Dataset Information

Multivariate Spatial Prediction of Air Pollutant Concentrations with INLA.

ABSTRACT: Estimates of daily air pollution concentrations with complete spatial and temporal coverage are important for supporting epidemiologic studies and health impact assessments. While numerous approaches have been developed for modeling air pollution, they typically only consider each pollutant separately. We describe a spatial multipollutant data fusion model that combines monitoring measurements and chemical transport model simulations that leverages dependence between pollutants to improve spatial prediction. For the contiguous United States, we created a data product of daily concentration for 12 pollutants (CO, NOx, NO₂, SO₂, O₃, PM₁₀, and PM_2.5 species EC, OC, NO₃, NH₄, SO₄) during the period 2005 to 2014. Out-of-sample prediction showed good performance, particularly for daily PM_2.5 species EC (R² = 0.64), OC (R² = 0.75), NH₄ (R² = 0.84), NO₃ (R2 = 0.73), and SO₄ (R² = 0.80). By employing the integrated nested Laplace approximation (INLA) for Bayesian inference, our approach also provides model-based prediction error estimates. The daily data product at 12km spatial resolution will be publicly available immediately upon publication. To our knowledge this is the first publicly available data product for major PM_2.5 species and several gases at this spatial and temporal resolution.

SUBMITTER: Gong W

PROVIDER: S-EPMC9187197 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature

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Multivariate Spatial Prediction of Air Pollutant Concentrations with INLA.

Gong Wenlong W Reich Brian J BJ Chang Howard H HH

Environmental research communications 20211027 10

Estimates of daily air pollution concentrations with complete spatial and temporal coverage are important for supporting epidemiologic studies and health impact assessments. While numerous approaches have been developed for modeling air pollution, they typically only consider each pollutant separately. We describe a spatial multipollutant data fusion model that combines monitoring measurements and chemical transport model simulations that leverages dependence between pollutants to improve spatia ...[more]

PMID: 35694083

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Dataset Information

Multivariate Spatial Prediction of Air Pollutant Concentrations with INLA.

Publications

Multivariate Spatial Prediction of Air Pollutant Concentrations with INLA.

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