Machine Learning Tools to Assess the Impact of COVID-19 Civil Measures in Atmospheric Pollution

Kavouras, Ioannis; Kaselimi, Maria; Protopapadakis, Eftychios; Doulamis, Nikolaos

doi:10.1145/3453892.3461327

Cited by 2 publications

(2 citation statements)

References 21 publications

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“…Other related works, include, the [18], in which the densities of CO, O 3 , NO 2 and SO 2 were predicted one week ahead, while, also, an AQI was calculated, which estimated that in countries, such as India, where the lockdown measures were stricter, the air quality improved, while on the other hand in countries like Australia where the lockdown measures, weren't so strict the air quality followed an incremented trend. In Madrid and Barcelona (Spain) [19] the concentration of NO 2 reduced 62% and 50%, respectively, after a 75% in traffic volume.…”

Section: Related Workmentioning

confidence: 99%

Assessing the Lockdown Effects on Air Quality during COVID-19 Era

Kavouras¹,

Protopapadakis²,

Kaselimi³

et al. 2021

Preprint

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In this work we investigate the short-term variations in air quality emissions, attributed to the prevention measures, applied in different cities, to mitigate the COVID-19 spread. In particular, we emphasize on the concentration effects regarding specific pollutant gases, such as carbon monoxide (CO), ozone (O 3 ), nitrogen dioxide (NO 2 ) and sulphur dioxide (SO 2 ). The assessment of the impact of lockdown on air quality focused on four European Cities (Athens, Gladsaxe, Lodz and Rome). Available data on pollutant factors were obtained using global satellite observations. The level of the employed prevention measures is employed using the Oxford COVID-19 Government Response Tracker. The second part of the analysis employed a variety of machine learning tools, utilized for estimating the concentration of each pollutant, two days ahead. The results showed that a weak to moderate correlation exists between the corresponding measures and the pollutant factors and that it is possible to create models which can predict the behaviour of the pollutant gases under daily human activities.

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Section: Related Workmentioning

confidence: 99%

Assessing the Lockdown Effects on Air Quality during COVID-19 Era

Kavouras¹,

Protopapadakis²,

Kaselimi³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

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“…Modelling analysis of COVID-19 outbreaks is a common process to predict confirmed COVID-19 cases and deaths using Artificial Intelligence (AI) and strongly assists national health agencies in developing response plans and mitigation measures [13][14][15]. Machine learning (ML) and especially ensemble (supervised) learning algorithms are dominant in the field of regression and time-series prediction tasks, achieving high performance regarding dataset complexity [16][17][18][19][20]. ML algorithms accurately predict COVID-19 cases and deaths, but now the problem is shifted in identifying the risk factors that cause the spread in order to establish countermeasures to prevent the spread of the pandemic in urban environments.…”

Section: Introductionmentioning

confidence: 99%

Novel Insights in Spatial Epidemiology Utilizing Explainable AI (XAI) and Remote Sensing

et al. 2022

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The COVID-19 pandemic has affected many aspects of human life around the world, due to its tremendous outcomes on public health and socio-economic activities. Policy makers have tried to develop efficient responses based on technologies and advanced pandemic control methodologies, to limit the wide spreading of the virus in urban areas. However, techniques such as social isolation and lockdown are short-term solutions that minimize the spread of the pandemic in cities and do not invert long-term issues that derive from climate change, air pollution and urban planning challenges that enhance the spreading ability. Thus, it seems crucial to understand what kind of factors assist or prevent the wide spreading of the virus. Although AI frameworks have a very efficient predictive ability as data-driven procedures, they often struggle to identify strong correlations among multidimensional data and provide robust explanations. In this paper, we propose the fusion of a heterogeneous, spatio-temporal dataset that combine data from eight European cities spanning from 1 January 2020 to 31 December 2021 and describe atmospheric, socio-economic, health, mobility and environmental factors all related to potential links with COVID-19. Remote sensing data are the key solution to monitor the availability on public green spaces between cities in the study period. So, we evaluate the benefits of NIR and RED bands of satellite images to calculate the NDVI and locate the percentage in vegetation cover on each city for each week of our 2-year study. This novel dataset is evaluated by a tree-based machine learning algorithm that utilizes ensemble learning and is trained to make robust predictions on daily cases and deaths. Comparisons with other machine learning techniques justify its robustness on the regression metrics RMSE and MAE. Furthermore, the explainable frameworks SHAP and LIME are utilized to locate potential positive or negative influence of the factors on global and local level, with respect to our model’s predictive ability. A variation of SHAP, namely treeSHAP, is utilized for our tree-based algorithm to make fast and accurate explanations.

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Machine Learning Tools to Assess the Impact of COVID-19 Civil Measures in Atmospheric Pollution

Cited by 2 publications

References 21 publications

Assessing the Lockdown Effects on Air Quality during COVID-19 Era

Assessing the Lockdown Effects on Air Quality during COVID-19 Era

Novel Insights in Spatial Epidemiology Utilizing Explainable AI (XAI) and Remote Sensing

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