keywords:
  Air Pollution Air Pollution Sampling Air Quality Monitoring Author Keywords: Air Pollution Monitoring Environmental Monitoring Exposure Assessment Exposure Models Land Use Regression Models Nitrogen Oxides Partial Least Squares Particulates Universal Kriging

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• ISSN:
  1559-0631
• Document Type:
  Text
• Funding:
  Grant ; Cooperative Agreement
• Genre:
  Journal Article
• Place as Subject:
  OSHA Region 10 ; Washington
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Pages in Document:
  184-192
• Volume:
  27
• Issue:
  2
• NIOSHTIC Number:
  nn:20063808
• Citation:
  J Expo Sci Environ Epidemiol 2017 Mar; 27(2):184-192
• Contact Point Address:
  Professor Guangli Xiu, Department of Environmental Engineering, East China University of Science and Technology, Shanghai, China
• Email:
  xiugl@ecust.edu
• CAS Registry Number:
  Ozone (CAS RN 10028-15-6)
• Federal Fiscal Year:
  2017
• NORA Priority Area:
  Agriculture, Forestry and Fishing
• Performing Organization:
  University of Washington
• Peer Reviewed:
  True
• Start Date:
  20050701
• Source Full Name:
  Journal of Exposure Science and Environmental Epidemiology
• End Date:
  20250630
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:5c3bbf8f8a3fafb6bef205d77df0b5de5416d2f23e7182503b83716ee68110e19aae10b45cd366277adc1a64f8f6916d3f77edb6268e581fd6bfd810ddbe963a
• Download URL:
  https://stacks.cdc.gov/view/cdc/222361/cdc_222361_DS1.pdf
• File Type:
  [PDF - 2.50 MB ]