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

Details

• Personal Author:
  Hystad P ; Meng Q ; Ohman-Strickland P ; O'Neill MS ; Osornio-Vargas ÁR ; Schwander S ; Son Y ; Texcalac-Sangrador JL
• 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 PM2.5, PM10, O3, NO2, CO and SO2 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 PM2.5, PM10 and SO2. 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. [Description provided by NIOSH]
• Subjects:
  Air Pollution Environmental Monitoring Occupational Health Safety
• Keywords:
  Air Pollution Air Quality Measurement Air Sampling Techniques Author Keywords: Land Use Regression Automotive Emissions Exposure Exposure Assessment LASSO Mexico City Models
• ISSN:
  0048-9697
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Journal Article
• Place as Subject:
  Michigan ; New Jersey ; Oregon ; OSHA Region 10 ; OSHA Region 2 ; OSHA Region 5
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Pages in Document:
  40-48
• Volume:
  639
• NIOSHTIC Number:
  nn:20055259
• Citation:
  Sci Total Environ 2018 Oct; 639:40-48
• Contact Point Address:
  Qingyu Meng, School of Public Health, Rutgers University, 683 Hoes Lane West, Piscataway, NJ 08854, USA
• Email:
  mengqi@sph.rutgers.edu
• Federal Fiscal Year:
  2019
• Performing Organization:
  University of Michigan, Ann Arbor
• Peer Reviewed:
  True
• Start Date:
  20050701
• Source Full Name:
  Science of the Total Environment
• End Date:
  20280630
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:2a73270b389100a832e816bb105cbeb0ba993f3d0480a9ba1dff59537f88918f6b70470b3150372bec36fd8a88868e4ee1af258042214e92cc04af29f3f268d1
• Download URL:
  https://stacks.cdc.gov/view/cdc/217603/cdc_217603_DS1.pdf
• File Type:
  [PDF - 1.70 MB ]