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跨国多传感器个人暴露研究数据的协调和可视化。

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

机构信息

Department of Environmental Sciences, Jožef Stefan Institute, 1000 Ljubljana, Slovenia.

Jožef Stefan International Postgraduate School, 1000 Ljubljana, Slovenia.

出版信息

Int J Environ Res Public Health. 2021 Nov 4;18(21):11614. doi: 10.3390/ijerph182111614.


DOI:10.3390/ijerph182111614
PMID:34770131
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8583633/
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.

摘要

使用多传感器方法可以让市民全面了解其周围环境的空气质量和个人对城市压力源的暴露情况。我们的工作是作为 ICARUS H2020 项目的一部分,该项目有来自七个欧洲城市的 600 多名参与者,我们讨论了多种多传感器数据流的数据融合和协调,以便为参与者提供全面且易于理解的报告。协调数据流时,我们发现传感器设备和协议存在问题,例如非均匀时间戳、数据空白、难以从商业设备中检索数据以及活动数据记录粗略等问题。我们的数据融合和协调过程允许我们自动化可视化和报告,从而为每个参与者提供详细的个性化报告。结果表明,一个关键的解决方案是简化代码并加快流程,这就需要在可视化数据方面做出某些妥协。经过深思熟虑的数据融合和协调多种多传感器数据流的过程,大大提高了研究参与者接收到的浓缩数据的质量和数量。尽管自动化大大加快了报告的制作速度,但强烈建议进行手动和结构化的双重检查。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/98d2/8583633/5221f99b2faf/ijerph-18-11614-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/98d2/8583633/b7babe918f79/ijerph-18-11614-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/98d2/8583633/4677ca69144b/ijerph-18-11614-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/98d2/8583633/8bd65d18673d/ijerph-18-11614-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/98d2/8583633/fd003adad23d/ijerph-18-11614-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/98d2/8583633/387793dcb441/ijerph-18-11614-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/98d2/8583633/7579c4b38a88/ijerph-18-11614-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/98d2/8583633/b6f4ce5f759c/ijerph-18-11614-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/98d2/8583633/5221f99b2faf/ijerph-18-11614-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/98d2/8583633/b7babe918f79/ijerph-18-11614-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/98d2/8583633/4677ca69144b/ijerph-18-11614-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/98d2/8583633/8bd65d18673d/ijerph-18-11614-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/98d2/8583633/fd003adad23d/ijerph-18-11614-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/98d2/8583633/387793dcb441/ijerph-18-11614-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/98d2/8583633/7579c4b38a88/ijerph-18-11614-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/98d2/8583633/b6f4ce5f759c/ijerph-18-11614-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/98d2/8583633/5221f99b2faf/ijerph-18-11614-g008.jpg

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引用本文的文献

[1]
Personal airborne particulate matter exposure and intake dose, indoor air quality, biometric, and activity dataset from the city of Ljubljana, Slovenia.

Data Brief. 2023-11-23

[2]
Empowering Participatory Research in Urban Health: Wearable Biometric and Environmental Sensors for Activity Recognition.

Sensors (Basel). 2023-12-18

[3]
Assessment of Individual-Level Exposure to Airborne Particulate Matter during Periods of Atmospheric Thermal Inversion.

Sensors (Basel). 2022-9-20

[4]
User-Centred Design of a Final Results Report for Participants in Multi-Sensor Personal Air Pollution Exposure Monitoring Campaigns.

Int J Environ Res Public Health. 2021-11-28

本文引用的文献

[1]
User-Centred Design of a Final Results Report for Participants in Multi-Sensor Personal Air Pollution Exposure Monitoring Campaigns.

Int J Environ Res Public Health. 2021-11-28

[2]
Urban population exposure to air pollution in Europe over the last decades.

Environ Sci Eur. 2021

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Int J Environ Res Public Health. 2020-9-9

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Environ Int. 2020-10

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Environ Int. 2020-6-23

[6]
Indoor Air Pollution, Related Human Diseases, and Recent Trends in the Control and Improvement of Indoor Air Quality.

Int J Environ Res Public Health. 2020-4-23

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Comparing Airborne Particulate Matter Intake Dose Assessment Models Using Low-Cost Portable Sensor Data.

Sensors (Basel). 2020-3-4

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BMJ. 2019-11-27

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Application of a Fusion Method for Gas and Particle Air Pollutants between Observational Data and Chemical Transport Model Simulations Over the Contiguous United States for 2005-2014.

Int J Environ Res Public Health. 2019-9-9

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