The D-NOSES Methodology relies on citizen science. While it can be easily understood that this means that citizens will be involved in creating scientific data, it can be more difficult to understand how this can produce reliable, unbiased data. It can never be overstated how important it is for scientists to follow well established protocols that ensure that the experimental data they collect is as accurate and reliable as possible. If the data is unreliable, then so are the conclusions, and so the data cannot be used. How can citizens, with no formal scientific training, be expected to deliver useable data?

D-NOSES has tackled the data reliability issue in several ways:

The first significant factor to increase the accuracy of data is to simplify the collection. The complex, traditional, odour recognition and impact measuring methodologies have been reduced to 2 observations a citizen must make. These are related to the intensity of the perceived odour and the hedonic tone. While the intensity represents the strength of the odour, the hedonic tone essentially is a measure of how pleasant or unpleasant the odour is. This simplification of the measurements reduces the chances of mistakes or misperceptions in recording observations.

The simple interface and odour model enable everyone to produce reliable data.

Another significant factor to increase the reliability of data coming from untrained citizens is, quite simply, to train them. One of the activities in the citizen engagement process is a simplified odour recognition workshop that is used to educate people in the basic principles and practice of recognising and reporting odours. While not all people reporting odour observations will have necessarily been trained, by increasing the number that have, the overall reliability is also increased.

Odour Training of citizen volunteers for the Barcelona Pilot

Even with training, there is always the risk of personal bias affecting individual data points. This possible bias can usually be detected through a statistical approach. If more people report an odour, at a particular location and at a certain time, it can be determined that this is a reliable observation. The real-time and continuous nature of the data collection in this case offers a clear advantage over the more traditional survey response methods.

While some statistics may remove individual bias, what about collective bias? After all, the people reporting odour issues are also the ones affected by it. This is where some of the more advanced techniques come into play. The D-NOSES method implements the use of something called retrotrajectory modelling. This means that using an advanced model that incorporates geographical and weather data for the regions being monitored, it is possible to demonstrate the path that an odour has taken before being observed by a citizen volunteer.

Using the model, an odour packet can be traced to its origin

The retrotrajectory model contributes to the datasets in two important ways. In the first place, it can be used as a validation of the observations by checking to see if there are other observations that match the calculated path taken. The path calculation also allows the identification of the probable source of the odours by examining the path taken and checking if there are odour emitters along the way. Perhaps even more importantly, if the source has been established, the model can also be used to link the perceived odours to the time the odour was released. With access to information about the emitters activities, the D-NOSES team of experts can diagnose the possible problems and suggest relevant solutions. This takes the guesswork out of odour management, and provides an easier way for emitters to control their odour emissions.

As stated earlier. the validation of the data is a vital component of the D-NOSES method and citizen science in general. Only if we can show that the data is reliable can we expect the relevant stakeholders to take appropriate action.The techniques outlined above are enough to show that it is in theory possible to collect reliable scientific data from regular citizens. The various pilots, some of which have already started, will then provide the scientific evidence that this is true. Everyone can contribute to this effort by using the OdourCollect app, either online or on their mobile phones. The more data we can collect, the stronger the case that can be made for remedial action.