Cost-Effective Air Quality Monitoring with Decision Support
More than one million deaths every year in India is attributable to air pollution, as estimated by the Global Burden of Disease study.
India has one of the highest annual average ambient particulate matter exposure levels in the world – significantly higher than those recommended by the World Health Organization. Despite the poor air quality, the monitoring of air pollution levels is limited even in large urban areas in India and virtually absent in small towns and rural areas. Existing tools such as the SDG India Dashboard launched by NITI Aayog and MOSPI measure goals and targets at the national level. Although these provide data-driven insights to support development plans, negligible information is available at the town and city level. Many cities have launched air quality apps, but do not provide any tools to guide the city towards improving air quality. In essence, they do not provide a way for local administrators and planners to perform what- if analysis and evaluate the impact of interventions on air quality without specific studies, which can be expensive and time consuming.
What is needed is an easy-to-use platform which will be a visual tool to provide real-time, local information about outdoor air quality, along with the measured or estimated values of various air quality parameters, and one which can provide a simple assessment of the health impact of outdoor air quality on a straightforward scale, for instance, by using classifications such as “healthy,” “unhealthy,” or “very unhealthy.” Such qualitative categorization will allow a user to make informed decisions about outdoor activities and needed preventive measures such as filter masks. It will also allow the public to make their concerns known to local authorities, and possibly at the national level via social media.
We propose a cost- effective and scalable one-stop system for air quality monitoring and assessment with decision support capabilities. Air quality data comes in from multiple disparate and heterogeneous data sources. In addition to real-time feeds from satellite- based sensors and monitoring stations, we will use mobile crowdsensing to enrich the data. We will develop technologies for cleaning, integrating, and curating the data for ensuring data reliability. Moreover, we will develop technologies to contextualize, discover, ingest, reason, and infer about various kinds of data such as multi-modal data (e.g., sensor, text, image & video data), including data on related parameters such as ambient temperature and land- use/land-cover (LULC), to develop a system that will provide air quality information over a range of geographic scales. To ensure consumability, we will incorporate decision support capabilities through what-if scenario analytics and perform data visualization to cater to the needs of various stakeholders such as municipal authorities.
Researchers : Anirban Mondal, Girish Agarwal, P. Krishna Reddy, Neil Chowdhary, Raghav Mittal, Chandra Shekar, Srinivas Annappalli, Nohrit Mandavi, Namrata Banjare, Durgesh Kumar, Aditya Singh, Namrata Rajesh Menon, Anirudh Patteri, Taha Mohammed Mama, Anamika Sarker, Tharun CM, Ginaishwarya Anne Jacob, Ishita Mittal, Radhika Narang