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...

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Autores principales:	Ivan Vajs, Dejan Drajic, Zoran Cica
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	air pollution monitoring COVID-19 emergency lockdown low-cost PM and gas sensors (CO and NO<sub>2</sub>) neural network sensor calibration Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/38b68a3b70fe41baa8e55a0bf2c182e2
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spelling	oai:doaj.org-article:38b68a3b70fe41baa8e55a0bf2c182e22021-11-25T16:31:34ZCOVID-19 Lockdown in Belgrade: Impact on Air Pollution and Evaluation of a Neural Network Model for the Correction of Low-Cost Sensors’ Measurements10.3390/app1122105632076-3417https://doaj.org/article/38b68a3b70fe41baa8e55a0bf2c182e22021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/10563https://doaj.org/toc/2076-3417In 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.Ivan VajsDejan DrajicZoran CicaMDPI AGarticleair pollution monitoringCOVID-19emergency lockdownlow-cost PM and gas sensors (CO and NO<sub>2</sub>)neural networksensor calibrationTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10563, p 10563 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	air pollution monitoring COVID-19 emergency lockdown low-cost PM and gas sensors (CO and NO<sub>2</sub>) neural network sensor calibration Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	air pollution monitoring COVID-19 emergency lockdown low-cost PM and gas sensors (CO and NO<sub>2</sub>) neural network sensor calibration Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Ivan Vajs Dejan Drajic Zoran Cica COVID-19 Lockdown in Belgrade: Impact on Air Pollution and Evaluation of a Neural Network Model for the Correction of Low-Cost Sensors’ Measurements
description	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.
format	article
author	Ivan Vajs Dejan Drajic Zoran Cica
author_facet	Ivan Vajs Dejan Drajic Zoran Cica
author_sort	Ivan Vajs
title	COVID-19 Lockdown in Belgrade: Impact on Air Pollution and Evaluation of a Neural Network Model for the Correction of Low-Cost Sensors’ Measurements
title_short	COVID-19 Lockdown in Belgrade: Impact on Air Pollution and Evaluation of a Neural Network Model for the Correction of Low-Cost Sensors’ Measurements
title_full	COVID-19 Lockdown in Belgrade: Impact on Air Pollution and Evaluation of a Neural Network Model for the Correction of Low-Cost Sensors’ Measurements
title_fullStr	COVID-19 Lockdown in Belgrade: Impact on Air Pollution and Evaluation of a Neural Network Model for the Correction of Low-Cost Sensors’ Measurements
title_full_unstemmed	COVID-19 Lockdown in Belgrade: Impact on Air Pollution and Evaluation of a Neural Network Model for the Correction of Low-Cost Sensors’ Measurements
title_sort	covid-19 lockdown in belgrade: impact on air pollution and evaluation of a neural network model for the correction of low-cost sensors’ measurements
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/38b68a3b70fe41baa8e55a0bf2c182e2
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_version_	1718413130324770816

COVID-19 Lockdown in Belgrade: Impact on Air Pollution and Evaluation of a Neural Network Model for the Correction of Low-Cost Sensors’ Measurements

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