Scalable penalized spatiotemporal land-use regression for ground-level nitrogen dioxide

TL;DR

This paper introduces a scalable land-use regression model with penalization and Gaussian process approximation to accurately predict daily ground-level NO$_2$ concentrations across the US, aiding health risk assessments.

Contribution

It develops a novel scalable method combining variable selection, spatiotemporal modeling, and Gaussian process approximation for land-use regression of air pollution data.

Findings

Improved model selection sensitivity and specificity.

Better prediction accuracy and interpretability.

Revealed spatiotemporal NO$_2$ patterns across the US.

Abstract

Nitrogen dioxide (NO$_2$) 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 NO$_2$ 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 comparisons 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 NO$_2$ data, our approach was more accurate, and produced a sparser and more interpretable model. Our daily predictions elucidate spatiotemporal patterns of NO$_2$ 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.