Air Quality Estimation and Forecasting via Data Fusion With Uncertainty Quantification: Theoretical Framework and Preliminary Results

Abstract Integrating air quality information from models, satellites, and in situ monitors allows for both better estimation of air quality and better quantification of uncertainties in this estimation. Uncertainty quantification is important to appropriately convey confidence in these estimates and forecasts to users who will base decisions on these. Uncertainty quantification also allows tracing the value of information provided by different data sources. This can identify gaps in the monitoring network where additional data could further reduce uncertainties. This paper presents a framework for data fusion with uncertainty quantification, applicable to multiple air‐quality‐relevant pollutants. Testing of this framework in the context of nitrogen dioxide forecasting at sub‐city scales shows promising results, with confidence intervals typically encompassing the expected number of actual measurements during cross‐validation. The framework is now being implemented into an online tool to support local air quality management decision‐making. Future work will also include the incorporation of low‐cost air sensor data and the quantification of uncertainty at hyper‐local scales.