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The Indian Institute of Technology Kanpur (IIT-Kanput) has set up a Centre of Excellence (CoE), the Advanced Technologies for Monitoring Air-quality iNdicators (ATMAN). It aims to build indigenous, low-cost sensors and improve artificial intelligence/machine learning (AI/ML) capabilities to improve air quality in urban and rural regions.
According to a press release, the CoE is a significant step towards the government’s Atma Nirbhar Bharat (Self-Reliant India) initiative. It also aligns with the vision of the Principal Scientific Advisor of India, aspiring to translate sustainable technologies and business models into tangible products and services accessible to millions worldwide.
There are several projects underway at the ATMAN, with one notable initiative being the Ambient Air Quality Monitoring of Rural Areas using Indigenous Technology (AMRIT). This flagship project involves deploying a dense sensor ambient air quality monitor (SAAQM) network with 1,400 nodes across rural areas in Bihar and Uttar Pradesh.
AMRIT represents a pioneering effort to comprehensively monitor air quality in these regions, which have traditionally lacked extensive data collection and been limited to towns and cities. The CoE team is collaborating with the State Pollution Control Board of Bihar and the Uttar Pradesh Department of Environment, Forests, and Climate Change.
“The State of Bihar took the initiative to collaborate with IIT-Kanpur towards [the] installation of sensor-based air quality monitors in all its 534 administrative blocks,” D.K. Shukla, Chairman of the Bihar State Pollution Control Board, said.
The collected air quality data will undoubtedly assist in devising an action plan to protect the respiratory health of the rural population in the state. In doing so, Bihar would pioneer the first-of-its-kind collaboration with an institution such as IIT-Kanpur, Shukla noted.
Sachchida Nand Tripathi, leading the CoE, expressed enthusiasm about the impactful research in progress, stating that CoE ATMAN is at the forefront of integrating indigenous technology for a self-dependent India. Working with state government departments in Bihar and Uttar Pradesh is instrumental in bridging gaps in the data-driven Air Quality Management (AQM) policy.
Meanwhile, the ATMAN’s Dynamic Hyper-Local Source Apportionment (DHSA) represents a cost-effective approach to source apportionment. It is currently being pioneered in Lucknow and Kanpur, Uttar Pradesh. Measuring and understanding the levels and causes of air pollution is the first step towards addressing it. DHSA is geared towards providing city authorities with valuable data to make informed decisions in their air quality action planning. The long-term goal is to scale DHSA systems to cities throughout India, enabling dynamic and real-time insights into emissions and sources of air pollution.
The ATMAN’s PM2.5 Prediction and Airshed Management project leverages micro-satellite imagery, sensor-based ambient air quality networks, and machine learning techniques to forecast PM2.5 levels at finer resolutions. The CoE is also actively working on an airshed approach to tackle air pollution at a broader scale, relying on data-driven policy decisions.
Projects like these put the CoE at the forefront of advancing indigenous air quality sensor fabrication, incorporating AI/ML models. The optimisation of this technology includes strategic sensor placement to enhance overall citizen satisfaction with publicly available air quality information.
The Principal Scientific Adviser to the government, Ajay Sood, said, “The CoE ATMAN has been set up to strategise and execute projects in the field of air quality with indigenous sensor manufacturing that is scalable globally. I appreciate the nurturing of Indian start-ups by this Centre. The coordination between science and government agencies is at the centre of citizens’ welfare.”
