Prediction of Fine Particulate Matter Chemical Components with a Spatio-Temporal Model for the Multi-Ethnic Study of Atherosclerosis Cohort

Details

• Personal Author:
  Bergen S ; Kaufman JD ; Kim S-Y ; Sampson PD ; Sheppard L ; Szpiro AA ; Vedal S
• Description:
  Although cohort studies of the health effects of PM2.5 have developed exposure prediction models to represent spatial variability across participant residences, few models exist for PM2.5 components. We aimed to develop a city-specific spatio-temporal prediction approach to estimate long-term average concentrations of four PM2.5 components including sulfur, silicon, and elemental and organic carbon for the Multi-Ethnic Study of Atherosclerosis cohort, and to compare predictions to those from a national spatial model. Using 2-week average measurements from a cohort-focused monitoring campaign, the spatio-temporal model employed selected geographic covariates in a universal kriging framework with the data-driven temporal trend. Relying on long-term means of daily measurements from regulatory monitoring networks, the national spatial model employed dimension-reduced predictors using universal kriging. For the spatio-temporal model, the cross-validated and temporally-adjusted R2 was relatively higher for EC and OC, and in the Los Angeles and Baltimore areas. The cross-validated R2s for both models across the six areas were reasonably high for all components except silicon. Predicted long-term concentrations at participant homes from the two models were generally highly correlated across cities but poorly correlated within cities. The spatio-temporal model may be preferred for city-specific health analyses, whereas both models could be used for multi-city studies. [Description provided by NIOSH]
• Subjects:
  Aerosols Air Pollution Cohort Studies Environmental Monitoring Epidemiology Occupational Health Particulate Matter Safety
• Keywords:
  Air Pollution Air Quality Monitoring Author Keywords: Empirical/Statistical Models Cohort Studies Environmental Monitoring Exposure Modeling Models Particulate Matter Particulates
• ISSN:
  1559-0631
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Journal Article
• Place as Subject:
  Minnesota ; OSHA Region 10 ; OSHA Region 5 ; Washington
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Volume:
  26
• Issue:
  5
• NIOSHTIC Number:
  nn:20063721
• Citation:
  J Expo Sci Environ Epidemiol 2016 Sep; 26(5):520-528
• Contact Point Address:
  Dr. Sun-Young Kim, Institute of Health and Environment, Seoul National University, Room 717, Building 221, 1, Gwanak-ro, Gwanak-gu, Seoul 08826, Korea
• Email:
  puha0@snu.ac.kr
• Federal Fiscal Year:
  2016
• 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-512:012be95df5b711ef856439589981884ed62ead6ac24d2e983e5a3d63f20fdb2c6e2be0ac57e60c7c06bc037be41bcb109fefdad7c3e416d4c457d4e05ad1b339
• Download URL:
  https://stacks.cdc.gov/view/cdc/222282/cdc_222282_DS1.pdf
• File Type:
  [PDF - 1.60 MB ]