The World Economic Forum 2016 just concluded at Davos with climate action only occupying the lower end of the agendas. Some organizations though are aware of the impacts of business-as-usual (BAU) on climate change and incidentally on air quality, which is one of its societal consequences.

Among the pollutants of concern for public health are surface ozone and particulate matter. The latter consists of particles of 2.5 or 10 micro-meters which can be inhaled into the lungs and can accumulate in the bronchus. Lung problems can result in exacerbation of pre-existing condition (e.g. asthma), increased absence from work/school, increased hospital admissions and even premature death. The persistent problems with air pollution under BAU conditions exacerbate distortions in taxes and subsidies as recently highlighted by a WHO Regional Office for Europe report and will lead to changes in regulation and investment decisions.

Traditionally, air quality has been tackled by national or transnational organizations such as the US EPA which maintains AirData, or the European EEA through AirBase. These data repositories are fed by a combination of high-cost, high-precision sensor stations and mathematical models to infer indices in locations not reached by stations. The emergence of open low-cost microcontroller platforms combined with increased awareness of air quality conditions in our cities, lead to a new range of lower-cost lower-fidelity devices. Aclima, which designs and deploys environmental sensor networks, recently announced its partnership with Google mounting mobile sensors on its Street View cars. In Switzerland, the OpenSense project has also made progress with mid-range sensors mounted on top of public transportation. Mayors are also tapping into the innovations in IoT to provide open data sets in cities such as the Array of Things in Chicago, which measures air quality as well as light, noise and other environmental measures.

As we progress into an urban future with a slow transition towards clean energy, we remain vulnerable to air quality. Yale’s Environmental Performance Index (EPI) points to 3.5 billion people as living in areas in which the air quality is below levels set by the World Health Organization.

Measuring air quality and deriving actionable risk data is a transdisciplinary proposition which involves data science, environmental science, embedded systems design, platform design, as well as environmental governance. Sensors undergo systematic calibration to ensure coherence with officially-recognized reference instruments. Collection and processing of time-series data also plays a role in the design of the solution, in particular when there are many sensors involved. Visualizations and statistical procedures for detection of changes in the series are elements that we at Ness would potentially explore during the discovery phase of our Ness Connected approach. This approach is precisely grounded on transdisciplinarity and designed to bring complementary stakeholders around the table in order to achieve outstanding results in product design and implementation.

Today, sensor solutions are tied to data centers which are running most server-side logic in the cloud. The arrival of smart contract and blockchain technology will also likely see a new breed of air quality networks with autonomous sensors which operate in a peer-to-peer fashion and potentially enforce local air quality ordinances through smart contracts directly. Externalizing costs may thus become just difficult enough that we do steer away from BAU towards a brighter and cleaner future.