Comparing Universal Kriging and Land-Use Regression for Predicting Concentrations of Gaseous Oxides of Nitrogen (NOx) for the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air)

Details

• Personal Author:
  Adar SD ; Allen RW ; Avol EL ; Kaufman JD ; Larson T ; Lindström J ; Liu L-JS ; Mercer LD ; Oron AP ; Sheppard L ; Szpiro AA
• Description:
  Background: Epidemiological studies that assess the health effects of long-term exposure to ambient air pollution are used to inform public policy. These studies rely on exposure models that use data collected from pollution monitoring sites to predict exposures at subject locations. Land-use regression (LUR) and universal kriging (UK) have been suggested as potential prediction methods. We evaluate these approaches on a dataset including measurements from three seasons in Los Angeles, CA. Methods: The measurements of gaseous oxides of nitrogen (NOx) used in this study are from a "snapshot" sampling campaign that is part of the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air). The measurements in Los Angeles were collected during three two-week periods in the summer, autumn, and winter, each with about 150 sites. The design included clusters of monitors on either side of busy roads to capture near-field gradients of traffic-related pollution. LUR and UK prediction models were created using geographic information system (GIS)-based covariates. Selection of covariates was based on 10-fold cross-validated (CV) R2 and root mean square error (RMSE). Since UK requires specialized software, a computationally simpler two-step procedure was also employed to approximate fitting the UK model using readily available regression and GIS software. Results: UK models consistently performed as well as or better than the analogous LUR models. The best CV R2 values for season-specific UK models predicting log(NOx) were 0.75, 0.72, and 0.74 (CV RMSE 0.20, 0.17, and 0.15) for summer, autumn, and winter, respectively. The best CV R2 values for season-specific LUR models predicting log(NOx) were 0.74, 0.60, and 0.67 (CV RMSE 0.20, 0.20, and 0.17). The two-stage approximation to UK also performed better than LUR and nearly as well as the full UK model with CV R2 values 0.75, 0.70, and 0.70 (CV RMSE 0.20, 0.17, and 0.17) for summer, autumn, and winter, respectively. Conclusion: High quality LUR and UK prediction models for NOx in Los Angeles were developed for the three seasons based on data collected for MESA Air. In our study, UK consistently outperformed LUR. Similarly, the 2-step approach was more effective than the LUR models, with performance equal to or slightly worse than UK. [Description provided by NIOSH]
• Subjects:
  Air Pollution Environmental Monitoring Epidemiology Occupational Health Safety
• Keywords:
  Air Pollution Air Sampling Author Keywords: Universal Kriging Exposure Assessment Land-use Regression Long Term Exposure Los Angeles Models Nitrogen Oxides Spatial Modeling
• ISSN:
  1352-2310
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Journal Article
• Place as Subject:
  California ; Michigan ; OSHA Region 10 ; OSHA Region 5 ; OSHA Region 9 ; Washington
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Volume:
  45
• Issue:
  26
• NIOSHTIC Number:
  nn:20054715
• Citation:
  Atmos Environ 2011 Aug; 45(26):4412-4420
• Contact Point Address:
  Adam A. Szpiro, Department of Biostatistics, University of Washington, F-600, Health Sciences Building, Seattle, WA 98195-7232, USA
• Email:
  aszpiro@u.washington.edu
• Federal Fiscal Year:
  2011
• Performing Organization:
  University of Washington
• Peer Reviewed:
  True
• Start Date:
  20050701
• Source Full Name:
  Atmospheric Environment
• End Date:
  20250630
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:a22f9d3765f76874919e004bd88eeda2fc5b55c67f851c79c1af8885d647b049751b0ea76ed5b53553aad636cb4abf7a38990575043069d460c2281fe308e96d
• Download URL:
  https://stacks.cdc.gov/view/cdc/217165/cdc_217165_DS1.pdf
• File Type:
  [PDF - 1.13 MB ]