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Cleaner Production and Pollution Prevention Good Practices for Small-and-Medium Scale...
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Cleaner Production and Pollution Prevention Good Practices for Small-and-Medium Scale Businesses and Organizations

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Pollution Prevention, Mitigation and Sustainability".

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 2518

Special Issue Editors

Dr. Luis Velazquez

E-Mail Website
Guest Editor
Industrial Engineering Department, University of Sonora, Hermosillo 87287, Mexico
Interests: sustainability; cleaner production; climate change education; energy efficiency
Special Issues, Collections and Topics in MDPI journals
Dr. Nora Munguia

E-Mail Website
Guest Editor
Industrial Engineering Department, University of Sonora, Hermosillo 87287, Mexico
Interests: industry; sustainability; cleaner production; occupational health
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

For decades, cleaner production and pollution prevention good practices have driven big enterprise’s efforts to strengthen their corporate sustainability. However, little is known about implementing such practices in small-and-medium scale businesses and organizations. Through case study analysis, this Special Issue aims to disseminate cleaner production and pollution prevention strategies. In particular, this Special Issue will focus on how cleaner production and pollution prevention good practices have supported small-and-medium enterprises and organizations to increase their resilience to COVID-19 disruptions. This Special Issue will comprise but is not limited to sustainability topics related to the engineering process, supply chains, sustainability corporation, climate change, hazardous waste, social sustainability, outreach and partnership, public policies, sustainable production and consumption, behavioral changes. All submissions must state the importance of the proposal toward meeting the Sustainable Development Goals, highlighting at least one SDG.  

Dr. Luis Velazquez
Dr. Nora Munguia
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. Sustainability is an international peer-reviewed open access semimonthly 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 2400 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

  • SME
  • CP & PP
  • The Agenda 2030
  • Decade of Action

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Published Papers (1 paper)

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Research

22 pages, 16083 KiB  
Article
An Ensemble Model with Adaptive Variational Mode Decomposition and Multivariate Temporal Graph Neural Network for PM2.5 Concentration Forecasting
by Yadong Pei, Chiou-Jye Huang, Yamin Shen and Yuxuan Ma
Sustainability 2022, 14(20), 13191; https://doi.org/10.3390/su142013191 - 14 Oct 2022
Cited by 8 | Viewed by 2090
Abstract
Accurate prediction of PM2.5 concentration for half a day can provide valuable guidance for urban air pollution prevention and daily travel planning. In this paper, combining adaptive variational mode decomposition (AVMD) and multivariate temporal graph neural network (MtemGNN), a novel PM2.5 prediction model [...] Read more.
Accurate prediction of PM2.5 concentration for half a day can provide valuable guidance for urban air pollution prevention and daily travel planning. In this paper, combining adaptive variational mode decomposition (AVMD) and multivariate temporal graph neural network (MtemGNN), a novel PM2.5 prediction model named PMNet is proposed. Some studies consider using VMD to stabilize time series but ignore the problem that VMD parameters are difficult to select, so AVMD is proposed to solve the appealing problem. Effective correlation extraction between multivariate time series affects model prediction accuracy, so MtemGNN is used to extract complex non-Euclidean distance relationships between multivariate time series automatically. The outputs of AVMD and MtemGNN are integrated and fed to the gate recurrent unit (GRU) to learn the long-term and short-term dependence of time series. Compared to several baseline models—long short-term memory (LSTM), GRU, and StemGNN—PMNet has the best prediction performance. Ablation experiments show that the Mean Absolute Error (MAE) is reduced by 90.141%, 73.674%, and 40.556%, respectively, after adding AVMD, GRU, and MtemGNN to the next 12-h prediction. Full article
(This article belongs to the Special Issue Cleaner Production and Pollution Prevention Good Practices for Small-and-Medium Scale Businesses and Organizations)
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