Translational data analytics in exposure science and environmental health: a citizen science approach with high school students
Abstract 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...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2020-07-01
|
| Series: | Environmental Health |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12940-020-00627-5 |
| _version_ | 1828473525509292032 |
|---|---|
| author | Ayaz Hyder Andrew A. May |
| author_facet | Ayaz Hyder Andrew A. May |
| author_sort | Ayaz Hyder |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-12-11T05:45:46Z |
| format | Article |
| id | doaj.art-3cb60ac1cf26418c91f79741927c748b |
| institution | Directory Open Access Journal |
| issn | 1476-069X |
| language | English |
| last_indexed | 2024-12-11T05:45:46Z |
| publishDate | 2020-07-01 |
| publisher | BMC |
| record_format | Article |
| series | Environmental Health |
| spelling | doaj.art-3cb60ac1cf26418c91f79741927c748b2022-12-22T01:18:58ZengBMCEnvironmental Health1476-069X2020-07-0119111210.1186/s12940-020-00627-5Translational data analytics in exposure science and environmental health: a citizen science approach with high school studentsAyaz Hyder0Andrew A. May1Division of Environmental Health Sciences, College of Public Health, The Ohio State UniversityDepartment of Civil, Environmental and Geodetic Engineering, College of Engineering, The Ohio State UniversityAbstract 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.http://link.springer.com/article/10.1186/s12940-020-00627-5Air pollutionLow-cost sensorsCitizen scienceTranslational data analytics |
| spellingShingle | Ayaz Hyder Andrew A. May Translational data analytics in exposure science and environmental health: a citizen science approach with high school students Environmental Health Air pollution Low-cost sensors Citizen science Translational data analytics |
| title | Translational data analytics in exposure science and environmental health: a citizen science approach with high school students |
| title_full | Translational data analytics in exposure science and environmental health: a citizen science approach with high school students |
| title_fullStr | Translational data analytics in exposure science and environmental health: a citizen science approach with high school students |
| title_full_unstemmed | Translational data analytics in exposure science and environmental health: a citizen science approach with high school students |
| title_short | Translational data analytics in exposure science and environmental health: a citizen science approach with high school students |
| title_sort | translational data analytics in exposure science and environmental health a citizen science approach with high school students |
| topic | Air pollution Low-cost sensors Citizen science Translational data analytics |
| url | http://link.springer.com/article/10.1186/s12940-020-00627-5 |
| work_keys_str_mv | AT ayazhyder translationaldataanalyticsinexposurescienceandenvironmentalhealthacitizenscienceapproachwithhighschoolstudents AT andrewamay translationaldataanalyticsinexposurescienceandenvironmentalhealthacitizenscienceapproachwithhighschoolstudents |