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41 pages, 12863 KiB  
Review
Factors Affecting the Indoor Air Quality and Occupants’ Thermal Comfort in Urban Agglomeration Regions in the Hot and Humid Climate of Pakistan
by Muhammad Usama Haroon, Bertug Ozarisoy and Hasim Altan
Sustainability 2024, 16(17), 7869; https://doi.org/10.3390/su16177869 - 9 Sep 2024
Viewed by 1046
Abstract
The World Air Quality Index indicates that Pakistan ranks as the third most polluted country, regarding the average (Particulate Matter) PM2.5 concentration, which is 14.2 times higher than the World Health Organization’s annual air quality guideline. It is crucial to implement a [...] Read more.
The World Air Quality Index indicates that Pakistan ranks as the third most polluted country, regarding the average (Particulate Matter) PM2.5 concentration, which is 14.2 times higher than the World Health Organization’s annual air quality guideline. It is crucial to implement a program aimed at reducing PM2.5 levels in Pakistan’s urban areas. This review paper highlights the importance of indoor air pollution in urban regions such as Lahore, Faisalabad, Gujranwala, Rawalpindi, and Karachi, while also considering the effects of outdoor air temperature on occupants’ thermal comfort. The study aims to evaluate past methodological approaches to enhance indoor air quality in buildings. The main research question is to address whether there are statistical correlations between the PM2.5 and the operative air temperature and whether other indoor climatic variables have an impact on the thermal comfort assessment in densely built urban agglomeration regions in Pakistan. A systematic review analysis method was employed to investigate the effects of particulate matter (PM2.5), carbon oxides (COx), nitrogen oxides (NOx), sulfur oxides (SOx), and volatile organic compounds (VOCs) on residents’ health. The Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA) protocol guided the identification of key terms and the extraction of cited studies. The literature review incorporated a combination of descriptive research methods to inform the research context regarding both ambient and indoor air quality, providing a theoretical and methodological framework for understanding air pollution and its mitigation in various global contexts. The study found a marginally significant relationship between the PM2.5 operative air temperature and occupants’ overall temperature satisfaction, Ordinal Regression (OR) = 0.958 (95%—Confidence Interval (CI) [0.918, 1.000]), p = 0.050, Nagelkerke − Regression (R2) = 0.042. The study contributes to research on the development of an evidence-based thermal comfort assessment benchmark criteria for the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) Global Thermal Comfort Database version 2.1. Full article
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<p>Working schematic of pre-filters and HEPA filters [<a href="#B41-sustainability-16-07869" class="html-bibr">41</a>].</p>
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<p>Working schematic of multi-stage filtration system [<a href="#B43-sustainability-16-07869" class="html-bibr">43</a>].</p>
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<p>Air conditioning system active carbon filter [<a href="#B47-sustainability-16-07869" class="html-bibr">47</a>].</p>
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<p>Köppen–Geiger climate classification map, Lahore, Pakistan [<a href="#B52-sustainability-16-07869" class="html-bibr">52</a>].</p>
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<p>Flow design of conceptual framework. Drawn by author.</p>
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<p>(<b>a</b>) Input variables selected to complete the data mining process. (<b>b</b>) Missing patterns were excluded from the dataset.</p>
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<p>Dry bulb temperature during day- and night-time. Data source: <a href="https://clima.cbe.berkeley.edu" target="_blank">https://clima.cbe.berkeley.edu</a> (accessed on 25 March 2024).</p>
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<p>Bubble plot of age and relative humidity. Data source: <a href="https://repository.uel.ac.uk/item/8q774" target="_blank">https://repository.uel.ac.uk/item/8q774</a> (accessed on 25 March 2024).</p>
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<p>Yearly relative humidity chart for Lahore. Data source: <a href="https://clima.cbe.berkeley.edu" target="_blank">https://clima.cbe.berkeley.edu</a> (accessed on 25 March 2024).</p>
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<p>Psychometric chart. Data source: <a href="https://clima.cbe.berkeley.edu" target="_blank">https://clima.cbe.berkeley.edu</a> (accessed on 25 March 2024).</p>
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<p>Natural ventilation yearly bar Chart. Data source: <a href="https://clima.cbe.berkeley.edu" target="_blank">https://clima.cbe.berkeley.edu</a> (accesses on 25 March 2024).</p>
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<p>Universal thermal climate index (UTCI) heat stress map. Data source: <a href="https://clima.cbe.berkeley.edu" target="_blank">https://clima.cbe.berkeley.edu</a> (accesses on 25 March 2024).</p>
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<p>Bubble plot between length of residency and outdoor heat stress index. Data source: <a href="https://repository.uel.ac.uk/item/8q774" target="_blank">https://repository.uel.ac.uk/item/8q774</a> (accessed on 25 March 2024).</p>
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<p>Forest plot of outdoor air temperature and thermal sensation vote of occupants. Data source: <a href="https://repository.uel.ac.uk/item/8q774" target="_blank">https://repository.uel.ac.uk/item/8q774</a> (accessed on 25 March 2024).</p>
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<p>Boxplot for the distribution of indoor air temperature. Data source: <a href="https://cbe-berkeley.shinyapps.io/comfortdatabase/" target="_blank">https://cbe-berkeley.shinyapps.io/comfortdatabase/</a> (accessed on 25 March 2024).</p>
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<p>Boxplot for the distribution of PMV values. Data source: <a href="https://cbe-berkeley.shinyapps.io/comfortdatabase/" target="_blank">https://cbe-berkeley.shinyapps.io/comfortdatabase/</a> (accessed on 25 March 2024).</p>
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<p>Scatter plot relationship between PMV and indoor air temperature. Data source: <a href="https://cbe-berkeley.shinyapps.io/comfortdatabase/" target="_blank">https://cbe-berkeley.shinyapps.io/comfortdatabase/</a> (accessed on 25 March 2024).</p>
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<p>Scatter plot relationship between TSV and indoor air temperature. Data source: <a href="https://cbe-berkeley.shinyapps.io/comfortdatabase/" target="_blank">https://cbe-berkeley.shinyapps.io/comfortdatabase/</a> (accessed on 25 March 2024).</p>
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<p>ASHRAE thermal comfort adaptive model between indoor radiant temperature and monthly mean outdoor temperature. Data source: <a href="https://cbe-berkeley.shinyapps.io/comfortdatabase/" target="_blank">https://cbe-berkeley.shinyapps.io/comfortdatabase/</a> (accessed on 25 March 2024).</p>
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<p>Satisfaction graph between PMV and percentage dissatisfied. Data source: <a href="https://cbe-berkeley.shinyapps.io/comfortdatabase/" target="_blank">https://cbe-berkeley.shinyapps.io/comfortdatabase/</a> (accessed on 25 March 2024).</p>
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<p>(<b>a</b>) Skewness and kurtosis of in situ recorded indoor relative humidity (RH); (<b>b</b>) histogram of indoor RH; (<b>c</b>) normality analysis of indoor RH; (<b>d</b>) whisker graph of indoor RH.</p>
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<p>(<b>a</b>) Skewness and kurtosis of in situ recorded operative air temperature; (<b>b</b>) histogram of operative air temperature; (<b>c</b>) normality analysis of operative air temperature; (<b>d</b>) whisker graph of operative air temperature.</p>
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<p>(<b>a</b>) Skewness and kurtosis of in situ recorded operative air temperature; (<b>b</b>) histogram of operative air temperature; (<b>c</b>) normality analysis of operative air temperature; (<b>d</b>) whisker graph of operative air temperature.</p>
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12 pages, 572 KiB  
Article
Fuzzy Evaluation Model for Operational Performance of Air Cleaning Equipment
by Kuen-Suan Chen, Tsun-Hung Huang, Chun-Min Yu and Hui-E Lee
Mathematics 2024, 12(17), 2630; https://doi.org/10.3390/math12172630 - 24 Aug 2024
Viewed by 590
Abstract
Global warming has led to the continuous deterioration of the living environment, in which air quality directly affects human health. In addition, the severity of the COVID-19 pandemic has further increased the attention to indoor air quality. Indoor clean air quality is not [...] Read more.
Global warming has led to the continuous deterioration of the living environment, in which air quality directly affects human health. In addition, the severity of the COVID-19 pandemic has further increased the attention to indoor air quality. Indoor clean air quality is not only related to human health but also related to the quality of the manufacturing environment of clean rooms for numerous high-tech processes, such as semiconductors and packaging. This paper proposes a comprehensive model for evaluating, analyzing, and improving the operational performance of air cleaning equipment. Firstly, three operational performance evaluation indexes, such as the number of dust particles, the number of colonies, and microorganisms, were established. Secondly, the 100(1 α)% upper confidence limits of these three operational performance evaluation indexes were deduced to construct a fuzzy testing model. Meanwhile, the accumulated value of ϕ was used to derive the evaluation decision-making value. The proposed model can help companies identify the key quality characteristics that need to be improved. Furthermore, the competitiveness of cooperative enterprises towards smart manufacturing can be strengthened, so that enterprises can not only fulfill their social responsibilities while developing the economy but also take into account the sustainable development of enterprises and the environment. Full article
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<p>Fuzzy membership function <math display="inline"><semantics> <mrow> <msub> <mi>η</mi> <mi>h</mi> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> and vertical line <math display="inline"><semantics> <mrow> <mi>x</mi> <mo>=</mo> <msub> <mi>R</mi> <mi>h</mi> </msub> </mrow> </semantics></math>.</p>
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17 pages, 3697 KiB  
Article
Evaluating Indoor Air Quality in Schools: Is the Indoor Environment a Haven during High Pollution Episodes?
by Li Sun, Peng Wei, Dane Westerdahl, Jing Xue and Zhi Ning
Toxics 2024, 12(8), 564; https://doi.org/10.3390/toxics12080564 - 2 Aug 2024
Viewed by 814
Abstract
Pollution data were collected at five schools in Hong Kong using low-cost, sensor-based monitors both indoors and outdoors during two consecutive high pollution episodes. The pollutants monitored included NO2, O3, PM2.5, and PM10, which were [...] Read more.
