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COVID-19 Lockdown in Belgrade: Impact on Air Pollution and Evaluation of a Neural Network Model for the Correction of Low-Cost Sensors' Measurements
| Content Provider | MDPI |
|---|---|
| Author | Vajs, Ivan Drajic, Dejan Cica, Zoran |
| Copyright Year | 2021 |
| Description | In this paper, we explore the impact of the COVID-19 lockdown in Serbia on the air pollution levels of CO, $NO_{2}$ and $PM_{10}$ 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 $NO_{2}$ (primarily related to traffic density), while on the other hand CO and $PM_{10}$, 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 $NO_{2}$ and $PM_{10}$ 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 $NO_{2}$ and an improvement of 0.13 for the R2 of $PM_{10}$, 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. |
| Starting Page | 10563 |
| e-ISSN | 20763417 |
| DOI | 10.3390/app112210563 |
| Journal | Applied Sciences |
| Issue Number | 22 |
| Volume Number | 11 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-11-10 |
| Access Restriction | Open |
| Subject Keyword | Applied Sciences Characterization and Testing of Materials Air Pollution Monitoring Covid-19 Emergency Lockdown Low-cost Pm and Gas Sensors (co and No2) Neural Network Sensor Calibration |
| Content Type | Text |
| Resource Type | Article |
