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.
journal_name
Environ Health Perspectjournal_title
Environmental health perspectivesauthors
Adam-Poupart A,Brand A,Fournier M,Jerrett M,Smargiassi Adoi
10.1289/ehp.1306566subject
Has Abstractpub_date
2014-09-01 00:00:00pages
970-6issue
9eissn
0091-6765issn
1552-9924journal_volume
122pub_type
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