Integration of experimental, computational & live data from ventilation subprojects

Prof. Krishnendu Sinha, Prof. Soumyo Mukherji & Prof. Sarika Mehra

Sterilization of air via ultraviolet germicidal irradiation (UVGI) is an effective and efficient method to inactivate the presence of pathogenic microorganisms as it is automatic, cost- effective, energy- efficient, and doesn’t leave any residue. Wavelengths in the UVC band (200-280 nm) are absorbed by the DNA, RNA, and protein of the cell and therefore leading to microbe inactivation or failure to replicate. This project aims at developing an IoT-based system to monitor and Control UVGIbased Air Disinfection Units for safe and efficient air disinfection of lecture hall complexes/ seminar rooms/ laboratories, etc. that are frequently occupied by a large number of students. Each of said units in the proposed system will consist of a plurality of germicidal UVC lamps, various sensors required for operation and safety, node microcontroller; wireless communication module, camera and necessary power supplies along with safety mechanism. Each of the disinfection units will be finally integrated into an IoT platform enabling the user to monitor and control every operation from nearby and/or remotely

IoT based system to monitor and control UVGI-based air disinfection units

Prof. Ambarish Kunwar & Prof. Kiran Kondabagil

Sterilization of air via ultraviolet germicidal irradiation (UVGI) is an effective and efficient method to inactivate the presence of pathogenic microorganisms as it is automatic, cost- effective, energy- efficient, and doesn’t leave any residue. Wavelengths in the UVC band (200-280 nm) are absorbed by the DNA, RNA, and protein of the cell and therefore leading to microbe inactivation or failure to replicate. This project aims at developing an IoT-based system to monitor and Control UVGIbased Air Disinfection Units for safe and efficient air disinfection of lecture hall complexes/ seminar rooms/ laboratories, etc. that are frequently occupied by a large number of students. Each of said units in the proposed system will consist of a plurality of germicidal UVC lamps, various sensors required for operation and safety, node microcontroller; wireless communication module, camera and necessary power supplies along with safety mechanism. Each of the disinfection units will be finally integrated into an IoT platform enabling the user to monitor and control every operation from nearby and/or remotely.

Developing a self-calibrating, self-diagnosable and optimum low cost (LCS) air quality (AQ) sensor network

Prof. Abhishek Chakraborty & Prof. Rajesh Zele

Low-cost sensors are often touted as the game changer in air-quality monitoring domain as they could provide a very high spatio-temporal coverage at a fraction of the cost compared to the traditional AQ monitoring network. AQ is generally very heterogeneous in nature and often changes significantly even within a small area like an academic campus, classrooms, and office spaces. AQ within a city/urban/non-urban area could be highly heterogeneous and vary significantly from one intra-city location to another. Thus, policymakers need to consider that before proposing any policy for air pollution mitigation. However, LCS often suffers from poor accuracy and reliability issues, hindering its widespread adoption and applicability.

To counter this problem, we need to develop an LCS network that could recalibrate itself against reference/research grade analyzers /equipment from time to time. It is also vital that the LCS network self-diagnose itself to alert the user about the necessity of replacement or any other maintenance activities so that high quality data can be obtained continuously. Several AQ and sensor related parameters could be utilized to achieve the proposed purpose. The project requires the involvement of a multidisciplinary team consisting of members with different expertise, from air quality to networking and data acquisition.

Ventilation CFD to determine the optimum placement of minimum sensors

Prof. Krishnendu Sinha, Prof. Sarika Mehra & Prof. Janani Muralidharan

Ventilation of indoor spaces is traditionally determined by air changes per hour (ACH). This is based on the assumption that the pollutants are uniformly distributed throughout the room, and every corner of the room is equally ventilated. This is actually not the case. Recirculating air and dead air zones present at corners and around obstacles can trap air and infectious particles. This can significantly delay their evacuation by the ventilation system. Knowing the location and size of recirculating zones is therefore important for the placement of sensors that detect the quality of air. It can also help in deciding the minimum number of sensors required to cover a given indoor space.

