Abstract
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.
Highlights
IntroductionThe work from home culture has been increasingly adopted leading to increased number of people utilizing the household buildings for longer durations
In the wake of the COVID-19 pandemic, it is crucial to maintain the norms of social distancing and keeping oneself indoors to minimize the odds of catching the virus.The work from home culture has been increasingly adopted leading to increased number of people utilizing the household buildings for longer durations
It is the COVID-19 that can be spread through poor indoor conditions, there are other viruses, allergens, and diseases which generally spread through a central HVAC system
Summary
The work from home culture has been increasingly adopted leading to increased number of people utilizing the household buildings for longer durations Such practices can ensure the compliance of COVID-19 standard operating procedures (SOPs) but at the same time they can adversely affect the indoor air quality. In order to minimize the chances of being infected with this disease, it is important to understand and monitor the air quality of enclosed environment (such as home, hospitals, offices, shopping malls, meeting rooms etc.) which are centrally controlled by Heating, Ventilation, and Air Conditioning (HVAC) systems It is the COVID-19 that can be spread through poor indoor conditions, there are other viruses, allergens, and diseases which generally spread through a central HVAC system
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