scholarly journals Machine Learning on the COVID-19 Pandemic, Human Mobility and Air Quality: A Review

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Md. Mokhlesur Rahman ◽  
Kamal Chandra Paul ◽  
Md. Amjad Hossain ◽  
G. G. Md. Nawaz Ali ◽  
Md. Shahinoor Rahman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Human Mobility

scholarly journals Machine Learning on the COVID-19 Pandemic, Human Mobility, and Air Quality: A Review

2021 ◽  
Author(s):  
Md Mokhlesur Rahman ◽  
Kamal Chandra Paul ◽  
Md. Amjad Hossain ◽  
G. G. Md. Nawaz Ali ◽  
Md. Shahinoor Rahman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Urban Form ◽  
Human Mobility ◽  
Virus Transmission ◽  
Past Research ◽  
Policy Implications ◽  
Viral Spread ◽  
Global Pandemic ◽  
Review Study

The ongoing COVID-19 global pandemic is affecting every facet of human lives (e.g., public health, education, economy, transportation, and the environment). This novel pandemic and citywide implemented lockdown measures are affecting virus transmission, people’s travel patterns, and air quality. Many studies have been conducted to predict the COVID-19 diffusion, assess the impacts of the pandemic on human mobility and air quality, and assess the impacts of lockdown measures on viral spread with a range of Machine Learning (ML) techniques. This review study aims to analyze results from past research to understand the interactions among the COVID-19 pandemic, lockdown measures, human mobility, and air quality. The critical review of prior studies indicates that urban form, people's socioeconomic and physical conditions, social cohesion, and social distancing measures significantly affect human mobility and COVID-19 transmission. During the COVID-19 pandemic, many people are inclined to use private transportation for necessary travel purposes to mitigate coronavirus-related health problems. This review study also noticed that COVID-19 related lockdown measures significantly improve air quality by reducing the concentration of air pollutants, which in turn improves the COVID-19 situation by reducing respiratory-related sickness and deaths of the people. It is argued that ML is a powerful, effective, and robust analytic paradigm to handle complex and wicked problems such as a global pandemic. This study also discusses policy implications, which will be helpful for policymakers to take prompt actions to moderate the severity of the pandemic and improve urban environments by adopting data-driven analytic methods.

scholarly journals Subtle Changes or Dramatic Perceptions of Air Pollution in Sydney during COVID-19

Environments ◽  
2021 ◽  
Vol 8 (1) ◽  
pp. 2
Author(s):  
Peter Brimblecombe ◽  
Yonghang Lai
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
New South ◽  
Human Mobility ◽  
Ground Level ◽  
Physical Reality ◽  
Social Perceptions ◽  
Search Terms ◽  
South Wales ◽  
Significant Difference

The COVID-19 pandemic made it critical to limit the spread of the disease by enforcing human isolation, restricting travel and reducing social activities. Dramatic improvements to air quality, especially NO2, have often characterised places under COVID-19 restrictions. Air pollution measurements in Sydney in April 2019 and during the lockdown period in April 2020 show reduced daily averaged NO2 concentrations: 8.52 ± 1.92 and 7.85 ± 2.92 ppb, though not significantly so (p1~0.15) and PM2.5 8.91 ± 4.94 and 7.95 ± 2.64 µg m−3, again a non-significant difference (p1~0.18). Satellite imagery suggests changes that parallel those at ground level, but the column densities averaged over space and time, in false-colour, are more dramatic. Changed human mobility could be traced in increasing times spent at home, assessed from Google Mobility Reports and mirrored in decreased traffic flow on a major road, suggesting compliance with the restrictions. Electricity demand for the State of New South Wales was low under lockdown in early April 2020, but it recovered rapidly. Analysis of the uses of search terms: bushfires, air quality, haze and air pollution using Google Trends showed strong links between bushfires and pollution-related terms. The smoke from bushfires in late 2019 may well have added to the general impression of improved air quality during lockdown, despite only modest changes in the ground level measurements. This gives hints that successful regulation of air quality requires maintaining a delicate balance between our social perceptions and the physical reality.

scholarly journals Machine Learning-Based Prediction of Air Quality

2020 ◽  
Vol 10 (24) ◽  
pp. 9151
Author(s):  
Yun-Chia Liang ◽  
Yona Maimury ◽  
Angela Hsiang-Ling Chen ◽  
Josue Rodolfo Cuevas Juarez
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Random Forest ◽  
Prediction Models ◽  
Superior Performance ◽  
Support Vector ◽  
Economic Activities ◽  
Adaptive Boosting ◽  
Series Of Experiments ◽  
Artificial Neural Network Ann

