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Ladau J, Brodie EL, Falco N, Bansal I, Hoffman EB, Joachimiak MP, Mora AM, Walker AM, Wainwright HM, Wu Y, Pavicic M, Jacobson D, Hess M, Brown JB, Abuabara K. Estimating geographic variation of infection fatality ratios during epidemics. Infect Dis Model 2024; 9:634-643. [PMID: 38572058 PMCID: PMC10990719 DOI: 10.1016/j.idm.2024.02.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 02/10/2024] [Accepted: 02/16/2024] [Indexed: 04/05/2024] Open
Abstract
Objectives We aim to estimate geographic variability in total numbers of infections and infection fatality ratios (IFR; the number of deaths caused by an infection per 1,000 infected people) when the availability and quality of data on disease burden are limited during an epidemic. Methods We develop a noncentral hypergeometric framework that accounts for differential probabilities of positive tests and reflects the fact that symptomatic people are more likely to seek testing. We demonstrate the robustness, accuracy, and precision of this framework, and apply it to the United States (U.S.) COVID-19 pandemic to estimate county-level SARS-CoV-2 IFRs. Results The estimators for the numbers of infections and IFRs showed high accuracy and precision; for instance, when applied to simulated validation data sets, across counties, Pearson correlation coefficients between estimator means and true values were 0.996 and 0.928, respectively, and they showed strong robustness to model misspecification. Applying the county-level estimators to the real, unsimulated COVID-19 data spanning April 1, 2020 to September 30, 2020 from across the U.S., we found that IFRs varied from 0 to 44.69, with a standard deviation of 3.55 and a median of 2.14. Conclusions The proposed estimation framework can be used to identify geographic variation in IFRs across settings.
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Affiliation(s)
- Joshua Ladau
- Departments of Computational Precision Health and Dermatology, University of California, San Francisco, CA, 94115, USA
- Arva Intelligence, Inc., Salt Lake City, UT, 84101, USA
- Computational Biosciences Group, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Eoin L. Brodie
- Earth and Environmental Sciences Area, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Nicola Falco
- Earth and Environmental Sciences Area, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Ishan Bansal
- Computational Biosciences Group, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Elijah B. Hoffman
- Arva Intelligence, Inc., Salt Lake City, UT, 84101, USA
- Graduate Group in Biostatistics, University of California, Berkeley, CA, 94720, USA
| | - Marcin P. Joachimiak
- Biosystems Data Science, Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Ana M. Mora
- Center for Environmental Research and Community Health (CERCH), School of Public Health, University of California, Berkeley, CA, 94720, USA
| | - Angelica M. Walker
- Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee, Knoxville, TN, 37996, USA
| | - Haruko M. Wainwright
- Department of Nuclear Science and Engineering, Massachusetts Institute of Technology, Boston, MA, 02139, USA
| | - Yulun Wu
- Graduate Group in Biostatistics, University of California, Berkeley, CA, 94720, USA
| | - Mirko Pavicic
- Biosciences, Oak Ridge National Laboratory, Oak Ridge, TN, 37830, USA
| | - Daniel Jacobson
- Biosciences, Oak Ridge National Laboratory, Oak Ridge, TN, 37830, USA
| | | | - James B. Brown
- Arva Intelligence, Inc., Salt Lake City, UT, 84101, USA
- Computational Biosciences Group, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
- Statistics Department, University of California, Berkeley, CA, 94720, USA
| | - Katrina Abuabara
- Departments of Computational Precision Health and Dermatology, University of California, San Francisco, CA, 94115, USA
- Division of Epidemiology and Biostatistics, University of California Berkeley School of Public Health, 2121 Berkeley Way, Berkeley, CA, 94720, USA
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Ayoub A, Wainwright HM, Sansavini G, Gauntt R, Saito K. Resilient design in nuclear energy: Critical lessons from a cross-disciplinary analysis of the Fukushima Dai-ichi nuclear accident. iScience 2024; 27:109485. [PMID: 38571761 PMCID: PMC10987892 DOI: 10.1016/j.isci.2024.109485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 01/21/2024] [Accepted: 03/08/2024] [Indexed: 04/05/2024] Open
Abstract
This paper presents a multidisciplinary analysis of the Fukushima Dai-ichi Nuclear Power Plant accident. Along with the latest observations and simulation studies, we synthesize the time-series and event progressions during the accident across multiple disciplines, including in-plant physics and engineering systems, operators' actions, emergency responses, meteorology, radionuclide release and transport, land contamination, and health impacts. We identify three key factors that exacerbated the consequences of the accident: (1) the failure of Unit 2 containment venting, (2) the insufficient integration of radiation measurements and meteorology data in the evacuation strategy, and (3) the limited risk assessment and emergency preparedness. We conclude with new research and development directions to improve the resilience of nuclear energy systems and communities, including (1) meteorology-informed proactive venting, (2) machine learning-enabled adaptive evacuation zones, and (3) comprehensive risk-informed emergency planning while leveraging the experience from responses to other disasters.
