J
2021
Harmonization and Visualization of Data from a Transnational Multi-Sensor Personal Exposure Campaign
NOVAK, Rok; Ioannis PETRIDIS; David KOCMAN; Johanna Amalia ROBINSON; Tjasa KANDUC et al.
Basic information
Original name
Harmonization and Visualization of Data from a Transnational Multi-Sensor Personal Exposure Campaign
Authors
NOVAK, Rok; Ioannis PETRIDIS; David KOCMAN; Johanna Amalia ROBINSON; Tjasa KANDUC; Dimitris CHAPIZANIS; Spyros KARAKITSIOS; Benjamin FLUCKIGER; Danielle VIENNEAU; Ondřej MIKEŠ; Céline DEGRENDELE; Ondřej SÁŇKA; Saul Garcia DOS SANTOS-ALVES; Thomas MAGGOS; Demetra PARDALI; Asimina STAMATELOPOULOU; Dikaia SARAGA; Marco Giovanni PERSICO; Jaideep VISAVE; Alberto GOTTI and Dimosthenis SARIGIANNIS
Edition
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, BASEL, MDPI AG, 2021, 1660-4601
Other information
Type of outcome
Article in a journal
Country of publisher
Switzerland
Confidentiality degree
is not subject to a state or trade secret
Marked to be transferred to RIV
Yes
RIV identification code
RIV/00216224:14310/21:00124127
Organization
Přírodovědecká fakulta – Repository – Repository
Keywords in English
data fusion; multi-sensor; data visualization; data treatment; participant reports; air quality; exposure assessment
Links
CZ.02.1.01/0.0/0.0/16_013/0001315, interní kód Repo. EF16_013/0001761, research and development project. EF17_043/0009632, research and development project. 690105, interní kód Repo. 857560, interní kód Repo. RECETOX RI, large research infrastructures. ACTRIS-CZ II, large research infrastructures.
In the original language
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.
Displayed: 3/5/2026 09:08