Citizen monitoring during hazards: validation of Fukushima radiation measurements
- 266 Downloads
Citizen-led movements producing scientific hazard data during disasters are increasingly common. After the Japanese earthquake-triggered tsunami in 2011, and the resulting radioactive releases at the damaged Fukushima Daiichi nuclear power plants, citizens monitored on-ground levels of radiation with innovative mobile devices built from off-the-shelf components. To date, the citizen-led Safecast project has recorded 50 million radiation measurements worldwide, with the majority of these measurements from Japan. The analysis of data which are multi-dimensional, not vetted, and provided from multiple devices presents big data challenges due to their volume, velocity, variety, and veracity. While the Safecast project produced massive open-source radiation measurements at specific coordinates and times, the reliability and validity of the overall data have not yet been assessed. The nuclear disaster at the Fukushima Daiichi nuclear-power plant provides a case for assessing the Safecast data with official aerial remote sensing radiation data jointly collected by the governments of the United States and Japan. This study spatially analyzes and statistically compares the citizen-volunteered and government-generated radiation data. An assessment of the Safecast dataset requires several preprocessing steps. First, it was necessary to convert the data from the Safecast ionized radiation sensors since they were collected using different units of measure than the government data. Secondly, the normally occurring radiation and decay rates of cesium from deposition surveys were used to properly compare measurements in space and time. Finally, the GPS located points were selected within overlapping extents at multiple spatial resolutions. Quantitative measures were used to assess the similarity and differences in the observed measurements. Radiation measurements from the same geographic extents show similar spatial variations and statistically significant correlations. The results suggest that? actionable scientific data for disasters and emergencies can be inferred from non-traditional and not vetted data generated through citizen science projects. This project provides a methodology for comparing datasets of radiological measurements over time and space. Integrating data for assessment from different Earth sensing systems is paramount for societal and environmental problems.
KeywordsVolunteered geographic information Citizen science Environmental monitoring Fukushima Radiation Hazards
This research was partially funded by ONR grants N00014-13-1-0784 and N00014-14-1-0208. This research was also partially supported by the National Science Foundation under IGERT Grant DGE-1144860, Big Data Social Science.
- Abe, Y. (Feb. 2014). Safecast or the production of collective intelligence on radiation risks after 3.11 Yasuhiko Abe. The Asia-Pacific Journal, 12 (5), 1–10.Google Scholar
- AIST. (2007). Geological survey of Japan. https://gbank.gsj.jp/geochemmap/data/download.htm
- Bander, T. J. (1982). PAVAN : An atmospheric-dispersion program for evaluating design-basis accidental releases of radioactive materials from nuclear power stations. Technical report, Pacific Northwest Laboratory and US Nuclear Regulatory Commission.Google Scholar
- Bonner, S., Brown, A., & Cheung, A., (March 2015). The safecast report. http://blog.safecast.org/2015/03/the-safecast-report/
- Brown, A. (Jan. 2014). Fukushima across the Pacific. http://blog.safecast.org/2014/01/fukushima-across-the-pacific/
- Brown, A., Franken, P., & Bonner, S. (March 2016a). The safecast report. http://www.slideshare.net/safecast/safecast-report-2016-final01print
- Cervone, G., & Franzese, P. (2014). Source term estimation for the 2011 Fukushima nuclear accident. In: G. Cervone, J. Lin, N. Waters (Eds.), Data mining for geoinformatics: Methods and applications (pp. 49–64) Springer New York.Google Scholar
- Creative Commons. (December 2015). Cc0 1.0 universal (cc0 1.0) public domain dedication. http://creativecommons.org/publicdomain/zero/1.0/
- Department of Energy. (2011). US DOE/NNSA response to 2011 Fukushima incident- raw aerial data and extracted ground exposure rates and cesium deposition. https://catalog.data.gov/dataset/us-doe-nnsa-response-to-2011-fukushima-incident-raw-aerial-data-and-extracted-ground-expos-20e73
- FEMA. (1996). Federal Emergency Management Agency (FEMA), US Department of Homeland Security, and United States of America, Guide for all-hazard emergency operations planning, September 1996. https://www.fema.gov/pdf/plan/0-prelim.pdf.
- Franken, P. (2014). Volunteers crowdsource radiation monitoring to map potential risk on every street in Japan, Democracy Now!, January 17, 2014. https://www.democracynow.org/2014/1/17/volunteers_crowdsource_radiation_monitoring_to_map.
- Idogawa, K. (2014). Mayor of town that hosted Fukushima nuclear plant says he was told: “no accident could ever happen”, Democracy Now!, January 17, 2014. https://www.democracynow.org/2014/1/17/mayor_of_town_that_hosted_fukushima.
- Japan Atomic Energy Agency. (2014). Airborne monitoring in the distribution survey of radioactive substances. http://emdb.jaea.go.jp/emdb/en/portals/b224/
- Japanese Nuclear Regulation Authority. (2014). Monitoring information of environmental radioactivity level. http://radioactivity.nsr.go.jp/en/
- Langley, S. A. (2014). Science in the digital age: Overcoming uncertainty and the adoption of volunteered geographic information for science. Ph.D. thesis, Michigan State University.Google Scholar
- Lyons, C. (2011). DOE/NNSA Aerial Measuring System (AMS): Flying the ‘Real’ thing. Technical report, Nevada Test Site/National Security Technologies, USDOE National Nuclear Security Administration (NNSA), Palm Beach, FL. http://www.osti.gov/scitech/biblio/1054702/
- Meybatyan, S. (2014). Nuclear disasters and displacement. Forced Migration Review, (45), 63–66.Google Scholar
- Moran, A., Gadepally, V., Hubbell, M., & Kepner, J. (2015). Improving big data visual analytics with interactive virtual reality. In 2015 IEEE high performance extreme computing conference (HPEC 15), pp. 0–5.Google Scholar
- Povinec, P. P., Hirose, K., & Aoyama, M. (2013). 3-Fukushima accident. In Fukushima accident (pp. 55–102). Boston: Elsevier.Google Scholar
- Safecast. (December 2015a). About calibration and the bgeigie nano. http://blog.safecast.org/faq/about-calibration-and-the-bgeigie-nano/
- Safecast. (December 2015b). Nano operation manual. https://github.com/Safecast/bGeigieNanoKit/wiki/Nano-Operation-Manual
- Safecast. (March 2015c). Safecast history. http://blog.safecast.org/faq/data/
- Safecast Real Time Radiation Monitoring (2016). Real time radiation monitoring. http://realtime.safecast.org/
- Snell, W. G., & Jubach, R. W., (1981). Atmospheric dispersion models for potential accident consequence assessments at nuclear power plants. Technical report, NUS Corporation and US Nuclear Regulatory Commission. http://pbadupws.nrc.gov/docs/ML1204/ML12045A197.pdf
- Sugiyama, G., Nassstrom, J., Foster, K., Pobanz, B., Vogt, P., Aluzzi, F., & Homann, S. (2012). National atmospheric release advisory center dispersion modeling during the Fukushima Daiichi nuclear power plant accident. In: NIRS symposium on reconstruction of early internal dose due to the TEPCO Fukushima Daiichi nuclear power station accident. Lawrence Livermore National Laboratory, pp. 1–12.Google Scholar
- Sui, D., Elwood, S., & Goodchild, M. (2013). Crowdsourcing geographic knowledge: Volunteered geographic information (VGI) in theory and practice 9789400745, 1–396.Google Scholar
- Terada, H., Katata, G., Chino, M., & Nagai, H. (Oct. 2012). Atmospheric discharge and dispersion of radionuclides during the Fukushima Dai-ichi Nuclear Power Plant accident. Part II: verification of the source term and analysis of regional-scale atmospheric dispersion. Journal of Environmental Radioactivity, 112, 141–154. http://www.sciencedirect.com/science/journal/0265931X/112/supp/C
- Tominski, C., Schumann, H., Andrienko, G., & Andrienko, N. (2012). Stacking-based visualization of trajectory attribute data. IEEE Transactions on Visualization and Computer Graphics, 18(12), 1–10.Google Scholar