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Affiliation(s)
- Ali Ayoub
- Department of Nuclear Science and Engineering, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
| | - Haruko M. Wainwright
- Department of Nuclear Science and Engineering, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
| | - Giovanni Sansavini
- Department of Mechanical and Process Engineering, ETH Zurich, Zurich, Switzerland
| | - Randall Gauntt
- Severe Accident Analysis Department, Sandia National Laboratories, Albuquerque, NM, USA
| | - Kimiaki Saito
- Fukushima Environmental Safety Center, Japan Atomic Energy Agency, Fukushima, Japan
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3
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Meray A, Sturla S, Siddiquee MR, Serata R, Uhlemann S, Gonzalez-Raymat H, Denham M, Upadhyay H, Lagos LE, Eddy-Dilek C, Wainwright HM. PyLEnM: A Machine Learning Framework for Long-Term Groundwater Contamination Monitoring Strategies. Environ Sci Technol 2022; 56:5973-5983. [PMID: 35427133 PMCID: PMC9069689 DOI: 10.1021/acs.est.1c07440] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Revised: 03/08/2022] [Accepted: 03/21/2022] [Indexed: 06/14/2023]
Abstract
In this study, we have developed a comprehensive machine learning (ML) framework for long-term groundwater contamination monitoring as the Python package PyLEnM (Python for Long-term Environmental Monitoring). PyLEnM aims to establish the seamless data-to-ML pipeline with various utility functions, such as quality assurance and quality control (QA/QC), coincident/colocated data identification, the automated ingestion and processing of publicly available spatial data layers, and novel data summarization/visualization. The key ML innovations include (1) time series/multianalyte clustering to find the well groups that have similar groundwater dynamics and to inform spatial interpolation and well optimization, (2) the automated model selection and parameter tuning, comparing multiple regression models for spatial interpolation, (3) the proxy-based spatial interpolation method by including spatial data layers or in situ measurable variables as predictors for contaminant concentrations and groundwater levels, and (4) the new well optimization algorithm to identify the most effective subset of wells for maintaining the spatial interpolation ability for long-term monitoring. We demonstrate our methodology using the monitoring data at the Savannah River Site F-Area. Through this open-source PyLEnM package, we aim to improve the transparency of data analytics at contaminated sites, empowering concerned citizens as well as improving public relations.
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Affiliation(s)
- Aurelien
O. Meray
- Applied
Research Center, Florida International University, 10555 W Flagler Street, Miami, Florida 33174, United States
| | - Savannah Sturla
- Department
of Environmental Science, Policy, and Management, University of California Berkeley, Mulford Hall, 2521 Hearst Avenue, Berkeley, California 94709, United States
| | - Masudur R. Siddiquee
- Applied
Research Center, Florida International University, 10555 W Flagler Street, Miami, Florida 33174, United States
| | - Rebecca Serata
- Department
of Civil and Environmental Engineering, University of California Berkeley, Davis Hall, 2521 Hearst Avenue, Berkeley, California 94709, United States
| | - Sebastian Uhlemann
- Climate
and Ecosystem Sciences Division, Lawrence
Berkeley National Laboratory, 1 Cyclotron Road, MS 74R-316C, Berkeley 94704, United States
| | - Hansell Gonzalez-Raymat
- Savannah
River National Laboratory, Savannah River Site, Aiken, South Carolina 29808, United States
| | - Miles Denham
- Panoramic
Environmental Consulting, LLC, P.O. Box
906, Aiken, South Carolina 29802, United States
| | - Himanshu Upadhyay
- Applied
Research Center, Florida International University, 10555 W Flagler Street, Miami, Florida 33174, United States
| | - Leonel E. Lagos
- Applied
Research Center, Florida International University, 10555 W Flagler Street, Miami, Florida 33174, United States
| | - Carol Eddy-Dilek
- Savannah
River National Laboratory, Savannah River Site, Aiken, South Carolina 29808, United States
| | - Haruko M. Wainwright
- Climate
and Ecosystem Sciences Division, Lawrence
Berkeley National Laboratory, 1 Cyclotron Road, MS 74R-316C, Berkeley 94704, United States
- Department
of Nuclear Science & Engineering, Massachusetts
Institute of Technology, Cambridge, Massachusetts 02139, USA
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4
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Denham ME, Amidon MB, Wainwright HM, Dafflon B, Ajo-Franklin J, Eddy-Dilek CA. Improving Long-term Monitoring of Contaminated Groundwater at Sites where Attenuation-based Remedies are Deployed. Environ Manage 2020; 66:1142-1161. [PMID: 33098454 DOI: 10.1007/s00267-020-01376-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Accepted: 10/05/2020] [Indexed: 06/11/2023]
Abstract
This study presents an effective approach to tackle the challenge of long-term monitoring of contaminated groundwater sites where remediation leaves residual contamination in the subsurface. Traditional long-term monitoring of contaminated groundwater sites focuses on measuring contaminant concentrations and is applicable to sites where contaminant mass is removed or degraded to a level below the regulatory standard. The traditional approach is less effective at sites where risk from metals or radionuclides continues to exist in the subsurface after remedial goals are achieved. We propose a long-term monitoring strategy for this type of waste site that focuses on measuring the hydrological and geochemical parameters that control attenuation or remobilization of contaminants while de-emphasizing contaminant-concentration measurements. We demonstrate how this approach would be more effective than traditional long-term monitoring, using a site in South Carolina, USA, where groundwater is contaminated by several radionuclides. A comprehensive enhanced attenuation remedy has been implemented at the site to minimize discharge of contamination to surface water. The immobilization of contaminants occurs in three locations by manipulation of hydrological and geochemical parameters, as well as by natural attenuation processes. Deployment of our proposed long-term monitoring strategy will combine subsurface and surface measurements using spectroscopic tools, geophysical tools, and sensors to monitor the parameters controlling contaminant attenuation. The advantage of this approach is that it will detect the potential for contaminant remobilization from engineered and natural attenuation zones, allowing potential adverse changes to be mitigated before contaminant attenuation is reversed.
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Affiliation(s)
- Miles E Denham
- Panoramic Environmental Consulting, LLC, Aiken, SC, USA.
| | | | | | | | - Jonathan Ajo-Franklin
- Lawrence Berkeley National Laboratory, Berkeley, CA, USA
- Rice University, Houston, TX, USA
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5
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Sun D, Wainwright HM, Oroza CA, Seki A, Mikami S, Takemiya H, Saito K. Optimizing long-term monitoring of radiation air-dose rates after the Fukushima Daiichi Nuclear Power Plant. J Environ Radioact 2020; 220-221:106281. [PMID: 32560882 DOI: 10.1016/j.jenvrad.2020.106281] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2020] [Revised: 03/30/2020] [Accepted: 04/26/2020] [Indexed: 06/11/2023]
Abstract
Radiation air dose rates near the Fukushima Daiichi Nuclear Power Plant (FDNPP) have been steadily decreasing over the past eight years since the release of radioactive elements in March 2011. Currently, the radiation monitoring program is expected to transition to long-term monitoring after most of the remediation activities are completed. The main long-term monitoring objectives are to (1) confirm the continuing reduction of contaminant and hazard levels, (2) provide assurance for the public, (3) accumulate the basic datasets for scientific knowledge and future preparation, and (4) detect changes or anomalies in contaminant mobility (if they occur), or any unexpected processes or events. In this work, we have developed a methodology for optimizing the monitoring locations of radiation air dose-rate monitoring. Our approach consists of three steps in order to determine monitoring locations in a systematic manner: (1) prioritizing the critical locations, such as schools or regulatory requirement locations, (2) diversifying locations that cover the key environmental controls that are known to influence contaminant mobility and distributions, and (3) capturing the heterogeneity of radiation air-dose rates across the domain. For the second step, we use a Gaussian mixture model to identify the representative locations among multiple environmental variables, such as elevation and land-cover types. For the third step, we use a Gaussian process model to capture and estimate the heterogeneity of air-dose rates across the domain. Employing an integrated dose-rate map derived from Bayesian geostatistical methods as a reference map, we distribute the monitoring locations in such a way as to capture the heterogeneity of the reference map. Our results have shown that this approach allows us to select monitoring locations in a systematic manner such that the heterogeneity of air dose rates is captured by the minimal number of monitoring locations.
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Affiliation(s)
- Dajie Sun
- Department of Nuclear Engineering, University of California, Berkeley, CA, USA
| | - Haruko M Wainwright
- Earth Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA; Department of Nuclear Engineering, University of California, Berkeley, CA, USA.
| | - Carlos A Oroza
- Department of Civil and Environmental Engineering, University of Utah, Salt Lake City, UT, USA
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6
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Wainwright HM, Seki A, Mikami S, Saito K. Characterizing regional-scale temporal evolution of air dose rates after the Fukushima Daiichi Nuclear Power Plant accident. J Environ Radioact 2019; 210:105808. [PMID: 30337102 DOI: 10.1016/j.jenvrad.2018.09.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2017] [Revised: 03/25/2018] [Accepted: 04/08/2018] [Indexed: 06/08/2023]
Abstract
In this study, we quantify the temporal changes of air dose rates in the regional scale around the Fukushima Dai-ichi Nuclear Power Plant in Japan, and predict the spatial distribution of air dose rates in the future. We first apply the Bayesian geostatistical method developed by Wainwright et al. (2017) to integrate multiscale datasets including ground-based walk and car surveys, and airborne surveys, all of which have different scales, resolutions, spatial coverage, and accuracy. This method is based on geostatistics to represent spatial heterogeneous structures, and also on Bayesian hierarchical models to integrate multiscale, multi-type datasets in a consistent manner. We apply this method to the datasets from three years: 2014 to 2016. The temporal changes among the three integrated maps enables us to characterize the spatiotemporal dynamics of radiation air dose rates. The data-driven ecological decay model is then coupled with the integrated map to predict future dose rates. Results show that the air dose rates are decreasing consistently across the region. While slower in the forested region, the decrease is particularly significant in the town area. The decontamination has contributed to significant reduction of air dose rates. By 2026, the air dose rates will continue to decrease, and the area above 3.8 μSv/h will be almost fully contained within the non-residential forested zone.
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Affiliation(s)
- Haruko M Wainwright
- Earth Sciences Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, MS 74R-316C, Berkeley, CA 94720-8126, USA.
| | - Akiyuki Seki
- Japan Atomic Energy Agency, 2-4 Shirakata, Tokai-mura, Naka-gun, Ibaraki 319-1195, Japan.
| | - Satoshi Mikami
- Japan Atomic Energy Agency, 7-1 Omachi, Taira, Iwaki-shi, Fukushima 970-8026, Japan.
| | - Kimiaki Saito
- Japan Atomic Energy Agency, 2-2-2 Uchisawai-cho, Chiyoda, Tokyo, 100-0011, Japan.
