Indoor Air Quality Monitoring System Research Articles

Reengineering Indoor Air Quality Monitoring Systems to Improve End-User Experience.

This paper presents an indoor air quality (IAQ) monitoring system designed for a better end-user experience. The monitoring system consists of elements, from the monitoring sensor to the monitoring interface, designed and implemented by the research team, especially for the proposed monitoring system. The monitoring solution is intended for users who live in houses without automatic ventilation systems. The air quality sensor is designed at a minimum cost and complexity to allow multi-zone implementation without significant effort. The user interface uses a spatial graphic representation that facilitates understanding areas with different air quality levels. Presentation of the outdoor air quality level supports the user's decision to ventilate a space. An innovative element of the proposed monitoring interface is the real-time forecast of air quality evolution in each monitored space. The paper describes the implementation of an original monitoring solution (monitoring device, Edge/Cloud management system, innovative user monitoring interface) and presents the results of testing this system in a relevant environment. The research conclusions show the proposed solution's benefits in improving the end-user experience, justified both by the technical results obtained and by the opinion of the users who tested the monitoring system.

Easy-to-Use MOX-Based VOC Sensors for Efficient Indoor Air Quality Monitoring.

This research paper presents a case study on the application of Metal Oxide Semiconductor (MOX)-based VOC/TVOC sensors for indoor air quality (IAQ) monitoring. This study focuses on the ease of use and the practical benefits of these sensors, drawing insights from measurements conducted in a university laboratory setting. The investigation showcases the straightforward integration of MOX-based sensors into existing IAQ monitoring systems, highlighting their user-friendly features and the ability to provide precise and real-time information on volatile organic compound concentrations. Emphasizing ease of installation, minimal maintenance, and immediate data accessibility, this paper demonstrates the practicality of incorporating MOX-based sensors for efficient IAQ management. The findings contribute to the broader understanding of MOX sensor capabilities, providing valuable insights for those seeking straightforward and effective solutions for indoor air quality monitoring. This case study outlines the feasibility and benefits of utilizing MOX-based sensors in various environments, offering a promising avenue for the widespread adoption of user-friendly technologies in IAQ management.

Indoor Air Quality Monitoring and Automatic Ventilation System

Indoor Air Quality Monitoring and Automatic Ventilation System

Low-cost IoT-enabled indoor air quality monitoring systems: A systematic review

Indoor air quality (IAQ) is a critical challenge much less controlled in comparison with outdoor air quality. Bad IAQ is related to significant health complications such as respiratory problems, heart disease, and cancer. Many people spend most of their days inside buildings and don’t have air quality monitoring systems. Therefore, the occupants don’t know when the space has a higher quantity of pollutants than recommended, saturating the environment, and compromising people’s health. This is a problem that can be addressed by using Internet of Things (IoT) technologies to develop monitoring systems that allow a greater number of possibilities regarding the storage and processing of data and access to information by the end user, assisting the decision-making process regarding the indoor air pollution problem. Real-time data can be compared to default values, alerting the user of that situation, and suggesting an action to decrease the air pollutants concentration. There already are multiple solutions involving IoT-based technologies, many of them using low-cost sensors. Those are analyzed in this systematic review. Furthermore, the COVID-19 pandemic pointed out the importance of IAQ monitoring to evaluate the risk of contamination. The microcontrollers, IAQ parameters, sensors, data storage and visualization methods used in monitoring systems have been analyzed. The results show that most of the studies store data in Cloud systems and use Web platforms for data consulting. However, sensor calibration and efficient energy consumption are challenges that still exist.

