Academic literature on the topic 'Real-time IAQ index'

Indoor Air Quality (IAQ) issues have a direct impact on the health and comfort of building occupants. In this paper, an experimental low-cost system has been developed to address IAQ issues by using a distributed internet of things platform to control and monitor the indoor environment in building spaces while adopting a data-driven approach. The system is based on several real-time sensor data to model the indoor air quality and accurately control the ventilation system through algorithms to maintain a comfortable level of IAQ by balancing indoor and outdoor pollutant concentrations using the Indoor Air Quality Index approach. This paper describes hardware and software details of the system as well as the algorithms, models, and control strategies of the proposed solution which can be integrated in detached ventilation systems. Furthermore, a mobile app has been developed to inform, in real time, different-expertise-user profiles showing indoor and outdoor IAQ conditions. The system is implemented in a small prototype box and early-validated with different test cases considering various pollutant concentrations, reaching a Technology Readiness Level (TRL) of 3–4.

In recent years, more and more occupants have suffered from respiratory illness due to poor indoor air quality (IAQ). In order to address this issue, this paper presents a method to achieve efficient monitoring and adaptive control of IAQ. Firstly, an indoor air quality monitoring and control system (IAQMCS) is developed using IoT technology. Then, based on fuzzy inference, a novel fuzzy air quality index (FAQI) model is proposed to effectively assess IAQ. Furthermore, a simple adaptive control mechanism, called SACM, is designed to automatically control the IAQMCS according to a real-time FAQI value. Finally, extensive experiments are performed by comparing with regular control (time-based control), which show that our proposed method effectively measures various air parameters (CO2, VOC, HCHO, PM2.5, PM10, etc.) and has good performance in terms of evaluation accuracy, average FAQI value, and overall IAQ.

This study was designed to verify the effectiveness of smart gardens by improving indoor air quality (IAQ) through the installation of an indoor garden with sensor-based Internet-of-Things (IoT) technology that identifies pollutants such as particulate matter. In addition, the study aims to introduce indoor gardens for customized indoor air cleaning using the data and IoT technology. New apartments completed in 2016 were selected and divided into four households with indoor gardens installed and four households without indoor gardens. Real-time data and data on PM2.5, CO2, temperature, and humidity were collected through an IoT-based IAQ monitoring system. In addition, in order to examine the effects on the health of occupants, the results were analyzed based on epidemiological data, prevalence data, current maintenance, and recommendation criteria, and were presented and evaluated as indices. The indices were classified into a comfort index, which reflects the temperature and humidity, an IAQ index, which reflects PM2.5 and CO2, and an IAQ composite index. The IAQ index was divided into five grades from “good” to “hazardous”. Using a scale of 1 to 100 points, it was determined as follows: “good (0–20)”, “moderate (21–40)”, “unhealthy for sensitive group (41–60)”, “bad (61–80)”, “hazardous (81–100)”. It showed an increase in the “good” section after installing the indoor garden, and the “bad” section decreased. Additionally, the comfort index was classified into five grades from “very comfortable” to “very uncomfortable”. In the comfort index, the “uncomfortable” section decreased, and the “comfortable” section increased after the indoor garden was installed.

Indoor air quality (IAQ) is not only a determinant of occupational health but also influences all indoor human behaviours. In most university establishments, laboratories are also used as classrooms. On one hand, indoor environment quality (IEQ) conditions supervision in laboratories is relevant for experimental activities. On the other hand, it is also crucial to provide a healthy and productive workplace for learning activities. The proliferation of cost-effective sensors and microcontrollers along with the Internet of Things (IoT) architectures enhancements, enables the development of automatic solutions to supervise the Laboratory Environmental Conditions (LEC). This paper aims to present a real-time IEQ-laboratory data collection system-based IoT architecture named iAQ Plus (iAQ+). The iAQ+ incorporates an integrated Web management system along with a smartphone application to provide a historical analysis of the LEC. The iAQ+ collects IAQ index, temperature, relative humidity and barometric pressure. The results obtained are promising, representing a meaningful contribution for IEQ supervision solutions based on IoT. iAQ+ supports push notifications to alert people in a timely way for enhanced living environments and occupational health, as well as a work mode feature, so the user can configure setpoints for laboratory mode and schoolroom mode. Using the iAQ+, it is possible to provide an integrated management of data information of the spatio-temporal variations of LEC parameters which are particularly significant not only for enhanced living environments but also for laboratory experiments.