Pollution data were collected at five schools in Hong Kong using low-cost, sensor-based monitors both indoors and outdoors during two consecutive high pollution episodes. The pollutants monitored included NO2, O3, PM2.5, and PM10, which were also used as input to a health risk communication protocol known as Air Quality Health Index (AQHI). CO2 was also measured simultaneously. The study aimed to assess the relationship between indoor pollutant concentrations and AQHI levels with those outdoors and to evaluate the efficacy of building operating practices in protecting students from pollution exposure. The results indicate that the regular air quality monitoring stations and outdoor pollutant levels at schools exhibit similar patterns. School AQHI levels indoors were generally lower than those outdoors, with PM10 levels showing a larger proportional contribution to the calculated values indoors. NO2 levels in one school were in excess of outdoor values. CO2 monitored in classrooms commonly exceeded indoor guidelines, suggesting poor ventilation. One school that employed air filtration had lower indoor PM concentrations compared to other schools; however, they were still similar to those outdoors. O3 levels indoors were consistently lower than those outdoors. This study underscores the utility of on-site, sensor-based monitoring for assessing the health impacts of indoor and community exposure to urban air pollutants. The findings suggest a need for improved ventilation and more strategic air intake placement to enhance indoor air quality. Full article
(This article belongs to the Special Issue Atmospheric Emissions Characteristics and Its Impact on Human Health)
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<p>Locations of the investigated schools and AMQSs. The red stars represent the investigated schools, the yellow dots indicate the AQMSs employed for comparison, and the blue dots are the other AQMSs running in Hong Kong.</p>
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<p>(<b>a</b>) Window-type air conditioner and (<b>b</b>) split air conditioner.</p>
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<p>Temporal variation in (<b>a</b>) NO<sub>2</sub>, (<b>b</b>) O<sub>3</sub>, (<b>c</b>) SO<sub>2</sub>, (<b>d</b>) PM<sub>10</sub>, (<b>e</b>) PM<sub>2.5</sub> concentrations and (<b>f</b>) AQHIs measured by MK roadside AQMS, KT community AQMS, SSP community AQMS, and TAP background AQMS during the period from 10:00 9 September to 23:59 22 September 2017. The shaded parts indicate high pollution episodes. The gray dashed lines represent the 2005 WHO air quality guidelines and the black dashed lines indicate the 2014 HKEPD air quality guidelines.</p>
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<p>Contribution of NO<sub>2</sub>, O<sub>3</sub>, SO<sub>2</sub>, and PM<sub>10</sub> to the AQHI on a daily basis calculated from the sampling data of (<b>a</b>) MK roadside AQMS, (<b>b</b>) KT community AQMS, (<b>c</b>) SSP community AQMS, and (<b>d</b>) TAP background AQMS.</p>
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<p>Temporal variation in (<b>a</b>) NO<sub>2</sub>, (<b>b</b>) O<sub>3</sub>, (<b>c</b>) PM<sub>10</sub>, and (<b>d</b>) PM<sub>2.5</sub> concentrations at the outdoor sites of the 5 investigated schools compared to data of KT AQMS and SSP AQMS. The shaded parts indicate high pollution episodes.</p>
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<p>I/O ratio of (<b>a</b>) NO<sub>2</sub>, (<b>b</b>) O<sub>3</sub>, (<b>c</b>) PM<sub>10</sub>, and (<b>d</b>) PM<sub>2.5</sub> calculated from the data collected only during the school time of each investigated school and compared between weekdays and Sundays. S1 has two classrooms (in purple and blue) and one auditorium (in green); S2 and S3 both have three classrooms (in purple, blue, and green); and S4 and S5 both have one classroom (in purple) and one auditorium (in green). Dark purple, blue, and green colors represent the I/O ratio on weekdays; light purple, blue, and green colors represent the I/O ratio on Sundays. The gray dashed lines indicate that the indoor concentration levels are equal to the outdoor ones.</p>
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<p>Temporal variation in (<b>a</b>) NO<sub>2</sub>, (<b>b</b>) O<sub>3</sub>, (<b>c</b>) PM<sub>10</sub>, and (<b>d</b>) PM<sub>2.5</sub> measured at the indoor and outdoor sites of S2. The shaded part indicates a high pollution episode.</p>
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<p>Temporal variation in (<b>a</b>) NO<sub>2</sub>, (<b>b</b>) O<sub>3</sub>, (<b>c</b>) PM<sub>10</sub>, and (<b>d</b>) PM<sub>2.5</sub> measured at the indoor and outdoor sites of S4. The shaded part indicates a high pollution episode.</p>
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<p>Temporal variation in (<b>a</b>) NO<sub>2</sub>, (<b>b</b>) O<sub>3</sub>, (<b>c</b>) PM<sub>10</sub>, and (<b>d</b>) PM<sub>2.5</sub> measured at the indoor and outdoor sites of S5. The shaded part indicates a high pollution episode.</p>
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<p>Temporal variation in the AQHI calculated from the indoor and outdoor pollutant concentrations of 5 schools during individual air quality monitoring periods.</p>
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<p>Contributions of NO<sub>2</sub>, O<sub>3</sub>, and PM<sub>10</sub> to the indoor and outdoor AQHIs of 5 schools.</p>
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<p>Temporal variation in CO<sub>2</sub> concentrations measured at the indoor sites of (<b>a</b>) S1, (<b>b</b>) S2, (<b>c</b>) S3, (<b>d</b>) S4, and (<b>e</b>) S5.</p>
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14 pages, 845 KiB  
Review
Sustainable Building Construction Materials in the United Arab Emirates: A Review
by Khalid Mehmood Sadar Din and Md Sayuti Ishak
Sustainability 2024, 16(15), 6565; https://doi.org/10.3390/su16156565 - 31 Jul 2024
Viewed by 2342
Abstract
The construction industry, a major player in economic development, is facing increased pressure to address sustainability concerns amidst rapid population growth and urbanization. With global projections indicating a significant rise in building demand by 2050, sustainability has emerged as a crucial focus area [...] Read more.
The construction industry, a major player in economic development, is facing increased pressure to address sustainability concerns amidst rapid population growth and urbanization. With global projections indicating a significant rise in building demand by 2050, sustainability has emerged as a crucial focus area and paradigm shift to enhance environmental friendliness, quality, and project outcomes. The UAE, renowned for its vibrant construction industry, offers a unique context for examining the integration of sustainable practices. The use of sustainable construction practices is growing in the UAE, where the built environment plays a key role in economic growth and environmental stewardship. The United Arab Emirates (UAE) aims to foster long-term sustainability while enhancing the standard of living for current and future generations by integrating social, environmental, and economic aspects within construction projects, while also reevaluating conventional sustainable development frameworks and embracing a triple bottom line approach. This research was conducted to explore sustainable construction material usage and evaluation in the United Arab Emirates. The literature was reviewed for sustainable building construction materials across the UAE in the SCOPUS index from 2014 to 2024, as well as the regional regulations concerning the subject. This study evaluated the increasing trend of sustainable construction material research works, as well as the sound regional parameters of sustainable construction materials implemented across the country. Through an exploration of the significance of sustainable construction materials, this research underscores the multifaceted benefits of locally sourced, recyclable, and renewable materials in reducing environmental impacts, fostering economic and social well-being, and improving overall project performance and project management practices. The construction sector’s role in economic development and its substantial environmental impact are discussed in alignment with sustainable construction materials, sustainable construction practices, and the need to enhance environmental sustainability and create healthier built environments. In the realm of sustainable construction materials, project management knowledge areas encompass a range of factors. These include the properties of materials sourced regionally; the incorporation of recycled content; considerations for indoor air quality, energy, and water efficiency parameters; and how these properties relate to project scopes, scheduling constraints, and challenges. Additionally, the availability of resources and competency levels, quality control standards, specifications, communication strategies, and stakeholder involvement play crucial roles. It is important to assess both the positive and negative risks associated with these elements across construction projects. Full article
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<p>Sustainable construction material research in the UAE from 2014 to 2024.</p>
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<p>Sustainable construction material life cycle.</p>
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<p>Life cycle of sustainable construction materials.</p>
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12 pages, 1072 KiB  
Article
Second-Hand Tobacco Smoke Exposure: Results of Particulate Matter (PM2.5) Measurements at Hospitality Venues in Addis Ababa, Ethiopia
by Selamawit Hirpa, Noreen Dadirai Mdege, Terefe Gelibo Argefa, Yifokire Tefera, Selam Abraham Kassa, Winnie Awuor and Wakgari Deressa
Int. J. Environ. Res. Public Health 2024, 21(8), 1011; https://doi.org/10.3390/ijerph21081011 - 31 Jul 2024
Viewed by 989
Abstract
Introduction: In Ethiopia, a comprehensive smoke-free law that bans smoking in all public areas has been implemented since 2019. This study aimed to evaluate compliance with these laws by measuring the air quality and conducting covert observations at 154 hospitality venues (HVs) in [...] Read more.
