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

• URL: http://arxiv.org/abs/2106.13750v1
• Date: Fri, 25 Jun 2021 16:39:44 GMT
• Title: Assessing the Lockdown Effects on Air Quality during COVID-19 Era
• Authors: Ioannis Kavouras, Eftychios Protopapadakis, Maria Kaselimia, Emmanuel Sardis, Nikolaos Doulamis
• Abstract summary: In particular, we emphasize on the concentration effects regarding specific pollutant gases, such as carbon monoxide (CO), ozone (O3), nitrogen dioxide (NO2) and sulphur dioxide (SO2) The assessment of the impact of lockdown on air quality focused on four European Cities (Athens, Gladsaxe, Lodz and Rome) The level of the employed prevention measures is employed using the Oxford COVID-19 Government Response Tracker. The results showed that a weak to moderate correlation exists between the corresponding measures and the pollutant factors and it is possible to create models which can predict the behaviour of the pollutant gases under daily human activities.
• Score: 8.733926566837676
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 (O3), nitrogen dioxide (NO2) and sulphur dioxide (SO2). 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.

Related papers

• Machine Learning for Methane Detection and Quantification from Space -- A survey [49.7996292123687]
  Methane (CH_4) is a potent anthropogenic greenhouse gas, contributing 86 times more to global warming than Carbon Dioxide (CO_2) over 20 years. This work expands existing information on operational methane point source detection sensors in the Short-Wave Infrared (SWIR) bands. It reviews the state-of-the-art for traditional as well as Machine Learning (ML) approaches.
  arXiv  Detail & Related papers  (2024-08-27T15:03:20Z)
• Back to the Future: GNN-based NO$_2$ Forecasting via Future Covariates [49.93577170464313]
  We deal with air quality observations in a city-wide network of ground monitoring stations. We propose a conditioning block that embeds past and future covariates into the current observations. We find that conditioning on future weather information has a greater impact than considering past traffic conditions.
  arXiv  Detail & Related papers  (2024-04-08T09:13:16Z)
• Autonomous Detection of Methane Emissions in Multispectral Satellite Data Using Deep Learning [73.01013149014865]
  Methane is one of the most potent greenhouse gases. Current methane emission monitoring techniques rely on approximate emission factors or self-reporting. Deep learning methods can be leveraged to automatize the detection of methane leaks in Sentinel-2 satellite multispectral data.
  arXiv  Detail & Related papers  (2023-08-21T19:36:50Z)
• Counting Carbon: A Survey of Factors Influencing the Emissions of Machine Learning [77.62876532784759]
  Machine learning (ML) requires using energy to carry out computations during the model training process. The generation of this energy comes with an environmental cost in terms of greenhouse gas emissions, depending on quantity used and the energy source. We present a survey of the carbon emissions of 95 ML models across time and different tasks in natural language processing and computer vision.
  arXiv  Detail & Related papers  (2023-02-16T18:35:00Z)
• Using Machine Learning to Predict Air Quality Index in New Delhi [0.0]
  We use a Support Vector Regression (SVR) model to forecast the levels of various pollutants and the air quality index. The model predicts levels of various pollutants, like, sulfur dioxide, carbon monoxide, nitrogen dioxide, particulate matter 2.5, and ground-level ozone, at an accuracy of 93.4 percent.
  arXiv  Detail & Related papers  (2021-12-10T00:20:05Z)
• Effects of a nuclear disturbed environment on a quantum free space optical link [52.77024349608834]
  This manuscript investigates the potential effect of a nuclear-disturbed atmospheric environment on the signal attenuation of a ground/satellite transmitter/receiver system. Attenuation of a signal transmitted through the rising nuclear cloud and the subsequently transported debris is modeled climatologically for surface-level detonations of 10 kt, 100 kt, and 1 Mt.
  arXiv  Detail & Related papers  (2021-08-10T19:25:54Z)
• Evaluation of Time Series Forecasting Models for Estimation of PM2.5 Levels in Air [0.0]
  The study adopts ARIMA, FBProphet, and deep learning models such as LSTM, 1D CNN, to estimate the concentration of PM2.5 in the environment. Our predicted results convey that all adopted methods give comparative outcomes in terms of average root mean squared error.
  arXiv  Detail & Related papers  (2021-04-07T16:24:39Z)
• Interpretable and Transferable Models to Understand the Impact of Lockdown Measures on Local Air Quality [5.273501657421094]
  COVID-19 related lockdown measures offer a unique opportunity to understand how changes in economic activity and traffic affect ambient air quality. We estimate pollution reduction over the lockdown period by using the measurements from ground air pollution monitoring stations. We show that our models achieve state-of-the-art performance on the data from air pollution measurement stations in Switzerland and in China.
  arXiv  Detail & Related papers  (2020-11-19T23:09:30Z)
• SOCAIRE: Forecasting and Monitoring Urban Air Quality in Madrid [0.0]
  We present SOCAIRE, an operational tool based on neural, statistical and nested models. It focuses on modeling each and every available component which might play a role in air quality: past concentrations of pollutants, human activity, numerical pollution estimation, and numerical weather predictions. This tool is currently in operation in Madrid, producing daily air quality predictions for the next 48 hours.
  arXiv  Detail & Related papers  (2020-11-19T09:39:10Z)
• Averaging Atmospheric Gas Concentration Data using Wasserstein Barycenters [68.978070616775]
  Hyperspectral satellite images report greenhouse gas concentrations worldwide on a daily basis. We propose using Wasserstein barycenters coupled with weather data to average gas concentration data sets and better concentrate the mass around significant sources.
  arXiv  Detail & Related papers  (2020-10-06T14:31:25Z)
• HazeDose: Design and Analysis of a Personal Air Pollution Inhaled Dose Estimation System using Wearable Sensors [6.284628903370058]
  We extend the paradigm to HazeDose system, which can personalize the individuals' air pollution exposure. Users can visualize their personalized air pollution exposure information via a mobile application. One algorithm is also introduced to balance the execution time and dosage reduction for alternative routes scenarios.
  arXiv  Detail & Related papers  (2020-05-28T02:35:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.