Optimizing long-term monitoring of radiation air-dose rates after the Fukushima Daiichi Nuclear Power Plant
| Autor: | Carlos A. Oroza, Dajie Sun, Haruko Wainwright, Hiroshi Takemiya, Satoshi Mikami, Akiyuki Seki, Kimiaki Saito |
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| Rok vydání: | 2020 |
| Předmět: |
010504 meteorology & atmospheric sciences
Health Toxicology and Mutagenesis Bayesian probability 010501 environmental sciences Radiation 01 natural sciences law.invention symbols.namesake Japan Radiation Monitoring law Nuclear power plant Fukushima Nuclear Accident Environmental Chemistry Waste Management and Disposal Gaussian process 0105 earth and related environmental sciences Remote sensing Elevation Bayes Theorem General Medicine Mixture model Pollution Hazard Air Pollutants Radioactive Cesium Radioisotopes Nuclear Power Plants symbols Environmental science Radiation monitoring |
| Zdroj: | Journal of Environmental Radioactivity. :106281 |
| ISSN: | 0265-931X |
| DOI: | 10.1016/j.jenvrad.2020.106281 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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