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Добірка наукової літератури з теми "Indoor air quality index"

Автор: Grafiati

Опубліковано: 29 березня 2025

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Статті в журналах з теми "Indoor air quality index"

Eltzov, Evgeni, Abri Lavena De Cesarea, ‘Yuen Kei Adarina Low, and Robert S. Marks. "Indoor air pollution and the contribution of biosensors." EuroBiotech Journal 3, no. 1 (January 1, 2019): 19–31. http://dx.doi.org/10.2478/ebtj-2019-0003.

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Abstract A vast majority of people today spend more time indoors than outdoors. However, the air quality indoors may be as bad as or even worse than the air quality outside. This is due to the continuous circulation of the same air without proper ventilation and filtration systems, causing a buildup of pollutants. As such, indoor air quality monitoring should be considered more seriously. Indoor air quality (IAQ) is a measure of the air quality within and around buildings and relates to the health and comfort of building occupants. To determine the IAQ, computer modeling is done to simulate the air flow and human exposure to the pollutant. Currently, very few instruments are available to measure the indoor air pollution index. In this paper, we will review the list of techniques available for measuring IAQ, but our emphasis will be on indoor air toxicity monitoring.

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Wagdi, Dalia, Khaled Tarabieh, and Mohamed Nagib Abou Zeid. "Indoor air quality index for preoccupancy assessment." Air Quality, Atmosphere & Health 11, no. 4 (January 26, 2018): 445–58. http://dx.doi.org/10.1007/s11869-018-0551-y.

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Khadim, Hussein, Faik Obaed, and Nahla Ajeel. "Wireless Sensing Network for Implementation of Air Quality Monitoring System and Indoor Air Quality Index Application." Iraqi Geological Journal 57, no. 2D (October 31, 2024): 210–20. http://dx.doi.org/10.46717/igj.57.2d.17ms-2024-10-27.

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Indoor air quality significantly impacts respiratory health and mental activity. This study utilizes a wireless sensing network (WSN) based on the Internet of Things (IoT) to monitor indoor air quality, referred to as an indoor air quality monitoring system. The system was installed and applied on campus at the University of Baghdad. The present study aims to monitor air quality parameters continuously within laboratories. Carbon monoxide, sulfur dioxide, nitrogen dioxide, ammonia, and particulate objects are the pollutants chosen to be monitored by the installed system in this study. These pollutants were selected because they affect indoor facilities' comfort, health, and working conditions. Colored coded data was employed in the monitoring system; defined ranges for each pollutant were also integrated. Sensor nodes, wireless modules that connect to the IoT server, and user applications are the main components of the IAQMS system. LCDs, mobile applications, the ThingSpeak web server, and the LabView platform are examples of techniques used to present data collected by the system. Additionally, the system includes a notification function that alerts students and lab personnel when indoor air quality index IAQI values signal unhealthy indoor air quality. The proactive approach ensures a regulated standard for indoor air quality and pollutants.

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Sun, Li, Peng Wei, Dane Westerdahl, Jing Xue, and Zhi Ning. "Evaluating Indoor Air Quality in Schools: Is the Indoor Environment a Haven during High Pollution Episodes?" Toxics 12, no. 8 (August 2, 2024): 564. http://dx.doi.org/10.3390/toxics12080564.

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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.

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Erfianto, Bayu, and Andrian Rahmatsyah. "Application of ARIMA Kalman Filter with Multi-Sensor Data Fusion Fuzzy Logic to Improve Indoor Air Quality Index Estimation." JOIV : International Journal on Informatics Visualization 6, no. 4 (December 31, 2022): 771. http://dx.doi.org/10.30630/joiv.6.4.889.

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Air quality monitoring is a process that determines the number of pollutants in the air, one of which is indoor air quality. The Fuzzy Indoor Air Quality Index was developed in this research. It is a method for determining the indoor air quality index using sensor fusion and fuzzy logic. By combining several different time series determinants of air quality, a fuzzy logic-based sensor fusion method is used to build a knowledge base about indoor air quality levels. Without the use of complicated calculation models, fuzzy logic-based fusion will make it easier to determine indoor air quality levels based on various sensor parameters. The input for fuzzy-based data fusion is obtained from the ARIMA method with Kalman Filter's air quality parameter values estimation. The application of ARIMA with a Kalman Filter was used to improve the accuracy of indoor air quality estimation in this study. ARIMA(3,1,3) had a MAPE of 0.1 percent on the CO2 dataset, and ARIMA(1,0,1) had a MAPE of 0.63 percent on the TVOC dataset based on approximately three experimental days. ARIMA (3,1,3) estimation with a Kalman Filter results in a MAPE of 0.03 percent for the CO2 dataset and a MAPE of 0.24 percent for ARIMA(1,0,1) Kalman Filter estimation on TVOC dataset. As a result, the Fuzzy Indoor Air Quality Index (FIAQI) developed in this research reasonably estimates indoor air quality. This can be seen by examining the percentage of estimation errors obtained from the experiment.

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Altamirano-Astorga, Jorge, J. Octavio Gutierrez-Garcia, and Edgar Roman-Rangel. "Forecasting Indoor Air Quality in Mexico City Using Deep Learning Architectures." Atmosphere 15, no. 12 (December 20, 2024): 1529. https://doi.org/10.3390/atmos15121529.

