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.Objectives: 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.Citation: Adam-Poupart A, Brand A, Fournier M, Jerrett M, Smargiassi A. 2014. Spatiotemporal modeling of ozone levels in Quebec (Canada): a comparison of kriging, land-use regression (LUR), and combined Bayesian maximum entropy–LUR approaches. Environ Health Perspect 122:970–976; http://dx.doi.org/10.1289/ehp.1306566

A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods

“…This study showed a strong association between NO 2 and lung cancer. Jerrett and his colleagues later estimated the O 3 exposure in Quebec, QC, Canada using a variety of models, including a Land-Use mixed-effects Regression (LUR) model, a mixture of a Bayesian Maximum Entropy (BME) model and the land-use mixed model as well as a kriging method model [89]. The experimental results proved the superiority of the mixture model (BME-LUR).…”

“…Although researchers chose different predictor variables according to local environments in their LUR models, most of them used linear regression to model the relationship between the pollutant level and the predictor variables [13,15,16,[77][78][79][80][81][82][83][84][85][86][87][88][89][90][91][92][93][94][95]. One of the assumptions for linear regression is that the observations should be independent of each other.…”

Applying land use regression model to estimate spatial variation of PM2.5 in Beijing, China

“…Several recent LUR studies have attempted to capture temporal variation, mainly in three ways: (1) by modifying the intercept of LUR models based on a temporal variation observed at background monitoring sites (Gan et al 2011;Nethery et al 2008;Saraswat et al 2013;Slama et al 2007); (2) by adding dummy variables to reflect different periods (Dons et al 2013;Noth et al 2011);and (3) by building LUR models for different periods with dynamic or static variables (Dons et al 2013;Gulliver et al 2011). Other studies even integrated the above methods (Adam-Poupart et al 2014;Patton et al 2014). The first two approaches are limited to producing the same spatial pattern of pollutants over time, because they are based on a typical LUR model.…”

Using UAV-Based Systems to Monitor Air Pollution in Areas with Poor Accessibility

“…These pollution maps were generated using the R Graph tool [43] and following a Kriging-based interpolation [44]. In particular, a Gaussian distribution is used to adjust the parameters coming from UAV [2] UAV [3] UAV [4] UAV [5] UAV [6] UAV [7] UAV [8] UAV [9] UAV [10] UAV [11] UAV [12] UAV [13] UAV [14] UAV [15] UAV [16] UAV [17] UAV [18] UAV [19] UAV [20] UAV [21] UAV [22] UAV [23] UAV [24] UAV [25] UAV [26] UAV [27] UAV [28] UAV [29] UAV [30] UAV [31] UAV [32] UAV [33] UAV [34] UAV [35] UAV [36] UAV [37] UAV [38] UAV [39] UAV [40] UAV [41] UAV [42] UAV [43] UAV [44] UAV [45] UAV [46] UAV UAV [9]…”