COVID-19 Lockdown in Belgrade: Impact on Air Pollution and Evaluation of a Neural Network Model for the Correction of Low-Cost Sensors’ Measurements
In this paper, we explore the impact of the COVID-19 lockdown in Serbia on the air pollution levels of CO, NO<sub>2</sub> and PM<sub>10</sub> alongside the possibility for low-cost sensor usage during this period. In the study, a device with low-cost sensors collocated with a...
Guardado en:
| Autores principales: | , , |
|---|---|
| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
MDPI AG
2021
|
| Materias: | |
| Acceso en línea: | https://doaj.org/article/38b68a3b70fe41baa8e55a0bf2c182e2 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| Sumario: | In this paper, we explore the impact of the COVID-19 lockdown in Serbia on the air pollution levels of CO, NO<sub>2</sub> and PM<sub>10</sub> 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<sub>2</sub> (primarily related to traffic density), while on the other hand CO and PM<sub>10</sub>, 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<sub>2</sub> and PM<sub>10</sub> 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<sub>2</sub> and an improvement of 0.13 for the R2 of PM<sub>10</sub>, 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. |
|---|