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

• 成果类型: Article
• 署名作者: Messier, Kyle P.; Katzfuss, Matthias
• 署名单位: National Institutes of Health (NIH) - USA; NIH National Institute of Environmental Health Sciences (NIEHS); Texas A&M University System; Texas A&M University College Station
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/20-AOAS1422
• 发表日期: 2021
• 页码: 688-710
• 关键词: particle number concentration; gaussian process models; urban air-pollution; particulate matter; variable selection; pm2.5; no2; variability; exposure; regularization

摘要

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.