Physics-Regularized Neural Networks for Urban Air Quality Prediction Under Meteorological Variability: Emission Source Estimation from Sparse Observations in London
This paper proposes a physics-regularized neural network framework that integrates sparse urban monitoring data with meteorological reanalysis and atmospheric transport physics to reconstruct spatially continuous PM2.5 emission-related fields in London, effectively constraining ill-posed inverse solutions to provide physically plausible diagnostic insights under data-scarce conditions.