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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; 18:11614. [PMID: 34770131 PMCID: PMC8583633 DOI: 10.3390/ijerph182111614] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 10/26/2021] [Accepted: 11/01/2021] [Indexed: 11/17/2022]
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.
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Affiliation(s)
- Rok Novak
- Department of Environmental Sciences, Jožef Stefan Institute, 1000 Ljubljana, Slovenia; (D.K.); (J.A.R.); (T.K.)
- Jožef Stefan International Postgraduate School, 1000 Ljubljana, Slovenia
| | - Ioannis Petridis
- Environmental Engineering Laboratory, Department of Chemical Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece; (I.P.); (D.C.); (S.K.); (D.S.)
| | - David Kocman
- Department of Environmental Sciences, Jožef Stefan Institute, 1000 Ljubljana, Slovenia; (D.K.); (J.A.R.); (T.K.)
| | - Johanna Amalia Robinson
- Department of Environmental Sciences, Jožef Stefan Institute, 1000 Ljubljana, Slovenia; (D.K.); (J.A.R.); (T.K.)
- Jožef Stefan International Postgraduate School, 1000 Ljubljana, Slovenia
| | - Tjaša Kanduč
- Department of Environmental Sciences, Jožef Stefan Institute, 1000 Ljubljana, Slovenia; (D.K.); (J.A.R.); (T.K.)
| | - Dimitris Chapizanis
- Environmental Engineering Laboratory, Department of Chemical Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece; (I.P.); (D.C.); (S.K.); (D.S.)
| | - Spyros Karakitsios
- Environmental Engineering Laboratory, Department of Chemical Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece; (I.P.); (D.C.); (S.K.); (D.S.)
- HERACLES Research Centre on the Exposome and Health, Center for Interdisciplinary Research and Innovation, 54124 Thessaloniki, Greece
| | - Benjamin Flückiger
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, CH-4051 Basel, Switzerland; (B.F.); (D.V.)
- University of Basel, CH-4001 Basel, Switzerland
| | - Danielle Vienneau
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, CH-4051 Basel, Switzerland; (B.F.); (D.V.)
- University of Basel, CH-4001 Basel, Switzerland
| | - Ondřej Mikeš
- RECETOX, Faculty of Science, Masaryk University, 62500 Brno, Czech Republic; (O.M.); (C.D.); (O.S.)
| | - Céline Degrendele
- RECETOX, Faculty of Science, Masaryk University, 62500 Brno, Czech Republic; (O.M.); (C.D.); (O.S.)
- LCE, CNRS, Aix-Marseille University, 13003 Marseille, France
| | - Ondřej Sáňka
- RECETOX, Faculty of Science, Masaryk University, 62500 Brno, Czech Republic; (O.M.); (C.D.); (O.S.)
| | - Saul García Dos Santos-Alves
- Department of Atmospheric Pollution, National Environmental Health Centre, Institute of Health Carlos III, 28220 Madrid, Spain;
| | - Thomas Maggos
- Atmospheric Chemistry and Innovative Technologies Laboratory, INRASTES, NCSR “Demokritos”, Aghia Paraskevi, 15310 Athens, Greece; (T.M.); (D.P.); (A.S.); (D.S.)
| | - Demetra Pardali
- Atmospheric Chemistry and Innovative Technologies Laboratory, INRASTES, NCSR “Demokritos”, Aghia Paraskevi, 15310 Athens, Greece; (T.M.); (D.P.); (A.S.); (D.S.)
| | - Asimina Stamatelopoulou
- Atmospheric Chemistry and Innovative Technologies Laboratory, INRASTES, NCSR “Demokritos”, Aghia Paraskevi, 15310 Athens, Greece; (T.M.); (D.P.); (A.S.); (D.S.)
| | - Dikaia Saraga
- Atmospheric Chemistry and Innovative Technologies Laboratory, INRASTES, NCSR “Demokritos”, Aghia Paraskevi, 15310 Athens, Greece; (T.M.); (D.P.); (A.S.); (D.S.)
| | - Marco Giovanni Persico
- Department of Science, Technology and Society, University School of Advanced Study IUSS, 27100 Pavia, Italy; (M.G.P.); (J.V.)
- Eucentre Foundation, Via A. Ferrata, 1, 27100 Pavia, Italy;
| | - Jaideep Visave
- Department of Science, Technology and Society, University School of Advanced Study IUSS, 27100 Pavia, Italy; (M.G.P.); (J.V.)