During the last two years, the COVID-19 pandemic continues to wreak havoc in many areas of the world, as the infection spreads through person-to-person contact. Transmission and prognosis, once infected, are potentially influenced by many factors, including indoor air pollution. Particulate Matter (PM) is a complex mixture of solid and/or liquid particles suspended in the air that can vary in size, shape, and composition and recent scientific work correlate this index with a considerable risk of COVID-19 infections. Early Warning Systems (EWS) and the Internet of Things (IoT) have given rise to the development of Low Power Wide Area Networks (LPWAN) based on sensors, which measure PM levels and monitor In-door Air pollution Quality (IAQ) in real-time. This article proposes an open-source platform architecture and presents the development of a Long Range (LoRa) based sensor network for IAQ and PM measurement. A few air quality sensors were tested, a network platform was implemented after simulating setup topologies, emphasizing feasible low-cost open platform architecture.

In the face of the severe global epidemic, indoor architectural space has become one of the critical issues, and the construction of a new type of “built environment” while solving “health and epidemic prevention” has become the goal of active development in countries around the world (SDGs & Pandemic Response); Pollutant concentration, optimization of indoor heat and humidity environment, and release of indoor environmental monitoring data, etc. It can not only protect the short-term needs of building users but also provide long-term health protection for building users and ultimately achieve the purpose of physical and mental health of building users. This study uses GIA-K007-12 Air Box to collect “environmental characteristics” variables; IAQ, PM1, PM2.5, PM10, CO2, TVOC, HCHO, Fungi index, TEMP, and HUMD are input variables for XGBOOST, using IBM SPSS Statistics 20.0 performs statistical analysis, modelling and using PYTHON to simulate the accuracy of the building fresh air system model and the decision ranking of essential factors. The test results are based on the XGBOOST decision tree. The accuracy value reaches 94.24%, and the order of critical environmental factors for the indoor fresh air system is PM1, HCHO, IAQ, Fungi index, TVOC, etc. The research results can provide the basis for constructing a teaching space for epidemic prevention and demonstrate that the establishment of an “air quality control platform that can be calculated in real-time” can improve the environmental health awareness (EHL) of stakeholders and provide for future development of epidemic prevention space planning and design in the post-epidemic era Reference and application of operation management.

Light and active mobility, as well as multimodal mobility, could significantly contribute to decarbonization. Air quality is a key parameter to monitor the environment in terms of health and leisure benefits. In a possible scenario, wearables and recharge stations could supply information about a distributed monitoring system of air quality. The availability of low-power, smart, low-cost, compact embedded systems, such as Arduino Nicla Sense ME, based on BME688 by Bosch, Reutlingen, Germany, and powered by suitable software tools, can provide the hardware to be easily integrated into wearables as well as in solar-powered EVSE (Electric Vehicle Supply Equipment) for scooters and e-bikes. In this way, each e-vehicle, bike, or EVSE can contribute to a distributed monitoring network providing real-time information about micro-climate and pollution. This work experimentally investigates the capability of the BME688 environmental sensor to provide useful and detailed information about air quality. Initial experimental results from measurements in non-controlled and controlled environments show that BME688 is suited to detect the human-perceived air quality. CO2 readout can also be significant for other gas (e.g., CO), while IAQ (Index for Air Quality, from 0 to 500) is heavily affected by relative humidity, and its significance below 250 is quite low for an outdoor uncontrolled environment.

This article describes the design and development of a low-cost, portable monitoring system for indoor environment quality (IEQ). IEQ is a holistic concept that encompasses elements of indoor air quality (IAQ), indoor lighting quality (ILQ), acoustic comfort, and thermal comfort (temperature and relative humidity). The unit is intended for the monitoring of temperature, humidity, PM2.5, PM10, total VOCs (×3), CO2, CO, illuminance, and sound levels. Experiments were conducted in various environments, including a typical indoor working environment and outdoor pollution, to evaluate the unit’s potential to monitor IEQ parameters. The developed system was successfully able to monitor parameter variations, based on specific events. A custom IEQ index was devised to rate the parameter readings with a simple scoring system to calculate an overall IEQ percentage. The advantages of the proposed system, with respect to commercial units, is associated with better customisation and flexibility to implement a variety of low-cost sensors. Moreover, low-cost sensor modules reduce the overall cost to provide a comprehensive, portable, and real-time monitoring solution. This development facilities researchers and interested enthusiasts to become engaged and proactive in participating in the study, management, and improvement of IEQ.

