SCALABLE PENALIZED SPATIOTEMPORAL LAND-USE REGRESSION FOR GROUND-LEVEL NITROGEN DIOXIDE

Nitrogen dioxide (NO2) is a primary constituent of traffic-related air pollution and has well-established harmful environmental and human-health impacts. Knowledge of the spatiotemporal distribution of NO2 is critical for exposure and risk assessment. A common approach for assessing air pollution exposure is linear regression involving spatially referenced covariates, known as land-use regression (LUR). We develop a scalable approach for simultaneous variable selection and estimation of LUR models with spatiotemporally correlated errors, by combining a general-Vecchia Gaussian-process approximation with a penalty on the LUR coefficients. In comparison to existing methods using simulated data, our approach resulted in higher model-selection specificity and sensitivity and in better prediction in terms of calibration and sharpness, for a wide range of relevant settings. In our spatiotemporal analysis of daily, US-wide, ground-level NO2 data, our approach was more accurate, and produced a sparser and more interpretable model. Our daily predictions elucidate spatiotemporal patterns of NO2 concentrations across the United States, including significant variations between cities and intra-urban variation. Thus, our predictions will be useful for epidemiological and risk-assessment studies seeking daily, national-scale predictions, and they can be used in acute-outcome health-risk assessments.

Automated summary

The study developed a scalable approach for simultaneous selection of variables and estimation of LUR models with spatiotemporally correlated errors through a combination of general Vecchia Gaussian-process approximation with a penalty on the LUR coefficients. Compared to existing methods using simulated data, the approach resulted in higher model-selection specificity and sensitivity, as well as better prediction in terms of calibration and sharpness, across a wide range of settings. The spatiotemporal analysis of daily ground-level NO2 data in the United States using this approach was more accurate and produced a sparser and more interpretable model, revealing significant variations between cities and intra-urban variation. The daily predictions can be valuable for epidemiological and risk-assessment studies seeking daily, national-scale predictions and for use in acute-outcome health-risk assessments.