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Advances in Indoor Environmental Quality
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Advances in Indoor Environmental Quality

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Environmental Science and Engineering".

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 6498

Special Issue Editor

Dr. Ling Tim Wong

E-Mail Website
Guest Editor
Department of Environment and Energy Engineering, The Hong Kong Polytechnic University, Hong Kong, China
Interests: environmental control; indoor air quality; built environment; water and sanitation; building safety
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Collaborative research efforts towards understanding indoor environmental health risks and practical solutions for improving indoor environmental quality in buildings, while reducing cost, resource and energy consumption impacts, can lead to advances in sustainable built environments. Related topics include, but are not limited to, environmental exposure; health risk assessment and modelling; bioaerosols; climate change and adaptation; indoor air quality; thermal comfort; ventilation; sustainable housing; occupant perception, acceptance and responses; health impact and policy; and quality of life. All research outcomes are intended to contribute to the development of best management practices in indoor and built environments in public and environmental health sciences. This Special Issue is open to any subject area relating to indoor environmental quality and health. Research papers, analytical reviews, case studies, conceptual frameworks, and policy-relevant articles are welcome.

Dr. Ling Tim Wong
Guest Editor

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. International Journal of Environmental Research and Public Health 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 2500 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

  • environmental exposure
  • health risk assessment and modelling
  • bioaerosols
  • climate change and adaptation
  • indoor air quality
  • thermal comfort
  • ventilation
  • sustainable housing
  • occupant perception
  • acceptance and response
  • health impact and policy
  • quality of life

Published Papers (3 papers)

