## 1 Introduction
The AirNet model was developed in [10] and uses dual encoder neural networks to calibrate CO and O\({}_{3}\) low-cost sensors. The developed AirNet architecture has been proven to be superior via comparison to several baseline algorithms. A mobile PM\({}_{2.5}\) sensing system was developed and calibrated using fully connected ANNs in [11]. It considers relative humidity and temperature in addition to PM\({}_{2.5}\) measurements and was shown to greatly increase the accuracy of the developed system. In [12], the authors explore three different machine learning algorithms for the calibration of low-cost CO and O\({}_{3}\) sensors based on a number of pollutant measurements and weather conditions. Univariate and multivariate linear regression are implemented as well as fully connected ANNs. Several training algorithms for the ANNs are implemented as well, and it was concluded that the ANN outperforms the linear algorithms for both pollutants. The authors have explored the possibility of applying linear models and fully connected ANNs to data collected from a location different than the one used for the acquisition of training data in [13]. O\({}_{3}\) and CO\({}_{2}\) measurements have been observed. It was shown that the ANN is overall more effective, but that there are some exceptions influenced by the selection of training and test scenarios. An evaluation of linear models and fully connected ANNs is performed in [14], with an assortment of observed pollutants: O\({}_{3}\), CO, CO\({}_{2}\) and CH\({}_{4}\). It was shown that the ANN has better performance when the modeling of complex nonlinear behavior is concerned, using a wide range of pollutant and weather condition measurements. Regardless of the variety of the papers that have covered air quality sensor calibration, to the best of the authors' knowledge no paper has attempted to train algorithms on data from a single year and evaluate it on data that is gathered a year later during a different day-to-day regime. The scope and contributions of our research are twofold. The first part is to explore the impact of the COVID-19 lockdown in Serbia on air pollution levels. The second part includes a device with low-cost sensors which is collocated with a reference public monitoring station. The goal here is to validate the device measurements by comparing them with reference measurements, and to apply corrections due to the influence of temperature and relative humidity on the measurements by using convolutional ANNs. Our results show that by using data from the year preceding the COVID-19 lockdown, a convolutional ANN model can be trained and successfully applied to low-cost sensors' measurements during the lockdown period. This shows that our model can be applied to low-cost measurements that are acquired a year later than the ones used for training data. This indicates that the sensors can be successfully used for multiple years and that our calibration algorithm can be implemented at least one year after it is trained (showing a decent level of robustness). Furthermore, the pollutant concentrations are different in 2019 and 2020 (COVID-19 lockdown), and the algorithm was trained in 2019 and tested in 2020, showing that it is useful even if the regimes during training and testing are different. The rest of the paper is organized as follows: in Section 2 our research methods are presented. The results and subsequent discussion are presented in Section 3. Finally, Section 4 concludes the paper.