Respire

The challenge

Globally, 98% of cities with more than 100,000 inhabitants fail to meet the World Health Organization’s air quality guidelines ( PM2.5<25ug/m3 and PM10<50ug/m3). Air pollution is the biggest environmental health risk of our time, killing more 19 thousand people each day throughout the year. Research shows that air pollution is not evenly distributed in a city and can in fact, be up to eight times worse on one end of a city block than another. While most conventional monitoring systems can provide a general sense of a city’s air quality, they are large, stationary, and very few in number. Thus, they cannot account for air pollution variations at the neighbourhood level, where people live, work, and play. As a result,we do not have air quality distribution maps of these cities and no air pollution readings of a localised area. At the same time, more and more individuals and industries utilising fleet management are engaging in the purchase of vehicle tracker devices, mainly to prevent car theft and monitoring driving habits. These devices costing about €10 contain the basic hardware needed to build a wireless sensor network.

The solution

We propose a sensor network of air quality monitoring devices which is relatively inexpensive (€20), self-powered and small enough to be attachable to any moving infrastructure (deployed on cars, buses, etc.). Respire will be an IoT device that consists of particulate matter counters: PM 2.5 and PM 10, three gas sensors CO, NO and Ozone, 6W solar panel with an ultracapacitor, GPS for location and LoRa module for reporting data. This device offers twin benefits: (i) for individuals and industries, it functions as a traditional vehicle tracker for fleet management and vehicle theft (ii) for governmental organisations, it provides a highly localised air quality index(AQI) of a particular area using the sensors. Further, being deployed on mobile infrastructure, makes it suitable to send air pollution data of the updated location as the vehicle moves. These sensors, forming an interconnected network of nodes, are used to build a pollution map of the entire city. Thus, providing data in real time and at high spatial resolution . By overlaying this live environmental data with live transportation data, we can modify our traffic through adaptive and intelligent traffic signaling to reduce emission, and reduce air pollution concentration levels in extremely targeted areas. The same insights are also fed to a forecasting model along with various atmospheric parameters to suggest healthy routes and timings with the least pollution levels to the general public and bicyclists.