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Pollutants
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Stochastic Behavior of Environmental Pollution
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Stochastic Behavior of Environmental Pollution

A special issue of Pollutants (ISSN 2673-4672).

Deadline for manuscript submissions: closed (20 August 2024) | Viewed by 2448

Special Issue Editors

Dr. Thomas Plocoste

E-Mail Website
Guest Editor
Department of Research in Geoscience, KaruSphère, Les Abymes, Guadeloupe (F.W.I.), France
Interests: air pollution; atmospheric sciences; air quality; multifractal analysis; stochastics methods; machine learning, deep learning; urban climatology; climate change
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Adarsh Sankaran

E-Mail Website
Guest Editor
Department of Civil Engineering, TKM College of Engineering, Kollam 691005, India
Interests: hydrology; water; climate; climate change; geophysics; sediment load modeling; machine learning; risk management; disaster management; statistical analysis; land and climate

Special Issue Information

Dear Colleagues,

In the current scenario of rapid industrialization and population growth, pollution in different forms is also amplified and poses a serious threat to human health. Systematic monitoring and analysis of pollutant data is essential to resist against such threats. On the other hand, the behavior of pollutant datasets is very complex in nature, which often presents difficulties for simulation and forecasting.

This complex behavior includes non-linearity, non-stationarity, multiscaling, stochasticity, fractality, etc. Advancements in statistical theory and mathematical transforms will help to improve the characterization of the complex time series of pollutants and advancements in the artificial intelligence paradigms, which will in turn help towards improved forecasting of pollutants.

In this Special Issue, we invite submissions of original research or review papers related to the analysis, characterization, and prediction of pollutant time series (water, air, noise, or any related forms). Examples of topics that could be addressed include but are not limited to new research methodologies, analysis of stochastic behavior and complexity analysis of pollutant time series, time-frequency characterization of pollutants, fractal and multifractal analysis of pollutants, pollution monitoring, statistical analysis and imputation of pollutant data, and predictive modeling of pollutants using machine learning and deep learning.

Dr. Thomas Plocoste
Prof. Dr. Adarsh Sankaran
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Pollutants is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • pollutants
  • water pollution
  • air pollution
  • trend and complexity analysis
  • time-frequency characterization
  • stochastic fluctuations
  • scaling and multifractality
  • data imputation
  • machine learning
  • deep learning

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Published Papers (2 papers)

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Research

17 pages, 3211 KiB  
Article
Scaling Correlation Analysis of Particulate Matter Concentrations of Three South Indian Cities
by Adarsh Sankaran, Susan Mariam Rajesh, Muraleekrishnan Bahuleyan, Thomas Plocoste, Sumayah Santhoshkhan and Akhila Lekha
Pollutants 2024, 4(4), 498-514; https://doi.org/10.3390/pollutants4040034 - 13 Nov 2024
Viewed by 519
Abstract
Analyzing the fluctuations of particulate matter (PM) concentrations and their scaling correlation structures are useful for air quality management. Multifractal characterization of PM2.5 and PM10 of three cities in India wase considered using the detrended fluctuation procedure from 2018 to 2021. The cross-correlation [...] Read more.
Analyzing the fluctuations of particulate matter (PM) concentrations and their scaling correlation structures are useful for air quality management. Multifractal characterization of PM2.5 and PM10 of three cities in India wase considered using the detrended fluctuation procedure from 2018 to 2021. The cross-correlation of PM concentration in a multifractal viewpoint using the multifractal cross-correlation analysis (MFCCA) framework is proposed in this study. It was observed that PM2.5 was more multifractal and complex than PM10 at all the locations. The PM–gaseous pollutant (GP) and PM–meteorological variable (MV) correlations across the scales were found to be weak to moderate in different cities. There was no definite pattern in the correlation of PM with different meteorological and gaseous pollutants variables. The nature of correlation in the pairwise associations was found to be of diverse and mixed nature across the time scales and locations. All the time series exhibited multifractality when analyzed pairwise using multifractal cross-correlation analysis. However, there was a reduction in multifractality in individual cases during PM–GP and PM–MV paired analyses. The insights gained into the scaling behavior and cross-correlation structure from this study are valuable for developing prediction models for PMs by integrating them with machine learning techniques. Full article
(This article belongs to the Special Issue Stochastic Behavior of Environmental Pollution)
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16 pages, 2151 KiB  
Article
Forecasting End-of-Life Vehicle Generation in the EU-27: A Hybrid LSTM-Based Forecasting and Grey Systems Theory-Based Backcasting Approach
by Selman Karagoz
Pollutants 2024, 4(3), 324-339; https://doi.org/10.3390/pollutants4030022 - 2 Jul 2024
Viewed by 823
Abstract
End-of-life vehicle (ELV) forecasting constitutes a crucial aspect of sustainable waste management and resource allocation strategies. While the existing literature predominantly employs time-series forecasting and machine learning methodologies, a dearth of studies leveraging deep learning techniques, particularly Long Short-Term Memory (LSTM) networks, is [...] Read more.
End-of-life vehicle (ELV) forecasting constitutes a crucial aspect of sustainable waste management and resource allocation strategies. While the existing literature predominantly employs time-series forecasting and machine learning methodologies, a dearth of studies leveraging deep learning techniques, particularly Long Short-Term Memory (LSTM) networks, is evident. Moreover, the focus on localized contexts within national or municipal boundaries overlooks the imperative of addressing ELV generation dynamics at an international scale, particularly within entities such as the EU-27. Furthermore, the absence of methodologies to reconcile missing historical data presents a significant limitation in forecasting accuracy. In response to these critical gaps, this study proposes a pioneering framework that integrates grey systems theory (GST)-based backcasting with LSTM-based deep learning methodologies for forecasting ELV generation within the EU until 2040. By introducing this innovative approach, this study not only extends the methodological repertoire within the field but also enhances the applicability of findings to supranational regulatory frameworks. Moreover, the incorporation of backcasting techniques addresses data limitations, ensuring more robust and accurate forecasting outcomes. The results indicate an anticipated decline in the recovery and recycling of ELVs, underscoring the urgent need for intervention by policymakers and stakeholders in the waste management sector. Through these contributions, this study enriches our understanding of ELV generation dynamics and facilitates informed decision-making processes in environmental sustainability and resource management domains. Full article
(This article belongs to the Special Issue Stochastic Behavior of Environmental Pollution)
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