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 levels of pollution. Levels can also vary at different times of the day. However, setting up more stations is not practical because of the high costs.

Towards tackling this issue, IIT Madras Researchers, have developed a new IoT-based mobile air pollution monitoring technology wherein low-cost air quality sensors are mounted on vehicles to gather spatio-temporal 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. 

Led by Prof. Raghunathan Rengaswamy, Dean (Global Engagement) and Faculty, Department of Chemical Engineering, IIT Madras, Project Kaatru (air in Tamil) leverages IoT, big data and data science to achieve the following goals:

• Obtain pan-India hyperlocal air quality map
• Exposure assessment for each Indian citizen
• Data driven solutions for policy, intervention and mitigation strategies

A data science and IoT based mobile monitoring framework for performing high resolution spatio-temporal assessment was recently published in the reputed, peer-reviewed journal Building and Environment (https://doi.org/10.1016/j.buildenv.2022.109597) in a paper co-authored by SathishSwaminathan, Anand Guntuku, Sumeer S, Amita Gupta and Prof. Raghunathan Rengaswamy.

Elaborating on the findings of this Research, Prof. Raghunathan Rengaswamy, Faculty, Department of Chemical Engineering, IIT Madras, said,“Interestingly, one specific location showed a significant spike of PM2.5 pollution between 2 am and 3 am. This was associated 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.”

Prof. Raghunathan Rengaswamy added,“Mobile air quality sensors would find extensive use in both 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 spatio-temporal awareness that could allow for informed mitigation and policy decisions.”

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. Figure 1 shows the parameters that can be sensed by the IoT mobile monitoring devices.

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. Data Science principles areused to analyse the large volume of data generated from these IoT devices.

The researchers undertook two case studies as a part of this work.

The first study was aimed at assessing hyper local air quality assessments to evaluate the effects of vehicular traffic, urban topography and urban functions. Measurements were made across a 15 sq. km. area of carefully selected region in western Chennai to study and authenticate as to how pollution concentration varied.

The pilot area was chosen carefully to include different land use such as commercial, industrial, residential, hospital and school zones, that would have an impact on unevenly distributed emission sources, dilution, and physicochemical transformations over short distances. Additionally, the impact of different factors such as: vehicular density, urban, industrial & residential functions on hyperlocal level air quality were assessed. Diurnal trends in specific zones were identified and correlated with human activity in those zones.

The study was able to capture even subtle variation in PM2.5 concentrations at various locations across time. The gradation in PM2.5 concentration between main roads and arterial roads was also captured through this assessment. This case study was done jointly with the Centre for Urbanization Buildings and Environment (CUBE), a Centre of Excellence at IIT Madras.

The second case study was the analysis of PM2.5 levels around a specific high intensity event – Deepavali (Diwali).  The auto-rickshaw-mounted sensors were made to ply on South Chennai roads two days before Deepavali of 2019, two days of the festival, and two days after. Four specific areas were chosen - a commercial area, a heavily wooded residential academic campus, an upscale residential area and an industrial area with small-scale workshops. 

The published Research Paper also validates the reliability of the data collected by the IoT devices by comparing it with a CPCB station in one of the locations of study. Data collected from the devices followed the same profile/trend across the 6 days of study and showed a high qualitative match with the nearby CPCB station.

A detailed analysis of the variation in PM2.5 levels before, during and after the high intensity event was done across locations and time. Through such an assessment, it was possible to gauge the impact of the event on an area and associate it with the type of area (land use).  This insight confutes the popular opinion that the entire region experiences the same impact during such high intensity events.

 

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