Learning physically interpretable deep networks from reanalysis data for medium-term regional PM_2.5 forecasts

Abstract Medium-term regional forecasts of fine particulate matter (PM 2.5 ) are critical to mitigate its hazardous environmental effects. Deep learning (DL) has recently emerged as a promising technique to improve traditional forecasts based on chemistry-transport models (CTMs). However, challenges remain in (1) representing the complex physics of the PM 2.5 pollution processes including the emission, transport, and chemical conversion of the pollutants, and (2) probing the interpretability of DL models for trustworthy forecasts and mitigations. Here we learn U-Net deep networks from a reanalysis dataset—the data fusion information by blending available pollutant observations and CTM simulations in China—to represent the best available physics of PM 2.5 pollution in this region, and then assess this representation using interpretability methods. This hybridization between CTM and U-Net results in robust improvements by over 30% against the baseline CTM forecasts in root mean square error over 1391 stations of the China National Environmental Monitoring Centre network. Interpretability analysis demonstrates physical consistency, manifesting the roles of wind fields and precursor pollutants in modulating PM 2.5 concentration variations through dispersion and chemical processes. A decline in the emission importance from 2013 to 2020 suggests the potential of the deep networks to capture the evolving PM 2.5 pollution processes and growing emission uncertainties under China’s continuous significant emission reduction in this period. Such a combination of learning of physics and interpretability analysis is valuable for PM 2.5 forecasts that are accurate yet trustworthy for effective pollution mitigation.