Atmospheric Chemical Transformations, Regeneration and Transport: The Invisible Bridge Enabling the Integration of Positive Matrix Factorisation, Remote Sensing, for Air-Quality Assessment

We propose an ‘Invisible Bridge’ framework that links in situ measurements to remote sensing observations of atmospheric pollutants via atmospheric chemical transformations. An exploratory multivariate workflow, non-negative matrix factorization (NMF, used here as a PMF-type decomposition), principal component analysis (PCA) and k-means clustering identified three robust atmospheric regimes. One factor is a primary circulation signal (high levels of NOx and VOCs linked to traffic), another is a magnification/resuspension regime, and a third is a secondary ‘regenerated’ regime dominated by O3, SO2 and secondary PM. Compositions based on remote sensing data regimes show that the secondary/regenerated regime represents the crucial chemical and vertical link between surface emissions and satellite measurements integrated in the column, thus improving the interpretation of satellite observations in terms of surface exposure. The results show that chemical regeneration and regime context strongly modulate surface-column consistency, which has implications for the interpretation of satellite air quality indicators considering regime.