Introduction: In Ethiopia, a comprehensive smoke-free law that bans smoking in all public areas has been implemented since 2019. This study aimed to evaluate compliance with these laws by measuring the air quality and conducting covert observations at 154 hospitality venues (HVs) in Addis Ababa. Methods: Indoor air quality was measured using Dylos air quality monitors during the peak hours of the venues, with concentrations of particulate matter <2.5 microns in diameter (PM2.5) used as a marker of second-hand tobacco smoke. A standardized checklist was used to assess compliance with smoke-free laws during the same peak hours. The average PM2.5 concentrations were classified as good, moderate, unhealthy for sensitive groups, unhealthy for all, or hazardous using the World Health Organization’s (WHO) standard air quality index breakpoints. Results: Only 23.6% of the venues complied with all smoke-free laws indicators. Additionally, cigarette and shisha smoking were observed at the HVs. Overall, 63.9% (95% confidence interval: 56–72%) of the HVs had PM2.5 concentrations greater than 15 µg/m3. The presence of more than one cigarette smoker in the venue, observing shisha equipment in the indoor space, and the sale of tobacco products in the indoor space were significantly associated with higher median PM2.5 concentration levels (p < 0.005). Hazardous level of PM2.5 concentrations—100 times greater than the WHO standard—were recorded at HVs where several people were smoking shisha and cigarettes. Conclusions: Most HVs had PM2.5 concentrations that exceeded the WHO average air quality standard. Stricter enforcement of smoke-free laws is necessary, particularly for bars and nightclubs/lounges. Full article
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<p>Source of smoke in hospitality venues in Addis Ababa.</p>
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<p>Indicators of compliance with smoke-free laws by type of hospitality venue in Addis Ababa.</p>
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<p>PM<sub>2.5</sub> (µg/m<sup>3</sup>) measurement at a nightclub with several tobacco users (Addis Ababa, December 2022).</p>
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18 pages, 5868 KiB  
Article
Indoor Air Quality Evaluation in Rural Houses Using Different Heating Methods in Northern Shanxi, China
by Mengying Zhang, Xujuan Dong and Jing Feng
Sustainability 2024, 16(14), 5912; https://doi.org/10.3390/su16145912 - 11 Jul 2024
Viewed by 757
Abstract
It has been shown that heating methods have a large impact on rural indoor air quality. Previous studies on indoor air quality in rural houses involved a limited number of heating methods and lacked comprehensive comparative research on the three heating methods: coal-fired [...] Read more.
It has been shown that heating methods have a large impact on rural indoor air quality. Previous studies on indoor air quality in rural houses involved a limited number of heating methods and lacked comprehensive comparative research on the three heating methods: coal-fired boiler radiator heating, air-source heat pump radiator heating, and Chinese stove–kang heating. In this paper, subjective surveys and objective tests were conducted on indoor air quality in rural houses using these three heating methods in northern Shanxi, China. The gray relational analysis method and the comprehensive index method were used to evaluate the indoor air pollution levels of the three heating methods. The results were as follows: The subjective evaluations of most rural residents were overly optimistic about the indoor air quality of coal-fired boiler radiator heating and Chinese stove–kang heating. The indoor TVOC concentrations from these two heating methods far exceeded the standard limit of 0.6 mg/m3 at night. The indoor PM2.5 and PM10 concentrations from Chinese stove–kang heating varied greatly over a day and showed intermittent peak fluctuations that far exceeded the standard limits in the initial period of fuel combustion. The pollution levels from coal-fired boiler radiator heating, air-source heat pump radiator heating, and Chinese stove–kang heating were evaluated as light pollution, non-pollution, and medium or heavy pollution, respectively. Full article
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<p>Survey area.</p>
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<p>Plans of typical houses monitored for 24 h: (<b>a</b>) coal-fired boiler radiator heating, (<b>b</b>) air-source heat pump radiator heating, and (<b>c</b>) Chinese stove–kang heating.</p>
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<p>Survey site photographs.</p>
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<p>Indoor air quality questionnaire results: (<b>a</b>) air circulation statistics, (<b>b</b>) indoor odor statistics, (<b>c</b>) indoor dust amount statistics, and (<b>d</b>) overall indoor air quality satisfaction statistics.</p>
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<p>Results of on-site tracking tests in 300 rural houses: (<b>a</b>) PM<sub>2.5</sub> concentrations, (<b>b</b>) PM<sub>10</sub> concentrations, (<b>c</b>) TVOC concentrations, and (<b>d</b>) CO<sub>2</sub> concentrations.</p>
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<p>All-day continuous test results for the three heating methods: (<b>a</b>) PM<sub>2.5</sub> 24 h concentrations, (<b>b</b>) PM<sub>10</sub> 24 h concentrations, (<b>c</b>) TVOC 24 h concentrations, and (<b>d</b>) CO<sub>2</sub> 24 h concentrations.</p>
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25 pages, 3954 KiB  
Article
Enhanced Indoor Air Quality Dashboard Framework and Index for Higher Educational Institutions
by Farah Shoukry, Sherif Goubran and Khaled Tarabieh
Buildings 2024, 14(6), 1640; https://doi.org/10.3390/buildings14061640 - 3 Jun 2024
Viewed by 646
Abstract
This research proposes a 10-step methodology for developing an enhanced IAQ dashboard and classroom index (CI) in higher educational facilities located in arid environments. The identified parameters of the enhanced IAQ dashboard–inspired by the pandemic experience, result from the literature review and the [...] Read more.
This research proposes a 10-step methodology for developing an enhanced IAQ dashboard and classroom index (CI) in higher educational facilities located in arid environments. The identified parameters of the enhanced IAQ dashboard–inspired by the pandemic experience, result from the literature review and the outcome of two electronic surveys of (52) respondents, including health professionals and facility management experts. On the other hand, the indicators included in the CI are based on (80) occupant survey responses, including parameters related to IAQ, Indoor Environmental Quality (IEQ), and thermal comfort, amongst other classroom operative considerations. The CI is further tested in four learning spaces at the American University in Cairo, Egypt. The main contribution of this research is to suggest a conceptual visualization of the dashboard and a practical classroom index that integrates a representative number of contextual indicators to recommend optimal IAQ scenarios for a given educational facility. This study concludes by highlighting several key findings: (1) both qualitative and quantitative metrics are necessary to capture indoor air quality-related parameters accurately; (2) tailoring the dashboard as well as the CI to specific contexts enhances its applicability across diverse locations; and finally, (3) the IAQ dashboard and CI offer flexibility for ad-hoc applications. Full article
(This article belongs to the Special Issue Healthy, Digital and Sustainable Buildings and Cities)
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<p>Research Methodology (Self-Produced).</p>
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<p>Respondents’ Profile (Self-Produced).</p>
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<p>Experts’ Panel Brainstorming Exercise (Screenshot of the Google Slides—Self-Produced).</p>
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<p>Dashboard Visualization (Self-Produced).</p>
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<p>Conceptual Diagram for the Classroom Indoor Environmental Quality Index (CIEQI)—(Self-Produced).</p>
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<p>CIEQI—Classroom 01 (Self-Produced).</p>
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<p>CIEQI—Classroom 02 (Self-Produced).</p>
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<p>CIEQI—Studio 01(Self-Produced).</p>
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<p>CIEQI—Studio 02 (Self-Produced).</p>
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22 pages, 2650 KiB  
Article
A Field Survey on Indoor Climate in Land Transport Cabins of Buses and Trains
by John Omomoluwa Ogundiran, Jean-Paul Kapuya Bulaba Nyembwe, Anabela Salgueiro Narciso Ribeiro and Manuel Gameiro da Silva
Atmosphere 2024, 15(5), 589; https://doi.org/10.3390/atmos15050589 - 13 May 2024
Cited by 1 | Viewed by 1094
Abstract
Assessing indoor environmental quality (IEQ) is fundamental to ensuring health, well-being, and safety. A particular type of indoor compartment, land transport cabins (LTCs), specifically those of trains and buses, was surveyed. The global rise in commute and in-cabin exposure time gives relevance to [...] Read more.
Assessing indoor environmental quality (IEQ) is fundamental to ensuring health, well-being, and safety. A particular type of indoor compartment, land transport cabins (LTCs), specifically those of trains and buses, was surveyed. The global rise in commute and in-cabin exposure time gives relevance to the current study. This study discusses indoor climate (IC) in LTCs to emphasize the risk to the well-being and comfort of exposed occupants linked to poor IEQ, using objective assessment and a communication method following recommendations of the CEN-EN16798-1 standard. The measurement campaign was carried out on 36 trips of real-time travel on 15 buses and 21 trains, mainly in the EU region. Although the measured operative temperature, relative humidity, CO2, and VOC levels followed EN16798-1 requirements in most cabins, compliance gaps were found in the indoor climate of these LTCs as per ventilation requirements. Also, the PMV-PPD index evaluated in two indoor velocity ranges of 0.1 and 0.3 m/s showed that 39% and 56% of the cabins, respectively, were thermally inadequate. Also, ventilation parameters showed that indoor air quality (IAQ) was defective in 83% of the studied LTCs. Therefore, gaps exist concerning the IC of the studied LTCs, suggesting potential risks to well-being and comfort and the need for improved compliance with the IEQ and ventilation criteria of EN16798-1. Full article
(This article belongs to the Special Issue Enhancing Indoor Air Quality: Monitoring, Analysis and Assessment)
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<p>Use of an IEQ multiprobe device in bus and train cabins.</p>
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<p>Graphical illustration of the CO<sub>2</sub> charging and decay processes in indoor spaces [<a href="#B61-atmosphere-15-00589" class="html-bibr">61</a>].</p>
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<p>Time evolution of indoor climate parameters during a selected bus trip. (<b>a</b>) T<sub>O</sub> (°C) considering HVAC use for heating; (<b>b</b>) T<sub>O</sub> (°C) considering HVAC use for cooling; (<b>c</b>) relative humidity; (<b>d</b>) CO<sub>2</sub> level; and (<b>e</b>) VOC level.</p>
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<p>Mean and SD of thermal parameter series in all trips, (<b>a</b>) heating season and (<b>b</b>) cooling season, according to HVAC application.</p>
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<p>PMV index using two air velocities (<b>a</b>) Va = 0.1 m/s and (<b>b</b>) Va = 0.3 m/s for all the bus and train trips investigated.</p>
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<p>IAQ parameters for all trips: (<b>a</b>) CO<sub>2</sub> level averages and SD; (<b>b</b>) VOC averages and SD for all trips.</p>
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<p>CO<sub>2</sub> concentrations and fresh air flow rate correlation with the IAQ conditions [<a href="#B78-atmosphere-15-00589" class="html-bibr">78</a>].</p>
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16 pages, 4575 KiB  
Article
Evaluation of Air Quality and Thermal Comfort in University Dormitories in China
by Yanpeng Wu, Xiaoyu Li, Sheng Zhao, Qianglong Wang, Shanxin Wang, Liyang Yu and Faming Wang
Atmosphere 2024, 15(5), 586; https://doi.org/10.3390/atmos15050586 - 11 May 2024
Viewed by 837
Abstract
Most studies on Chinese dormitories are carried out in summer, while few focus on a transition season or winter. This study evaluated the air quality of a student dormitory in a university in the Beijing area by using a questionnaire survey and on-site [...] Read more.
