scholarly journals Pollutant Recognition Based on Supervised Machine Learning for Indoor Air Quality Monitoring Systems

2017 ◽  
Vol 7 (8) ◽  
pp. 823 ◽  
Author(s):  
Shaharil Mad Saad ◽  
Allan Andrew ◽  
Ali Md Shakaff ◽  
Mohd Mat Dzahir ◽  
Mohamed Hussein ◽  
...  
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Indoor Air Quality ◽  
Indoor Air ◽  
Supervised Machine Learning ◽  
Quality Monitoring ◽  
Monitoring Systems ◽  
Air Quality Monitoring

Indoor Air Quality Monitoring Systems

2021 ◽  
Vol 11 (3) ◽  
pp. 1-14
Author(s):  
Rasha AbdulWahhab ◽  
Karan Jetly Jetly ◽  
Shqran Shakir
Keyword(s):  
Air Quality ◽  
Indoor Air Quality ◽  
Indoor Air ◽  
Ad Hoc ◽  
Quality Monitoring ◽  
Monitoring Systems ◽  
Air Quality Monitoring ◽  
Research Activity ◽  
Pollution Levels ◽  
Closed Area

Research activity in the field of monitoring indoor quality systems has increased dramatically in recent years. Monitoring closed areas can reduce health-related risks due to poor or contaminated air quality. In the current COVID pandemic, the population has observed that improving ventilation in the closed area can significantly reduce infection risk. However, the significance of air quality statistics makes highly accurate real-time monitoring systems vital. In this paper, several researchers' protocols and the methodologies for monitoring a good high indoor air quality system are presented. The majority of the reviewed works are aimed to reduce air pollution levels of the atmosphere. The vast majority of the identified works utilized IoT and WSN technology to fix the partial access to sensed data, high cost, and non-scalability of conventional air monitoring systems. Furthermore, ad-hoc approaches are predominantly used to help society change its attitude and impose corrective actions to improve air quality. This paper presents a short but comprehensive review of several researchers works with different approaches to ecological trend analysis capabilities, drawing on existing literature works. Overall, the findings highlight the need for developing systematic protocols for these systems and establishing smart air quality monitoring systems capable of measuring pollutant concentrations in the air.

scholarly journals Indoor Air Quality Monitoring Systems in the IoT Environment

2015 ◽  
Vol 40 (5) ◽  
pp. 886-891 ◽  
Author(s):  
Chang-Se Oh ◽  
Min-Seok Seo ◽  
Jung-Hyuck Lee ◽  
Sang-Hyun Kim ◽  
Young-Don Kim ◽  
...  
Keyword(s):  
Air Quality ◽  
Indoor Air Quality ◽  
Indoor Air ◽  
Quality Monitoring ◽  
Monitoring Systems ◽  
Air Quality Monitoring

scholarly journals Indoor Air Quality Monitoring Systems Based on Internet of Things: A Systematic Review

2020 ◽  
Vol 17 (14) ◽  
pp. 4942 ◽  
Author(s):  
Jagriti Saini ◽  
Maitreyee Dutta ◽  
Gonçalo Marques
Keyword(s):  
Systematic Review ◽  
Air Quality ◽  
Internet Of Things ◽  
Indoor Air Quality ◽  
Indoor Air ◽  
Well Being ◽  
Quality Monitoring ◽  
Monitoring Systems ◽  
Air Quality Monitoring ◽  
Research Knowledge

Indoor air quality has been a matter of concern for the international scientific community. Public health experts, environmental governances, and industry experts are working to improve the overall health, comfort, and well-being of building occupants. Repeated exposure to pollutants in indoor environments is reported as one of the potential causes of several chronic health problems such as lung cancer, cardiovascular disease, and respiratory infections. Moreover, smart cities projects are promoting the use of real-time monitoring systems to detect unfavorable scenarios for enhanced living environments. The main objective of this work is to present a systematic review of the current state of the art on indoor air quality monitoring systems based on the Internet of Things. The document highlights design aspects for monitoring systems, including sensor types, microcontrollers, architecture, and connectivity along with implementation issues of the studies published in the previous five years (2015–2020). The main contribution of this paper is to present the synthesis of existing research, knowledge gaps, associated challenges, and future recommendations. The results show that 70%, 65%, and 27.5% of studies focused on monitoring thermal comfort parameters, CO2, and PM levels, respectively. Additionally, there are 37.5% and 35% of systems based on Arduino and Raspberry Pi controllers. Only 22.5% of studies followed the calibration approach before system implementation, and 72.5% of systems claim energy efficiency.

scholarly journals Internet of Things (IoT) Based Indoor Air Quality Sensing and Predictive Analytic—A COVID-19 Perspective

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 184
Author(s):  
Rafia Mumtaz ◽  
Syed Mohammad Hassan Zaidi ◽  
Muhammad Zeeshan Shakir ◽  
Uferah Shafi ◽  
Muhammad Moeez Malik ◽  
...  
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Internet Of Things ◽  
Indoor Air Quality ◽  
Indoor Air ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Ambient Conditions ◽  
Public Facilities

Indoor air quality typically encompasses the ambient conditions inside buildings and public facilities that may affect both the mental and respiratory health of an individual. Until the COVID-19 outbreak, indoor air quality monitoring was not a focus area for public facilities such as shopping complexes, hospitals, banks, restaurants, educational institutes, and so forth. However, the rapid spread of this virus and its consequent detrimental impacts have brought indoor air quality into the spotlight. In contrast to outdoor air, indoor air is recycled constantly causing it to trap and build up pollutants, which may facilitate the transmission of virus. There are several monitoring solutions which are available commercially, a typical system monitors the air quality using gas and particle sensors. These sensor readings are compared against well known thresholds, subsequently generating alarms when thresholds are violated. However, these systems do not predict the quality of air for future instances, which holds paramount importance for taking timely preemptive actions, especially for COVID-19 actual and potential patients as well as people suffering from acute pulmonary disorders and other health problems. In this regard, we have proposed an indoor air quality monitoring and prediction solution based on the latest Internet of Things (IoT) sensors and machine learning capabilities, providing a platform to measure numerous indoor contaminants. For this purpose, an IoT node consisting of several sensors for 8 pollutants including NH3, CO, NO2, CH4, CO2, PM 2.5 along with the ambient temperature & air humidity is developed. For proof of concept and research purposes, the IoT node is deployed inside a research lab to acquire indoor air data. The proposed system has the capability of reporting the air conditions in real-time to a web portal and mobile app through GSM/WiFi technology and generates alerts after detecting anomalies in the air quality. In order to classify the indoor air quality, several machine learning algorithms have been applied to the recorded data, where the Neural Network (NN) model outperformed all others with an accuracy of 99.1%. For predicting the concentration of each air pollutant and thereafter predicting the overall quality of an indoor environment, Long and Short Term Memory (LSTM) model is applied. This model has shown promising results for predicting the air pollutants’ concentration as well as the overall air quality with an accuracy of 99.37%, precision of 99%, recall of 98%, and F1-score of 99%. The proposed solution offers several advantages including remote monitoring, ease of scalability, real-time status of ambient conditions, and portable hardware, and so forth.

Indoor Air Quality Monitoring Systems and COVID-19

2021 ◽  
pp. 133-147
Author(s):  
Jagriti Saini ◽  
Maitreyee Dutta ◽  
Gonçalo Marques
Keyword(s):  
Air Quality ◽  
Indoor Air Quality ◽  
Indoor Air ◽  
Quality Monitoring ◽  
Monitoring Systems ◽  
Air Quality Monitoring

scholarly journals Indoor Air Quality Monitoring Systems for Enhanced Living Environments: A Review toward Sustainable Smart Cities

2020 ◽  
Vol 12 (10) ◽  
pp. 4024 ◽  
Author(s):  
Gonçalo Marques ◽  
Jagriti Saini ◽  
Maitreyee Dutta ◽  
Pradeep Kumar Singh ◽  
Wei-Chiang Hong
Keyword(s):  
Air Quality ◽  
Internet Of Things ◽  
Indoor Air Quality ◽  
Indoor Air ◽  
Smart Cities ◽  
Well Being ◽  
Quality Monitoring ◽  
Monitoring Systems ◽  
Air Quality Monitoring ◽  
Health And Well Being

Smart cities follow different strategies to face public health challenges associated with socio-economic objectives. Buildings play a crucial role in smart cities and are closely related to people’s health. Moreover, they are equally essential to meet sustainable objectives. People spend most of their time indoors. Therefore, indoor air quality has a critical impact on health and well-being. With the increasing population of elders, ambient-assisted living systems are required to promote occupational health and well-being. Furthermore, living environments must incorporate monitoring systems to detect unfavorable indoor quality scenarios in useful time. This paper reviews the current state of the art on indoor air quality monitoring systems based on Internet of Things and wireless sensor networks in the last five years (2014–2019). This document focuses on the architecture, microcontrollers, connectivity, and sensors used by these systems. The main contribution is to synthesize the existing body of knowledge and identify common threads and gaps that open up new significant and challenging future research directions. The results show that 57% of the indoor air quality monitoring systems are based on Arduino, 53% of the systems use Internet of Things, and WSN architectures represent 33%. The CO2 and PM monitoring sensors are the most monitored parameters in the analyzed literature, corresponding to 67% and 29%, respectively.

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