A New Delhi-based study involving researchers from the Energy Policy Institute at the University of Chicago (EPIC), Yale University, and New York University offers a model that can help identify local pollution hotspots and catalyze policy actions.
The study uses a high-precision spatiotemporal prediction model drawn from two years of data from a low-cost monitoring network of 28 custom-designed low-cost portable air quality sensors to derive fine-grained pollution maps. It proposes that a network of inexpensive, portable air quality monitors in densely populated cities can significantly enhance pollution forecasting and play an important role in developing precise policy recommendations and public health alerts.
The paper emphasizes how high costs often prevent city governments from widely deploying reference-grade air quality monitors, which are the standard for providing data to the public air-quality index. For instance, only 33 reference-grade monitors are available for Delhi, India, which has a population of 15 million citizens and a land area of 1500 sq km. Researchers at Inclusion Economics at Yale University, Warwick, EPIC, Swiss Data Center, Kai Air Monitoring Pvt Ltd, and New York University propose a new methodology to model and predict urban air quality at a fine-grained level using dense noisy, low-cost sensors.
Anant Sudarshan, Senior Fellow, EPIC, and Assistant Professor, University of Warwick, explains, “Our paper develops a model that can offer more precise forecasts and assessments of air quality. So we used machine-learning methods to combine high and low-quality sensors and generate accurate, fine-grained pollution maps.”