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.

The final version of this paper has been published and is available (open-access) from the Earth & Space Science JournalWhile multiple information sources exist concerning surface-level air pollution, no individual source simultaneously provides large-scale spatial coverage, fine spatial and temporal resolution, and high accuracy. It is therefore necessary to integrate multiple data sources, using the strengths of each source to compensate for the weaknesses of others. In this paper, we propose a method incorporating outputs of NASA’s GEOS Composition Forecasting model system with satellite information from the TROPOMI instrument and ground measurement data on surface concentrations. Although we use ground monitoring data from the EPA network in the continental United States (US), the model and satellite data sources used have the potential to allow for global application. This method is demonstrated using surface measurements of nitrogen dioxide as a test case in regions surrounding five major US cities. The proposed method is assessed through cross-validation against withheld ground monitoring sites. In these assessments, the proposed method demonstrates major improvements over two baseline approaches which use ground-based measurements only. Results also indicate the potential for near-term updating of forecasts based on recent ground measurements.