Motor transport related harmful PM2.5 and PM10: from onroad measurements to the modelling of air pollution by neural network approach on street and urban level

Lozhkina, Olga; Lozhkin, Vladimir; Nevmerzhitsky, N; Tarkhov, Dmitriy; Vasilyev, Alexander

doi:10.1088/1742-6596/772/1/012031

Cited by 20 publications

(15 citation statements)

References 11 publications

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“…By order, Paris is the most populated, followed by Marseille (2nd), Lyon (3rd) (French National Institute of Statistics and Information about the Economy (INSEE), 2020). We used two types of Particulate Matter: PM 10 and PM 2.5 (expressed in µg/m 3 ) whose concentration in urban cities is directly linked to the combustion of fossil fuels for road traffic [11] . For each city and each pollutant, we collected and average the concentrations measures given by operating environmental monitoring stations.…”

Section: Data Collection and Methodologymentioning

confidence: 99%

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The relationship between air pollution and COVID-19-related deaths: An application to three French cities

2020

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Being heavily dependent to oil products (mainly gasoline and diesel), the French transport sector is the main emitter of Particulate Matter (PMs) whose critical levels induce harmful health effects for urban inhabitants. We selected three major French cities (Paris, Lyon, and Marseille) to investigate the relationship between the Coronavirus Disease 19 (COVID-19) outbreak and air pollution. Using Artificial Neural Networks (ANNs) experiments, we have determined the concentration of PM 2.5 and PM 10 linked to COVID-19-related deaths. Our focus is on the potential effects of Particulate Matter (PM) in spreading the epidemic. The underlying hypothesis is that a pre-determined particulate concentration can foster COVID-19 and make the respiratory system more susceptible to this infection. The empirical strategy used an innovative Machine Learning (ML) methodology. In particular, through the so-called cutting technique in ANNs, we found new threshold levels of PM 2.5 and PM 10 connected to COVID-19: 17.4 µg/m 3 (PM 2.5 ) and 29.6 µg/m 3 (PM 10 ) for Paris; 15.6 µg/m 3 (PM 2.5 ) and 20.6 µg/m 3 (PM 10 ) for Lyon; 14.3 µg/m 3 (PM 2.5 ) and 22.04 µg/m 3 (PM 10 ) for Marseille. Interestingly, all the threshold values identified by the ANNs are higher than the limits imposed by the European Parliament. Finally, a Causal Direction from Dependency (D2C) algorithm is applied to check the consistency of our findings.

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Section: Data Collection and Methodologymentioning

confidence: 99%

“…The origin of PMs emissions come from road traffic as it relies almost exclusively on oil products (gasoline and diesel) [11] . In urbanized areas, these particles have recorded a constant growth over the past decades with detrimental impacts on human health [12] , [13] .…”

Section: Introductionmentioning

confidence: 99%

The relationship between air pollution and COVID-19-related deaths: An application to three French cities

2020

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“…The mobility in the regions of Northern Italy was reduced by 77% [17], while in France, mobility shrunk by 79% during the lockdown [18]. National lockdown presented itself as a solution to the question of finding a way to improve air quality when air pollution is a global threat leading to a large impact on human health and ecosystems [19,20]. The transport sector represents almost a quarter of greenhouse gas emissions and it's considered to be the main cause of air pollution in cities.…”

Section: Introductionmentioning

confidence: 99%

COVID-19 and Air Pollution: Measuring Pandemic Impact to Air Quality in Five European Countries

Skirienė

Stasiškienė

2021

Atmosphere

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The rapid spread of the coronavirus (COVID-19) pandemic affected the economy, trade, transport, health care, social services, and other sectors. To control the rapid dispersion of the virus, most countries imposed national lockdowns and social distancing policies. This led to reduced industrial, commercial, and human activities, followed by lower air pollution emissions, which caused air quality improvement. Air pollution monitoring data from the European Environment Agency (EEA) datasets were used to investigate how lockdown policies affected air quality changes in the period before and during the COVID-19 lockdown, comparing to the same periods in 2018 and 2019, along with an assessment of the Index of Production variation impact to air pollution changes during the pandemic in 2020. Analysis results show that industrial and mobility activities were lower in the period of the lockdown along with the reduced selected pollutant NO2, PM2.5, PM10 emissions by approximately 20–40% in 2020.

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“…We will explain the essence of the neural network approach that we have developed by using the example of the simplest boundary‐value problem

A false(u false) = sans-serif g, u = u false(boldx false), boldx \in normalΩ \subset R^{p}, B false(u false) |_{Γ} = h .

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Section: Introductionmentioning

confidence: 99%

Unified approach to constructing the neural network models of real objects. Part 1

Antonov

Tarkhov

Vasilyev

2018

Math Methods in App Sciences

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This article is a two-part composition. It describes the basics of the methodology developed by the authors to construct approximate neural network solutions of ordinary and partial differential equations. The methodology is illustrated with the sample tasks, ie, for compound domains (with a different type of equations in the subdomains) in the first part, for a variable boundary (caused by phase transition), for a selected boundary, with some heterogeneous information, which includes differential equations, initial and boundary conditions, and experimental and other data in the second part. We show that these different tasks are solved uniformly with the application of general principles. KEYWORDSartificial neural network, differential equation, error functional, heterogeneous data, initial-boundary value problem, network learning 9244

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Motor transport related harmful PM2.5 and PM10: from onroad measurements to the modelling of air pollution by neural network approach on street and urban level

Cited by 20 publications

References 11 publications

The relationship between air pollution and COVID-19-related deaths: An application to three French cities

The relationship between air pollution and COVID-19-related deaths: An application to three French cities

COVID-19 and Air Pollution: Measuring Pandemic Impact to Air Quality in Five European Countries

Unified approach to constructing the neural network models of real objects. Part 1

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