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7
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Arora B, Wainwright HM, Dwivedi D, Vaughn LJS, Curtis JB, Torn MS, Dafflon B, Hubbard SS. Evaluating temporal controls on greenhouse gas (GHG) fluxes in an Arctic tundra environment: An entropy-based approach. Sci Total Environ 2019; 649:284-299. [PMID: 30173035 DOI: 10.1016/j.scitotenv.2018.08.251] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2018] [Revised: 07/23/2018] [Accepted: 08/19/2018] [Indexed: 06/08/2023]
Abstract
There is significant spatial and temporal variability associated with greenhouse gas (GHG) fluxes in high-latitude Arctic tundra environments. The objectives of this study are to investigate temporal variability in CO2 and CH4 fluxes at Barrow, AK and to determine the factors causing this variability using a novel entropy-based classification scheme. In particular, we analyzed which geomorphic, soil, vegetation and climatic properties most explained the variability in GHG fluxes (opaque chamber measurements) during the growing season over three successive years. Results indicate that multi-year variability in CO2 fluxes was primarily associated with soil temperature variability as well as vegetation dynamics during the early and late growing season. Temporal variability in CH4 fluxes was primarily associated with changes in vegetation during the growing season and its interactions with primary controls like seasonal thaw. Polygonal ground features, which are common to Arctic regions, also demonstrated significant multi-year variability in GHG fluxes. Our results can be used to prioritize field sampling strategies, with an emphasis on measurements collected at locations and times that explain the most variability in GHG fluxes. For example, we found that sampling primary environmental controls at the centers of high centered polygons in the month of September (when freeze-back period begins) can provide significant constraints on GHG flux variability - a requirement for accurately predicting future changes to GHG fluxes. Overall, entropy results document the impact of changing environmental conditions (e.g., warming, growing season length) on GHG fluxes, thus providing clues concerning the manner in which ecosystem properties may be shifted regionally in a future climate.
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Affiliation(s)
- Bhavna Arora
- Lawrence Berkeley National Laboratory, Berkeley, United States of America.
| | | | - Dipankar Dwivedi
- Lawrence Berkeley National Laboratory, Berkeley, United States of America
| | - Lydia J S Vaughn
- Lawrence Berkeley National Laboratory, Berkeley, United States of America
| | - John B Curtis
- University of Colorado, Boulder, United States of America
| | - Margaret S Torn
- Lawrence Berkeley National Laboratory, Berkeley, United States of America
| | - Baptiste Dafflon
- Lawrence Berkeley National Laboratory, Berkeley, United States of America
| | - Susan S Hubbard
- Lawrence Berkeley National Laboratory, Berkeley, United States of America
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8
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Maco B, Bardos P, Coulon F, Erickson-Mulanax E, Hansen LJ, Harclerode M, Hou D, Mielbrecht E, Wainwright HM, Yasutaka T, Wick WD. Resilient remediation: Addressing extreme weather and climate change, creating community value. ACTA ACUST UNITED AC 2018. [DOI: 10.1002/rem.21585] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Barbara Maco
- U.S. Sustainable Remediation Forum; Piedmont California
| | | | | | | | | | | | - Deyi Hou
- School of Environment; Tsinghua University; Beijing China
| | | | | | - Tetsuo Yasutaka
- National Institute of Advanced Industrial Science and Technology; Tsukuba Japan
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9
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Wainwright HM, Seki A, Mikami S, Saito K. Characterizing regional-scale temporal evolution of air dose rates after the Fukushima Daiichi Nuclear Power Plant accident. J Environ Radioact 2018; 189:213-220. [PMID: 29702453 DOI: 10.1016/j.jenvrad.2018.04.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2017] [Revised: 03/25/2018] [Accepted: 04/08/2018] [Indexed: 06/08/2023]
Abstract
In this study, we quantify the temporal changes of air dose rates in the regional scale around the Fukushima Dai-ichi Nuclear Power Plant in Japan, and predict the spatial distribution of air dose rates in the future. We first apply the Bayesian geostatistical method developed by Wainwright et al. (2017) to integrate multiscale datasets including ground-based walk and car surveys, and airborne surveys, all of which have different scales, resolutions, spatial coverage, and accuracy. This method is based on geostatistics to represent spatial heterogeneous structures, and also on Bayesian hierarchical models to integrate multiscale, multi-type datasets in a consistent manner. We apply this method to the datasets from three years: 2014 to 2016. The temporal changes among the three integrated maps enables us to characterize the spatiotemporal dynamics of radiation air dose rates. The data-driven ecological decay model is then coupled with the integrated map to predict future dose rates. Results show that the air dose rates are decreasing consistently across the region. While slower in the forested region, the decrease is particularly significant in the town area. The decontamination has contributed to significant reduction of air dose rates. By 2026, the air dose rates will continue to decrease, and the area above 3.8 μSv/h will be almost fully contained within the non-residential forested zone.
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Affiliation(s)
- Haruko M Wainwright
- Earth Sciences Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, MS 74R-316C, Berkeley, CA 94720-8126, USA.
| | - Akiyuki Seki
- Japan Atomic Energy Agency, 2-4 Shirakata, Tokai-mura, Naka-gun, Ibaraki 319-1195, Japan.
| | - Satoshi Mikami
- Japan Atomic Energy Agency, 7-1 Omachi, Taira, Iwaki-shi, Fukushima 970-8026, Japan.
| | - Kimiaki Saito
- Japan Atomic Energy Agency, 2-2-2 Uchisawai-cho, Chiyoda, Tokyo, 100-0011, Japan.
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10
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Schmidt F, Wainwright HM, Faybishenko B, Denham M, Eddy-Dilek C. In Situ Monitoring of Groundwater Contamination Using the Kalman Filter. Environ Sci Technol 2018; 52:7418-7425. [PMID: 29932644 DOI: 10.1021/acs.est.8b00017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
This study presents a Kalman filter-based framework to establish a real-time in situ monitoring system for groundwater contamination based on in situ measurable water quality variables, such as specific conductance (SC) and pH. First, this framework uses principal component analysis (PCA) to identify correlations between the contaminant concentrations of interest and in situ measurable variables. It then applies the Kalman filter to estimate contaminant concentrations continuously and in real-time by coupling data-driven concentration-decay models with the previously identified data correlations. We demonstrate our approach with historical groundwater data from the Savannah River Site F-Area: We use SC and pH data to estimate tritium and uranium concentrations over time. Results show that the developed method can estimate these contaminant concentrations based on in situ measurable variables. The estimates remain reliable with less frequent or no direct measurements of the contaminant concentrations, while capturing the dynamics of short- and long-term contaminant concentration changes. In addition, we show that data mining, such as PCA, is useful to understand correlations in groundwater data and to design long-term monitoring systems. The developed in situ monitoring methodology is expected to improve long-term groundwater monitoring by continuously confirming the contaminant plume's stability and by providing an early warning system for unexpected changes in the plume's migration.
