Predicting Intra-Urban Variation in Air Pollution Concentrations with Complex Spatio-Temporal Dependencies

Details

• Personal Author:
  Adar SD ; Kaufman JD ; Lumley T ; Sampson PD ; Sheppard L ; Szpiro AA
• Description:
  We describe a methodology for assigning individual estimates of long-term average air pollution concentrations that accounts for a complex spatio-temporal correlation structure and can accommodate spatio-temporally misaligned observations. This methodology has been developed as part of the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air), a prospective cohort study funded by the US EPA to investigate the relationship between chronic exposure to air pollution and cardiovascular disease. Our hierarchical model decomposes the space--time field into a "mean" that includes dependence on covariates and spatially varying seasonal and long-term trends and a "residual" that accounts for spatially correlated deviations from the mean model. The model accommodates complex spatio-temporal patterns by characterizing the temporal trend at each location as a linear combination of empirically derived temporal basis functions, and embedding the spatial fields of coefficients for the basis functions in separate linear regression models with spatially correlated residuals (universal kriging). This approach allows us to implement a scalable single-stage estimation procedure that easily accommodates a significant number of missing observations at some monitoring locations. We apply the model to predict long-term average concentrations of oxides of nitrogen (NOx) from 2005 to 2007 in the Los Angeles area, based on data from 18 EPA Air Quality System regulatory monitors. The cross-validated IR2 is 0.67. The MESA Air study is also collecting additional concentration data as part of a supplementary monitoring campaign. We describe the sampling plan and demonstrate in a simulation study that the additional data will contribute to improved predictions of long-term average concentrations. [Description provided by NIOSH]
• Subjects:
  Air Pollution Cardiovascular Diseases Cardiovascular System Environmental Exposure Occupational Exposure Occupational Health Safety
• Keywords:
  Air Pollution Air Pollution Sampling Author Keywords: Air Pollution Cardiovascular Disease Chronic Exposure Exposure Assessment Exposure Levels Hierarchical Modeling Maximum Likelihood Monitoring Systems Spatio-Temporal Modeling Universal Kriging

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• ISSN:
  1180-4009
• Document Type:
  Text
• Funding:
  Grant
• 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
• Volume:
  21
• Issue:
  6
• NIOSHTIC Number:
  nn:20054874
• Citation:
  Environmetrics 2009 Sep; 21(6):606-631
• Contact Point Address:
  Adam A. Szpiro, Department of Biostatistics, University of Washington, Box 357232, Seattle, WA, 98195- 7232, U.S.A.
• Email:
  aszpiro@u.washington.edu
• Federal Fiscal Year:
  2009
• Performing Organization:
  University of Washington
• Peer Reviewed:
  True
• Start Date:
  20050701
• Source Full Name:
  Environmetrics
• End Date:
  20250630
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:64e87518f18a8b971583d2665b00db7bc87c6c90eff42399e0ffe07482ae1fceae7b6a86e8a165cc8d468208d30c4882754da24cdec60a3e331548875aa2bdb4
• Download URL:
  https://stacks.cdc.gov/view/cdc/217288/cdc_217288_DS1.pdf
• File Type:
  [PDF - 768.10 KB ]