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Buildings
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New Technologies in Assessment of Indoor Environment
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New Technologies in Assessment of Indoor Environment

A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Building Energy, Physics, Environment, and Systems".

Deadline for manuscript submissions: 31 May 2025 | Viewed by 2039

Special Issue Editors

Dr. Mengtao Han

E-Mail Website
Guest Editor
School of Architecture and Urban Planning, Huazhong University of Science and Technology, No. 1037 Luoyu Road, Wuhan, China
Interests: urban & indoor wind environment; ventilation & pollutant; CFD modeling & simulation; green building design
Dr. Chao Lin

E-Mail Website
Guest Editor
Institute of Industrial Science, University of Tokyo, Tokyo 153-8505, Japan
Interests: wind environment; indoor environment; ventilation; wind tunnel experiment; CFD simulation; pollutant dispersion; gas chemistry; aerosol dynamics; high temperature gas; buoyant gas; concentration model
Dr. Shan Gao

E-Mail Website
Guest Editor
School of Architecture and Urban Planning, Chongqing University, Chongqing 400045, China
Interests: thermal comfort; urban/building environment and health; sustainable buildings and building energy efficiency

Special Issue Information

Dear Colleagues,

Modern human life predominantly occurs indoors, where indoor air quality, lighting, temperature, and humidity profoundly influence our health and emotional well-being. Poor indoor environments directly and indirectly impact physiological health, work efficiency, and overall quality of life. Research on indoor environments safeguards human health and drives advancements in architectural engineering and environmental science.

Throughout the history of indoor environment research, the continuous emergence of new research methods and technologies has dramatically enhanced our understanding. These advancements enable researchers to assess and improve indoor environmental quality more precisely and comprehensively. Novel simulation technologies allow for systematic and profound virtual experiments under controlled conditions. The development of real-time monitoring devices enables researchers to obtain high-frequency, high-precision data on indoor environmental parameters. Advanced analytical instruments and data science facilitate the in-depth studies of complex chemical and biological pollutants in indoor environments. Furthermore, the latest AI technologies support applications ranging from intelligent monitoring and personalized optimization of indoor environments to pollution management, building design optimization, and prediction of emergency events.

In summary, the emergence of new research methods and technologies enhances the scientific rigor and reliability of indoor environment research and provides powerful tools for creating healthier and more comfortable indoor living environments. These advancements contribute to safeguarding individual health and promoting sustainable development in architecture and urban design, thereby enhancing global indoor environmental quality.

In this Special Issue, we invite original contributions introducing new technologies, methodologies, case studies, projects, reviews, and discussions relevant to indoor environments. Submitted materials may encompass various engineering disciplines, such as building physics, environmental science, architecture, fluid dynamics, and experimental sciences.

We welcome manuscripts on topics including, but not limited to, the following:

  • Simulation and modeling: novel simulation methods for indoor environments;
  • Methods and case studies of indoor environment monitoring and experiments;
  • Multi-domain and multiphysics investigations on indoor environment;
  • Indoor environment and artificial intelligence.

Dr. Mengtao Han
Dr. Chao Lin
Dr. Shan Gao
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. Buildings 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 2600 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

  • indoor environment
  • new methodologies and technologies
  • environmental monitoring
  • simulation and modeling
  • artificial intelligence
  • case studies and projects

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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

16 pages, 4637 KiB  
Article
Indoor Air Pollution Source Localization Based on Small-Sample Training Convolutional Neural Networks
by Tiancheng Ye and Mengtao Han
Buildings 2025, 15(8), 1244; https://doi.org/10.3390/buildings15081244 - 10 Apr 2025
Viewed by 300
Abstract
In addressing the problem of indoor air pollution source localization, traditional methods have limitations such as strong sample dependence and low computational efficiency. This study uses a convolutional neural network to establish a pollution source inversion method based on small samples. By integrating [...] Read more.
In addressing the problem of indoor air pollution source localization, traditional methods have limitations such as strong sample dependence and low computational efficiency. This study uses a convolutional neural network to establish a pollution source inversion method based on small samples. By integrating computational fluid dynamics simulation data and deep learning techniques, a spatial pollution source identification model suitable for limited-sample conditions was constructed. In a benchmark scenario, the optimized model achieved a localization of 82.3% weighted accuracy within a prediction radius of 1 m, and the corresponding normalized error of the detected area was of less than 0.26%. In cross-scenario verification, the localization accuracy within a 1 m radius increased to 100%, and the corresponding predicted Euclidean distance error decreased by 21.43%. By using the optimal cutting ratio (α = 0.25) and a rotation-enhanced dataset (θ = 10°, n = 36), the model reduced the cross-space sample requirement to 1/5 of that of the benchmark scenario while ensuring the accuracy of spatial representation. The research findings provide an efficient and reliable deep learning solution for the localization of pollution sources in complex spaces. Full article
(This article belongs to the Special Issue New Technologies in Assessment of Indoor Environment)
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19 pages, 8743 KiB  
Article
Improving Building Floor Acoustics with Innovative Inorganic Sound Insulation Coating
by Jie Li, Qinglin Meng, Haowei Lu and Genyu Xu
Buildings 2024, 14(11), 3663; https://doi.org/10.3390/buildings14113663 - 18 Nov 2024
Cited by 1 | Viewed by 1121
Abstract
Floor impact sound insulation is essential for improving living environments and has become a mandatory requirement for green buildings in Southern China. This study introduces an innovative inorganic sound insulation coating technology for enhancing building floor acoustic performance. Through comprehensive laboratory experiments and [...] Read more.
Floor impact sound insulation is essential for improving living environments and has become a mandatory requirement for green buildings in Southern China. This study introduces an innovative inorganic sound insulation coating technology for enhancing building floor acoustic performance. Through comprehensive laboratory experiments and field tests, we evaluated inorganic coatings of 3 mm and 5 mm thickness, comparing their performance against traditional methods, including organic coatings and soundproof mortar. Standardized impact sound pressure level measurements, conducted in accordance with the China GB/T 50121 standard, demonstrated significant acoustic improvements. Laboratory testing revealed impact sound reductions of 6–7 dB and 9–10 dB for the 3 mm and 5 mm inorganic coatings, respectively, while field applications of the 3 mm coating achieved an average reduction of 14.3 dB. The inorganic coating exhibited superior performance characteristics compared to both organic coatings and soundproof mortar in terms of sound insulation efficiency, fire resistance, and application feasibility, demonstrating particularly effective attenuation in the mid- to high-frequency range. This investigation presents an innovative, cost-effective, and environmentally sustainable solution for improving floor sound insulation in green buildings. Full article
(This article belongs to the Special Issue New Technologies in Assessment of Indoor Environment)
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