Most studies on Chinese dormitories are carried out in summer, while few focus on a transition season or winter. This study evaluated the air quality of a student dormitory in a university in the Beijing area by using a questionnaire survey and on-site measurements. The CO2 concentration was used as an indoor air quality evaluation index to characterize the freshness of the air, and different window opening conditions in the dormitory were simulated, with corresponding improvement plans proposed. The results of this study revealed that the air quality and thermal comfort of the student dormitories during a transition season and winter fell short of expectations. According to the survey, students who opened their windows frequently had a better subjective perception of the air quality. However, due to the large temperature difference between day and night, more than 80% of the students felt too cold when opening the windows. For daytime conditions, the area of unilateral ventilation window opening should not be less than 0.39 m2, the area of bilateral ventilation window opening should not be less than 0.13 m2, and the time taken to close the windows and doors should not exceed the maximum ventilation interval. Empirical equations were fitted for nighttime conditions based on the CO2 concentration, number of people in the room, and window opening area, resulting in a reasonable window opening area of 0.349 m2~0.457 m2. In sum, this study assessed the air quality status within typical university dormitories across varying seasons, gaining a clear understanding of how different ventilation strategies and occupant densities influence air freshness and thermal comfort. Based on these insights, a practical and optimized window area recommendation was formulated to enhance the indoor environmental quality in these dormitories. Full article
(This article belongs to the Special Issue Contributions of Emission Inventory to Air Quality)
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<p>Test site and floor plan.</p>
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<p>The simplified model.</p>
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<p>Perception of air freshness with different numbers of permanent residents in rooms.</p>
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<p>Perception of air freshness with different window opening frequencies.</p>
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<p>Frequency distribution of sensation of cold among dormitory residents when opening windows.</p>
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<p>Measurement of indoor and outdoor parameters of dormitory in transitional season. (<b>a</b>) Actual measurements of indoor and outdoor temperatures in a typical dormitory. (<b>b</b>) Actual measurements of outdoor wind speed around a typical dormitory. (<b>c</b>) Actual measurements of indoor CO<sub>2</sub> concentrations in different dormitories.</p>
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<p>Measurement of indoor and outdoor parameters of dormitory in transitional season. (<b>a</b>) Actual measurements of indoor and outdoor temperatures in a typical dormitory. (<b>b</b>) Actual measurements of outdoor wind speed around a typical dormitory. (<b>c</b>) Actual measurements of indoor CO<sub>2</sub> concentrations in different dormitories.</p>
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<p>Actual measurements of indoor and outdoor parameters in dormitories during winter seasons. (<b>a</b>) Actual measurements of indoor and outdoor temperatures in a typical dormitory during winter. (<b>b</b>) Actual measurements of outdoor wind speed around a typical dormitory during winter. (<b>c</b>) Actual measurements of indoor CO<sub>2</sub> concentrations in different dormitories during winter.</p>
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<p>Actual measurements of indoor and outdoor parameters in dormitories during winter seasons. (<b>a</b>) Actual measurements of indoor and outdoor temperatures in a typical dormitory during winter. (<b>b</b>) Actual measurements of outdoor wind speed around a typical dormitory during winter. (<b>c</b>) Actual measurements of indoor CO<sub>2</sub> concentrations in different dormitories during winter.</p>
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<p>Changes in indoor CO<sub>2</sub> concentrations for different numbers of occupants when doors and windows are closed.</p>
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<p>Simulation statistics of daytime working conditions in the dormitory. The X-axis means the opening width of the window, and the height of the window is always 1.3 m.</p>
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<p>The experimental results.</p>
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17 pages, 4424 KiB  
Article
An Experimental Study on the Efficacy of Local Exhaust Systems for the Mitigation of Exhaled Contaminants in a Meeting Room
by Muhammad Farhan Ejaz, Simo Kilpeläinen, Panu Mustakallio, Weixin Zhao and Risto Kosonen
Buildings 2024, 14(5), 1272; https://doi.org/10.3390/buildings14051272 - 1 May 2024
Cited by 2 | Viewed by 894
Abstract
In industrial applications, local exhaust systems have been used extensively for capturing and confining contaminants at their source. The present study investigates the efficacy of these systems in mitigating the spread of exhaled pollutants by combining them with mixing and displacement ventilation. Experiments [...] Read more.
In industrial applications, local exhaust systems have been used extensively for capturing and confining contaminants at their source. The present study investigates the efficacy of these systems in mitigating the spread of exhaled pollutants by combining them with mixing and displacement ventilation. Experiments were conducted in a simulated meeting room with six closely situated workstations, featuring five exposed persons (simulated with heated dummies) and one infected person (simulated with a breathing manikin). Six overhead local exhaust units, merged with panels, corresponding to workstations, were installed using a lowered false ceiling. Additionally, a table plenum setting for air inlets was introduced to enhance displacement ventilation effectiveness along with local exhaust systems. Results from 16 experimental cases are presented, using the local air quality index and ventilation effectiveness in the breathing zone. The local exhaust system improved the local air quality at the measuring locations closest to the infector in almost all test scenarios. The improvement, particularly significant with displacement ventilation, marked a maximum 35% increase in the local air quality index adjacent to the infector and 25% in the entire breathing zone of the tested meeting room. Moreover, the table plenum settings, coupled with displacement ventilation, further enhanced conditions in the breathing zone. Under the specific conditions of this investigation, the number of operational local exhausts had a marginal impact on mixing ventilation but a significant one on displacement ventilation tests. The efficacy of local exhaust systems was also influenced by the levels of heat gains present in the room. Overall, the study aims to contribute to ongoing efforts to identify sustainable solutions to mitigate indoor airborne diseases with a combination of supply and local exhaust units. Full article
(This article belongs to the Topic Indoor Air Quality and Built Environment)
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<p>The layout of the test chamber: (<b>a</b>) top view; (<b>b</b>) side view.</p>
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<p>Experimental arrangement.</p>
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<p>Table plenum design: (<b>a</b>) side view; (<b>b</b>) table plenum configuration.</p>
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<p>Supply air openings with table plenum: (<b>a</b>) all openings; (<b>b</b>) close-up for infector’s opening.</p>
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<p>Exhaust concentration for high heat gain reference cases.</p>
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<p>Local air quality (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>ε</mi> </mrow> <mrow> <mi>p</mi> </mrow> </msub> </mrow> </semantics></math>) at the breathing zones (P1–P5) for low heat gain mixing distribution cases. (<b>a</b>) Reference case (no local exhaust). (<b>b</b>) Two operational local exhausts (at infector and location P3). (<b>c</b>) Six operational local exhausts.</p>
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<p>Local air quality (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>ε</mi> </mrow> <mrow> <mi>p</mi> </mrow> </msub> </mrow> </semantics></math>) at the breathing zones (P1–P5) for high heat gain mixing distribution cases. (<b>a</b>) Reference case (no local exhaust). (<b>b</b>) Two operational local exhausts (at infector and location P3). (<b>c</b>) Six operational local exhausts.</p>
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<p>Local air quality <math display="inline"><semantics> <mrow> <mo>(</mo> <msub> <mrow> <mi>ε</mi> </mrow> <mrow> <mi>p</mi> </mrow> </msub> <mo>)</mo> <mo> </mo> </mrow> </semantics></math> at the breathing zones (P1–P5) for low heat gain displacement distribution cases. (<b>a</b>) Reference case (no local exhaust). (<b>b</b>) Two operational local exhausts (at infector and location P3). (<b>c</b>) Two operational local exhausts with table plenum. (<b>d</b>) Six operational local exhausts. (<b>e</b>) Six operational local exhausts with table plenum.</p>
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<p>Local air quality <math display="inline"><semantics> <mrow> <mo>(</mo> <msub> <mrow> <mi>ε</mi> </mrow> <mrow> <mi>p</mi> </mrow> </msub> <mo>)</mo> </mrow> </semantics></math> at the breathing zones (P1–P5) for high heat gain displacement distribution cases. (<b>a</b>) Reference case (no local exhaust). (<b>b</b>) Two operational local exhausts (at infector and location P3). (<b>c</b>) Two operational local exhausts with table plenum. (<b>d</b>) Six operational local exhausts. (<b>e</b>) Six operational local exhausts with table plenum.</p>
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<p>Point source ventilation effectiveness for the entire breathing zone.</p>
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28 pages, 13624 KiB  
Review
State-of-the-Art Low-Cost Air Quality Sensors, Assemblies, Calibration and Evaluation for Respiration-Associated Diseases: A Systematic Review
by Hasan Tariq, Farid Touati, Damiano Crescini and Adel Ben Mnaouer
Atmosphere 2024, 15(4), 471; https://doi.org/10.3390/atmos15040471 - 11 Apr 2024
Cited by 1 | Viewed by 3973
Abstract
Indoor air quality and respiratory health have always been an area of prime interest across the globe. The significance of low-cost air quality sensing and indoor public health practices spiked during the pandemic when indoor air pollution became a threat to living beings, [...] Read more.
