Land use regression models to assess air pollution exposure in Mexico City using finer spatial and temporal input parameters.

ABSTRACT: 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_2.5, 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 traditiona

SUBMITTER: Son Y 

PROVIDER: S-EPMC10896644 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature