Land Use Regression Models to Assess Air Pollution Exposure in Mexico City Using Finer Spatial and Temporal Input Parameters

Details

• Alternative Title:
  Sci Total Environ
• Personal Author:
  Son, Yeongkwon ; Osornio-Vargas, Álvaro R. ; O’Neill, Marie S. ; Hystad, Perry ; Texcalac-Sangrador, José L. ; Ohman-Strickland, Pamela ; Meng, Qingyu ; Schwander, Stephan
• Description:
  The Mexico City Metropolitan Area (MCMA) is one of the largest and most populated urban environments in the world and experiences high air pollution levels. To develop models that estimate pollutant concentrations at fine spatiotemporal scales and provide improved air pollution exposure assessments for health studies in Mexico City. We developed finer spatiotemporal land use regression (LUR) models for PM|, PM|, O|, NO|, CO and SO| using mixed effect models with the Least Absolute Shrinkage and Selection Operator (LASSO). Hourly traffic density was included as a temporal variable besides meteorological and holiday variables. Models of hourly, daily, monthly, 6-monthly and annual averages were developed and evaluated using traditional and novel indices. The developed spatiotemporal LUR models yielded predicted concentrations with good spatial and temporal agreements with measured pollutant levels except for the hourly PM|, PM| and SO|. Most of the LUR models met performance goals based on the standardized indices. LUR models with temporal scales greater than one hour were successfully developed using mixed effect models with LASSO and showed superior model performance compared to earlier LUR models, especially for time scales of a day or longer. The newly developed LUR models will be further refined with ongoing Mexico City air pollution sampling campaigns to improve personal exposure assessments.
• Keywords:
  Air Pollutants Air Pollution Cities Environmental Monitoring Exposure Land Use Regression LASSO Mexico Mexico City Particulate Matter
• Source:
  Sci Total Environ. 639:40-48
• Pubmed ID:
  29778680
• Pubmed Central ID:
  PMC10896644
• Document Type:
  Journal Article
• Funding:
  T42 OH008455/OH/NIOSH CDC HHSUnited States/ ; P30 ES005022/ES/NIEHS NIH HHSUnited States/ ; R01 ES016932/ES/NIEHS NIH HHSUnited States/ ; T42OH008455/ACL/ACL HHSUnited States/ ; R01 ES020382/ES/NIEHS NIH HHSUnited States/ ; P30 ES017885/ES/NIEHS NIH HHSUnited States/ ; R01 ES017022/ES/NIEHS NIH HHSUnited States/
• Volume:
  639
• Collection(s):
  CDC Public Access
• Main Document Checksum:
  urn:sha256:c19d18011858ae81a6a523cda379b01892761a596ec75ecf2a764af5a97dc134
• Download URL:
  https://stacks.cdc.gov/view/cdc/150351/cdc_150351_DS1.pdf
• File Type:
  [PDF - 1.55 MB ]