Air pollution in cities is usually measured at only a few fixed spots, missing how it truly spreads. We placed a network of light-based gas sensors across the university campus in Munich and combined the values using a statistical computer model to map nitrogen dioxide in three dimensions over time. Pollution often builds up in corners and sheltered areas away from the street, suggesting official monitoring stations may underestimate the true exposure to nitrogen dioxide.

This study presents a Bayesian framework for retrieving vertical NO₂ profiles from sparse measurements. By reconstructing a 2+1D spatiotemporal NO₂ density field using the NIFTy framework, height profiles are derived from combined long-path DOAS and in-situ measurements during the CINDI-III campaign. The reconstructed fields reproduce key atmospheric features and agree well with independent MAX-DOAS retrievals, demonstrating the feasibility of Bayesian inference for atmospheric tomography.

Nitrogen dioxide (NO₂) is an air pollutant whose concentration often exceeds air quality guideline values, especially in urban areas. To map the spatial distribution of NO₂ in Munich, we conducted the Munich NO₂ Imaging Campaign (MuNIC), where NO₂ was measured with stationary, mobile, and airborne in situ and remote sensing instruments. The campaign provides a unique dataset that has been used to compare the different instruments and to study the spatial variability of NO₂ and its sources.

In this work, an improved tropospheric NO₂ retrieval algorithm from TROPOMI measurements over Europe is presented. The stratospheric estimation is implemented with correction for the dependency of the stratospheric NO₂ on the viewing geometry. The AMF calculation is implemented using improved surface albedo, a priori NO₂ profiles, and cloud correction. The improved tropospheric NO2 data show good correlations with ground-based MAX-DOAS measurements.