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Air pollution causes millions of premature deaths per year due to its strong association with several diseases and respiratory afflictions. Consequently, air quality monitoring and forecasting systems have been deployed in large urban areas. However, those systems forecast outdoor air quality while people living in relatively large cities spend most of their time indoors. Hence, this work proposes an indoor air quality forecasting system, which was trained with data from Mexico City, and that is supported by deep learning architectures. The novelty of our work is that we forecast an indoor air quality index, taking into account seasonal data for multiple horizons in terms of minutes; whereas related work mostly focuses on forecasting concentration levels of pollutants for a single and relatively large forecasting horizon, using data from a short period of time. To find the best forecasting model, we conducted extensive experimentation involving 133 deep learning models. The deep learning architectures explored were multilayer perceptrons, long short-term memory neural networks, 1-dimension convolutional neural networks, and hybrid architectures, from which LSTM rose as the best-performing architecture. The models were trained using (i) outdoor air pollution data, (ii) publicly available weather data, and (iii) data collected from an indoor air quality sensor that was installed in a house located in a central neighborhood of Mexico City for 17 months. Our empirical results show that deep learning models can forecast an indoor air quality index based on outdoor concentration levels of pollutants in conjunction with indoor and outdoor meteorological variables. In addition, our findings show that the proposed method performs with a mean squared error of 0.0179 and a mean absolute error of 0.1038. We also noticed that 5 months of historical data are enough for accurate training of the forecast models, and that shallow models with around 50,000 parameters have enough predicting power for this task.

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Tariq, Hasan, Farid Touati, Damiano Crescini, and Adel Ben Mnaouer. "State-of-the-Art Low-Cost Air Quality Sensors, Assemblies, Calibration and Evaluation for Respiration-Associated Diseases: A Systematic Review." Atmosphere 15, no. 4 (April 11, 2024): 471. http://dx.doi.org/10.3390/atmos15040471.

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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.

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Wei, Qing Tao, Li Na Zhao, and Hai Ting Lv. "Fuzzy Comprehensive Evaluation of Air Quality in Home." Applied Mechanics and Materials 484-485 (January 2014): 484–87. http://dx.doi.org/10.4028/www.scientific.net/amm.484-485.484.

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Based on the indoor air quality and the evaluation index, utilize the fuzzy mathematical theory, comprehensively consider on the three important factors which influence the air quality, and through the calculation and determination the weight vector, divide the air quality into four levels as "qualified"," mild overweight"," overweight", "severe overweight". Through sampling the indoor air in a family, according to the fuzzy comprehensive evaluation method and the maximum membership degree principle, get every sample point levels from data. It will provide the method of getting a more objective evaluation of indoor air quality situation.

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Wang, Wei, Zhuang Yu, Hui Zhang, and Hai Tao Wang. "Assessment of Indoor Air Quality Using Different Air-Condition for Cooling." Advanced Materials Research 518-523 (May 2012): 910–13. http://dx.doi.org/10.4028/www.scientific.net/amr.518-523.910.

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Based on observation of characteristics of NAI concentration of part of an office-building in Shenzhen, air quality of different equipments for cooling were assessed using ion polarity ratio (q) and air ion assessment index (CI). The result show that the air cleanness degree of the natural ventilation indoor is better than mechanical ventilation, using renewable energy for cooling is better than normal air-conditioning, and placed the negative ion generator has improved indoor air quality significantly. So the authors suggest to take NAI concentration as a monitoring and assessment indicators of the indoor environment, so as to provide a scientific basis and design concept for energy planning and environmental protection in the future.

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Kaewrat, Jenjira, Rungruang Janta, Surasak Sichum, and Thongchai Kanabkaew. "Indoor Air Quality and Human Health Risk Assessment in the Open-Air Classroom." Sustainability 13, no. 15 (July 25, 2021): 8302. http://dx.doi.org/10.3390/su13158302.

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Indoor air quality is associated with academic performance and harmful health effects on students and teachers who participate in the classroom. Outdoor sources always contribute to classroom air quality. This study aims to estimate the amounts of indoor and outdoor pollutants and the influence of outdoor sources on open-air classrooms in a school located in the city. A health risk assessment was applied to assess the non-carcinogenic risk to students and teachers from exposure to the pollutants in the classroom. The concentrations of indoor NO2 ranged between 46.40 and 77.83 µg/m3, which is about 0.8 times that of outdoor NO2. A strong correlation and a high indoor/outdoor (I/O) ratio (>0.5) without a source, indicated that indoor NO2 is significantly influenced by outdoor sources. The range of indoor PM2.5 concentrations was 1.66 to 31.52 µg/m3 which was influenced by meteorological conditions. The indoor PM2.5 concentrations were affected by both indoor and outdoor sources. Although the level of indoor air pollutants met the official standard, the young children were exposed to indoor air pollutants which were above the recommended limits to human health with regard to the hazard index (HI) of 1.12. Instant measures such as regularly cleaning the classrooms, zoning the students, and installation of solid and vegetation barriers are recommended to reduce the daily dose of pollutants affecting students in open-air classrooms.

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Більше джерел

Дисертації з теми "Indoor air quality index"

Assy, Eliane. "Study of indoor air quality by multi-sensor systems." Electronic Thesis or Diss., Université de Lille (2018-2021), 2021. http://www.theses.fr/2021LILUR056.

