Understanding spatial variability of air pollutant concentrations is critical for public health assessments. Our goal is to examine ground-level ozone and comparatively evaluate method performance for predicting and mapping national concentrations across the United States, while assessing the importance of accounting for spatial variability.Cross-sectional US EPA ozone monitoring data was acquired for three days in 2006, plus environmental covariates of land use, traffic, temperature, elevation, and population. Evaluation of ozone variability was assessed with land use regression (LUR) and spatially explicit kriging models. Ozone concentration was predicted with four approaches, including LUR, inverse distance weighting (IDW), ordinary kriging, and universal kriging, and evaluated with a Monte Carlo cross-validation simulation. Results were mapped for the continental United States.Temperature, elevation, and distance to major roads were significantly related to ozone concentrations and examination of spatial dependence on LUR models revealed the presence of residual spatial variation. Cross-validation results found kriging outperformed both LUR and IDW in terms of root mean squared prediction error. We demonstrate that national-level ozone is best evaluated using the statistically optimal kriging models, which account for residual spatial variation. Universal kriging was preferred over ordinary kriging by allowing us to assess the significance of environmental covariates both for inference and prediction of ozone concentrations.
Bibliographical noteFunding Information:
We thank Nick Mangus at US EPA for the acquisition of AQS ozone data. Jesse Berman was supported by a training grant from the National Institute for Occupational Safety and Health Education and Research Center ( ERC #T 420H008428 ).
© 2014 Elsevier B.V.
- Method comparison
- Spatial prediction