Research Review Inquisitive on Indoor Air Quality Monitoring System Facilitate with Internet of Things

In recent days, Indoor Air Quality (IAQ) has become a matter of interest for researchers from both scientific and industrial community. During pandemic situation public settle more time at indoor in place of outdoor environment. With rapid growth in science and technology people have progressive necessity for active facility parameters with respect to indoor temperature, humidity and wind movement etc. Asthma, lung cancer, hypertension, cardiovascular issues, and respiratory illnesses are just a few of the chronic health issues that have been linked to frequent exposure to hazardous material in enclosed spaces. However, real-time monitoring technologies are being adopted in smart city projects to identify and remedy such unfavourable conditions, ultimately improving the general quality of life. This paper surveyed the current proposed IAQ monitoring systems in terms of Internet of Things (IoT). In addition, the research work highlights composition of diverse monitoring systems, research summary, challenges together implying future directions for the researchers. The current level of knowledge on the close relationship between IoT and IAQ is presented in this research.

Feedforward Neural Network-Based Indoor Air Quality Detection System

Indoor air quality is crucial for the sustainability of human life quality. Therefore, improving indoor air quality is critical for enhancing life quality. In this study, an artificial intelligence-based indoor air quality monitoring system is designed. The system consists of two main parts, hardware and software. The hardware part includes a control card and various sensors. The software part includes a C-based IDE software and a feedforward network, a deep learning algorithm, for establishing the connection between the control card and the sensors. The temperature, humidity, and gas concentration values obtained from the sensors at certain intervals were fed to the feedforward network's input layer through the control card. The feedforward network consists of the input layer, hidden layer, and output layer, and the decision on whether the air quality is normal or not was made at the output. The system described in this study is intended to provide a real-time monitoring solution for indoor air quality. By using a feedforward neural network, the system is able to learn patterns in the sensor data and detect changes in air quality that may indicate a problem. The system can be customized to suit the needs of different environments and can be used in a variety of settings, including homes, offices, and public spaces. Ultimately, the goal of the system is to improve human health and well-being by ensuring that indoor air quality is at a safe and healthy level.

Fusing Long Short-Term Memory and Autoencoder Models for Robust Anomaly Detection in Indoor Air Quality Time-Series Data

People spend most of their time indoors by choice or by need. Carbon dioxide (CO2) accumulation can cause various adverse health effects, including vertigo, headache, and fatigue. Therefore, monitoring indoor air quality(IAQ) is necessary for various health reasons. The market is flooded with air quality monitoring devices. However, the ordinary public does not make use of them because they are expensive and difficult to obtain. Several research studies have been carried out to monitor indoor air quality with the help of the Internet of Things(IoT), which has greatly simplified the method for monitoring IAQ. In this research, we offer an improved IoT based IAQ monitoring system with AI-powered recommendations. Our suggested system relies on the Message Queuing Telemetry Transport(MQTT) protocol for communication between IoT devices. In addition, the gathered CO2 occupancy data is used together with the deep learning approach of Long Short-Term Memory and Autoencoder (LSTM-AE) to detect anomalies or outliers in CO2 concentrations.  Due to a close connection between air quality and human health and well-being, the detection of anomalies in the data of  IAQ has emerged as an essential topic of study. Anomalies requiring the observation of correlations spanning numerous data points (i.e., often referred to as long-term dependencies) were not detectable by conventional statistical and basic machine learning (ML) related techniques in the sector of  IAQ.  Hence this research uses the LSTM-AE model to address this issue.  In comparison to previous similar models, our experimental results on a generated CO2 occupancy time series reveal a robust and powerful accuracy of 99.49%.

Co-Dependency of IAQ in Functionally Different Zones of Open-Kitchen Restaurants Based on Sensor Measurements Explored via Mutual Information Analysis.

