This story was originally published by Data-Smart City Solutions.
According to the World Health Organization (WHO), more than 5.5 million people worldwide die each year as a result of air pollution. Many of these deaths occur in large cities, where exhaust from cars, factories, and power plants fills the air with hazardous particles.
In response to a growing concern about the effects of air pollution, many cities have improved their efforts to measure pollution using the Internet of Things (IoT)—networks of connected sensors that gather and send data. Using this data, cities can map areas of high pollution, track changes over time, identify polluters, and analyze potential interventions.
City air quality initiatives using sensors fall into three main categories: initiatives that integrate sensors into existing infrastructure, those that leverage mobile sensors, and others that analyze cell phone data to understand resident exposure to poor air quality. No one model is correct, but rather each has advantages and drawbacks that cities should consider when implementing an air quality monitoring initiative.
Many cities have integrated air quality sensors into existing infrastructure in order to track air quality in key areas. Chicago, for example, deployed its Array of Things in 2014, a citywide network of sensors mounted on lampposts developed with Argonne National Library and the Chicago Department of Innovation and Technology. Using a technology called “waggle chips,” these sensors track the presence of a number of air pollutants, including carbon monoxide, nitrogen dioxide, ozone, and particulate matter, with plans to monitor volatile organic compounds (VOCs) in the near future. Chicago has used this data to predict air quality incidents in order to take preventative action and has released data to the public via the city’s open data portal. Barcelona has pursued a similar strategy with its Barcelona Lighting Masterplan, deploying a smart lighting system with embedded air quality sensors that relay information to city agencies and the public.
Other cities have integrated sensors into multi-purpose solutions. More than 65 cities worldwide including Boston, Los Angeles, and Miami have installed Soofa benches, park benches equipped with a solar panel that channels electricity via USB ports to charge devices. These benches not only serve as a social space and sustainable source of energy, but also house sensors that record air quality, temperature, traffic, and radiation.
A distinct advantage of infrastructure-embedded air sensors is their longevity: integrating sensors into lasting features of the urban landscape allows cities to collect data over time and map trends without additional interventions. Moreover, these systems can provide instantaneous air quality data, which cities may be able to use to nudge citizens to action during times of poor air quality. However, these sensor nodes are also expensive, often running around $5,000 per sensor, and cities may therefore be unable to install them ubiquitously. As of 2016, Chicago had installed 50 nodes, and although there are plans to increase to number to 500 by 2018, these sensors will likely be unable to map detailed air quality in every corridor of the city.
However, low-cost air quality sensors like the Airbeam or Aclima’s triangular sensor nodes, which usually cost around $250, may provide an opportunity for cities to engage in ubiquitous, long-term monitoring. Because these sensors are much less expensive, cities may be able to invest in more of them, installing sensors across the city landscape in order to produce fine-grained and real-time air quality data.
There are concerns with the quality of the data that such sensors can produce. According to a study by Nature, “reducing cost inevitably reduces specificity or sensitivity, or both.” Low-cost air quality sensors are often unable to pick up finer particles and their readings may be influenced by meteorological conditions. Unlike more expensive sensors that normally come out of academic or research institutions, these sensors do not undergo peer review or academic evaluation and their accuracy is therefore unclear. Yet, even if not perfectly accurate, the information from these sensors can help inform discussions about air quality and increase political capital for making changes.
However, the monetary and environmental costs of these sensor networks may not justify their use as a political motivator. While the sensors themselves are inexpensive, in some cases the costs involved in their installation and maintenance, as well as in analyzing the data they produce, are still prohibitively high. Some academics are also concerned with these sensors’ e-waste burden after they have reached the end of their usable life, as there has not yet been a proper analysis of their carbon footprint.
