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Products of CORINE Land Cover (CLC), the National Land Cover Dataset (NLCD), the FAO/UNEP Land Cover Classification System (LCCS), etc. currently provide an important source of information used for the assessment of issues such as landscape change, landscape fragmentation and the planning of urbanization. Assuming that the data from these various databases are often used in searching for solutions to environmental problems, it is necessary to know which classes of different databases exist and to what extent they are similar, i.e. their possible compatibility and interchangeability. An expert assessment of the similarity between the CLC and NLCD 1992 nomenclatures is presented. Such a similarity assessment in comparison with the ‘geometric model’, the ‘feature model’ and the ‘network model’ is not frequently used. The results obtained show the similarity of assessments completed by four experts who marked the degree of similarity between the compared land cover classes by 1 (almost similar classes), 0.5 (partially similar classes) and 0 (not similar classes). Four experts agreed on assigning 1 in only three cases; 0.5 was given 33 times. A single expert assigned 0.5 a total of 17 times. Results confirmed that the CLC and NLCD nomenclatures are not very similar.

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Air pollution is currently considered one of the most serious problems facing humans. Fine particulate matter with a diameter smaller than 2.5 micrometres (PM2.5) is a very harmful air pollutant that is linked with many diseases. In this study, we created a machine learning-based scheme to estimate PM2.5 using various open data such as satellite remote sensing, meteorological data, and land variables to increase the limited spatial coverage provided by ground-monitors. A space-time extremely randomised trees model was used to estimate PM2.5 concentrations over Europe, this model achieved good results with an out-of-sample cross-validated R2 of 0.69, RMSE of 5 μg/m3, and MAE of 3.3 μg/m3. The outcome of this study is a daily full coverage PM2.5 dataset with 1 km spatial resolution for the three-year period of 2018–2020. We found that air quality improved throughout the study period over all countries in Europe. In addition, we compared PM2.5 levels during the COVID-19 lockdown during the months March–June with the average of the previous 4 months and the following 4 months. We found that this lockdown had a positive effect on air quality in most parts of the study area except for the United Kingdom, Ireland, north of France, and south of Italy. This is the first study that depends only on open data and covers the whole of Europe with high spatial and temporal resolutions. The reconstructed dataset will be published under free and open license and can be used in future air quality studies.