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 NO RESULTS: Using bneR, we harmonized the spatial scale of four input NO 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.