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L'exposition à la pollution de l'air intérieur est considérée comme un enjeu sanitaire majeur pour toute population en général, entraînant des maladies respiratoires et cardiovasculaires voir des décès prématurés. Malgré un nombre croissant d'études au cours des dernières décennies, les données sur la pollution de l'air intérieur sont encore limités. Ce manque est dû notamment aux différents environnements, publics ou privés à étudier, et à la disponibilité des techniques d’analyse qui peuvent être déployées dans ces environnements de manière à ne pas gêner les occupants. Pour ces raisons, les capteurs chimiques à faible coût désormais présents dans le commerce constituent des instruments prometteurs pour l'étude de la QAI, sous réserve qu'ils soient bien caractérisés.Dans ce travail, les systèmes multi-capteurs conçus dans le cadre d'un projet multidisciplinaire au sein de l'Université de Lille, ont été testés dans des conditions semi-contrôlées en laboratoire afin d'évaluer leurs performances métrologiques et leurs limites. Les résultats ont révélé que les capteurs étaient capables de quantifier avec une résolution temporelle élevée (30 secondes), les concentrations de CO2, CO, NOx, O3, VOC et PM, en dépit de certains problèmes de calibration liés aux interférences chimiques et à la dépendance de la réponse des capteurs à l'humidité relative.Ces capteurs ont été déployés dans divers bâtiments résidentiels et non résidentiels de l'agglomération lilloise. Les mesures ont montré que, la plupart du temps, les concentrations de polluants de l'air intérieur sont en dessous des valeurs seuils recommandées par la communauté scientifique. Les mesures ont également permis, lorsqu'elles sont couplées aux registres spatio-temporels d'activité remplis par les occupants, d'identifier et de caractériser les événements conduisant à des concentrations supérieures aux valeurs recommandées. Ces déterminants de la QAI incluent la cuisson, même sur une cuisinière électrique, les processus de combustion tels que la fumée de cigarette ou la brûlure de bougies ou d'encens, la consommation de produits de soins corporels et de nettoyage de la maison, et même la simple présence des occupants.Les mesures des capteurs ont été utilisées afin de calculer un indice de la qualité de l'air intérieur en temps quasi-réel, basé sur l'indice Int’Air®. Cet indice modifié converge rapidement vers l'indice Int’Air® permettant ainsi d'effectuer une évaluation simple et peu coûteuse de la QAI, comme exigé par les autorités réglementaires. Par ailleurs, ce nouvel indice réagit immédiatement aux événements de pollution, ce qui pourrait être utilisé par les gestionnaires de bâtiments pour prendre des mesures visant à améliorer la QAI lorsque cela s'avère nécessaire
Exposure to indoor air pollution is a major health hazard for the general population, leading to respiratory and cardiovascular diseases and even to premature death. In spite of an increasing number of studies in the last decades, indoor air pollution data are still scarce. This is due in part to the many different environments, public or private, to be investigated, and to the availability of instruments that can be deployed in such environments without disturbing the occupants. For these reasons, the now commercially available low-cost chemical sensors are promising instruments for the study of IAQ, provided they are well characterized.In the present work, sensor nodes developed in a multidisciplinary project within the University of Lille, were tested in laboratory semi-controlled conditions to assess their performances and limitations. They were found adequate to quantify with a high time resolution (30 seconds) the concentrations of CO2, CO, NOx, O3, VOC and PM, in spite of some calibration issues linked to chemical interferences and to the dependence of the sensors response on the relative humidity.These sensors nodes were deployed in various residential and non-residential buildings in the metropolitan area of Lille. These measurements showed that, most of the time, the indoor air pollutants concentrations are below the threshold values recommended by the scientific community. The measurements also allowed, when coupled to space-time-activity logs filled by the occupants, to identify and characterize the events leading to concentrations in excess of the recommended values. Such IAQ determinants include cooking, even on electric stove, combustion processes such as cigarette smoking or burning candles or incense, use of body care and housecleaning products, and even the mere presence of occupants.The sensors data were used to calculate a quasi-real time indoor air quality index, based on the INT’AIR® index. This modified index converges quickly with INT’AIR®, therefore allowing to perform an easy and cheap assessment of IAQ as mandated by regulatory instances. At the same time, the new index also responds immediately to pollution events, which could be used by building managers to take actions to improve IAQ when necessary

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Miranda, Cavalcante Neto Luiz. "Dynamic indicator of individual exposure to air quality based on multi-sensor measurements : a tool for personalized prevention." Electronic Thesis or Diss., Ecole nationale supérieure Mines-Télécom Lille Douai, 2024. http://www.theses.fr/2024MTLD0009.

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L'évolution récente des technologies de détection des gaz a popularisé l’usage des micro-capteurs dans de nombreuses applications : analyse de la qualité des produits alimentaires, nuisances olfactives, surveillance de la pollution de l'air ambiant et intérieur. Les capteurs de gaz à base d'oxyde métallique (MOX) dominent le marché des capteurs prêts à l'emploi grâce à leur miniaturisation, leur coût réduit et leur disponibilité. Cependant, les capteurs MOX sont rarement utilisés seuls pour mesurer un gaz unique, car ils sont sensibles à de nombreux paramètres, dont plusieurs gaz simultanément, et sujets à la dérive au fil du temps. Ils sont généralement regroupés en grappes (ou « nez électroniques ») combinant différents modèles de capteurs MOX aux sensibilités variées. Avec un traitement de données approprié via des algorithmes de reconnaissance des formes, ces systèmes fournissent des informations précieuses sur l'échantillon mesuré. Pour la qualité de l'air intérieur (QAI), ces grappes de capteurs MOX servent à mesurer la concentration de composés organiques volatils (COV), avec des résultats parfois comparables aux équipements analytiques de laboratoire. Cette thèse étudie les informations fournies par ces grappes dans les applications de QAI, et comment les transmettre à l'occupant sous la forme d'un indice individuel dynamique de QAI, d'où le titre de la thèse. L'approche retenue a d’abord consister à étudier le nombre de degrés de liberté d'un système multi-capteurs MOX à l'aide d'un outil d'analyse dimensionnelle : la dimensionnalité intrinsèque (ID). L’objectif était d’identifier une configuration optimale pour un moniteur de QAI. Pour cela, plusieurs ensembles de données, illustrant différentes situations de QAI, ont été analysés. Nous avons ensuite développé notre propre base de données, comprenant 10 activités intérieures quotidiennes, surveillées par un grand nombre de capteurs MOX. Lors de l'analyse de ces données, nous avons constaté que l'ID pouvait aussi indiquer l'état de la pollution de l'environnement surveillé. Après avoir approfondi les effets des activités sur l'ID du système, un article a été publié sur ces résultats
Recent developments in gas sensing technology have made the use of microsensors popular for a large variety of applications, such as the analysis of quality of food products, odor nuisances, and air pollution monitoring in the ambient and in the indoor air. Notably, metal-oxide-based gas sensors (MOX sensors) have dominated the market for off-the-shelf gas sensor due to their miniaturization, cost-effectiveness, and availability. Despite that, MOX sensors are usually not used individually to measure a single gas as they are notoriously known to be sensitive to a large number of parameters, including multiple gases at the same time, as well as being prone to drift in their measurement during their lifetime. The solution to that is that is most applications, these sensors are grouped in clusters (sometimes called electronic noses) containing different models of MOX sensors capable of measuring different species of gases with different levels of sensitivity and, with proper data treatment in the form of a pattern recognition algorithm, they can provide valuable information about the sample presented to them. For indoor air quality (IAQ) applications, these clusters of MOX sensors are typically used to measure concentration of volatile organic compounds (VOCs)in the indoor air, with results sometimes comparable to analytical laboratory equipment. In this thesis, we study which type of information these clusters of sensors can provide to us, specifically in IAQ applications and how we can convey this information to the occupant of a monitored indoor environment in the form of a dynamic individual IAQ index, hence the title of the thesis. The chosen approach was, at first, to study the number of degrees of freedom of a system containing multiple MOX sensors using a dimensional analysis tool (the intrinsic dimensionality, or ID, of the system) to try to find an ideal configuration for an IAQ monitor to. To do so, multiple datasets were analyzed, which contained different IAQ situations. We ended up developing our own dataset containing reproductions of 10 different day-to-day indoor activities monitored by a large number of MOX sensors. During the analysis of this dataset, we realized that the ID can also be an important indicator of the state of the air pollution in the monitored indoor environment, so after further exploring the effects of the performed activities in the ID of the system, a paper was published with the findings of this study

