A cost-effective mobile air pollution monitoring framework has been created by researchers from the Indian Institute of Technology Madras (IIT Madras). This system uses pollution sensors installed on public vehicles to monitor air quality continuously and dynamically across a wide area, providing detailed spatial and temporal resolution.
Conventional air quality assessment relies on monitoring stations that measure ambient air quality and report it as the ‘Air Quality Index’ (AQI). However, due to their fixed locations, these stations can only capture the air quality data of a limited geographical region. Furthermore, air pollution displays dynamic patterns where locations within a few hundred metres of each other can have varying levels of pollution. Moreover, pollution levels can fluctuate throughout the day. However, setting up more stations is not practical because of the high costs.
To address the issue, researchers have introduced an IoT-based mobile air pollution monitoring technology. It involves equipping vehicles with affordable air quality sensors to collect detailed spatiotemporal data on air quality. 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.
The devices can measure multiple parameters, ranging from PM1, PM2.5, and PM10, and gases such as NOx and SOx. Apart from pollutants, the devices can evaluate road roughness, potholes, and UV index. The modular design of the device allows for sensors to be replaced as needed. The IoT devices are also equipped with GPS and GPRS systems to collect and transmit location data.
Under the guidance of Raghunathan Rengaswamy, Dean (Global Engagement) and Faculty, Department of Chemical Engineering, Project Kaatru (meaning “air” in Tamil) uses IoT, big data, and data science. It aims to create a comprehensive hyperlocal air quality map across India, assess individual exposure to air pollution for every citizen, and develop data-driven solutions to inform policies, interventions, and mitigation strategies related to air pollution.
Commenting on the findings, Rengaswamy said that one specific location showed a significant spike of particulate matter (PM) 2.5 pollution between 2 am and 3 am. This was attributed to 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.
Mobile air quality sensors would have a wide range of applications in both personal and public health initiatives. Personal monitoring devices enable individuals to assess the pollution levels in their neighbourhoods, empowering them to take necessary precautions and protective measures. Additionally, having access to real-time local pollution data can facilitate informed decision-making, such as rerouting traffic to avoid highly polluted areas and reducing exposure to harmful pollutants.
Government policies and smart city planning would benefit from the technology. The IoT-based mobile monitoring network, combined with data science principles to analyse the large volume of data generated provides advantages in collecting hyperlocal air quality data. It currently stands as the most viable option, capable of offering high-resolution and real-time awareness of air pollution levels. The information enables informed decision-making for effective mitigation strategies and policy formulation. By leveraging the power of mobile air quality trackers, governments can make data-driven choices that lead to improved air quality and enhanced public health outcomes.