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.

Air pollution is increasing profusely in Indian cities as well as throughout the world, and it poses a major threat to climate as well as the health of all living things. Air pollution is the reason behind degraded indoor air quality (IAQ) in urban buildings. Carbon dioxide (CO2) is the main contributor to indoor pollution as humans themselves are one of the generating sources of this pollutant. The testing and monitoring of CO2 consume cost and time and require smart sensors. Thus, to solve these limitations, machine learning (ML) has been used to predict the concentration of CO2 inside an office room. This study is based on the data collected through real-time measurements of indoor CO2, number of occupants, area per person, outdoor temperature, outer wind speed, relative humidity, and air quality index used as input parameters. In this study, ten algorithms, namely, artificial neural network (ANN), support vector machine (SVM), decision tree (DT), Gaussian process regression (GPR), linear regression (LR), ensemble learning (EL), optimized GPR, optimized EL, optimized DT, and optimized SVM, were used to predict the concentration of CO2. It has been found that the optimized GPR model performs better than other selected models in terms of prediction accuracy. The result of this study indicated that the optimized GPR model can predict the concentration of CO2 with the highest prediction accuracy having R , RMSE, MAE, NS, and a20-index values of 0.98874, 4.20068 ppm, 3.35098 ppm, 0.9817, and 1, respectively. This study can be utilized by the designers, researchers, healthcare professionals, and smart city developers to analyse the indoor air quality for designing air ventilation systems and monitoring CO2 level inside the buildings.

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

Indoor air quality (IAQ) is among the topmost environmental hazards associated with the health of human beings. The concentrations of indoor pollutants could be several times more than outdoors. Increasing environmental pollution and global warming are also responsible for climate change. Variations in climatic conditions also add to the worsening of IAQ. The majority of time is spent indoors and adequate ventilation, thermal performance and desirable IAQ are important parameters of concern in indoor settings. Usage of HVAC (heating, ventilation, air conditioning) equipment accounts for the huge consumption of energy and reduced energy consumption can be met by reduced air circulation leading to more airtight buildings which compromise the air quality and health of inhabitants. Several strategies have been devised and being implemented to monitor indoor air quality. Smart environments are insidious systems consisting of integrable net-aware devices. Smart environments are augmented with computational resources providing information and services when and where needed. Over the last few years, IAQ monitoring has developed into smart environment monitoring (SEM) which is based on the internet of things (IoT) and the development of sensor technology. This chapter is an attempt to summarize the automated, computational aids and machine learning techniques that can predict the IAQ in smart environment. It is imperative to know the pollutants and factors governing the IAQ and the chapter has critically analyzed the available technological interventions based on IoT like sensors, Fuzzy logic controller and cloud computing technology which aid in the prediction of air quality in smart environment. Different types of sensors including infrared and electrochemical cells, Metal oxide semiconductor (MOS) gas sensor along with their principle has been discussed in context to IAQ. Recent developments in the field like the usage of the fuzzy logic controller for the calculation of air quality index by combining PM10, PM2.5, CO, and NO2 etc. has also been explored. The information can be utilized in dynamic situations to suggest alternative methods https://worldpopulationreview.com/world-cities/lucknow-population for the improvement of air quality which can be influenced by artificial intelligence and machine learning for futuristic predictions. However, there are some challenges as well including the development of systems working on a real-time basis and evaluation of the impact of different pollutants in diverse geographic conditions and variable living set-ups by highly accurate and calibrated systems. Nevertheless, as compared to the conventional solutions which predict IAQ instantly, the computational predictions furnish futuristic data and imminent crucial changes in the indoor air quality to implement anticipatory measures to prevent hazardous health impacts. Nevertheless there are several challenges like data security, data conversion, and connectivity issues etc. which have been discussed in the chapter.

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.