Open Access
June 2021 Scalable penalized spatiotemporal land-use regression for ground-level nitrogen dioxide
Kyle P. Messier, Matthias Katzfuss
Author Affiliations +
Ann. Appl. Stat. 15(2): 688-710 (June 2021). DOI: 10.1214/20-AOAS1422


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.

Funding Statement

Messier’s research was partially conducted while at Oregon State University, Department of Environmental and Molecular Toxicology, and supported by NIEHS K99 ES029523. Messier is currently supported by NIH institutes NIEHS/NTP and NIMHD as an intramural investigator. Katzfuss’ research was partially supported by National Science Foundation (NSF) Grants DMS-1654083 and DMS-1953005.


The authors would like to thank Shahzad Gani, Jianhua Huang, Irina Gaynanova, Anirban Bhattacharya and Joe Guinness for helpful comments and suggestions.

Simulations were run on computing resources at the Oregon State University Center for Genome Research and Biocomputing and the NIEHS/NTP Office of Data Science computing cluster.


Download Citation

Kyle P. Messier. Matthias Katzfuss. "Scalable penalized spatiotemporal land-use regression for ground-level nitrogen dioxide." Ann. Appl. Stat. 15 (2) 688 - 710, June 2021.


Received: 1 June 2020; Revised: 1 November 2020; Published: June 2021
First available in Project Euclid: 12 July 2021

MathSciNet: MR4298961
zbMATH: 1478.62356
Digital Object Identifier: 10.1214/20-AOAS1422

Keywords: Air pollution , Gaussian process , General Vecchia approximation , kriging , spatial statistics , Variable selection

Rights: Copyright © 2021 Institute of Mathematical Statistics

Vol.15 • No. 2 • June 2021
Back to Top