High-quality indoor air is essential in open-kitchen restaurants for ensuring a healthy workplace and comfortable conditions for visitors. In this study, indoor air quality interdependence between the kitchen and the dining zones in open-kitchen restaurants was analyzed. The method was based on measurements of selected air parameters using a sensor technique and mutual information (MI) analysis. A long-term approach (based on a several-hour time series) and a short-term approach (based on a several-minute time series) were applied. This study involved four open-kitchen restaurants. The indoor conditions were represented by the temperature, relative humidity, CO2 concentration, and content of the total volatile organic compounds (TVOC) in the air. The MI analyses showed that the long-term co-dependence of the indoor conditions between the kitchen and the dining zones was smaller during business hours (MI = 0.12 ÷ 0.40) compared to night hours (MI = 0.24 ÷ 0.58). The ranking of the long-term MI values for the individual air parameters was MICO2 (0.34) ≅ MIT (0.34) > MIRH (0.28) > MITVOC (0.23). The short-term interdependencies were smaller during night hours (median MI = 0.01 ÷ 0.56) compared to business hours (MI = 0.23 ÷ 0.61). Additionally, the short-term MI was subject to high temporal variability. The ranking of the short-term MI values for the individual air parameters was MICO2 (0.48) > MIT (0.46) > MIRH (0.37) > MITVOC (0.26). Due to the weak and highly variable co-dependence of the air parameters between the kitchen and dining areas, multi-zone monitoring of air parameters with an emphasis on TVOC measurements is recommended to ensure proper indoor conditions in open-kitchen restaurants. The presented approach may be applied to design indoor air quality monitoring and ventilation systems not only in open-kitchen restaurants but also in other interiors with functionally different zones.

Perceived air quality and satisfaction during implementation of an automated indoor air quality monitoring and control system

Cooking and cleaning are among the largest sources of home pollutant emissions. To reduce these emissions, we developed an automated indoor air quality (IAQ) control intervention that operated based on real-time sensor readings of particulate matter (PM2.5) and evaluated the perceptual differences between this intervention and a baseline condition. We employed a 14-participant crossover study design in a one-bedroom apartment module. Participants experienced one of two conditions: (1) Advanced Control–automated IAQ interventions including a stove hood, two portable air cleaners, and a bathroom exhaust powered on/off based on predefined PM2.5 thresholds measured by environmental sensors; and (2) Standard Control–participants controlling IAQ interventions (e.g., stove hood) manually. Each condition lasted two weeks. Participants followed standardized cooking and cleaning protocols and filled out surveys assessing psychosocial and perceptual outcomes. Observations indicated that weekly IAQ satisfaction, perception, and preferences were similar between the two conditions—despite lower PM2.5 concentrations during cooking and cleaning for the Advanced Control versus Standard Control Condition. When pairing IAQ complaints with PM2.5 concentrations during cooking, we observed participants made complaints when PM2.5 concentrations >∼80 μg/m3 but few complaints when <∼60 μg/m3. Lower PM2.5 concentrations were observed for the Advanced Control versus Standard Control Condition during cleaning, but these lower concentrations did not appear perceivable by participants due to far lower PM2.5 concentrations during cleaning overall. Our observations suggest a connection between PM2.5 concentration and IAQ complaints made by participants, thereby providing possible thresholds for perceivable IAQ changes.

Air Watch: An Ample Design of Indoor Air Quality Monitoring System

The environment is getting contaminated drastically by introducing harmful materials into the atmosphere through the excessive activities of human in adding comfort and luxurious style in their living. The pollutant level in air has high impact of healthiness of the person inhaling it. Air not outside as well indoor is infected by various hazardous particles and gases.  To assess the air quality in the particular environment, it stimulates the need to monitor the hazardous elements listed. The internet of things (IoT) and artificial intelligence (AI) became the part of human life by adding smartness in their daily routines from facilitating control over appliances to own health factor as well automate operations. The primary objective of the effort is to identify the gases that cause air pollution, measure the air quality, and assess the level of pollution so that we can determine which gases cause pollution and at what place is the air being impacted. An IoT based indoor air quality monitoring system is built through incorporating carbon monoxide (CO), carbon dioxide (CO2), Ozone (O3), particulate matters (PM) and volatile organic components (VOC) sensors into Arduino board. The design ensures a complete air monitor, extends reliable service at low cost. A rule based system is developed to automate events upon the estimated air quality index (AQI) out of the sensory circuit.

