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Updated: May 30, 2025

Impact Assessment of Repeated Exposure of Organotypic 3D Bronchial and Nasal Tissue Culture Models to Whole Cigarette Smoke
Published on: February 12, 2015
bneR: A collaborative workflow for air pollution exposure modeling and uncertainty characterization using the
Jaime Benavides1, Carlos Carrillo-Gallegos2, Vijay Kumar1
1Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, NY, USA.
Background:
Air pollution is a major public health threat globally. Health studies, regulatory actions, and policy evaluations typically rely on air pollutant concentrations from single exposure models, assuming accurate estimations and ignoring related uncertainty. We developed a modeling framework, bneR, to apply the Bayesian Nonparametric Ensemble (BNE) prediction model that combines existing exposure models as inputs to provide air pollution estimates and their spatio-temporal uncertainty.
Methods:
The bneR modeling framework (1) harmonizes air pollutant datasets to use standardized inputs for the BNE algorithm; (2) applies the BNE algorithm to obtain the posterior predictive distribution of pollutant concentrations; and (3) generates visualizations. We applied bneR to estimate NO2 concentrations and characterize uncertainty levels at high spatio-temporal resolution (daily, 1 km2) over New York State (NYS) for 2015. We met with stakeholders and modelers to discuss bneR user-friendliness and interpretation of its estimates.
Results:
Using bneR, we harmonized the spatial scale of four input NO2 models (using the finer resolution, 1 km2 for BNE estimations), applied BNE to obtain the NO2 daily posterior predictive distribution, and visualized the results. Over NYS, the daily average NO2 concentration was 6.0 (interquartile range, IQR: 4.6-6.8) pbb with daily average uncertainty (as SD) of 1.2 (IQR: 1.0-1.3) ppb. BNE performed well with cross-validated RMSE=2.84 ppb and R2=0.80.
Conclusion:
Meeting stakeholders and modelers allowed us to understand that efficient communication on how uncertainty is estimated and interpreted is a key feature for these communities to engage in using bneR and its data products.
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