Air, an essential natural resource, has been compromised in terms of quality by economic activities. Considerable research has been devoted to predicting instances of poor air quality, but most studies are limited by insufficient longitudinal data, making it difficult to account for seasonal and other factors. Several prediction models have been developed using an 11-year dataset collected by Taiwan’s Environmental Protection Administration (EPA). Machine learning methods, including adaptive boosting (AdaBoost), artificial neural network (ANN), random forest, stacking ensemble, and support vector machine (SVM), produce promising results for air quality index (AQI) level predictions. A series of experiments, using datasets for three different regions to obtain the best prediction performance from the stacking ensemble, AdaBoost, and random forest, found the stacking ensemble delivers consistently superior performance for R2 and RMSE, while AdaBoost provides best results for MAE.

scholarly journals Developing Relative Humidity and Temperature Corrections for Low-Cost Sensors Using Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3338
Author(s):  
Ivan Vajs ◽  
Dejan Drajic ◽  
Nenad Gligoric ◽  
Ilija Radovanovic ◽  
Ivan Popovic
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Relative Humidity ◽  
Low Cost ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Lower Accuracy ◽  
Wide Range ◽  
The Impact ◽  
Monitoring Stations

Existing government air quality monitoring networks consist of static measurement stations, which are highly reliable and accurately measure a wide range of air pollutants, but they are very large, expensive and require significant amounts of maintenance. As a promising solution, low-cost sensors are being introduced as complementary, air quality monitoring stations. These sensors are, however, not reliable due to the lower accuracy, short life cycle and corresponding calibration issues. Recent studies have shown that low-cost sensors are affected by relative humidity and temperature. In this paper, we explore methods to additionally improve the calibration algorithms with the aim to increase the measurement accuracy considering the impact of temperature and humidity on the readings, by using machine learning. A detailed comparative analysis of linear regression, artificial neural network and random forest algorithms are presented, analyzing their performance on the measurements of CO, NO2 and PM10 particles, with promising results and an achieved R2 of 0.93–0.97, 0.82–0.94 and 0.73–0.89 dependent on the observed period of the year, respectively, for each pollutant. A comprehensive analysis and recommendations on how low-cost sensors could be used as complementary monitoring stations to the reference ones, to increase spatial and temporal measurement resolution, is provided.

Air quality analysis in critical zones of Mexico using unsupervised machine learning

2020 ◽  
Author(s):  
Eli G. Pale-Ramon ◽  
Luis J. Morales-Mendoza ◽  
Sonia L. Mestizo-Gutierrez ◽  
Mario Gonzalez-Leee ◽  
Rene F. Vazquez-Bautista ◽  
...  
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Quality Analysis ◽  
Unsupervised Machine Learning ◽  
Critical Zones

scholarly journals Research on air quality prediction method in Hangzhou based on machine learning

2021 ◽  
Vol 2010 (1) ◽  
pp. 012011
Author(s):  
Zhongjie Fu ◽  
Haiping Lin ◽  
Bingqiang Huang ◽  
Jiana Yao
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Prediction Method ◽  
Quality Prediction ◽  
Air Quality Prediction

scholarly journals Estimating lockdown-induced European NO<sub>2</sub> changes using satellite and surface observations and air quality models

2021 ◽  
Vol 21 (9) ◽  
pp. 7373-7394
Author(s):  
Jérôme Barré ◽  
Hervé Petetin ◽  
Augustin Colette ◽  
Marc Guevara ◽  
Vincent-Henri Peuch ◽  
...  
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Urban Areas ◽  
Gradient Boosting ◽  
Quality Models ◽  
Pollution Levels ◽  
Surface Station ◽  
Concentration Changes ◽  
Monitoring Service ◽  
No2 Pollution

Abstract. This study provides a comprehensive assessment of NO2 changes across the main European urban areas induced by COVID-19 lockdowns using satellite retrievals from the Tropospheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5p satellite, surface site measurements, and simulations from the Copernicus Atmosphere Monitoring Service (CAMS) regional ensemble of air quality models. Some recent TROPOMI-based estimates of changes in atmospheric NO2 concentrations have neglected the influence of weather variability between the reference and lockdown periods. Here we provide weather-normalized estimates based on a machine learning method (gradient boosting) along with an assessment of the biases that can be expected from methods that omit the influence of weather. We also compare the weather-normalized satellite-estimated NO2 column changes with weather-normalized surface NO2 concentration changes and the CAMS regional ensemble, composed of 11 models, using recently published estimates of emission reductions induced by the lockdown. All estimates show similar NO2 reductions. Locations where the lockdown measures were stricter show stronger reductions, and, conversely, locations where softer measures were implemented show milder reductions in NO2 pollution levels. Average reduction estimates based on either satellite observations (−23 %), surface stations (−43 %), or models (−32 %) are presented, showing the importance of vertical sampling but also the horizontal representativeness. Surface station estimates are significantly changed when sampled to the TROPOMI overpasses (−37 %), pointing out the importance of the variability in time of such estimates. Observation-based machine learning estimates show a stronger temporal variability than model-based estimates.

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