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Affiliation(s)
- Franziska Schmidt
- Department of Nuclear Engineering , University of California Berkeley , Etcheverry Hall, 2521 Hearst Avenue , Berkeley , California 94709 , United States
| | - Haruko M Wainwright
- Climate and Ecosystem Sciences Division , Lawrence Berkeley National Laboratory , 1 Cyclotron Road, MS 74R-316C , Berkeley , California 94720-8126 , United States
| | - Boris Faybishenko
- Energy Geosciences Division , Lawrence Berkeley National Laboratory , 1 Cyclotron Road , Berkeley , California 94720-8126 , United States
| | - Miles Denham
- Panoramic Environmental Consulting, LLC, P.O. Box 906, Aiken , South Carolina 29802 , United States
| | - Carol Eddy-Dilek
- Savannah River National Laboratory, Savannah River Site, Aiken , South Carolina 29808 , United States
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11
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Arora B, Davis JA, Spycher NF, Dong W, Wainwright HM. Comparison of Electrostatic and Non-Electrostatic Models for U(VI) Sorption on Aquifer Sediments. Ground Water 2018; 56:73-86. [PMID: 28683163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Received: 12/01/2016] [Revised: 04/28/2017] [Accepted: 05/29/2017] [Indexed: 06/07/2023]
Abstract
A non-electrostatic generalized composite surface complexation model (SCM) was developed for U(VI) sorption on contaminated F-Area sediments from the U.S. Department of Energy Savannah River Site, South Carolina. The objective of this study was to test if a simpler, semi-empirical, non-electrostatic U(VI) sorption model (NEM) could achieve the same predictive performance as a SCM with electrostatic correction terms in describing U(VI) plume evolution and long-term mobility. One-dimensional reactive transport simulations considering key hydrodynamic processes, Al and Fe minerals, as well as H+ and U surface complexation, with and without electrostatic correction terms, were conducted. The NEM was first calibrated with laboratory batch H+ and U(VI) sorption data on F-Area sediments, and then the surface area of the NEM was adjusted to match field observations of dissolved U(VI). Modeling results indicate that the calibrated NEM was able to perform as well as the previously developed electrostatic model in predicting the long-term evolution of H+ and U(VI) at the site, given the variability of field-site data. The electrostatic and NEM models yield somewhat different results for the time period when basin discharge was active; however, it is not clear which modeling approach may be better to model this early time period because groundwater quality data during this period were not available. A key finding of this study is that the applicability of NEM (and thus robustness of its predictions) to the field system evolves with time and is strongly dependent on the pH range that was used to develop the model.
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Affiliation(s)
| | - James A Davis
- Climate & Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Rd., Berkeley, CA, 94720
| | - Nicolas F Spycher
- Energy Geosciences Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Rd., Berkeley, CA, 94720
| | - Wenming Dong
- Energy Geosciences Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Rd., Berkeley, CA, 94720
| | - Haruko M Wainwright
- Climate & Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Rd., Berkeley, CA, 94720
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12
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Yabusaki SB, Wilkins MJ, Fang Y, Williams KH, Arora B, Bargar J, Beller HR, Bouskill NJ, Brodie EL, Christensen JN, Conrad ME, Danczak RE, King E, Soltanian MR, Spycher NF, Steefel CI, Tokunaga TK, Versteeg R, Waichler SR, Wainwright HM. Water Table Dynamics and Biogeochemical Cycling in a Shallow, Variably-Saturated Floodplain. Environ Sci Technol 2017; 51:3307-3317. [PMID: 28218533 DOI: 10.1021/acs.est.6b04873] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Three-dimensional variably saturated flow and multicomponent biogeochemical reactive transport modeling, based on published and newly generated data, is used to better understand the interplay of hydrology, geochemistry, and biology controlling the cycling of carbon, nitrogen, oxygen, iron, sulfur, and uranium in a shallow floodplain. In this system, aerobic respiration generally maintains anoxic groundwater below an oxic vadose zone until seasonal snowmelt-driven water table peaking transports dissolved oxygen (DO) and nitrate from the vadose zone into the alluvial aquifer. The response to this perturbation is localized due to distinct physico-biogeochemical environments and relatively long time scales for transport through the floodplain aquifer and vadose zone. Naturally reduced zones (NRZs) containing sediments higher in organic matter, iron sulfides, and non-crystalline U(IV) rapidly consume DO and nitrate to maintain anoxic conditions, yielding Fe(II) from FeS oxidative dissolution, nitrite from denitrification, and U(VI) from nitrite-promoted U(IV) oxidation. Redox cycling is a key factor for sustaining the observed aquifer behaviors despite continuous oxygen influx and the annual hydrologically induced oxidation event. Depth-dependent activity of fermenters, aerobes, nitrate reducers, sulfate reducers, and chemolithoautotrophs (e.g., oxidizing Fe(II), S compounds, and ammonium) is linked to the presence of DO, which has higher concentrations near the water table.