An Improvement Strategy for Indoor Air Quality Monitoring Systems.

Air quality has a huge impact on the comfort and healthiness of various environments. According to the World Health Organization, people who are exposed to chemical, biological and/or physical agents in buildings with low air quality and poor ventilation are more prone to be affected by psycho-physical discomfort, respiratory tract and central nervous system diseases. Moreover, in recent years, the time spent indoors has increased by around 90%. If we consider that respiratory diseases are mainly transmitted from human to human through close contact, airborne respiratory droplets and contaminated surfaces, and that there is a strict relationship between air pollution and the spread of the diseases, it becomes even more necessary to monitor and control these environmental conditions. This situation has inevitably led us to consider renovating buildings with the aim of improving both the well-being of the occupants (safety, ventilation, heating) and the energy efficiency, including monitoring the internal comfort using sensors and the IoT. These two objectives often require opposite approaches and strategies. This paper aims to investigate indoor monitoring systems to increase the quality of life of occupants, proposing an innovative approach consisting of the definition of new indices that consider both the concentration of the pollutants and the exposure time. Furthermore, the reliability of the proposed method was enforced using proper decision-making algorithms, which allows one to consider measurement uncertainty during decisions. Such an approach allows for greater control over the potentially harmful conditions and to find a good trade-off between well-being and the energy efficiency objectives.

Architecture for determining the cleanliness in shared vehicles using an integrated machine vision and indoor air quality-monitoring system

In an attempt to mitigate emissions and road traffic, a significant interest has been recently noted in expanding the use of shared vehicles to replace private modes of transport. However, one outstanding issue has been the hesitancy of passengers to use shared vehicles due to the substandard levels of interior cleanliness, as a result of leftover items from previous users. The current research focuses on developing a novel prediction model using computer vision capable of detecting various types of trash and valuables from a vehicle interior in a timely manner to enhance ambience and passenger comfort. The interior state is captured by a stationary wide-angled camera unit located above the seating area. The acquired images are preprocessed to remove unwanted areas and subjected to a convolutional neural network (CNN) capable of predicting the type and location of leftover items. The algorithm was validated using data collected from two research vehicles under varying conditions of light and shadow levels. The experiments yielded an accuracy of 89% over distinct classes of leftover items and an accuracy of 91% among the general classes of trash and valuables. The average execution time was 65 s from image acquisition in the vehicle to displaying the results in a remote server. A custom dataset of 1379 raw images was also made publicly available for future development work. Additionally, an indoor air quality (IAQ) unit capable of detecting specific air pollutants inside the vehicle was implemented. Based on the pilots conducted for air quality monitoring within the vehicle cabin, an IAQ index was derived which corresponded to a 6-level scale in which each level was associated with the explicit state of interior odour. Future work will focus on integrating the two systems (item detection and air quality monitoring) explicitly to produce a discrete level of cleanliness. The current dataset will also be expanded by collecting data from real shared vehicles in operation.

LSTM-Based IoT-Enabled CO2 Steady-State Forecasting for Indoor Air Quality Monitoring

Whether by habit or necessity, people tend to spend most of their time indoors. Built-up Carbon dioxide (CO2) can lead to a series of negative health effects such as nausea, headache, fatigue, and so on. Thus, indoor air quality must be monitored for a variety of health reasons. Various air quality monitoring systems are available on the market. However, since they are expensive and difficult to obtain, they are not commonly employed by the general population. With the advent of the Internet of Things (IoT), the Indoor Air Quality (IAQ) monitoring system has been simplified, and a number of studies have been conducted in order to monitor the IAQ using IoT. In this paper, we propose an improved IoT-based, low-cost IAQ monitoring system using Artificial Intelligence (AI) to provide recommendations. In our proposed system, the IoT sensors transmit data via Message Queuing Telemetry Transport (MQTT) protocol which can be visualised in real time on a user-friendly dashboard. Furthermore, the AI technique referred to as Long Short-Term Memory (LSTM) is applied to the collected CO2 data for the purpose of predicting future CO2 concentrations. Based on the predicted CO2 concentration, our system can compute CO2 steady state in advance with an error margin of 5.5%.

