Air quality in cities and its impact on public health is currently a growing concern, receiving ample attention from policymakers, scientists and general public. Very small concentrations can already be harmful, stressing the urge on information about current state, exposure time, pollution sources and mitigation measures.
Despite a dozen official air monitoring stations installed in the city of Amsterdam measuring numerous air pollutants (Luchtmeetnet, 28-09-2015), it is known that no traditional monitoring network is able to capture the local variations in air quality. Still keeping us in ignorance about the actual state or the impact of air quality on our wellbeing. And as extending traditional monitoring networks is too expensive, we have to find new ways to collect information about the impact of air quality on our health. In particular, the fast development of new emerging sensor technology makes that more and more citizens start collecting data themselves. It is logic to question whether in this case society can meet science. In other words: does societal information have any added value to existing monitoring networks in such a way that it contributes to new insights?
Despite policymakers and scientists consider crowdsourcing to be one of the most promising alternatives to collect huge amount of data, there is a huge debate going on about the quality of societal data. Can laity carry out proper measurements and are their cheap sensors reliable enough? As a consequence, one can observe a strong focus on developing new emerging sensor technologies, capable of dealing with the uncertain expectations of the scientific world.
Looking from a distance, this development confuses me. What accuracy should a sensor have to become accepted by the scientific world? And if there is a kind of threshold, how much value does it have in this era, in which we continuously expect information to be more precise. Apart from the sensors specifications itself, how does the data gathering procedure of laymen influence the value of the data? Should we therefore not invest in the developing of data gathering procedures, so societal data becomes of more scientific value?
But also as a citizen I have my questions. Like RIVM found out that the crowd is willing to invest no more than 13 euro’s in any measuring device. Personally I think spending 13 euro’s for a measuring device you only use once or twice and that collects some difficult to interpret values, is already quite something. On the other hand, if one could recommend me in bringing my children to day-care either by bike or by car because the exposure time to polluted air significantly influences their health, my willingness to pay for this information increases and I could easily spend a couple of hundred euro’s on this issue. In other words: the crowd might have individual motives to contribute and the challenge is how to relate the gathered information back to these individual motives.
And there is one final issue more I want to address. Apart from the crowd willingness to invest financially in measuring equipment, the willingness to invest in data gathering. Whether these sensors require a physical location or are considered as wearable, each new sensor will compete with either its predecessor or with other existing sensors. So in that sense: what will be the life time of measuring devices used by the public, and does this life time match the expectations of the scientific world?
Personally, I think the world of crowdsourcing is very fascinating, and indeed contains a lot of potential, but we still have a long way to go. I have the feeling scientists often consider the crowd source option, as a cheap way of gathering the information they want, forgetting the individual motives of society. On the other hand, the crowd expectation of knowledge derived from the societal data is overestimated and it is challenging to maintain their attention. In the AMS Institute Stimulus Project ‘UrbanAirQ’, we work on bridging these two worlds. Through interactive workshops, we explore; a) the added value of societal data, b) the motives of residents to gather information, c) how to connect available knowledge to information need, but perhaps most important; d) how to identify the blind spot to tackle in our follow-up.
Matthijs Danes, Theme coordinator Smart city and Complex systems – Alterra Wageningen UR