A well-adopted strategy for disentangling the effects of meteorological factors and emission is meteorological normalization or de-weathering. This is especially useful when studying imission, i.e., concentrations of air pollutants measured at monitoring stations or sensors which can be influence by local meteorology. While the concept was known, it gained a lot of momentum due to its successful synergy with machine learning and the availability of an easy-to-use R-scripting language library . Indeed, according to Google Scholar and at the time of writing this paper, the two establishing works and were cited over 300 and 200 times respectively. This points to the increasing popularity of the method, which we also used in our previous works in a Python adaption to assess the effect of the COVID-19 lockdowns in 2020.