Abstract
In this paper, we explore the impact of the COVID-19 lockdown in Serbia on the air pollution levels of CO, NO2 and PM10 alongside the possibility for low-cost sensor usage during this period. In the study, a device with low-cost sensors collocated with a reference public monitoring station in the city of Belgrade is used for the same period of 52 days in 2019 (pre-COVID-19 period), 2020 (COVID-19 lockdown) and 2021 (post-COVID-19 period). Low-cost sensors’ measurements are improved by using a convolutional neural network that applies corrections of the influence of temperature and relative humidity on the low-cost sensors. As a result of this study we have noticed a remarkable decrease in NO2 (primarily related to traffic density), while on the other hand CO and PM10, related to domestic heating sources and heating plants, showed constant or slightly higher levels. The obtained results are in accordance with other published work in this area. The low-cost sensors have shown a satisfactory correlation with the reference CO measurements during the lockdown, while the NO2 and PM10 measurements of 2020 were corrected using a convolutional neural network trained on meteorological and pollutant data from 2019. The results include an improvement of 0.35 for the R2 of NO2 and an improvement of 0.13 for the R2 of PM10, proving that our neural network model trained on data from 2019 can improve the performance of the sensor in the lockdown period in 2020. This means that our neural network model is very robust, as it exhibits good performance even in the case where training data from the prior year (2019) are used in the following year (2020) in very different environment circumstances—a lockdown.
Highlights
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
The presented results show that, for NO2, every train/test scenario for the artificial neural networks (ANNs) shows an improvement over the simple linear regression (LR) correction results
It is notable that the results for the test year 2021 are better when data from both 2019 and 2020 are used as the training set. When it comes to the results for the PM10, the most prominent improvement is present when the ANN is trained on 2019 data and tested on 2020 data
Summary
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]. 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
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