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

ABSTRACT: 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 comparisons to existing methods using simulated data, our approach resulted in higher model-selection specificity and sensitivity

SUBMITTER: Messier KP 

PROVIDER: S-EPMC8774268 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature