Next Article in Journal
Evaluating Quantitative Precipitation Forecasts Using the 2.5 km CReSS Model for Typhoons in Taiwan: An Update through the 2015 Season
Next Article in Special Issue
Screening Approach for Short-Term PM2.5 Health Co-Benefits: A Case Study from 15 Metropolitan Cities around the World during the COVID-19 Pandemic
Previous Article in Journal
Spatio-Temporal Characteristics of Dry-Wet Conditions and Boundaries in Five Provinces of Northwest China from 1960 to 2020
Previous Article in Special Issue
The Impact of Large-Scale Social Restriction Phases on the Air Quality Index in Jakarta
Article

Implications of COVID-19 Restriction Measures in Urban Air Quality of Thessaloniki, Greece: A Machine Learning Approach

1
Department of Meteorology and Climatology, School of Geology, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
2
Department of Environment and Hydroeconomy, Directorate of Environment, Industry, Energy and Natural Resources, of the Region of Central Macedonia, 54642 Thessaloniki, Greece
3
Department of Environment of the Municipality of Thessaloniki, 54642 Thessaloniki, Greece
*
Author to whom correspondence should be addressed.
Academic Editors: Gunnar W. Schade, Nicole Mölders, Daniele Contini, Gabriele Curci, Francesca Costabile, Prashant Kumar and Chris G. Tzanis
Atmosphere 2021, 12(11), 1500; https://doi.org/10.3390/atmos12111500
Received: 14 September 2021 / Revised: 2 November 2021 / Accepted: 10 November 2021 / Published: 14 November 2021
(This article belongs to the Special Issue Coronavirus Pandemic Shutdown Effects on Urban Air Quality)
Following the rapid spread of COVID-19, a lockdown was imposed in Thessaloniki, Greece, resulting in an abrupt reduction of human activities. To unravel the impact of restrictions on the urban air quality of Thessaloniki, NO2 and O3 observations are compared against the business-as-usual (BAU) concentrations for the lockdown period. BAU conditions are modeled, applying the XGBoost (eXtreme Gradient Boosting) machine learning algorithm on air quality and meteorological surface measurements, and reanalysis data. A reduction in NO2 concentrations is found during the lockdown period due to the restriction policies at both AGSOFIA and EGNATIA stations of −24.9 [−26.6, −23.2]% and −18.4 [−19.6, −17.1]%, respectively. A reverse effect is revealed for O3 concentrations at AGSOFIA with an increase of 12.7 [10.8, 14.8]%, reflecting the reduced O3 titration by NOx. The implications of COVID-19 lockdowns in the urban air quality of Thessaloniki are in line with the results of several recent studies for other urban areas around the world, highlighting the necessity of more sophisticated emission control strategies for urban air quality management. View Full-Text
Keywords: COVID-19; air quality; machine learning; NO2; O3; Thessaloniki; Greece COVID-19; air quality; machine learning; NO2; O3; Thessaloniki; Greece
Show Figures

Figure 1

MDPI and ACS Style

Akritidis, D.; Zanis, P.; Georgoulias, A.K.; Papakosta, E.; Tzoumaka, P.; Kelessis, A. Implications of COVID-19 Restriction Measures in Urban Air Quality of Thessaloniki, Greece: A Machine Learning Approach. Atmosphere 2021, 12, 1500. https://doi.org/10.3390/atmos12111500

AMA Style

Akritidis D, Zanis P, Georgoulias AK, Papakosta E, Tzoumaka P, Kelessis A. Implications of COVID-19 Restriction Measures in Urban Air Quality of Thessaloniki, Greece: A Machine Learning Approach. Atmosphere. 2021; 12(11):1500. https://doi.org/10.3390/atmos12111500

Chicago/Turabian Style

Akritidis, Dimitris, Prodromos Zanis, Aristeidis K. Georgoulias, Eleni Papakosta, Paraskevi Tzoumaka, and Apostolos Kelessis. 2021. "Implications of COVID-19 Restriction Measures in Urban Air Quality of Thessaloniki, Greece: A Machine Learning Approach" Atmosphere 12, no. 11: 1500. https://doi.org/10.3390/atmos12111500

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop