Researchers at IIT Madras Develop IoT-Based Mobile Air Pollution Monitoring Technology
Towards tackling this issue, IIT Madras researchers have developed a new IoT-based mobile air pollution monitoring technology wherein low-cost air quality sensors are mounted on vehicles to gather spatiotemporal air quality data.
IIT Madras | File
Air pollution is no more a specific state issue, and researchers from IITs have understood it better. Researchers from the Indian Institute of Technology Madras (IIT Madras) have recently come up with air-pollution monitoring framework. Keeping in mind a constant dip in the air quality, this framework can dynamically monitor the air quality of an extended area at high spatial and temporal resolution.
In order to meticulously address the issue, Researchers at IIT Madras have developed this IoT-based mobile air pollution monitoring technology. This project was led by professor Raghunathan Rengaswamy, Dean (Global Engagement) and Faculty, Department of Chemical Engineering, IIT Madras, Project Kaatru (air in Tamil) leverages IoT, big data, and data science.
This project had goals to achieve which revolve around - obtaining pan-India hyperlocal air quality map, exposure assessment for each Indian Citizen.
While talking about the findings, prof. Raghunathan Rengaswamy said, "Interestingly, one specific location showed a significant spike of PM2.5 pollution between 2 am and 3 am. This was associated with trucks carrying milk from a major milk distribution hub in this location at that time. PM2.5 spikes were also found in school neighbourhoods during school start and end hours and in commercial zones during peak hours.”
A detailed analysis of the parameter PM2.5 has done before hold. This was during the high intensity event which was done across locations and times. The published Research paper also validates that the reliability of the data collected by the IoT devices by comparing it with a CPCB station in one of the location of study. Data collected from the devices followed the same profile/trend across the 6 days of study and showed a high qualitative match with the nearby CPCB station.
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