title: Spatiotemporal modeling of ozone levels in Quebec (Canada): a comparison of kriging, land-use regression (LUR), and combined Bayesian maximum entropy-LUR approaches.
abstract: BACKGROUND
  Ambient air ozone (O3) is a pulmonary irritant that has been associated with respiratory health effects including increased lung inflammation and permeability, airway hyperreactivity, respiratory symptoms, and decreased lung function. Estimation of O3 exposure is a complex task because the pollutant exhibits complex spatiotemporal patterns. To refine the quality of exposure estimation, various spatiotemporal methods have been developed worldwide.
  
  
  OBJECTIVE
  We sought to compare the accuracy of three spatiotemporal models to predict summer ground-level O3 in Quebec, Canada.
  
  
  METHODS
  We developed a land-use mixed-effects regression (LUR) model based on readily available data (air quality and meteorological monitoring data, road networks information, latitude), a Bayesian maximum entropy (BME) model incorporating both O3 monitoring station data and the land-use mixed model outputs (BME-LUR), and a kriging method model based only on available O3 monitoring station data (BME kriging). We performed leave-one-station-out cross-validation and visually assessed the predictive capability of each model by examining the mean temporal and spatial distributions of the average estimated errors.
  
  
  RESULTS
  The BME-LUR was the best predictive model (R2 = 0.653) with the lowest root mean-square error (RMSE ;7.06 ppb), followed by the LUR model (R2 = 0.466, RMSE = 8.747) and the BME kriging model (R2 = 0.414, RMSE = 9.164).
  
  
  CONCLUSIONS
  Our findings suggest that errors of estimation in the interpolation of O3 concentrations with BME can be greatly reduced by incorporating outputs from a LUR model developed with readily available data.
authors.affiliation: Department of Environmental and Occupational Health, Faculty of Public Health, Université de Montréal, Montréal, Québec, Canada.
authors.forename: Ariane
authors.initials: A
authors.lastname: Adam-Poupart
authors.forename: Allan
authors.initials: A
authors.lastname: Brand
authors.forename: Michel
authors.initials: M
authors.lastname: Fournier
authors.forename: Michael
authors.initials: M
authors.lastname: Jerrett
authors.forename: Audrey
authors.initials: A
authors.lastname: Smargiassi
mesh_terms: D000393:Air Pollutants / Q000032:analysis*; D000397:Air Pollution / Q000706:statistics & numerical data*; D001499:Bayes Theorem; D004784:Environmental Monitoring / Q000379:methods*; D008956:Models, Chemical; D010126:Ozone / Q000032:analysis*; D011792:Quebec; D012044:Regression Analysis; D012621:Seasons; D062206:Spatial Analysis; D013223:Statistics as Topic
chemical_list: D000393:Air Pollutants; D010126:Ozone