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. 2021 Nov 4;18(21):11614.
doi: 10.3390/ijerph182111614.

Harmonization and Visualization of Data from a Transnational Multi-Sensor Personal Exposure Campaign

Rok Novak  1   2 , Ioannis Petridis  3 , David Kocman  1 , Johanna Amalia Robinson  1   2 , Tjaša Kanduč  1 , Dimitris Chapizanis  3 , Spyros Karakitsios  3   4 , Benjamin Flückiger  5   6 , Danielle Vienneau  5   6 , Ondřej Mikeš  7 , Céline Degrendele  7   8 , Ondřej Sáňka  7 , Saul García Dos Santos-Alves  9 , Thomas Maggos  10 , Demetra Pardali  10 , Asimina Stamatelopoulou  10 , Dikaia Saraga  10 , Marco Giovanni Persico  11   12 , Jaideep Visave  11   12 , Alberto Gotti  12 , Dimosthenis Sarigiannis  3   4   11
Affiliations

Harmonization and Visualization of Data from a Transnational Multi-Sensor Personal Exposure Campaign

Rok Novak et al. Int J Environ Res Public Health. .

Abstract

Use of a multi-sensor approach can provide citizens with holistic insights into the air quality of their immediate surroundings and their personal exposure to urban stressors. Our work, as part of the ICARUS H2020 project, which included over 600 participants from seven European cities, discusses the data fusion and harmonization of a diverse set of multi-sensor data streams to provide a comprehensive and understandable report for participants. Harmonizing the data streams identified issues with the sensor devices and protocols, such as non-uniform timestamps, data gaps, difficult data retrieval from commercial devices, and coarse activity data logging. Our process of data fusion and harmonization allowed us to automate visualizations and reports, and consequently provide each participant with a detailed individualized report. Results showed that a key solution was to streamline the code and speed up the process, which necessitated certain compromises in visualizing the data. A thought-out process of data fusion and harmonization of a diverse set of multi-sensor data streams considerably improved the quality and quantity of distilled data that a research participant received. Though automation considerably accelerated the production of the reports, manual and structured double checks are strongly recommended.

Keywords: air quality; data fusion; data treatment; data visualization; exposure assessment; multi-sensor; participant reports.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Schematic representation of data collection devices and protocols, transfer paths, aggregation, visualization, and delivery protocols.
Figure 2
Figure 2
Faceted plots with meteorological variables—temperature, relative humidity, and air pressure; data from IAQ. Colored horizontal ribbons represent “optimal” values for each variable.
Figure 3
Figure 3
Faceted heatmaps of three pollutants (CO2, NO2, and TVOCs); data from IAQ.
Figure 4
Figure 4
Three size classes of PM (PM1, PM2.5, and PM10) and heart rate values for both seasons with each point colored according to the associated activity; data from PPM, SAT, and TAD.
Figure 5
Figure 5
Three size classes of PM and heart rate values for both seasons with each point colored according to the associated activity and each ribbon representing a location or means of transport for that time period; data from PPM, SAT, and TAD.
Figure 6
Figure 6
Faceted plots of average daily concentrations of three size classes of PM for each season, with WHO guidelines; data from PPM.
Figure 7
Figure 7
Aggregated data from SAT.
Figure 8
Figure 8
Faceted plots of average values of three size classes of PM (PM1, PM2.5, and PM10) for each specific activity and each season; data from PPM and TAD.
See this image and copyright information in PMC

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