Uncertainty aware Deep Learning for Downscaling Air Quality Concentrations

Poor air quality poses a major threat to public health globally. Fine particulate matter (PM_2.5) is of particular concern due to its ability to penetrate deep into the lungs and enter the cardiovascular system, contributing to respiratory disease, cancer and early mortality. These health impacts underpin the critical need for accurate, high-resolution estimates of population exposure to support effective intervention strategies and safeguard public health.

There are many sources of information detailing air quality, including ground observations, remote sensing and atmospheric models (AM). Ground networks can provide accurate local measurements but are often spatially sparse, while satellite products and AMs often provide good spatial coverage but may lack local detail and may be affected by indirect measurement errors or model misspecification. Data integration modelling techniques can be employed to bring these complimentary data sources together and enable accurate, spatially continuous, high-resolution maps of air quality estimates.

Statistical downscaling approaches are commonly employed for this purpose, but often their high computational cost and limited scalability have motivated the adoption of downscaling through machine learning (ML) methods. However, ML models are traditionally deterministic, not providing explicit quantification of prediction uncertainty which is vital for risk-based decision making. We can address this gap by developing a probabilistic ML downscaling framework based on a Bayesian convolutional neural network (BCNN) where predictive uncertainty deriving from both model structure and random error is quantified using Monte Carlo dropout.

In this study, a BCNN is designed to enhance Copernicus Atmosphere Monitoring Service (CAMS) PM_2.5 forecasts from their native 10 x 10 km resolution to 1 km in Western Europe. CAMS spatial data is spatially located with PM_2.5 ground observations such that each extracted image corresponds to an observed concentration at a given time and location. The BCNN is trained to learn the relationships between largescale atmospheric patterns and local PM_2.5 concentrations, enabling the creation of high-resolution prediction maps even in regions where ground monitoring in limited.  

The resulting framework produces spatially detailed, probabilistic PM_2.5 estimates at relatively low computational cost compared to traditional statistical downscaling methods. The downscaled pollution data enables improved assessments of population exposure to poor air quality and the identification of pollution hotspots. This approach demonstrates strong potential for broader applications in data-sparse regions and for supporting urban-scale air quality planning.