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Riffelli, Stefano. "Sustainable comfort in indoor environments: global comfort indices and virtual sensors." Doctoral thesis, Urbino, 2022. http://hdl.handle.net/11576/2700929.

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Rahmani, Mariam. "Indoor Air Quality Measurements." Honors in the Major Thesis, University of Central Florida, 2003. http://digital.library.ucf.edu/cdm/ref/collection/ETH/id/415.

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This item is only available in print in the UCF Libraries. If this is your Honors Thesis, you can help us make it available online for use by researchers around the world by following the instructions on the distribution consent form at http://library.ucf
Bachelors
Engineering and Computer Science
Environmental Engineering

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Cony, Louis. "Élaboration et développement d’un indice de la qualité sanitaire de l’habitat : outil de quantification de la « favorabilité » à la santé." Thesis, La Rochelle, 2020. http://www.theses.fr/2020LAROS002.

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Si nous n’amoindrissons pas l’importance de la qualité de l’air extérieur (en particulier dans les zones à trafic routier important, dans les zones à proximité de sites industriels…) ou dans les transports (comme les espaces confinés souterrains) dans l’exposition des personnes aux polluants de l’air, la prise en compte de l’exposition aux polluants des occupants dans leurs logements est primordiale puisque les gens y passent en moyenne autour de 80% de leur temps. La première étape de ce travail a consisté à définir tout d’abord un nombre réduit de polluants à considérer à l’intérieur des logements par un processus de hiérarchisation consistant à comparer les niveaux d’exposition aux différents polluants par rapport à leurs valeurs sanitaires de référence. L’analyse des indices mono et multi polluants existants nous a permis d’aboutir à la définition d’un nouvel indice multi polluants, nommé ULR-QAI, qui a été utilisé comme indicateur principal dans la suite de l’étude. Le deuxième chapitre était quant à lui dédié au développement de l’outil numérique nécessaire à reproduire les situations diverses et variées qui peuvent être rencontrées dans les logements. L’objectif était ici de reproduire le transport des polluants de l’air extérieur vers l’intérieur, les sources intérieures de polluants ainsi que les phénomènes physiques essentiels (transferts de polluants entre les différentes pièces d’un logement, variation de l’humidité relative de l’air, dépôt de particules, filtration…) pour l’évaluation des niveaux de concentration des polluants cibles définis dans le chapitre précédent. Ainsi, un environnement de simulation hygrothermique, aéraulique et de QAI a été construit par couplage des logiciels TRNSYS et CONTAM. Enfin, une analyse des éléments impactant la QAI des logements a été développée dans le dernier chapitre. Le but ici n’était pas uniquement d’observer l’influence de certains paramètres mais bien de quantifier et de hiérarchiser, à travers le calcul de l’indice ULR-QAI, les polluants, leurs sources, les systèmes ainsi que les actions pouvant être entreprises par les occupants pour améliorer la QAI de leurs logements
Without lessening the importance of outdoor air quality (especially in areas with heavy road traffic or near industrial sites...) or transport (such as confined underground spaces) in people's exposure to air pollutants, considering the exposure of occupants to pollutants in their dwellings is essential since people spend around 80% of their time there. The first step of this work consisted in defining a reduced number of pollutants to be considered inside the dwellings through a prioritization process consisting in comparing the levels of exposure to the different pollutants in relation to their health reference values. The analysis of the existing single and multi-pollutant indices led to the definition of a new multi-pollutant index, called ULR-IAQ, which was used as the main indicator in the rest of the study. The second chapter was dedicated to the development of the numerical tool necessary to reproduce the various and varied situations that can be encountered in dwellings. The objective here was to reproduce the transport of pollutants from outdoor to indoor, indoor sources of pollutants as well as the main physical phenomena (pollutant transfers between the different rooms, variation in air relative humidity, deposition of particles, filtration...) for the evaluation of the concentration levels of the target pollutants defined in the previous chapter. Thus, a simulation environment combining energy, airflow and pollutant transport for multizone buildings has been developed by coupling TRNSYS and CONTAM software. Finally, an analysis of the elements impacting the IAQ of dwellings was developed in the last chapter. The goal here was not only to observe the influence of certain parameters but also to quantify and prioritize, through the ULR-IAQ index calculation, the pollutants, their sources, the systems as well as the actions that can be taken by the occupants to improve the IAQ of their dwellings

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Curti, Valerio. "Indoor air quality and moulds." Thesis, Georgia Institute of Technology, 2002. http://hdl.handle.net/1853/22721.

