Indian Institute of Technology Madras (IIT Madras) researchers have developed a low-cost mobile air pollution monitoring framework in which pollution sensors mounted on public vehicles can dynamically monitor the air quality of an extended area at high spatial and temporal resolution.
Traditionally, ambient air quality is measured in monitoring stations and reported as 'Air Quality Index' (AQI). Since these stations are at fixed locations, they only measure the air quality of a small geographic area. Air pollution however is dynamic with locations just a few hundred meters away from each other exhibiting different pollution levels. Levels can also vary at different times of the day. However, setting up more stations is not practical because of the high costs.
The IoT-based mobile air pollution monitoring technology developed by IIT Madras allows low-cost air quality sensors to be mounted on vehicles to gather spatiotemporal air quality data. For the cost of a single reference monitoring station, it would be possible to map an entire city at high resolution using these low-cost mobile monitoring devices, researchers say.
"Mobile air quality sensors would be extensively used in personal and public health initiatives. Personal monitoring devices can help people know the extent of pollution in their neighbourhood so that they can take protective measures. Traffic can be rerouted if local pollution levels are known. Government policy changes and smart city planning would benefit enormously from the use of mobile air quality trackers. Our affordable IoT based mobile monitoring network, coupled with data science principles offers unprecedented advantage in gathering hyperlocal insights into air quality. It is the only viable option at present, capable of offering high spatiotemporal awareness that could allow for informed mitigation and policy decisions," says lead researcher, Prof. Raghunathan Rengaswamy, Dean (Global Engagement) and Faculty, Department of Chemical Engineering, IIT Madras.
According to him, Project Kaatru (air in Tamil) leverages IoT, big data and data science to obtain pan-India hyperlocal air quality map, carry out exposure assessment for each Indian citizen and help develop data driven solutions for policy, intervention and mitigation strategies.
"Interestingly, one specific location showed a significant spike of PM2.5 pollution between 2 am and 3 am. This was associated with trucks carrying milk from a major milk distribution hub in this location at this time. PM2.5 spikes were also found in school neighbourhoods during school start and end hours and in commercial zones during peak hours," Prof. Rengaswamy states.
The devices are capable of measuring multiple parameters, ranging from PM1, PM2.5, PM10 and gasses such as NOx and SOx. In addition to pollutants, the devices can assess road roughness, potholes and UV index among others. The modular design of the device allows for sensors to be replaced on demand. The patented IoT side view mirror design enables the devices to be retrofitted on any kind of vehicle, ranging from buses to cars and even two wheelers. The IoT devices are also equipped with GPS and GPRS systems to collect and transmit location information while data science principles are used to analyse the large volume of data generated from these IoT devices.