Presented by Andreas Ikonomopoulos (NCSR-Demokritos) during the 2nd BDE SC5 workshop, 11 October 2016, in Brussels, Belgium

1. SECOND SC5 PILOT
IDENTIFYING THE
RELEASE LOCATIONRELEASE LOCATION
OF A SUBSTANCE
NCSR Demokritos11 October 2016

2. ContentContent
 Environment contamination
 The Chernobyl plumey p
 The Algeciras plume
 Th F k hi l The Fukushima plume
 Problem definition
 Leveraging BDE
 Issues to be explored Issues to be explored
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3. Environment contaminationEnvironment contamination
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4. DepositionDeposition
Wet deposition
Dry deposition
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5. Exposure pathwaysExposure pathways
Cloudshine
Inhalation
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Ingestion
Groundshine

6. FactorsFactors
 Climate and weather are important factors
affecting accident consequences
 In the absence of rain dry deposition takes place
o radioactive particles settle under the influence ofo radioactive particles settle under the influence of
gravity, wind and turbulence
 Snow and rain result Snow and rain result
in wet deposition
SSource:
http://www.irsn.fr/EN/publications/thematic/fukushima/
Documents/IRSN_Fukushima-Environment-
consequences_28022012.pdf

7. The Chernobyl plumeThe Chernobyl plume
 At 01:23 on 26/4/1986 a severe accident took
place at Chernobyl-4 nuclear power plant
 Atmospheric dispersion models were applied to the
137Cs atmospheric releasep
o The meteorological conditions in Europe following the
accident were reconstructedacc de we e eco s uc ed
Source: http://www.irsn.fr/EN/publications/thematic-safety/chernobyl/Pages/The-Chernobyl-Plume.aspx
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8. Source: http://www.europarl.europa.eu/news/en/news-room/20140514STO47018/forsmark-how-sweden-alerted-the-world-about-the-danger-of-chernobyl-disaster
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9. Source: http://www.europarl.europa.eu/news/en/news-room/20140514STO47018/forsmark-how-sweden-alerted-the-world-about-the-danger-of-chernobyl-disaster
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10. The Algeciras plumeThe Algeciras plume
HYSPLIT simulation of 137Cs dispersion between 0 and 500m altitude every 3 hours over a 3-day period.
Source: http://www-dase.cea.fr/public/dossiers_thematiques/modelisation_et_simulation_du_transport_atmospherique/description_en.html
p y y p
It is presumed that the source (red dot) emits 100 Bq between 00:00 and 03:00 on May 30, 1998.
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11. The Fukushima plumeThe Fukushima plume
 At 14:46 on 11/3/2011 the Tohoku-Chihou-
Taiheiyo-Oki earthquake rocked Fukushima Daiichi
nuclear power station
 An hour later a tsunami invaded the site
 Hydrogen explosions
E l ti di th F k hi D ii hi Explanations regarding the Fukushima Daiichi
accident consequences on the environment appear
h // i f /EN/ bli i / h iat: http://www.irsn.fr/EN/publications/thematic-
safety/fukushima/Pages/2-fukushima-
understanding-environment.aspx
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12. Issue at handIssue at hand
 Release of a hazardous substance in the
atmosphere
 Support the decision making process for
countermeasure takingg
 Estimation of consequences on:
h manso humans
o environment
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13. Problem statementProblem statement
 Identify the release location of a substance
 Available information:
o Measurements of the substance level at certain
locations
o Current weather conditions
o Past weather conditionso Past weather conditions
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14. Current approachesCurrent approaches
 Computational approach:
o work backwards – inverse modelling – from the current
atmospheric conditions to estimate source location
 Long computation times required, especially forg p q , p y
complex cases
o complicated topographyo complicated topography
o weather conditions
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15. Leveraging BDELeveraging BDE
 Use BDE to manage:
o A large number of pre-computed dispersion data
o Historical atmospheric conditions
 In this manner: In this manner:
o The computationally hardest part is pre-computed
M t hi t t h i diti t tho Matching current atmospheric conditions to the pre-
computed cases formulates a smaller problem
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16. The computational essenceThe computational essence
 Match current weather against a database of
historical atmospheric conditions by:
o Building upon the NetCDF data management and
searching tools created during the first SC5 pilot
o Encoding weather patterns onto maps
 Weather similarity  map/image similarity
o Developing operators that search 7 consecutive hourly
slices for similar weather
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17. Preliminary architecturePreliminary architecture
Transparent ingestion
and exporting of
NetCDF dataNetCDF data
W3C PROV
Analytics on climate data
SC5 NetCDF tools
SC7 image/map change
detection tools?
Apache Hive
Hadoop
MapReduce
Spark
Apache HDFS
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18. Open issuesOpen issues
 Open issues to be investigated during the pilot
implementation:
o What exactly does “similar enough weather” mean for
this pilot purposes?
o What volumes need to be pre-computed to always
have in the database “similar enough weather”?
o Are these volumes manageable and searchable?
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19. Questions?Questions?
 BigDataEurope Web site:
https://www.big-data-europe.eu
 Thank you for your attention!
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