Indoor air quality and respiratory health have always been an area of prime interest across the globe. The significance of low-cost air quality sensing and indoor public health practices spiked during the pandemic when indoor air pollution became a threat to living beings, especially human beings. Problem Definition: Indoor respiration-associated diseases are hard to diagnose if they are due to indoor environmental conditions. A major challenge was observed in establishing a baseline between indoor air quality sensors and associated respiratory diseases. Methods: In this work, 10,000+ articles from top literature databases were reviewed using six bibliometric analysis methods (Lorenz Curve of Citations, Hirch’s H-Index, Kosmulski’s H2-Index, Harzing’s Hl-Norm-Index, Sidoropolous’s HC-Index, and Schrieber’s HM-index) to formulate indoor air quality sensor and disease correlation publication rubrics to critically review 482 articles. Results: A set of 152 articles was found based on systematic review parameters in six bibliometric indices for publications that used WHO, NIH, US EPA, CDC, and FDA-defined principles. Five major respiratory diseases were found to be causing major death toll (up to 32%) due to five key pollutants, measured by 30+ low-cost sensors and further optimized by seven calibration systems for seven practical parameters tailored to respiratory disease baselines evaluated through 10 cost parameters. Impact: This review was conducted to assist end-users, public health facilities, state agencies, researchers, scientists, and air quality protection agencies. Full article
(This article belongs to the Special Issue Air Pollution Exposure and Health Impact Assessment (2nd Edition))
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<p>Indoor respiration-assisted disease-focused systematic review hierarchy [<a href="#B1-atmosphere-15-00471" class="html-bibr">1</a>,<a href="#B2-atmosphere-15-00471" class="html-bibr">2</a>,<a href="#B3-atmosphere-15-00471" class="html-bibr">3</a>,<a href="#B4-atmosphere-15-00471" class="html-bibr">4</a>,<a href="#B5-atmosphere-15-00471" class="html-bibr">5</a>,<a href="#B6-atmosphere-15-00471" class="html-bibr">6</a>,<a href="#B7-atmosphere-15-00471" class="html-bibr">7</a>,<a href="#B8-atmosphere-15-00471" class="html-bibr">8</a>,<a href="#B9-atmosphere-15-00471" class="html-bibr">9</a>,<a href="#B10-atmosphere-15-00471" class="html-bibr">10</a>,<a href="#B11-atmosphere-15-00471" class="html-bibr">11</a>,<a href="#B12-atmosphere-15-00471" class="html-bibr">12</a>,<a href="#B13-atmosphere-15-00471" class="html-bibr">13</a>,<a href="#B14-atmosphere-15-00471" class="html-bibr">14</a>,<a href="#B15-atmosphere-15-00471" class="html-bibr">15</a>,<a href="#B16-atmosphere-15-00471" class="html-bibr">16</a>,<a href="#B17-atmosphere-15-00471" class="html-bibr">17</a>,<a href="#B18-atmosphere-15-00471" class="html-bibr">18</a>,<a href="#B19-atmosphere-15-00471" class="html-bibr">19</a>,<a href="#B20-atmosphere-15-00471" class="html-bibr">20</a>,<a href="#B21-atmosphere-15-00471" class="html-bibr">21</a>,<a href="#B22-atmosphere-15-00471" class="html-bibr">22</a>,<a href="#B23-atmosphere-15-00471" class="html-bibr">23</a>,<a href="#B24-atmosphere-15-00471" class="html-bibr">24</a>,<a href="#B25-atmosphere-15-00471" class="html-bibr">25</a>,<a href="#B26-atmosphere-15-00471" class="html-bibr">26</a>,<a href="#B27-atmosphere-15-00471" class="html-bibr">27</a>,<a href="#B28-atmosphere-15-00471" class="html-bibr">28</a>,<a href="#B29-atmosphere-15-00471" class="html-bibr">29</a>,<a href="#B30-atmosphere-15-00471" class="html-bibr">30</a>,<a href="#B31-atmosphere-15-00471" class="html-bibr">31</a>,<a href="#B32-atmosphere-15-00471" class="html-bibr">32</a>,<a 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class="html-bibr">82</a>,<a href="#B83-atmosphere-15-00471" class="html-bibr">83</a>,<a href="#B84-atmosphere-15-00471" class="html-bibr">84</a>,<a href="#B85-atmosphere-15-00471" class="html-bibr">85</a>,<a href="#B86-atmosphere-15-00471" class="html-bibr">86</a>,<a href="#B87-atmosphere-15-00471" class="html-bibr">87</a>,<a href="#B88-atmosphere-15-00471" class="html-bibr">88</a>,<a href="#B89-atmosphere-15-00471" class="html-bibr">89</a>,<a href="#B90-atmosphere-15-00471" class="html-bibr">90</a>,<a href="#B91-atmosphere-15-00471" class="html-bibr">91</a>,<a href="#B92-atmosphere-15-00471" class="html-bibr">92</a>,<a href="#B93-atmosphere-15-00471" class="html-bibr">93</a>,<a href="#B94-atmosphere-15-00471" class="html-bibr">94</a>,<a href="#B95-atmosphere-15-00471" class="html-bibr">95</a>,<a href="#B96-atmosphere-15-00471" class="html-bibr">96</a>,<a href="#B97-atmosphere-15-00471" class="html-bibr">97</a>,<a href="#B98-atmosphere-15-00471" class="html-bibr">98</a>,<a href="#B99-atmosphere-15-00471" class="html-bibr">99</a>,<a href="#B100-atmosphere-15-00471" class="html-bibr">100</a>,<a href="#B101-atmosphere-15-00471" class="html-bibr">101</a>,<a href="#B102-atmosphere-15-00471" class="html-bibr">102</a>,<a href="#B103-atmosphere-15-00471" class="html-bibr">103</a>,<a href="#B104-atmosphere-15-00471" class="html-bibr">104</a>,<a href="#B105-atmosphere-15-00471" class="html-bibr">105</a>,<a href="#B106-atmosphere-15-00471" class="html-bibr">106</a>,<a href="#B107-atmosphere-15-00471" class="html-bibr">107</a>,<a href="#B108-atmosphere-15-00471" class="html-bibr">108</a>,<a href="#B109-atmosphere-15-00471" class="html-bibr">109</a>,<a href="#B110-atmosphere-15-00471" class="html-bibr">110</a>,<a href="#B111-atmosphere-15-00471" class="html-bibr">111</a>,<a href="#B112-atmosphere-15-00471" class="html-bibr">112</a>,<a href="#B113-atmosphere-15-00471" class="html-bibr">113</a>,<a href="#B114-atmosphere-15-00471" class="html-bibr">114</a>,<a 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href="#B131-atmosphere-15-00471" class="html-bibr">131</a>,<a href="#B132-atmosphere-15-00471" class="html-bibr">132</a>,<a href="#B133-atmosphere-15-00471" class="html-bibr">133</a>,<a href="#B134-atmosphere-15-00471" class="html-bibr">134</a>,<a href="#B135-atmosphere-15-00471" class="html-bibr">135</a>,<a href="#B136-atmosphere-15-00471" class="html-bibr">136</a>,<a href="#B137-atmosphere-15-00471" class="html-bibr">137</a>,<a href="#B138-atmosphere-15-00471" class="html-bibr">138</a>,<a href="#B139-atmosphere-15-00471" class="html-bibr">139</a>,<a href="#B140-atmosphere-15-00471" class="html-bibr">140</a>,<a href="#B141-atmosphere-15-00471" class="html-bibr">141</a>,<a href="#B142-atmosphere-15-00471" class="html-bibr">142</a>,<a href="#B143-atmosphere-15-00471" class="html-bibr">143</a>,<a href="#B144-atmosphere-15-00471" class="html-bibr">144</a>,<a href="#B145-atmosphere-15-00471" class="html-bibr">145</a>,<a href="#B146-atmosphere-15-00471" class="html-bibr">146</a>,<a href="#B147-atmosphere-15-00471" class="html-bibr">147</a>,<a href="#B148-atmosphere-15-00471" class="html-bibr">148</a>,<a href="#B149-atmosphere-15-00471" class="html-bibr">149</a>,<a href="#B150-atmosphere-15-00471" class="html-bibr">150</a>,<a href="#B151-atmosphere-15-00471" class="html-bibr">151</a>,<a href="#B152-atmosphere-15-00471" class="html-bibr">152</a>].</p>
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<p>PRISMA diagram of workflow for inclusion of articles based on literature search and their applied exclusion criteria to scope in the focus of sensors and respiratory diseases.</p>
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<p>Statistical bibliometric indices for IAQ and indoor epidemiological relationship and public health baseline: (<b>a</b>) Lorenz Curve of Citations; (<b>b</b>) Hirch’s H-Index; (<b>c</b>) Kosmulski’s H2-Index; (<b>d</b>) Harzing’s Hl-Norm-Index; (<b>e</b>) Sidoropolous’s HC-Index; and (<b>f</b>) Schrieber’s HM-index.</p>
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<p>Summary of global institutional guidelines for indoor pollutant exposure limits as per WHO IAQ 2010, US EPA, ASHRAE, NIH, and CEN Indoor Air Quality Standards, Standards for IAQ GB/T 18883-2022 and ISIAQ STC34 Data.</p>
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<p>Two fundamental approaches in real-time AQMap [<a href="#B26-atmosphere-15-00471" class="html-bibr">26</a>,<a href="#B27-atmosphere-15-00471" class="html-bibr">27</a>,<a href="#B28-atmosphere-15-00471" class="html-bibr">28</a>,<a href="#B29-atmosphere-15-00471" class="html-bibr">29</a>,<a href="#B30-atmosphere-15-00471" class="html-bibr">30</a>,<a href="#B31-atmosphere-15-00471" class="html-bibr">31</a>,<a href="#B32-atmosphere-15-00471" class="html-bibr">32</a>]. (<b>a</b>) Real-time indoor air quality mapping. (<b>b</b>) Real-time outdoor air quality mapping [<a href="#B33-atmosphere-15-00471" class="html-bibr">33</a>,<a href="#B34-atmosphere-15-00471" class="html-bibr">34</a>].</p>