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Adler, Stuart Alan. "Indoor air quality and architecture." Thesis, Georgia Institute of Technology, 1988. http://hdl.handle.net/1853/23178.

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Schuh, Christine. "Performance indicators for indoor air quality." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape4/PQDD_0016/NQ54809.pdf.

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Yontz, Raymond Reese. "AN OVERVIEW OF INDOOR AIR QUALITY." MSSTATE, 2003. http://sun.library.msstate.edu/ETD-db/theses/available/etd-04082003-080526/.

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This thesis is designed to introduce beginning and experienced heating, ventilation and air conditioning (HVAC) engineers to common indoor air quality (IAQ) problems and solutions. The bulk of the work is a literature review of common pollutants, pollutant sources, HVAC equipment and systems, and remediation techniques. Pollutants covered include fungi, bacteria, dust mites, viruses, biofilms, microbiological volatile organic compounds (MVOC?s), volatile organic compounds (VOC?s), carbon dioxide, ozone, and radon. The HVAC systems covered are ventilation, direct expansion (DX), desiccant dehumidification, and system filters. The remediation techniques discussed are proper hygiene and maintenance, increased ventilation, humidity control, and proper selection of building materials.

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Amissah, Patrick Ken. "Indoor air quality : combining air humidity with construction moisture." Thesis, University of Strathclyde, 2005. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=21574.

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The project aims to improve the modelling of moisture transfers at internal surfaces by linking the finite volumes representing the Heat, Air and Moisture (HAM) and Computational Fluid Dynamics (CFD) domains. Conflation of both models facilitates the detailed study of moisture flow as it impacts on indoor air quality and occupant health. The thesis lays down the conceptual framework for the subsequent development of an indoor air quality analytical tool. The work thus improves the modelling of construction feature risk assessment, for example, moisture absorption and desorption at the internal fabric surfaces in as much as it relates to indoor air quality. Through such an improvement, an indoor air quality analytical tool for the prediction of time-varying temperature/humidity conditions at specific locations within the building is enabled and subsequently these conditions may be related to the likely occurrence of mould. Humidity in indoor spaces is one of the most important factors in the determination of indoor air quality. High indoor humidity is a major contributor to the accumulation of moisture in the building envelope. This often results in dampness within the building envelope and subsequent health-related problems for the occcupants. Moderation of the indoor relative humidity, temperature and moisture content of the indoor air amongst others is a pre-requisite for a healthy building because it affects the perception of indoor air quality, thermal comfort, occupant health (asthma, respiratory illness, etc), building durability, material emission and energy consumption. Excessively high relative humidity promotes the growth of moulds and mildew on building surfaces. The basis for the envisaged conflation evolves around the boundary layer theory as it pertains to the velocity, thermal and concentration profiles associated with flow parallel to a flat surface, a phenomenon which is recognised as being similar in nature to buoyancy-driven convective heat transfer within building enclosures (White 1988). Within the framework of modelling of indoor air flows, the conflated modelling approach is very much dependent upon the treatment of the internal surface convection, for example, in the conflation of HAM and CFO models. This is referred to as the pivot point for the handshaking between HAM and CFO modelling domains. Within the framework of this project, the pivot point refers to the treatment of surface convection mass transfer at the internal surface to facilitate the hand shaking between HAM and CFO modelling domains. The two-time step coupling approach based on the loose coupling algorithm is adapted for the conflation. The technique involves a process whereby the HAM and CFO models are processed independently but exchange information at the interface at every time-step. The numerical method for the solution of the Navier-Stokes equations is based on the co-located grid arrangement, whereby all flow variables are defined in the centre of the grid cells. The transport equations are integrated for each grid cell and the Gauss Theorem applied to yield an integral over the cell face. These cell face integrals are then approximated using interpolation of the cell centred data. For the resolution of flow in the near-wall regime, the Low-Reynolds number k-ε turbulence model is used. A configuration mechanism with a rules-based moisture control algorithm to facilitate the handshaking of the HAM and CFO domains is presented. Methods for the solution of problems due to moisture migration across the interface, which are effected through variation of the convective mass transfer coefficient, hm, through variation of the standard k-ε turbulence model, namely the lowReynolds number model with its embedded wall damping functions and through adjustment of the source terms of governing transport equations of the CFO and HAM models are also discussed.

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Книги з теми "Indoor air quality index"

Hess-Kosa, Kathleen. Indoor Air Quality. Third edition. | Boca Raton : CRC Press/Taylor & Francis, 2019.: CRC Press, 2018. http://dx.doi.org/10.1201/9781315098180.

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Kasuga, Hitoshi, ed. Indoor Air Quality. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-83904-7.

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V, Gobbell Ronald, and Ganick Nicholas R, eds. Indoor air quality. New York: McGraw-Hill, 1995.

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Sheet Metal and Air Conditioning Contractors' National Association (U.S.), ed. Indoor air quality. 2nd ed. Chantilly, VA: Sheet Metal and Air Conditioning Contractors National Association, 1993.

