## 2 Methods  
The IoT (Internet of Things)-based air quality monitoring ekoNET device AQ10x (DunavNET, AQ103, Novi Sad, Serbia) with low-cost sensors for outdoor air quality measurements [15] has been collocated with a reference public monitoring station in Belgrade (Serbia). The system contains the following components: an AQ10x device (Figure 1), a cloud database for permanent data storage, a data analytics engine (Microsoft Azure), a visualization engine, an administration module and a web application. The AQ10x is a portable device that can be installed both indoors and outdoors, as well as on vehicles to enable larger coverage. The device's power consumption is 2.5 W, weight is 1.5 kg and dimensions are 180 \(\times\) 180 \(\times\) 265 mm\({}^{3}\). AQ10x devices support different transmission technologies (WiFi, BLE (Bluetooth Low Energy), LoRa (Long Range), SigFox, GPRS (General Packet Radio Service), 3G, LTE (Long-Term Evolution) and NB-IoT (Narrowband Internet of Things), and measurements are sent to the back-end server via one of the selected communication modules. The average payload for one data transmission (one measurement period) is 100 B. The transmission period can be set by the user, and for a recommended period of 1 min the total monthly payload is about 4.5 MB. The data are visualized in real time using the web application, which provides a rich set of tools for data processing (averaging, filtering, noise elimination, etc.). In comparison to the public monitoring station, for the same set of observed pollutants, the cost of the low-cost device is about 25 times lower (i.e., for the cost of 1 public reference station, 25 low-cost devices can be purchased). Furthermore, the public reference station additionally requires a significantly higher amount of maintenance. The device contains the following B4 series Alphasense electrochemical gas sensors, intended to monitor air quality in urban areas [16]: CO-B4 gas sensor (measurement range: 0-50 ppm, unit ppm or mg/m\({}^{3}\), accuracy \(\pm\) 2% FS), NO\({}_{2}\)-B43F gas sensor (measurement range: 0-20 ppm, unit ppb or ug/m\({}^{3}\), accuracy \(\pm\) 2% FS), air temperature (t) and relative humidity (RH) sensors (Bosch BME 280) in addition to PM\({}_{1}\), PM\({}_{2.5}\) and PM\({}_{10}\) Plantower PMS7003 optical counters (measurement range: 0-1000 ug/m\({}^{3}\), unit ug/m\({}^{3}\), accuracy \(\pm\) 2% FS) [17]. The measurement point is located in the biggest municipality in Belgrade (and in Serbia), i.e., New Belgrade. The station is located a few tens of meters away from one of the main intersections in New Belgrade with a high traffic volume, and a few hundred meters from the Sava River as well as the Ada Lake. The main sources of air pollution in this area are transport (traffic), a heating plant (about 1 km from the measurement point), industrial and construction activities, domestic heating and dust. The device sampling resolution is 1 minute, and data are sent via a GPRS network to the back-end server in the cloud, where data are stored and processed. Obtained values are averaged for each hour and statistically correlated to values captured from the public monitoring station in the same time intervals. Results are collected and analyzed for the periods of 16 March-6 May 2019, 16 March-6 May 2020 (COVID lockdown) and 16 March-6 May 2021. In this study, CO, NO\({}_{2}\) and PM\({}_{10}\) measurements are used, alongside weather data. The data from the period of the Aralkum desert storm are excluded from the 2020 dataset to avoid interference with the results [3]. The raw measurement data are collected from low-cost sensors. Since low-cost sensors do not provide highly precise measurements, the collected measurements should be corrected. As we stated in the Introduction section, we used convolutional ANNs for this purpose. The goal regarding the ANN calibration performed in this paper is to train the network to correct the raw low-cost sensors' measurements, using air temperature and relative humidity measurements.