A peer-reviewed open access journal published by the National Institute of Environmental Health Sciences

National-scale empirical models for air pollution can include hundreds of geographic variables. The impact of model parsimony (i.e., how model performance differs for a large versus small number of covariates) has not been systematically explored. We aim to (1) build annual-average integrated empirical geographic (IEG) regression models for the contiguous U.S. for six criteria pollutants during 1979–2015; (2) explore systematically the impact on model performance of the number of variables selected for inclusion in a model; and (3) provide publicly available model predictions. We compute annual-average concentrations from regulatory monitoring data for PM₁₀, PM_2.5, NO₂, SO₂, CO, and ozone at all monitoring sites for 1979–2015. We also use ~350 geographic characteristics at each location including measures of traffic, land use, land cover, and satellite-based estimates of air pollution. We then develop IEG models, employing universal kriging and summary factors estimated by partial least squares (PLS) of geographic variables. For all pollutants and years, we compare three approaches for choosing variables to include in the PLS model: (1) no variables, (2) a limited number of variables selected from the full set by forward selection, and (3) all variables. We evaluate model performance using 10-fold cross-validation (CV) using conventional and spatially-clustered test data. Models using 3 to 30 variables selected from the full set generally have the best performance across all pollutants and years (median R² conventional [clustered] CV: 0.66 [0.47]) compared to models with no (0.37 [0]) or all variables (0.64 [0.27]). Concentration estimates for all Census Blocks reveal generally decreasing concentrations over several decades with local heterogeneity. Our findings suggest that national prediction models can be built by empirically selecting only a small number of important variables to provide robust concentration estimates. Model estimates are freely available online.

Ozone is known to cause bronchial inflammation and airway hyper-responsiveness via oxidative injury and inflammation. While other ambient air pollutants such as particulate matter (PM) and NO2 showed decreasing trends of mean annual concentrations, ozone concentration did not decline recently in most countries worldwide. Short-term exposure to high concentration of ozone has been associated with increased mortality and cardiovascular and respiratory morbidity in many regions of the world. However, long-term effects of ozone were less investigated than short-term exposure due to the difficulty of modeling for ozone exposure and linking between individual exposure and health outcome data. Recently developed modeling of ozone exposure enabled investigation of long-term ozone effects on health outcomes. Recent findings suggested that long-term exposure to ozone is associated with an increased risk of cardiovascular and respiratory mortality. Longitudinal studies using large cohorts also revealed that long-term exposure to ozone was associated with a greater decline in lung function and progression of emphysema. Keeping standards for ozone as well as PM is needed to protect respiratory health of the general population and subjects with chronic respiratory diseases.