Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Data Fusion and Visualization of a Multi-Sensor Personal Exposure Campaign

Version 1 : Received: 29 September 2021 / Approved: 30 September 2021 / Online: 30 September 2021 (14:13:52 CEST)

How to cite: 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. Data Fusion and Visualization of a Multi-Sensor Personal Exposure Campaign. Preprints 2021, 2021090518 (doi: 10.20944/preprints202109.0518.v1). 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. Data Fusion and Visualization of a Multi-Sensor Personal Exposure Campaign. Preprints 2021, 2021090518 (doi: 10.20944/preprints202109.0518.v1).

Abstract

Use of a multi-sensor approach can provide citizens a holistic insight in the air quality in their immediate surroundings and assessment of personal exposure to urban stressors. Our work, as part of the ICARUS H2020 project, which included over 600 participants from 7 European cities, discusses data fusion and harmonization on a diverse set of multi-sensor data streams to provide a comprehensive and understandable report for participants, and offers possible solutions and improvements. Harmonizing the data streams identified issues with the used 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 the process of generating 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 on a diverse set of multi-sensor data streams considerably improved the quality and quantity of data that a research participant receives. Though automatization accelerated the production of the reports considerably, manual structured double checks are strongly recommended.

Keywords

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

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