Computational fluid dynamics (CFD) is an effective tool to obtain the air flow circulation in a room, and identify the recirculation zones. It can predict the air residence time and the time scale of evacuation of pollutants. The CFD data and pathogen information fed into a Reactor model framework gives the infection probability and safe exposure time in a room. The CFD model predictions are to be validated with sensor data obtained from other sub-projects. The CFD data can also be used to optimize the operation of interventions required to improve ventilation and their energy usage.

Development of Sensors for VOC-s (Volatile organic compounds) in an indoor environment

Prof. Soumyo Mukherji & Prof. Nirmal Punjabi

VOC-s are not only harmful by themselves but can also act as markers for various other contaminants, including viruses, in an indoor setting. As such, VOC-s need to be monitored, measured and controlled, which will also alleviate the entities that they proxy for. In the Biosensors and Bioinstrumentation (BSBI) lab, we have developed inexpensive paper polymer based sensors for measuring VOC-s in breath. We would like to extend this towards monitoring other compounds which have a negative impact on indoor air quality.

Real-time assessment of ventilation using commercial CO2 sensors in high-occupancy spaces

Prof. Srinidhi Balasubramaniam, Prof. Virendra Sethi & Prof. Krishnendu Sinha

Good indoor air quality is essential for good occupant health and productivity, particularly in classrooms with high occupancy and a high risk of airborne disease transmission. It is imperative to dynamically measure indoor air quality in response to occupant behaviours and develop suitable mitigation measures. Carbon dioxide (CO2), which occupants naturally emit, has been widely used to quantify ventilation effectiveness and more recently implemented in the design of automated systems for real-time, energy-efficient ventilation. This sub-project aims to develop a hybrid approach that combines real-time measurements of CO2 with spatiotemporal modeling of tracer gas patterns to assess classroom ventilation under different occupancy conditions and deploy sensor-based real-time ventilation controls.

Role of thermal stratification on indoor ventilation

Prof.Rajneesh Bhardwaj & Prof.Amit Agarwal

Improving ventilation in an indoor environment is central to mitigating the airborne transmission of airborne diseases. While there are standard guidelines for enhancing the ventilation of space in isothermal conditions with first-order models or standard CFD models, the effect of temperature stratification has not been considered in previous studies. The temperature distribution could be such that a stable stratification gets set up inside the room and the thermal plume being emitted by an infected person as she breathes out does not reach the ceiling, rather the infected aerosols remain suspended at some intermediate level owing to stratification in the room. This may increase the chances of infection even with some ventilation available in the room and the chances could be higher in a crowded room, such as a classroom.

The role of the exhaust fan near the ceiling on disrupting the stratification and its consequent effect on ventilation remains unclear. We will employ high fidelity CFD simulations to understand the flow and thermal fields in the presence of infected subjects. We will determine the possible stagnation and recirculation zones in the room in the presence of thermal fields. In these zones, the virus-laden droplets may get trapped, thereby increasing the chances of infection for the disease. The CFD data will be combined with available IoT temperature sensors, installed on walls at different heights in the room. This will allow us to explore inexpensive interventions for reducing the infections, such as limiting usage of AC as a function of time and number of students in the classroom.

Laboratory scale benchtop model for ventilation experiments

Prof. Manaswita Bose, Prof. Parthasarathy Goswami & Prof. Krishnendu Sinha

Transmission of airborne disease in confined spaces like classrooms is strongly influenced by indoor ventilation. Computational Fluid Dynamics (CFD) has emerged as a diagnostic tool for detecting indoor air movement; however, the CFD results need to be validated against experimental measurements before a final conclusion is made. Particle Image Velocimetry is the state of art measurement methodology that can be used to determine the local air velocity. The same can be used for validating the CFD results. The objective of this sub-project is to build a benchtop model classroom of geometrically similar dimensions of an actual classroom of IIT Bombay. The benchtop model classroom will be fitted with exhaust fans. Fans may be fitted on the roof of the benchtop model classroom to mimic the ceiling fans. Air flow velocity will be measured at different heights of the benchtop model classroom. The stagnation zones will be identified. Concentration of aerosol particles will be estimated from the images acquired using long distance microscopic lenses. The possible correlation between the clustering of aerosol particles and air flow will be investigated.

