Air Quality Monitoring

The majority of in-depth work in environmental IoT has been done here. For the most part, research in this area covers sensor networks used to measure and report air quality metrics for a given area. Air quality is typically measured in one of two ways; using a reactive component in a sensor to calculate the parts per million in a volume of air (ppm/v), or by measuring light wavelengths over a period of time and detecting particles in the air [1]. One of the objectives of monitoring air quality in this manner is to provide information about the air to the population that was previously not available. It has been suggested that air quality sensor networks measuring information like this could produce “ozone alerts”, like traffic alerts, and commuters could use this information when planning a route [2].

A few academic studies have been performed in this area. One of them out of Lancaster University (UK), used consumer-grade “maker” electronics to perform a study on how pollution levels impact individual commuters. They developed wearable sensors using Arduino boards that they gave to study participants to wear as they commuted to and from campus. The information was reported back to a central instance using an application on a smart phone. The results did not agree on exact concentrations of pollution at given times, but using data plotted on a map, the researchers were able to identify “high” and “low” areas of pollution during peak travel times. The data inconsistencies were attributed to the low-quality sensors used, a requirement of both the cost and power requirements of a wearable device. The research team did propose doing a follow-up study where they performed an exit interview with the participants. Many participants reported feeling like they had traveled through an area with “bad pollution”, only to find the data did not back that up. This seems to indicated there is difference between perceived and measurable levels of air pollution in our environment [3].

Another system to measure pollution was set up in Cape Town, South Africa. This system was established to be a proof-of-concept “WaspNet” for sensor networks. The idea here being that with a central system many types of metrics for different environmental factors could be collected centrally and be made available for use. The system was designed to address several of the problems surrounding deployments of this kind; namely poor network connectivity and power. The central technology used for data collection and reporting was Zygbee which is a low-power network communication protocol. The author of the study stressed the importance of well documented standards and open-source sensors as a strategy for building a robust and sustainable solution. The centralized and open nature of such a system also lowers the barrier of entry for developing nations, meaning organizations that could not afford to build this from the ground up could purchase a few sensors and participate, gathering valuable information. At the time of this writing, the data collected by the system was not available for public consumption, but there are plans to offer that in the future [4].

I was surprised that I did not find many city-scale projects like this in my research. I did, however, find quite a number of smaller grassroots efforts. Many of these had similar approaches and scope, some even using the same technology. The end goal was developing a network that private citizens could afford to tap into and get useful data. The Air Quality Egg provides a $185 boxed kit, and has a website with a real-time map showing data collected from participants [5]. The AirPi project was featured on the Raspberry Pi homepage and features a set of do-it-yourself instructions, parts, and code to build an air quality ground station. It also has a map-based reporting interface [6]. The AirCasting project allows for a number of different sensors to connect to an Android OS application and upload data to a central location that way [7]. One of the projects to use this delivery method is CitiSense, which provides a wearable sensor about the size of a wallet intended to be clipped to a backpack. The sensor uploads data to the CitiSense reporting page using AirCasting as the passthrough [8]. HabitatMap also uses AirCasting for delivery, but instead uses it’s own “AirBeam” sensor, which is an Arduino board, intended to be carried. The site also has information and activities related to pollution awareness at the local level [9].