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<p>Two-step AQMP methodology practices in AQM [<a href="#B36-atmosphere-15-00471" class="html-bibr">36</a>,<a href="#B37-atmosphere-15-00471" class="html-bibr">37</a>,<a href="#B38-atmosphere-15-00471" class="html-bibr">38</a>,<a href="#B39-atmosphere-15-00471" class="html-bibr">39</a>,<a href="#B40-atmosphere-15-00471" class="html-bibr">40</a>,<a href="#B41-atmosphere-15-00471" class="html-bibr">41</a>,<a href="#B42-atmosphere-15-00471" class="html-bibr">42</a>] (<b>a</b>) Air quality modelling [<a href="#B36-atmosphere-15-00471" class="html-bibr">36</a>]. (<b>b</b>) Air quality management implementation scheme [<a href="#B37-atmosphere-15-00471" class="html-bibr">37</a>,<a href="#B38-atmosphere-15-00471" class="html-bibr">38</a>].</p>
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<p>Five respiratory disease-relevant approaches in AQ gas sensor technologies [<a href="#B49-atmosphere-15-00471" class="html-bibr">49</a>].</p>
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<p>Overview of OGS [<a href="#B50-atmosphere-15-00471" class="html-bibr">50</a>,<a href="#B53-atmosphere-15-00471" class="html-bibr">53</a>]. (<b>a</b>) Architecture. (<b>b</b>) Working principle.</p>
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<p>Overview of NDIR-GS [<a href="#B55-atmosphere-15-00471" class="html-bibr">55</a>,<a href="#B56-atmosphere-15-00471" class="html-bibr">56</a>] (<b>a</b>) Architecture. (<b>b</b>) Working principle.</p>
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<p>Overview of ECS [<a href="#B56-atmosphere-15-00471" class="html-bibr">56</a>,<a href="#B57-atmosphere-15-00471" class="html-bibr">57</a>,<a href="#B58-atmosphere-15-00471" class="html-bibr">58</a>] (<b>a</b>) Architecture. (<b>b</b>) Working principle.</p>
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<p>Overview of CGS [<a href="#B62-atmosphere-15-00471" class="html-bibr">62</a>]. (<b>a</b>) Architecture. (<b>b</b>) Working principle.</p>
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<p>Overview of CMGS [<a href="#B63-atmosphere-15-00471" class="html-bibr">63</a>,<a href="#B64-atmosphere-15-00471" class="html-bibr">64</a>,<a href="#B65-atmosphere-15-00471" class="html-bibr">65</a>]. (<b>a</b>) Architecture. (<b>b</b>) Working principle.</p>
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<p>Overview of AGS [<a href="#B66-atmosphere-15-00471" class="html-bibr">66</a>,<a href="#B67-atmosphere-15-00471" class="html-bibr">67</a>]. (<b>a</b>) Architecture. (<b>b</b>) Working principle.</p>
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<p>Sensing approach capabilities in non-invasive respiratory disease approximation [<a href="#B49-atmosphere-15-00471" class="html-bibr">49</a>,<a href="#B50-atmosphere-15-00471" class="html-bibr">50</a>,<a href="#B51-atmosphere-15-00471" class="html-bibr">51</a>,<a href="#B52-atmosphere-15-00471" class="html-bibr">52</a>,<a href="#B53-atmosphere-15-00471" class="html-bibr">53</a>,<a href="#B54-atmosphere-15-00471" class="html-bibr">54</a>,<a href="#B55-atmosphere-15-00471" class="html-bibr">55</a>,<a href="#B56-atmosphere-15-00471" class="html-bibr">56</a>,<a href="#B57-atmosphere-15-00471" class="html-bibr">57</a>,<a href="#B58-atmosphere-15-00471" class="html-bibr">58</a>,<a href="#B59-atmosphere-15-00471" class="html-bibr">59</a>,<a href="#B60-atmosphere-15-00471" class="html-bibr">60</a>,<a href="#B61-atmosphere-15-00471" class="html-bibr">61</a>,<a href="#B62-atmosphere-15-00471" class="html-bibr">62</a>,<a href="#B63-atmosphere-15-00471" class="html-bibr">63</a>,<a href="#B64-atmosphere-15-00471" class="html-bibr">64</a>,<a href="#B65-atmosphere-15-00471" class="html-bibr">65</a>,<a href="#B66-atmosphere-15-00471" class="html-bibr">66</a>].</p>
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<p>Limitations of sensing technologies and impact on their performance under different conditions [<a href="#B49-atmosphere-15-00471" class="html-bibr">49</a>,<a href="#B50-atmosphere-15-00471" class="html-bibr">50</a>,<a href="#B51-atmosphere-15-00471" class="html-bibr">51</a>,<a href="#B52-atmosphere-15-00471" class="html-bibr">52</a>,<a href="#B53-atmosphere-15-00471" class="html-bibr">53</a>,<a href="#B54-atmosphere-15-00471" class="html-bibr">54</a>,<a href="#B55-atmosphere-15-00471" class="html-bibr">55</a>,<a href="#B56-atmosphere-15-00471" class="html-bibr">56</a>,<a href="#B57-atmosphere-15-00471" class="html-bibr">57</a>,<a href="#B58-atmosphere-15-00471" class="html-bibr">58</a>,<a href="#B59-atmosphere-15-00471" class="html-bibr">59</a>,<a href="#B60-atmosphere-15-00471" class="html-bibr">60</a>,<a href="#B61-atmosphere-15-00471" class="html-bibr">61</a>,<a href="#B62-atmosphere-15-00471" class="html-bibr">62</a>,<a href="#B63-atmosphere-15-00471" class="html-bibr">63</a>,<a href="#B64-atmosphere-15-00471" class="html-bibr">64</a>,<a href="#B65-atmosphere-15-00471" class="html-bibr">65</a>,<a href="#B66-atmosphere-15-00471" class="html-bibr">66</a>].</p>
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<p>Major contributions in applied GSGs and ENBs. (<b>a</b>) Trio GSG for I-AQA [<a href="#B67-atmosphere-15-00471" class="html-bibr">67</a>]. (<b>b</b>) Wound infection ENB [<a href="#B68-atmosphere-15-00471" class="html-bibr">68</a>]; (<b>c</b>) eight-parameter GSG [<a href="#B69-atmosphere-15-00471" class="html-bibr">69</a>].</p>
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<p>Major contributions in applied GSAs, GSGs, and ENBs. (<b>a</b>) Test GSG for I-AQA [<a href="#B70-atmosphere-15-00471" class="html-bibr">70</a>]. (<b>b</b>) Eighteen-parameter GSA [<a href="#B71-atmosphere-15-00471" class="html-bibr">71</a>]. (<b>c</b>) Smart rig test ENB [<a href="#B72-atmosphere-15-00471" class="html-bibr">72</a>].</p>
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<p>Major contributions in GSAoC-based SOI for applied GSGs and ENBs. (<b>a</b>) SPEC DSPoC with GSA on chip [<a href="#B73-atmosphere-15-00471" class="html-bibr">73</a>,<a href="#B74-atmosphere-15-00471" class="html-bibr">74</a>,<a href="#B75-atmosphere-15-00471" class="html-bibr">75</a>,<a href="#B76-atmosphere-15-00471" class="html-bibr">76</a>]. (<b>b</b>) Monolithic GSA on chip with 3.5 nm wires [<a href="#B77-atmosphere-15-00471" class="html-bibr">77</a>].</p>
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<p>The AQ gas sensor calibration chamber-based system [<a href="#B74-atmosphere-15-00471" class="html-bibr">74</a>]. (<b>a</b>) P&amp;ID of a unit AQGS testing and calibration system. (<b>b</b>) System assembly and architecture.</p>
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<p>The computer-supervised AQGS testing systems [<a href="#B80-atmosphere-15-00471" class="html-bibr">80</a>,<a href="#B81-atmosphere-15-00471" class="html-bibr">81</a>]. (<b>a</b>) AQGS array testing and calibration system [<a href="#B80-atmosphere-15-00471" class="html-bibr">80</a>]. (<b>b</b>) The ENB testing and calibration system [<a href="#B81-atmosphere-15-00471" class="html-bibr">81</a>].</p>
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<p>IoT-based Smart AQ GSA testing and calibration system [<a href="#B84-atmosphere-15-00471" class="html-bibr">84</a>].</p>
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<p>Air quality mesh network testing and calibration system [<a href="#B92-atmosphere-15-00471" class="html-bibr">92</a>].</p>
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23 pages, 6836 KiB  
Article
Numerical Study of Air Distribution and Thermal Environment in Attached Ventilation Mode in the Generator Layer of a Hydropower Station
by Tong Ren, Mengzhuo Li, Long He and Panpan Sun
Buildings 2024, 14(4), 1030; https://doi.org/10.3390/buildings14041030 - 7 Apr 2024
Viewed by 1266
Abstract
Because they are in enclosed underground buildings, the generator layers of hydropower stations have limited ventilation. In order to reduce the influence of a hot and humid environment on equipment and staff health and create a good thermal environment with good air quality [...] Read more.
