Translational data analytics in exposure science and environmental health: a citizen science approach with high school students.

Background: Translational data analytics aims to apply data analytics principles and techniques to bring about broader societal or human impact. Translational data analytics for environmental health is an emerging discipline and the objective of this study is to describe a real-world example of this...

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Publicado en:Environmental Health: A Global Access Science Source Vol. 19; no. 1; pp. 1 - 13
Autores principales: Hyder, Ayaz, May, Andrew A.
Formato: research Journal Article
Publicado: BioMed Central 7/1/2020
Acceso en línea:Ver este registro en EBSCOhost
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      dt: 7/1/2020
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      pub: BioMed Central
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        atl: Translational data analytics in exposure science and environmental health: a citizen science approach with high school students.
      aug:
        au:
          Hyder, Ayaz
          May, Andrew A.
        affil: Division of Environmental Health Sciences, College of Public Health, The Ohio State University, 1841 Neil Ave., Cunz Hall, Room 380D, 43210, Columbus, OH, USA
      sug:
        subj:
          Environmental Exposure
          Environmental Health Methods
          Adolescence
          Students
          Schools
          Air Pollution Analysis
          Human
          Environmental Monitoring Methods
          Validation Studies
          Comparative Studies
          Evaluation Research
          Multicenter Studies
          Arthritis Impact Measurement Scales
          Ferrans and Powers Quality of Life Index
          Impact of Events Scale
          Scales
          Adolescent: 13-18 years
      ab: Background: Translational data analytics aims to apply data analytics principles and techniques to bring about broader societal or human impact. Translational data analytics for environmental health is an emerging discipline and the objective of this study is to describe a real-world example of this emerging discipline.Methods: We implemented a citizen-science project at a local high school. Multiple cohorts of citizen scientists, who were students, fabricated and deployed low-cost air quality sensors. A cloud-computing solution provided real-time air quality data for risk screening purposes, data analytics and curricular activities.Results: The citizen-science project engaged with 14 high school students over a four-year period that is continuing to this day. The project led to the development of a website that displayed sensor-based measurements in local neighborhoods and a GitHub-like repository for open source code and instructions. Preliminary results showed a reasonable comparison between sensor-based and EPA land-based federal reference monitor data for CO and NOx.Conclusions: Initial sensor-based data collection efforts showed reasonable agreement with land-based federal reference monitors but more work needs to be done to validate these results. Lessons learned were: 1) the need for sustained funding because citizen science-based project timelines are a function of community needs/capacity and building interdisciplinary rapport in academic settings and 2) the need for a dedicated staff to manage academic-community relationships.
      pubtype: Academic Journal
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        research
        Journal Article
      ougenre: Article
    language: English
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