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

The Science of the Total Environment
Yeongkwon SonStephan Schwander

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 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 sca...Continue Reading

Citations

Jun 6, 2020·Environmental Monitoring and Assessment·Jad ZalzalMarianne Hatzopoulou
Apr 27, 2019·International Journal of Environmental Research and Public Health·Lan WangPingping Bao
Feb 24, 2021·Environmental Pollution·Aslı BozdağÖznur Begüm Gökçek
Mar 17, 2021·Environmental Monitoring and Assessment·Anahi AguileraAvto Goguitchaichvili
Jul 3, 2021·International Journal of Environmental Research and Public Health·Yan ZhangChunbao Fu
Jul 16, 2021·Environmental Pollution·Shin ArakiUNKNOWN Japan Environment and Children's Study Group

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