Harmonization and Visualization of Data from a Transnational Multi-Sensor Personal Exposure Campaign
- Outputs resulting from multi-sensor and multi-parameter data flows;
- Aggregation and harmonization of data collected;
- Production of tailored visualizations by fusing data from multiple sources, and automated compilation of individualized final reports.
2. Materials and Methods
2.1. PPM Data
2.2. SAT Data
2.3. IAQ Data
2.4. ICARUS Data Portal
2.5. TAD Data
- A scatter plot was made for every PM size class and heart rate for both seasons. Additionally, the points were colored based on the activity at that minute, which allowed the reader to observe what activities took place at, for example, elevated levels of PM or elevated heart rate. Only the activities which the participant filled in were shown in the legend.
- A similar scatter plot as in (a) was constructed, with an additional layer which showed vertical bands or ribbons of different colors corresponding with the participant’s location and mode of transport. As this added another layer of complexity to the visualization, the decision was made to provide these plots only to specific individuals who expressed interest. Though activity information was missing in several TADs, the location and transport data were logged for almost the entire period of observation (for most participants). Consequently, participants could associate specific means of transport with elevated levels of PM, and corresponding activities with a higher heart rate.
- The third plot showed the average weekly PM values for each activity. Six plots were constructed, three per season, one for each PM size class.
2.6. Final Report Compilation and Production
- Generation of plots as described in points 2.1.–2.4., which was followed for all of the participants. These plots were saved locally in a jpeg format and labeled according to each participant ID.
- Plots were integrated in a rmarkdown script, with the customization of each report designated in an Excel file. Each participant had a custom greeting with their name and gender-appropriate pronoun. All plots and other graphics were inserted using the include_graphics function in the knitr package.
- Finally, the script was iterated over all participants in a separate script to allow some further customizations. Some participants had additional visualizations (see 2.5 point b), while others had some omitted due to missing data. After all the reports were generated in the participants’ local language, they were manually checked for errors by local organizers in each participating city and distributed to all the participants.
2.7. Temporal Resolution and Data Treatment
3. Results and Discussion
3.1. A Merged Dataset
- Specific characteristics for each participant (age and gender);
- PPM data (PM values, temperature, humidity, battery charge level, location coordinates, speed, and altitude);
- SAT data (where several columns proved to be somewhat redundant and were therefore removed);
- IAQ data (which proved to be easiest to handle as they had a correct timestamp for each recorded value, almost no missing values, and a simple interface to download the data);
- TAD data, presented the same way as they were recorded on the physical paper sheets: location of the participant (home, office, indoor, outdoor), transport data (bus, car, foot, etc.), indoor and outdoor activities (cooking, smoking, sports, etc.), and some specific conditions for the indoor space the participant was in (burning candle or fireplace, open windows, and/or AC turned on).
3.2. Visualizing the Data
3.3. The Final Report
3.4. Issues Faced and Recommendations for Future Studies
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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Novak, R.; Petridis, I.; Kocman, D.; Robinson, J.A.; Kanduč, T.; Chapizanis, D.; Karakitsios, S.; Flückiger, B.; Vienneau, D.; Mikeš, O.; Degrendele, C.; Sáňka, O.; García Dos Santos-Alves, S.; Maggos, T.; Pardali, D.; Stamatelopoulou, A.; Saraga, D.; Persico, M.G.; Visave, J.; Gotti, A.; Sarigiannis, D. Harmonization and Visualization of Data from a Transnational Multi-Sensor Personal Exposure Campaign. Int. J. Environ. Res. Public Health 2021, 18, 11614. https://doi.org/10.3390/ijerph182111614
Novak R, Petridis I, Kocman D, Robinson JA, Kanduč T, Chapizanis D, Karakitsios S, Flückiger B, Vienneau D, Mikeš O, Degrendele C, Sáňka O, García Dos Santos-Alves S, Maggos T, Pardali D, Stamatelopoulou A, Saraga D, Persico MG, Visave J, Gotti A, Sarigiannis D. Harmonization and Visualization of Data from a Transnational Multi-Sensor Personal Exposure Campaign. International Journal of Environmental Research and Public Health. 2021; 18(21):11614. https://doi.org/10.3390/ijerph182111614Chicago/Turabian Style
Novak, Rok, Ioannis Petridis, David Kocman, Johanna Amalia Robinson, Tjaša Kanduč, Dimitris Chapizanis, Spyros Karakitsios, Benjamin Flückiger, Danielle Vienneau, Ondřej Mikeš, Céline Degrendele, Ondřej Sáňka, Saul García Dos Santos-Alves, Thomas Maggos, Demetra Pardali, Asimina Stamatelopoulou, Dikaia Saraga, Marco Giovanni Persico, Jaideep Visave, Alberto Gotti, and Dimosthenis Sarigiannis. 2021. "Harmonization and Visualization of Data from a Transnational Multi-Sensor Personal Exposure Campaign" International Journal of Environmental Research and Public Health 18, no. 21: 11614. https://doi.org/10.3390/ijerph182111614