Projects > DST Air Quality Monitoring

Air quality sensors installed in an auto
Route covered by an vehicle mounted auto for 6 days.
Given measurements in time and few nodes in the city network, evolving algorithms to complete the spatiotemporal air pollution matrix.


To create a system of vehicle-mounted sensors that performs accurate air quality sensing.


We propose the creation of a dense air quality map by performing a spatiotemporal sampling using only a few moving sensors followed by the suggested matrix completion technique. This is possible as the underlying low rank and slowly time-varying structure of the air quality data can be leveraged to create models that facilitate an effective Spatio-temporal extrapolation. We claim that to obtain a dense air quality map, the cities may require only a small fraction of moving sensors as compared to static sensors. Therefore moving air quality measurement could lead to an effective strategy against air pollution and climate change.


To identify the source of local pollution and its correlation with key geographical, meteorological, and local parameters (assuming pollution inventory).
To create a web portal for effective dissemination, visualization and analysis for the collected and extrapolated data.

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