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Introduction: Cross-border mobility (CBM) to visit social network members or for everyday activities is an important part of daily life for citizens in border regions, including the Meuse-Rhine Euroregion (EMR: neighboring regions from the Netherlands, Belgium, and Germany). We assessed changes in CBM during the COVID-19 pandemic and how participants experienced border restrictions., Methods: Impact of COVID-19 on the EMR' is a longitudinal study using comparative cross-border data collection. In 2021, a random sample of the EMR-population was invited for participation in online surveys to assess current and pre-pandemic CBM. Changes in CBM, experience of border restrictions, and associated factors were analyzed using multinomial and multivariable logistic regression analysis., Results: Pre-pandemic, 82% of all 3,543 participants reported any CBM: 31% for social contacts and 79% for everyday activities. Among these, 26% decreased social CBM and 35% decreased CBM for everyday activities by autumn 2021. Negative experience of border restrictions was reported by 45% of participants with pre-pandemic CBM, and was higher (p < 0.05) in Dutch participants (compared to Belgian; aOR= 1.4), cross-border [work] commuters (aOR= 2.2), participants with cross-border social networks of friends, family or acquaintances (aOR= 1.3), and those finding the measures 'limit group size' (aOR= 1.5) and 'minimalize travel' (aOR= 2.0) difficult to adhere to and finding 'minimalize travel' (aOR= 1.6) useless., Discussion: CBM for social contacts and everyday activities was substantial in EMR-citizens, but decreased during the pandemic. Border restrictions were valued as negative by a considerable portion of EMR-citizens, especially when having family or friends across the border. When designing future pandemic control strategies, policy makers should account for the negative impact of CBM restrictions on their citizens., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 van Bilsen, Brinkhues, Hoebe, Stabourlos, Moonen, Demarest, Hanssen, van Loo, Savelkoul, Philippsen, van der Zanden and Dukers-Muijrers.)

The recent COVID-19 pandemic affected various aspects of life. Several studies established the consequences of pandemic lockdown on air quality using satellite remote sensing. However, such studies have limitations, including low spatial resolution or incomplete spatial coverage. Therefore, in this paper, we propose a machine learning-based scheme to solve the pre-mentioned limitations by training an optimized space-time extra trees model for each year of the study period. The results have shown that our trained models reach a prediction accuracy up to 95% when predicting the missing values in the MODIS MCD19A2 Aerosol Optical Depth (AOD) product. The outcome of the mentioned scheme was a geo-harmonized atmospheric dataset for aerosol optical depth at 550 nm with 1 km spatial resolution and full coverage over Europe. As an application, we used the proposed machine learning based prediction approach in AOD levels analysis. We compared the mean AOD levels between the lockdown period from March to June in 2020 and the mean AOD values of the same period for the past 5 years. We found that AOD levels dropped over most European countries in 2020 but increased in several eastern and western countries. The Netherlands had the most significant average decrease in AOD levels (19%), while Spain had the highest average increase (10%). Moreover, we analyzed the relationship between the relative percentage difference of AOD and four meteorological variables. We found a positive correlation between AOD and relative humidity and a negative correlation between AOD and wind speed. The value of the proposed prediction scheme is further emphasized by taking into consideration that the reconstructed dataset can be used for future air quality studies concerning Europe. [ABSTRACT FROM AUTHOR]