- Eucentre Foundation, Via A. Ferrata, 1, 27100 Pavia, Italy;
| | - Alberto Gotti
- Eucentre Foundation, Via A. Ferrata, 1, 27100 Pavia, Italy;
| | - Dimosthenis Sarigiannis
- Environmental Engineering Laboratory, Department of Chemical Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece; (I.P.); (D.C.); (S.K.); (D.S.)
- HERACLES Research Centre on the Exposome and Health, Center for Interdisciplinary Research and Innovation, 54124 Thessaloniki, Greece
- Department of Science, Technology and Society, University School of Advanced Study IUSS, 27100 Pavia, Italy; (M.G.P.); (J.V.)
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Yie KY, Chien TW, Yeh YT, Chou W, Su SB. Using Social Network Analysis to Identify Spatiotemporal Spread Patterns of COVID-19 around the World: Online Dashboard Development. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:2461. [PMID: 33802247 PMCID: PMC7967593 DOI: 10.3390/ijerph18052461] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 02/24/2021] [Accepted: 02/25/2021] [Indexed: 12/15/2022]
Abstract
The COVID-19 pandemic has spread widely around the world. Many mathematical models have been proposed to investigate the inflection point (IP) and the spread pattern of COVID-19. However, no researchers have applied social network analysis (SNA) to cluster their characteristics. We aimed to illustrate the use of SNA to identify the spread clusters of COVID-19. Cumulative numbers of infected cases (CNICs) in countries/regions were downloaded from GitHub. The CNIC patterns were extracted from SNA based on CNICs between countries/regions. The item response model (IRT) was applied to create a general predictive model for each country/region. The IP days were obtained from the IRT model. The location parameters in continents, China, and the United States were compared. The results showed that (1) three clusters (255, n = 51, 130, and 74 in patterns from Eastern Asia and Europe to America) were separated using SNA, (2) China had a shorter mean IP and smaller mean location parameter than other counterparts, and (3) an online dashboard was used to display the clusters along with IP days for each country/region. Spatiotemporal spread patterns can be clustered using SNA and correlation coefficients (CCs). A dashboard with spread clusters and IP days is recommended to epidemiologists and researchers and is not limited to the COVID-19 pandemic.
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Affiliation(s)
- Kyent-Yon Yie
- Department of Gastrointestinal Hepatobiliary, Chi Mei Jiali Hospital, Tainan 700, Taiwan;
| | - Tsair-Wei Chien
- Department of Medical Research, Chi-Mei Hospital, Tainan 700, Taiwan;
| | - Yu-Tsen Yeh
- Medical School, St. George’s University of London, London SW17 0RE, UK;
| | - Willy Chou
- Department of Physical Medicine and Rehabilitation, Chi Mei Medical Center, Tainan 700, Taiwan
| | - Shih-Bin Su
- Department of Occupational Medicine, Chi Mei Medical Center, Tainan 700, Taiwan
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Differences in the Estimation of Wildfire-Associated Air Pollution by Satellite Mapping of Smoke Plumes and Ground-Level Monitoring. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:ijerph17218164. [PMID: 33167314 PMCID: PMC7663802 DOI: 10.3390/ijerph17218164] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Revised: 11/03/2020] [Accepted: 11/03/2020] [Indexed: 12/30/2022]
Abstract
Wildfires, which are becoming more frequent and intense in many countries, pose serious threats to human health. To determine health impacts and provide public health messaging, satellite-based smoke plume data are sometimes used as a proxy for directly measured particulate matter levels. We collected data on particulate matter <2.5 μm in diameter (PM2.5) concentration from 16 ground-level monitoring stations in the San Francisco Bay Area and smoke plume density from satellite imagery for the 2017–2018 California wildfire seasons. We tested for trends and calculated bootstrapped differences in the median PM2.5 concentrations by plume density category on a 0–3 scale. The median PM2.5 concentrations for categories 0, 1, 2, and 3 were 16, 22, 25, and 63 μg/m3, respectively, and there was much variability in PM2.5 concentrations within each category. A case study of the Camp Fire illustrates that in San Francisco, PM2.5 concentrations reached their maximum many days after the peak for plume density scores. We found that air pollution characterization by satellite imagery did not precisely align with ground-level PM2.5 concentrations. Public health practitioners should recognize the need to combine multiple sources of data regarding smoke patterns when developing public guidance to limit the health effects of wildfire smoke.
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