Vayuayan and VayuayanR: Scalable analysis of air quality data from satellite, public and private sensor networks

Every winter, India’s news outlets run headlines about pollution and the various intervention schemes being planned. Despite air pollution being one of India's biggest public health concerns, data on it in India remains scattered. The Central Pollution Control Board (CPCB) runs hundreds of ground monitors across Indian cities. Private organizations have also installed low-cost sensor networks to fill in the gaps between them. Satellites also provide coverage of PM2.5. However, each data source has its own format, portal, and quirks. So, if you want to ask a simple question, like "what was PM2.5 in this district last November, and how has it changed over the last 10 years?" you will end up stitching together different pipelines by hand.

In this session, we'll demo vayuayan and vayuayanR, an open-source toolkit that pulls all of these sources through a single interface and returns tidy, analysis-ready data. Both packages are built around satellite PM2.5 from Washington University (global PM2.5), and India-specific data sources, including historical and live CPCB and Open Air quality (OAQ) sensor data.

The packages can be used to ask analytical questions, like how optimally sensors are placed, or to run gap analysis on disagreements between ground and satellite PM2.5 data.

What makes it unique:

- Satellites, government monitors, and sensor networks behind one interface.

- Both historical and live CPCB data, including station and city listings and nearest-station lookup.

- Same workflow in Python and R, so mixed teams learn it once.

- Few general air-quality packages read CPCB or OAQ data, so this combination is hard to find elsewhere.