Spatial distribution of bacterial pathogens in indoor spaces

Prof. Sarika Mehra, Prof. Krishnendu Sinha & Prof. Parthasarathy Goswami

The concentration of air-borne pathogens in indoor spaces is influenced by the airflow patterns. Thus, infectivity will vary in a room based on the presence of wellmixed flow and dead zones. CFD studies have predicted that the mean residence time of particles can be 10 times greater in stagnation zones. In this project, we will experimentally test these predictions using bioaerosols. The aerosols will be generated using Mycobacterium smegmatis, a model organism to study tuberculosis, another airborne infection. The experiments will be performed in a labscale benchtop model of the classroom where the airflow pattern can also be experimentally measured (subproject 7). Scaling the experiments to the 10 ft x 10 ft Life-size test bed will also be attempted.

To generate bioaerosols, the bacteria will be aerosolized in a controlled manner. As the infection dose can vary over many orders of magnitude depending on the source of generation such as cough or normal breathing, the bioaerosol concentration will also be varied accordingly. Various methods of detection of viable bacteria will need to be optimized to increase the sensitivity and to also distinguish from any background bacteria present in air. The experimental data will also be compared with the air flow pattern computed using CFD (subproject 3) and that based on experimental studies (subproject 7). The data obtained will also be correlated to measurements of CO2 and PM levels to validate their use as proxy metrics for assessing infectivity in closed spaces.

Testing facility for Indoor ventilation experiments at IITB

Prof. Krishnendu Sinha, Prof. Soumyo Mukherji & Prof. Virendra Sethi

Ventilation studies proposed under different sub-projects are mostly targeted towards the LT001 classroom in the Lecture Hall Complex at IIT Bombay. Controlled experimentation is possible in the classroom, when not occupied by students. This restricts the availability of the room and limits the type of equipment that can be used for experimentation. As an alternative, it is proposed to develop a dedicated life-size test bed (approximately 10ft x 10ft) for ventilation experiments, which replicates the key features of ventilation in LT001 classroom. It will simulate the airflow in the class room with full-scale ceiling and exhaust fans, doors and windows, along with real furniture and mannequins. It will also have air conditioning identical to what is available in the real classroom. The test bed will provide a controlled environment for careful testing, with injection of CO2 and PM as per requirement. It will be always available for testing and experimentation, and will avoid scheduling issues that are unavoidable for the real classroom with lectures and other commitments.

Development of a methodology for pathogen capture and quantification of inactivation

Prof. Kiran Kondabagil, Prof. Ambarish Kunwar & Prof. Soumyo Mukherji

A major objective of this project is to establish a methodology for capturing and quantifying airborne viruses. The method developed will be useful for validating the viral inactivation strategies developed by other investigators as part of this umbrella project. For inactivation studies, surrogate non-pathogenic bacterial RNA and DNA viruses will be used. These viruses, also known as bacteriophages, such as MS2 and phi-6, serve as good surrogates of human pathogenic viruses such as coronaviruses, rhinoviruses, influenza viruses, etc. In terms of size, stability, and aerosol behavior, these viruses are very similar to coronaviruses and influenza viruses. Hence, the parameters optimized for inactivation of these bacterial viruses will be directly applicable to inactivation of human pathogenic viruses. Detection of viruses in aerosols presents several challenges. Viruses are usually present in the air at a very high dilution. Hence, for measuring virus load one needs to process large volumes of sample and also needs decent capture efficiency.

For measuring the load of viable viruses, one has to use a gentler method for virus capture. Traditional air sampling devices usually have low flow rates and depending on the dilution, one needs to collect samples from air for a long duration. Another major impediment while collecting samples, especially in the context of inactivation, is contamination from environmental microorganisms including other bacteria and other phages. A controlled environment and an air sampler with high flow rate that minimizes damage to virus integrity will help in establishing the inactivation efficiencies of the methods developed. Indoor testing facility developed as part of sub-project 9 will be used for this purpose. Another objective of the study is to establish a method for characterization of airborne viruses from various environments. This objective will give us an idea on the virus load (both pathogenic and non-pathogenic) and helps in parameter optimization for various inactivation methodol.