Nowcasting aerosol particle size distributions from PM2.5 with physics-regularized flow matching
The mass concentration of particles smaller than 2.5um (PM2.5) is cheap to measure and widely monitored, whereas the particle number size distribution (PSD) requires costly instruments and is measured at only a few research stations. Yet many aerosol effects, such as vapor uptake, cloud droplet formation and deposition in the human respiratory tract, depend on the PSD rather than on mass. We explore whether recent generative deep learning models can nowcast the PSD: from the PM2.5 record and eight meteorological parameters over a 24h window, a model generates an ensemble of plausible hourly size distributions for the same window, reflecting that one mass can arise from many size distributions. Rather than proposing a single best model, we use two contrasting Finnish stations, the boreal forest site Hyytiälä and the urban site Kumpula, as a controlled testbed. On it, we benchmark six generative models against deterministic and climatological baselines, locally and after transfer between sites, and test physical constraints for neural network training. Flow matching performs best overall; its guided variant has the lowest continuous ranked probability score (CRPS) at both sites. A constraint based on a reduced aerosol general dynamic equation (GDE) lowers the CRPS by 5.5-11.5% at the training site and reduces errors after transfer between sites. Transfer remains harder than local estimation, identifying training on large multi-station data sets as the next step towards PSD estimates at unmonitored locations. This benchmark establishes physics-informed generative models as a promising route from routine air-quality monitoring to size-resolved aerosol information.