Because they are in enclosed underground buildings, the generator layers of hydropower stations have limited ventilation. In order to reduce the influence of a hot and humid environment on equipment and staff health and create a good thermal environment with good air quality for underground buildings, in this paper, vertical wall-attached ventilation was combined with the generator layer of a hydropower station to replace traditional ventilation. The influence of air supply velocity, air supply outlet position, and the opening mode of the generator layer on indoor velocity and temperature field distribution were analyzed via numerical simulation, and the evaluation indices of different cases were also compared. In the single-sided vertical wall-attached ventilation mode, when the velocity was increased from 4 m/s to 8 m/s, the maximum increment in the energy utilization coefficient was 41%, and the maximum reduction in the velocity non-uniformity coefficient was 9.5%. The results show that the single-sided mode can offer a higher ventilation efficiency than the double-sided mode, with a higher energy efficiency and a more uniform air distribution. Based on the mean temperature and velocity, and the key evaluation indices (head-foot temperature difference, percentage of dissatisfaction, non-uniformity coefficient, energy utilization coefficient, and air diffusion performance index), it is suggested that the single-sided air supply mode should be adopted for this kind of tall building, with an air supply velocity of v = 6 m/s and two open air supply outlets at each interval. Full article
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<p>Schematic diagram of airflow structure under the vertical wall-based attached jet.</p>
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<p>Flow chart of research methods for vertical wall-attached jet ventilation.</p>
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<p>Three-dimensional geometry diagram of generator layer.</p>
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<p>(<b>a</b>) Arrangement of double-sided vents; (<b>b</b>) meshing of double-sided vents (Model 1).</p>
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<p>(<b>a</b>) Arrangement of single-sided vents; (<b>b</b>) meshing of single-sided vents (Model 2).</p>
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<p>Monitoring points in the generator layer.</p>
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<p>Model mesh independence verification.</p>
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<p>(<b>a</b>)The position of the test device; (<b>b</b>) arrangement of measuring points.</p>
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<p>Measured and simulated variations in the shaft temperature of air supply outlet.</p>
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<p>Jet velocity attenuation of the air supply outlet under different velocities [<a href="#B46-buildings-14-01030" class="html-bibr">46</a>].</p>
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<p>Velocity distribution of the double-sided mode under different air supply velocities. (* The reasonable air supply velocity range is between 0.2 m/s and 0.8 m/s, the same as below).</p>
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<p>Velocity distribution of the single-sided mode under different air supply velocities.</p>
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<p>Velocity nephogram under different air supply velocities (x = 60 m).</p>
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<p>Temperature distribution of the double-sided mode under different air supply velocities.</p>
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<p>Temperature distribution of the single-sided mode under different air supply velocities.</p>
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<p>Temperature nephogram under different air supply velocities (x = 60 m).</p>
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<p>Velocity distribution of the double-sided air supply under different opening modes. (* The reasonable air supply velocity range between dimensionless velocity 0.25 and 0.10, the same as below).</p>
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<p>Velocity distribution of the single-sided air supply under different opening modes.</p>
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<p>The temperature distribution of the double-sided air supply under different opening modes.</p>
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<p>Temperature distribution of the single-sided air supply under different opening modes.</p>
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<p>Comparison of air supply modes between double-sided (Case A) and single-sided (CaseA1): (<b>a</b>) cloud diagram of velocity distribution; and (<b>b</b>) cloud diagram of temperature distribution.</p>
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19 pages, 4319 KiB  
Article
The Relationship between Mechanical Ventilation, Indoor Air Quality Classes, and Energy Classes in a Romanian Context
by Vasilica Vasile, Vlad Iordache, Valentin Mihai Radu and Claudiu-Sorin Dragomir
Atmosphere 2024, 15(4), 444; https://doi.org/10.3390/atmos15040444 - 3 Apr 2024
Cited by 1 | Viewed by 1218
Abstract
Nowadays, indoor air quality (IAQ) and the energy performance of buildings are two main scientific and technical challenges because they are in direct connection with human health and the depletion of energy resources. In this study, we analyzed the influence of an outdoor [...] Read more.
Nowadays, indoor air quality (IAQ) and the energy performance of buildings are two main scientific and technical challenges because they are in direct connection with human health and the depletion of energy resources. In this study, we analyzed the influence of an outdoor air flow introduced through a mechanical ventilation system, focusing on the two aforementioned topics. A standardized ventilation rate (25 m3/h/person) led to an increase in the indoor O3 concentration (from 5 μg/m3 to 50 μg/m3) and, simultaneously, to a decrease in the indoor CO2 concentration (from 2000 mg/m3 to 800 mg/m3), a decrease in the PM2.5 concentration (from 300 μg/m3 to 150 μg/m3), and the maintenance of a constant indoor HCHO concentration. In our study, a new, single indoor air quality index, IIAQ, is proposed. This new index presents different implications: on the one hand, it has the ability to simultaneously take into account several pollutant species, and on the other hand, it can prioritize the ventilation strategy that responds to the extreme values of a certain pollutant. Moreover, indoor air quality classes were elaborated, similar to energy classes. The possibility of using this new index simultaneously with energy consumption may lead to ventilation strategies that are adaptative to dynamic outdoor pollutant concentrations. Full article
(This article belongs to the Special Issue Science and Technology of Indoor and Outdoor Environment)
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<p>Roadmap of the methodology.</p>
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<p>Schema of the IAQ and energy consumption modeling.</p>
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<p>The physical model of an indoor space.</p>
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<p>Energy scale for one hour of heating, valid for the studied space, in kWh.</p>
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<p>The influence of fresh air flow introduced through the mechanical ventilation, q<sub>0</sub>, on indoor O<sub>3</sub> concentration at the moment τ, C<sub>i</sub>(τ), and the energy consumption, Q.</p>
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<p>The influence of fresh air flow introduced through the mechanical ventilation, q<sub>0</sub>, on indoor PM<sub>2.5</sub> concentration at the moment τ, C<sub>i</sub>(τ), and the energy consumption, Q.</p>
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<p>The influence of fresh air flow introduced through the mechanical ventilation, q<sub>0</sub>, on indoor HCHO concentration at the moment τ, C<sub>i</sub>(τ), and the energy consumption, Q.</p>
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<p>The influence of fresh air flow introduced through the mechanical ventilation, q<sub>0</sub>, on indoor CO<sub>2</sub> concentration at the moment τ, C<sub>i</sub>(τ), and the energy consumption, Q.</p>
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<p>The influence of fresh air flow introduced through the mechanical ventilation, q<sub>0</sub>, on indoor air quality index, I<sub>IAQ</sub>, and the energy consumption, Q.</p>
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26 pages, 6605 KiB  
Article
Design and Evaluation of Wireless DYU Air Box for Environment-Monitoring IoT System on Da-Yeh University Campus
by Lun-Min Shih, Huan-Liang Tsai and Cheng-Yu Tsai
Appl. Sci. 2024, 14(5), 2201; https://doi.org/10.3390/app14052201 - 6 Mar 2024
Cited by 1 | Viewed by 1231
Abstract
This paper presents an original wireless DYU Air Box of an environment-monitoring IoT (EMIoT) system on a campus to offer information on environmental conditions through the public ThingSpeak IoT platform for stakeholders including all the students and employees on the Da-Yeh University (DYU) [...] Read more.
This paper presents an original wireless DYU Air Box of an environment-monitoring IoT (EMIoT) system on a campus to offer information on environmental conditions through the public ThingSpeak IoT platform for stakeholders including all the students and employees on the Da-Yeh University (DYU) campus in Taiwan. Firstly, the proposed wireless heterogeneous multi-sensor module aggregates BME680, SCD30, PMS7003, and BH1750 sensors with a TTGO ESP32 Wi-Fi device based on the I2C and UART interface standards of series communication. Through the DYU-802.1X Wi-Fi network with the WPA2 Enterprise security directly, the wireless multi-sensor monitoring module further forwards the observation data of environmental conditions on campus via the DYU-802.1X Wi-Fi network to the public ThingSpeak IoT platform, which is a cloud service platform to aggregate, visualize, and analyze live sensing data of air quality index (AQI), concentrations of PM1.0/2.5 and CO2, brightness, ambient temperature, and relative humidity (RH). The results illustrate the proposed DYU Air Box for monitoring the indoor environmental conditions on campus and validate them with sufficient accuracy and confidence with commercialized measurement instruments. In this work, the wireless smart environment-monitoring IoT system features monitoring and automatic alarm functions for monitoring AQI, CO2, and PM concentrations, as well as ambient illumination, temperature, and RH parameters and collaboration and interoperability through the Enterprise Intranet. All the organizational stakeholders interested in the environmental conditions of the DYU campus can openly access the information according to their interests. In the upcoming future, the information of the environmental conditions in the DYU campus will be developed to be simultaneously accessed by all the stakeholders through both the public ThingSpeak IoT platform and the private EMIoT system. Full article