Instead of installing air quality sensors into fixed, permanent features, some cities have chosen to implement mobile sensors attached to devices or objects that move throughout a city. Cars provide one fairly obvious vehicle for air quality sensors, as they can traverse and map entire cities quickly. In partnership with Google, the Environmental Defense Fund (EDF) has used Street View cars to measure methane levels in eleven cities by equipping cars with an intake tube and methane analyzer. Using this data, EDF has created methane maps and identified more than 5,500 leaks. In 2014, Google began exploring more broad air quality monitoring, equipping Street View cars with Aclima’s Environmental Intelligence (Ei) mobile platform, which includes sensors that can measure particulate matter, NO2, CO2, black carbon, and more. During a pilot test in Denver, the car collected more than 150 million data points over 750 hours of driving, creating a comprehensive air quality map of the city.
Using air quality sensors in bike share programs may be another means of gathering citywide air quality data. In a trial in 2014, the city of Dublin fitted 30 bikes with air sensors measuring carbon dioxide, carbon monoxide, smoke, and particulates. In three days, these bikes gathered data for the entire city, which researchers across the country studied and mapped. While this trial only produced data for this three-day period, building sensors into bike share programs more broadly would allow cities to gather consistent air quality data. Because bikes are mobile, such a program may require fewer air quality sensors than lamppost-embedded systems, collecting similar data at a lower cost.
London has taken a unique approach to air quality monitoring, attaching air quality sensors to ten pigeons in order to monitor air quality over three days of flights. In March 2016, the city sent the pigeons across London carrying 25-gram sensors that monitored levels of nitrogen dioxide, ozone, and other volatile compounds. During the flights, Londoners could inquire about pollution levels in their areas by tweeting @PigeonAir, which would respond with readings ranging from moderate to extreme.
Rather than deploying traditional air quality sensors, Louisville chose to pursue an alternative means of gauging air quality in the city. In 2012, the city deployed its AIR Louisville initiative, giving 300 local residents a sensor that fits on top of their inhaler, which tracks locations of inhaler use with the intention of helping residents manage asthma. The city collected 5,400 data points over the 13 months of the initial phase and has started identifying hotspots with high inhaler use in order to pinpoint areas with particularly bad air quality. AIR Louisville has since expanded to two thousand residents, with the goal of not only helping residents with asthma, but also of informing city leaders of air quality concerns in order to direct policy. With more residents navigating the city with the sensors, the city will get a more complete picture of air quality across Louisville.
While these one-time initiatives lack the capacity to monitor air quality over time without repeated interventions, they can provide more complete, timely, and inexpensive maps of city air quality. Because mobile sensors are able to traverse an entire city in a few days, cities can use them to quickly capture a comprehensive picture of air quality at any given time. Moreover, cities do not need to invest in a large number of sensors, as one mobile sensor can theoretically map an entire city.
While systems of air quality sensors can provide a picture of air quality in a city, it may be unclear how this air quality affects particular residents. However, by mapping residents’ routes using anonymized cellphone data, cities can better understand citizen exposure to areas of poor air quality. In New York City, MIT’s Senseable City Lab used anonymized cellphone data paired with air quality measures to determine the amounts of different chemicals to which New Yorkers are exposed. For example, the study determined that those who live and work in Manhattan are exposed to more pollution than residents who commute to the outer boroughs. This type of analysis goes beyond efforts to merely describe air quality in a city, outlining direct impacts on residents.
While there are many options, the model of air quality monitoring that a city chooses to pursue should match that city’s needs and capabilities. For example, a one-time mobile sensing effort may be a good fit in a city that does not have a great deal of money and is just breaking into the smart city arena. An air quality mapping effort may be a useful first step for such a city to bring awareness to pollution or boost public support for a proposed environmental reform. On the other hand, in a city that has deeper coffers, a more mature civic tech environment, or an impending infrastructure upgrade planned, it may make more sense to build air quality sensors into their infrastructure. If such a city has a well-developed smart city agenda and advanced data management capabilities, it may want to measure not only air quality, but many other factors including traffic, climate, and noise, justifying the development of an advanced sensor network. Technological development will continue to enable sensors to measure more at lower cost, making sensor-based data ever more accessible and useful to cities.