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Berggren, J. L. Indoor air quality. Hayward, CA: LAMA Books, 1999.

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1921-, Kasuga H., Council for Environment and Health (Japan), and International Conference on Indoor Air Quality (1987 : Tokyo, Japan), eds. Indoor air quality. Berlin: Springer-Verlag, 1990.

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Birgitta, Berglund, Grimsrud David T, and Seifert Bernd, eds. Indoor air quality. Oxford: Pergamon Press, 1989.

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Sheet Metal and Air Conditioning Contractors' National Association., ed. Indoor air quality. Vienna, VA: Sheet Metal and Air Conditioning Contractors National Association, 1988.

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Fromme, Hermann. Indoor Air Quality. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-40078-0.

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J, Milko Robert, ed. Indoor air quality. Ottawa: Library of Parliament, 1987.

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Частини книг з теми "Indoor air quality index"

Drechsler, Andreas, Steffi Reinhold, Andreas Ruff, Martin Schneider, and Berndt Zeitler. "Airborne Sound Insulation of Sustainable Building Facades." In iCity. Transformative Research for the Livable, Intelligent, and Sustainable City, 335–57. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-92096-8_22.

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AbstractTwo trends are currently leading to an increased risk of indoor noise pollution. Firstly, urban densification causes traffic noise sources to be closer to the building facades which makes them louder at the facades. Secondly, airtightness of buildings, due to energy regulations, leads to the need of natural or mechanical ventilation to ensure a “healthy” indoor air quality, thereby allowing noise to easily pass from outdoors to indoors. In the case of mechanical ventilation, an additional noise source is also created. This study investigates the risk reduction of an indoor noise problem by optimizing the facade elements regarding sound insulation. Noise levels of different transportation noise sources (cars, trucks, trains) are used to calculate the resulting indoor noise levels after passing through the facade elements. The amount of noise transmitted into the indoors is dependent on the frequency spectra of the sources and of the sound reduction properties of the facade elements. Facade elements such as masonry walls, open windows, and ventilators are investigated and modified regarding their sound insulation properties. Through passive means, the weighted sound reduction index of an open window and an open ventilator was increased by 12 dB and 3 dB, respectively. Also, the indoor self-noise of the ventilator was investigated and reduced for different airflow rates.

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Sree, Naragam Bhanu, Aditya Kumar Patra, Penchala Abhishek, and Nazneen. "Determination of AER, Ventilation Rate and Indoor Air Quality Index for a Community Kitchen." In Lecture Notes in Civil Engineering, 87–94. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-4681-5_9.

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Pandey, Garvitraj, Priyansh Goel, Dishita Bhasin, Kantipudi M. V. V. Prasad, Prabhat Thakur, Pritesh Shah, Sudhanshu Gonge, Rahul Joshi, and Ketan Kotecha. "Using Plant as the Natural Air Purifier and Monitoring Indoor Air Quality Index Using Random Forest." In Lecture Notes in Networks and Systems, 459–73. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-4228-8_31.

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Tiwary, Abhishek, and Ian Williams. "Indoor air quality." In Air Pollution, 289–311. Fourth edition. | Boca Raton : CRC Press, 2018. | Earlier editions written by Jeremy Colls.: CRC Press, 2018. http://dx.doi.org/10.1201/9780429469985-7.

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Shiva Nagendra, S. M., and V. S. Chithra. "Indoor Air Quality." In Urban Air Quality Monitoring, Modelling and Human Exposure Assessment, 69–73. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5511-4_5.

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Vural, S. Müjdem. "Indoor Air Quality." In Sick Building Syndrome, 59–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-17919-8_3.

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Khazaii, Javad. "Indoor Air Quality." In Energy-Efficient HVAC Design, 47–51. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11047-9_6.

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Campagna, Anthony C., and Dhruv Desai. "Indoor Air Quality." In Lifestyle Medicine, 639–49. Third edition. | Boca Raton : Taylor & Francis, 2019.: CRC Press, 2019. http://dx.doi.org/10.1201/9781315201108-52.

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Halligan, Kyle T., and Anthony C. Campagna. "Indoor Air Quality." In Lifestyle Medicine, Fourth Edition, 647–56. 4th ed. Boca Raton: CRC Press, 2024. http://dx.doi.org/10.1201/9781003227793-62.

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Halliwell, Jack L. "Indoor Air Quality." In Energy Management Handbook, 499–510. Ninth edition. | Louisville, Kentucky : Fairmont Press, Inc., [2018]: River Publishers, 2020. http://dx.doi.org/10.1201/9781003151364-17.

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Тези доповідей конференцій з теми "Indoor air quality index"

Badurova, Andrea, Petra Stiborova, and Iveta kotnicov. "FACTORS AFFECTING INDOOR AIR QUALITY IN KINDERGARTEN." In 24th SGEM International Multidisciplinary Scientific GeoConference 24, 403–10. STEF92 Technology, 2024. https://doi.org/10.5593/sgem2024/4.1/s19.53.

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This article deals with the issue of the indoor environment in terms of optimal conditions of thermal comfort and the satisfactory quality of indoor air. A weekly measurement of the parameters of the indoor environment was carried out in the building of the day rehabilitation centre in order to verify the state of the indoor environment, to determine the processes that create the indoor environment and to define the factors that affect the resulting state of the indoor microclimate. One of the important factors is air quality, where the main factor that influences the indoor environment is the concentration of carbon dioxide, whose value affects the biological functions of the human organism. The analysis of the measured parameters found that the concentration of carbon dioxide in the monitored indoor space is not, as expected, the riskiest factor that may be the cause of dissatisfaction with the indoor microclimate.

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Anto, Tony Rosset, Borja Albert Gramaje, Lukasz Wisniewski, and Stylianos Karatzas. "Data Management Platform for Indoor Air Quality Management." In 2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation (ETFA), 1–4. IEEE, 2024. http://dx.doi.org/10.1109/etfa61755.2024.10710780.