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Affiliation(s)
- Steven B Yabusaki
- Pacific Northwest National Laboratory , Richland, Washington 99354, United States
| | | | - Yilin Fang
- Pacific Northwest National Laboratory , Richland, Washington 99354, United States
| | - Kenneth H Williams
- Lawrence Berkeley National Laboratory , Berkeley, California 94720, United States
| | - Bhavna Arora
- Lawrence Berkeley National Laboratory , Berkeley, California 94720, United States
| | - John Bargar
- Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory , Menlo Park, California 94025, United States
| | - Harry R Beller
- Lawrence Berkeley National Laboratory , Berkeley, California 94720, United States
| | - Nicholas J Bouskill
- Lawrence Berkeley National Laboratory , Berkeley, California 94720, United States
| | - Eoin L Brodie
- Lawrence Berkeley National Laboratory , Berkeley, California 94720, United States
| | - John N Christensen
- Lawrence Berkeley National Laboratory , Berkeley, California 94720, United States
| | - Mark E Conrad
- Lawrence Berkeley National Laboratory , Berkeley, California 94720, United States
| | | | - Eric King
- Lawrence Berkeley National Laboratory , Berkeley, California 94720, United States
| | | | - Nicolas F Spycher
- Lawrence Berkeley National Laboratory , Berkeley, California 94720, United States
| | - Carl I Steefel
- Lawrence Berkeley National Laboratory , Berkeley, California 94720, United States
| | - Tetsu K Tokunaga
- Lawrence Berkeley National Laboratory , Berkeley, California 94720, United States
| | - Roelof Versteeg
- Subsurface Insights , Hanover, New Hampshire 03755, United States
| | - Scott R Waichler
- Pacific Northwest National Laboratory , Richland, Washington 99354, United States
| | - Haruko M Wainwright
- Lawrence Berkeley National Laboratory , Berkeley, California 94720, United States
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Wainwright HM, Seki A, Chen J, Saito K. A multiscale Bayesian data integration approach for mapping air dose rates around the Fukushima Daiichi Nuclear Power Plant. J Environ Radioact 2017; 167:62-69. [PMID: 27939095 DOI: 10.1016/j.jenvrad.2016.11.033] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/24/2016] [Revised: 11/27/2016] [Accepted: 11/28/2016] [Indexed: 06/06/2023]
Abstract
This paper presents a multiscale data integration method to estimate the spatial distribution of air dose rates in the regional scale around the Fukushima Daiichi Nuclear Power Plant. We integrate various types of datasets, such as ground-based walk and car surveys, and airborne surveys, all of which have different scales, resolutions, spatial coverage, and accuracy. This method is based on geostatistics to represent spatial heterogeneous structures, and also on Bayesian hierarchical models to integrate multiscale, multi-type datasets in a consistent manner. The Bayesian method allows us to quantify the uncertainty in the estimates, and to provide the confidence intervals that are critical for robust decision-making. Although this approach is primarily data-driven, it has great flexibility to include mechanistic models for representing radiation transport or other complex correlations. We demonstrate our approach using three types of datasets collected at the same time over Fukushima City in Japan: (1) coarse-resolution airborne surveys covering the entire area, (2) car surveys along major roads, and (3) walk surveys in multiple neighborhoods. Results show that the method can successfully integrate three types of datasets and create an integrated map (including the confidence intervals) of air dose rates over the domain in high resolution. Moreover, this study provides us with various insights into the characteristics of each dataset, as well as radiocaesium distribution. In particular, the urban areas show high heterogeneity in the contaminant distribution due to human activities as well as large discrepancy among different surveys due to such heterogeneity.
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Affiliation(s)
- Haruko M Wainwright
- Earth Sciences Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, MS 74R-316C, Berkeley, CA 94720-8126, USA.
| | - Akiyuki Seki
- Japan Atomic Energy Agency, Center for Computational Science & E-system, 178-4-4 Wakashiba, Kashiwa, Chiba, 227-0871, Japan.
| | - Jinsong Chen
- Earth Sciences Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, MS 74R-316C, Berkeley, CA 94720-8126, USA.
| | - Kimiaki Saito
- Japan Atomic Energy Agency, Fukushima Environmental Safety Center, 2-2-2 Uchisawai-cho, Chiyoda, Tokyo, 100-0011, Japan.
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