1. Introduction                                                                                                  6. Discussion

2. Data Domain and Design Principles for Air Pollution Sonification         7. Conclusions

3. Sonification Design                                                                                      Acknowledgements

4. Preliminary Findings                                                                                    Biographies

5. Study Design - Focus Groups                                                        References

4.   Preliminary Findings


 

Ultimately, a successful sonification design solution reveals auditory gestalts, perceptible artifacts, or patterns in the data that hopefully inspire further research in the respective data domain. Based on listening experiences during several initial presentations of this sonification – to expert publics first – the most notable sonic effect is present in Edmonton’s sonified air quality data: it exhibits a unique temporal emissions pattern that is not present in any of the other cities. Short bursts of emissions from different sources can be heard at extremely regular and predictable intervals, followed each time by a gradual increase/decrease cadence in ozone levels. Ground level O3is caused by the reaction of other pollutants (usually those produced by the burning of fossil fuels) with UV rays in the lower atmosphere. The informal experience of listening to this dataset generated further questions and investigations into the causes and patterns of air pollution in that geographic area. For instance, the cadence in ozone can be partly indexed to the time of day, since more ground level O3will be created as reactions with available UV occur. This can also be heard in other cities like Toronto. Rush hour, although initially thought to be the cause, is not a possible explanation for the bursts; other cities would yield the same pattern if this were the case. What is curious is that, even though the short bursts sound at regular intervals, the source of the burst changes between the pollutants. At times there are prominent bursts in SO2and CO, but they will then switch to O3and NO2and back again. Potentially, the bursts are the result of a shared industrial practice across multiple sources of these pollutants, e.g. an active industrial complex or factory. However, another possibility is that they might be weather-related.

 

 

The point is, considered within the scientific domain, this sonification model certainly presents the potential for identifying relevant shifts and patterns; however, it was the strong affective impact of the sound that emerged as a promising direction towards building engagementand shifts in awarenessof the subject matter. On the basis of these preliminary listening tests with the sonification of air pollution, we worked to create an evaluation protocol specifically aimed at non-expert listeners, in order to determine the ways in which members of the general public might comprehend and engage with sonification of socially relevant data of this type.

1. Introduction                                                                                                  6. Discussion

2. Data Domain and Design Principles for Air Pollution Sonification         7. Conclusions

3. Sonification Design                                                                                      Acknowledgements

4. Preliminary Findings                                                                                    Biographies

5. Study Design - Focus Groups                                                        References