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Christakis, Ioannis, Elena Sarri, Odysseas Tsakiridis, Konstantinos Moutzouris, Dimos Triantis, and Ilias Stavrakas. "Integrated Open Source Indoor Air Quality Monitoring Platform." In 2024 9th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM), 183–88. IEEE, 2024. http://dx.doi.org/10.1109/seeda-cecnsm63478.2024.00041.

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Saad, S. M., A. Y. M. Shakaff, A. R. M. Saad, A. M. Yusof, A. M. Andrew, A. Zakaria, and A. H. Adom. "Development of indoor environmental index: Air quality index and thermal comfort index." In 11TH ASIAN CONFERENCE ON CHEMICAL SENSORS: (ACCS2015). Author(s), 2017. http://dx.doi.org/10.1063/1.4975276.

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Saad, Shaharil Mad, Ali Yeon Md Shakaff, Abdul Rahman Mohd Saad, and Azman Muhamad Yusof Kamarudin. "Implementation of index for real-time monitoring indoor air quality system." In 2014 2nd International Conference on Electronic Design (ICED). IEEE, 2014. http://dx.doi.org/10.1109/iced.2014.7015770.

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Feigley, C., D. Salzberg, and C. Toole. "196. Limitations of Carbon Dioxide as an Index of Indoor Air Quality." In AIHce 2005. AIHA, 2005. http://dx.doi.org/10.3320/1.2758551.

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Rastogi, Krati, Anurag Barthwal, Divya Lohani, and Debopam Acharya. "An IoT-based Discrete Time Markov Chain Model for Analysis and Prediction of Indoor Air Quality Index." In 2020 IEEE Sensors Applications Symposium (SAS). IEEE, 2020. http://dx.doi.org/10.1109/sas48726.2020.9220077.

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Badea, Elena, Cristina Carsote, Cristina Balaceanu, Oana Orza, Sabina Bosoc, Robert Streche, George Suciu, Zóra Barta, Valéria Tálai, and Zsolt Viniczay. "Understanding and Controlling the Environmental Quality in Museums through IoT: An International Research and Practice Collaboration to Support Museums in the Implementation of Climate Action." In The 9th International Conference on Advanced Materials and Systems. INCDTP - Leather and Footwear Research Institute (ICPI), Bucharest, Romania, 2022. http://dx.doi.org/10.24264/icams-2022.w.1.

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MUSEION project aims at developing an integrated IoT based platform for the sustainable management of environmental control and adaptation to climate change of museum collections. The MUSEION solution will thus provide the optimization of resources such costs, energy, staff workload, while contributing to carbon footprint reduction. This solution is a replicable IoT-based system, which will solve the problems of real objects in real conditions (sustainable environmental control and adaptation to climate change). It will consider the main components of the museum system that influence its optimal climate (i.e, museum itself, artworks and visitors) and will continuously monitor and allow visualization of environmental and air quality markers. The monitoring reports will be elaborated by a software designed to real-time calculate the overall Indoor Air Quality (IAQ) Index. The main advantage provided by the MUSEION system consist in the simultaneous monitoring and evaluation of the environment quality and its impact on various artefacts in various conservation condition.

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Bencheikh, Hamida, and Boussebsi Khalida. "The Effect of Atrium on the Thermal Comfort in Buildings in Hot Arid Zones." In The 2nd International Conference on Civil Infrastructure and Construction. Qatar University Press, 2023. http://dx.doi.org/10.29117/cic.2023.0060.

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The atrium is an open interior space that may be linked to the external environment; it is becoming more and more popular and a key element in the architectural design of many buildings, due to its attractive and symbolic aesthetic characteristics for the public. It is a filter against unwanted external environmental phenomena such as rain, snow and wind. A well-designed atrium can contribute towards having a significant effect on the indoor environment, affecting the comfort of the occupants. However, in certain hot and arid regions such as the city of Laghouat in the south of Algeria characterized by a scalding and dry summer, and cold winter, these fully enclosed atrium spaces with their untouched typological and architectural diversity, and due to lack of a good renewal of the indoor air, can cause considerable thermal discomfort to the occupants of space, and thermal stratification inside, especially in summer. The present work studies the impact of the atrium configuration on the inside thermal environment, for summers and winter periods, and to provide a sufficient air renewal within the atrium to ensure good air quality. As a result, a rectangular, fully enclosed, unventilated central atrium building was examined with its adjacent spaces, by a series of field measurements to study two geometric factors that have a considerable impact on the interior thermal comfort, the height width ratio (SAR Index) and the glazed coverage ratio. The impact of SAR index and glazed area ratio on thermal comfort and stratification of the air in summer and winter period were also examined.

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Burzo, Mihai G., Hussein Kokash, and Khalil Khanafer. "Investigating the Design and Locations of Inlet and Exhaust Diffusers and Airflow Patterns and Airborne Contaminants in Surgical Settings." In ASME 2024 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2024. https://doi.org/10.1115/imece2024-145848.

