Principal Investigator: Bharadwaj Amrutur
Is it possible to determine the amount of pollution we get exposed to every time we travel to our work place? Knowing the personal exposure could allow people travelling to their work place to choose a route based on an estimate of the exposure to pollution? We (along with other collaborators from IISc and around the world) are currently developing low-cost sensors that can enable this and many more applications.
A recent WHO study identified that India is home to 14 of the 15 most polluted cities in the world, with primary sources of pollution being vehicle emissions, traffic congestion and biomass and fuel-wood burning. Outdoor air pollution has been identified to be the fifth biggest killer in India and has been implicated in respiratory and cardiovascular diseases as well as asthma, bronchitis, lung cancer, acidosis, etc. First step towards getting rid of air pollution is to reliably measure the amount of pollutants in air at a given location. The air quality index (AQI) is the measure of how good or bad the quality of air is over a region and is calculated based on measured concentrations of 8 different pollutants including CO, SO2 and particulate matter (PM). In India, an AQI between 0-100 is considered to be safe while an AQI above 200 is considered harmful for humans. Concentration of pollutants are typically measured at a few monitoring stations using expensive reference grade equipments. However, in view of their cost it is impossible to deploy many such monitoring stations to get data with high spatial resolution. In this context, developing low-cost sensors that can reliably measure the concentration of the pollutants and can be deployed in large numbers is important. IISc, in collaboration with the Central Electronics Engineering Research Institute (CSIR-CEERI) and the University of Southern California (USC), is currently working on a project funded by Indo-U.S. Science and Technology Forum (IUSSTF) to develop and calibrate low-cost air quality sensors.
The low-cost sensors (airCENSE) are built by the Centre for Nano Science and Engineering at IISc and consist of a metal oxide semiconductor thin film whose resistance changes deterministically on exposure to a particular amount of a pollutant, thereby allowing us to determine the concentration of the gas. But one of the challenges of using low-cost sensors is the issue of calibration. Calibration is the process of comparing the values measured by a device with a reference standard of known accuracy. Since the sensors will be deployed in harsh environments, the components of the sensors will degrade and the measurements become less accurate over a period of time. Thus for getting reliable measurements, sensors would have to calibrated regularly. Since calibrating each of these sensors can be a tedious and expensive task, it is important to come up with low-cost calibrating techniques. Scientists at RBCCPS along with colleagues from the Electrical Communication Engineering department at IISc and USC are working on developing new calibration techniques by integrating sophisticated machine learning techniques with dispersion models, using data collected from a network of sensors instead of just one device.