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Research

26 pages, 9346 KiB  
Article
Numerical Investigation of Very Low Reynolds Cross Orifice Jet for Personalized Ventilation Applications in Aircraft Cabins
by Florin Ioan Bode and Ilinca Nastase
Int. J. Environ. Res. Public Health 2023, 20(1), 740; https://doi.org/10.3390/ijerph20010740 - 31 Dec 2022
Cited by 2 | Viewed by 1419
Abstract
This study focuses on the numerical analysis of a challenging issue involving the regulation of the human body’s microenvironment through personalized ventilation. We intended to first concentrate on the main flow, namely, the personalized ventilation jet, before connecting the many interacting components that [...] Read more.
This study focuses on the numerical analysis of a challenging issue involving the regulation of the human body’s microenvironment through personalized ventilation. We intended to first concentrate on the main flow, namely, the personalized ventilation jet, before connecting the many interacting components that are impacting this microenvironment (human body plume, personalized ventilation jet, and the human body itself as a solid obstacle). Using the laminar model and the large eddy simulation (LES) model, the flow field of a cross-shaped jet with very low Reynolds numbers is examined numerically. The related results are compared to data from laser doppler velocimetry (LDV) and particle image velocimetry (PIV) for a reference jet design. The major goal of this study is to evaluate the advantages and disadvantages of the CFD approach for simulating the key features of the cross-shaped orifice jet flow. It was discovered that the laminar model overestimated the global jet volumetric flow rate and the flow expansion. LES looks more suitable for the numerical prediction of such dynamic integral quantities. In light of the computational constraints, it quite accurately mimics the mean flow behavior in the first ten equivalent diameters from the orifice, where the mesh grid was extremely finely tuned. From the perspective of the intended application, the streamwise velocity distributions, streamwise velocity decay, and volumetric flow rate anticipated by the LES model are rather well reproduced. Full article
(This article belongs to the Special Issue Advances in Indoor Environmental Quality)
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27 pages, 19917 KiB  
Article
Event-Specific Transmission Forecasting of SARS-CoV-2 in a Mixed-Mode Ventilated Office Room Using an ANN
by Nishant Raj Kapoor, Ashok Kumar, Anuj Kumar, Dilovan Asaad Zebari, Krishna Kumar, Mazin Abed Mohammed, Alaa S. Al-Waisy and Marwan Ali Albahar
Int. J. Environ. Res. Public Health 2022, 19(24), 16862; https://doi.org/10.3390/ijerph192416862 - 15 Dec 2022
Cited by 7 | Viewed by 1685
Abstract
The emerging novel variants and re-merging old variants of SARS-CoV-2 make it critical to study the transmission probability in mixed-mode ventilated office environments. Artificial neural network (ANN) and curve fitting (CF) models were created to forecast the R-Event. The R-Event is defined as [...] Read more.
The emerging novel variants and re-merging old variants of SARS-CoV-2 make it critical to study the transmission probability in mixed-mode ventilated office environments. Artificial neural network (ANN) and curve fitting (CF) models were created to forecast the R-Event. The R-Event is defined as the anticipated number of new infections that develop in particular events occurring over the course of time in any defined space. In the spring and summer of 2022, real-time data for an office environment were collected in India in a mixed-mode ventilated office space in a composite climate. The performances of the proposed CF and ANN models were compared with respect to traditional statistical indicators, such as the correlation coefficient, RMSE, MAE, MAPE, NS index, and a20-index, in order to determine the merit of the two approaches. Thirteen input features, namely the indoor temperature (TIn), indoor relative humidity (RHIn), area of opening (AO), number of occupants (O), area per person (AP), volume per person (VP), CO2 concentration (CO2), air quality index (AQI), outer wind speed (WS), outdoor temperature (TOut), outdoor humidity (RHOut), fan air speed (FS), and air conditioning (AC), were selected to forecast the R-Event as the target. The main objective was to determine the relationship between the CO2 level and R-Event, ultimately producing a model for forecasting infections in office building environments. The correlation coefficients for the CF and ANN models in this case study were 0.7439 and 0.9999, respectively. This demonstrates that the ANN model is more accurate in R-Event prediction than the curve fitting model. The results show that the proposed ANN model is reliable and significantly accurate in forecasting the R-Event values for mixed-mode ventilated offices. Full article
(This article belongs to the Special Issue Advances in Indoor Environmental Quality)
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16 pages, 4367 KiB  
Article
A Physiological-Signal-Based Thermal Sensation Model for Indoor Environment Thermal Comfort Evaluation
by Shih-Lung Pao, Shin-Yu Wu, Jing-Min Liang, Ing-Jer Huang, Lan-Yuen Guo, Wen-Lan Wu, Yang-Guang Liu and Shy-Her Nian
Int. J. Environ. Res. Public Health 2022, 19(12), 7292; https://doi.org/10.3390/ijerph19127292 - 14 Jun 2022
Cited by 13 | Viewed by 2388
Abstract
Traditional heating, ventilation, and air conditioning (HVAC) control systems rely mostly on static models, such as Fanger’s predicted mean vote (PMV) to predict human thermal comfort in indoor environments. Such models consider environmental parameters, such as room temperature, humidity, etc., and indirect human [...] Read more.
Traditional heating, ventilation, and air conditioning (HVAC) control systems rely mostly on static models, such as Fanger’s predicted mean vote (PMV) to predict human thermal comfort in indoor environments. Such models consider environmental parameters, such as room temperature, humidity, etc., and indirect human factors, such as metabolic rate, clothing, etc., which do not necessarily reflect the actual human thermal comfort. Therefore, as electronic sensor devices have become widely used, we propose to develop a thermal sensation (TS) model that takes in humans’ physiological signals for consideration in addition to the environment parameters. We conduct climate chamber experiments to collect physiological signals and personal TS under different environments. The collected physiological signals are ECG, EEG, EMG, GSR, and body temperatures. As a preliminary study, we conducted experiments on young subjects under static behaviors by controlling the room temperature, fan speed, and humidity. The results show that our physiological-signal-based TS model performs much better than the PMV model, with average RMSEs 0.75 vs. 1.07 (lower is better) and R2 0.77 vs. 0.43 (higher is better), respectively, meaning that our model prediction has higher accuracy and better explainability. The experiments also ranked the importance of physiological signals (as EMG, body temperature, ECG, and EEG, in descending order) so they can be selectively adopted according to the feasibility of signal collection in different application scenarios. This study demonstrates the usefulness of physiological signals in TS prediction and motivates further thorough research on wider scenarios, such as ages, health condition, static/motion/sports behaviors, etc. Full article
(This article belongs to the Special Issue Advances in Indoor Environmental Quality)
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