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Atmosphere
Special Issues
Air Quality and Energy Transition: Interactions and Impacts
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Air Quality and Energy Transition: Interactions and Impacts

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Air Quality".

Deadline for manuscript submissions: closed (15 January 2025) | Viewed by 7155

Special Issue Editors

Dr. Domenico Toscano

E-Mail Website
Guest Editor
Department of Chemical, Materials and Industrial Production Engineering, University of Naples Federico II, Piazzale V. Tecchio 80, 80125 Naples, Italy
Interests: atmospheric pollution; air quality; air pollution studies; air pollution modeling; atmospheric dispersion model; emission inventories; air pollution monitoring; GIS system; renewable energy
Dr. Grazia Fattoruso

E-Mail Website
Guest Editor
TERIN/FSD/SAFS Lab, Department of Energy Technologies and Renewable Sources, ENEA Research Center Portici, P.le Enrico Fermi, 1, 80055 Portici, Italy
Interests: geomatics; spatial multicritical analysis; spatial statistics; smart water network; site suitability analysis; (Agri-)PV site suitability mapping; solar cadaster; optimal sensor placement; GIS/DSS systems; urban air quality mapping
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Air pollution is not solely an environmental issue—it also poses a major risk for global health, economies, and nature's balance. In view of this, the UN's 2030 Agenda for Sustainable Development emphasizes the urgent need to reduce air pollution to meet its Sustainable Development Goals (SDGs).

Improving air quality, via greater efficiency and increased deployment of renewables, goes hand in hand with the broader energy sector transformation and decarbonization. Reducing pollutant emissions improves the global quality of life, specifically relating to water and soil quality, crop yields, and food security. We are inviting you to contribute articles, perspectives, and reviews that investigate new methods, models, technologies, and systems which can deliver through combining better air quality with developments in renewable energy. By focusing on the role of energy in all walks of life, from mobility to living comfort, from agricultural to industrial production, and so on, the scope of this Special Issue encompasses the broader move towards an understanding that clean air is not simply a necessity but a crucial aspect of global sustainable progress. We welcome contributions of original research focusing on how to address this foremost challenge.

The topics of interest for this Special Issue include but are not limited to the following:

  • Pollution control systems
  • Sustainability of the energy development
  • Renewable energy systems and models for better air quality
  • Energy efficiency for air quality improvement
  • Relationship between air quality and Sustainable Development Goals (SDGs).
  • Policies and best practices aimed at controlling emissions and reducing greenhouse gases.
  • Effects of air quality on human health and climate change.
  • Citizen science, urban mobility, and air quality.

Dr. Domenico Toscano
Dr. Grazia Fattoruso
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. Atmosphere is an international peer-reviewed open access monthly 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

  • air quality
  • renewable energy systems
  • sustainable development goals (SDGs)
  • energy efficiency
  • air quality monitoring and modelling
  • citizen science
  • renewable energy community
  • human health
  • climate change
  • urban heat

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

45 pages, 24880 KiB  
Article
Future Low-Cost Urban Air Quality Monitoring Networks: Insights from the EU’s AirHeritage Project
by Saverio De Vito, Antonio Del Giudice, Gerardo D’Elia, Elena Esposito, Grazia Fattoruso, Sergio Ferlito, Fabrizio Formisano, Giuseppe Loffredo, Ettore Massera, Paolo D’Auria and Girolamo Di Francia
Atmosphere 2024, 15(11), 1351; https://doi.org/10.3390/atmos15111351 - 10 Nov 2024
Viewed by 1840
Abstract
The last decade has seen a significant growth in the adoption of low-cost air quality monitoring systems (LCAQMSs), mostly driven by the need to overcome the spatial density limitations of traditional regulatory grade networks. However, urban air quality monitoring scenarios have proved extremely [...] Read more.
The last decade has seen a significant growth in the adoption of low-cost air quality monitoring systems (LCAQMSs), mostly driven by the need to overcome the spatial density limitations of traditional regulatory grade networks. However, urban air quality monitoring scenarios have proved extremely challenging for their operative deployment. In fact, these scenarios need pervasive, accurate, personalized monitoring solutions along with powerful data management technologies and targeted communications tools; otherwise, these scenarios can lead to a lack of stakeholder trust, awareness, and, consequently, environmental inequalities. The AirHeritage project, funded by the EU’s Urban Innovative Action (UIA) program, addressed these issues by integrating intelligent LCAQMSs with conventional monitoring systems and engaging the local community in multi-year measurement strategies. Its implementation allowed us to explore the benefits and limitations of citizen science approaches, the logistic and functional impacts of IoT infrastructures and calibration methodologies, and the integration of AI and geostatistical sensor fusion algorithms for mobile and opportunistic air quality measurements and reporting. Similar research or operative projects have been implemented in the recent past, often focusing on a limited set of the involved challenges. Unfortunately, detailed reports as well as recorded and/or cured data are often not publicly available, thus limiting the development of the field. This work openly reports on the lessons learned and experiences from the AirHeritage project, including device accuracy variance, field recording assessments, and high-resolution mapping outcomes, aiming to guide future implementations in similar contexts and support repeatability as well as further research by delivering an open datalake. By sharing these insights along with the gathered datalake, we aim to inform stakeholders, including researchers, citizens, public authorities, and agencies, about effective strategies for deploying and utilizing LCAQMSs to enhance air quality monitoring and public awareness on this challenging urban environment issue. Full article
(This article belongs to the Special Issue Air Quality and Energy Transition: Interactions and Impacts)
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20 pages, 3602 KiB  
Article
Machine Learning for Optimising Renewable Energy and Grid Efficiency
by Bankole I. Oladapo, Mattew A. Olawumi and Francis T. Omigbodun
Atmosphere 2024, 15(10), 1250; https://doi.org/10.3390/atmos15101250 - 19 Oct 2024
Cited by 7 | Viewed by 4632
Abstract
This research investigates the application of machine learning models to optimise renewable energy systems and contribute to achieving Net Zero emissions targets. The primary objective is to evaluate how machine learning can improve energy forecasting, grid management, and storage optimisation, thereby enhancing the [...] Read more.
This research investigates the application of machine learning models to optimise renewable energy systems and contribute to achieving Net Zero emissions targets. The primary objective is to evaluate how machine learning can improve energy forecasting, grid management, and storage optimisation, thereby enhancing the reliability and efficiency of renewable energy sources. The methodology involved the application of various machine learning models, including Long Short-Term Memory (LSTM), Random Forest, Support Vector Machines (SVMs), and ARIMA, to predict energy generation and demand patterns. These models were evaluated using metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). Key findings include a 15% improvement in grid efficiency after optimisation and a 10–20% increase in battery storage efficiency. Random Forest achieved the lowest MAE, reducing prediction error by approximately 8.5%. The study quantified CO2 emission reductions by energy source, with wind power accounting for a 15,000-ton annual reduction, followed by hydropower and solar reducing emissions by 10,000 and 7500 tons, respectively. The research concludes that machine learning can significantly enhance renewable energy system performance, with measurable reductions in errors and emissions. These improvements could help close the “ambition gap” by 20%, supporting global efforts to meet the 1.5 °C Paris Agreement targets. Full article
(This article belongs to the Special Issue Air Quality and Energy Transition: Interactions and Impacts)
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