A GPS Based Unmanned Drone Technology for Detecting and Analyzing Air Pollutants

Air pollution is now becoming a global issue because it directly affects human health, by creating cancer, respiratory and cardiac diseases, and the earth's ecosystem, by causing climate change and depletion of ozone layers. The rapid expansion of civilization and industrialization, leading to hazardous emissions from automobiles, chemical industries, and fossil fuel burning, plays a significant role in air and atmospheric pollution. Unmanned aerial systems and the internet of things-based sensing devices have provided new impetus for automatic air pollution monitoring and analysis. In this paper, an automatic indoor and outdoor air quality monitoring system has been developed using an unmanned aerial vehicle: a drone. The proposed device is constructed from an ultra-strength DJI F450 frame, various gas sensors, a particulate matter detector, and a camera to detect and distinguish air pollution data. The precision and sensitivity of the proposed device have been evaluated by collecting air emissions data from two places in Dhaka, Bangladesh. The drone has been navigated in Uttara and Bashundhara areas to measure the amount of CO, CO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> , O <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</inf> , SO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> , NOx, and dust in indoor and outdoor environments. We obtained higher air pollution concentrations in Uttara than in Bashundhara, as it is an area with more industries and construction sites. The proposed system composed of an uncrewed aerial device and low-cost gas monitoring sensors is expected to create new possibilities in environmental and air pollution monitoring.

Monitoring indoor air quality using smart integrated gas sensor module (IGSM) for improving health in COPD patients.

Technology advancement, industrialisation, and globalisation have been significant reasons for air pollution outdoors and indoors. It may surprise us that we spend about 80% of our time indoors breathing toxic, stale, polluted air, making us sluggish and fretful. In contrast to outdoor air, indoor air does not recycle consistently. It traps and builds pollutants from wood and coal stoves, furniture and building materials, paints and solvents, cigarette smoke, and cleaning supplies. The prolonged exposure to these hidden pollutants can prompt respiratory disorders such as lung disease, pneumonitis, asthma, pulmonary hypertension, and chronic obstructive pulmonary disease (COPD). It is enduring and is not curable, which has been a threat to humanity for ages. COPD's major cause is airborne particulate matter and other toxic compounds emitted from indoor and outdoor sources. Outdoor air pollution can be controlled only by acquiring changes in the vast population, wherein for indoor, every individual may create a major impact on improving air purity, thereby promoting health. The proposed design model for monitoring indoor air quality was tested in a normal and stimulating environment where we live. Parameters tested included temperature, humidity, amount of PM2.5, and the concentration of CO, CO2, and NH3. These parameters were monitored for five to 6 h per day for 8 days. Results indicate that the total air quality lies in the moderate range. Further study will be helpful to utilise this module as an effective Indoor air quality (IAQ) monitoring system. HIGHLIGHTS: • A simple, effective, inexpensive integrated gas sensor module (IGSM) has been proposed in this study to monitor the indoor air quality index (IAQI). • Indoor air quality was tested in a normal and stimulating environment for 8 days. • The integrated gas sensor module (IGSM) was composed of sensors in series, and the outputs for the respective parameter were measured easily. • Among the parameters tested, CO and PM2.5 lie in the moderate range, while other pollutants within the normal range reveal that the tested air quality is moderate.

