Using data science and IoT, Indian Institute of Technology Madras (IIT Madras) Researchers have developed a low-cost mobile air pollution monitoring framework in which, pollution sensors mounted on public vehicles can dynamically monitor the air quality of an extended area at high spatial and temporal resolution.
The devices are capable of measuring multiple parameters, ranging from PM1, PM2.5, PM10 and gasses such as NOx and SOx. In addition to pollutants, the devices can assess road roughness, potholes and UV index among others. The modular design of the device allows for sensors to be replaced on demand. The patented IoT side view mirror design enables the devices to be retrofitted on any kind of vehicle, ranging from buses to cars and even two wheelers.
The IoT devices are also equipped with GPS and GPRS systems to collect and transmit location information. Data Science principles are used to analyse the large volume of data generated from these IoT devices.
Led by Prof. 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 to achieve the following goals:
- Obtain pan-India hyperlocal air quality map
- Exposure assessment for each Indian citizen
- Data driven solutions for policy, intervention and mitigation strategies
A data science and IoT based mobile monitoring framework for performing high resolution spatio-temporal assessment was recently published in the reputed, peer-reviewed journal Building and Environment (https://doi.org/10.1016/j.buildenv.2022.109597) in a paper co-authored by Sathish Swaminathan, Anand Guntuku, Sumeer S, Amita Gupta and Prof. Raghunathan Rengaswamy.
Prof. Raghunathan Rengaswamy comments, “Our affordable IoT based mobile monitoring network, coupled with data science principles offers unprecedented advantage in gathering hyperlocal insights into air quality. It is the only viable option at present, capable of offering high spatio-temporal awareness that could allow for informed mitigation and policy decisions.”
The researchers undertook two case studies as a part of this work.
