Satellite-Based NO2 and Model Validation in a National Prediction Model Based on Universal Kriging and Land-Use Regression

详细信息    查看全文

• 作者：Michael T. Young ; Matthew J. Bechle ; Paul D. Sampson ; Adam A. Szpiro ; Julian D. Marshall ; Lianne Sheppard ; Joel D. Kaufman
• 刊名：Environmental Science & Technology
• 出版年：2016
• 出版时间：April 5, 2016
• 年：2016
• 卷：50
• 期：7
• 页码：3686-3694
• 全文大小：416K
• ISSN：1520-5851

文摘

Epidemiological studies increasingly rely on exposure prediction models. Predictive performance of satellite data has not been evaluated in a combined land-use regression/spatial smoothing context. We performed regionalized national land-use regression with and without universal kriging on annual average NO₂ measurements (1990–2012, contiguous U.S. EPA sites). Regression covariates were dimension-reduced components of 418 geographic variables including distance to roadway. We estimated model performance with two cross-validation approaches: using randomly selected groups and, in order to assess predictions to unmonitored areas, spatially clustered cross-validation groups. Ground-level NO₂ was estimated from satellite-derived NO₂ and was assessed as an additional regression covariate. Kriging models performed consistently better than nonkriging models. Among kriging models, conventional cross-validated R² (R²_cv) averaged over all years was 0.85 for the satellite data models and 0.84 for the models without satellite data. Average spatially clustered R²_cv was 0.74 for the satellite data models and 0.64 for the models without satellite data. The addition of either kriging or satellite data to a well-specified NO₂ land-use regression model each improves prediction. Adding the satellite variable to a kriging model only marginally improves predictions in well-sampled areas (conventional cross-validation) but substantially improves predictions for points far from monitoring locations (clustered cross-validation).