## 1 Introduction  
Due to the COVID-19 pandemic, in order to protect citizens and stop the virus spreading, most governments around the world declared a state of emergency and conducted a partial or total lockdown for a certain period. In Serbia, an emergency lockdown was introduced on 16 March 2020 and lasted until 6 May 2020 (i.e., 52 days) [1]. During that period, some days had tighter emergency measures (complete lockdown) while during other days there was a partial lockdown. Generally, Belgrade is a moderately polluted city, mostly affected by traffic and transport, construction, industrial activities, dust and domestic-heating-related pollution. As a consequence of the lockdown, vehicle traffic volume, manufacturing, construction and industrial activities were reduced. To understand the influence of these factors on pollution, it is of interest to analyze air quality during the emergency lockdown and compare it with the preceding and following periods. There are many analyses, studies and pieces of research related to the influence of COVID-19 lockdowns on air quality [2]. In [3], the authors compare the consecutive intervals that occur immediately pre-COVID, COVID and post-COVID in terms of their influence on air quality in the city of Novi Sad (Serbia) for PM\({}_{2.5}\), PM\({}_{10}\), CO and NO\({}_{\rm X}\). The authors have noticed a significant reduction in NO\({}_{2}\), while CO and PM\({}_{10}\) mostly remained on the same level (depending on the location in Novi Sad). PM\({}_{2.5}\), PM\({}_{10}\), NO\({}_{2}\) and O\({}_{3}\) concentrations are explored and compared for the same periods in 2019 and 2020 (during the COVID lockdown in Florence, Pisa and Lucca in Italy) in [4]. In this study, a significant reduction in NO\({}_{2}\) was observed, while the pollution level of O\({}_{3}\) remained the same; PM\({}_{10}\), in most of the analyzed cases, remained on the same level. Major urban cities are explored in [5], and a reduction in NO\({}_{2}\) pollution is highly noticeable (in comparison to 2019) in the cities where there was a strict lockdown (Madrid, Milan and Paris), while in the cities with a partial lockdown this decrease was much lower (Warsaw, Bismarck and Lincoln). The French National Institute (INERIS) has conducted a comprehensive study related to NO\({}_{2}\) and PM\({}_{10}\) around Europe, and a decrease in NO\({}_{2}\) pollution was also noticed, but the PM\({}_{10}\) pollution was shown to depend on the observed region (an increase/decrease was observed) [6]. The same behavior, regarding NO\({}_{2}\) and PM pollution levels, is also noticed in Canada and presented in [7]. The impact of the COVID-19 lockdown on traffic-related air pollution (PM\({}_{2.5}\), black carbon (BC), NO, NO\({}_{2}\) and CO) in a northwestern US city is evaluated in [8]. Based on the multiple studies in this area, it was concluded that road traffic is a major urban pollutant source. The measurements were conducted near a major freeway. During the lockdown period, the median traffic volume decreased by a third. This caused a decrease in the level of pollutants, where the median BC concentration decreased by a fourth, PM\({}_{2.5}\), NO and NO\({}_{2}\) decreased by a third and CO decreased by a sixth in comparison to the pre-COVID-19 state. The modeling of the influence of weather conditions on low-cost air quality sensors is quite present in modern research. More specifically, artificial neural networks (ANNs), as very promising machine learning algorithms, can be found in several papers related to low-cost sensors. In [9], the authors use a deep ANN consisting of convolutional, fully connected and long short-term memory layers, and train it on the O\({}_{3}\), PM\({}_{2.5}\) and PM\({}_{10}\) measurements, taking into consideration various weather conditions. They have concluded that by training a network using reference, low-cost measurements, such as wind speed, wind direction, temperature and relative humidity, the error of low-cost sensors can be drastically decreased.