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<p>Schematic of wireless multi-sensor EMIoT system on Da-Yeh University campus.</p>
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<p>Schematics of wireless DYU Air Box with heterogeneous and multi-sensor module: (<b>a</b>) Wiring connection; (<b>b</b>) sensor shield; (<b>c</b>) PCB layout.</p>
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<p>Schematics of heterogeneous sensors: (<b>a</b>) BME680 air quality sensor; (<b>b</b>) PMS7003T dust sensor; (<b>c</b>) SCD30 CO<sub>2</sub> gas sensor; (<b>d</b>) BH1750 light sensor.</p>
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<p>Schematics of heterogeneous sensors: (<b>a</b>) BME680 air quality sensor; (<b>b</b>) PMS7003T dust sensor; (<b>c</b>) SCD30 CO<sub>2</sub> gas sensor; (<b>d</b>) BH1750 light sensor.</p>
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<p>The flowchart of the proposed wireless DYU Air Box.</p>
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<p>Photographs of the proposed wireless heterogeneous multi-sensor module: (<b>a</b>) TTGO ESP32 Wi-Fi module, BH1750 light sensor, BME680 AQI sensor, SCD30 CO<sub>2</sub> sensor, and PMS7003T dust sensor; (<b>b</b>) demo DYU Air Box for environmental monitoring of classroom.</p>
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<p>Screenshot of the ThingSpeak public channel for the proposed DYU Air Box.</p>
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<p>The 24 h in situ measurement results of DYU Air Box for environmental monitoring of H708 classroom at Da-Yeh University: (<b>a</b>) IAQ index; (<b>b</b>) IAQ level; (<b>c</b>) PM<sub>1.0</sub> concentration; (<b>d</b>) PM<sub>2.5</sub> concentration; (<b>e</b>) CO<sub>2</sub> concentration; (<b>f</b>) brightness; (<b>g</b>) temperature; (<b>h</b>) humidity.</p>
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<p>The 24 h in situ measurement results of DYU Air Box for environmental monitoring of H708 classroom at Da-Yeh University: (<b>a</b>) IAQ index; (<b>b</b>) IAQ level; (<b>c</b>) PM<sub>1.0</sub> concentration; (<b>d</b>) PM<sub>2.5</sub> concentration; (<b>e</b>) CO<sub>2</sub> concentration; (<b>f</b>) brightness; (<b>g</b>) temperature; (<b>h</b>) humidity.</p>
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<p>The 24 h in situ measurement results of DYU Air Box for environmental monitoring of H708 classroom at Da-Yeh University: (<b>a</b>) IAQ index; (<b>b</b>) IAQ level; (<b>c</b>) PM<sub>1.0</sub> concentration; (<b>d</b>) PM<sub>2.5</sub> concentration; (<b>e</b>) CO<sub>2</sub> concentration; (<b>f</b>) brightness; (<b>g</b>) temperature; (<b>h</b>) humidity.</p>
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<p>The 24 h in situ measurement results of DYU Air Box for environmental monitoring of H708 classroom at Da-Yeh University: (<b>a</b>) IAQ index; (<b>b</b>) IAQ level; (<b>c</b>) PM<sub>1.0</sub> concentration; (<b>d</b>) PM<sub>2.5</sub> concentration; (<b>e</b>) CO<sub>2</sub> concentration; (<b>f</b>) brightness; (<b>g</b>) temperature; (<b>h</b>) humidity.</p>
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<p>Difference analysis of 24 h in situ measurement results for DYU Air Box compared with corresponding measurement instruments: (<b>a</b>) IAQ index; (<b>b</b>) IAQ level; (<b>c</b>) PM<sub>1.0</sub> concentration; (<b>d</b>) PM<sub>2.5</sub> concentration; (<b>e</b>) CO<sub>2</sub> concentration; (<b>f</b>) brightness; (<b>g</b>) temperature; (<b>h</b>) humidity.</p>
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<p>Difference analysis of 24 h in situ measurement results for DYU Air Box compared with corresponding measurement instruments: (<b>a</b>) IAQ index; (<b>b</b>) IAQ level; (<b>c</b>) PM<sub>1.0</sub> concentration; (<b>d</b>) PM<sub>2.5</sub> concentration; (<b>e</b>) CO<sub>2</sub> concentration; (<b>f</b>) brightness; (<b>g</b>) temperature; (<b>h</b>) humidity.</p>
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<p>Difference analysis of 24 h in situ measurement results for DYU Air Box compared with corresponding measurement instruments: (<b>a</b>) IAQ index; (<b>b</b>) IAQ level; (<b>c</b>) PM<sub>1.0</sub> concentration; (<b>d</b>) PM<sub>2.5</sub> concentration; (<b>e</b>) CO<sub>2</sub> concentration; (<b>f</b>) brightness; (<b>g</b>) temperature; (<b>h</b>) humidity.</p>
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<p>Difference analysis of 24 h in situ measurement results for DYU Air Box compared with corresponding measurement instruments: (<b>a</b>) IAQ index; (<b>b</b>) IAQ level; (<b>c</b>) PM<sub>1.0</sub> concentration; (<b>d</b>) PM<sub>2.5</sub> concentration; (<b>e</b>) CO<sub>2</sub> concentration; (<b>f</b>) brightness; (<b>g</b>) temperature; (<b>h</b>) humidity.</p>
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24 pages, 9734 KiB  
Article
Impact of Diffuser Location on Thermal Comfort Inside a Hospital Isolation Room
by Mustafa Alkhalaf, Adrian Ilinca, Mohamed Yasser Hayyani and Fahed Martini
Designs 2024, 8(2), 19; https://doi.org/10.3390/designs8020019 - 20 Feb 2024
Viewed by 2042
Abstract
Thermal comfort is increasingly recognized as vital in healthcare facilities, where patients spend 80–90% of their time indoors. Sensing, controlling, and predicting indoor air quality should be monitored for thermal comfort. This study examines the effects of ventilation design on thermal comfort in [...] Read more.
Thermal comfort is increasingly recognized as vital in healthcare facilities, where patients spend 80–90% of their time indoors. Sensing, controlling, and predicting indoor air quality should be monitored for thermal comfort. This study examines the effects of ventilation design on thermal comfort in hospital rooms, proposing four distinct ventilation configurations, each with three airflow rates of 9, 12, and 15 Air Changes per Hour (ACH). The study conducted various ventilation simulation scenarios for a hospital room. The objective is to determine the effect of airflow and the diffuser location distribution on thermal comfort. The Reynolds-Averaged Navier–Stokes (RANS) equations, along with the k–ε turbulence model, were used as the underlying mathematical representation for the airflow. The boundary conditions for the simulations were derived from the ventilation standards set by the American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE) and insights from previous studies. Thermal comfort and temperature distribution were assessed using indices like Predicted Percentage Dissatisfaction (PPD), Predicted Mean Vote (PMV), and Air Diffusion Performance Index (ADPI). Although most of the twelve scenarios failed to attain thermal comfort, two of those instances were optimal in this simulation. Those instances involved the return diffuser behind the patient and airflow of 9 ACH, the minimum recommended by previous studies. It should be noted that the ADPI remained unmet in these cases, revealing complexities in achieving ideal thermal conditions in healthcare environments. This study extends the insights from our prior research, advancing our understanding of ventilation impacts on thermal comfort in healthcare facilities. It underscores the need for comprehensive approaches to environmental control, setting the stage for future research to refine these findings further. Full article
(This article belongs to the Topic Building Energy and Environment, 2nd Volume)
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<p>Isolation Room and its Content.</p>
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<p>The layout of the isolation room with different diffuser locations (<b>a</b>) cases 1, 2, and 3; (<b>b</b>) cases 4, 5, and 5; (<b>c</b>) cases 7, 8, and 9; (<b>d</b>) cases 10, 11, and 12.</p>
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<p>Domain meshes with zoom-in on the body edge.</p>
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<p>Temperature and air velocity percentage error at the control point vs. mesh size.</p>
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<p>Illustration of the Position of the Measuring Line.</p>
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<p>The Comparison with an Experimental Study.</p>
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<p>Front view for Temperature Case 1 at 9 ACH.</p>
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<p>Front view for Velocity Case 1 at 9 ACH.</p>
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<p>Front view for PMV (<b>a</b>) Case 1 at 9 ACH, (<b>b</b>) Case 2 at 12 ACH, (<b>c</b>) Case 3 at 15 ACH.</p>
Full article ">Figure 9 Cont.
<p>Front view for PMV (<b>a</b>) Case 1 at 9 ACH, (<b>b</b>) Case 2 at 12 ACH, (<b>c</b>) Case 3 at 15 ACH.</p>
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<p>Front view for Fluid PPD (<b>a</b>) Case 1 at 9 ACH, (<b>b</b>) Case 2 at 12 ACH, (<b>c</b>) Case 3 at 15 ACH.</p>
Full article ">Figure 10 Cont.
<p>Front view for Fluid PPD (<b>a</b>) Case 1 at 9 ACH, (<b>b</b>) Case 2 at 12 ACH, (<b>c</b>) Case 3 at 15 ACH.</p>
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<p>Top view (<b>a</b>) Case 4 at 9 ACH, (<b>b</b>) Case 5 at 12 ACH, (<b>c</b>) Case 6 at 15 ACH.</p>
Full article ">Figure 11 Cont.
<p>Top view (<b>a</b>) Case 4 at 9 ACH, (<b>b</b>) Case 5 at 12 ACH, (<b>c</b>) Case 6 at 15 ACH.</p>
Full article ">Figure 12
<p>Front view for PMV (<b>a</b>) Case 4, at 9 ACH, (<b>b</b>) Case 5 at 12 ACH, (<b>c</b>) Case 6 at 15 ACH.</p>
Full article ">Figure 12 Cont.
<p>Front view for PMV (<b>a</b>) Case 4, at 9 ACH, (<b>b</b>) Case 5 at 12 ACH, (<b>c</b>) Case 6 at 15 ACH.</p>
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<p>Top view for Fluid PPD (<b>a</b>) Case 4 at 9 ACH, (<b>b</b>) Case 5 at 12 ACH, (<b>c</b>) Case 6 at 15 ACH.</p>
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<p>Front view for PMV (<b>a</b>) Case 7 at 9 ACH, (<b>b</b>) Case 8 at 12 ACH, (<b>c</b>) Case 9 at 15 ACH.</p>
Full article ">Figure 14 Cont.
<p>Front view for PMV (<b>a</b>) Case 7 at 9 ACH, (<b>b</b>) Case 8 at 12 ACH, (<b>c</b>) Case 9 at 15 ACH.</p>
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<p>Front view of PPD (<b>a</b>) Case 7 at 9 ACH, (<b>b</b>) Case 8 at 12 ACH, (<b>c</b>) Case 9 at 15 ACH.</p>
Full article ">Figure 15 Cont.
<p>Front view of PPD (<b>a</b>) Case 7 at 9 ACH, (<b>b</b>) Case 8 at 12 ACH, (<b>c</b>) Case 9 at 15 ACH.</p>
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<p>Front view for PMV (<b>a</b>) Case 10 at 9 ACH, (<b>b</b>) Case 11 at 12 ACH, (<b>c</b>) Case 12 at 15 ACH.</p>
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<p>Front view for Fluid PPD (<b>a</b>) Case 10 at 9 ACH, (<b>b</b>) Case 11 at 12 ACH, (<b>c</b>) Case 12 at 15 ACH.</p>
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<p>Illustration of the position of the measuring line.</p>
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<p>Comparison of PPD % at 9 ACH.</p>
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<p>Comparison of PPD % at 12 ACH.</p>
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<p>Comparison of PPD % at 15 ACH.</p>
Full article ">Figure 22
<p>Comparison of ADPI Values.</p>
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