A Design of Indoor Air-Quality Monitoring System

With the deepening of people’s understanding of the harm of air pollution, indoor air quality is getting more and more attention. This paper designs an indoor air quality monitoring system based on sensor technology and Internet of things technology, the system function includes air quality detection, real-time data display, server transfer, remote display throught Wechat mini programe and others, the system can detect six important environmental parameters and allows users to view the data on mobile phone. Based on the data collected, this paper also designs an air quality evaluation algorithm.

Review of Internet of Things-Based Artificial Intelligence Analysis Method through Real-Time Indoor Air Quality and Health Effect Monitoring: Focusing on Indoor Air Pollution That Are Harmful to the Respiratory Organ.

Everyone is aware that air and environmental pollutants are harmful to health. Among them, indoor air quality directly affects physical health, such as respiratory rather than outdoor air. However, studies that have examined the correlation between environmental and health information have been conducted with public data targeting large cohorts, and studies with real-time data analysis are insufficient. Therefore, this research explores the research with an indoor air quality monitoring (AQM) system based on developing environmental detection sensors and the internet of things to collect, monitor, and analyze environmental and health data from various data sources in real-time. It explores the usage of wearable devices for health monitoring systems. In addition, the availability of big data and artificial intelligence analysis and prediction has increased, investigating algorithmic studies for accurate prediction of hazardous environments and health impacts. Regarding health effects, techniques to prevent respiratory and related diseases were reviewed.

Modeling indoor PM2.5 using Adaptive Dynamic Fuzzy Inference System Tree (ADFIST) on Internet of Things-based sensor network data

Indoor air pollution is crucial for the building environment as it has a direct impact on human health, prosperity, comfort, and productivity levels. Repeated exposure to toxic materials and harmful chemicals leaves a considerable impact on occupant well-being. The degraded air quality in the indoor environment is closely connected to the rising cases of respiratory health issues, cardiovascular disease, lung cancer, and a wide spectrum of symptoms. Therefore, it is required to design a reliable system to assess indoor air quality (IAQ) in residential and commercial spaces to improve the health outcomes and productivity levels of the building occupants. The main contribution of this paper is to provide a rigorous design and development approach for the IAQ monitoring system using the Internet of Things-based sensor network. Furthermore, a forecasting model was designed using Adaptive Dynamic Fuzzy Inference System Tree to predict PM2.5 conditions in the target building environment. The performance of the proposed system is tested over multiple datasets obtained from different rural and urban locations; moreover, the findings are further validated against benchmark datasets. The updates about real-time IAQ conditions in the target building environment were made accessible to occupants via an online web portal, and the entire system was named “Vayuveda” – an approach for enhanced air quality in the living spaces. The authors tested multiple methods at every step of the development process and compared performance at each stage to come out with the best model for real-time IAQ assessment.

Predicting the opening state of a group of windows in an open-plan office by using machine learning models

Window operation is among one of the most influencing factors on the indoor air quality (IAQ). The opening state of the windows can modify the air exchange rate and as such the pollutant transfer between indoor and outdoor environments. In this paper, we focus on the modeling of the windows opening state in a real open-plan office with five windows. For this purpose, three machine learning-based models were implemented: (i) Decision Tree, (ii) k-Nearest Neighbors and (iii) Kernel Approximation. IAQ, climatic parameters and the opening state of the windows have been monitored during an entire period of 18 months. The information about: (i) the environmental factors from the previous 24th hour and (ii) the current time (month, day of the week, hour of the day) was used to predict the current state of the windows. The predictor importance estimation and the calculated autocorrelation functions showed that the three most relevant factors were: the previous 24th hour of the windows status, the current time and the previous 24th hour of the prevailing mean outdoor air temperature. The three models perform well with the testing sets according to the different evaluation indicators. The developed methods can be helpful for understanding occupant behavior and also for controlling indoor air pollutants levels in buildings, either as a standalone model or a part of a real-time IAQ monitoring system.

Efficient Monitoring and Adaptive Control of Indoor Air Quality Based on IoT Technology and Fuzzy Inference

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.