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Abstract Maintaining air quality and thermal comfort in operating rooms (ORs) is crucial for the health, safety and well-being of patients and medical staff. During surgical procedures, various gases are emitted, including volatile organic compounds (VOCs) from disinfectants, anesthetic gases, pathogens, and smoke from high-frequency electric knives. This surgical smoke, laden with harmful particles, is often termed a “silent killer”, posing a significant contamination risk. Ventilation systems are crucial for maintaining a comfortable thermal environment and regulating airborne particle concentrations within prescribed limits critical for sterile conditions in ORs. We aim to comprehensively assess airflow dynamics, temperature distribution and contaminant distribution in typical ORs using numerical analysis. We proposed new air diffuser arrangements and a corresponding optimization framework to effectively control airborne contaminants, ensuring high standards of indoor air quality and thermal comfort during surgical procedures. Two airflow configurations were considered: Modified Laminar Airflow (M-LAF) and Coanda diffuser layouts. It was found that the M-LAF, with adjusted inlet velocities at the diffuser corners, enhances airflow uniformity around medical personnel and patients, outperforming traditional setups. Our results show a reduction in Air Diffusion Performance Index (ADPI) of 75.6%, 35.6%, and 83.3% between M-LAF and LAF at 0.15 m/s, 0.5 m/s, and 0.75 m/s, respectively. However, it also indicates an increase in Local Air Change Index (LACI) of 50.2% at 0.15 m/s and 172.5% at 0.75 m/s, while LACI decreases by 72.5% between LAF and M-LAF at 0.5 m/s. Particle simulations confirm that the M-LAF better disperses smoke particles, suggesting lower contamination risks during surgery. The Coanda diffuser layout also improves airflow and temperature distribution, ensuring better thermal comfort and is expected to offer improved energy savings. ADPI for Coanda diffuser was found to increase by 88% compared with LAF. Its design effectively removes airborne particles by directing them to exit diffusers, decreasing the concentration near staff. Altogether, the research indicates that these designs could substantially improve ventilation in healthcare settings, boosting both thermal comfort and air quality in ORs.

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Звіти організацій з теми "Indoor air quality index"

Lee, Jusang, John E. Haddock, Dario D. Batioja Alvarez, and Reyhaneh Rahbar Rastegar. Quality Control and Quality Assurance of Asphalt Mixtures Using Laboratory Rutting and Cracking Tests. Purdue University, 2019. http://dx.doi.org/10.5703/1288284317087.

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The main objectives of this project were to review the available balanced-mix design (BMD) methodologies, understand the I-FIT and Hamburg Wheel Tracking Test (HWTT) test methods using INDOT asphalt mixtures, and to explore the application of these tests to both a BMD approach and as performance-related Quality Control (QC) and Quality Acceptance (QA) methods. Two QA mixture specimen types, plant-mixed laboratory-compacted (PMLC) and plant-mixed field-compacted (PMFC) were used in the determination of cracking and rutting parameters. Distribution functions for the flexibility index (FI) values and rutting parameters were determined for various mixture types. The effects of specimen geometry and air voids contents on the calculated Flexibility Index (FI) and rutting parameters were investigated. The fatigue characteristics of selected asphalt mixtures were determined using the S-VECD test according to different FI levels for different conditions. A typical full-depth pavement section was implemented in FlexPAVE to explore the cracking characteristics of INDOT asphalt mixtures by investigating the relationship between the FI values of QA samples with the FlexPAVE pavement performance predictions. The FI values obtained from PMFC specimens were consistently higher than their corresponding PMLC specimens. This study also found that FI values were affected significantly by variations in specimen thickness and air voids contents, having higher FI values with higher air voids contents and thinner specimens. These observations do not agree with the general material-performance expectations that better cracking resistance is achieved with lower air voids content and thicker layers. Additionally, PG 70-22 mixtures show the lowest mean FI values followed by the PG 76-22 and 64-22 mixtures. The same order was observed from the ΔTc (asphalt binder cracking index) of INDOT’s 2017 and 2018 projects. Finally, it was found that the HWTT showed reasonable sensitivity to the different characteristics (e.g., aggregate sizes, binder types, and air voids contents) of asphalt mixtures. Mixtures containing modified asphalt binders showed better rut resistance and higher Rutting Resistance Index (RRI) than those containing unmodified binders.

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Duffield, George, and Sarah Bunn. Indoor air quality. Parliamentary Office of Science and Technology, UK Parliament, September 2023. http://dx.doi.org/10.58248/pb54.

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McNall, Preston, George Walton, Samuel Silberstein, James Axley, Kunimichi Ishiguro, Richard Grot, and T. Kusuda. Indoor air quality modeling :. Gaithersburg, MD: National Bureau of Standards, 1985. http://dx.doi.org/10.6028/nbs.ir.85-3265.

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Axley, James. Indoor air quality modeling :. Gaithersburg, MD: National Bureau of Standards, 1987. http://dx.doi.org/10.6028/nbs.ir.87-3661.

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Ronyak, James P., Karen A. Fox, Ian C. Rybczynski, and Kenneth L. Cox. Guide for Indoor Air Quality Surveys. Fort Belvoir, VA: Defense Technical Information Center, February 2003. http://dx.doi.org/10.21236/ada414423.

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Bright, P. D., Michael J. Mader, David R. Carpenter, and Ivette Z. Hermon-Cruz. Guide for Indoor Air Quality Surveys. Fort Belvoir, VA: Defense Technical Information Center, May 1992. http://dx.doi.org/10.21236/ada251638.

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McNeil, Preston E. Indoor air quality modeling workshop report. Gaithersburg, MD: National Bureau of Standards, 1985. http://dx.doi.org/10.6028/nbs.ir.85-3150.

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Singh, H., J. Jones, and P. Rojeski. Effectiveness of Variable Ventilation on Indoor Air Quality. Fort Belvoir, VA: Defense Technical Information Center, March 1997. http://dx.doi.org/10.21236/ada325326.

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Rempel, Jane. An Innovative Reactor Technology to Improve Indoor Air Quality. Office of Scientific and Technical Information (OSTI), March 2013. http://dx.doi.org/10.2172/1115742.

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Rudd, Armin, and Daniel Bergey. Ventilation System Effectiveness and Tested Indoor Air Quality Impacts. Office of Scientific and Technical Information (OSTI), February 2014. http://dx.doi.org/10.2172/1126286.

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