Air Pollution Sensing and Causal Modelling

Over 91% of the world’s population lives in areas that exceed WHO guideline limits for air quality. Globally, up to 10 million deaths annually are attributed to ambient air pollution — higher than malaria and HIV. Unfortunately, India registers a large portion of these deaths, approximately over 2 million deaths – a number greater than all the deaths in 2020-21 due to Covid-19, Tuberculosis, Malaria, and AIDS combined. There are number of studies that have shown direct causality of air pollution with cardiovascular, cerebrovascular, pre/neo-natal diseases and pediatric health. Air pollution not only poses a significant health risk to humans but also is a major factor leading to climate change. To ensure quality of life for the people, present and future, it is critical we reduce air pollution across the world.

There have been a lot of public policy measures taken to address pollution in the country but for it to be effective we need a nuanced data-driven approach to understand and predict air pollution. We need to be able gather air pollution data and strengthen the ability to monitor air quality across locations, especially in areas close to hospitals, schools, and workplaces. Low-cost sensors and other emerging technologies can help improve and expand air pollution monitoring in areas that are currently underserved. Further, we need models that can help determine the factors causing air pollution to identify the right policy approaches for effective interventions.

At the lab we have been working on the problems of enabling better collection of granular spatiotemporal air pollution data and developing models that can help determine the causal factors of air pollution. Through our efforts we have designed and implemented improved approaches to air pollution data collection in Delhi and Bangalore. We have also developed site-specific models to infer causal factors and predict air pollution in Delhi.

Learn more about our work: