Artificial Radionuclides in the Environment2021
環境における人工放射能の研究（2021 年版）

Meteorological Research Institute
JMA
March 2022

気象庁

気象研究所

最終ページに図の詳しい説明があります。
See figure caption at the end page of this publication.

「環境における人工放射能の研究（2021 年版）」について

気象研究所では、1954 年以来、主に現在の原子力規制庁放射能調査研究費により、大気及び海洋の環境放射能の研究を実施してきました。特に人工放射性核種の降下量を 60 年余りの長期にわたり、毎月、東京・高円寺で、そして気象研究所の筑波研究学園都市移転後（1980 年～）は同地で、精密に測定してきました。この長期モニタリング観測は、世界でも最長の人工放射性核種の定点観測です。この観測期間に、1950 年代から 1960 年代にかけては、旧ソ連や米国などによる大気圏核実験が行われ、その後 1986 年には旧ソ連のチェ
ルノブイリ原子力発電所で爆発事故が発生するなどがありましたが、この長期にわたる精密
観測によって、それらの影響を含めたバックグラウンドの人工放射性核種の長期的変動を明らかにし、国民の安全・安心に寄与してきました。2011 年には不幸にして東日本大震災に伴う福島第一原子力発電所事故が発生し、それによって放出された放射性核種の推移を調査するという課題も担うこととなりました。今後も長期的な視点に立って、人工放射性核種の変動を監視する必要があります。
長寿命の放射性核種を含む物質の濃度や分布は、その物質の性質に応じて再飛散・輸送・沈着といった複雑なメカニズムにより変動しています。そのため、それらの長期的な変動を予測するためには、長期観測や再飛散観測、サンプルの分析等による変動メカニズムの理解に基づいた、数値計算モデルの開発が必要です。気象研究所では、気候変動や降水予測の研究のため、大気中の微粒子の動きを表現する数値モデルを開発してきましたが、そのような数値モデルの技術を利用して、 バックグラウンド放射能の変動メカニズムを解析する研究も行ってきました。
これまでの研究から、長期的変動に対しては、降水などにより一旦地面などに沈着した人
工放射性核種が、土壌粒子や生物由来の粒子等を通じて空気中へ再飛散する過程が重要であることがわかりました。福島事故直前の人工放射能の降下量は、黄砂等による再浮遊放射能の長距離輸送が関与していたと推定されます。また現在の降下量は、主に事故後に降水など
とともに地面に沈着した放射能が、土壌粒子や生物活動によって空気中に再浮遊するフラックス（流速）によって決まっていると推定されます。
本論文集「環境における人工放射能の研究（2021 年版）」 は、その研究成果を､関係省庁の担当者の方々及び大学や試験研究機関の研究者の方々に広く知って頂くために、2019 年
から 2021 年までの期間に出版された論文（主に英語論文）を、過去から現在までの成果と最近のトピックスに関するテーマ毎に分類し、各テーマの冒頭に簡単な日本語の解説を加えて､ 一冊にまとめたものです。

令和 4年 3月
気象研究所 研究総務官 石井 雅男

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Contents
0. Preface
1. Long-term trends in deposition of Sr-90 and Cs-137
2. Reassessment of resuspension of radioactive materials
3. Publications

2019-2021

4. Publication list 1954-2021
5. Publication list (in Japanese)

1954-2019

6. History of the studies at the Meteorological Research Institute
7. A caption of cover art

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目次
0. はじめに
1. Sr-90 と Cs-137 の降下量の長期変化
2. 放射性物質の再浮遊の再評価
3. 出版論文 2019-2021
4. 論文リスト（英文誌）1954-2021
5. 論文リスト（和文誌）1954-2019
6. 表：研究の歴史
7. 表紙説明-4-

0. はじめに気象研究所（2021 年 1 月現在は予報気象研究部および全球大気海洋研究部）では、1954 年
以来、環境放射能の観測・測定法の開発、放射能汚染の実態の把握、大気や海洋における物質輸送解明のトレーサーとしての利用を目的として、60 年余にわたって環境放射能の研究を実施してきた。1957 年以降、各省庁がそれぞれの所掌で実施してきた環境放射能調査研究関連
業務は、まず旧科学技術庁、その後文部科学省が所管してきた。さらに原子力規制委員会と原子力規制庁の発足に伴って 2013 年からは、原子力規制庁が所管することとなった。このような長期にわたり多数の研究者が係わり、観測・研究を継続してきた結果、環境放射能について
世界的にも他に類を見ない貴重な時系列データが内外に提供され、また様々な気象学・海洋学的発見をもたらしてきた。この間の研究成果は 200 編以上の原著論文や解説資料として内外の雑誌や成書で公表されている。
1954 年 3 月 1 日に米国によりビキニ環礁で行われた水爆実験により、危険水域外で操業していた第五福竜丸乗組員が放射性物質を含む降灰（いわゆる死の灰）による被ばくを受けた事件を契機にして、日本における環境放射能研究が本格的に始まった。当時の気象研究所地球化学研究室は環境の放射能の分析・研究において日本で有数の研究室であり、海洋及び大気中の放射能汚染の調査・研究に精力的に取り組んだ。その結果、当時予想されていなかった海洋の
放射能汚染、さらに大気を経由した日本への影響など放射能汚染の拡大の実態を明らかにすることができた。1958 年には、放射能調査研究費による特定研究課題の一つである「放射化学分析（落下塵・降水・海水中の放射性物質の研究）」を開始し、以降、地点の変動はあるが、札幌、仙台、東京、大阪、福岡の五つの管区気象台、秋田、稚内、釧路、石垣島の 4 地方気象
台、輪島、米子の 2 測候所の全国 11 気象官署及び観測船で採取した海水中の人工放射性核種（90Sr、137Cs、3H 及びプルトニウム）の分析を実施してきた。
東京・つくばにおける大気中の人工放射性核種の降下量は 1961 年から 1962 年に行われた大規模な大気圏核実験の翌年である 1963 年に最大値を観測した。その後、「部分的核実験禁止条約」の締結により米ソの大気圏核実験が中止された結果、降下量はおよそ１年の半減時間で減少した。この放射性核種の降下量の減少率は成層圏にまきあげられた物質の滞留時間を反映している。その後も、中国及びフランスにより大気圏核実験は続けられ、人工放射性核種の降下量は増減を繰り返した。1980 年の中国による最後の大気圏核実験の後、フォールアウトは
再度 1 年の半減期間で減少し、1985 年には 1957 年の観測開始以降最も低いレベルになった。
しかし、1986 年の旧ソ連のチェルノブイリ原子力発電所事故により、大気中の人工放射性核種濃度（特に揮発性の高い 131I、134Cs、137Cs など）は日本でも 1963 年に近いレベルに達する
ほど著しく増加した。大部分の放射性核種は対流圏の滞留時間（25 日）で減少したが一部 137Cs
は成層圏にも輸送されていることが分かった。1988 年以降は低いレベルで推移しているが、明瞭な減少の傾向は見られなかった。この原因は一度地上に降下した放射性核種の再飛散に由来すると考えている。さらに、再飛散がどこで起るかについて研究を進め、東アジアの砂漠域で発生する黄砂が有力な候補であることを明らかにした。黄砂の発生は大陸域の環境変化と関連しており、降下物中の人工放射性核種は、砂漠化や関連する植生変化など大陸域の環境変化の指標となりうることが分かった。
大気フォールアウトの研究とともに、海洋における放射性核種の挙動も長期的に調査研究を実施してきた。日本周辺海域ばかりでなく、太平洋の広域に亘って海水試料の採取を実施し、放射能汚染の実態を明らかにした。1960 年代後半から 1970 年代の調査で、海洋表面水中の放射能が北半球の中緯度で高いことを明らかにし、フォールアウトの緯度分布を反映していることが分かった。その後、海洋表面水中の放射性核種は海洋の物質循環に支配されていることを-5-

明らかにした。さらに、海水中の人工放射性核種の分析法の高度化を実現し少量の試料で分析可能にし、海洋の 137Cs 濃度の精密鉛直断面を描き、核実験由来の 137Cs の主な部分は北太平洋の亜熱帯中層に存在していることを明らかにした。フォールアウトによる人工放射性核種の海洋への主な降下以来、数 10 年以上経過し、その広がりは北太平洋から、インド洋など他の海盆に及んでいる。これに関連して 2003－4 年に実施された BEAGLE2003 の航海で採取された海水について人工放射性核種の分析を行って、その広がりについて知見が得られた。1993
年旧ソ連/ロシアによる放射性廃棄物の日本海等への海洋投棄の実態が明らかにされ、それに伴う日本海の放射能調査の実施に参加した。放射性廃棄物による影響は検出されなかったが、調査の結果を踏まえ、日本海における固有水の生成過程及び生成場所（ウラジオストック沖）についての知見を得ることができた。これらは、海洋放射能のデータベースとして公開されている。
1990 年以降の環境放射能汚染として、1997 年の旧動力炉核燃料開発事業団「アスファルト
固化処理施設」の火災爆発事故や 1999 年の JCO ウラン燃料工場の臨界事故があるが、いずれも環境中に放出された放射能は極めて低いレベルで、その影響は殆どなかった。しかしながら、
137Cs
2011 年 3 月に起きた東日本大震災にともなう東京電力福島第一原子力発電所事故により、の総量だけで 10 PBq（ペタベクレル）以上という過去日本では経験したことのない大量の人
工放射性核種が環境中に放出され、その 2 割程度が日本の国土に降下し、残りの 8 割は北太平洋に降下したと考えられている。これらの影響評価やその後の環境中での拡散状況の把握のためにも、環境放射能調査・研究は今後とも重要である。
2006 年 4 月より 2011 年 3 月まで、気象研究所では放射能調査研究費による特定研究課題
として「放射性降下物の長期変動と再浮遊に関する研究」及び「海洋環境における放射性核種の長期挙動に関する研究」の二課題で環境放射能研究に取り組んできた。また、2011 年 8月からは、「大気を通じた人工放射性核種の陸圏・水圏への沈着およびその後の移行過程の解明研究」の課題のもと、過去の知見を十分に活用しながら、同年 3 月 11 日に発生した東日本大震災にともなう福島第一原子力発電所の事故以降の新たな事態に対応し、放出された
大量の人工放射性核種の挙動を解明する研究を行った。目の前の大気に加えて、北太平洋におけるこれらの核種による汚染実態の把握やその動態の解明に取り組んだ。
過去 60 年余にわたり実施されてきたこうした研究成果を踏まえ、2015 年 4 月からは「人工放射性核種のバックグラウンド大気監視と数値解析に関する研究」を新たに開始した。本報告書では、この研究課題で得られた成果を含め、最新の成果やトピックスをテーマ毎に記述した。関係各位の今後の研究や業務に役立つ資料とすべく、編集作業を行った。我が国における環境放射能研究や大気科学研究に多少なりとも寄与できたとすれば、著者一同の望外の喜びである。

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1. Sr-90 と Cs-137 の降下量の長期変化について
1） 福島事故由来以前の長期変化
気象研究所では、東京（高円寺；1957-1980）とつくば（気象研究所; 1980-）で人工放射性同位体の月ごとの降下量（乾性沈着と湿性沈着の総量）のモニタリングを継続している。
図１に、これまで測定された 90Sr と 137Cs の降下量の変化を示す。1945 年より前には、人工の放射性物質は環境中に存在しなかったが、核実験等により大気中に放出された。1963 年までの降下量は、核実験により成層圏に巻き上げられた放射能が、徐々に対流圏へ降下する過程（グローバルフォールアウト）により、高い値が継続していたが、1963 年に部分的核実
験禁止条約が発効してから減少し始めた。減少速度は、中国とフランスが大気中の核実験を終了してから速まった。1986 年にチェルノブイリで事故が起こると、降下量は一時的に大きく増加した。その後、1990 年頃から降下量の減少速度は再び低下した。これは、一旦地面に沈着した放射性同位体が、ローカルダストと長距離輸送ダスト（黄砂）を担体として再浮遊するプロセスが、グローバルフォールアウトよりも相対的に重要になったためと考えられる。
2） 福島事故由来の 3 つのフェーズ
2011 年 3 月の福島の原発事故により、大量の人工放射性同位体が環境中に放出された。
環境中の放射性同位体は、壊変と環境要因による除去プロセスにより減少するが、90Sr と137Cs の壊変速度(半減期)は、どちらも約 30 年と非常に長いため、減少速度を決めるのは
主に環境要因である。しかし、そのプロセスは、気象や担体の物理化学的性質、生物活動、人間活動等に依存し、非常に複雑である。福島事故直後につくばで観測された 137Cs の月間
降下量は、2.31×104Bqm-2 であり、核実験が行われていた頃、例えば 1963 年 6 月(548 Bqm2)よりもずっと高い。しかし、90Sr に関しては、福島事故直後の値(5.2 Bqm-2)は、1960 年
6 月(170 Bqm-2)と比べて低い。これは、福島事故により放出された 90Sr の総放射能量が137Cs に比べて非常に少なかった（90Sr/137Cs=0.02 と見積もられている）ことで説明される。つくばと榛名山で観測された月間降下量の変化曲線から、期間を３つのフェーズに分類可能である。第１は、事故直後の 2011 年 3 月の期間で、原発からの直接の放出が効いているフェーズである。第２は、2011 年 4 月から 11 月の期間で、放出された物質がまだ空気中に残っており、対流圏での循環や除去プロセスが重要になるフェーズである。第 3 は、2012
年以降であり、再浮遊が重要になるフェーズである。
2021 年 5 月につくばで測定された 137Csの月間降下量は、ピーク時の 1/26000 まで減少したが、事故前のレベルと比較すると 130 倍にもなる。一方 90Sr については、事故前のレベルと同等まで低下している。
3） 環境中に 137Cs の減少率（回帰による推定）
事故前後の 137Cs 降下量について指数関数による回帰を行った。事故前の 1990 年 1 月から 2010 年 7 月の期間については１個の指数関数を、事故後の 2012 年から 2018 年(フェーズ３)の期間について、２つの指数関数（短寿命、長寿命の核種に対応）を用いて回帰を行った。事故後の実効半減期は、つくばについては、195 日と 4.7 年、榛名山については、148 日と 5.9 年であった。つくばにおける事故前の実効半減期は 8.5 年であったのでそれより短い。137Cs が事故前と同レベルに戻るには、計算上、2011 年から 42 年を要すると推定される。

-8-図１． Cs-137(a)と Sr-90(b)の月間降下量。2007 年以降の赤い四角は榛名山での観測デー
タを示す。

参考文献
Kinase, T., Adachi, K., Sekiyama, T. T., Kajino, M., Zaizen, Y., & Igarashi, Y. (2020). Temporal variationsof 90 Sr and 137 Cs in atmospheric depositions after the Fukushima Daiichi Nuclear Power Plant
accident with long-term observations. Scientific reports, 10(1), 1-8.

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2. 放射性物質の再浮遊の再評価
1） 福島原子力事故由来の放射性セシウムの地表面からの再浮遊過程の再評価
Kajino et al. (2016) もしくは前報（環境における人口放射能の研究（2018 年版）第 3 章）で報告した再浮遊の試算は、137Cs の担体エアロゾルの仮定が誤っていたために、再浮遊量を過小評価した。具体的には、2013 年の計算において 137Cs の担体エアロゾルを 2011 年 3 月の一次放出（原子炉からの直接放出）と同様に、サブミクロン粒子（直径 1 μm より小さい）と仮定した。しかし、その後の研究から再浮遊は土壌粒子やバイオエアロゾルなど、スーパーミク
ロン（直径 1 -10 μm）の粒子が主たる担体粒子である可能性が指摘されて来た。スーパーミク
ロン粒子は、サブミクロン粒子に比べて乾性沈着速度（vd）や湿性沈着率（Ec）が 1 桁もしく
はそれ以上に高い可能性があり、実際、福島市における 137Cs の沈着量を大幅に過小評価していた（Watanabe et al., 2021）。そこで本研究（Kajino et al., 2021）では、福島県浪江町津島地区、
福島県福島市（福島大学）、および茨城県つくば市（気象研究所）、の 3 か所における大気濃度と沈着量を整合的に説明する粒径分布を仮定した計算（Fig.1）により、再浮遊量と再沈着量の再評価を実施した。Fig. 1 は、大気濃度の月平均値と月間降下量をそれぞれ対数軸でとったプロットであり、傾きが 1 に近ければ縦軸の切片が沈着速度の次元となる。Kajino et al. (2016)
は、観測値（紫）や本研究（緑）と比べて沈着速度が 1 桁以上過小評価していることが分かる。
また紫と緑が近いことから、本研究の見積もりが現実と整合的であることも見てとれる。

Figure 1: Scatter diagram of the depositions of 137Cs over the monthly mean (purple crosses) observed
surface concentrations at Namie (Tsushima), Fukushima, and Tsukuba in 2013 and those simulated by
Kajino et al., 2016 (K16) (Ec and vd are 0.04 and 0.1 cm s-1, respectively) considering different emission
sources: the open orange squares represent mineral dust particles from bare soil (dust aerosols), and theclosed orange squares denote bioaerosols emitted from forest ecosystems (forest aerosols). The green open
and closed squares are the same as the orange squares but are simulated by this study (Ec and vd are 0.4
and 10 cm s-1, respectively). The purple, orange, and green lines indicate the regression lines of the purple
crosses, orange squares (open plus closed), and green squares (open plus closed), respectively.

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Figure 2 に年間再浮遊・再沈着量を旧評価（上）と新評価（下）に分けて示す。新評価では、
Cs の年間再浮遊量は 25.7 TBq であり、これは初期沈着量（2.68 PBq）の 0.96%に相当した。
また沈着率が速いため、年間の再沈着量も 10.6 TBq と大きく、再浮遊した 137Cs のうち 40%は再び地上に沈着した（残り 60%は系外に流出）。この年間再浮遊率（0.96% y-1（=2.6×10-5 d-1））
は一見小さく見えるが、実際の福島県で得られた空間ガンマ線量率の減少率と比較すると、無視できないかもしれない。2013 年の 137Cs と 134Cs の放射性壊変を除外した減少率（つまり、陸面過程や除染などによるものと考えられる）は 1.0 – 7.9×10-4 d-1 であり、再浮遊はそれらの 110%程度を占める可能性がある。
依然、本研究もまた Kajino et al. (2016)と同様、再浮遊のメカニズムを解明した研究ではなく、実際の観測結果と整合的になるように領域収支（発生・輸送・沈着）を算出した結果であり、そのメカニズムはまだ未解明のままである。今後も、メカニズム解明の実験的研究に基づいた影響評価を継続していく必要がある。
137

Figure 2: Horizontal distributions of (a,c) the annual total amounts of resuspended 137Cs (Bq m-2) and(b,d) redeposited amounts of resuspended 137Cs (Bq m-2) obtained from the simulations assuming (a,b)
submicron (K16; Ec and vd are 0.04 and 0.1 cm s-1, respectively) and (c,d) supermicron (this study; Ec andvd are 0.4 and 10 cm s-1, respectively) sizes of 137Cs-bearing particles. The areal total amounts are embedded
at the bottom right of each panel.

- 12 -

参考文献
Kajino, M., M. Ishizuka, Y. Igarashi, K. Kita, C. Yoshikawa, M. Inatsu. Long-term assessment of airborne
radiocesium after the Fukushima nuclear accident: Re-suspension from bare soil and forest ecosystems,
Atmos. Chem. Phys., 16, 13149-13172, https://doi.org/10.5194/acp-16-13149-2016, 2016.
Watanabe, M., M. Kajino, K. Ninomiya, Y. Nagahashi, and A. Shinohara, Eight-year variations inatmospheric radiocesium in Fukushima city, Atmos. Chem. Phys. Discuss. [preprint],
https://doi.org/10.5194/acp-2021-591, in review, 2021.
Kajino, M., A. Watanabe, M. Ishizuka, K. Kita, Y. Zaizen, T. Kinase, R. Hirai, K. Konnai, A. Saya, Y.
Shiroma, H. Hasegawa, N. Akata, M. Hosoda, S. Tokonami, and Y. Igarashi, Reassessment of theradiocesium resuspension flux from contaminated ground surfaces in East Japan, submitted to Atmos.
Chem. Phys.

- 13 -

3. Publications 2019-2021
出版論文 2019-2021- 14 -

Abe et al. Progress in Earth and Planetary Science
https://doi.org/10.1186/s40645-020-00403-6(2021) 8:13

RESEARCH ARTICLE

Progress in Earth andPlanetary Science

Open AccessWidespread distribution of radiocesiumbearing microparticles over the greater
Kanto Region resulting from the Fukushima
nuclear accident
Yoshinari Abe1,2* , Seika Onozaki1, Izumi Nakai1, Kouji Adachi3, Yasuhito Igarashi4,5, Yasuji Oura6,
Mitsuru Ebihara6,7, Takafumi Miyasaka3,8,9, Hisashi Nakamura8, Keisuke Sueki10, Haruo Tsuruta11 andYuichi Moriguchi12

Abstract
The Fukushima Daiichi Nuclear Power Plant (FDNPP) accident in March 2011 emitted a considerable amount ofradioactive materials. This study isolated radiocesium-bearing microparticles (CsMPs), a form of radioactive materials
emitted from the FDNPP at the early stage of the accident, from aerosols collected hourly on filter tapes at sevenmonitoring stations at the greater Kanto Region, including the Tokyo metropolitan area, on 15 March 2011. The
aerosols had a spherical shape ~ 1 μm in diameter with activity of less than 1 Bq of 137Cs per particle. Their physicaland chemical characteristics, including radioactivity ratio 134Cs/137Cs as well as chemical composition and state, areessentially the same as previously reported CsMPs. This study demonstrated that air parcels containing CsMPs
emitted from the FDNPP were widespread over the greater Kanto Region, more than 250 km away from the
FDNPP, during the daytime of 15 March. Trajectory analysis indicated that these particles were emitted from thereactor No. 2 of FDNPP between 14 March evening and 15 March early morning. The information obtained on thewidespread distribution of CsMPs can be useful for assessing the actual impacts of radioactive contamination fromthe FDNPP accident on the environment and human health.
Keywords: Fukushima Daiichi Nuclear Power Plant accident, Radiocesium-bearing microparticle, Suspended
particulate matter, Synchrotron radiation X-ray analysis, Trajectory analysis

1 Introduction
Considerable amounts of radioactive materials were released into the environment following the Fukushima
Daiichi Nuclear Power Plant (FDNPP) accident causedby tsunamis associated with the Tohoku Earthquake on
11 March 2011 (e.g., MEXT 2020; Steinhauser et al.
2014). To reveal the time evolution of atmospheric* Correspondence: y.abe@mail.dendai.ac.jp
1
Department of Applied Chemistry, Faculty of Science, Tokyo University of
Science, 1-3 Kagurazaka, Shinjuku-ku, Tokyo 162-8601, Japan
2
Division of Material Science and Engineering, Graduate School ofEngineering, Tokyo Denki University, 5 Senju Asahi-cho, Adachi-ku, Tokyo
120-8551, Japan
Full list of author information is available at the end of the article

radionuclide concentrations immediately after the accident, Tsuruta et al. (2014, 2018) and Oura et al. (2015)
investigated suspended particulate matter (SPM) collected on filter-tape at automated air pollution monitoring stations across Eastern Japan. More than 400
stations in Eastern Japan were in operation during theinitial period of the FDNPP accident. Analyzing atmospheric radiocesium concentrations at 99 of those SPM
monitoring stations, these studies revealed that radioactive materials emitted into the air from the FDNPP
were transported over Eastern Japan via several major
plumes during 12–23 March (Tsuruta et al. 2014, 2018).

© The Author(s). 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give
appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate ifchanges were made. The images or other third party material in this article are included in the article's Creative Commonslicence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commonslicence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain
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Abe et al. Progress in Earth and Planetary Science(2021) 8:13

As a form of radioactive materials emitted from the
FDNPP at the early stage of the accident, radiocesiumbearing microparticles (CsMPs) have been investigatedby numerous researchers in recent years (Igarashi et al.
2019). They were solid particles and not easily dissolved
into water, which first discovered by Adachi et al. (2013)
from aerosols collected at the Meteorological ResearchInstitute (MRI) in Tsukuba, 170 km south-southwest of
FDNPP, during 2110 JST 14 March and 0910 JST 15
March 2011. After the discovery of the CsMP, manystudies focused on their physical and chemical characteristics as well as their environmental distributions(Igarashi et al. 2019). While several types of CsMPs havebeen reported, almost CsMPs examined previously were
nearly spherical, several μm in diameter, and had relatively high specific radioactivity, i.e., ~ 1 Bq per particle
as 137Cs (Adachi et al. 2013; Furuki et al. 2017). Theirmain matrices were silicate glass (Satou et al. 2016) with
Fe and Zn (Adachi et al. 2013; Abe et al. 2014), and trace
amounts of various heavy elements associated with nuclear fuel and fission products (FPs) were detected (Abeet al. 2014). Physical and chemical characterizations ofradioactive materials, including their water solubility,
shape, size, and chemical composition and state, are critical factors that determine their behavior within the environment and human body. CsMPs and otherparticulate radioactive materials have previously been
identified in various environmental samples collected in
Fukushima Prefecture as follows: soils collected aroundthe FDNPP (e.g., Satou et al. 2016, 2018, Ono et al.
2017, Furuki et al. 2017, Martin et al. 2019, 2020), a
non-woven fabric cloth laid on an agricultural field(Yamaguchi et al. 2016), and river sediments (Miuraet al. 2018). Similar radioactive microparticles were also
found on masks worn during cleaning work in residential areas near the FDNPP 5 years after the accident(Higaki et al. 2017). As mentioned above, these CsMPs
found previously consist primarily of silicate glass. It isthus concerned that they have a long-term impact on
the environment compared to water-soluble radioactive
materials, whereas some recent investigations indicate avery slow rate of dissolution of CsMP into pure-water orseawater (Okumura et al. 2019; Suetake et al. 2019).
Meanwhile, some CsMPs containing chloride as a watersoluble compound had been recently found from aerosols collected near the FDNPP after the hydrogen explosion of the reactor No. 1 of the FDNPP (Onozaki et al.
2019). Comprehensive investigations of CsMPs withvarious physical/chemical properties are therefore vitally
important in accurately assessing the impacts of radioactive contamination from the FDNPP accident on theenvironment and human health.
Among the major polluted plumes identified by Tsuruta et al. (2014), the second plume (P2) carried thePage 2 of 10

aerosols collected at the MRI on 14 and 15 March 2011
(Adachi et al. 2013). After being observed at SPM monitoring stations in the vicinity of the MRI (Tsukuba) during the morning of 15 March, P2 was then spread into
the Kanto Plain, including the Tokyo metropolitan area,
one of the most populated areas in the world. The
southward spread of P2 was due to low-level northerlies
associated with an eastward-moving low-pressure system
located south of the Kanto Region (Takemura et al.
2011). Hypothesizing that CsMPs were the major carrierof radioactive Cs in P2, this study aims to verify thewidespread distribution of CsMP in P2 over the greater
Kanto Region (in and around the Kanto Region) on 15
March, focusing on how far CsMPs were transported
southwest from the FDNPP by the local wind system.
We examined radioactive aerosol particles from the
SPM filter-tape samples collected hourly on 15 March atseveral stations in the greater Kanto Region to compare
their physical/chemical characteristics with those ofpreviously reported CsMPs isolated from various
environmental samples. To investigate detailed chemical
characteristics of the radioactive aerosols isolated fromthe SPM filter-tape samples, we applied multiple X-ray
analytical techniques using a synchrotron radiation
microbeam (SR-μ) X-ray in a nondestructive manner.
The SR-μ-X-ray analysis is an analytical technique thatis commonly used for microscopic chemical
characterization of various materials in the nondestructive manner and is quite suitable for CsMPs as demonstrated by several previous studies (Abe et al. 2014; Onoet al. 2017; Onozaki et al. 2019; Miura et al. 2020; Kurihara et al. 2020a). This study has implications for the
impacts of radioactive materials on the environment andhuman health as well as the reactor condition during theearly stage of the accident.

2 Methods/experimental
2.1 SPM filter-tape samples and separation method ofparticles

The SPM monitors within the Japan air pollution monitoring network are routinely operated by local governments (prefectures and municipalities designated byordinance). Particulates less than 10 μm in diameter
were automatically collected on the filter tape installedin the SPM monitors as a sample spot (11 or 16 mm indiameter) for 1 h at a flow rate of 15.0, 16.7, or 18.0 l/
min. The filter tape was made of glass fiber or polytetrafluoroethylene. Detailed information of the SPM filtertape samples has been described in previous studies(Tsuruta et al. 2014, 2018; Oura et al. 2015). Seven
pieces of the filters sampled at seven monitoring stations
designated as A, B, C, D, E, F, and G within the greater
Kanto Region (see Table 1 and Fig. 1) were used in thisstudy. Each piece was selected which had the highest

Abe et al. Progress in Earth and Planetary Science(2021) 8:13

Page 3 of 10

Table 1 Information of seven monitoring stations (A~G) and SPM filter-tape samples investigated in the present study
Cs/Bq m−3

StationLatitudeLongitude

Distance from FDNPP

Sampling time (JST)

137

A

35.85

140.25

189 km

15 March 0800–0900

22.1

B

36.19

139.13218 km

15 March 1200–1300

59.1

C

35.78

139.62229 km

15 March 1000–1100

81.9

D

35.65

139.59236 km

15 March 1000–1100

29.4

E

35.37

139.22280 km

15 March 1400–1500

29.3

F

36.33

138.44261 km

15 March 1600–1700

22.1

G

36.41

138.24273 km

15 March 1600–1700

32.8

137

Cs concentration on March 15 at each station under
the direct influence of P2. We cut these filters into fouror eight portions in collecting radioactive particles. An
imaging plate (GE Measurement and Control, CR × 25P
computed radiography scanner) and micromanipulator(AP-xy-01; Micro Support Corp.) were used to detectand separate radioactive particles from the filters. The
amount and distribution of radioactive particles on thefilter was observed in a process similar to previous studies (Adachi et al. 2013; Abe et al. 2014). Prior to the SR
experiments, a low-vacuum scanning electron

microscope (SEM; SU 3500; Hitachi High-Technologies)
was used to observe the shapes of individual particles
isolated from the filters. The radioactivity of 134Cs and137
Cs in each particle was determined by using a Ge
semiconductor detector (GC4018; CANBERRA) coupledwith a multichannel analyzer (Lynx Digital Signal
Analyzer; CANBERRA). The gamma-ray spectrum wascollected for more than 400,000 s per particle. Twostandard radioactive sources, 134Cs standard by Japan
Radioisotope Association and 137Cs standard by Amersham plc, were measured in the same manner toFig. 1 Locations of monitoring stations A~G in the greater Kanto Region and radioactive particles isolated in this study. Coordinates andcollection time of the filters are shown for each station. White dashed lines on the map indicate distance from the FDNPP. Eight CsMPs wereisolated from these seven filters as displayed by SEM images around the map. They are nearly spherical in shape with a diameter of 1.3 μm(particle A1), 1.3 μm (B1), 1.4 μm (C1), 1.5 μm (D1), 1.2 μm (D2), 1.3 μm (E1), 0.9 μm (F1), and 1.8 μm (G1). The scale bars under the SEM images
show a length of 1 μm

Abe et al. Progress in Earth and Planetary Science(2021) 8:13

calibrate radioactivity of the particles. Absolute value
and statistical error of radioactivity of 134Cs and 137Cs inindividual particles were calculated as decay-corrected
data at the time of 1446 JST 11 March 2011. After theseanalyses, the radioactive particles on carbon tape fragments were removed and then placed on a flat Kapton
tape with a plastic holder for the SR-μ-X-ray analyses.
2.2 Synchrotron radiation X-ray analyses of CsMPs

The SR experiments using an X-ray microbeam werecarried out at the BL37XU (Terada et al. 2004, 2010), a
hard X-ray undulator beamline at SPring-8, located at
the Japan Synchrotron Radiation Research Institute(JASRI). The sample was placed on an automatic XY
stage in the experimental hatch. Monochromatic X-rays
were obtained with a Si (111) double crystal monochromator, and the X-ray microbeam with the size of ~1 μm (V) × ~1 μm (H) was produced by focusing
Kirkpatrick−Baez mirrors. We applied three X-ray analytical techniques: SR-μ-X-ray fluorescence (XRF) analysis,
SR-μ-X-ray absorption near edge structure (XANES) analysis, and SR-μ-X-ray powder diffraction (XRD) analysis.
The measurement conditions of the SR-experiments were
the same as in our previous investigations (Abe et al.
2014; Ono et al. 2017; Onozaki et al. 2019).
The SR-μ-XRF analysis was carried out using 37.5 keV
X-rays that enable to excite K-edges of Cs (36.0 keV)
and Ba (37.4 keV). Three types of energy-dispersive Xray detectors were used depending on the beamtime: aSi (Li) detector for the beamtimes until 2017, and eightelements silicon drift detectors or a Ge semiconductor
detector for the beamtimes in 2018 and 2019. The SR-μXRF spectrum was measured for 200 s in live time persample. The intensity of each spectrum was normalized
to that of the Thomson scattering peak detected at 37.5
keV. The SR-μ-XANES spectra of the particles and thereference samples were measured in fluorescence mode
for the following absorption edges: Fe-K edge (7111 eV),
Zn-K edge (9661 eV), Mo-K edge (20,000 eV), and Sn-K
edge (29,200 eV). The intensity of Kα line of each target
elements in individual particles was scanned with ameasurement step of ~ 1 eV/step, integration times of
3.0~10.0 s/step, and an energy range of ~ 100 eV fromthe lower to the higher energy sides of the absorption
edge. To normalize the Kα intensity of the target elements, an intensity of the incident X-ray beam (I0) wasmonitored using an ionization chamber. Reference materials (powders of metals, typical oxides, sulfides, silicates,and synthesized glass samples) containing each target elements were also measured as a same manner. In the
SR-μ-XRD analysis, the X-ray diffraction patterns of thesamples were measured with a Debye−Scherrer opticalsystem using a two-dimensional detector (CMOS flat
panel) placed 200 mm behind the sample. The energy ofPage 4 of 10

the incident X-ray was set to 15.0 keV with an exposure
time of 440 ms and an integration of 100 times/sample.
2.3 Trajectory analysis for the radioactive plume transport

A meteorological trajectory analysis was conducted toevaluate air parcel positions every 10 min based on windfields from the Japan Meteorological Agency mesoscalemodel objective analysis. The analysis has a horizontal
resolution of 0.0625 × 0.05° with 50 vertical levels up to
21,800 m. Three-hourly analysis data of wind on modellevels were used, as well as 3-hourly analysis and 1hourly forecast data of 10 m surface wind. The threedimensional wind data cannot resolve small-scale turbulence that is most vigorous within the mixed layer. Airparcels were initially placed at 50 m intervals from 50 m
to 1000 m above the surface. For each parcel, the trajectory calculation was terminated if it hit the surface.

3 Results and discussion
3.1 Physical/chemical characteristics of CsMPs isolated
from SPM filter-tape

In total, eight CsMPs were successfully isolated fromseven pieces of SPM filter-tape samples. As illustrated inFig. 1, the eight CsMPs were labelled as A1, B1, C1, D1,
D2, E1, F1, and G1 in corresponding to the seven SPM
monitoring stations. The particles were spherical withdiameters of ~ 1 μm, and their radioactivity was lessthan 1 Bq of 137Cs per particle (Fig. 2). The radioactivity
ratios between 134Cs and 137Cs (134Cs/137Cs) were ~ 1.0,
suggesting that these particles were emitted from either
reactor No. 2 or 3 of FDNPP (Nishihara et al. 2010) (seeadditional data Table S1 online). As first pointed out by
Satou et al. (2018), CsMPs from the FDNPP can be categorized into two major types, type A and type B, basedon the 134Cs/137Cs ratio of individual particles. Type A
particles are characterized by 134Cs/137Cs ratio of ~ 1.0,
in contrast to type B particles with 134Cs/137Cs ratio of ~0.9.
As a result of the SR-μ-XRF analyses of individual particles, A1~G1 were found to have qualitatively-similar
chemical compositions. The SR-μ-XRF spectra of four
representative particles (A1, B1, C1, and E1) are shownin Fig. 3 with that of a type A CsMP collected at the
MRI in a previous study (Abe et al. 2014). Note that allthese spectra were measured using the same Si (Li)detector in several beamtimes. The XRF analysis using a
monochromatic SR-μ-X-ray with high-energy (37.5 keV)for the excitation can detect trace amounts of heavy elements within individual particles, although lighter elements (such as Si) that are major components of theparticle could not be detected in the spectrum. In
addition to sharp K-line peaks of Cs which had been
identified by the gamma-ray spectroscopy, the following
eight heavy elements were detected in all particles: Fe,Abe et al. Progress in Earth and Planetary Science(2021) 8:13

Page 5 of 10

Fig. 2 Radioactivity of 137Cs and 134Cs/137Cs activity ratio of individual particles. The radioactivity of 137Cs (± 1 × standard deviation, σ) of eightparticles (A1~G1) are as follows: 0.471 ± 0.016 Bq (particle A1), 0.426 ± 0.015 Bq (B1), 0.546 ± 0.019 Bq (C1), 0.425 ± 0.014 Bq (D1), 0.318 ± 0.011
Bq (D2), 0.156 ± 0.008 Bq (E1), 0.317 ± 0.011 Bq (F1), and 0.665 ± 0.023 Bq (G1). The three dashed lines show estimates of the 134Cs/137Cs ratio of
nuclear fuel in FDNPP reactors No. 1, 2, and 3, respectively, at the time of the accident, as calculated using ORIGEN2 Code (Nishihara et al. 2010).
Activity values shown here were decay-corrected as of 11 March 2011

Zn, Rb, Mo, Sn, Sb, Te, and Ba. Several trace elements
specific to certain particles were also found: Zr fromseven particles except for particle B1; Nb from particle
C1; Ag from particle F1, Cd from particles C1 and G1;
Pb from particles A1, B1, C1, E1, F1, and G1; and U
from particles A1, B1, and F1.
The SR-μ-XANES analysis examined the chemical
states of four metal elements (Fe, Zn, Mo, and Sn) andindicated that these elements exist as cations in silicate
glass with high oxidation numbers (see additional data
Fig. S1 online). SR-μ-XRD analysis of individual particles
showed no diffraction peaks caused by crystal structure
for any of the particles (see additional data Fig. S2 online), confirming that these particles have glass bodies.

As discussed above, physical/chemical characteristics
obtained for the eight CsMPs (A1~G1) collected at theseven SPM monitoring stations in the greater Kanto Region are essentially the same as those of type A CsMPs
found in the previous studies (e.g., Abe et al. 2014; Igarashi et al. 2019). As first reported by Utsunomiya et al.
(2019), it is already-known fact that air parcels containing type A CsMPs passed over Tokyo City at some pointon 15 March. Our results strongly support their pioneering report. At the same time, this paper first demonstrated that CsMPs emitted from the FDNPP werewidespread over the greater Kanto Region, includingWest side of Tokyo metropolitan area, during the daytime of 15 March with a temporal resolution of an hour.

Fig. 3 SR-μ-XRF spectra of four representative CsMPs (A1, B1, C1, and E1) isolated from the SPM filter-tape samples, showing the heavy elemental
composition of individual particles. The intensity of each spectrum is displayed on a logarithmic scale and shifted in a longitudinal direction. The
spectrum of MRI-A shows datum obtained for one of type A CsMPs (particle A) collected at the MRI in the previous study (Abe et al. 2014)

(2021) 8:13

Page 6 of 10

3.2 Transport pathway of CsMP from FDNPP to greater
Kanto Region

Kanto Region on 15 March were emitted from the
FDNPP between the evening of 14 March and earlymorning 15 March. After being released into the mixed
layer, these polluted air parcels were advected southward
by low-level northerlies and later by northeasterlies. The
parcels very likely underwent vigorous mixing down tonear-surface levels, within the mixed layer above the stations. The earliest emission time is estimated for theparcels above station E, as the trajectories detoured far
offshore under the northwesterlies shortly after emitted.
Our trajectory analysis implies that individual aerosol
particles that were emitted locally from the FDNPP subsequently spread widely over the greater Kanto Region
within a relatively short period of time (within 18 h at
the longest) under time-varying flow conditions. Basedon the results of our backward trajectory analysis, we
calculated forward trajectories for air parcels over the
FDNPP starting at 0100 JST 15 March 2011 and showed
them on the map with the seven monitoring stations
A~G (Fig. 5). The air parcels situated above the FDNPP
at that time moved southward and then widespread overthe greater Kanto Region within a day.

Abe et al. Progress in Earth and Planetary Science

To estimate the emission time of the CsMPs and identify
their transport pathway(s) from the FDNPP to thegreater Kanto Region, we conducted trajectory analysis
of air parcels that passed over the seven monitoring
stations A~G. As an example, Fig. 4 shows backward
trajectories for air parcels situated at different heights
over station B starting at 1200 JST 15 March, where the
SPM aerosols including particle B1 were collected during 1200–1300 JST. The trajectories are color-coded to
distinguish parcel heights at station B in Fig. 4a and alsocolor-coded by parcel height at each time step in Fig. 4b.
Figure 4a, b suggests that the air parcels situated at 150–
600 m above station B at 1200 JST were likely to belocated below the 500 m level within 20 km range fromthe FDNPP sometime between 0040 and 0330 JST on
the same day. The vertical profile of potential
temperature at station B (Fig. 4c) indicates that the atmospheric mixed layer was as deep as 800 m above thesurface, suggesting that air parcels within the layer
should have experienced vigorous turbulent mixing andthus be well-mixed down to near-surface levels. Likewise, air parcels from the FDNPP were released into thenight-time mixed layer. Advected by low-level northerlies, these air parcels then travelled along the coast
mostly within the marine mixed layer (Fig. 4b), which
was probably well developed in the early morning given
the cool offshore northerlies over the relatively warm
ocean.
Results of our backward trajectory analysis for theseven monitoring stations are summarized in Table 2.
Although the estimated emission times have ranges thatspan several hours, we concluded that polluted air parcels containing CsMPs that passed over the greater

3.3 Possible source of CsMPs distributed over greater
Kanto Region

The chemical composition of particles emitted primarily
from the FDNPP could reflect the reactor condition during the early stages of the accident. As indicated by Okumura et al. (2019), we cannot ignore the possibility that
CsMP collected from soils years after the accident wasaltered physically or chemically in the environment even
if they are hardly soluble into the water. Unlike suchfield samples, there would be little change in physicaland chemical properties of the CsMPs as they were sampled on the SPM filter-tape shortly after emission. The

Fig. 4 Backward trajectories calculated for air parcels placed above station B at 1200 JST 15 March 2011. The trajectories are color-coded (a) to
distinguish heights (m) of the parcels at station B and (b) according to their heights at each time step. Small and large dots on the trajectories
indicate intervals of every hour and 3 h, respectively, after the calculation start time. c Vertical profile of potential temperature (K) above station B
at the trajectory start timeAbe et al. Progress in Earth and Planetary Science(2021) 8:13

Page 7 of 10

Table 2 Summary of the backward trajectory analysis for seven monitoring stations (A~G)
StationStarting time(JST) ofbackward
trajectories

Depth ofatmospheric
mixed layer
above the station

Estimated paths of air parcels between the station and 20 km range from FDNPP
Height above
the station

Height above 20
km range from FDNPP

Estimated emission time(JST) from FDNPP

A

15 March 0800

~500 m

750-800 m

0-600 m

15 March 0000-0220

B

15 March 1200

~800 m

150-600 m

0-500 m

15 March 0040-0330

C

15 March 1100

~900 m

800-900 m

100-700 m

15 March 0210-0340

D

15 March 1100

~900 m

850-900 m

50-650 m

15 March 0120-0310

E

15 March 1400

~700 m

750-850 m

100-650 m

14 March 2000- 15 March 0130

F

15 March 1600

~200 m50-650 m

50-550 m

15 March 0040-0340

G

15 March 1600

~200 m50-300 m

100-300 m

14 March 2330- 15 March 0200

chemical compositions of the CsMPs discussed in thisstudy are thus likely to be preserved except for radioactive
decay effects. Therefore, our analysis offers important information regarding raw materials and the generation processof CsMP in the reactor. All elements identified in theCsMPs can be associated with materials in the FDNPP. As
a result of the nuclear fission reaction of 235U, the FPs
yielded 11 elements (Rb, Zr, Nb, Mo, Ag, Cd, Sn, Sb, Te,
Cs, and Ba) (Crouch 1977, Burns et al. 2012, Yamamoto2012). It is also possible that Zr and Sn in the CsMPs originate to Zr−Sn alloy used for fuel cladding within the reactors. On the other hand, vaporous elements, such as Rb(e.g., the boiling points of RbOH and RbI are about 1660 K
and 1577 K under 1 atm, respectively) and Cs (e.g., the boiling points of CsOH and CsI are about 1263 K and 1553 K
under 1 atm, respectively), in the CsMPs were richer thanthe original composition of FPs derived from U fuel. Therefore, condensation of these elements by vaporization in thereactor pressure vessel (RPV) could happen during the particle generation process. Moreover, the amounts of Cs in
the particles would be higher than those of Mo evenFig. 5 Forward trajectories calculated for air parcels placed above
the FDNPP at 0100 JST 15 March 2011. The trajectories are colorcoded to distinguish heights (m) of the parcels at FDNPP. Small andlarge dots on the trajectories indicate intervals of every hour and 3 hsince the calculation start time, respectively

considering the difference in excitation efficiency frommonochromatic X-rays (37.5 keV). Although Cs2MoO4 wassuggested as one possible source for the vapor phases carrying Cs at high temperature (Kissane and Drosik 2006;
Gouello et al. 2013; Do et al. 2018), our result indicates thatthe volatilization and condensation of Cs2MoO4 were notpredominant processes of the generating of the CsMPs. Do
et al. (2018) have pointed out that CsOH is the predominant cesium species when the damaged fuel temperature ishigher than 2000 K at higher steam pressures, but
Cs2MoO4 would become more important at lower
temperature. In this connection, Imoto et al. (2017) reported the presence of nanoparticles of CsFeSi2O6 within
CsMP and pointed out that the material could be formedby the CsOH chemisorption onto Si-bearing stainlesssteel. Iron, Cr, Mn, Ni, and Mo could originate from stainless steel, which composed RPV and FDNPP buildings.
Regarding a possible source for Zn, we previously identified an additive agent of primary cooling water (Abe et al.
2014), but a thin plating of steel and inorganic paint on
RPV and buildings could be other potential sources (Itouet al. 2018). Lead metal and Pb-containing materials arecommonly used for shielding of radiation. To make amore detailed interpretation of the generation process of
the CsMPs, it is indispensable to carry out further scientific investigation using other analytical methods to reveal
more quantitative chemical composition and isotopic features of individual particles, such as secondary ion mass
spectrometry (Imoto et al. 2017; Kurihara et al. 2020a;
Kurihara et al. 2020b).
As described above, type A CsMPs including eightparticles investigated in this study have been thought tobe originated to either reactors No. 2 or No. 3 of the
FDNPP based on their 134Cs/137Cs ratio: there are two
opinions about from which the type A CsMPs were
emitted (e.g., Igarashi et al. 2019; Ikehara et al. 2020).
We therefore consider the source of the type A CsMPs
in relationship to the accident progress, making use ofhigh time-resolution of the SPM filter-tape samples. Our
trajectory analysis suggests that the emission time for theAbe et al. Progress in Earth and Planetary Science(2021) 8:13

type A CsMPs isolated from the greater Kanto Region was
between midnight and early morning 15 March, althoughemission time could have been as early as the evening of
14 March for parcels that reached station E (Table 2).
Consistent with these estimated emission times, pressure
inside the RPV in reactor No. 2 decreased after the usageof a safety relief valve around 1903 JST on 14 March,followed by three sharp RPV pressure peaks around 2100
and 2300 JST 14 March and 0100 JST 15 March (TEPCO2015). Around 0300 JST on 15 March, the pressure inside
the primary containment vessel of reactor No. 2 exceeded
its designed value (TEPCO 2015). In contrast, no incident
was reported for reactor No. 3 during the same period except for a hydrogen explosion at 1101 JST 14 March. We
therefore hypothesized that the incident(s) in reactor No.
2 were the most likely cause of the type A CsMPs, rather
than reactor No. 3. Our hypothesis is strongly supported
by recent investigation of isotopic ratios of U and Cs in
CsMPs (Kurihara et al. 2020b).

4 Conclusions
Eight CsMPs were isolated successfully from aerosol particles collected hourly on filter tapes at seven monitoring
stations in the greater Kanto Region, including the Tokyo
metropolitan area, on 15 March 2011. Our finding demonstrates clearly that air parcels containing CsMPs emitted from the FDNPP were widespread over the greater
Kanto Region, farther than 250 km away from the FDNPP,
during the daytime of 15 March. Detailed physical andchemical properties of individual CsMPs were investigatedby SR-μ-X-ray analyses. As a result, it was concluded thatthe incident(s) in reactor No. 2 of FDNPP were the most
likely cause of CsMPs distributed over the greater Kanto
Region. Our trajectory analysis also suggests that air parcels containing the CsMPs as water-insoluble microparticles with radionuclides likely passed over the greater
Kanto Region including Tokyo metropolitan area on 15
March. Some of those particles could have been depositedon the ground or suspended in the near-surface air, although most of them were transported to the ocean. Further investigation is necessary to estimate theenvironmental and health impacts from the CsMPs thattravelled into the metropolitan area. Information regarding widespread distribution of CsMPs can be useful toward calculating an inhalation dose of radionuclides
during the early stage of the accident.
5 Supplementary Information
The online version contains supplementary material available at https://doi.
org/10.1186/s40645-020-00403-6.
Additional file 1: Table S1. 134Cs and 137Cs radioactivities andCs/137Cs activity ratio of eight particles (A1~G1). Figure S1. SR-μXANES spectra of four representative CsMPs (A1, B1, C1, and E1) isolated
134

Page 8 of 10

from the SPM filter and reference materials. (a) Fe-K edge, (b) Zn-K edge,(c) Mo-K edge, and (d) Sn-K edge. Figure S2. SR-μ-XRD patterns of four
representative radioactive particles (A1, B1, C1, and E1) isolated from the
SPM filter and reference material. (a) Particle A1, (b) particle B1, (c) particle
C1, (d) particle E1, and (e) silicon powder (NIST SRM 640c). In contrast to
sharp diffraction peaks detected in silicon powder, no obvious peaks
were detected in four radioactive particles.
Abbreviations
FDNPP: Fukushima Daiichi Nuclear Power Plant; SPM: Suspended particulatematter; CsMP: Radiocesium-bearing microparticle; JST: Japan Standard Time;
MRI: Meteorological Research Institute; FP: Fission product; SR-μ-Xray: Synchrotron radiation microbeam X-ray; JASRI: Japan Synchrotron
Radiation Research Institute; XRF: X-ray fluorescence; XANES: X-ray absorption
near edge structure; XRD: X-ray powder diffraction; SEM: Scanning electron
microscope; RPV: Reactor pressure vessel
Acknowledgements
We thank all local governments for allowing us to investigate the SPM filtertape samples. We are deeply grateful to Dr. Y Terada, Dr. K Nitta, and Dr. O
Sekizawa (SPring-8/JASRI) for their assistances in the SR experiment at
BL37XU, SPring-8. The synchrotron radiation experiments were performed
with the approval of the SPring-8 Program Advisory Committee (proposal
numbers 2016A1705, 2016B1811, 2017A1719, 2017B1757, 2018A1704,
2018B1748, and 2019B1308).
Authors’ contributions
YA performed the SR measurements, data analysis, and wrote themanuscript. SO performed the separation of particles and SR measurements.
IN and YI conceived the study idea. KA conducted the SEM analysis. YO andME controlled the SPM filter-tape samples. TM and HN performed the trajectory analysis. KS conducted the gamma-ray spectrometry. HT and YM helped
to devise the study idea and assisted with cooperation and coordination
with local governments. All authors read and approved the final manuscript.
Funding
This work was supported by MEXT/JSPS KAKENHI Grants (24110003,
15H00978, 19H05702), in collaboration with projects of Environment
Research and Technology Development Fund of the Environmental
Restoration and Conservation Agency, Japan (JPMEERF20155001,
JPMEERF20181002, and JPMEERF20192004). This work was also supported by
Joint Research on Risk Evaluation and Management in Nuclear Technology(Japan-U.K.), the Collaborative Laboratories for Advanced Decommissioning
Science, JAEA (JPJA18F18072860).
Availability of data and materials
Data sharing not applicable to this article as no datasets were generated oranalyzed during the current study. Please contact corresponding author fordata requests.
Competing interests
The authors declare that they have no known competing financial interests
or personal relationships that could have appeared to influence the work
reported in this paper.
Author details
1
Department of Applied Chemistry, Faculty of Science, Tokyo University of
Science, 1-3 Kagurazaka, Shinjuku-ku, Tokyo 162-8601, Japan. 2Division ofMaterial Science and Engineering, Graduate School of Engineering, Tokyo
Denki University, 5 Senju Asahi-cho, Adachi-ku, Tokyo 120-8551, Japan.
3
Meteorological Research Institute, 1-1 Nagamine, Tsukuba-shi, Ibaraki
305-0052, Japan. 4Institute for Integrated Radiation and Nuclear Science,
Kyoto University, 2-chome, Asashiro-Nishi, Kumatori-cho, Sennan-gun, Osaka
590-0494, Japan. 5College of Science, Ibaraki University, 2-1-1 Bunkyo,
Mito-shi, Ibaraki 310-8215, Japan. 6Graduate School of Science, Tokyo
Metropolitan University, 1-1 Minami-Osawa, Hachioji-shi, Tokyo 192-0397,
Japan. 7Department of Earth Sciences, School of Education and Integrated
Arts & Sciences, Waseda University, 1-6-1 Nishi-waseda, Shinjuku-ku, Tokyo
169-8050, Japan. 8Research Center for Advanced Science and Technology,
The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8904, Japan.

Abe et al. Progress in Earth and Planetary Science(2021) 8:13

9
Japan Meteorological Business Support Center, 1-1 Nagamine, Tsukuba-shi,
Ibaraki 305-0052, Japan. 10Faculty of Pure and Applied Sciences, University ofTsukuba, 1-1-1 Tennodai, Tsukuba-shi, Ibaraki 305-8571, Japan. 11Remote
Sensing Technology Center of Japan, 3-17-1 Toranomon, Minato-ku, Tokyo
105-0001, Japan. 12Department of Urban Engineering, The University ofTokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan.

Received: 23 September 2020 Accepted: 21 December 2020

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Page 10 of 10

RESEARCH ARTICLE
10.1029/2020JD033460
Key Points:• T he regional impacts of two types(A and B) of Cs-bearing solid
microparticles (CsMPs) werenumerically examined for the firsttime• Type B CsMPs could partly explain
the underestimation of simulated
total deposition over land
• The environmental behaviors of type
A CsMPs could vary substantiallydepending on size
Correspondence to:
M. Kajino,kajino@mri-jma.go.jp
Citation:
Kajino, M., Adachi, K., Igarashi,Y., Satou, Y., Sawada, M., Thomas
Sekiyama, T., et al. (2021). Depositionand dispersion of radio-cesium
released due to the Fukushima nuclear
accident: 2. Sensitivity to aerosol
microphysical properties of Cs-bearing
microparticles (CsMPs). Journal ofGeophysical Research: Atmospheres,
126, e2020JD033460. https://doi.
org/10.1029/2020JD033460
Received 7 JUL 2020
Accepted 5 DEC 2020

Deposition and Dispersion of Radio-Cesium Released
due to the Fukushima Nuclear Accident: 2. Sensitivity
to Aerosol Microphysical Properties of Cs-Bearing
Microparticles (CsMPs)
Mizuo Kajino1,2,3 , Kouji Adachi1 , Yasuhito Igarashi4,5 , Yukihiko Satou6 ,Morihiro Sawada7 , Tsuyoshi Thomas Sekiyama1 , Yuji Zaizen1 , Akane Saya1
Haruo Tsuruta8, and Yuichi Moriguchi9,

1
Meteorological Research Institute (MRI), Japan Meteorological Agency (JMA), Tsukuba, Ibaraki, Japan, 2Faculty ofLife and Environmental Sciences, University of Tsukuba, Tsukuba, Ibaraki, Japan, 3Institute of Radiation Emergency
Medicine (IREM), Hirosaki University, Hirosaki, Aomori, Japan, 4Institute for Integrated Radiation and Nuclear
Science (KURNS), Kyoto University, Kumatori, Osaka, Japan, 5College of Science, Ibaraki University, Mito, Ibaraki,
Japan, 6Collaborative Laboratories for Advanced Decommissioning Science (CLADS), Japan Atomic Energy Agency(JAEA), Tomioka, Fukushima, Japan, 7Graduate School of Life and Environmental Sciences, University of Tsukuba,
Tsukuba, Ibaraki, Japan, 8Remote Sensing Technology Center of Japan (RESTEC), Minato, Tokyo, Japan, 9Graduate
School of Engineering, University of Tokyo, Bunkyo, Tokyo, Japan

Abstract The dispersion and deposition of radio-cesium (137Cs) carried by two types (type A andtype B) of water-insoluble Cs-bearing solid microparticles (CsMPs) released due to the Fukushima
nuclear accident were simulated for the first time. The presence of type B CsMPs (70–400 μm foundin soil and 1–5 μm found in air), associated with the hydrogen explosion of Unit 1 in the afternoon ofMarch 12, could partly explain the simulated underestimation of total deposition over land by assumingthat 100% of the Cs carriers were water-soluble submicron particles (WSPs). Type A CsMPs (0.1–10 μm),
released from Units 2 or 3 in the midnight between March 14 and 15, traveled over the Kanto Plain, themost populated plain in Japan. Differences in the size distribution of type A CsMPs altered the surface
air concentration over Kanto substantially, by up to more than one order of magnitude. The major
deposition mechanisms varied among dry, wet, and fog (and/or cloud) depositions depending on thesize distribution and locations. The simulated activity fractions due to the CsMPs in the total deposition
were compared to those observed in surface soil for the first time. The observations could be explainedby the simulations for the locations under the influence of type B CsMPs. However, the simulations were
substantially underestimated for the locations influenced by type A CsMPs. There could be more fractionsof type A CsMPs emission in the source term and/or the simulated deposition rates of type A CsMPs were
underestimated.

1. Introduction© 2020. The Authors.
This is an open access article under
the terms of the Creative Commons
Attribution-NonCommercial-NoDerivs
License, which permits use anddistribution in any medium, provided
the original work is properly cited,
the use is non-commercial and no
modifications or adaptations are made.

KAJINO ET AL.

The Fukushima Daiichi Nuclear Power Plant (F1NPP or FDNPP) accident released substantial amountsof fission products into the atmosphere (Chino et al., 2011; Hirao et al., 2013; Katata et al., 2015; Saunieret al., 2013; Stohl et al., 2012; Terada et al., 2020, 2012; Winiarek et al., 2014; Yumimoto et al., 2016). The
contaminated air mass traveled over Japan and the radionuclides were deposited into and contaminatedterrestrial ecosystems; this phenomenon was discovered by field measurements (Igarashi et al., 2015; Ikehara et al., 2020; NRA [Nuclear Regulation Authority], 2012; Oura et al., 2015; Sanada et al. 2014, 2018; Torii
et al., 2012; Tsuruta et al., 2014, 2017, 2018, 2019) and has been investigated by numerical simulations (Kajino et al., 2018, 2019a; Morino et al, 2011, 2013; Nakajima et al., 2017; Sekiyama & Iwasaki, 2018; Sekiyama& Kajino, 2020; Saya et al., 2018; Sekiyama et al, 2015, 2017; Terada et al., 2020). The radionuclides weretransported and deposited over the ocean (Aoyama et al., 2016) and further to North America (Wetherbee
et al., 2012) and Europe (Masson et al., 2011, 2013). Radio-Cs (134Cs and 137Cs) is among the key radioisotopes due to its abundance and relatively long half-lives (2.06 and 30.1 years, respectively).
Thanks to extensive field measurements and numerical simulations, knowledge has been accumulated(Mathieu et al., 2018). Between 7 and 20 PBq of 137Cs was released to the atmosphere (Mathieu et al., 2018)
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and ∼3 PBq was deposited in terrestrial areas in Japan (NRA, 2012; Torii et al., 2012). Nakajima et al. (2017)
identified the transport pathways of radio-Cs over Japan by using numerical simulations together with hourly surface air activity concentration measurements from one hundred stations in Japan (Oura et al., 2015;
Tsuruta et al., 2014). Deposition mechanisms over land were systematically investigated by the altitudinal
analysis of aircraft measurements and a numerical simulation by Sanada et al. (2018). To better simulate theatmospheric behaviors of radio-Cs, multimodel ensemble studies have been conducted (Draxler et al., 2015,
Kitayama et al., 2018; Kristiansen et al., 2016; Sato et al., 2018, 2020; SCJ [Science Council of Japan], 2014).
However, although the properties of radio-Cs in the atmosphere are relatively easy to predict, that is, it ischemically inert and has an extremely low vapor pressure, there are still significant uncertainties in multimodel simulations, especially for deposition.
Several issues remain regarding the accurate simulation of radio-Cs, such as (1) deposition modeling, (2)horizontal resolution, and (3) aerosol microphysical properties. Due to the large uncertainty in deposition
modeling, even using the same meteorological field and the same source term, different transport models have predicted very different deposition fields and relative magnitudes of different deposition mechanisms (i.e., dry or wet depositions) (Sato et al., 2018). Most models do not include the fog and cloud droplet
deposition process, which could play a key role in radio-Cs depositions over the mountain forests of Japan (Hososhima & Kaneyasu, 2015; Imamura et al., 2020; Katata et al., 2015; Kajino et al., 2019a; Sanada
et al., 2018). There is also considerable uncertainty in meteorological modeling, which was indicated fromPart 1 of the current study (Kajino et al., 2019a). Even a single transport model with a single model domain
predicted very different deposition fields and contributions from different deposition mechanisms (dry, fog,in-cloud, or below-cloud wet depositions) depending on the selection of meteorological fields. Due to thecomplex topography in the Fukushima area and the surrounding prefectures, the horizontal resolution isimportant for accurate simulations of plume transport, as indicated by a comparison of 5, 3, and 1-km resolution simulations (Sekiyama & Kajino, 2020). However, the effect of aerosol microphysical properties has
not been fully investigated by numerical simulations.
Kaneyasu et al. (2012) showed that the size distributions of radio-Cs were very close to those of sulphate, which is water-soluble particles (WSPs) with submicron size ranges (0.1–1 μm in diameter) andconcluded that sulphate was the potential carrier of radio-Cs. WSPs were assumed to be the carrier ofradio-Cs for all previous simulations, except that of Adachi et al. (2013). Adachi et al. (2013) discovered Cs-glassy and spherical particles that were totally hydrophobic and larger than 1 μm, the environmental behaviors of which should be very different from those of WSPs. The particles were named
Cs-bearing microparticles (CsMPs) and extensively studied in the subsequent literatures (e.g., Igarashi
et al., 2019). Based on limited knowledge, Adachi et al. (2013) conducted a transport simulation of
CsMPs for a limited period (March 15) and showed a significant difference in the deposition patterns.
However, there was no knowledge on the abundance of CsMPs at that time, and only a few particles
were found from an air sample.
After Adachi et al. (2013), successful isolations of CsMPs from various samples, such as soil (Furuki
et al., 2017; Ikehara et al., 2018; Satou et al., 2016, 2018), plant leaves and agricultural materials (Kogure
et al., 2016; Okumura et al., 2019a; Yamaguchi et al, 2016, 2018), masks (Higaki et al., 2017), and rivers(Miura et al., 2018), were performed. Extensive studies have been performed on the physical and chemical
properties of CsMPs (Abe et al., 2014; Adachi et al., 2013; Kogure et al., 2016; Okumura et al., 2019b, 2019c;
Satou et al., 2015, 2016, 2018; Yamaguchi et al., 2016); thus, knowledge has been accumulated (Igarashi
et al., 2019). Based on the chemical compositions of CsMPs, locations of soil samples, and locations andtimings of air samples, power plant units involved, formation mechanisms, emission events, and transport
pathways were investigated (Hidaka, 2019; Igarashi et al., 2019; Ikehara et al., 2020; Onozaki et al., 2019;
Satou et al, 2015, 2016, 2018). Recently, Ikehara et al. (2020) reported the activity fractions of CsMPs in thesurface soil at 20 sites in the Fukushima prefecture, located 4.42–61.0 km from the F1NPP, which ranged
from 1.63% to 80.2% of the total activity. As a result, we have observational evidence that the radioactivityof CsMPs was significant in the environment, and we can quantitatively compare those observational datawith simulated CsMP deposition.
Based on the knowledge of CsMPs obtained so far, we conducted the first simulations for the dispersion anddeposition of radio-Cs in March 2011 by taking the presence of CsMPs into account. Then we summarized

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Table 1
Summary of Information for Type B and Type A Nonhygroscopic CsMPs
Type B

Type Aac

Sample contained CsMPs

Soil (<10 km NNW of F1NPP) , Air (25 km N of
F1NPP)bAir (170 km SW of F1NPP) , Soil (20 km NW of F1NPP)d, clothon vegetable fielde

Diameter found

70–400 μma, 1–5 μmb

1–10 μmc,d, < 1 μme

Hypothesized emission event

Unit 1, hydrogen explosion (March 12, 15:36 LT)

Unit 2, SRV openings (March 14, 21:30 and 23:25; March 15, 1:02
LT)f or Unit 3, core cooling water injection on March 15, 2:30
LT (Hidaka, 2019)fMarch 15, evening (Satou et al., 2015)f

Simulated duration of emission

March 12, 15:30–16:00 LT

f

Total emission amount

0.7 PBq

0.475 PBqf

March 14, 21:00–22:00 LTMarch 14, 23:00–March 15, 0:00
LTMarch 15, 01:00–02:00 LTa

Satou et al. (2018). bOnozaki et al. (2019). cAdachi et al. (2013). dSatou et al. (2016, 2018). eOkumura et al. (2019a). fThe emission event of type A CsMPs is still
controversial, but the timings of the SRV openings of Unit 2 were in the simulation, because emission amount associated with the water injection event of Unit
3 is not currently available. Also, there could be additional source of type A CsMPs, March 15 evening, as suggested by Satou et al. (2015).

the environmental behaviors of CsMPs and their differences from the previous simulations, assuming 100%of submicron WSPs.

2. Materials and Methods
Many parts of the simulation methods used in this study are the same as those in our Part 1 paper (Kajino
et al., 2019a). The common methods are roughly described herein, but the novel methods are extensively
described in this section.

2.1. Meteorological Simulation
In Kajino et al. (2019a), an ensemble analysis of multiple meteorological models and modules was conducted with a 3-km grid resolution. The results showed that the chemical transport simulation that used
the meteorological ensemble mean field (Met_EnsMean) was successful in terms of modeling cumulative
precipitation, total 137Cs deposition, and cumulative 137Cs surface air concentrations. Thus, in this study,
Met_EnsMean was selected as a reference meteorological field with which to conduct sensitivity tests of the
microphysical properties of CsMPs using a chemical transport model.
Each meteorological simulation and the methods used to produce the Met_EnsMean are explained in Table 1 and Figure 1 of Kajino et al. (2019a). Two meteorological models were used: the Japan Meteorological
Agency (JMA)’s non-hydrostatic model (NHM ver. 3.5; Saito et al., 2007) and the weather research andforecasting model (WRF ver. 3.5.1; Skamarock et al., 2008). Three types of simulations were conducted:
NHM using JMA’s meso-regional objective analysis (MANAL) as the boundary conditions, NHM using the
local ensemble transform Kalman filter (LETKF; Kunii, 2013), and WRF driven by MANAL. Several WRF
simulations with different cloud microphysics modules and different boundary layer turbulence schemes
were also conducted to depict the variations in surface air concentration and deposition of 137Cs dependingon the physics modules and to produce the ensemble mean of the WRF simulations (WRF_EnsMean).
Met_EnsMean was the ensemble mean of the NHM, NHM-LETKF, and WRF_EnsMean.
2.2. Transport Model
NHM-Chem (Kajino et al., 2019b) has been used for simulating the dispersion and deposition of radionuclides. NHM-Chem is a Eulerian chemical transport model (CTM) that can be offline-coupled or online-coupled with the NHM. The offline coupling mode was used for this study. Because the transport process is embedded as a subroutine of the NHM in the online coupling mode, the CTM is driven only by the
NHM. On the other hand, the offline coupling mode is composed of a standalone CTM and the interface
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Figure 1. (a) Model domain and the topography of the region. The cross indicates the location of the F1NPP. (b) Deposition area defined in this study. (0)Whole area indicates the sum of areas (1) to (9).

processor, which converts the meteorological model output into the input for the CTM; thus, other meteorological models can be used to drive NHM-Chem by preparing interface processors for other meteorologicalfields, such as Met_EnsMean and those made by WRF.
NHM-Chem considers major tropospheric photochemical reactions and aerosol dynamic processes, but
a simplified version for radionuclides was used in this study, as presented in Kajino et al. (2019a): no
chemical reactions and the relevant aerosol processes, such as nucleation, condensation and coagulation, were considered, while changes in size distribution due to deposition were considered by usinga triple-moment modal method, assuming log-normal size distributions. Changes in size distribution
during transport can be ignored for submicron particles, as in Kajino et al. (2019a) and almost all theprevious simulations, because their gravitational settling velocities are negligibly small. In contrast, thesize distributions of CsMPs, the diameters of which are as large as 10–100 μm, should vary significantly
during transport, as the larger particles are readily deposited on the surface within 10–100 km (e.g., asshown later in Figure 3). The predicted size distributions were applied for the calculations of the dry
deposition, gravitational settling, and below-cloud scavenging processes. For in-cloud scavenging, theprescribed cloud condensation nuclei (CCN) activation fractions were applied as described later in Section 2.3.2. This is the only difference from Kajino et al. (2019a) in the elementary processes of the CTM:
the prescribed “hygroscopicity” (κ = 0.4) was applied for the simulation of WSPs for the calculation of
CCN activation in Kajino et al. (2019a).
2.3. Simulation Setup
2.3.1. Model Domain and Simulation Period
The model domain, which covers the eastern and northeastern parts of Japan (213 × 257 grid cells with a
3-km grid resolution using a Lambert conformal coordinate system) is presented in Figure 1a. There are 48
vertical layers, up to ∼22 km above sea level (ASL), for the NHM and 27 layers, up to 100 hPa, for the WRF
on a terrain-following coordinate system with vertically stretched grids (with more grids at lower levels to
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resolve boundary layer dynamics and fewer grids in the free troposphere). Since the vertical grid structuresof the NHM and WRF are different, as is the standalone CTM part of the offline NHM-Chem, the meteorological fields were vertically interpolated to the vertical layers of the CTM, which include 19 layers up to
10 km ASL. The output time interval of the NHM and WRF are 1 h, and thus the input/output time interval
for the CTM is also 1 h. The horizontal grid of the CTM was the same as that of the meteorological models.
The simulation period is from 00 Cooridated Universal Time (UTC) on March 11 to 00 UTC on April 1, witha spin-up period of 20 h; the onset of emission was 20 UTC on March 11.
A tagged simulation method was used in this study. Tagged tracers (137Cs) were released every 30 min at
a rate of 1 TBq h−1. Each simulation stopped when the maximum concentration over the model domain
was below a very small value (10−15 Bq m−3). After all the simulations, the prescribed emission rate at each
30 min interval was multiplied by the corresponding tagged concentration and deposition fields, and all thetagged simulations were summed to derive the full simulation results. The tagged method is useful because
the environmental behaviors of radionuclides should not be very nonlinear. However, note that due to numerical errors, the tagged results were different from those of the standard simulation: the results assuming
WSPs for the whole period presented in this study were different from those of Met_EnsMean presented in
Kajino et al. (2019a). The difference is also due to using different settings for the microphysical propertiesof the WSPs (see Section 2.3.3) and different formulations of CCN activation between Kajino et al. (2019a)
and this study (see Section 2.2).
2.3.2. Emission Amount and Period for 137Cs and CsMPs
The most commonly used estimation by the Japan Atomic Energy Agency (JAEA) (Katata et al., 2015) wasused for the emission inventory of 137Cs in the simulation. WSPs were assumed to be the radionuclide carrier particles in the same manner as in Kajino et al. (2019a), except for two periods, when two types of CsMPs
were assumed to be emitted.
The current knowledge on the two types of CsMPs is summarized in Table 1. The readers can find very
detailed information on CsMPs from the extensive review by Igarashi et al. (2019); this informationis briefly described here. Type A CsMPs were first found by Adachi et al. (2013) in a sample from ahigh-volume air sampler filter in Tsukuba, ∼170 km southwest (SW) of the F1NPP. Type A CsMPs
were also found by Satou et al. (2016) in the soil sampled ∼20 km northwest (NW) of the F1NPP. The
diameters of the isolated type A CsMPs thus far range from 1 to 10 μm; these particles have sphericalshapes and high specific radioactivity (Adachi et al., 2013; Igarashi et al., 2019; Satou et al., 2016). Additionally, type A CsMPs smaller than 0.5 μm in diameter were recently found (Okumura et al., 2019a).
Type B CsMPs were first found by Satou et al. (2018) in soil samples in Fukushima, within 10 km
north-northwest (NNW) of the F1NPP. The diameters of the isolated type B CsMPs so far range from 70
to 400 μm; these particles have irregular shapes and low specific radioactivity but high activity values,
reaching beyond 1 kBq (Igarashi et al., 2019; Satou et al., 2018). Recently, type B CsMPs with a few
micrometers in diameter (1–5 μm) were also found in an air sample at a station ∼25 km north of the
F1NPP (Onozaki et al., 2019).
Satou et al. (2018) discussed that the emission of type B CsMPs is associated with the hydrogen explosionof Unit 1, which occurred on March 12, 15:36 local time (LT; UTC+9). This is because the isotopic activity
ratios (134Cs/137Cs) of the type B CsMPs were similar to those of the estimated fuel composition of Unit 1
and because the dispersion of such large particles (∼100 μm in diameter) over significant distances (∼20 km
away from the F1NPP) should require considerable energy, such as a hydrogen explosion. According to
Katata et al. (2015), the simulated duration of the emission on March 12 lasted from 15:30 to 16:00 LT, andthe total emission amount was 0.7 PBq (Table 1).
On the other hand, the timing of the emission of type A CsMPs is still controversial. The activity ratios of thetype A CsMPs were significantly different from those of Unit 1, but it is difficult to identify the origin of theparticles because the isotopic activity ratios of Units 2 and 3 were similar to each other (Igarashi et al., 2019;
Satou et al., 2018). Judging from the air sample that contained type A CsMPs (sampled from March 14 at
21:00 to March 15 at 9:10 LT) and the tagged simulation results, Adachi et al. (2013) estimated that the air
mass that contained type A CsMPs should have started in the F1NPP between March 14 at 17:00 to March
15 at 2:00 LT. According to Katata et al. (2015), during this period, three peak emission events occurred, andthe safety relief valve (SRV) of Unit 2 opened at 21:00 and 23:00 LT on March 14 and at 1:00 LT on March 15.
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In contrast, based on the engineering of the reactor, Hidaka (2019) argued that the formation of type A CsMPsin Unit 2 is unlikely. The author proposed that type A CsMPs were also formed due to the hydrogen explosionof Unit 3 at 11:01 LT on March 14. The particles were mostly dispersed toward the ocean during the time offormation, but part of them were deposited on the reactor building of Unit 3 and could have been resuspended
and released into the environment due to a restart of the core cooling water injection at 2:30 LT on March 15.
This argument is consistent with the result of the atmospheric simulation: the environmental dose rate measured in Tsukuba peaked twice (at 4:24 (0.5 μSv h−1) and 9:12 (1.1 μSv h−1) LT on March 15); the latter peak
was associated with the water injection time according to the tagged simulation of Adachi et al. (2013). Nevertheless, we had to use the events of Unit 2 as the source for the type A CsMPs, which amounted to 0.475 PBq(Table 1), because the Katata et al. (2015)’s source term does not consider emissions of the Unit 3 during thetime. There could be other emission events of type A CsMPs, such as on the evening of March 15, as suggested
by Satou et al. (2015). However, the measurement of these particles was conducted using soil samples, so it isdifficult to associate the samples with the emission events. Thus, the events on the evening of March 15 were
not assessed in the current study as a CsMP type A emission source.
2.3.3. Microphysical Properties of 137Cs and CsMPs
In Kajino et al. (2019a), the 137Cs-bearing particles were assumed to be WSPs in all cases, and the microphysical properties of these particles were set as follows: the number-equivalent geometric mean dry diameter(Dg,n,dry) was 102 nm, the geometric standard deviation (σg) was 1.6, the particle density (ρp) was 1.83 × 103 kg
m−3 (assuming ammonium sulfate) and the hygroscopicity (κ) was 0.4 (to calculate hygroscopic growth and
CCN activation). In Adachi et al. (2013), for simplicity, we excluded the in-cloud scavenging processes (CCN
activation and subsequent cloud microphysical processes), as the CsMPs were water-insoluble and thus not
activated as CCN. However, this is not true in all cases: even relatively large nonhygroscopic particles canbe activated at higher supersaturations (e.g., Petters and Kreidenweis, 2007). To consider the activation of
nonsoluble particles and to make the simulation simpler, the CCN activation fractions of the CsMPs wereprescribed for the sensitivity simulations.
Tables 2 and 3 summarize the sensitivity tests for the microphysical properties of type B and type A CsMPs,
respectively. The number-equivalent geometric mean diameters (Dg,n) at emission were set at logarithmic
intervals from 0.1 to 100 μm for type B and 0.1 to 10 μm for type A. Note that Dg,n in the environment = Dg,n,dry
because CsMPs are nonhygroscopic. σg was set to unity (monodispersed) for the type B simulation to see thesimple relationship between size and transportation/deposition. The weather pattern was simple, the wind
was mostly southerly during the 30-min emission duration, and in reality, most of the type B CsMPs were
deposited within 10 km of the F1NPP, as these CsMPs were large. On the other hand, type A CsMPs traveleda longer distance (more than 100 km) over complex terrain and/or within complex meteorological fields,
so sensitivity tests were conducted for σg values ranging from monodispersal (σg = 1.0) to broad dispersal(σg = 2.0). Information on σg has not yet been yielded from field observations, but this is a key parameter
for atmospheric simulations because larger particles are deposited more rapidly and substantial changes
in size distribution during transport could occur for broadly dispersed particles larger than 1 μm in diameter. For example, when Dg,n = 1 μm, the mass-equivalent mean diameter is Dg,m = 1 μm for σg = 1.0 but
Dg,m = 4.2 μm for σg = 2.0, according to the relationships between number-equivalent and mass-equivalentmean diameters as follows:





2

(1)
ln
Dg,m ln Dg,n  3 ln  g .
Table 2
Sizes and Prescribed Activated Fractions of Type B CsMPs
Experiment name

B15

B14

B13

B12

B11

B10

B09

B08

B07

B06

B05

B04

B03

B02

B01

B00

Dg,n (μm)a

100

63.1

39.8

25.1

15.8

10.

6.31

3.98

2.51

1.58

1.0

0.631

0.398

0.251

0.158

0.1

b

1

1

1

1

1

1

0

0

0

0

0

0

0

0

0

0

Cloudb

1

1

1

1

1

1

1

1

1

0

0

0

0

0

0

0

Fog
a

b

Number-equivalent geometric mean diameter at emission. Derived using prescribed supersaturations (0.01% for fog and 0.1 for clouds); κ = 0.

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The settling velocity is 17.6-fold larger for particles with σg = 2.0 than for particles with σg = 1.0 for same
Dg,n. The prescribed activated fractions were calculated using prescribed supersaturations of 0.01% and 0.1%
for fog and clouds, respectively. The activated fractions of fog were applied for 137Cs at the bottom layer ofthe model domain, and those of clouds were applied above the bottom layer. This method comprises several
uncertainties. (1) Changes in activated fractions during transport due to changes in size distributions cannot be considered. (2) Substantial electrical charges of the type A CsMPs significantly enhanced scavenging
by cloud droplets (Dépée et al., 2019), but this effect was not considered. Electrical charges can reduce thesurface tension of droplets so that the activated fraction can be enhanced (Holländer & Schumann, 1979).
(3) Fog deposition over mountains could occur when clouds over the plains approach the mountain surface(Hososhima & Kaneyasu et al., 2015; Imamura et al., 2020; Sanada et al., 2018); therefore, it might be better
to use the activated fraction for clouds.
As shown in Tables 2 and 3, we defined the experiment names as follows: For the type B case, B00-B15
represent simulations from 0.1 to 100 μm of Dg,n, respectively. For the type A case, A00 to A10 represent
simulations from 0.1 to 10 μm of Dg,n. In addition, for the type A case, symbols from a to d are added to theend of the experiment names indicating the sensitivity of σg from 1.0 to 2.0, respectively. For example, B10A05c means a sensitivity study with (Dg,n, σg) = (10 μm, 1.0) for type B CsMPs and (Dg,n, σg) = (1.0 μm, 1.6)
for type A CsMPs.
For the WSPs, the Dg,n and σg at emission were set as 0.5 μm and 1.6, respectively, with prescribed activation
fractions of 1.0 for both fog and clouds. Note that Dg,n in the environment = Dg,n,dry because hygroscopic
growth was not considered in the simulation method. ρp was assumed to be 1.83 × 103 kg m-3 in all cases
because the particle densities of the CsMPs were not available.
2.4. Observation Data and Deposition Areas
The radioactivity observation data sets used in Kajino et al. (2019a) were also used in this study: aircraft
measurement data for cumulative depositions of 137Cs were provided by NRA (2012) and Torii et al. (2012)and hourly surface air concentrations of 137Cs were provided by Tsuruta et al. (2014) and Oura et al. (2015),
except for the Tokai site (station d of Figure 11), for which data were obtained from Okura et al. (2012).
Tsuruta et al. (2014) developed a method to retrieve hourly surface air concentrations of 137Cs using thefilter tapes for the suspended particle matter (SPM; 100% cut-off at 10 μm) monitoring. 137Cs concentration
data measured at 99 stations were released by Tsuruta et al. (2014) and Oura et al. (2015). For the Tokai
site, the filter sampling was made only when the gamma dose rates were high with sampling intervals from20 min to half a day (Okura et al., 2012). For the sake of comparison against the hourly simulation data,
we produced hourly data from the raw data of time intervals less than 1 hour (20 and 45 min). Because
NRA (2012) did not provide deposition amounts for the restricted flight zone of the F1NPP (an area 3 km

Table 3Size Distributions and Prescribed Activated Fractions of Type a CsMPs
Experiment name

A10a-da

A09

A08

A07

A06

A05

A04

A03

A02

A01

A00

b

Dg,n (μm)

10.

6.31

3.98

2.51

1.58

1.0

0.631

0.398

0.251

0.158

0.1

Fog (σg = 1.0)c

1

0

0

0

0

0

0

0

0

0

0

c

Fog (σg = 1.3)

0.97

0.53

0.05

0

0

0

0

0

0

0

0

Fog (σg = 1.6)c

0.98

0.84

0.51

0.17

0.03

0

0

0

0

0

0

c

Fog (σg = 2.0)

0.99

0.96

0.87

0.68

0.43

0.20

0.07

0.01

0

0

0

Cloud (σg = 1.0)c

1

1

1

1

0

0

0

0

0

0

0

Cloud (σg = 1.3)c

1

1

1

0.99

0.74

0.14

0

0

0

0

0

Cloud (σg = 1.6)c

1

1

1

0.99

0.91

0.64

0.27

0.05

0

0

0

c

1

1

1

1

0.98

0.91

0.76

0.51

0.26

0.1

0.03

Cloud (σg = 2.0)aba–d correspond to σg, namely, a, b, c, and d are experiments with σg = 1.0, 1.3, 1.6, and 2.0 respectively. Same for A00 to A09. Number-equivalent geometricmean diameter at emission. cDerived using prescribed supersaturations (0.01% for fog and 0.1 for clouds); κ = 0.

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in radius), in this study, radiation monitoring data, which totaled 45 TBq, collected using an unmanned
helicopter and provided by Sanada et al. (2014) were used. Precipitation data were not used in the study because the simulated precipitation was already evaluated in Kajino et al. (2019a). The detection limits of observed surface air concentration of 137Cs were ∼0.1–0.6 Bq m−3 and 0.1–0.3 Bq m−3 for Tsuruta et al. (2014)
and Oura et al. (2015), respectively. The detection limit of observed deposition was ∼15 kBq m−2 (Torii
et al., 2012).
The deposition areas used for the process analysis in both Kajino et al. (2019a) and this study are shown in
Figure 1b. To focus on the areas in which CsMPs were deposited, we discussed only Hamadori (Area 1), a
coastal area in Fukushima prefecture, which includes the F1NPP, Tochigi and Gunma (Area 6 and Area 7),
where the mountains were contaminated mainly due to fog (or cloud) deposition, and Iwaki-Ibaraki and
Ibaraki-Chiba (Area 8 and Area 9), which are south and downwind of the F1NPP for assessing type A particle transportation. Fog deposition partly contributed to deposition in Iwaki-Ibaraki (Kajino et al., 2019a;
Sanada et al., 2018), but there was almost no fog contribution to Ibaraki-Chiba because the contaminatedareas were located over the Kanto Plain (Figure 1a). The Kanto Plain, which includes Ibaraki, Chiba, Tochigi, Gunma, and Tokyo prefectures, is the most populated plain in Japan, as shown in Figure 1b.
The observed total amount of 137Cs deposited over the entire land area of Japan (denoted as Area 0) was 2.59
PBq excluding the restricted flight zone and 2.64 PBq including it. The largest deposition (1.43 PBq) was
observed in Area 1 (including deposition in the restricted zone). The observed depositions over Areas 6 and7, and Areas 8 and 9 were 0.21 and 0.16 PBq, respectively.

3. Results
3.1. Type B
Surface weather charts for the periods during which emissions of type B and type A CsMPs occurred arepresented in Figure 2, together with the timings of their possible emission sources. The hourly mean measured and simulated wind vectors at F1NPP are also presented. The wind speed and direction were measured on the premises of the F1NPP by a monitoring car, https://www.tepco.co.jp/nu/fukushima-np/f1/

Figure 2. (a–d) Surface weather charts provided by the Japan Meteorological Agency for the assumed emission events of type B and type A CsMPs from March
12 to 15, 9 local time (LT) (0 UTC). (e) Observed (red) and simulated (blue) hourly wind vectors at the F1NPP.

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indexold-j.html (last accessed: June 5, 2020). There are significant discrepancies in the wind field due tothe differences in the measurement height (probably at 2 m) and simulation height (10 m) and the coarse
resolution (Δx = 3 km) needed to resolve the wind field by the coast.
During the emission of type B CsMPs (from the hydrogen explosion of Unit 1), the southerly wind associated with a migrating anticyclone situated to the south of the F1NPP transported type B CsMPs northwardand deposited them as shown in Figure 3. The wind directions of the measurement and simulation weresimilar, but the simulated wind direction was shifted slightly toward the east, in the direction of the ocean.
Figure 3 shows the cumulative concentration and deposition of 137Cs at different sizes of CsMPs (0.1, 1, 10,
and 100 μm). The transport distances and deposition amounts are summarized in Figure 4. The simulated
deposition amounts and the deposition processes that contributed to the model are summarized in Figure 5.
There were significant differences in both the concentration and deposition fields for the CsMPs with diameters ranging between 0.1 and 100 μm, as shown in Figure 3. Under the influences of the anticyclone, noprecipitation or fog events occurred during the emission and transport of type B CsMPs in the afternoon ofMarch 12; thus, only dry deposition contributed to the overall deposition (Figure 5). In Figure 3, the horizontal distributions of the cumulative surface air concentrations remain unchanged from a Dg,n of 0.1 μm
up to a Dg,n of 10 μm because the gravitational settling velocities are low for this size range. On the other
hand, the deposition amounts significantly increased from a Dg,n of 10 μm due to the significant increase in
dry deposition velocity because of the large inertia of the particles. Consequently, the regional impact of dry
deposition (or the deposition amount over downwind areas) was largest at a Dg,n of 10 μm (these particles
were widely distributed in significant amounts). Certainly, the total deposition amount was largest at a Dg,nof 100 μm, but the contaminated area was limited (to only near the emission source).
This trend is clearly shown in Figure 4. In Figure 4a, the transport distances (defined as Dist66, the smallest radius of a circle around the F1NPP in which 66% of the total deposition occurred) below a Dg,n of
10 μm were almost the same (>100 km), which indicates that dry deposition velocities were unsignificant, such that the meteorology determined the dispersion and deposition of the particles. On the other
hand, when Dg,n was larger than 10 μm, the gravitational settling velocities became significantly larger

Figure 3. The simulated (upper panels) cumulative concentration and (lower panels) deposition of 137Cs carried by type B CsMPs for different Dg,n values (0.1,
1, 10, and 100 μm, from left to right). The experiment names are depicted in the bottom.

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Figure 4. (a) The simulated transport distance (Dist66: the smallest radius of a circle around the F1NPP in which 66%
of the total deposition amount is included). The dashed line indicates the model grid resolution (Δx = 3 km), andthe dashed dotted line is a slope inversely proportional to the square of Dg,n. (b) The total deposition amounts of 137Cs
carried by type B CsMPs at different Dg,n values in the model domain over land (open circles and dashed line) and over
the whole area (land and ocean; closed circles/solid line).

and affected the transport distances of the particles. For the size ranges, the decrease in Dist66 was inversely proportional to the square of Dg,n (dashed dotted line). Certainly, Dist66 cannot be lower than themodel Δx (= 3 km) because NHM-Chem is a Eulerian model. It cannot resolve the phenomena smaller
than Δx. However, judging from the dashed dotted line (inversely proportional to square of diameter andthus inversely proportional to gravitational settling velocity), Dist66 could be extrapolated to ∼1 km at a
Dg,n of 100 μm.
While the transport distance changes with Dg,n values from 10 to 100 μm, the deposition amounts started toincrease for particles with Dg,n values from 1 to 10 μm due to a significant increase in dry deposition velocity(Figure 4b). The total deposition amounts over the model domain were unchanged above 10 μm, but thecontaminated areas significantly differed (Figures 3 and 4a). Figure 4b shows the simulated depositions
over both the whole area (land and ocean) and land only. The aircraft observation data were collected only
over the land, but the simulation caused depositions over both the land and ocean due to its crude Δx (themodel grid including the F1NPP was covered partly with ocean (39%) and partly with land (61%) and deviations in the wind direction (the simulated wind direction was shifted slightly eastward compared to theobserved wind direction). Based on observational studies (Chino et al., 2016; Satou et al., 2015), the contamination caused by type B CsMPs occurred mainly over land. Serious contamination occurred northwestof the F1NPP, as shown in Figure 9. Satou et al. (2015) and Chino et al. (2016) used isotopic activity ratiosof 110mAg/137Cs from soil samples and 134Cs/137Cs from an unmanned helicopter survey, respectively, andsuccessfully identified the source reactor units. The 137Cs deposition due to type B CsMPs occurred not in
the center of the northwestern zone but in the vicinity of the zone and toward the north (i.e., in the NNW
direction), ∼15 km from the F1NPP.

In Kajino et al. (2019a), from an ensemble analysis of multiple meteorological models and physical modules, we concluded that the simulated total deposition amounts over land (1.23 PBq of Met_EnsMean), assuming WSPs as carriers of 137Cs, significantly underestimated the aircraft observation (2.59 PBq) due to theunderestimation of deposition rates of NHM-Chem. Among the deposition processes, we hypothesized thatthe below-cloud scavenging rate could be the main reason for underestimation because the value we used
was based on theories that have been known to underestimate values derived by field experiments (e.g.,
Wang et al., 2010; Zhang et al., 2013). Figure 5 indicates another reason for the discrepancy between thesimulated and observed total deposition. The difference between the aircraft observations (2.64 PBq, withadditional deposition within 3 km of the F1NPP) and the current simulation, assuming 100% WSP carriers(1.19 PBq), was 1.45 PBq. Approximately half of this underestimation can be explained by the presence oftype B CsMPs (0.7 PBq), if the emission estimated by Katata et al. (2015) during the hydrogen explosion of
Unit 1 consisted solely of type B CsMPs and the diameters of all the particles were much larger than 10 μm

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10.1029/2020JD033460(as suggested by Satou et al., 2018 [70–400 μm]) so that they were readily deposited onto the ground surface within less than 100 km from the
F1NPP. Onozaki et al. (2019) found type B CsMPs smaller than 10 μm in
diameter from air samples collected at a station located 25 km north of
the F1NPP. The relative contributions of radioactivity from type B CsMPslarger than 10 μm and smaller than 10 μm would have been a key factor
controlling the surface air concentration and deposition of radio-Cs during the emission event. By considering the presence of type B CsMPs, theestimated emission amounts during the emission event could be significantly different from the value previously estimated by assuming WSP
carrier aerosols (e.g., Katata et al., 2015), as presented in Section 3.4.

3.2. Type A
During the emission of type A CsMPs (SRV openings of Unit 2 orhydrogen explosion of Unit 3 and subsequent water injection), twin
extratropical cyclones, often observed in the early and late stages of
winter, were situated north and south of the Japanese archipelago andmigrated eastward (Figure 2). Both the observed and simulated winddirections were northerly during the emission period, although there
were some deviations between them. The simulated wind direction
was more inland (toward the west) and more toward the ocean (toward the east) before and after 0:00 LT on March 15. Figure 6 shows
the cumulative concentration and deposition of 137Cs for CsMPs withdifferent Dg,n values (0.1, 1, and 10 μm) and different σg values (1.0
and 2.0). As shown in Figure 6, the anticyclonic circulation between
the two cyclones moved the air mass containing the type A CsMPs
to the Kanto Plain (Figure 6). The transport distances and deposition
amounts are summarized in Figure 7.
In the type A simulation, σg was altered between 1.0 (monodispersed),
1.3 (narrowly dispersed), 1.6 (standard dispersal), and 2.0 (broadly
dispersed). The type A CsMPs were transported over a long distance;
therefore, substantial changes in the size distribution (Dg and σg) of
the particles could occur during transport. This is also because, in
addition to Dg, σg is also a key factor: atmospheric behaviors change
substantially depending on σg even for the same Dg. For example, theratios of the concentration of particles with a median diameter characterized by mass (Dg,m) to those of particles with a median diametercharacterized by number (Dg,n) are 1, 1.2, 1.9, and 4.2 for σg values of 1.0, 1.3, 1.6, and 2.0, respectively(see Equation 1).

Figure 5. Upper panels: the observed (solid line) and simulated total
depositions of 137Cs for (circles) different sensitivity tests of type B CsMPsand (dashed line) 100% WSPs over the entire land area of Japan (denoted as
Area 0), as defined in Figure 1. Note that the simulation includes deposition
over both land and ocean. Lower panels: the simulated contributions ofeach deposition process to the deposition of 137Cs by type B CsMPs: red,dry deposition; green, fog deposition; sky blue, wet deposition by solid
precipitation; navy, wet deposition by liquid precipitation.

In Figure 6, the Dg,m values are 0.1, 0.42, 1.0, 4.2, 10.0, and 42.2 μm. For the type A CsMPs, other deposition
processes, such as fog deposition and wet deposition, occurred, as shown in Figure 8, while only dry deposition affected the type B CsMPs. However, features similar to those in Figure 3 are observed: no significant
difference in the cumulative concentration of particles smaller than 1.0 μm (for Dg,m), gradually increasing
deposition amounts and gradually decreasing cumulative concentrations of particles ranging between 1.0
and 10 μm (for Dg,m), and a significant decrease in the cumulative concentration of particles with Dg,mgreater than 10 μm. Figures 7a and 7b also show similar features. The Dist66 decreased in a manner inversely proportionally to the square of Dg,n for σg of 2.0 and Dg,n values larger than ∼3 μm (Dg,m = 12.2 μm) in
Figure 7a. Adachi et al. (2013) found substantial amounts of type A CsMPs in the air samples in Tsukuba,
170 km from the F1NPP; therefore, we can safely conclude that Dg,m could be much smaller than 42.2 μm(Dg,n = 10 μm, σg = 2.0) (see the rightmost panels of Figures 6 and 7a). This finding is also supported by thefact that type A CsMPs with diameters much larger than 10 μm have not been found in any field experiments so far (Igarashi et al., 2019).
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Figure 6. The information presented in this figure is the same as that in Figure 3, but for the type A CsMPs with different Dg,n (0.1, 1, and 10 μm) and σg (1.0
and 2.0) values. The corresponding Dg,m and experiment names are depicted in the bottom.

Figure 8 presents the total deposition amounts over the whole terrestrial area of Japan (Area 0) and regions
affected by the deposition and transport of type A CsMPs (i.e., Areas 1, 6, 7, 8, and 9), together with thecontributions of processes to the total deposition of type A CsMPs. In Area 1, the contribution of dry deposition to type A deposition was almost 100%. As the particle sizes increased, the total deposition amountapproached the observed value, but it is unlikely as described before (Dg,m could be much smaller than
42.2 μm). In Areas 6 and 7, the simulated deposition based on an assumption of 100% WSPs (dashed line)
underestimated the observed deposition by ∼50% but was larger than the results of any other sensitivity
tests for the type A CsMPs. This is because the prescribed activated fractions of the type A CsMPs, withsmaller diameters for clouds and fog, were either 0 or less than 1 (Table 3), whereas those for WSPs are
assumed to be 1. As the sizes were larger for σg values of 1.0 and 2.0, the contributions of fog deposition andwet deposition were enhanced and peaked at 0.07 PBq in the areas. However, the total deposition decreases
as the sizes increase further (i.e., Dg,n > 2 μm for σg = 2.0) because the type A CsMPs were deposited before
reaching areas such as the Ibaraki and Saitama prefectures. The contributions of wet and fog depositions
increased to 60% and 50%, respectively, at larger sizes. This is the major difference from the study by Adachi
et al. (2013), in which the activated fractions of the CsMPs are assumed to be zero. Whereas the presenceof type B CsMPs could partly explain the underestimation of simulated deposition, the presence of typeFigure 7. The information presented in this figure is the same as that in Figure 4, but for type A CsMPs at different σgvalues: 1.0 (black), 1.3 (red), 1.6 (blue), and 2.0 (green). Note that the simulated depositions over the whole area (landand ocean) (shown by the closed circles and solid lines) are presented in Figure 7b.

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Figure 8. The information presented in this figure is the same as that in Figure 5, but for type A CsMPs at different σg values (1.0 and 2.0) over the whole landarea (Area 0), Hamadori (Area 1), Tochigi and Gunma (Areas 6 and 7), and Iwaki-Ibaraki and Ibaraki-Chiba (Areas 8 and 9). Unlike Figure 5, the simulation
results shown in this figure do not include depositions over the ocean, consistent with the observations.

A particles could not explain the underestimation of deposition in Areas 6 and 7. For Areas 8 and 9, thecontribution of dry deposition was dominant, but the same patterns were observed: the deposition amounts
increased as the sizes increased but started to decrease as the sizes grew even larger (i.e., Dg,n > 3 μm for
σg = 2.0). Because Areas 8 and 9 were closer than Areas 6 and 7 to the F1NPP along with the transport
pathway of type A CsMPs, the Dg,n showing a deposition peak of σg = 2.0 for Areas 8 and 9, which was larger
than that for Areas 6 and 7. The simulated peak depositions (0.18 PBq) exceeded the observed deposition(0.16 PBq). For Areas 8 and 9, the presence of type A CsMPs could explain the simulated underestimationof deposition. At smaller sizes, the total deposition in the sensitivity tests for type A CsMPs was smaller than
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that for the WSPs. This is because type A CsMPs were deposited over the ocean off the coastline of Ibaraki
prefecture by wet deposition. As presented in the upper panels of Figure 6, a part of the air mass containing
type A CsMPs was transported over the ocean. Overall, for Area 0, the presence of type A CsMPs could not
explain the underestimation of the simulated depositions. The contributions of dry deposition were thelargest, and those of wet and fog depositions were up to 45% and 10%, respectively, depending on the size
parameters.
The sizes of the type A CsMPs isolated from field experiments ranged from 0.1 to 10 μm (Adachi et al., 2013;
Satou et al., 2016, 2018; Okumura et al., 2019a; Igarashi et al., 2019 and references therein). The sensitivity,
especially focusing on the observational data, can be obtained from the simulation differences at σg = 1.0(monodispersal), as shown in Figures 6–8; the environmental behaviors of the CsMPs with diameters of 0.1,
1, and 10 μm were drastically different. The horizontal distributions were also different (Figure 6). Amongthe total emissions assumed in the simulation (0.475 PBq), only 0.01–0.03 PBq was deposited over the model
domain for particles with a size of 1 μm or smaller, while more than 10 times (0.4 PBq; more than 80% of theemission) that amount was deposited for particles with a size of 10 μm (Figure 7b). The deposition mechanisms could be drastically different, especially for Areas 6 and 7 (Figure 8): the contribution of dry deposition was almost 100% for particles with a size of 1 μm or smaller, whereas the contribution of fog deposition
was the largest (50%) for particles with a size of 10 μm. For particles ranging in size between 0.1 and 10 μm,
wet deposition also significantly contributed to the overall deposition and varied substantially up to 60%.

3.3. Horizontal Distributions of Cumulative Concentrations and Depositions
Figures 9 shows the horizontal distributions of the observed and simulated cumulative surface air concentrations (Bq m−3 h), while Figure 10 shows the horizontal distributions of the observed and simulated depositions (kBq m−2). From left to right, the figures show the simulation results for the models using WSPs forthe whole period, sensitivity simulation results showing the maximum areal total deposition (Max_depo;
B13-A07 d), minimum areal total deposition (Min_depo; B01-A01a), maximum areal mean cumulative
concentration (Max_conc; B11-A01a), and minimum areal mean cumulative concentration (Min_conc;
B15-A10 d). We found that the results showing the best R in deposition was identical to Min_depo.
The correlation coefficients of the sensitivity simulations did not significantly differ for the cumulative
concentrations and showed relatively high values (0.8), as found in Kajino et al. (2019a) (Figure 9). On the

Figure 9. Horizontal distributions of the observed and simulated cumulative surface air concentrations of 137Cs from 13 to 23 March and scattergrams between
observations and the sensitivity simulations (from left to right, with only WSPs, maximum and minimum areal total depositions (Max_depo and Min_depo,
respectively), and maximum and minimum areal mean cumulative concentrations (Min_conc and Min_conc, respectively). Note that best R in deposition
was identical to Min_depo. The correlation coefficient (R), observed areal average (Obs. Ave.), and mean bias (MB) values are embedded in the panels. The
observation data in each model grid cell were averaged and used for the comparison. The combinations of experiment names are embedded in the bottom.

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other hand, MB varied substantially, from −33.1 to −109.1 Bq m−3 h, with an observed average of 376.1
Bq m−3 h. In terms of MB, the best performances were yielded from the simulations with low deposition
rates (Min_depo, Max_conc), but the spatial R values were somewhat smaller than those in the other cases.
Cases with relatively high MB values were somewhat unlikely because the observed CsMPs were muchlarger than these cases. However, these less likely smaller size cases (Min_depo) showed better R values
for depositions (Figure 10). In the cases in which the performance of the MB was better for given depositions (Max_depo and Min_conc), the R values were much lower (0.3–0.4). These cases were also somewhat
unlikely based on the observations (type B CsMPs might be reasonable, but type A CsMPs should not be
so large). These contradictory statistical metrics indicate that the discrepancies between the observed andsimulated depositions have not yet been resolved by considering the presence of type A and type B CsMPs.
Further studies for the improvements of simulated depositions are indispensable.

3.4. Transport Events and CsMP Pathways
Figure 11 shows the time series of the observed and simulated surface air concentrations of 137Cs at several
stations in the Fukushima, Ibaraki, and Saitama prefectures over the transport pathways of type A CsMPs.
The pathway of type B CsMPs was not depicted in the figure, because the pathway was simple and no observed values were available during the period. It was not depicted also because most of 137Cs from type B
CsMPs might not be captured by the SPM filters as most of their sizes could be larger than the cut-off sizeof the SPM sampling (100% cut-off at 10 μm). For the type B CsMPs case, the comparison of simulated
and observed deposition may be more useful, which was presented later in Section 3.5. The type B CsMPs
were emitted in the afternoon of March 12 and arrived at a SPM station, located 25 km NNW of the F1NPP, after 1 h. The simulated concentration was enhanced for 3 h, from 16 to 19 LT. Onozaki et al. (2019)
isolated CsMPs from air samples at the same station, but collected them later, from 20 to 21 LT and 0 to
1 LT on March 13. The emission of type B CsMPs could have lasted longer after the hydrogen explosion.
The simulated 3-h cumulative concentration spanned more than a 4-fold difference, from 1,470 to 6,900
Bq m−3 h, depending on the particle sizes (0.1–100 μm), which could substantially affect the source term
estimation, as discussed in Section 3.1. An estimated amount (0.7 PBq) was obtained from the simulation,
assuming submicron carrier particles (Katata et al., 2015). If the typical sizes of type B CsMPs (70–400 μm)
were considered, more emission would be required to explain the observed surface air concentration (ordose rate from cloud shine). On the other hand, the dose rate from ground shine should be enhanced due
to fast settling velocities; therefore, the assumption of the relative fractions of cloud shine and ground shine
should be modified. There were also type B CsMPs with diameters of 1–5 μm floating in the air (Onozaki

Figure 10. The information presented in this figure is the same as that in Figure 9, but for the observed and simulated cumulative deposition of 137Cs. The areal
total deposition amounts over land are also embedded in the upper panels.

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et al., 2019). Thus, it is currently still not clear whether the estimated amount will be increased or decreased
by considering the microphysical properties of type B CsMPs.
In the time series panels on the left in Figure 11, the red shaded areas indicate the range of simulated 137Csconcentrations obtained from the size distribution sensitivity studies. The simulated type A particles were
emitted during the night from March 14 to 15 and transported south and southwest over southern Fukushima, Ibaraki, Saitama, and Gunma prefectures due to anticyclonic circulation, which was identified asplume P2 (Nakajima et al., 2017; Tsuruta et al., 2014). Hereafter, note that the “observed event” was defined
as a surface air concentration exceeding 1 Bq m−3 (well above the higher detection limit, i.e., 0.6 Bq m−3) andthe “simulated event” was defined as a surface air concentration larger than 0.5 Bq m−3 (half the criteria ofobserved event). The “simulated event affected by the presence of type A CsMP” (or simply “simulated type
A event”) was defined as a relative difference between the simulated maximum and minimum surface airconcentration for the type A CsMPs sensitivity tests exceeded 10%. The emission timing of the type A CsMPs
is still controversial, but both the observed and simulated surface air concentrations started to increase at 1
LT on March 15 at the earliest among the selected stations (i.e., station [d]). The observed event ended at 10
LT, while the simulated event lasted longer, up to 17 LT. The simulated event was affected by type A for thewhole event. The observed 9-h (1–10 LT) cumulative concentration was 770 Bq m−3 h, whereas the simulated 16-h (1–17 LT) cumulative concentration, which was significantly underestimated, ranged from 16 to
202 Bq m−3 h for the ranges of Dg,n = 0.1–10 μm and σg = 1.0–2.0. The underestimation was due probably tothe discrepancy of simulated plume center, because the horizontal plume size was narrow. It was also due
probably to the underestimation of emission. Still, however, the emission could be reasonable because theobserved values were well within the simulated range in the further downwind locations (e.g., station [f]).
It must also be noted here that during the transport events of the type A CsMPs, substantial amounts of
WSPs existed; as such, both the simulated and observed cumulative concentrations included both types ofparticles. The cumulative concentrations purely from type A CsMPs are shown in the upper panels of Figure 6 and the right panel of Figure 11. Thus, the discrepancies between the simulated and observed cumulative concentrations for the transport periods of the type A CsMPs were not associated solely with the uncertainty in the type A simulations. However, the difference between the simulated maximum and minimum
concentrations was associated with the differences in the assumed size parameters for the type A CsMPs.

Figure 11. Left panels: time series of the observed (black) and simulated (red) surface air concentrations of 137Cs (Bq m−3) at monitoring stations (a) to (g) in
Fukushima, Ibaraki, and Saitama prefectures over the transport pathways of type A CsMPs. The x-axis indicates the dates in LT. The red shaded areas indicate
the range of simulated 137Cs concentrations obtained from the sensitivity tests. The timing of assumed emissions of the type A CsMPs are shown by the reddashed lines in the panels. The rough sequences of plume arrivals at the stations for type A CsMPs along with the plumes P2 and P3 (Nakajima et al., 2017;
Tsuruta et al., 2014) are indicated with arrows. Right panel: the locations of the monitoring stations are depicted over a map of the cumulative concentration oftype A CsMPs for Dg,n = 1.0 μm and σg = 1.0 (A05a). Latitude and longitude information of each station is also depicted.

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Along with the plume P2 (stations d, g, f, and e), simulated type A CsMPs arrived at station (g) in Ibaraki
2 h after their arrival at station (d), at 3 LT on March 15, and remained for 8 h, up to 11 LT. The observed
event started at 2 LT, 1 h earlier than the simulation, remained for 16 h. The observed 16-h (2–18 LT) cumulative concentration was 230 Bq m−3 h, whereas the simulations ranged from 5.9 to 94 Bq m−3 h. Next,
the simulated type A CsMPs arrived at station (f) at 5 LT (7 LT based on the observations) and at station (e)at 8 LT (9 LT based on observations). The durations of observed event, simulated event, and simulated type
A events at station (f) were 15, 12, and 10 h and those at station (e) were 19, 10, and 9 h, respectively. The
observed cumulative concentrations at stations (f) and (e) were 250 and 180 Bq m−3 h, respectively, whereas
the simulated cumulative concentrations were 25–400 and 5.8–160 Bq m−3 h, respectively. There were noobservation data available for Gunma prefecture, but station (e) in Saitama prefecture is the closest to Gunma prefecture. The data suggest that type A CsMPs were next transported to Gunma prefecture.
The plume P3, identified by Tsuruta et al. (2014) and Nakajima et al. (2017) could also be affected by type A
CsMPs. Along with P3, (stations c, b, and a), the simulated type A CsMPs arrived at station (c) in Fukushima
at 11 LT on March 15, 3 h after the onset of observed event (8 LT). The durations of observed event, simulated event, and simulated type A events at station (c) were 15, 21, and 17 h, respectively. The observed cumulative concentration at station (c) was 240 Bq m−3 h, and the simulated concentration ranged from 187 to
1,036 Bq m−3 h. Next, simulated type A CsMPs arrived at station (b) at 14 LT, 2 h after the onset of observed
event (12 LT). The durations of observed event, simulated event, and simulated type A events at station (b)were 13, 17, and 13 h, respectively. The observed cumulative concentration at station (b) was 280 Bq m−3 h,and the simulated cumulative concentration ranged from 141 to 740 Bq m−3 h. The simulated type A CsMPs
arrived at stations (a) at 17 LT (same as the observation time). The durations of observed event, simulated
event, and simulated type A events at station (a) were 10, 13, and 13 h, respectively. The observed cumulative concentrations at station (a) was 91 Bq m−3 h and the simulated cumulative concentrations at station (a)was 43–120 Bq m−3 h. Efforts to isolate CsMPs from samples along with pathway P3 are currently ongoing.
There may be a contradiction between the observed and simulated transport pathways of type A CsMPs,
given the fact that relatively large type A CsMPs (up to 6.4 μm in diameter) were found in soil samples at
20 km NW of the F1NPP (Satou et al., 2015). For the assumed emission periods of the type A CsMPs, anortherly wind prevailed, causing most of the type A CsMPs to be transported first toward the south alongwith the plume P2 (Figure 2). Type A CsMPs were also transported along with the plume P3 traveling northback to Fukushima after traveling for a long distance (∼100 km). It is slightly difficult to believe that suchlarge type A CsMPs can travel that distance (certainly, “lucky” particles can travel long distances). There isa possibility that another emission of type A CsMPs occurred when the wind direction was southeasterly,
from 12 to 22 LT (Figure 2), as proposed by Satou et al. (2015). During this time (on the evening of March
15), there was a significant enhancement in the estimated radio-Cs emissions (Katata et al., 2015). However,
it is still difficult to estimate the timing of emission because the particles were not observed in the air sample
but in the soil.
3.5. Activity Fractions of CsMPs to the Total Cumulative Depositions
The simulated activity fractions of the CsMPs to the total cumulative depositions for all the sensitivity
tests are illustrated and compared against the observed values from the surface soil at 20 sites in Fukushima (Ikehara et al., 2020) in Figure 12. The simulated spatial distributions are shown in the top panelswith the two combinations of sensitivity tests. The top-left panel shows that the fraction exceeded 90%
in the grid cell including the F1NPP due to the large size of the type B CsMPs (Dg,n = 100 μm), and thefraction ranged from 30% to 90% along with the plume P2 (stations d, g, f, and e; Figure 11) due to thelarge dry deposition velocity of the type A CsMPs (Dg,n = 10 μm). Because the activated fraction of thesize of the type A CsMPs in fog was one (Table 3), significant depositions of type A CsMPs occurred overthe mountain forests in the Gunma prefecture due to fog (also see Figure 8). In the top-right panel, theactivity fraction of the CsMPs in the F1NPP grid cell was lower than 5% due to the low gravitational settling velocities of type A and type B CsMPs. The activity fractions exceeded 90% over wide areas north andnortheast of the F1NPP, as type B CsMPs (Dg,n = 10 μm) could travel long distances and be deposited toa greater extent than type A CsMPs (Figure 3). The activity fractions of the type A CsMPs (Dg,n = 1 μm),
which are much smaller than the type B CsMPs (Dg,n = 10 μm), ranged from 5% to 40% along the plume
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Figure 12. Top panels: simulated activity fractions of the CsMPs to the total cumulative depositions for the twosensitivity tests of the type B and type A CsMPs (left: B15-A10a, right: B10-A05a). The values are depicted where
the simulated deposition exceeded 1 kBq m−2. Bottom panel: The observed (O) activity fractions of CsMPs withmeasurement errors at the 20 sites in Fukushima based on a study conducted by Ikehara et al. (2020) and ranges of thesimulated (S) maximum and minimum activity fractions of CsMPs for all the sensitivity tests.

P2 mainly due to the lower dry deposition velocity. The bottom panel of Figure 12 shows a comparison
between the observations (with the measurement errors) and simulations (with the maximum and minimum values of the sensitivity tests). Note that all the stations were located in Fukushima prefecture,
4.42–61.0 km from the F1NPP. The station numbers were reordered based on geographical categories(W, NW, SSW, and NNW of the F1NPP and Nakadori valley) (Figure 1b), including stations (a) and (c) in
Figure 11 and the simulated pathway of the type A CsMPs along with the plume P3. It is apparent thatthe simulated activity fractions of the CsMPs were substantially underestimated except at site #1 (W ofthe F1NPP) and sites NNW of the F1NPP (#16 and #18). Site #1 is located only 4.42 km from the F1NPP;
therefore, its location was difficult to simulate using a 3-km model. In fact, the model grid cell in whichsite #1 was located was the same as the grid cell in which the F1NPP was located, so the large variationin the simulation (1.95%–92.2%) was simply due to the large variation in the settling velocity of the type B
CsMPs (0.01–100 μm in diameter). Sites #16–18 were also located along the simulated pathway of the typeB CsMPs. The relatively better performance of the simulated activity fractions at the north-northwesternsites indicated the successful simulations of emission, transport, and deposition of type B CsMPs. On
the other hand, the simulated values affected by the presence of type A CsMPs were all underestimated.
Among the south-southwestern sites, the location of the observed peak (#10) was the same as that for thesimulation, but the values were much smaller (the observed peak was 80.2% and the simulated peak was
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0.85%–16.2%), even though the emission during the events (0.475 PBq) was assumed to be 100% CsMPs.
The simulated cumulative surface air concentrations were not very different from those observed along
the pathways of the type A CsMPs (Figure 11), and there were two reasons for the large underestimation: there were more type A particle fractions in the source term of Katata et al. (2015) or the simulated
deposition rates were underestimated. A similar assumption can be made for the Nakadori sites, alongwith the plume P3, on which the observed values were ∼40% and the simulated maximum was 5% atsite #8. The simulated performance at the northwestern sites (#2–7 and #19) was the worst among thegeographical categories, simply because the type A particle emissions that might have occurred during
the evening of March 15 were not included in the simulation. The observed values ranged from 7.25%
to 34.0%, whereas the simulated maximum value was 0.75% (#2). Further observational and simulation
studies are required to constrain the source term estimation and size distribution of CsMPs, especiallyfor type A CsMPs, to better understand the abundance and environmental behaviors of type A CsMPs.

4. Conclusions
The dispersion and deposition of radio-cesium (137Cs) carried by two types (type A and type B) of water-insoluble Cs-bearing microparticles (CsMPs) released due to the Fukushima nuclear accident were simulated for the
first time by using the multimodel meteorological ensemble mean field created in Part 1 of the current study(Kajino et al., 2019a). The results were compared against field observations of the surface air concentrationand deposition. The results were also compared with the previous simulation, in which it was assumed that
100% of the carrier aerosols of 137Cs were water-soluble particles (WSPs) with a submicron size range.
The presence of type B CsMPs (70–400 μm in soil (Satou et al., 2018) and 1–5 μm in air (Onozaki
et al., 2019) arising from the hydrogen explosion of Unit 1 occurred on March 12, 15:36 LT; UTC+9; 0.7
PBq) could partly explain the simulated underestimation of the total deposition over land when assuming 100% WSPs. The observed deposition over land totaled 2.64 PBq, whereas the simulated deposition
assuming submicron carriers was 1.19 PBq. The type B CsMPs were so large that almost 100% of theemission was deposited near the F1NPP. The deposition of type B CsMPs (0.7 PBq) accounts for ∼50% of
the total underestimation (1.45 PBq). The relative magnitudes of the radioactivity of the type B CsMPslarger than 10 μm and smaller than 10 μm should be a key factor in the surface air concentration anddeposition during the emission event. Considering the presence of type B CsMPs in the source term
estimation (e.g., Katata et al., 2015) could alter the estimated amount of radio-Cs emitted by the Unit 1hydrogen explosion.
The environmental behaviors of type A CsMPs of 0.1, 1, and 10 μm could be drastically different. The origin of type A CsMPs is still controversial (it could be Units 2 or 3), but these particles were emitted in themidnight between March 14 and 15 and transported southward over the Kanto Plain, the most populated
plain in Japan, along with the plume P2 (Nakajima et al., 2017; Tsuruta et al., 2014) and also transported to
Fukushima, along with the plume P3 (Nakajima et al., 2017; Tsuruta et al., 2014). The size of the particles
ranged from 1 to 10 μm both in the air in Tsukuba, located in Kanto (Adachi et al., 2013), and in the soil
in Fukushima (Satou et al., 2016); however, recently, type A CsMPs smaller than 1 μm were also found in
Fukushima (Okumura et al., 2019a). Due to the differences in size distributions, the surface air concentration over Kanto varied substantially, by up to more than one order of magnitude. The major deposition
mechanisms varied among dry, wet, and fog deposition depending on the size distribution of the particles.
There might have been other emission events of type A CsMPs from Units 2 or 3 in the evening of March 15,
followed by transport toward the northwest and deposition in the mountain region in Fukushima.
The simulated activity fractions of CsMPs to the total cumulative depositions were compared to those in thesurface soil observed in Fukushima (Ikehara et al., 2020) for the first time. The observed activity fractions
could have settled within the ranges of the simulated sensitivity tests for the locations under the influenceof the type B CsMPs (near the F1NPP and NNW of the F1NPP). On the other hand, the simulation substantially underestimated the observed activity fractions for the locations along the transport pathways of thetype A CsMPs (SSW of the F1NPP and Nakadori and NW of the F1NPP). There could be more fractions oftype A CsMPs in the estimated source term than assumed in this study, and/or the simulated deposition
rates might be underestimated.
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Works on the isolation of CsMPs from hourly air filters at one hundred locations (Oura et al., 2015; Tsuruta
et al., 2014) are currently ongoing (partly reported by Onozaki et al., 2019). Information on the relative magnitudes of radioactivity from CsMPs and WSPs is essential. These efforts will certainly promote the precise
estimations of emission events, the timing and origins of emission events, and the amounts of radioactivity
associated with CsMPs. This may help to improve the understanding of the formation mechanisms of CsMPs
in reactors, together with the mechanisms of emission into the environment, which are still controversial.
The elementary process modeling of numerical simulations needs to be improved. The possible underestimation of deposition suggested in Part 1 (Kajino et al., 2019a) has not yet been resolved. An improvement in
the below-cloud scavenging rate (Kajino et al., 2019a) and the implementation of the substantial electricalcharges of type A CsMPs (Dépée et al., 2019) will be incorporated in the future. The self-electrical charge oftype A CsMPs should enhance the rates of dry deposition and below-cloud scavenging, which may improve
the significant underestimation of the deposition fraction from CsMPs SSW of the F1NPP and Nakadori.
The charging effect should be assessed in the future steps of the current work.
Numerical simulations also need to be improved in terms of horizontal grid resolution. A new simulation witha finer grid resolution (Δx = 1 km; Sekiyama & Kajino, 2020) indicated that our Δx = 3 km simulation would besignificantly improved in terms of the reproduction of wind fields along the coastline of Fukushima, which maylead to improvements in plume directions starting from the F1NPP (e.g., for type B CsMPs). Due to the complex
topography in inland areas of Fukushima (with mountains and valleys), air masses transported inland will also
be improved by fine-scale grid simulations (e.g., type A CsMPs in Nakadori along with plume P3 and type A
CsMPs which might have been emitted in the evening of March 15 and transported to the northwest).

Data Availability Statement
The simulated and observed data used in all the figures are available at https://mri-2.mri-jma.go.jp/owncloud/s/pz8T2P2XfKJMi3X (last accessed: June 5, 2020). In terms of the raw observation data sets, the surface air activity concentration data are available in Appendix A of Oura et al. (2015), and the activity deposition data are available at https://emdb.jaea.go.jp/emdb/en/portals/b1010301/ (last accessed: June, 5 2020).

Acknowledgments
The authors are thankful for the usefulcomments of Drs Akihide Hidaka
and Hiroaki Terada of JAEA on thepossible formation mechanisms of type
A CsMPs, Dr Yukihisa Sanada of JAEA
for providing the unmanned helicopter
measurement data within 3 km of the
F1NPP, and Prof. Yoshinari Abe ofTokyo Denki University for providing
information on CsMPs isolated fromair samples. The authors are gratefulfor the support of Mr. Shoichi Akami
of the University of Tsukuba for
figure generation. This research was
mainly supported by the Environmental
Research and Technology Development Fund of the Environmental
Restoration and Conservation Agency(ERCA) (JPMEERF20155001 and JPMEERF20181002) and by Grants-in-Aid
for Scientific Research on Innovative
Areas 24110002 and 24110003 of the
Ministry of Education, Culture, Sports,
Science and Technology (MEXT). It waspartially supported by the Japanese Radioactivity Survey from the NRA, Japan,
and the Japanese Society for the Promotion of Sciences (JSPS) (KAKENHI
grant nos. JP15H02811, JP16KK0018,
JP19H02649, and JP19H01155).

KAJINO ET AL.

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August
Journal 2021of the Meteorological Society of Japan,
T. T. 99(4),SEKIYAMA
1089−1098,
and M.
2021.
KAJINO
doi:10.2151/jmsj.2021-052

1089

NOTES AND CORRESPONDENCE
Performance of a 250-m Grid Eulerian Dispersion Simulation Evaluatedat Two Coastal Monitoring Stationsin the Vicinity of the Fukushima Daiichi Nuclear Power Plant
Tsuyoshi Thomas SEKIYAMA
Meteorological Research Institute, Tsukuba, Japanand
Mizuo KAJINO
Meteorological Research Institute, Tsukuba, Japan
Faculty of Life and Environmental Sciences, University of Tsukuba, Tsukuba, Japan(Manuscript received 16 March 2021, in final form 22 April 2021)

Abstract
Using two horizontal resolutions (3 km and 250 m), this study examined the performance of Eulerian models
in simulating dispersion fields at two coastal monitoring stations in the vicinity of a pollutant source (3.2 km and17.5 km distant) under the situation of the Fukushima 2011 nuclear accident. A 250-m grid simulation was newly
conducted for the examination and was able to reproduce the wind and concentration fields in detail over complex
terrain. The 3-km grid model could not reproduce the details of the winds and plumes around the Fukushima
Daiichi Nuclear Power Plant but occasionally yielded a higher performance with a lower undetected error ratecompared with the 250-m grid model due to the large numerical diffusion of the former. A disadvantage of Eulerian dispersion models is expected to be the artificial numerical diffusion in the advection process near emission
sources. The artificial numerical diffusion increases the false alarm ratio (number of strikeouts while swinging)but fortunately decreases the undetected error rate (number of strikeouts while looking). This characteristic isappropriate for environmental emergency response (EER) systems. Furthermore, the 250-m grid model result was
improved by a plume augmentation (i.e., max pooling) process, which enlarged the plume widths and masked
short time lags and small plume drifts. Plume augmentation was advantageous to the high-resolution model for
improving statistical scores, which is beneficial for EER systems.
Keywords high resolution modeling; weather modeling; advection modeling; radioactive cesium-137; environmental emergency response
Citation Sekiyama, T. T., and M. Kajino, 2021; Performance of a 250-m grid Eulerian dispersion simulation evaluated at two coastal monitoring stations in the vicinity of the Fukushima Daiichi Nuclear Power Plant. J. Meteor.
Soc. Japan, 99, 1089–1098, doi:10.2151/jmsj.2021-052.

Corresponding author: Tsuyoshi Thomas Sekiyama, Meteorological Research Institute, 1-1 Nagamine, Tsukuba, Ibaraki
305-0052, Japan
E-mail: tsekiyam@mri-jma.go.jp
J-stage Advance Published Date: 10 May 2021
©The Author(s) 2021. This is an open access article published by the Meteorological Society of Japan undera Creative Commons Attribution 4.0 International (CC BY 4.0) license (https://creativecommons.org/licenses/by/4.0).

1090

1.

Journal of the Meteorological Society of Japan

Introduction

The authors have previously investigated thedependence of dispersion model performance on thehorizontal resolution using the data of the Fukushima
2011 nuclear accident (Sekiyama et al. 2015, 2017;
Sato et al. 2018, 2020; Sekiyama and Kajino 2020).
These studies have essentially found that when using
15-, 5-, 3-, and 1-km grid models, higher-resolution
models yield better performance over complex Japa­
nese terrain. Specifically, while the 3-km and 1-km
grid models performed comparably over plain regions
more than 100 km from the Fukushima Daiichi
Nuclear Power Plant (FDNPP), the 1-km grid model
was evidently superior to the 3-km grid model overmountainous regions approximately 50 km from the
FDNPP. Unfortunately, the 15-km and 5-km gridmodels exhibited poorer performance compared withthe 3-km and 1-km grid models.
The Cs-137 concentration data used for validationin these studies were retrieved by Tsuruta et al. (2014)
and Oura et al. (2015) and recorded at approximately
100 monitoring stations. However, most of the stations
are located more than 50 km from the FDNPP; among
them, the nearest station is 26 km away. Consequently,
these studies did not evaluate the model performancein the area immediately surrounding the FDNPP. In
principle, Eulerian models, which were utilized inthese studies, are generally not expected to be good at
simulating plume dispersion in the vicinity of emission sources (cf. Rood 1987). However, it is not theoretically trivial to estimate the poor performance of a
Eulerian model, especially when the horizontal resolution is almost comparable to the distance between amonitoring point and an emission source. Therefore,
the model performance should be evaluated throughobservations at not only distant but also close ranges.
In this paper, the close-range performance of Eulerian dispersion simulations is evaluated using two horizontal resolutions, namely, 3 km and 250 m. The 3-km
grid simulation is derived from Sekiyama and Kajino(2020), whereas the 250-m grid simulation is newly
conducted for the evaluation. Fortunately, Tsuruta
et al. (2018) released new Cs-137 concentration data
retrieved at two monitoring stations in the vicinity of
the FDNPP (3.2 km and 17.5 km distant). This newdataset allows the authors to evaluate the close-range
dispersion model performance. The 250-m grid plumedispersion is categorized as a sub-kilometer-scale
simulation, which is currently being examined by anincreasing number of studies, mainly for complex
terrain (e.g., Bao et al. 2018; Wiersema et al. 2020)

Vol. 99, No. 4

and urban pollution (e.g., Lateb et al. 2016; Nakayama
et al. 2016; Li et al. 2018; Hamer et al. 2020). The
authors would like to contribute to the studies onhigh-resolution geophysical model by focusing on therural and coastal locations near the FDNPP.
In addition, in the case of environmental emergency
responses (EER; cf. World Meteorological Organization 2006), both the Eulerian and Lagrangian plumedispersion models would be required, especially inthe areas very close to emission sources. Hence, weshould understand beforehand what types of models,
what model resolution, and what model configuration
are needed for EER. The authors hope that this paper
will provide insight into what model resolution andconfiguration are suitable for supporting EER.
2.

Methodology

2.1 Models
The 3-km grid meteorological analysis was provided at an hourly resolution by Sekiyama et al. (2017)
and Sekiyama and Kajino (2020). This simulation was
performed using a 4-dimensional data assimilation
system consisting of a nonhydrostatic regional weather prediction model (referred to as the NHM; cf. Saito
et al. 2006, 2007), a local ensemble transform Kalmanfilter (LETKF; cf. Miyoshi and Aranami 2006; Kunii
2014), and Japan Meteorological Agency (JMA)
operational observation datasets. The NHM was being
operationally used by JMA at the time of the Fuku­
shima nuclear accident (March 2011) for daily national weather forecasts with four-dimensional variational
assimilation (cf. Honda et al. 2005). The analysis has
60 vertical layers from the surface to a 22-km elevation within the model domain over eastern Japan, as
presented in Fig. 1a. The boundary conditions for themodel domain were provided by the JMA operational
global analysis system. The details of the model anddata assimilation settings are described in Sekiyama
et al. (2017) and Sekiyama and Kajino (2020). Thismeteorological analysis has been employed not onlyby the above studies referenced but also by Sato et al.
(2018), Sekiyama and Iwasaki (2018), Iwasaki et al.
(2019), Takagi et al. (2020), and Goto et al. (2020) for
nuclear accident air pollution modeling.
The 250-m grid meteorological analysis was calculated by the NHM nested by the 1-km meteorologicalanalysis within the model domain, as presented in Fig.
1b. The 1-km analysis was provided by Sekiyama andKajino (2020), who implemented a one-way nested
data assimilation scheme (Kunii 2014) nested by the
3-km meteorological analysis aforementioned. Notethat the 3-km, 1-km, and 250-m grid analyses wereAugust 2021

T. T. SEKIYAMA and M. KAJINO

1091

calculated using the same model and physical configurations, except for the model domains and horizontal
resolution-dependent setups. All the resolution models
implemented the improved Mellor–Yamada level 3
closure model (Nakanishi and Niino 2004, 2006) asa turbulence scheme. A cumulus parameterization
was not utilized for any models in this study. The
250-m grid NHM calculation lasted 20 h for a 24-h
simulation using 96 cores of Fujitsu FX100 with 840
× 980 grids. On the other hand, the 3-km grid NHM
calculation lasted 4 h for a 24-h simulation using one
core of Fujitsu FX100 with 215 × 259 grids.
Cs-137 plume dispersion was calculated by anoffline Eulerian regional air quality model, which was
driven by either the 3-km or 250-m grid meteorological analysis. This model was previously developedand evaluated by Kajino et al. (2012, 2016, 2018,2019a, b) and Mathieu et al. (2018). The 3-km and
250-m grid meteorological analyses were input at 1-h(3-km grid) or 10-min (250-m grid) intervals into theoffline air quality model, in which the dynamical time
step was set to 24 s (3-km grid) or 2 s (250-m grid)using the time-interpolated meteorological analysis.
The Cs-137 emission scenario was provided by Katata
et al. (2015). In this model, Cs-137 was assumed tobe mixed in sulfate-organic mixture aerosol particles,
which were injected into a grid cell above the FDNPP
at 20 – 150-m heights, following the time-varying
emission scenario. The details of the model settings
are described in Sekiyama et al. (2015, 2017) andSekiyama and Kajino (2020). The 250-m grid offlinemodel calculation lasted 12 h for a 24-h dispersion
simulation using 112 cores of Intel Xeon (Haswell)with the same domain as the 250-m grid NHM. On theother hand, the 3-km grid offline model calculation
lasted 0.5 h for a 24-h dispersion simulation usingeight cores of Intel Xeon (Haswell) with the same
domain as the 3-km grid NHM.
2.2 Observations
The observational Cs-137 concentrations were retrieved hourly from filter tapes installed in prefectural
governments’ suspended particulate matter (SPM)
monitors, of which the theoretical detection limit
was 0.1 Bq m−3 (Tsuruta et al. 2014, 2018). The two
SPM monitoring stations, as reported by Tsuruta et al.
(2018), were located at Futaba and Naraha in the vicinity of the FDNPP along the east coast of Fuku­shima
Prefecture (Fig. 1c). The Futaba and Naraha monitoring stations were located 3.2-km northwest and 17.5km south–southwest of the FDNPP, respectively. The
plume arrivals were defined with a threshold of 1.5

Fig. 1. Model domains of (a) the 3-km grid modeland (b) the 250-m grid model. (c) Detailed locations of the Fukushima Daiichi Nuclear Power
Plant (FDNPP), Futaba monitoring station, andNaraha monitoring station with 50-m interval
elevation contours.

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Vol. 99, No. 4

Fig. 2. Time series of the hourly averaged Cs-137 concentrations in March 2011 local time at the (a) Futaba
monitoring station and (b) Naraha monitoring station. [A] and [B] indicate the snapshot times for Figs. 3a and 3b,
respectively. Closed and open circles are observations defined as plumes (equal to or more than 1.5 Bq m−3) andnon-plumes (less than 1.5 Bq m−3), respectively.

Bq m−3 using hourly averaged concentrations. The
modeled concentrations were spatially linear-interpolated at each monitoring station. Sekiyama et al. (2017)
and Sekiyama and Kajino (2020) utilized a thresholdof 1.0 Bq m−3; the reason why this study used a threshold of 1.5 Bq m−3 is described below.
The major leakage of radioactive substances causedby the Fukushima nuclear accident lasted for 2 or
3 weeks starting on March 12, 2011, the day after
the great earthquake. Most of the plumes flowed
offshore to the Pacific Ocean with the Siberian wintermonsoon. Nakajima et al. (2017) reported that thetime windows of onshore plumes were limited to lessthan 50 h in total. However, the plumes often reachedboth Futaba and Naraha, as reported by Tsuruta et al.
(2018), as these stations are near the FDNPP along the
Pacific Ocean coast. Tsuruta et al. (2018) reported thathigh Cs-137 concentrations were observed at Futaba
intermittently at a local time (LT) between March
12 and March 25, 2011, and at Naraha intermittently(LT) between March 14 and March 23, 2011. The
observed concentrations are presented in Fig. 2, where
the closed (open) circles indicate the existence (nonexistence) of a plume defined by a threshold of 1.5
Bq m−3.

The background concentration seems to be approximately 1 Bq m−3 at Futaba (Fig. 2a), which isprobably due to equipment contamination and filter
tape cross-contamination (cf. Tsuruta et al. 2014,2018). The background concentration at Naraha isalso higher than the detection limit (0.1 Bq m−3) with alarge deviation, in which the maximum values seem toexceed 1 Bq m−3 (Fig. 2b). This is also probably due
to contamination and cross-contamination. Althoughturbulent diffusion may have partially influenced
the background concentrations near the FDNPP, it isunnatural that the values did not fall below 1 Bq m−3,
even during time slots in which winds are strongly
directed toward the ocean at the FDNPP. Therefore,
the threshold of plume existence/nonexistence wasdefined as 1.5 Bq m−3 to avoid contamination errors atthese two locations. The sensitivity of the statisticalscores to the threshold value was small between 1.5
Bq m−3 and 3 Bq m−3, although the scores evidently
deteriorated with the threshold of 1 Bq m−3.
In this study, the model performance was evaluatedby the following statistical scores, namely, the proportion correct (PC), false alarm ratio (FAR), undetected
error rate (UER), bias score (BS), and threat score(TS) (see Appendix). These statistics are based on

August 2021
Table 1.

FutabaNarahaStatistics (cf. Appendix) of each model at the Futaba monitoring station and the Naraha monitoring station.
FO

XO

FX

XX

Proportion
Correct

False Alarm
Ratio

Undetected
Error Rate

Bias
Score

ThreatScore

3 km-grid model
250 m-grid model
Max pooling
250 m-grid model

140
104

48
84

41
35

122
128

0.75
0.66

0.23
0.25

0.26
0.45

0.96
0.74

0.61
0.47

139

49

67

96

0.67

0.33

0.26

1.10

0.55

3 km-grid model
250 m-grid model
Max pooling
250 m-grid model

49
41

11
19

49
23

194
220

0.80
0.86

0.50
0.36

0.18
0.32

1.63
1.07

0.45
0.49

44

16

27

216

0.86

0.38

0.27

1.18

0.51

the occurrence of a binary event, in this study, theexistence/nonexistence of a Cs-137 plume. Thus, athreshold was required to determine the plume arrival
period. Plume arrivals can be detected by not only theconcentration but also the radiation dose rate at eachstation. However, since the equipment for measuring
the radiation dose rate is often seriously contaminatedin the environment, especially after rainfall, concentration observations are favorable for detecting plumes
with high temporal resolution (cf. Tsuruta et al. 2018).
Unlike the observational plume arrivals, the modeled
plume arrivals were easily detected without the influence of the threshold. This is because the plume edge
concentration is increased by more than five orders ofmagnitude in the model simulations (cf. Iwasaki et al.
2019).
3.

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T. T. SEKIYAMA and M. KAJINO

Results and discussion

3.1 Model simulations
Figure 2a presents the modeled concentrations at
Futaba, which is located in the grid immediately next
to the grid of the FDNPP in the 3-km grid model. The
3-km grid model (blue lines) and 250-m grid model(red lines) are often synchronized with each other.
However, the concentration spikes in the 3-km gridmodel tend to be broader than those in the 250-m gridmodel. In other words, the plumes tend to stay for alonger time in the 3-km grid model than in the 250-m
grid model upon their arrival. This tendency is expected as low-resolution Eulerian models experience large
numerical diffusion near emission sources. Moreover,at Naraha (five times farther from the FDNPP than
Futaba), the synchronization between the 3-km and
250-m grid models is less prominent, as presented inFig. 2b. The plumes in the 3-km grid model evidently
arrive more often at Futaba than at Naraha. This ismainly due to the large numerical diffusion of the
3-km grid model. Note that both models tend tooverestimate the concentration at Futaba (nearer tothe FDNPP), which is probably due to the model’s
dynamic errors rather than the emission scenario error,
as the models are less likely to overestimate the concentration at Naraha.
Within the time windows presented in Figs. 2a and2b, the statistical scores were calculated for Futaba
and Naraha (Table 1). At Futaba, the PC is higher
for the 3-km grid model as the UER (the number ofstrikeouts while looking, if using a baseball analogy)
is much lower. In this case, since the FARs (thenumber of strikeouts while swinging, if using a baseball analogy) are similar between the 3-km and 250-m
grid models, a lower UER results in a higher PC. The
lower UER is due to the larger numerical diffusion of
the 3-km grid model. Since the BS is smaller than 1
for the 250-m grid model, the horizontal diffusion in
the models might be weaker than that in reality. Thisis because the model probably underestimates thefrequency of high-concentration events when the BS
is much smaller than 1.
In contrast, at Naraha, the 250-m grid model has
a slightly higher PC than the 3-km grid model. In
this case, the 3-km grid model has a much lower
UER but a much higher FAR due to large numerical
diffusion, as half of the alarms are false (“the boy
who cried wolf” events). Consequently, the BS value
is inflated, and the TS value deteriorates in the 3-km
grid model. The combination of a high BS and a low
TS leads to people adopting a normalcy bias, which is
not appropriate for an EER model. Conversely, superhigh-resolution models generally tend to have a high
UER due to the narrowness of plumes, which is also
not appropriate for an EER model. Therefore, plumeaugmentation, or image processing for model results,
might raise the statistical scores when using superhigh-resolution models. Such processing is describedand evaluated in the next section.

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3.2 Max pooling model
To augment the plume widths, a max poolingprocess was applied to the 250-m grid model result.
At each grid point, an alternative value was sampled
by taking the maximum value inside of a circle witha radius of 1.5 km from the grid point. This process
is equivalent to the max-pooling layer of an artificial
neural network with a 1-grid stride and a 12-grid pooling size. In the output distributions, the plumes werebroadened to be comparable to the 3-km grid plumes.
The time series of the concentrations and statisticalscores for the max-pooling 250-m grid model arepresented in Fig. 2 indicated by green lines and listed
in Table 1, respectively.
As presented in Fig. 2a, compared with the original
250-m grid model, the plumes arrive more frequently
at Futaba (nearer to the FDNPP) in the max-poolingmodel, and the concentration spikes are evidently
broader and higher for the max-pooling model. Consequently, the BS value is improved, and the UER value
is successfully decreased to the level of the 3-km gridmodel (see the rows for Futaba in Table 1). In thecase of the original 250-m grid model, the BS value
was much smaller than 1 as FO was small while (FO+ XO) was large. Therefore, the improvement in the
BS indicates the increase in FO, which results in the
improvement in the TS. Although the improvementin the PC is small because of the deterioration in the
FAR, the TS value is significantly improved from 0.47
to 0.55. In contrast, the difference between the original
and max-pooling 250-m grid models is very small at
Naraha, as presented in Fig. 2b. This is probably because Naraha is farther from the FDNPP than Futaba,
and thus, the pooling size (1.5-km radius) is relatively
small in comparison with the plume width or distance
between the plumes and Naraha. Consequently, there
is slight improvement in the PC and FAR (see therows for Naraha in Table 1). However, since the UER
value is slightly improved, the TS value increases
from 0.49 to 0.51. Note that the TS is higher than
0.5 for the max-pooling 250-m grid model at both
stations, which is relatively good in comparison withthe operational scores of heavy rain (> 10 mm h−1)
weather forecasts (cf. Appendix).
The scores were improved by the max-poolingprocess, especially at Futaba (nearer to the FDNPP).
These improvements are presented in Fig. 3 for twotime slots, [A] and [B], indicated in Fig. 2a. At time[A], the 3-km grid model successfully simulates theobserved high concentration, but the original 250-m
grid model fails with a sharp drop in its concentration(see Fig. 2a). In this case, as presented in Fig. 3a,Vol. 99, No. 4

while the 3-km grid plume spreads over the monitoring station with large numerical diffusion, the original
250-m grid plume slightly misses covering the monitoring station. Since the difference between the 3-km
and 250-m grid surface wind fields is small aroundthe FDNPP, the plume coverage mainly depends onthe magnitude of numerical diffusion. In contrast, theedge of the max-pooling 250-m grid plume successfully covers the monitoring station.
At time [B], while the 3-km grid model completely
fails, the original 250-m grid model almost successfully simulates the observed high concentration (seeFig. 2a). However, since the observed concentration
rapidly fluctuates, the original 250-m grid model
regrettably fails with a very small time lag. In thiscase, as presented in Fig. 3b, the 3-km grid plume
misses covering the monitoring station as the surface
wind around the FDNPP continuously flows in one
direction opposite to the monitoring station. However,in the 250-m grid model, the surface wind around the
FDNPP is not homogeneous and instead follows thecomplicated terrain near the FDNPP. Consequently,
while the prevailing wind is in the opposite direction
to the monitoring station, the fluctuating edge of theplume sometimes covers the monitoring station. Even
if the timing of plume intrusion at Futaba is slightly
lagged in the original 250-m grid model, the augmented plume constantly covers Futaba in the max-pooling
250-m grid model, which provides a lower UER (buta higher FAR) and a better TS.
This study examined the concentration data fromtwo monitoring stations located along the Pacificcoast (not in a mountainous region) in the vicinity of
the FDNPP (only 3.2 km and 17.5 km distant). The
topographical difference was one of the reasons why
the higher-resolution model did not perform overwhelmingly better than the lower-resolution modelin this study. Previous studies (Sekiyama et al. 2015;
Sekiyama and Kajino 2020) mainly focused on inlandcomplex terrain. Although the 3-km grid model
could not reproduce the details of the wind direction
and plume dispersion near the FDNPP (e.g., at time[B]), the low-resolution model was superior to theoriginal 250-m grid model at Futaba due to the large
numerical diffusion therein. This empirical knowledge
is not trivial for the construction of EER systems.
In addition, a max-pooling (or plume augmentation)
process is probably beneficial to high-resolution EER
systems. However, the optimal pooling size (or plumeaugmentation width) depends on the situation. In thisstudy, 4-grid (500-m radius) and 40-grid (5-km radius)
pooling sizes were also tested (not shown), but the 12-

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1095

Fig. 3. Hourly averaged surface Cs-137 concentrations (green shading) and lowermost layer winds (red arrows)
near Futaba and the FDNPP calculated using the models at (a) 0800 – 0900 local time on March 14, 2011, and (b)2100 – 2200 local time on March 16, 2011. The 250-m grid winds are plotted every 6 grids (1.5 km). Gray shading
indicates the elevation used in the 250-m grid models. The open triangle indicates the FDNPP. The open circle indicates the Futaba monitoring station.

grid (1.5-km radius) pooling size employed above wassuperior to the other options.
4.

Conclusion

Generally, large numerical diffusion near an emission source is a disadvantage of Eulerian dispersion
models, as increasing numerical diffusion increases
the FAR (the number of strikeouts while swinging).
Fortunately, this drawback, however, will decrease theUER (the number of strikeouts while looking). This
characteristic is suitable for EER systems. We demonstrated that the performance of the 3-km grid model at
Futaba (very close to the FDNPP) is better than thatof the original 250-m grid model. On the one hand,
it is scientifically important to determine the causesof plume errors on a case-by-case basis. On the other
hand, it is technically troublesome that such a tiny
plume location error can cause the pollutant concentration to not exceed a warning threshold. Therefore, itwould be ideal for EER systems to use high-resolution
models with augmented plumes, i.e., the max-poolingprocess. Note that high-resolution models are able tosimulate the wind and dispersion fields affected bycomplex terrain in detail, as presented in Fig. 3b. In
addition, plume augmentation effectively masks shorttime lags and small plume drifts in sub-kilometer-scale
high-resolution models. Unfortunately, the computational burden is, however, theoretically 12 × 12 × 12
= 1728 times different between the 3-km and 250-m
grid models when compared in the same area according to the Courant–Friedrichs–Lewy (CFL) condition.
Therefore, enhancing the model resolution to thesub-kilometer-scale is not cost-effective, considering
the available computational resources as of 2021. The
operationalization of sub-kilometer-scale dispersion
models will be a future issue in the construction of
EER systems.

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Data availability statement
The 3-km and 250-m grid model data for both themeteorological and dispersion simulations are available upon request to the corresponding author. The
authors obtained the Cs-137 observation data from
Tsuruta et al. (2018) in text-file format upon request.
The numerical model source codes used in this study
are available under a collaborative framework between MRI and related institutes/universities.
Acknowledgments
This work was supported by the Environment Research and Technology Development Fund (1-1802)
at the Environmental Restoration and Conservation
Agency and by JSPS KAKENHI Grant Numbers
JP17K00533, JP19H01155, and 21H03593.
Appendix
This appendix highlights the statistical indices referenced in this study. First, we define the following:
FO is the number of positive simulations and positive
observations (correct hits),
XO is the number of negative simulations but positive
observations (misses),
FX is the number of positive simulations but negative
observations (false alarms), andXX is the number of negative simulations and negative observations (correct rejections),
where positive simulations/observations indicate highCs-137 concentrations (equal to or more than 1.5
Bq m−3) in this study.
The proportion correct (PC) is the ratio of thenumber of correct events (hits and rejections) to thenumber of total events, defined by
PC ≡FO + XX,
FO + XO + FX + XX

(0 ≤ PC ≤ 1).

A higher PC indicates higher model performance,
especially when the number of positive observations
is comparable to the number of negative observations.
The false alarm ratio (FAR) is the ratio of thenumber of false alarm events to the number of positive
simulations, defined by
FAR ≡

FX
,
FO + FX(0 ≤ FAR ≤ 1).

Cases with a high FAR are likened to “the boy
who cried wolf ” (or the number of strikeouts while
swinging). Therefore, a lower FAR is better to prevent
people from adopting a normalcy bias.
The undetected error rate (UER) is the ratio of theVol. 99, No. 4

number of missed events to the number of positive
observations, defined by
UER ≡

XO
,
FO + XO(0 ≤ UER ≤ 1).

High-UER forecast models are apt to miss disasters(strikeout while looking), which is not desirable for an
emergency management system.
The bias score (BS) is the ratio of the number of
positive simulations to the number of positive observations, defined by
BS ≡FO + FX,
FO + XO(0 ≤ BS).

If the frequency of positive simulations is equal to thatof positive observations, the BS is unity, which is thebest score. The models with a high FAR (the models
that “cry wolf ”) are more apt to yield a much higher
BS than 1.
The threat score (TS) is the ratio of the number ofcorrect hit events to the number of events other thancorrect rejections, defined by
TS ≡

FO
,
FO + FX + XO

(0 ≤ TS ≤ 1).

The TS is often referred to as the critical success index(CSI). When the number of negative observations
is increased, such as no rain or no tornado, XX (thenumber of correct rejections) tends to be large. In thatcase, the PC approaches unity and is less affected by
the informative values, namely, FO, FX, and XO. In
contrast, the TS excludes correct rejection events andthus is applicable to validation with a large number ofnegative observations. While the best value of the TS
is 1, the TS rarely approaches the best value. For example, operational TS values vary between 0.1 and 0.5
for heavy rain (> 10 mm h−1) forecasts in Japan with
0-h or 3-h lead times (Japan Meteorological Agency2019).
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atmosphere
ArticleEnsemble Dispersion Simulation of a Point-Source Radioactive
Aerosol Using Perturbed Meteorological Fields overEastern Japan
Tsuyoshi Thomas Sekiyama 1, * , Mizuo Kajino 1,2
1
2
3

*



Citation: Sekiyama, T.T.; Kajino, M.;
Kunii, M. Ensemble Dispersion
Simulation of a Point-Source
Radioactive Aerosol Using Perturbed
Meteorological Fields over Easternand Masaru Kunii 3

Meteorological Research Institute, Tsukuba 3050052, Japan; kajino@mri-jma.go.jp
Faculty of Life and Environmental Sciences, University of Tsukuba, Tsukuba 3058572, Japan
Japan Meteorological Agency, Tokyo 1058431, Japan; mkunii@met.kishou.go.jp
Correspondence: tsekiyam@mri-jma.go.jp

Abstract: We conducted single-model initial-perturbed ensemble simulations to quantify uncertainty
in aerosol dispersion modeling, focusing on a point-source radioactive aerosol emitted from the
Fukushima Daiichi Nuclear Power Plant (FDNPP) in March 2011. The ensembles of the meteorological variables were prepared using a data assimilation system that consisted of a non-hydrostatic
weather-forecast model with a 3-km horizontal resolution and a four-dimensional local ensemble
transform Kalman filter (4D-LETKF) with 20 ensemble members. The emission of radioactive aerosol
was not perturbed. The weather and aerosol simulations were validated with in-situ measurements at
Hitachi and Tokai, respectively, approximately 100 km south of the FDNPP. The ensemble simulations
provided probabilistic information and multiple case scenarios for the radioactive aerosol plumes.
Some of the ensemble members successfully reproduced the arrival time and intensity of the radioactive aerosol plumes, even when the deterministic simulation failed to reproduce them. We found thata small ensemble spread of wind speed produced large uncertainties in aerosol concentrations.
Keywords: probabilistic simulation; plume dispersion; data assimilation; ensemble spread; Fukushima
nuclear accident; radioactive cesium

Japan. Atmosphere 2021, 12, 662.
https://doi.org/10.3390/
atmos12060662

1. Introduction
Academic Editor: Chuen-Jinn Tsai
Received: 14 April 2021
Accepted: 19 May 2021
Published: 22 May 2021

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with regard to jurisdictional claims inpublished maps and institutional affiliations.

Copyright: © 2021 by the authors.
Licensee MDPI, Basel, Switzerland.
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distributed under the terms andconditions of the Creative Commons
Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

Ensemble simulation is a set of multiple numerical simulations that have slightly different initial conditions, boundary conditions, parameters, or models that are all geophysicallyplausible. Such a simulation enables the estimation of the predictability or reliability ofthe model simulation by providing a spread of ensemble forecasts. The simulation is mostcertain if the ensemble members are close to each other; otherwise, the ensemble provides
a possible range of different events. Thus, probabilistic model information can be obtained
from an ensemble simulation. Additionally, an ensemble simulation provides an ensemble
average that is often more accurate than a deterministic single simulation because themodel errors tend to be averaged out.
From a scientific viewpoint, the model predictability indicates not only the imperfection of simulation models but also the Lorenz’s deterministic chaos of Earth systems. The
error growth and propagation in the model simulation depend on the chaotic advection,diffusion, precipitation, thermodynamics, and chemistry, which all should be exploredin detail. From a practical perspective, probabilistic model information complements
deterministic model information, especially for atmospheric forecasts. Therefore, ensemble
prediction systems (EPSs) have been developed worldwide by operational weather forecast centers. These systems have adopted initial-condition ensemble simulations that aresuitable for error growth evaluation.
However, it is difficult to generate the ensemble perturbations of initial conditions
because randomly chosen (Monte Carlo) perturbations are likely to fade away or fail to

Atmosphere 2021, 12, 662. https://doi.org/10.3390/atmos12060662

https://www.mdpi.com/journal/atmosphereAtmosphere 2021, 12, 662

2 of 13grow through the simulations [1]. Therefore, more sophisticated perturbation methods
are generally used for weather forecasts, e.g., the singular vector method or the ensemble
Kalman filter method. The singular vector (SV) method was developed and implemented
initially by the European Centre for Medium-range Weather Forecasting (ECMWF) [2].
This method inevitably requires the adjoint code of the forecast model. In contrast, theensemble Kalman filter (EnKF) method, which is newer than the SV method, e.g., [3], does
not require the adjoint code of the forecast model and hence has been used throughout thisdecade. In the EnKF method, the generation of initial perturbations is united with the data
assimilation for building the initial conditions.
By contrast, atmospheric environmental EPSs have not been developed as extensively
as weather EPSs, and hence the application of ensemble dispersion simulations (EDSs) has
not been thoroughly explored. Most previous EDS studies were sensitivity tests validated
by parameter/model ensembles, e.g., [4–8] that were relatively easily executable with avery small number of ensemble members, or they were Monte Carlo tests that were simply
conducted with an offset modification of the initial/boundary conditions, e.g., [9–12]. Onlya few EDS studies have been conducted with sophisticated initial perturbations, e.g., theozone predictability experiments performed by Holt et al. [13] using an ensemble transform
method, the CO2 source/sink inversion experiments performed by Lauvaux et al. [14]using the SV method, the schematic dispersion experiments performed by Lattner andCervone [15] using an ensemble particle filter method, and the global aerosol dispersion
experiments performed by Haszpra et al. [16] using the ECMWF global ensemble forecasts.
Here, we have investigated the model uncertainty of a regional aerosol dispersion simulation with the meteorological initial perturbation generated by the EnKF. The knowledgeof the dispersion model uncertainty will provide insight regarding what model configuration is suitable for scientific and operational model usage. Unfortunately, the dispersion
model uncertainty has not been well explored with ensemble simulations because (1) itis difficult to prepare meteorological perturbations with sophisticated methods like the
SV and EnKF methods and (2) even if ensemble simulations are performed, it is difficult
to examine the probabilistic results in detail when tracer concentration observations andemission inventories are not sufficiently available.
Therefore, we introduced two original approaches to resolve these difficulties. (1) We
generated the meteorological initial ensembles ourselves using an EnKF data assimilation
system with an arbitrary model resolution and domain. (2) We examined the dispersion
process of the radioactive aerosol tracer (Cs-137) stemming from a point source, i.e., theFukushima Daiichi nuclear power plant (FDNPP). Point-source pollution data are ideal
for the validation of dispersion models. Especially in the case of the FDNPP accident, theemission location is exactly identified, the emission time and strength can be estimatedwithin a reasonable range to some extent, and the aerosol tracer concentration has been
accurately observed using radiation measurements. The model uncertainty will be effectively investigated with these approaches. Meanwhile, the emission term was unperturbed
and thus not investigated in this study.
2. Methodology
The EnKF is an approximate treatment of the Kalman filter for application to highdimensional systems such as the atmosphere cf. [17,18]. The Kalman filter defines ananalysis as an arithmetic weighted mean of forecasts and observations, imposing a minimum variance estimation of the analysis error on the weight optimization [19]. We
simultaneously obtain data assimilation products (i.e., analysis) and perturbations (i.e.,
ensemble members) by repeating the EnKF procedure. The analysis is the mean of theensemble members. The perturbations generated by the EnKF are qualitatively superior
to a random perturbation because they reflect the model uncertainty distribution and areflow-dependent, similar to the SV method. Furthermore, Wang et al. [20] reported thatwhen the ensemble size is small, the EnKF method has a statistical advantage because theother methods consistently generate symmetric positive–negative paired ensemble mem-

Atmosphere 2021, 12, 662

3 of 13bers to keep the average value and thus cannot make statistical full use of the ensemble
dimension. In contrast, the EnKF does not generate symmetric pairs but keeps the overall
average. While the EnKF has several numerical implementation methods, the square root
EnKF is implemented in this study. In the square root EnKF, each ensemble member retains
its identity through the data assimilation cycle because its relative position in the state
space among the ensemble members is invariant.
Prior to calculating the radioactive aerosol dispersion, we prepared the ensemble
analysis and forecast of the meteorological variables to drive the dispersion model using
an EnKF data assimilation system that was developed by Kunii [21]. This data assimilation
system consists of the local ensemble transform Kalman filter (LETKF), i.e., one of thesquare root EnKF implementations [22], and the Japan Meteorological Agency’s nonhydrostatic regional weather forecast model (JMA-NHM) [23,24]. The LETKF method has
been applied to weather forecast modeling, e.g., [21,25–30] and tracer dispersion modeling,e.g., [31–41].
In this study, the model domain covered eastern Japan as shown in Figure 1 and its
horizontal resolution was set to 3 km, which represented a typical grid scale for the regional
simulations implemented for the FDNPP accident cf. [37,39]. The model settings, such as
map projection, vertical coordinate, turbulence scheme, convective scheme, and terrain
features, were the same as those of the 3-km grid simulation performed by Sekiyama
et al. [37,39]. The domain consists of 215 × 259 horizontal grid points in the Lambertconformal projection and 60 vertical levels including 11 levels below 1 km above groundlevel. The terrain features were generated from the global digital elevation data with ahorizontal grid spacing of 30 arc seconds (GTOPO30) provided from the U.S. Geological
Survey. The turbulence scheme was based on the improved Mellor-Yamada level 3 closure
model [42,43]. A cumulus parameterization was not used in this study.
The data assimilation system was initiated at 06:00 UTC on 10 March 2011 with20 ensemble members and a 3-h time window. The assimilation settings, such as timeslots (3 h), prognostic variables (three wind components, temperature, pressure, water
vapor mixing ratio, and water/ice microphysics variables), inflation scheme (adaptively
multiplicative factors at each grid point), and covariance localization (1/e0.5 within 150 km
in the horizontal and 0.2 natural-logarithmical p-coordinate in the vertical), were the sameas those of the 3-km grid simulation performed by Sekiyama et al. [37,39]. We obtained theinitial condition and the boundary conditions from the JMA operational global 15-km grid
analysis.
We assimilated JMA’s operational observation dataset, which was integrated andquality-controlled for the JMA mesoscale Non-hydrostatic-model four-dimensional Variational data Assimilation system (JNoVA) [44], similarly to Kunii [21] and Sekiyama
et al. [37]. Additionally, we assimilated surface wind observations acquired by the Automated Meteorological Data Acquisition System (AMeDAS) similarly to Sekiyama et al. [39].
AMeDAS is a nationwide meteorological observation network managed by JMA. The data
assimilation system generated 20 ensemble members every 3 h (hereafter called ensemble
analysis members) and simultaneously calculated the mean value of the ensemble members as a deterministic analysis. The forecasts were calculated by the identical JMA-NHM
using these 20 ensemble members and a deterministic analysis as the initial conditions.
Hereafter, these forecasts are called ensemble forecast members and a deterministic forecast,respectively.
Using the meteorological analysis or forecast outputs, Eulerian dispersion simulations
were conducted with the Regional Air Quality Model version 2 (RAQM2) [45–50]. All of
the radioactive Cs-137 was contained in sulfate-organics-mixed aerosol particles when
it was transported in the atmosphere. The details of the modeled aerosol physics aredescribed in work of Kajino et al. [46] and Sekiyama et al. [37,39]. Note that the RAQM2
used in this study implements simplified aerosol dynamics compared with those of Kajino et al. [46] by assuming perpetual particle size distribution similarly to Sekiyama
et al. [37,39]. The combination of the JMA-NHM, the LETKF, the JNoVA+AMeDAS ob-Atmosphere 2021, 12, x FOR PEER REVIEW

Atmosphere 2021, 12, 662

4 of 14are described in work of Kajino et al. [46] and Sekiyama et al. [37,39]. Note that theRAQM2 used in this study implements simplified aerosol dynamics compared with those 4 of 13
of Kajino et al. [46] by assuming perpetual particle size distribution similarly to Sekiyama
et al. [37,39]. The combination of the JMA-NHM, the LETKF, the JNoVA+AMeDAS observations, and the RAQM2 has been successfully used for the Fukushima radioactive polluservations,and the RAQM2 has been successfully used for the Fukushima radioactivetion simulation[37,39–41,51–54].
pollution
simulation[37,39–41,51–54].
We used the emission scenarioof the radioactive Cs-137, which was released from
We
used
the
emissionof the radioactive
Cs-137,which
was released fromthe FDNPP, estimated by the Japan scenario
Atomic Energy
Agency (JAEA)[55–57].
Cs-containing
the
FDNPP,
estimated
by
the
Japan
AtomicEnergyAgency(JAEA)[55–57].
sulfate-organics-mixed aerosol particles [58] were injected at every time step into Cs-containing
a gridsulfate-organics-mixed
aerosolscenario
particles[58]
wereinjected
every
time stepinto
a grid cell
cell above
the FDNPP. The emission
has
been
revisedbyat
JAEA
several
times
after
above
FDNPP. The
has
been
revisedones
by JAEA
severaltimes
after 2012,2012, e.g.,[59].theHowever,the emission
differencescenario
betweenthepreviousand therevised
ones
e.g.,
[59].
However,thedifference
betweentheprevious
ones
and
the
revisedones
is not very large in comparison with the dispersion model uncertainty (i.e., ensemble is notlarge incomparison withthedispersion
model
uncertainty(i.e., ensemble
spread) of
spread)veryof Cs-137
concentrationsin thisstudy.
Since wefocus
on the quantificationof theCs-137concentrations
in
thisstudy.
Since
we
focus
on
the
quantificationofthedispersion
dispersion model uncertainty, the revision of the Cs-137 emission scenario scarcely affects
model
uncertainty,
theevaluation
revision ofinthe
Cs-137emission scenario scarcely affects the results
the resultsof theuncertainty
thisstudy.
of the uncertainty evaluation in this study.
The data assimilation cycle and the dispersion simulations were performed continuThe data assimilation cycle and the dispersion simulations were performed continuously from 11 March to 1 April 2011. The “analysis” run contained 20 ensemble simulaously from 11 March to 1 April 2011. The “analysis” run contained 20 ensemble simulations
tions and one deterministic simulation. Although the analysis is only provided every 3 h,and one deterministic simulation. Although the analysis is only provided every 3 h, thethe dispersion simulations require the meteorological variables at much smaller time indispersion simulations require the meteorological variables at much smaller time intervals.
tervals. Therefore, the meteorological variables were generated between the 3-h analysis
Therefore, the meteorological variables were generated between the 3-h analysis points
points by the 3-h forecast runs using the identical JMA-NHM. The variables were storedby the 3-h forecast runs using the identical JMA-NHM. The variables were stored at every
at every 10 min of simulation time and inputted into the RAQM2, and the variables were
10 min of simulation time and inputted into the RAQM2, and the variables were linearlylinearly interpolated during each 10-min interval. Additionally, we performed a “foreinterpolated during each 10-min interval. Additionally, we performed a “forecast” runcast” run for 24 h in two specific periods (15 March and 21 March, see Section 3). Eachfor 24 h in two specific periods (15 March and 21 March, see Section 3). Each forecast was
forecast was started at 21:00 local time (JST) of the previous day using the “analysis” run
started at 21:00 local time (JST) of the previous day using the “analysis” run as the initial
as the initialcondition.
The “forecast”
also contained
20 ensemble
simulations
andcondition.
The “forecast”
run alsorun
contained
20 ensemble
simulations
and one deterministic
one deterministic
simulation.
simulation.
215 grid points = 645 km

HitachiTokai

259 grid points = 777 km

FDNPP

Tokyo

(m a.s.l.)

Figure 1.Figure
Model1.domain
the JMA-NHM
and the RAQM2
used in thisstudy,
instudy,whichinthe
model
Model ofdomainof the JMA-NHM
and the RAQM2
used
in thiswhich
the model
resolution
is 3 km. isThedistance
betweenbetween
the Fukushima
Daiichi nuclear(FDNPP)
resolution
3 km.
The distancethe Fukushima
Daiichi power
nuclearplant
power
plant (FDNPP) andand Tokai
is approximately
100100km.km.
Tokai
is approximately

3. Resultsand Discussion
3. Resultsand Discussion
Here,
we focused
on 15
March
15 (Period1) (Period
and 21 (Period
2011
local
Here, wefocusedon March(Period
1) and 212), 20112),
local
time
to timeinves-to investigate
theradioactive
plumesthatwerecarriedlandward.
Nakajima
et
al.
[60]
tigate the radioactive plumes that were carried landward. Nakajima et al. [60] pointed
outpointed
out
that
the
plume
intrusion
inlandoccurred
twice
on
a
large
scale;
i.e.,
15
March
that the plume intrusion inland occurred twice on a large scale; i.e., 15 March and 20–21 and
20–21 March. The comparison of the radioactive aerosol concentration was performed at
the model grid corresponding to the location of Tokai, where JAEA has been operationallymonitoring radionuclide concentrations and clearly detected highly radioactive plumes
on both 15 and 21 March, 2011 [61]. The JAEA Tokai facilities are located approximately
100 km south of the FDNPP (Figure 1). The meteorological components were comparedAtmosphere 2021, 12, 662

March. The comparison of the radioactive aerosol concentration was performed at themodel grid corresponding to the location of Tokai, where JAEA has been operationally
5 of 13monitoring radionuclide concentrations and clearly detected highly radioactive plumes
on both 15 and 21 March, 2011 [61]. The JAEA Tokai facilities are located approximately
100 km south of the FDNPP (Figure 1). The meteorological components were compared
themodelmodelgrid
gridcorresponding
correspondingtotothe
thelocation
locationofofHitachi
Hitachi(Figure(Figure1),1),where
wherethethenearest
nearestatattheAMeDASstation
station(10(10kmkmnorth)north)totoTokai
Tokaiwaswaslocated.
located.
AMeDAS3.1. Period 1 (15 March 2011)
3.1. Period 1 (15 March 2011)
Figure 2a shows the time series of the meteorological ensemble analysis at the model
Figure 2a shows the time series of the meteorological ensemble analysis at the modelsurface layer (below 40 m) of the Hitachi AMeDAS station from 21:00 14 March to 21:00
surface layer (below 40 m) of the Hitachi AMeDAS station from 21:00 14 March to 21:00
15 March 2011 JST. The ensemble members are illustrated with the deterministic analysis,
15 March 2011 JST. The ensemble members are illustrated with the deterministic analysis,
AMeDAS observations, and the JMA operational 5-km gridded analysis. The wind speed
AMeDAS observations, and the JMA operational 5-km gridded analysis. The wind speed (u(uandandv)v)ensemble
ensemblehada smallspreadalmostsynchronized
the JMA
operhada smallspreadandandwaswasalmostsynchronizedwithwith
the JMA
operational
ationalanalysis
winds.
Thewind
errorsoftheanalysismembers(i.e.,
the
distance
analysis winds. The wind errors of the analysis members (i.e., the distance fromfrom
filledfilledcircles
to a bunchof blacklines)larger
were thanlargerthan
the differencewindcircles
to a bunchof blacklines) werethedifference
betweenbetween
the windthe
ensembleensemblethewidthspreadofwidthof aofbunch
blackFigurelines). 2b
Figure2b theshows
themembersmembers(i.e., the (i.e.,
spreada bunchblackoflines).
shows
forecast
forecast(initiated
21:00
14 March2011
in whichthe ensemble
wind ensemble
large(initiatedat 21:00at14
March2011 JST)
inJST)
which
the wind
had a hadlargea spread.
spread.
However,thewindforecast
spreadwasstillsmaller
than
the
wind
forecast
error
However, the wind forecast spread was still smaller than the wind forecast error (i.e.,the(i.e.,
theaverageddistancefromfilledcircles
to
a
bunch
of
black
lines).
averaged distance from filled circles to a bunch of black lines).

Figure2. (a) Ensemble
analysis members
a deterministicof east-westwind
Figure 2. (a) Ensemble analysis
members
and a deterministic
analysis andmemberof east-westanalysis
wind (umember
component),
north-south(ucomponent),
north-southwind(vcomponent),
andprecipitation
at
the
model
surfacelayerwind (v component), and precipitation at the model surface layer (below 40 m) of the grid corresponding to Hitachi from(below 40 m) of the grid corresponding to Hitachi from 21:00 14 March to 21:00 15 March local time.
21:00 14 March to 21:00 15 March local time. (b) Same as (a) except for forecast members, which were initiated at 21:00 14(b) Same as (a) except for forecast members, which were initiated at 21:00 14 March local time.
March local time. Circles indicate AMeDAS observations. Crosses indicate the JMA operational 5 km gridded analysis for
Circles indicate AMeDAS observations. Crosses indicate the JMA operational 5 km gridded analythe daily weather forecast.sis for the daily weather forecast.

The Cs-137 concentrations at the model surface layer (below 40 m) of the Tokai JAEAThe Cs-137 concentrations at the model surface layer (below 40 m) of the Tokai JAEA
station are shown in Figure 3a with the JAEA observations from 21:00 14 March to 21:00 15
station are shown in Figure 3a with the JAEA observations from 21:00 14 March to 21:00
March 2011 JST. In contrast to the wind speed ensemble, the Cs-137 ensemble concentrations
15 March 2011 JST. In contrast to the wind speed ensemble, the Cs-137 ensemble concenhad a large spread. Some of the ensemble members successfully represented the real peaktrations had a large spread. Some of the ensemble members successfully represented theconcentration but failed to represent the peak timing, appearing two hours early. The
real peak concentration but failed to represent the peak timing, appearing two hours early.
deterministic analysis underestimated the real concentration. In Figure 3b, the Cs-137
The
deterministic
analysis
underestimatedthe real
concentration.
In Figure 3b, Some
the Cs-137
forecast
memberspresented
a larger ensemble
spreadfor the concentration.
of theforecast
memberspresentedalargerensemble
spreadfortheconcentration.
Someof theCs-137 forecast members presented large overestimations before and after the peak.
Cs-137In
forecast
memberspresented large
overestimations
beforethe topeak.
this period,
the radioactive
aerosol
plume projected
fromandtheafter
FDNPP
the south,
coastwise, and then swept across Tokai (Figure 4). The percentile distribution of the 20 ensemble members was narrow in the analysis (Figure 4a) but relatively broad in the forecast(Figure 4b), which was in agreement with the time series of the ensemble analysis/forecast
concentrations at Tokai that are shown in Figure 3. The threshold (15 Bq/m3 ) used here
was defined as the half value of the air quality standard of Japan’s radioisotope regulations.

Atmosphere 2021, 12, 662

6 of 13

The percentile distribution of aerosol concentrations can be usefully applied to probabilistic
forecasts such as the chance-of-rain forecast. The probabilistic forecasts provide multiple
scenarios for environmental pollution or disasters. Generally, the accuracy of the forecast
decreases with time, and consequently the percentile distribution tends to diffuse withtime. Figure 4 shows that the forecast percentile distributions were very similar to theanalysis percentile distributions in the short forecasts (02:00 15 March and 06:00 15 March),
indicating the high accuracy of the forecast. However, the distributions were less similarin the longer forecast (10:00 15 March), in which the forecast percentile distribution was
diffused.
Atmosphere 2021, 12, x FOR PEER REVIEW The ensemble analysis of the surface wind speed (u and v) exhibited a relatively
6 of 14
small spread, which was usually less than 1 m/s even though the analysis errors (i.e.,analysis minus observation) were generally 1 or 2 m/s. For example, the relative standard
deviation(RSD) ofensembleaerosol
analysisfor thespecificfrom
4 h during
Period(02:00–08:00
In this period,
thetheradioactive
plume
projected
the FDNPP
to1thesouth,
15 March)was
5%swept
on average
By contrast,
ensembleof the surface
coastwise,andthenacross(Table
Tokai 1).
(Figure4). The thepercentile
distributionof theCs-137
20
concentration
hadwasa largespread,inanalysis
which somemembers
occasionallypresented
almostensemblemembers
narrowin the(Figure4a) but relatively
broad
in the forezero
concentrations,presented
very
high
concentrations.
The RSDof thecast(Figure4b), whichwhereas
was in others
agreementwith thetimeseriesof the ensemble
analyCs-137
concentration
analysis
for
the
same
time
periods
was93%
on
average(Table1).
sis/forecast concentrations at Tokai that are shown in Figure 3. The threshold (15 Bq/m3)This
resultindicatesthat aensemble
meteorology
large ensemble
used
here
was defined
assmallthe half
value ofspreadthe air inqualitystandardproducesof Japan’saradioisotope
spreadforaerosolconcentration.
Thus,
the
uncertainty
ontheconcentrationis amplified
regulations. The percentile distribution of aerosol concentrations can be usefully
applied incomparison
with
that
on
the
wind
field.
to probabilistic forecasts such as the chance-of-rain forecast. The probabilistic forecasts
ensemble
forecast
the samebehavior
as the analysis.
TheprovideThemultiple
scenarios
for exhibited
environmental
pollutionor disasters.
Generally,
theensemble
accuspread
wasrelatively
small inwith
the meteorologicalsimulation
andvery large
in the disperracyof theforecast
decreases
time, and consequently
thepercentile
distribution
siontosimulation.
Thetime.
forecast
RSDof the wind
speed
for thesame time
periods mentioned
tends
diffuse with
Figure4 shows
that theforecast
percentile
distributions
were
above
(02:00–08:00
15
March)
was7%
onaverage(Table1).
Incontrast,
theforecast
very similar to the analysis percentile distributions in the short forecasts (02:00 15 MarchRSD
of 06:00
the Cs-137
concentration
was1). Aofcomparison
the analysis
forecast
and
15 March),
indicating
the82%high(Table
accuracy
the forecast.ofHowever,
the and
distribuRSDs
indicatesthattheerrorsofthedispersion
modelsarenotlinearly
correlated
withtions were less similar in the longer forecast (10:00 15 March), in which the forecast per- theerrorsdistributionof the meteorologicalmodels.
centilewas diffused.

Figure3. (a)
Ensembleanalysismembersa deterministic
analysismemberof Cs-137
concentraFigure
3. (a)
Ensembleanalysismembers
andanda deterministic
analysismemberof Cs-137
concention
at
the
model
surfacelayer(below
40
m)
of
the
grid
corresponding
to
Tokai
from
21:00
14 March
tration at the model surface layer (below
March
to
21:00
15
March
local
time.
(b)
Same
as
(a)
exceptfor
forecast
members,which
were
initito 21:00 15 March local time. (b) Same as (a) except for forecast members, which were initiated at
ated
at 21:00
14 March
Circles
indicate
the measurements
at Tokai.
21:00
14 March
local local
time.time.
Circles
indicate
the measurements
at Tokai.

Atmosphere 2021, 12, x662
Atmosphere
FOR PEER REVIEW(a) Analysis 02:00 March 15

of 14
13
77 of

Analysis 06:00 March 15

Analysis 10:00 March 15

[%]

(b)

Forecast 02:00 March 15

Forecast 06:00 March 15

Forecast 10:00 March 15

FT = 5 h

FT = 9 h

FT = 13 h[%]

Figure 4. (a) Percentile distributions of the 20 ensemble analysis members, where the surface Cs-137 concentration is higher
Figure 4. (a) Percentile distributions of the 20 ensemble analysis members, where the surface Csthan the threshold (15 Bq/m3 ). White contour lines indicate the 15 Bq/m3 concentration of the deterministic analysis
137 concentration is higher than the threshold (15 Bq/m3). White contour lines indicate the 15
member. The open triangle and circle illustrate the locations of the FDNPP and Tokai, respectively. The local time of the
Bq/m3 concentration of the deterministic analysis member. The open triangle and circle illustratesnapshots was 2:00, 6:00, andon 15 of
March,
respectively.
(b) Sameas (a) except
thetime
forecast,
was initiatedthe10:00
locations
the FDNPP
and Tokai,
respectively.
The for
local
of thewhich
snapshots
was 2:00,at6:00,
21:00 14 March local time. The
was5, 9, and 13(b)h, respectively.
andforecast
10:00 onduration
15 March,
respectively.
Same as (a) except for the forecast, which was initiated at

21:00 14 March local time. The forecast duration
was 5, 9, and 13 h, respectively.
Table 1. Relative Standard Deviations (RSD) a of the 20-member Ensembles.

The ensemble analysis of the2:00–8:00
surface 15
wind
speed (u and v) exhibiteda 21
relatively
March
4:00–10:00
March small
spread, which was usually less
than
1
m/s
even
thoughtheanalysis
errors(i.e.,
analysisAnalysis
Forecast
Analysis
Forecast
minus observation)
example, the relative
standard 23%
deviation
Wind speed were generally
5% 1 or 2 m/s. For
7%
10%
(RSD)
of theensemble analysis 93%
for the specific 482%
h during Period77%
1 (02:00–08:00 235%
15 March)
Cs-137concentration
a RSD
was
5%
oncalculated
averagefor(Table1). By4contrast,
the ensemble
thesurfaceCs-137wasthe specified
h during Period
1 or Periodof
2 at
Hitachi(wind
speed)concentrationor Tokai (Cs-137
concentration).
hada large spread, in which some members occasionally presented almost zero concentrations, whereas others presented very high concentrations. The RSD of the Cs-137 con3.2. Period analysis
2 (21 Marchcentrationfor 2011)
the same time periods was 93 % on average (Table 1). This result
In comparison
Periodspread
1, the inensemble
spreads
tendedato
be larger
in Period
indicatesthat a smallwith
ensemble
meteorology
produces
large
ensemble
spread2.
Figure5 shows
time seriesThus,of thethemeteorologicalensemblesat the Hitachi
AMeDASinstationforaerosolconcentration.
uncertainty on
the concentrationis amplified
comsimilar to
those
Figure2 butfield.
for the time period from 21:00 20 March to 21:00 21 March,parisonwiththatinonthe wind
2011TheJST. ensemble
Note that forecast
it was rainingor snowing
onbehavior
this day inwidearea ofTheeastern
Japan.
exhibitedthe same
as atheanalysis.
ensembleAs
seenin
Figure5b,
theensemble
forecast(initiated
at
21:00
20
March
2011
JST)
presented
spread was relatively small in the meteorological simulation and very large in the disperchaotic
motionsTheof thewindRSD
speedv).speed
Furthermore,
the time
ensemble
spreadof thesion
simulation.
forecastof (utheandwind
for the same
periods
mentioned
precipitation
forecast
wasextremely
large.
above (02:00–08:00 15 March) was 7% on average (Table 1). In contrast, the forecast RSD
timeconcentration
series of the Cs-137
concentrations
at the Tokaiof
JAEA
station areinof theThe
Cs-137
was 82%(Table 1). A comparison
the analysis
andshown
forecast
Figure6
similarto
thosein
Figure3,
except
that
they
are
in
the
time
periodfrom
21:00
RSDs indicates that the errors of the dispersion models are not linearly correlated with the
20 March
to meteorological
21:00 21 Marchmodels.
2011 JST. The ensemble spreads were very large, similar toerrorsof thethose in Period 1. As shown in Figure 6a, the deterministic analysis failed to represent thepeak timing with a two-hour delay. However, some of the ensemble analysis members
successfully represented the real peak timing and concentration. In contrast to the deterministic analysis, the deterministic forecast completely failed to represent the plume arrival

Atmosphere 2021, 12, 662

8 of 13(Figure 6b). Many of the ensemble forecast members behaved similarly to the deterministic
Atmosphere 2021, 12, x FOR PEER REVIEW
9 of 14
Atmosphere 2021, 12, x FOR PEER REVIEW
9 ofthat
14
forecast. This failure was caused by the wet deposition (below-cloud scavenging)occurred before the plume arrived at Tokai in the forecast simulations.

5. Sameas period
Figure from
2 but 21:00
for thetime
periodfrom 21:00
20 March
to 21:00
21
March local
Figure 5. Same as Figure Figure
2Figurebut fortime
March
to 21:00
March
local time.
The21
forecasts
were
5. theSame
as Figure
2 but forthe20time
periodfrom 2121:00
20 March
to 21:00
March local
time. The forecasts were initiated at 21:00 20 March local time.
initiated at 21:00 20 Marchtime.
localThe
time.
forecasts were initiated at 21:00 20 March local time.

Figure6. Sameas Figure3 3butfor thetime
period
from
21:00
March
20 toto21:00
21
March
local
FigureSame
Figurebut
thetime
period
from
21:00
March
21:0021
21MarchMarchlocal
local time.
Figure
6. 6.
Same
as as
Figure3 but
forforthetimeperiod
from
21:00
March
2020
to 21:00
time.
The
forecasts
wereinitiated
at 21:00
20 March
local
time.
The
forecasts
wereinitiated
at
21:00
20
March
local
time.
time. The forecasts were initiated at 21:00 20 March local time.

In Period 2, the RSD of the wind speed analysis for the specified 4 h (04:00–10:00
21 March) was 10% on average (Table 1). However, the RSD of the Cs-137 concentration
analysis for the same time periods was 77% on average (Table 1). These scores also
indicate that the uncertainty on the concentration is amplified as shown in Period 1. This

Atmosphere 2021, 12, 662

9 of 13finding implies that if we deterministically pursue an accurate dispersion, it is necessary
to unrealistically increase the accuracy of the meteorological simulation. As expected, thepercentile distributions of the Cs-137 concentration (Figure 7) tended to be broader thanthose in Period 1 in both the analysis and forecast before raining (approximately 08:00 JST).
The percentile distributions rapidly shrank after the rain because the radioactive aerosols
were deposited through precipitation. In the forecast, the precipitation area or timing was
Atmosphere 2021, 12, x FOR PEER REVIEW
10 of 14slightly shifted from the actual values, and consequently, the distribution of wet deposition
was inadequately distorted.

(a) Analysis 04:00 March 21

Analysis 08:00 March 21

Analysis 12:00 March 21

[%]

(b)

Forecast 04:00 March 21

Forecast 08:00 March 21

Forecast 12:00 March 21

FT = 7 h

FT = 11 h

FT = 15 h[%]

Figure 7. Same as Figure 4 but for 21 March. The local time of the snapshots was 4:00, 8:00, and 12:00 on 21 March,
Figure 7. Same as Figure 4 but for 21 March. The local time of the snapshots was 4:00, 8:00, andrespectively. In the lower panels,
forecast
duration
was 7,In11,
15 h,respectively.
12:00 theon 21
March,
respectively.
theandlower
panels,the forecast duration was 7, 11, and 15 h, respectively.

The forecast RSD of the wind speed for the same time periods was 23% on averageand that
the Cs-137
was 235%(Table1).time
The extremely
largeforecast
errors
Theofforecast
RSDconcentrationof the wind speed
for thesame
periods was
23%
on averageoftheCs-137concentration
were
causedbytheforecast
errorsoftheprecipitation(notand that of the Cs-137 concentration was 235% (Table 1). The extremely large forecast only
erthe
but also
the timing). Specifically,
forecast
completely
rorsstrengthof the Cs-137
concentration
were causedthe
bydeterministic
the forecast errorsof almostthe precipitation
failedto reproduce
the but
Cs-137
arriving
at Tokai (Figure6b). In comparison
the(not only
the strength
alsoplumethe timing).
Specifically,
the deterministic
forecast withalmostextreme
error
of
the
Cs-137concentration,theprecipitation
error
was
moderateasshowncompletely failed to reproduce the Cs-137 plume arriving at Tokai (Figure 6b). In comparin
thewithrightthepanelof Figureison
extremeerror 5b.
of the Cs-137 concentration, the precipitation error was modOn
this
day,
light
precipitation
was widespreadover Japan (Figure 8a; derived from
erate as shown in the rightpanel of Figure
5b.
the JMA
Radar/rain-gauge
Analyzed
Precipitationdata
andreasonably
reproduced
On this day, light precipitation was widespread over[62])Japan(Figure8a; derived
from
by
the
deterministic
analysis(Figure8b).
Thedeterministic
forecast(Figure8c)
produced
the JMA Radar/rain-gauge Analyzed Precipitation data [62]) and reasonably reproduced
abydifferent
distribution
from(Figure
the observed(Figureforecast8a) and(Figure
the analyzed
valuesthe deterministic
analysis8b). Thevalues
deterministic8c) produced(Figure8b).
Thisdifference
causedtheCs-137concentration
to
have
a
high
error,
indicatinga different distribution from the observed values (Figure 8a) and the analyzed values (Figthat
the errorof the dispersion
is notonly amplified
in comparison
withthe error
ure 8b).
This difference
caused models
the Cs-137
concentration
to havea high error,
indicating
of
the
wind
speed
but
also
cruciallymagnified
by
the
error
of
the
precipitation
becausethat the error of the dispersion models is not only amplified in comparison with the errorofthehighsensitivityof alsoaerosoldeposition
on precipitation.
However,even so, someof theof thewind
speed but
cruciallymagnifiedby the error of
the precipitation
becauseof
ensemblememberswere
successfulinreproducing
thehighconcentration
at
Tokai.
This
the high sensitivity of aerosol deposition on precipitation. However, even so, some of theis
theadvantageof ensemble
simulations.
ensemblemembers
were successful
in reproducing the high concentration at Tokai. Thisis the advantage of ensemble simulations.

Atmosphere 2021, 12,
12, x662
Atmosphere
FOR PEER REVIEW

10 of
of 14
13
11

(a) Radar Obs 12:00 March 21(b) Analysis 12:00 March 21(c) Forecast 12:00 March 21

20
10
5
2
1
0.5
0.2
0.1

20
10
5
2
1
0.5
0.2
0.1

(mm/hr)(mm/hr)

20
10
5
2
1
0.5
0.2
0.1
FT=15hrs(mm/hr)Figure 8. Precipitation (mm/hr) from 11:00 to 12:00 21 March local time derived from (a) the JMA operational radar/rainFigure 8. Precipitation (mm/hr) from 11:00 to 12:00 21 March local time derived from (a) the JMA
gauge analyzed observations, (b) the deterministic analysis, (c) and the deterministic 15-h forecast. The cross mark illustrates
operational radar/rain-gauge analyzed observations, (b) the deterministic analysis, (c) and thethe location of Tokai.
deterministic 15-h forecast. The cross mark illustrates the location of Tokai.

4. Conclusions
4. Conclusions
We conducted ensemble simulations for the dispersion of a point-source aerosolWe
conducted
ensemble simulations
forensemble
the dispersion
of a point-source
aerosol ususing perturbed
meteorologicalfields. The
simulations
provided probabilistic
ing
perturbed
meteorologicalfields.
The
ensemblesimulations
providedprobabilistic
ininformation and multiple case scenarios for the aerosol dispersion. We found that a small
formationandmultiplecase
scenarios
fortheaerosol
dispersion.
We
found
that
a
small
ensemble spread of wind speed resulted in a large uncertainty in aerosol concentrations,ensemble
spread of wind
resulted
in a large
aerosol concentrations,
i.e., the uncertainty
on thespeed
aerosol
dispersion
was uncertainty
amplified inincomparison
with that oni.e.,
uncertainty
on Thisthe aerosolwasamplifiedin comparison
withthat onthe thewind
simulation.
findingdispersion
implies thata highaccuracyof dispersion
modelingthewindmuchsimulation.
finding
implies that amodeling,high accuracyof dispersiona modelingrequireshigher Thisaccuracyof meteorologicalthus representing
limitation
requiresmuch higher
accuracyof meteorologicalmodeling, thus therepresenting
a limitationof deterministic
dispersion
simulations
for analyzing/predicting
location andintensityofdeterministic
dispersion
simulations
for
analyzing/predicting
thelocationandintensityof aerosol plumes. Therefore, the probabilistic information of ensemble simulations exhibitsofaerosol
plumes.
Therefore,
the probabilistic
information of ensemble simulations exgreat
potential
for aerosol
analysisand prediction.
hibitsThegreatpotential foraerosol analysis
prediction.
deterministic
simulation
did not andprovidethe best analysis/prediction in this study.
The deterministic
simulationmembers
did not successfully
provide the reproduced
best analysis/predictionin thisHowever,some of the ensemble
the arrival timeandintensityof the aerosol
plumes.
With only
a deterministic
simulation,
it isthenotarrival
possibletostudy.
However,some ofthe ensemble
members
successfully
reproducedtime
account
for anotherevent. Regrettably,in only
the fieldof atmosphericchemistrytooandintensityof the aerosol
plumes. Witha deterministic
simulation,
it ismodeling,
not possiblemuchemphasishas beenplacedon deterministic
simulations
uncritically.
The usefulnesstoaccount
for anotherevent.
Regrettably,in the fieldof atmosphericchemistrymodeling,of ensemble
simulations
shouldbe recognized
to a greater
extent.uncritically. The usefultoo
much emphasis
has beenplacedon deterministic
simulations
The
errors insimulations
the aerosol simulation
were not onlywith the errors in windness ofensembleshould be recognized
to acumulative
greater extent.
speed
simulation
but aerosol
also cruciallymagnifiedby theerrorsin the precipitation
simulationThe
errors in thesimulation
were notonly
cumulativewith the errors
in windbecauseof the dependenceof aerosol
deposition
onerrors
precipitation.
Althoughsimulation
the singlespeed
simulation
but also cruciallymagnifiedby thein the precipitation
model initial-perturbed
ensemblesimulationas usedin this studyAlthough
is a powerfultool tobecauseof the dependenceof aerosol
deposition
on precipitation.
the singleexplore
probabilistic
analysis/prediction,
thelimitations
ofthesingle-model
simulation
model initial-perturbed ensemble simulation as used in this study is a powerful tool toshould also
be considered
because the single-model
simulationa single
exploreprobabilistic
analysis/prediction,
the limitationsof the implements
single-modelonly
simulation
modulealso
for betheconsidered
precipitation.
shouldbecause the single-model simulation implements only a single
module for the precipitation.

Author Contributions: Conceptualization, T.T.S.; Methodology, T.T.S., M.K. (Mizuo Kajino), and
M.K. (Masaru
Kunii); Software,
M.K. (Mizuo
Kajino)and M.K. (Masaru
Kunii);
Validation,
AuthorContributions:Conceptualization,
T.T.S.;
Methodology,
T.T.S., M.K.
(MizuoKajino),T.T.S.;
andFormalAnalysis,
T.T.S.;
Investigation,
T.T.S.;
Data
Curation,
T.T.S.
andM.K.
(MizuoKajino);Writing—
M.K. (Masaru Kunii); Software, M.K. (Mizuo Kajino) and M.K. (Masaru Kunii); Validation,
T.T.S.;
Original
Draft Preparation,
T.T.S.; Writing—Review& Editing,
T.T.S.;
Visualization,
Funding
FormalAnalysis,
T.T.S.; Investigation,
T.T.S.; Data Curation,
T.T.S.
and
M.K. (MizuoT.T.S.;
Kajino);WritAcquisition,
T.T.S.
andM.K.
(MizuoKajino).
Allauthors
have
read
and
agreedtothepublished
ing—Original Draft Preparation, T.T.S.; Writing—Review & Editing, T.T.S.; Visualization, T.T.S.;
version of
the manuscript.
Funding
Acquisition,
T.T.S. and M.K. (Mizuo Kajino). All authors have read and agreed to the pub-lishedversionthe manuscript.
Funding:
Thisofwork
was supported by the Environment Research and Technology Development
Fund(1-1802)
atthe Environmental
and Conservation
and by JSPS
KAKENHI
Funding: This work
was supported Restorationby the Environment
ResearchAgency
and Technology
Development
Grant
Numbers
24340115,
17K00533,
19H01155,
and
21H03593.
Fund (1-1802) at the Environmental Restoration and Conservation Agency and by JSPS KAKENHI
Grant
Numbers
24340115,
17K00533,
19H01155,
and 21H03593.
Institutional
Review
Board
Statement:
Not applicable.
Institutional
Review
Board Statement:
Not applicable.
Informed Consent
Statement:
Not applicable.
Informed Consent Statement: Not applicable.

Atmosphere 2021, 12, 662

11 of 13

Data Availability Statement: We thank M. Chino of JAEA for providing Cs-137 emission inventory
data from their published papers [55–57]. We thank JAEA for providing their Cs-137 observation
data from their published technical note [61]. The JMA data are available freely at their website or
can be purchased via the Japan Meteorological Business Support Center. The numerical model source
codes used in this study are available under a collaborative framework between MRI and related
institutes/universities. The ensemble analysis and forecast data generated in this study is available
on request to the corresponding author.
Acknowledgments: We greatly acknowledge K. Saito of JMA and T. Iwasaki of Tohoku University
for their help and encouragement. We deeply thank S. Koizumi for his technical support.
Conflicts of Interest: The authors declare no conflict of interest.

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https://doi.org/10.5194/acp-2021-591
Preprint. Discussion started: 30 July 2021
c Author(s) 2021. CC BY 4.0 License.

Eight-year variations in atmospheric radiocesium in Fukushima city
Akira Watanabe1,2, Mizuo Kajino3,4,5, Kazuhiko Ninomiya6,7, Yoshitaka Nagahashi1, and Atsushi
Shinohara6
51Faculty of Symbiotic Systems Science, Fukushima University, Fukushima, Fukushima 960-1296, Japan
Institute for Climate Change, Fukushima, Fukushima 960-0231, Japan
3
Meteorological Research Institute (MRI), Japan Meteorological Agency (JMA), Tsukuba, Ibaraki 305-0052, Japan
4
Faculty of Life and Environmental Sciences, University of Tsukuba, Tsukuba, Ibaraki 305-8572, Japan
5
Institute of Radiation Emergency Medicine (IREM), Hirosaki, Aomori 036-8564, Japan
6
Graduate School of Science, Osaka University, Toyonaka, Osaka 560-0043, Japan
7
Intitute for Radiation Sciences, Osaka University, Toyonaka, Osaka 560-0043, Japan
2

10

15

20
Correspondence to: A. Watanabe (watamay1948@yahoo.co.jp), M. Kajino (kajino@mri-jma.go.jp), and K. Ninomiya(ninomiya@rirc.osaka-u.ac.jp)

25

1

https://doi.org/10.5194/acp-2021-591
Preprint. Discussion started: 30 July 2021
c Author(s) 2021. CC BY 4.0 License.

Abstract. After the Fukushima nuclear accident, atmospheric 134Cs and 137Cs measurements were taken in Fukushima city foreight years, from March 2011 to March 2019. The surface air concentrations and deposition of radio-Cs were high in winterand low in summer; these trends are the opposite of those observed in a contaminated forest area. The half-lives of 137Cs in the5concentrations and deposition before 2015 (275 d and 1.11 y) were significantly shorter than those after 2015 (756 d and 4.69y). The dissolved fractions of precipitation were larger than the particulate fractions before 2015, but the particulate fractions
were larger after 2016. The half-lives of 137Cs in the concentrations and deposition were shorter before 2015, probably because
the dissolved radio-Cs was discharged from the local terrestrial ecosystems more rapidly than the particulate radio-Cs. X-ray
fluorescence analysis suggested that biotite may have played a key role in the environmental behavior of particulate forms of

10radio-Cs after 2014. However, the causal relationship between the seasonal variations in particle size distributions and thepossible sources of particles is not yet fully understood. The current study also proposes a method of evaluating the consistencyof a numerical model for radio-Cs resuspension and suggests that improvements to the model are necessary.
Keywords: Fukushima nuclear accident, long-term observation, radiocesium, atmospheric radioactivity, precipitation

15

radioactivity

2https://doi.org/10.5194/acp-2021-591
Preprint. Discussion started: 30 July 2021
c Author(s) 2021. CC BY 4.0 License.

1 Introduction
We conducted eight-year measurements of atmospheric 134Cs and 137Cs in Fukushima city after the Fukushima Daiichi Nuclear
Power Plant (FDNPP) accident that occurred in March 2011 to understand the time variations in and emission sources of 134Cs
and 137Cs and to propose effective ways to reduce atmospheric radioactivity. Among the various radionuclides released to the5environment, radio-Cs is particularly important due to its abundance in terrestrial ecosystems (the impacts of other nuclides
were negligibly small 100 days after the accident; Yoshimura et al., 2020), long half-lives (2.06 y for 134Cs and 30.17 y for
137Cs), and bioaccumulation (accumulation in muscle tissues, with biological half-lives of 30-150 d; WHO, 2011). Radio-Cs

forms aerosols in the air and is therefore efficiently deposited onto the ground surface via precipitation in addition to via dry
deposition. Approximately 30 % of the radio-Cs released in March 2011 was deposited onto the ground surface in Japan (the10aircraft-measured deposition on the ground was 2.7 PBq for 137Cs; NRA, 2012, and the most updated estimate of 137Cs
emissions by the Japan Atomic Energy Agency is 10 PBq; Terada et al., 2020). (The activity of 134Cs in the environment was
equivalent to that of 137Cs in March 2011). Once radio-Cs is deposited onto the ground surface, it circulates within local
terrestrial ecosystems, so the discharge from the local environment to downstream or downwind regions may not be substantial(0.02 - 0.3 % y-1 to river; Iwagami et al., 2017, approximately 1 % y-1 to atmosphere 1 ). Thus, long-term monitoring of

15

atmospheric radio-Cs at even one station may allow us to understand the mechanisms of its circulation in the local terrestrial
ecosystems, to estimate the external and inhalation exposure risks to the local residents, to propose efficient ways to reduce
health risks to the residents, and to assess the effectiveness of decontamination efforts.
To date, a great number of studies have focused on the circulation of radio-Cs in terrestrial ecosystems (Onda et al.,2020). In terms of the long-term monitoring of atmospheric radio-Cs with a focus on resuspension from the ground surface to20

the atmosphere, several papers have been published. Based on atmospheric measurements taken in the contaminated forest
area of the Abukuma Highlands (30 km northwest of the FDNPP) from October 2012 to December 2014, Ochiai et al. (2016)
reported that the surface activity concentrations of 137Cs were higher in summer and lower in winter and that the time variationsof the fine-mode (< 1.1 μm in diameter using an impactor) and coarse mode (> 1.1 μm) behaved differently. The coarse-mode
fractions were larger in summer, and the fine-mode fractions were larger in winter. Kinase et al. (2018) conducted surface

25

concentration measurements at four locations in the forest area of the Abukuma Highlands from July 2011 to March 2014 andfound that the concentrations of 134Cs and 137Cs were lower in winter and early spring and higher from late spring to autumn.
Their size-resolved measurements with a six-stage cascade impactor showed that the backup filter (< 0.39 μm) activity
concentrations were high in winter, consistent with Ochiai et al. (2016). However, Kinase et al. (2018) found through scanningelectron microscopy (SEM) that there were significant amounts of soil dust particles in the backup filter; these particles were30

larger but bounced off the upper impactor stages. Therefore, they concluded that the sizes of radioactive particles were not
1

The annual resuspension rate to the atmosphere was estimated as 0.047 % y-1 by Kajino et al. (2016). However, the current
study found that the resuspension rate was likely substantially underestimated (see Sect. 3.5 and Fig. 9). A value ofapproximately 1 % y-1 was obtained from improved simulations, but that manuscript is still in preparation.
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small but were actually large (coarse-mode particles). In late spring, the surface concentrations were positively correlated withthe wind speed, so they concluded that the wind-blown soil particles carried radio-Cs in this season. In the summer and autumn,
the concentrations were positively correlated with temperature but negatively correlated with wind speed, so they concluded
that the resuspension mechanisms were different in the winter and summer. The SEM analysis revealed that there were more
5abundant bioaerosols in summer than in winter. Based on simulations, Kaijno et al. (2016) indicated that the summer peaks insurface concentrations in the Abukuma Highlands could be accounted for by the bioaerosol emissions from forest ecosystems,
even though the emission mechanism remains unknown. Igarashi et al. (2019a) further investigated the mechanisms of
bioaerosol emissions in forests in summer by using fluorescent optical microscopic observation and high-throughput DNA
sequencing techniques. They suggested that the fungal spores that accumulate radio-Cs may be significantly involved in

10

resuspension in the forest in summer. Kita et al. (2020) suggested that rain induced the emission of radio-Cs associated withfungal spores in the forest in summer. Minami et al. (2020) combined aerosol flux measurements and a multilayer atmospheresoil-vegetation model and estimated that the bioaerosol emission flux was on the order of 10-2 μg m-2 s-1, which could accountfor the surface concentrations of 137Cs in the forests in summer (Kajino et al., 2016; Kinase et al., 2018; Igarashi et al., 2019a).
Kinase et al. (2018) also showed that there was no enhancement in the 137Cs concentration associated with forest fire events in15

the region. The surface concentration of 137Cs was not correlated with that of levoglucosan, which is often used as a marker ofbiomass burning. These results are distinct from those from Chernobyl, where wildfire plays a key role in the migration ofradio-Cs associated with the event (Ager et al., 2019; Igarashi et al., 2020). The contributions of additional 137Cs emissions
from the nuclear reactor buildings of FDNPP to the surface concentrations in Japan were negligibly small compared to theresuspensions from the ground surface (Kajino et al., 2016). On the other hand, unintentional emissions in the premises of

20

FDNPP such as debris removal operations contributed to some observed sporadic peaks (Steinhouse et al., 2015; Kajino et al.,2016), although the impacts of such events might be small in terms of long-term averages and trends.
The current study is distinct from other studies, as it includes long-term comprehensive measurements (time-resolved
and size-resolved measurements of surface air activity concentrations together with measurements of dissolved and particulate
forms of activity in precipitation) at an urban/rural location in the Fukushima Basin in the vicinity of contaminated forests in

25

the Abukuma Highlands. The field observation and the simulation methods are described in Sect. 2. Sect. 3 presents the results
for the surface concentrations (Sect. 3.1), deposition amounts (Sect. 3.2), size distribution (Sect. 3.3), chemical compositions(Sect. 3.4), comparison with simulations (Sect. 3.5), and comparison with measurements taken outside Fukushima Prefecture(Sect. 3.6). The seasonal variations and possible emission sources are discussed in Sect. 4.1, the impacts of decontaminationand natural variations on the differences in trends before and after approximately 2015 are discussed in Sect. 4.2, the reasons30

for the substantial deposition amount in January in Fukushima city are discussed in Sect. 4.3, and major findings and futureissues are summarized in Sect. 5. The observation data used in the study are provided as a Microsoft Excel file in the
Supplement.

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Figure 1: Map of Fukushima Prefecture and the surrounding prefectures. The locations mentioned in this study and terrestrial
elevations are depicted in the map.
2 Methods
5

2.1 Sampling site
The observation site, Fukushima University, is located in Fukushima city, located in the northernmost basin (Fukushima basin)in the Nakadori Valley, surrounded by the Ou mountains to the west and the Abukuma Highlands to the east (Fig. 1). The
distance of the observation site from the FDNPP is approximately 60 km. The Nakadori Valley was formed by the Abukuma
River, which starts in the mountains in Fukushima Prefecture near the border of Tochigi Prefecture and flows northeast through10

the central parts of Fukushima city to the Pacific Ocean in Miyagi Prefecture. The major radioactive plumes arrived twice inFukushima city, on March 15 and 20 (plume #3 and #8, as identified by Nakajima et al. (2017), respectively). These plumes
were transported over the Abukuma Highlands (where the peaks are mostly lower than 1,000 m) but were blocked by the5https://doi.org/10.5194/acp-2021-591
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higher Ou Mountains (peaks are 1,000 - 2,000 m) and thus transported along the Nakadori Valley (Nakajima et al., 2017). The
land surface of Fukushima city was contaminated mainly on the afternoon of March 15 with plume #3. The air dose rate in
Fukushima city started to increase at 17:00 local time (LT), associated with the weak rain that started at 13:00 LT, and peaked
at 19:30 LT at a value of 24.0 μSv h-1.
5

2.2 Surface air concentrations
2.2.1 High-volume air sampler, cascade impactor, and radioactivity measurement
The air samples were collected using high-volume air samplers (Kimoto electric Co., Ltd., Model-120SL) placed on the roofof the building at Fukushima University (37.68°N, 140.45°E) at a height of 25 m from ground level. In this study, we carriedout two types of air sampling: time-resolved observations and aerosol size-resolved observations. In the former case, aerosol

10

samples were collected on a quartz fiber filter (Tisch Environmental, Inc., TE-QMA-100). The air suction rate of the sampler
was 700 L min-1. The typical duration of each sample collection was 24 hours, from May 8 to September 2, 2011. Then, we
switched to 72 hours of collection until December 27, 2017; after that, 1 week of continuous collection was performed until
March 28, 2019. For the latter observations, a cascade impactor system (Shibata Scientific Technology Ltd., HV-RW) was
placed into a high-volume air sampler. The air suction rate was 566 L min-1. The aerosols were collected separately by diameter15

on six quartz filters (Kimoto, TE-236). The range of particle sizes in this system was 0.39-0.69, 0.69-1.3, 1.3-2.1, 2.1-4.2, 4.210.2, and >10.2 μm. (Note that the sizes in the manuscript indicate the 50 % cutoff aerodynamic diameters.) Fine particles
with a size of <0.39 μm were captured on a backup filter (Kimoto, TE-230-QZ). The typical sample collection time for thesize-resolved observations was three weeks. In types of both observations, activated carbon fiber filters (Toyobo Co., Ltd.,KF-1700F 84 mmφ) were also placed at the exit of the high-volume air samplers to collect gas-state aerosols.

20

The collected aerosol samples were sealed into polyethylene bags at Fukushima University. After being shaped into
definite shapes, the gamma rays from the samples were measured by high-purity germanium detectors (coaxial with 15, 35
and 40 % relative efficiencies, SEIKO EG&G, ORTEC and coaxial with 40 and 60 % relative efficiencies, CANBERRA)
connected to a multichannel analyzer system (MCA7600, SEIKO EG&G) at the Radioisotope Research Center, Osaka
University. The radioactivities of 134Cs and 137Cs were identified at gamma-ray intensities of 605 keV and 662 keV,

25

respectively. The detection efficiencies of the respective detectors for each gamma ray were determined from the same-shapefilter samples from standard 134Cs and 137Cs solutions obtained from the Japan Radioisotope Association. The typicalmeasurement time of each sample was 1-3 days. Under these conditions, the detection limits of 134Cs and 137Cs wereapproximately 5 × 10-3 Bq. The errors in the measured values are derived from the systematic error of geometrical configurationand the standard sample itself in addition to statistical error. All radioactivities determined by our measurements were corrected30

at mid sampling times.

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The radioactivities of both 134Cs and 137Cs were identified for most filter samples. The deviation in concentration
between 134Cs and 137Cs became larger over time due to the relatively short half-life of 134Cs. According to the radioactive
decay correction performed in March 2011, the activity ratios of 134Cs/137Cs were approximately 1. These ratios are consistent
with those in other reports related to FDNPP accident, so we concluded that the detected radiocesium originated from FDNPP
5

accident. During the measurement period, no radioactivity from 134Cs and 137Cs was detected from the carbon filters; that is,the component of gaseous radioactive cesium was negligibly small.
2.2.2 Impactor/cyclone system
Since the filters for the high-volume air samples were quartz fiber filters, they could not be used for elemental analysis with
X-ray fluorescence spectrometry (XRF). For the XRF analysis, we used an impactor/cyclone system (Tokyo Dylec Corp., no-10

number special order, 1100 L min-1) in which the aerosols were separated by size into < 2.5 μm and > 2.5 μm using an impactor;
those < 2.5 µm and >0.1 μm were sampled in glass bottles (As One corp., 2-4999-07) using a 0.1 µm cyclone with samplingintervals of one month from September 2014 to January 2018. Aerosols larger than 2.5 µm were collected on quartz fiber
filters in the system. Aerosol samples in glass bottles (0.1 – 2.5 μm) were defined as fine-mode PM (PMf), and those on quartz
fiber filters (> 2.5 µm) were defined as coarse-mode PM (PMc). The radioactivities of 134Cs and 137Cs in the samples were also15

measured in the same manner.
2.2.3 Possible artifacts of impactor measurements
Size separation by an impactor is associated with the artifacts caused by bouncing effects. In fact, in cascade impactor
measurements, Kinase et al. (2018) observed abundant coarse-mode particles such as mineral dust and bioaerosol particles inthe backup filters due to bouncing effects. In the impactor/cyclone system, the glass fiber filters used as an impaction surface

20

were immersed in silicone oil to prevent particles from bouncing (Okuda et al., 2015). In this study, silicone oil was not used
for the cascade impactor but was used in the impactor/cyclone system. However, the long-duration measurements (such as themonthlong measurements) could be associated with the larger particles that rebounded at the impactor and were collected inglass bottles (Okuda et al., 2015).
2.3 Deposition (dry plus wet deposition, dissolved and particulate fractions)25

The total deposition (dry plus wet deposition or fallout) samples were collected with a precipitation sampler (Miyamoto Riken
Ind. Co., Ltd., RS-20) with a funnel diameter of 20 cm. Since a heating device was not installed on the sampler, any snow in
the funnel was manually melted in a water bath in winter. The accumulated snow in the funnel never reached the top of thefunnel during the whole observation period. A filtration device was installed in the sampler using membrane filters (Advantec,
4-880-03) with a pore size of 0.45 μm. The radioactivities of 134Cs and 137Cs in the filtered water stored in the polyethylene

30bottle and those on the filters were both measured by high-purity germanium detectors at Osaka University and were defined7https://doi.org/10.5194/acp-2021-591
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as the dissolved and particulate fractions of the deposition, respectively. It should be noted here that this separation does not
perfectly differentiate water-soluble and insoluble radio-Cs. The clogging of the pores of the membrane filter can occur during
filtration. The measured total (dissolved plus particulate) deposition amounts were compared with those measured by theofficial method at the Fukushima Prefecture Nuclear Power Center (Fig. 1), which is located 6.5 km north-northwest of
5

Fukushima University. Our method was found to be consistent with their official method: the correlation coefficient R was
0.81, with a slope of 1.158 (the values from Fukushima University were larger by 16 %). Differences in locations and samplingintervals (daily at the Fukushima Prefecture Nuclear Power Center; monthly at Fukushima University) could also havecontributed to the differences in the measured values at the two sites.
2.4 X-ray fluorescence analysis (aerosols, deposition, and river sediments)

10

X-ray fluorescence (XRF) analysis was carried out by using a RIX1000 (Rigaku Corp.) at Fukushima University. The
measurement setup recommended by the manufacturer was used for the XRF. The major and trace element contents wereanalyzed by the fundamental parameter method and calibration curve method, respectively (Takase and Nagahashi, 2007).
Measurements were conducted for PMf (see Sect. 2.2.2), the particulate fractions of precipitation (see Sect. 2.3), and the river
sediments. River sediments were collected at 15 sites upstream and downstream of Fukushima city in the Abukuma River and15

its tributaries in 2010. Samples were taken from the gravel layer of the lower terrace at 5 sites, from alluvial fan deposits at 1
site, and from current riverbed sediments at 9 sites. The dried sediment samples were sieved and divided into two grain sizegroups: particles smaller than 180 μm (defined as fine sediment particles) and particles 180 μm - 2 mm (coarse sedimentparticles).
2.5 Numerical simulation and validation data

20

Kajino et al. (2016) used a Lagrangian model (LM) to simulate the atmospheric dispersion and deposition of 137Cs resuspended
from bare soil and forest ecosystems from January to December 2013. Since the resuspension fluxes and size distributions
were unknown, they adjusted the flux from bare soil (forest ecosystems) so that the simulated surface concentrations matchedthose measured in Namie (Tsushima) (Namie High School Tsushima Campus, 37.56°N, 140.77°E, 30 km northwest of the
FDNPP) (Fig. 1) in the winter (summer) of 2013, and they adjusted the dry and wet deposition parameters (reflecting the size

25

distributions and hygroscopicity) so that the simulated total (dry plus wet) deposition over land in March 2011 matched thosemeasured by the aircraft measurements (NRA, 2012). Thus, note that the size distribution of the simulation was assumed tohave submicron size ranges that were consistent with those of the primary emissions (the direct emissions associated with the
FDNPP accident in March 2011) but that may not be applicable for resuspension events; the carrier aerosols are presumed tobe soil dust or bioaerosols, which are usually larger than the submicron size range. Kajino et al. (2016) concluded that their30

simulations are likely reliable because the simulated differences between the surface concentrations in the contaminated area(or emission source area) (i.e., Tsushima) and those in the downwind area (Meteorological Research Institute (MRI), Tsukuba

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city, 36.06°N, 140.13°E, 170 km southwest of the FDNPP) (Fig. 1) were consistent with the observed differences at the two
locations.
However, Kajino et al. (2016) used only surface concentration measurements to validate the simulations. The current
study also used concentration and deposition measurements from Fukushima University for model validation. The previous
5

study compared only the two locations in the contaminated forest areas and in the downwind urban/rural regions; the current
study includes an additional location in the urban/rural region near the contaminated forest of the Abukuma Highlands (60 km
northwest of the FDNPP).

3 Results
3.1 Surface air concentrations
10

Figure 2 shows the time variations in the atmospheric radioactivity concentrations of 137Cs from May 2011 to March 2019.
Just after the accident, the 137Cs concentrations were higher than 0.01 Bq/m3, and the maximum concentration of 0.0169 Bq/m3
was detected on May 23, 2011. The concentration quickly decreased to a level of 10-4 Bq/m3, and the minimum concentrationof 4.05 × 10-6 Bq/m3 was obtained on December 5, 2018. By taking the annual averaged value, the decreasing tendency in theatmospheric concentration could be expressed as Y = 0.0418X-0.476, where Y indicates the annual mean 137Cs concentration and15

X means the number of years elapsed. The coefficient of determination, R2, is 0.993. This demonstrates that the surface
concentration decreased exponentially and halved in approximately 4 years; thus, the decrease rate was higher than the rate ofradioactive decay of 137Cs.
It is remarkable that the decreasing trends in the earlier stage and the later stage were different. The regression linesof the raw data time intervals for the whole period (red; May 2011 - March 2019), the earlier stage (blue; May 2011 - December

202014), and the later stage (green; January 2015 - March 2019) are shown in Fig. 2, with the half-life (Th) in days and thedecrease rate (Rd) in % y-1. The decreasing trend (Th = 275 d, Rd = 92.0 % y-1) of the earlier stage is approximately three times
faster than that of the later stage (Th = 756 d, Rd = 33.5 % y-1). It is shown later in Fig. 3 in Sect. 3.2 and discussed in Sect. 4.2,
but this could be related to the relative abundance of particulate and dissolved fractions of radio-Cs in the environment. The
dissolved fractions of radio-Cs may discharge faster than the particulate fractions from contaminated environments, such as

25

soils and plants. The relative abundance of the dissolved fractions was larger in the earlier stage than in the later stage suchthat the decreasing trend in the surface air concentration was faster than that in the later stage. In addition to the naturalvariability, decontamination work, which was completed by March 2018 in Fukushima city and the surrounding municipalities,
may also have contributed to the difference in the decrease rates; this possibility is also discussed in Sect. 4.2.

9

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5

Figure 2: Time series of surface air activity concentrations of 137Cs on the left axis. The red, blue, and green lines indicate theregression lines of the whole period, before 2015, and after 2015, respectively. The half-lives (Th) and decay rates (Rd) are also
depicted. The gray line indicates the ratio of the running mean of 20 data points (an approximately monthly cycle) to therunning mean of 160 data points (an approximately annual cycle) on the right axis to show its seasonal variation.

It is also interesting that our data show different seasonal variations from those measured in Tsushima by Ochiai etal. (2016) and Kinase et al. (2018). The levels in their studies were high in summer and low in winter, but as depicted in thegray line in Fig. 2, the concentration rose starting in October, with maxima in the spring season around March and minima in10

the summer. The maxima in the spring are approximately one order of magnitude larger than the minima in the summer. The
measurements of their studies were conducted in high-dose areas in the mountain forest (approximately 400 m above sea level(a.s.l.)), and the high-volume samplers were set near the ground surface. In contrast, the current air sampling was conductedin a relatively low-dose area (10 times lower than that in Tsushima) located in an urban/rural region on a hill (approximately
200 m a.s.l.) at the southern end of the Fukushima Basin. The heights of the samples were 25 m from the ground surface. Such

15

geographical and altitude differences could have caused these differences.

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3.2 Deposition amounts
Figure 3 shows the monthly cumulative deposition of 137Cs from March 2011 to March 2019. The monthly deposition amountpeaked in March 2011 as 202200 Bq m-2, decreased to 1 % of the initial amount after one year, and decreased to an order of 1
Bq m-2 after eight years. It also showed seasonal variation and was high from winter to spring. Nevertheless, the current level5

is from two to three orders of magnitude larger than that before the Fukushima nuclear accident. The monthly trend is expressed
as Y = 48232X-1.944, where Y indicates the monthly cumulative 137Cs deposition and X means the number of months elapsed.
R2 is 0.697.
The decrease rates of deposition (Th = 1.11 – 4.69 y) were generally slower than those of the surface concentrations(Th = 275 - 756 d). It is hard to identify the reason for this phenomenon. A perfect simulation could answer this question, but

10high uncertainties in atmospheric deposition modeling and land surface modeling inhibit a perfect understanding of these longterm circulations of radio-Cs in the environment. It is safe to presume here that the decreasing trends in deposition and surface
concentrations are different because the contributions of major emission sources to deposition and surface concentrations aredifferent. If the dominant source of the surface concentration is near (far from) the observation site and that for of deposition
is far from (near) the site, the faster decrease rate in concentration is due to the faster (slower) reduction rate in the nearby

15sources of emissions than in the far sources.
There is also a distinct difference in the decreasing trends before and after 2015. In addition to the effect of
decontamination work, as previously discussed in Sect. 3.1, the relative abundances of the dissolved and particulate fractionsof 137Cs could be a part of the reason. The particulate fraction made up 72.6 % of the deposition of March 2011, which ispresumed to be largely influenced by primary emissions. Here, it is interesting to note that most primary radio-Cs emissions

20

are thought to be composed of water-soluble submicron aerosol particles (e.g., Kaneyasu et al., 2012 and almost all numericalsimulations afterwards, such as Sato et al., 2020), while water-insoluble Cs-bearing microparticles (CsMP; Adachi et al., 2013,
Igarashi et al., 2019b) may contribute somewhat to primary emissions (Ikehara et al., 2020, Kajino et al., 2021). If the primaryradio-Cs in aerosols were 100 % in water-soluble forms, the particulate fraction should have made up 0% of the precipitationin March 2011 (although, some of the water-soluble Cs could have converted to a water-insoluble form through adsorption to25

soil particles accumulated on the membrane filter during filtration). After April 2011, as the contributions of resuspension
were thought to be dominant, the dissolved fractions became larger. The ratio varied, but the dissolved fractions were generally
higher before 2016, and the particulate fractions became dominant after 2016. There seemed to be a regime change in thephysicochemical properties of radio-Cs circulating in the environment in the area in approximately 2015 and 2016, which
could have changed the decreasing trends of both the surface concentrations and deposition before and after 2015. This result30

is consistent with the finding of Manaka et al. (2016), who reported that the exchangeable proportions of radio-Cs rapidly
decreased in forest soils from two to four years after the accident, i.e., from 2013 - 2015.

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5

Figure 3: (Top) Time series of 137Cs activity deposition. The red, blue, and green lines indicate the regression lines of thewhole period, before 2015, and after 2015, respectively. The half-lives (Th) and decay rates (Rd) are also depicted. (Bottom)
Time series of particulate and dissolved forms of 137Cs deposition on the left axis and the ratio of particulate to dissolved 137Cs
on the right axis.

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The seasonal variations in particulate and dissolved 137Cs were slightly different from each other and different fromthose of the surface concentration. The surface concentration peaked in March in almost all years, and the total deposition
peaked in January. The peaks of the total deposition in January coincided with those of the dissolved 137Cs before 2016, but
the peaks of the dissolved 137Cs became unclear afterwards. The peaks of particulate 137Cs occurred in March before 2016,
5

which coincided with those of the surface concentrations. After 2016, there were no clear seasonal variations in particulate
137

Cs. There are clear and different seasonal variations in the surface concentration and deposition. However, at the current

stage, we have no knowledge of or numerical tools to reveal the hidden mechanisms underlying these variations.
3.3 Size distributions
Figure 4 shows the time series of the seasonal mean atmospheric radioactivity concentrations of 137Cs obtained from the
10

cascade impactor measurements. The sampling interval for the cascade impactor measurements was three weeks. The seasonalmeans included a sampling period if any part of the sampling period was included in the season. For example, the raw datafrom the sampling period from February to March contributed to the averages of both DJF (December, January, and February,i.e., winter) and MAM (March, April, and May, i.e., spring). The seasonal mean total (all sizes) concentrations of cascade
impactor measurements during the sampling period agreed well with those of the time-resolved observations (Fig. 2), with R2

15= 0.93. The same seasonal variation discussed for the time-resolved observations (Sect. 3.1) was also observed; the atmospheric137

Cs concentration was relatively high in DJF and MAM compared to that in JJA (June, July, and August, i.e., summer) andSON (September, October, and November, i.e., autumn).
The most dominant size range in activity was the backup filter (< 0.39 μm, or rebounded particles such as soil dust
and bioaerosols; Kinase et al., 2018), and its seasonal variation agreed well with that of the total particle concentration (high20

in DJF and MAM). On the other hand, the second largest contribution was made by the size range of 4.2-10.2 μm, whichshowed the opposite seasonal variation and was relatively high in JJA and SON. The seasonal variations in the largest particle
fraction, larger than 10.2 μm, are interesting. The trend appears to be synchronized with that of the backup filter particles (highin DJF and MAM), but the opposite trend was observed in 2016 and 2017 (high in JJA). The contributions of other fractions,
i.e., 0.49-4.2 μm, were small in the measured period. Even though the contributions were small, the seasonal trend of 0.39-

25

0.69 μm was similar to that of the backup filter particles, but that of 1.3-2.1 μm was similar to that of 4.2-10.2 μm. The current
measurement indicates that the dominant particles and their sizes may be distinct depending on the season. The decrease ratesof each size were different before and after approximately 2015, as discussed in Sects. 3.1 and 3.2, but the size distribution of
the surface activity did not change substantially before and after approximately 2015.

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Figure 4: (Top) Time series of seasonal mean size-resolved surface activity concentrations of 137Cs and (bottom) their relative
fractions.
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5

Figure 5: (Top) Time series of surface activity concentrations of 137Cs in PMf (0.1-2.5 µm) and PMc (>2.5 µm) collected by
the impactor/cyclone system and those of the backup filter of the cascade impactor. (Bottom) Correlation coefficients of
temporal variations among seasonal mean 137Cs activity concentrations of different sizes measured by the impactor/cyclone
and the cascade impactor. Correlation coefficients higher than approximately 0.4 and lower than approximately -0.4 are colored
blue and orange, respectively.
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Cascade impactor sampling is associated with the bouncing effect, whereas filters for the impactor/cyclone systemwere immersed in silicone oil to prevent the bouncing effect. Thus, compared the cascade impactor and the impactor/cyclone
measurement data, as shown in Fig. 5. The top panel of Fig. 5 shows the data with the same measurement time intervals (three
weeks for the cascade impactor data and one month for the impactor/cyclone data). The surface activity concentrations of 137Cs
5

in the backup filters were well correlated with those of PMf. No remarkable seasonality was observed in PMc, but someenhancements were observed in JJA in 2015 and SON in 2016.
The bottom panel of Fig. 5 shows the correlation coefficients among the seasonal mean size-resolved data from thecascade impactor and impactor/cyclone measurements. If we assume that the bouncing effect on the impactor/cyclone
measurements was negligible, the cascade impactor data and the impactor/cyclone data were consistent. There was a positive10

correlation between PMf and the backup filter data. There were also positive correlations between PMc and the 1.3-2.1 µm and4.2-10.2 µm data. There was a negative correlation between PMc and PMf. We can assume that fine-mode particles are thedominant carriers of 137Cs in winter and spring and that coarse-mode particles are the dominant carriers of 137Cs in summer
and autumn. However, there was also a contradiction in the data. There were low or negative correlation coefficients between
the backup filter data and the cascade impactor data at smaller size ranges, such as 0.39-0.69, 0.69-1.3, and 1.3-2.1 µm, but

15

the backup filter data were positively correlated with the impactor data for > 10.2 µm. It appears that bouncing effect occurred;
particles larger than 10.2 µm bounced in the latter stages and were captured in the backup filter. However, as previously
discussed, the behaviors of the >10.2 μm-particle data were not consistent in time, i.e., they were generally high in DJF and
MAM and were high in JJA in 2016 and 2017 (Fig. 4). Kinase et al. (2018) and Igarashi et al. (2019a) considered that thedominant carriers of resuspended 137Cs were coarse-mode particles such as soil dust and bioaerosols. Ochiai et al. (2016)20

conducted two-stage impactor sampling and measured the surface activity concentrations of 134Cs and 137Cs above and below
1.1 μm from 2012 to 2014. They showed that the contributions of coarse-mode particles (> 1.1 μm) were dominant, withmaxima in summer. The contributions of the fine-mode particles (< 1.1 μm) were much smaller, and no significant seasonalvariations were found. All of their measurement sites were surrounded by contaminated forests in the Abukuma Highlands(Tsushima and the nearby sites), so the sampling sites were different from those in our study. Such larger particles may have25

contributed to the backup filter data in the current measurements; however, based on the fact that the backup filter data werepositively correlated with PMf and not with PMc, fine-mode particles (< 2.5 μm) should also play a key role in determining thesurface air concentrations in Fukushima city.
On the other hand, if we assume that the bouncing effect is also significant in the impactor/cyclone system due to thelong sampling duration, as suggested by Okuda et al. (2015), the positive correlation between the backup filter particles and30

PMf was simply due to the bouncing effects of the larger particles in both systems.

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Even though the emission sources of the dominant particles collected by the size-resolved measurements could not
be identified in this study, the possible aerosol sources that would explain the differences in size and seasonality of the two
locations are discussed later, in Sect. 4.1.
3.4 Chemical characterizations of particles in the air, rainfall, and river sediments
5

Figure 6 shows the relative abundance of the XRF-measured atomic number concentrations of elements in the PMf monthly
sample from September 2014 to January 2018. Among the 15 detected species, PMf was mainly composed of SiO2, Al2O3, andSO3. The fractions of SiO2 show clear seasonal variations and were higher around May. The seasonal variations in Al2O3 andSO3 are the opposite of that of SiO2. A positive temporal correlation was obtained between the 137Cs in PMf and SiO2 (R =0.30). Negative correlations were obtained for Al2O3 and SO3, with correlation coefficients of -0.36 and -0.35, respectively.

10

Note that these results do not prove that the SiO2-bearing aerosols are the carriers of resuspended 137Cs, but we can safely
conclude that the origins of SiO2 and 137Cs may be close to each other (i.e., that both come from the same source or the samearea/direction).
Figure 7 shows comparisons of the relative abundance of the periodic mean XRF measured atomic number
concentrations in different samples, fine sediment particles, coarse sediment particles, PMf, and particulate fractions of

15

precipitation. The PMf and precipitation data over the same period, from October 2014 and December 2012, were averaged.
The sediment samples were collected in 2010. The 10 species that were common to all samples are shown in Fig. 7. The
correlation coefficients for the compositions among samples are above 0.9, showing that the samples have similar origins. The
features of the PMf composition were distinct from the others. PMf included SO3 (17.8 %) and Cl (2.65 %), while the others
did not.

20

Weathered biotite is abundant in the soil in Fukushima and absorbs radio-Cs efficiently (Kogure et al., 2019). The
compositional correlation coefficients between the weathered biotite (Takase, 2020) and the four samples were high, at 0.73
to 0.87. However, when the two major components SiO2 and Al2O3 were excluded, the compositional correlation coefficientschanged significantly. The eight compositional correlation coefficients between the fine and coarse sediment particles were0.98, but those between the sediments and PMf were 0.01 and 0.19 for the fine and coarse sediment particles, respectively. The

25

eight compositional correlation coefficients for the particulate fractions of precipitation were moderate, at 0.36, 0.44, and 0.45
for fine sediment particles, coarse sediment particles, and PMf, respectively. The eight compositional correlation coefficientsfor weathered biotite were 0.76, 0.71, 0.50, and -0.14 for fine sediment particles, coarse sediment particles, particulate fractionsof precipitation, and PMf, respectively.

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Figure 6: Temporal variations in the chemical composition of PMf as measured by XRF.

5

Figure 7: Periodic mean chemical compositions of fine particles and coarse particles in sediments, PMf, and the precipitation
filter (the particulate fraction of precipitation) measured by XRF.
18

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The findings from the current section are summarized as follows. The mean compositions of both fine and coarse
sediment particles are similar to those of biotite, which absorbs radio-Cs efficiently. The similar composition feature wasobserved for the particulate fractions of precipitation. The composition of PMf was slightly different from those of the othersamples, but the 137Cs concentrations in PMf become larger when the relative fractions of SiO2, the major component of biotite,
5

increased. Thus, biotite may have played a key role in the environmental behavior of radio-Cs in Fukushima city since
September 2014. However, the major carriers of radio-Cs before September 2014 and those in the dissolved fractions inprecipitation are still unknown.
3.5 Comparison with the simulation results and climatological deposition velocity analysis
In Fig. 8, the surface concentrations of 137Cs in 2013 simulated by Kajino et al. (2016) are compared with the time-resolved

10

observation data (Fig. 2). Kajino et al. (2016) included 137Cs resuspended from bare soil, 137Cs resuspended from forest
ecosystems, and additional 137Cs emissions from the FDNPP. The additional 137Cs emissions were negligibly small, theconcentrations of which in East Japan were two to three orders of magnitude smaller than those from the two sources. Therefore,
they are not depicted in the figure. The simulation was successful in explaining the magnitude and seasonal variations insurface concentrations at Tsushima and the MRI, but the simulation at Fukushima city disagreed with the observations. The

15

simulation showed an enhancement of 137Cs from forests in the summer, but that was not detected in the observations. The
observed magnitude and seasonal trends are rather similar to those simulated for 137Cs from soil dust.

20

Figure 8: Time series of (black) measured surface activity concentrations of 137Cs and those simulated (by Kajino et al., 2016;
K16) considering different emission sources, (red) mineral dust from bare soil and (green) aerosols emitted from forest
ecosystems.

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Kajino et al. (2016) used only the observed surface concentrations to estimate the regional budget of resuspended
137

Cs in the air, but we used the observed deposition to evaluate the model, as shown in Fig. 9. Suppose there is a simple

nonlinear relationship between the deposition (D) and surface concentration (C):
𝐷

𝑎𝐶 ,

(1)

where a represents a removal rate and b represents nonlinearity, such as spatial and temporal variabilities. If one can take a5long-term average of D and C, Eq. (1) may hold. Eq. (1) is reformulated as𝑙𝑜𝑔 𝐷𝑏 𝑙𝑜𝑔 𝐶𝑙𝑜𝑔 𝑎 .

(2)

The log-log scatter plot between the monthly mean surface concentrations and monthly cumulative deposition of observed(purple) and simulated (orange) 137Cs are depicted in the left panel of Fig. 9. The coefficient of determination of the observation
was 0.678, with a risk factor for < 0.1 %. Eq. (1) holds for the monthly mean resuspended 137Cs at Fukushima University. As
seen in Eq. (2), the intercept of the Y-axis indicates the removal rate a. log(a) is dimensionless, but if b is close to one, the unit
10of a can be m s-1. From Fig. 9, b of observation is close to one. Therefore, the ratios of the monthly deposition amounts to themonthly mean surface concentrations are referred to as the climatological deposition velocity (m s-1). Time series of theclimatological deposition velocity are presented in the right panel of Fig. 9.

15

Figure 9: (Left) Scatter diagram of (purple circles) observed surface concentrations and deposition of 137Cs and those simulated(by Kajino et al., 2016; K16) considering different emission sources, (orange open squares) mineral dust from bare soil and(orange close squares) aerosols emitted from forest ecosystems. The purple and orange lines indicate the regression lines ofthe observed data and the simulated (both dust and forest) data. (Right) Time series of (blue) climatological deposition velocity
on the left axis and (gray) precipitation amounts on the right axis.

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The left panel of Fig. 9 clearly shows that the removal rate (a) used in Kajino et al. (2016) can be underestimated by
one to two orders of magnitude. The deposition velocities used in Kajino et al. (2016) were estimated from the observation of137

Cs in March 2011, which was supposed to be mainly composed of submicron water-soluble particles. However, the current

study and the series of previous studies regarding resuspended 137Cs indicated that the host particles of 137Cs could be5

substantially larger (e.g., soil and bioaerosols). This may be the reason for the overestimation of simulated 137Cs from forestsin the summer in Fukushima city. If the deposition velocities of the model increased by one to two orders of magnitude, thetransport of 137Cs from the contaminated forest to Fukushima city in summer may decrease such that the simulated surface
concentration in Fukushima city agrees with the observation. Certainly, their simulated regional budget needs to be reassessedusing the realistic deposition velocities indicated in the current study.

10

The observed climatological deposition velocity varied by more than one order of magnitude over time. There are
two main deposition mechanisms: dry deposition and wet deposition. Wet deposition is associated with precipitation. The
variations in the climatological deposition rate seem to agree with the observed precipitation, but almost no correlation wasobserved (R ~ 0.10). The mean climatological deposition velocity was 5.3×10-1 m s-1, and the peak values occurred in January.
The maximum value was 4.9 m s-1 in January 2013, when the monthly precipitation was not very high (81.2 mm). Possible

15

reasons for these peaks in January are discussed later, in Sect. 4.3. The typical order of the dry deposition velocity ofsupermicron (1–10 µm in diameter) particles is approximately 10-3–10-2 m s-1 (e.g., Petroff and Zhang, 2010), which issubstantially lower than the values in our climatological deposition velocity analysis. Certainly, the magnitudes of the instantdeposition velocity and our climatological deposition velocity are not directly comparable, but it seems that wet deposition
plays an important role in the removal of resuspended 137Cs-bearing atmospheric aerosols.

20

Figure 10: (Left) Scatter diagram of the observed surface deposition of 137Cs at Fukushima University and the MRI fromMarch 2011 to March 2019, with a regression line. (Right) Time series of the ratio of deposition at Fukushima University todeposition at the MRI, with a regression line.
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3.6 Comparison of deposition amounts at Fukushima University and the MRI
Figure 10 compares the deposition amounts of 137Cs at Fukushima University (60 km northwest of the FDNPP) and the MRI(170 km southwest of the FDNPP) from March 2011 to March 2019. The deposition data at the MRI are available from
Environmental Radioactivity and Radiation in Japan (https://www.kankyo-hoshano.go.jp/data/database/, last accessed: June5

14, 2021). There was a significant positive correlation between the deposition amounts of 137Cs at the two sites. The slope ofthe regression indicates that the ratio of deposition at Fukushima University to that at the MRI did not change significantly
from the initial ratio during the eight years, which is approximately 8-9 times (202200 Bq m-2 at Fukushima University and
23100 Bq m-2 at the MRI). This indicates that deposition was influenced by emissions from nearby sources and was notsubstantially influenced by long-range transport at either site. The right panel of Fig. 10 indicates that the deposition ratios at

10

the two sites were approximately 10, with a variation of more than one order of magnitude and peaks in winter (especially
January) that decreased slightly over time. The right panel of Fig. 9 shows that the January peak is a feature of Fukushima city
and was not observed at the MRI. The possible reasons for the January peak in Fukushima City are discussed later, in Sect.
4.3. The slight decreasing trend was probably due to decontamination, which was ongoing in Fukushima during the period
until 2018, as shown later in Table 1. Certainly, natural variations could also have contributed to the decreasing trend.

15

4 Discussion
Even eight years after the FDNPP accident, the surface air activity concentration of 137Cs had not fallen to the level before theaccident, which was at an order of magnitude of 10-6 Bq m-3. In difficult-to-return zones, the surface concentrations sometimes
still exceed 10-2 Bq m-3. Based on long-term measurements, this study tries to understand the characteristics of radio-Cs in the
air and its deposition and to reveal its origins in order to identify effective ways to reduce radioactivity in contaminated20

terrestrial ecosystems.
4.1 Seasonal variation and possible sources
The current study clearly shows that the surface concentrations of 137Cs are high from winter to spring, with peaks in Marchand lows from summer to autumn, in the urban/rural area of Fukushima city (60 km northwest of the FDNPP). It also showsthat the deposition amounts of 137Cs are high in the winter, especially in January, and low from summer to autumn. This25

seasonal trend is the opposite of that observed in a forested area in the Abukuma Highlands (Tsushima, 30 km northwest of
the FDNPP), which was high in the summer (Ochiai et al., 2016; Kinase et al., 2017). From winter to spring, northwesterly
winds prevail over the region associated with migrating disturbances, while southeasterly winds prevail over the region
associated with the Pacific high. The three simulated monthly mean surface wind fields for January to March and June toAugust are shown in Kajino et al. (2016).

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In summer, Fukushima city is downwind of Tsushima. The surface concentrations of 137Cs at Tsushima areapproximately ten times greater than those at Fukushima city, and Fukushima city is downwind of Tsushima, but there is noenhancement of 137Cs in summer. The traveling distances of carrier aerosols depend on their aerodynamic diameters. The
distance between the two sites is approximately 30 km. The traveling distances of aerosols below < 10 μm are not very different
5

and are larger than 100 km because their gravitational deposition velocities are negligibly small. On the other hand, thetraveling distances rapidly decrease proportionally to a square of the diameter above 10 μm, and the traveling distance of anaerosol with a diameter of 100 μm is an order of 1 km (Kajino et al., 2021). Aerosols below < 10 µm can travel longer distances,
but their dry deposition amounts increase significantly from 1 µm to 10 µm (Kajino et al., 2021). Igarashi et al. (2019a) reported
that the major proportions of bioaerosols in forests in summer are smaller than 5 μm in diameter and can travel a fairly long10

distance. Pollen is much larger than 10 μm, but pollen emission is limited in summer (Igarashi et al., 2019a). Consequently,
there was a significant enhancement in surface concentrations in the forests in summer but no enhancement in the downwind
urban/rural areas, probably because the carrier aerosols were efficiently deposited onto the ground surface before significant
amounts of atmospheric 137Cs reached the downwind areas. Consistent with our findings presented in Fig. 5, 137Cs in PMc wasmore abundant than that in PMf and the backup filter particles in summer. To obtain a quantitative understanding of the regional

15

cycle of atmospheric 137Cs in the northern part of Fukushima Prefecture, accurate simulations are required in the future.
In winter and spring, the surface concentrations of 137Cs are probably enhanced due to the local emissions from the
nearby sources because the location of the sampling site is upwind of the Abukuma Highlands and the ground surface in the
upwind areas of the sampling site in the season (northwest directions) is less contaminated than the site. In winter and spring,137

Cs in the backup filter particles and PMf are pronounced in Fukushima city. These characteristics are somewhat different

20

from those reported in previous studies. Miyamoto et al. (2014) measured the size distributions of radio-Cs with a cascade
impactor for two periods, from March 17 to April 1 and May 9 to 13, 2011, at a site 120 km southwest of the FDNPP. They
showed that the peak size ranges were 1.2 - 2.1 µm and 0.65 - 1.1 µm in the former and latter periods, respectively. Doi et al.
(2013) reported that the peak diameters of the 137Cs concentration from April 4 to 11 were 1.0 µm and 1.5 µm at Tsukuba, 170
km southwest of the FDNPP. Kaneyasu et al. (2017) measured the size distributions of 137Cs and other chemical components

25

six times at Tsukuba from April to September 2011. The peak diameter ranges were 0.49 - 0.7 μm in the earlier stages (before
June 9), but the contributions of coarse-mode particles (> 1 μm) increased after June 9, and the second modes appeared in theranges of 3.5 - 5.2 μm and 7.8 - 11 μm in July and September, respectively. Judging from their measured mass size distributionsof Ca, which is assumed to originate from mineral soil, Kaneyasu et al. (2017) concluded that soil particles could be the major
carrier of resuspended radio-Cs in Tsukuba. Our XRF analysis indicated that radio-Cs is carried mainly by soil particles in30

Fukushima city, but the size distributions are greater in PMf (<2.5 μm) and in the backup filter particles. If radio-Cs is carriedby soil particles, it is natural to presume that the fractions of radio-Cs in PMc would be large (e.g., Fig. 3 of Kaneyasu et al.,2012 or Fig. 4 of Kaneyasu et al., 2017). One could argue that the bounced coarse mode soil particles are observed in the
backup filters, but in fact, the seasonal mean Cs concentrations in the backup filter are positively correlated with PMf and23https://doi.org/10.5194/acp-2021-591
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negatively correlated with PMc (Fig. 5). One could further argue that bounced large particles are also collected in PMf despite
the special procedures employed to prevent rebound in the impactor/cyclone system.
There are four possible explanations for these results: (1) If the bouncing effect did not occur in either system, themajor sources of radio-Cs in Fukushima city are probably related to combustion (a mass peak below 0.39 μm means that the5number peak is approximately 100 nm). If the bouncing effect occurred only in the cascade impactor, (2) if the origin of radioCs is soil particles, the size distributions of soil particles in Fukushima city are smaller, or radio-Cs in the soil exists more
within finer particles; (3) if the origin of radio-Cs is soil particles, the coarse-mode fraction deposits to the ground surface
faster than the fine-mode fraction, such that the proportion of Cs in PMf is larger in Fukushima city. (4) The bouncing effectoccurs in both systems, and the origin of radio-Cs is coarse-mode soil particles. (1) is less likely because there is little chance

10of the artificial combustion of contaminated biomass. In fact, there were no temporal correlations between the 137Cs andlevoglucosan (a biomass burning marker) surface air concentrations at Tsushima during the forest fire event in the Abukuma
Highlands that occurred in March 2013 (Kinase et al., 2018). (3) is also less likely because long-range transport (at least 100km) is required for the major proportions of coarse-mode particles to deposit to the ground surface, whereas Fukushima city
is characterized as the emission source region in that season. In terms of (2), the latter sentence, “radio-Cs in the soil exists

15

more with finer particles”, contradicts Kaneyasu et al. (2017), suggesting that the radio-Cs is uniformly distributed on thesurface of soil particles. (4) is possible, but rebound is prevented in the impactor/cyclone system, and there is also no evidence
that rebound occurred in the impactor/cyclone system (certainly, there is also no evidence that rebound did not occur). Further
experiments are required to determine whether (2) or (4) is more likely and whether some sources are missing. As Kaneyasu
et al. (2012) and (2017) reported, comparing the size distributions of 137Cs with those of other chemical components in20

Fukushima city would be an effective way to investigate the origin of resuspended radio-Cs from winter to spring. Alternatively,a PM2.5 cyclone or virtual impactor could be used to separate the fine-mode and coarse-mode particles, that can completely
exclude the bouncing effect.
4.2 Differences in trends before and after approximately 2015 (natural variation and decontamination)
As described in Sects. 3.1 and 3.2, distinct decrease rates were observed before and after approximately 2015 in both the25

surface concentrations and the deposition. There may be two main reasons for this: natural variation and decontamination. The
natural variation (the dissolved fractions of precipitation or the exchangeable proportions of forest soils discharging faster thanother forms from the local ecosystems (Manaka et al., 2016)) was previously described, and the effect of decontamination ispresented in some detail here.

30

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Table 1: Decontamination achievement ratios in Fukushima City and the surrounding municipalities (Nihonmatsu City,
Kawamata Towna, Date City, and Koori Town)
March 2014

March 2015

March 2016

March 2017

March 2018

Fukushima City
Residential area (Number of houses)
Public facility (Number of facilities)
Road (km)Agricultural field (ha)Forest (living area)b (ha)50.2 %
89.3 %
9.1 %
94.0 %
5.0 %

62.3 %
92.3 %
16.1 %
94.4 %
6.3 %

100.0 %
97.5 %
39.6 %
95.2 %
37.3 %

100.0 %
100.0 %
50.2 %
96.0 %
80.8 %

100.0 %
100.0 %
100.0 %
100.0 %
100.0 %

87.2 %
34.3 %
48.7 %
99.0 %
23.9 %

97.4 %
55.3 %
56.8 %
99.0 %
36.7 %

99.6 %
80.2 %
67.5 %
99.6 %
64.1 %

100.0 %
94.9 %
82.0 %
100.0 %
88.6 %

100.0 %
100.0 %
100.0 %
100.0 %
100.0 %The surrounding municipalities
Residential area (Number of houses)
Public facility (Number of facilities)
Road (km)Agricultural field (ha)Forest (living area)b (ha)a5Only the western part of Kawamata town. The decontamination of areas with an annual cumulative dose exceeding 20 mmSv
was assigned to the central government, and that in areas with a dose below 20 mmSv was assigned to municipal governments.
The decontamination of the eastern part of Kawamata town was conducted by the central government.
bRemoval of the litter layer in forests within 20 meters of the forest edge.

Table 1 summarizes the achievement ratios of the scheduled decontamination of different land use types in Fukushimacity and the surrounding municipalities (available at https://www.pref.fukushima.lg.jp/site/portal/progress.html, last accessed:
10

June 14, 2021). The municipalities are in the northern part of Fukushima Prefecture, which comprises 55 % forest area, 15 %farmland area, 6 % residential area, and 23 % other areas (https://www.pref.fukushima.lg.jp/uploaded/attachment/42042.pdf,
last accessed: June 14, 2021). More than 94 % decontamination was achieved for the farmland area by March 2014. For the
residential and public facility areas, some parts were decontaminated by March 2014, but some others were not fully
decontaminated until March 2018. For the road and forest areas, decontamination was not completed in most areas by March

15

2014, but extensive decontamination was conducted from 2014 to 2018. Note that only a part of the forest (20 m from theforest edges) was decontaminated, which accounts for approximately 1 % of the whole forest area of the northern part of
Fukushima Prefecture. Additionally, only the litter layer of the forest was removed, and the soil layer remained.
Suppose that if contamination occurred independently of the land use type, approximately 30 % (farmland + half ofresidential and other) of northern Fukushima was decontaminated by 2014, and an additional 15 % (half of residential and20

other) was continuously decontaminated by 2018. The difference between the decrease rate from May 2011 to December 2014(93.1 % y-1) and that after (30.7 %y-1) was higher than the decontamination rate (30 - 45 % per three to seven years). If the25https://doi.org/10.5194/acp-2021-591
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surface concentration at Fukushima University was affected mainly by the emissions from nearby sources (i.e., within thenorthern part of Fukushima Prefecture), decontamination would not be the sole reason for the change in the decrease rates
before approximately 2015 and after. Natural variation (i.e., regime changes in the chemical forms of radio-Cs) would likely
occur during that period. As previously discussed, biotite may have played a key role in the environmental behaviors of radio5

Cs in Fukushima city after approximately 2015, but the current study could not identify the key aerosol particles that carrieddissolved (or exchangeable) radio-Cs and were abundant in Fukushima city before approximately 2015.
4.3 Substantial deposition amounts in January in Fukushima city
The climatological deposition velocities (or the ratios of the deposition rate to the mean surface concentration) in
Fukushima City were remarkably high in January 2013, 2014, 2015, and 2017 (Fig. 9). They were approximately one order of10

magnitude larger than those in the other months. The ratio of the deposition in Fukushima and to that at the MRI wasapproximately 10 on average, but those in January of those years exceeded 100 (Fig. 10). On the other hand, no peaks wereobserved in January of 2012, 2016, or 2018.
There are two possible explanations for these results: vertical distribution and the existence of superlarge particles.
In terms of the former, the substantial proportions of 137Cs in the upper air may have caused lower surface concentrations but

15higher deposition due to the wet removal of 137Cs aloft. However, due to the northwesterly winter monsoon, the upper air overFukushima city is also upwind of the Abukuma Highlands; thus, this possibility is less likely. In terms of the latter, superlarge
particles (~100 µm or larger in diameter), have settling velocities that are too high (as high as those of drizzle droplets) to enter
the high-volume air sampler but that allow them to settle efficiently in a deposition sampler. A similar feature has been
observed in the relationship of the deposition and surface concentration of sodium at observation sites near coastal areas (e.g.,

20

particles denoted as large sea salt particles (LSPs) in Kajino et al., 2012). The travel distance of such large particles isapproximately 1 km (e.g., Kajino et al., 2021), and Fukushima University is surrounded by major roads, such as Route 4 andNational Highway E4, within 1 km. January is the month when the highest snow depth occurs in the Fukushima Basin, andthe road surface may be wet and muddy due to snow removal work using deicing agents and daytime snow melt on pavements;
therefore, road dust emissions from busy transportation activities may be enhanced. The muddy surface conditions may

25

produce even larger road dust particles. Although there is no evidence of the existence of such superlarge particles, they may
be a possible reason for the substantial deposition amounts in January in Fukushima city. In fact, substantial amounts of roadsalt from deicing agents could contribute to roadside PM10 samples in winter (Denby et al., 2016), indicating that there could
be emissions of particles even larger than 10 µm in diameter. In addition to the direct deposition of 137Cs to the rain sampler
at Fukushima University, the immediate resuspension of deposited 137Cs associated with road dust or road salt from nearby

30

roads around the university could contribute additional deposition to the rain sampler. Unfortunately, analyses of the surface
meteorological observational data for Fukushima City from the JMA, such as temperature, precipitation, snow cover, and wind26

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speed data, did not reveal the differentiating features between the years with (2013, 2014, 2015, and 2017) and without (2012,2016, and 2018) high deposition peaks.

5 Conclusions
Eight-year measurements of atmospheric 134Cs and 137Cs conducted at Fukushima University from March 2011 to March 20195

are summarized in this study. A high-volume sampler, a cascade impactor, and an impactor/cyclone system were used to collectaerosol samples, and the activity concentrations of radio-Cs were detected by high-purity germanium detectors. A precipitation
sampler was used to collect deposition samples, and the dissolved and particulate fractions of radio-Cs in the samples were
measured. X-ray fluorescence (XRF) analysis was carried out to measure the elemental contents of the aerosol and precipitation
samples. The concentration and deposition data measured at Fukushima University were compared with numerical simulation

10

results.
The major findings are itemized as follows:(1) The observed surface concentrations and deposition at Fukushima University (an urban/rural area of Fukushima city, 60
km northwest of the FDNPP) were high in winter and low in summer; these seasonal trends are the opposite of those observedin a contaminated forest area (Ochiai et al., 2016; Kinase et al., 2018) (30 km northwest of the FDNPP, in the Abukuma

15

Highlands). Resuspension due to bioaerosol emissions (Kinase et al., 2018; Igarashi et al., 2019a) may be substantial in forests
but may not be in urban/rural areas. The half-life (Th) and decrease rate (Rd) for the eight years were 456 d and 55.6 % y-1 forthe concentrations and 2.35 y and 29.5 % y-1 for the deposition, respectively.
(2) The decreasing trends changed in approximately 2015 and were associated with changes in the dissolved/particulate
fractions of 137Cs in precipitation. The Th and Rd for concentrations before 2015 were 272 d and 93.1 % y-1, whereas they were20

825 d and 30.7 % y-1 after 2015. The Th and Rd for deposition before 2015 were 1.10 y and 63.2 % y-1, whereas they were 5.39
y and 12.9 % y-1 after 2015. The dissolved fractions were higher before 2015, whereas the particulate fractions were higher
after 2016. This may have been because the dissolved proportion of radio-Cs discharged faster than its particulate forms fromthe local terrestrial ecosystems. This is consistent with the findings of Manaka et al. (2016). Decontamination likely also
contributed to the difference because the decontamination of some land use types, such as agricultural fields, was completed25

before 2014, and 100 % of the planned decontamination was completed by March 2018. The contribution of decontamination
was estimated in this study to be 30 - 45 % for the three to seven years, which is significantly smaller than the differences in
the Rd of the concentrations (93.1 % y-1 before 2015 and 30.7 % y-1 after 2015). Therefore, decontamination may play a partial
role in explaining the differences in Th and Rd before and after 2015, but changes in the chemical forms of radio-Cs likely playa major role.

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(3) The size-resolved measurements showed that the dominant size range in activity in the cascade impactor data was the
backup filter (<0.39 μm in diameter, or particles rebounded from larger stages), followed by the 4.2-10.2 μm and >10.2 μm
sizes. The backup filter particles were abundant in winter. The seasonal mean 137Cs concentrations in the backup filter of thecascade impactor were positively correlated with those in the fine-mode aerosols collected by the impactor/cyclone system
5(PMf) and negatively correlated with those in the coarse-mode aerosols (PMc). PMc was high in summer. The impactor/cyclone
system prevented the bouncing effect, but bouncing may still have occurred during long-duration samplings. The XRF analysis
showed that biotite may have played a key role in the environmental circulation of particulate forms of resuspended radio-Cs
in Fukushima city after September 2014.
(4) The deposition amounts of 137Cs in January were remarkably high compared to the surface concentrations of 137Cs and the10

deposition amounts of 137Cs at the MRI. Although we have no observational evidence, we hypothesize that the existence ofsuperlarge particles (~100 µm or larger, with a distance of ~ 1 km or less) associated with snow removal operations on majorroads near Fukushima University may be one of the reasons for the remarkable high deposition amounts in January.
Certain issues remained unresolved, and topics for future study are summarized as follows:(1) The Abukuma Highlands are upwind of Fukushima city in summer. The enhancement of 137Cs in PMc in summer is

15

consistent with the fact that most bioaerosols exist in coarse mode. However, if radiocesium is carried mainly by biotite (i.e.,soil particles) in winter, there should be an enhancement of 137Cs in PMc because major proportions of soil particles exist incoarse mode (e.g., Kaneyasu et al., 2017). On the other hand, sources of Cs-bearing fine-mode particles such as combustion
emissions may be less likely. Thus, the main carrier of radio-Cs may be biotite in winter, but this is still not fully confirmed.
XRF measurements were conducted for PMf from September 2014 to January 2018, when the particulate proportions were20

dominant in the precipitation. Thus, the carrier aerosols of dissolved radio-Cs in Fukushima city are still unknown. As
Kaneyasu et al. (2012) and (2017) reported, comparisons of the size distributions of 137Cs with those of other chemicalcomponents in Fukushima city would be an effective way to investigate the origin of resuspended radio-Cs from winter tospring. Alternatively, a PM2.5 cyclone or virtual impactor could be applied to separate the fine-mode and coarse-mode particles,
which can completely exclude the bouncing effect.

25(2) The simulation used in this study was made to be consistent with the surface concentrations in a contaminated forest(Tsushima) and those in a downwind area (the MRI, 170 km southwest of the FDNPP). However, the current study found thatthe simulated seasonal variation in Fukushima city was totally opposite to the observations. The current study indicated thatthe deposition velocities applied in the simulation were significantly underestimated. Numerical simulation is a powerful toolfor quantitative assessment, but the current simulation requires further improvement. The reasons for the seasonal variations

30

in concentrations and deposition in the different locations need to be investigated with an improved model.

28

https://doi.org/10.5194/acp-2021-591
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c Author(s) 2021. CC BY 4.0 License.

Data availability
The observation data used in the study are provided as a Microsoft Excel file in the supplement.

Author contribution
AW conducted the long-term measurements together with KN and AS. YN performed the XRF analysis, and MK performed
5

the numerical simulation. AW, MK, and KN designed the manuscript structure and completed the draft together with all
authors.

Competing interests
The authors declare that they have no conflicts of interest.

Acknowledgments
10This work was supported by Grants-in-Aid for Scientific Research (KAKENHI), grant numbers JP16H01777 and JP24110009.
This work was also supported by the Sumitomo Foundation’s Environmental Studies Grant and partly supported by theEnvironment Research and Technology Development Fund (JPMEERF20215003) of the Environmental Restoration andConservation Agency of Japan. The authors thank Dr. Teruyuki NAKAJIMA of the National Institute for Environmental
Studies and Dr. Haruo TSURUTA of the Remote Sensing Technology Center for proving and installing a high-volume sampler

15

and Prof. Naohiro YOSHIDA of Tokyo Institute of Technology for the installation of an impactor observation system. The
authors thank Dr. Shogo TOGO of the Isotope Research Center of the University of Tokyo for the radioactivity measurementsof the initial samples and Dr. Yoshiaki YAMAGUCHI, Dr. Zijian ZHANG, Dr. Makoto INAGAKI, Mr. Shunsuke KAKITANI,
Mr. Kazuya FUJIHARA and Mr. Nobufumi FUJITA of Osaka University for their support with the radioactivity measurementsof the air samples. The authors also thank Mr. Kakeru KONNAI of the University of Tsukuba for data visualization, Dr. Akane

20

SAYA of the MRI for a useful discussion of aerosol deposition processes, and Prof. Tomoaki OKUDA of Keio University fora useful discussion of the possible artifacts in the impactor measurements.

29https://doi.org/10.5194/acp-2021-591
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OPEN

Temporal variations of 90Sr
and 137Cs in atmosphericdepositions after the Fukushima
Daiichi Nuclear Power Plant
accident with long‑term
observations
Takeshi Kinase1*, Kouji Adachi1, Tsuyoshi Thomas Sekiyama1, Mizuo Kajino1, Yuji Zaizen1 &Yasuhito Igarashi2,3
We have measured artificial radionuclides, such as 90Sr and 137Cs, in atmospheric depositions since1957 in Japan. We observed the variations in 90Sr and 137Cs, which were emitted from atmosphericnuclear tests and nuclear power plant accidents, due to their diffusion, deposition, and resuspension.
In March 2011, the Fukushima Daiichi Nuclear Power Plant accident occurred in Japan, and significant
increases in 90Sr and 137Cs were detected at our main site in Tsukuba, Ibaraki. Our continual
observations revealed that the 137Cs monthly deposition rate in 2018 declined to ~ 1/8100 of the peaklevel, but it remained more than ~ 400 times higher than that before the accident. Chemical analysis
suggested that dust particles were the major carriers of 90Sr and 137Cs during the resuspension period
at our main site. Presently, the effective half-life for 137Cs deposition due to radioactive decay andother environmental factors is 4.7 years. The estimation suggests that approximately 42 years from2011 are required to reduce the atmospheric 137Cs deposition to a state similar to that before theaccident. The current 90Sr deposition, on the other hand, shows the preaccident seasonal variation,and it has returned to the same radioactive level as that before the accident.
Atmospheric nuclear tests and nuclear power plant accidents have released artificial radionuclides into the atmosphere, land surface, and ocean. No artificial radionuclides occurred in the environment before 1945, and humanactivities have led to increases in environmental radioactivity levels. Thus, the monitoring of artificial radionuclides has been a global a­ ssignment1,2. We have continuously monitored artificial radionuclides in atmosphericdepositions for more than 63 years in the Kanto areas around Tokyo, Japan. Our long-term observations clarified
the historical variations in artificial radionuclides in atmospheric depositions as a result of nuclear tests and theiratmospheric transport and circulation from the 1950s to the ­1970s3–8. For example, after the Partial Test Ban
Treaty (PTBT) in 1963, atmospheric radionuclide deposition from the stratosphere, called global fallout, started
to decline. However, the decline of the deposition rate was slowed because China and France continued nuclear
tests until 1980. After the last nuclear test in 1980, the decrease rate increased until ~ 1990 (Fig. 1). In 1986, theChernobyl accident caused a temporary increase in radionuclide d­ eposition9–11. From ~ 1990 until March 2011,
the decrease of the deposition rate was slowed again because of the change in radionuclide deposition processes,
i.e., resuspension of artificial radionuclides hosted by local and remote dust ­particles12–15. These long-term observations of atmospheric deposition have demonstrated that the radionuclide changes in the environment dependon both global and local phenomena. The radionuclides in atmospheric deposition continued to decrease evenafter the cessation of their direct emissions.
In March 2011, an earthquake with a magnitude of 9.0 occurred and the subsequent tsunami severely damaged the Fukushima Daiichi Nuclear Power Plant (FDNPP). The accident resulted in enormous emissions of1
Meteorological Research Institute, 1‑1 Nagamine, Tsukuba, Ibaraki 305‑0052, Japan. 2Institute for Integrated
Radiation and Nuclear Science, Kyoto University (KURNS), 2, Asashiro‑Nishi, Kumatori, Sennan, Osaka 590‑0494,
Japan. 3Ibaraki University, 2‑1‑1, Bunkyo, Mito, Ibaraki 310‑8512, Japan. *email: tkinase@mri-jma.go.jp

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Figure 1.  Historical observation of the activity of (b) 90Sr and (a) 137Cs in atmospheric depositions (mBq ­m−2)
and the change from 1957 to 2019 at site A (closed black circles) and that after 2007 at site B (open red squares).
artificial radionuclides including 90Sr, 134Cs, and 137Cs (radiocesium) into the atmosphere and o­ cean16–21. Stud16,19,22
ies have estimated the amount of radioactive materials released from the ­accident
and their geographic­distributions23–25. Other studies showed the chemical and physical properties of the carriers of radionuclides,
such as glassy p­ articles26,27 and ­sulfate28, and estimated the resuspension processes of 137Cs in the a­ tmosphere29–36
through dust s­ uspensions32–34 or emissions of ­bioaerosols34–36.
The radionuclides released into the environment eventually decline due to radioactive decay and other environmental processes. The rate of radioactive decay is inversely proportional to the respective physical half-life,
which is 28.9, 2.1, and 30.2 years for 90Sr, 134Cs, and 137Cs, ­respectively37,38. However, the rate of decline due toenvironmental removal processes is complex and depends on the weather, environment, and physical and chemical properties of radionuclides. It is crucial to understand the time scale of environmental decay to predict thefate of radioactive materials from accidents and to evaluate their long-term influences on the environment andhuman health. Hence, this study aims (1) to show our long-term observation results, (2) to estimate the current
resuspension carriers of radionuclides, and (3) to evaluate their environmental decay period. To achieve this goal,
we measured the radioactivities of 90Sr, 134Cs, and 137Cs and stable elements and isotopes (Na, Mg, Al, K, Ca, Ti,
Mn, Fe, Ni, Cu, Zn, Sr, Ba, 9Be, 133Cs, 232Th, and 238U) of monthly atmospheric deposition samples collected at
two sites in different environments: suburban site A and mountain site B (Supplementary Fig. S1).

Results and discussion

Changes in radioactivity in atmospheric depositions after the accident. In March 2011, 134Cs was
detected with the same activity as that of 137Cs. As 134Cs had not been detected before the accident except during
the emission period resulting from the Chernobyl accident in 1­ 98611,40,41, the observed 134Cs/137Cs ratio verified
that the only source of 134Cs and 137Cs was the FDNPP (Supplementary Fig. S2). Our atmospheric aerosol samples indicated that at least three plumes resulting from the FDNPP accident passed across site A (Supplementary
Fig. S3). When these plumes arrived at site A, the activities of 90Sr and 137Cs in atmospheric deposition increased
to 2.7 × ­103 and 3.2 × ­106 times, respectively, higher than those before the accident (between July 2009 and June2010) (Fig. 1). The 137Cs/90Sr activity ratio calculated from our observational results in March 2011 was 4.5 × ­103.
This large difference in the rate of increase between 90Sr and 137Cs reflects the discrepancy between their emission rates, i.e., the total amounts of 90Sr and 137Cs released were estimated as 0.02 ­PBq39 and 14.5 ­PBq23, respectively. These estimations indicated that the 90Sr emission level was much lower than that of 137Cs. The monthly
137
Cs deposition peak due to the FDNPP accident (2.31 × ­104 Bq ­m−2) was much higher than those resulting from
nuclear weapon tests (548 Bq ­m−2; June 1963) and the Chernobyl accident (131 Bq ­m−2; May 1986) (Fig. 1a). On
the other hand, the 90Sr activity due to the FDNPP accident (5.2 Bq ­m−2) was lower than that due to the nuclear
tests in the 1960s (170 Bq ­m−2; June 1963) (Fig. 1b). For comparison, the average 137Cs values in atmosphericdepositions before the FDNPP accident (between July 2009 and June 2010) were 7.0 (1.2–22.5) mBq ­m−2 atsite A and 25.0 (6.1–76.4) mBq m­ −2 at site B, while those for 90Sr amounted to 1.9 (ranging from not detectable
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Figure 2.  Activity of 90Sr and 137Cs in atmospheric depositions after the FDNPP accident from 2011 to 2018. (a)Cesium-137 in atmospheric depositions. (b) Strontium-90 in atmospheric depositions. The black points indicate
the observational results. In panel (a), the pink lines indicate the regression curves. The green and blue curvesindicate the exponential curves obtained via multiple exponential fitting. The red lines indicate the preaccident
levels (the average monthly deposition between June 2009 and July 2010).
(N.D.)–6.0) mBq m­ −2 at site A and 26.0 (6.5–116.8) mBq ­m−2 at site B. The possible causes of the higher depositions rates at site B than those at site A are the differences in altitude (site A: 40 m; site B: ~ 1390 m) and local
environmental effects (site A: open area; site B: surrounded by forestland).
The activity of 90Sr and 137Cs in atmospheric depositions and that of 137Cs in aerosol samples rapidly decreased
after the first surge in March 2011 (Fig. 2 and Supplementary Fig. S3). The decrease rate of radioactivity inatmospheric depositions at site A was due to the change in radionuclide emission, transport, and deposition­processes29. We classify the period after the FDNPP accident into three phases. The first phase is dominated bydirect emissions (March 2011), the second phase is dominated by tropospheric circulation and removal (from
April to December 2011), and the third phase is dominated by resuspension (after January 2012). In the firstphase, the direct discharge/emission of radioactive materials during the FDNPP accident and meteorologicalconditions governed the radionuclide concentration in the ­environment29,42–44. In the second phase, tropospherictransport of the radioactive materials remaining in the atmosphere after the FDNPP accident and their removal
processes dominated atmospheric ­depositions17,29. The third phase (after January 2012) mainly depended on theresuspension of radioactive ­materials29–31,33–36. For comparison, the corresponding decrease rates (first, second,
and third phases) resulting from the Chernobyl accident were shorter than those resulting from the FDNPP
accident (for more discussion details, please refer to Supplementary Fig. S4 and the text). More discussions
regarding the first and second phases were also presented in previous s­ tudies29,34, and hence the scope of thepresent study is restricted to the third phase.
The latest average monthly 137Cs atmospheric depositions in 2018 at sites A and B reached ~ 1/8100(2.9 Bq ­m−2) and ~ 1/4500 (3.0 Bq ­m−2), respectively, with regard to the peak levels after the accident. But theselevels were still ~ 400 and ~ 130 times, respectively, higher than those before the accident (Figs. 1a and 2a, respectively). On the other hand, the 90Sr depositions in 2018 amounted to 3.0 (1.2–10.5) mBq ­m−2 and 33.8 (3.1–117)
mBq ­m−2 at sites A and B, respectively (Figs. 1b and 2b, respectively). These 90Sr deposition levels were almost
at the same level as the preaccident deposition levels, and we concluded that the 90Sr deposition levels at ourobservation sites had returned to the preaccident levels in at least 2015 (Fig. 2b).
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Figure 3.  Seasonal changes in 90Sr deposition from 2012 to 2018 at (a) site A and (b) site B. The black curvesindicate the median values in each month after the FDNPP accident (from 2012 to 2018). The gray curvesindicate those before the accident (from 2000 to 2010 at site A and from 2007 to 2010 at site B).

Before the FDNPP accident, the 90Sr and 137Cs activity in atmospheric deposition showed seasonal variations(Fig. 3 and Supplementary Figs. S5 and S6). The 137Cs deposition value peaks in spring (April) at site A. On
the other hand, it peaks twice in May and September at site B (Supplementary Figs. S4 and S5). Similarly, 90Sr
deposition reaches peak values during the spring season (March and April) at site A and during the fall season(September and October) at site B (Fig. 3). Studies have suggested that the 90Sr and 137Cs deposition peaks during
the spring season at site A are caused by local and long-range transported dust p­ articles14,15,34,45,46.
After the FDNPP accident, direct emissions and their tropospheric removal processes governed the 90Sr and137
Cs activity in atmospheric depositions at sites A and B, and seasonal variations were not apparent in the firstand second phases (Fig. 2). After 2012 (in the third phase), although the mean 137Cs deposition value at site A
had slightly increased in spring (peaking in April), no seasonal variations in 137Cs at either site were observed(Fig. 2 and Supplementary Figs. S5 and S6). After 2014, in contrast, the seasonal variations in the 90Sr radioactivity in atmospheric deposition at both sites showed similar trends to those before the accident (Figs. 2 and 3).

Possible carriers of 90Sr and 137Cs at sites A and B.

The radionuclides in the atmosphere are generally
carried by aerosol particles (host particles) emitted through, for example, geochemical and biological cycles. The
correlations between dust components (e.g., Al and Fe) and radionuclides (90Sr and 137Cs) within the collected
samples before the accident suggest that mineral dust particles are the dominant carriers of these radionuclides
at site A (Fig. 4a). Previous studies have also demonstrated that the sources of these radionuclides are mainly
resuspension of contaminated dust originating from long-range transport (large-scale phenomenon) and neighboring areas (local-scale phenomenon)14,15,33,34,45,46. After the accident, chemical analysis results indicate that
dust particles are the dominant carriers of 90Sr and 137Cs at site A, except from 2012 to 2014 for 90Sr when thecontributions from the accident were high (Fig. 2).
The correlations between the dust components and radionuclides after the accident at site B were poor
(Fig. 4b). However, the 90Sr activity showed correlations with inorganic salts such as Mg, K, and Ca at site B.
Scanning electron microscopy (SEM) observation exhibited the presence of inorganic salt particles including KCl,
NaCl, and ­CaSO4 in dried deposition samples (Supplementary Fig. S7). Although these salt particles had possibly
crystallized during the preparation of the atmospheric deposition samples, it is probable that 90Sr coexists withthese components in the environment as they are abundantly present in the samples. Studies have indicated thatthe biological cycle may be a source of these inorganic elements in ­forests47–49, i.e., the Mg, K, and Ca concentrations in throughfall depositions increase in forests due to foliar leaching. As Sr exhibits a similar geochemical
behavior to that of Ca, the occurrence of Sr could be synchronous to that of Ca in the neighboring forest.
Before the accident, the 137Cs activity at site B showed positive correlations with major mineral dust components such as Mg, Mn, Ca, K, Fe, and Al (Fig. 4), suggesting that dust particles were the dominant host particles

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Figure 4.  Correlations between radionuclides and stable elements at sites (a) A and (b) B. The units for 90Sr and137
Cs are mBq ­m−2, and those for the stable elements are mg ­m−2. The red points reveal that the correlations are
significant (p < 0.05) based on the correlation coefficient values. The gray points show that the correlations are
not significant (p ≥ 0.05).

for 137Cs. However, no significant correlation was detected between mineral dust and the 137Cs activity after 2014.
Previous studies have suggested that bioaerosols contribute to the resuspension of 137Cs at forest sites in thecontaminated area within the evacuation zone in Fukushima ­Prefecture35,36. Thus, it is possible that bioaerosols
carry 137Cs at site B. The differences between the possible carriers may cause the observed differences in theactivity ratios of sites B and A ­(RB/A) for 90Sr and 137Cs deposition after the accident (Supplementary Fig. S7).

Estimation of the environmental decrease in 137Cs.

With the use of regression curve fitting of theactivity of 137Cs in atmospheric deposition, we estimated its effective half-life due to radioactive decay andenvironmental removal processes (Fig. 2). We adopted a single-exponential function before the accident fromJanuary 1990 to July 2010 and a multiple exponential function after the accident (2012–2018; the resuspension
phase). The detailed method of the calculation is described in the Supplementary Information.
The effective half-lives of the short- and long-lived components (­ t1 and ­t2, respectively) of the 137Cs deposition
were 195 days and 4.7 years, respectively, at site A, and those at site B were 148 days and 5.9 years, respectively.
Interchange of the predominant short- and long-lived components occurred during the period between September and December 2013 (Fig. 2). Our estimation of the effective half-life of the long-lived component at site A is
longer than the estimation by the previous study (~ 1.1 years)29 possibly because our estimation 1) excluded the
direct emission period and 2) extended observation data by the end of 2018. The effective half-life of the longlived component of 137Cs at site A after the FDNPP accident is shorter than that before the accident (8.5 years).
There are two possible reasons for the difference between the effective half-lives before and after the accident.
First, the dominant resuspension processes are different before and after the accident. Second, the elapsed timeafter contaminations is different between the pre and postaccident periods, i.e., more than 30 years had passed
for the analysis period before the accident since the last atmospheric nuclear test, on the other hand, only 8 years
had passed since the significant pollution after the FDNPP accident.
The above estimated effective half-lives imply that, based on the atmospheric 137Cs deposition level, ~ 42
and ~ 48 years will be required from the year of the accident to reach the preaccident level at sites A and B,
respectively. These estimates contain uncertainties due to the short observation period compared to the effective
half-life before the FDNPP accident (8.5 years). A better understanding of the carriers, resuspension processes,
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and environmental circulation conditions of radionuclides is needed to confirm the above estimates. The radionuclide decreasing trend may change in the future if resuspension process, biological recycling, and their carriers changed. Finally, our observations only pertain to atmospheric deposition and provide limited featuresof the environmental radionuclide cycle. The contamination in other fields, such as surface soils, forests, andoceans, will exhibit different effective half-lives. Nevertheless, our continuous observations of the radionuclides
in atmospheric deposition before and after the accident enable the evaluation of the atmospheric phases and thechanges in various processes to regain the environmental conditions before the nuclear power plant accident.

Material and methods

We collected monthly atmospheric deposition samples at two sites: a suburban site in the Kanto Plain (site A;
36.1°N, 140.1° E) and a mountain site in the northwestern corner of the Kanto Plain (site B, 36.5°N, 138.9° E)(Supplementary Fig. S1). Site A is the main observation base and was established in 1980 at the MeteorologicalResearch Institute (MRI), Tsukuba, Japan. From 1957 to 1980, the main observation site was located in Koenji,
Tokyo, which was shifted to the current base due to the move of the MRI in ­198050. Sampling trays were placedon the rooftop of one-story (1980–2011) and six-story buildings (2011–) on the MRI campus. Site B was established in 2007 at the top of the Mt. Haruna (1390 m above sea level), Gunma, Japan. Sites A and B are 170 and
250 km away, respectively, from the FDNPP.
Atmospheric deposition samples, which include both rain (wet deposition) and dry deposition, were collectedusing the above plastic trays with a total open area of 1–4 m­ 2, depending on the sampling period. The samples
were sieved through a 106 µm mesh. The deposition samples were dried using rotary evaporators (Eyela NE-12,
Tokyo Rikakikai Ltd., Japan) and evaporating dishes followed by weight measurements. After March 2011, wecollected aerosol samples using high-volume air samplers (HV-1000F, Shibata Scientific Technology Ltd., Japan)
on quartz fiber filters (QR100, Advantech Ltd., USA) at a flow rate of 700 L per minute to observe the atmosphericradioactivity concentration.
The activity of radiocesium was measured by Ge semiconductor detectors (of the coaxial type from ORTEC
EG&G and Eurisys) coupled with a computed spectrometric analyzer (Oxford-Tennelec Multiport or Seiko
EG&G 92x) using a maximum live time of ­106 s after the FDNPP accident. After the radiocesium measurement,90
Sr was radiochemically separated, purified, and solidified as Sr-carbonate precipitates. After leaving the sample
for several weeks in order to achieve 90Sr and 90Y radioequilibrium, the β-activity was measured with an alpha/beta counting system (Tennelec LB5100, Mirion Technologies, USA) using a maximum live time of 1­ 03 min. The
detection limits were 1.55 and 39.6 mBq ­m−2 for 90Sr and 137Cs, respectively, in the deposition samples, which
were, obtained by multiplying each counting error measured in 2018 by three. Details on the sample preparationand measurement methods have been described in a previous ­study51.
The stable elements (Na, Mg, Al, K, Ca, Ti, Mn, Fe, Ni, Cu, Zn, Sr, and Ba) and isotopes (9Be, 133Cs, 232Th, and238U) were measured by inductively coupled plasma atomic emission spectrometry (CIROS-120, Rigaku Corp.,
Japan, or Vista-PRO, Varian Inc., USA) and inductively coupled plasma mass spectrometry (Agilent7500c orAgilent8000, Agilent, Ltd., USA), respectively, based on aliquots of the samples (3.6% in mass) during the aciddecomposition processes. The detection limit and quantification values were estimated as three and ten times
the standard deviation of ten measurements of 10 ppb standards. An SEM (SU-3500, Hitachi High Technologies
Co., Japan) with an energy-dispersive X-ray spectrometer (EDX; E-max 50 mm, Horiba Ltd., Japan) was adopted
for chemical and physical analysis of the dried deposition samples.
Received: 17 June 2020; Accepted: 18 November 2020

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Acknowledgements

The authors thank Kenzo Hama, Keiko Kamioka and Minami Ide-Tanimoto (ATOX Co. Ltd.) for the samplings
and measurements and Kayo Yanagida, Kyoko Kaneba and Joseph Ching (MRI) for sampling atmosphericdeposition at site A. This study was financially supported by the former Ministry of Education, Culture, Sports,
Science and Technology and the current Nuclear Regulation Authority (Japanese Radioactivity Survey) and theEnvironmental Restoration and Conservation agency (Grant Number: 1-1801).

Author contributions

T.K., Y.I. and Y.Z. collected samples. T.K. and M.K. analyzed the data. Y.I., T.S., M.K., and Y.Z. managed the
project. T.K. and K.A. wrote this paper, and all authors contributed to discussions.

Competing interests

The authors declare no competing interests.

Additional information

Supplementary information is available for this paper at https://​doi.​org/​10.​1038/​s41598-​020-​78312-3.
Correspondence and requests for materials should be addressed to T.K.
Reprints and permissions information is available at www.nature.com/reprints.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps andinstitutional affiliations.
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OPEN

Rain‑induced bioecologicalresuspension of radiocaesiumin a polluted forest in Japan
Kazuyuki Kita1*, Yasuhito Igarashi1,2,7*, Takeshi Kinase3,8, Naho Hayashi1,
Masahide Ishizuka4, Kouji Adachi3, Motoo Koitabashi5, Tsuyoshi Thomas Sekiyama3 &Yuichi Onda6
It is the conventional understanding that rain removes aerosols from the atmosphere. However, thequestion of whether rain plays a role in releasing aerosols to the atmosphere has recently been posedby several researchers. In the present study, we show additional evidence for rain-induced aerosol
emissions in a forest environment: the occurrence of radiocaesium-bearing aerosols in a Japanese
forest due to rain. We carried out general radioactive aerosol observations in a typical mountainous
village area within the exclusion zone in Fukushima Prefecture to determine the impacts and major
drivers of the resuspension of radiocaesium originating from the nuclear accident in March 2011. We
also conducted sampling according to the weather (with and without rain conditions) in a forest to
clarify the sources of atmospheric radiocaesium in the polluted forest. We found that rain induces
an increase in radiocaesium in the air in forests. With further investigations, we confirmed that thefungal spore sources of resuspended radiocaesium seemed to differ between rainy weather andnonrainy weather. Larger fungal particles (possibly macroconidia) are emitted during rainy conditions
than during nonrainy weather, suggesting that splash generation by rain droplets is the majormechanism of the suspension of radiocaesium-bearing mould-like fungi. The present findings indicatethat radiocaesium could be used as a tracer in such research fields as forest ecology, meteorology,
climatology, public health and agriculture, in which fungal spores have significance.
We found a novel rain-related mechanism of bioecological resuspension of radiocaesium in a contaminated area
in Japan. The research background is described below. It is widely known that atmospheric aerosols are removed
by rain (wet removal, including in-cloud and below-cloud scavenging). However, in recent atmospheric studies,
several examples of atmospheric aerosol releases supposedly related to rain have been r­ eported1–8. The existence of odours known as ­petricor9 and ­geosmin10, which occur with the start of rain or with light rain, has been
acknowledged for a long time, but their formation mechanism was revealed very ­recently3,7,8. In these cases, thesuspension flux from the surface overwhelms the deposition flux of the aerosols in question in the near-surface
air layer. The underlying mechanisms include (1) microbubbles bursting inside raindrops upon contact with
the Earth’s dried porous s­ urface3,7, (2) active fungal spore dispersion due to high humidity (e.g., ref.2,4), and (3)aerosol bursts caused by the splashing of raindrops (e.g. ref.11). Details of these phenomena are given in the Discussion section. Through such mechanisms, soil organics, fungal spores, bacteria and their fragments/contents

1

Graduate School of Science and Engineering, Ibaraki University, 2‑1‑1 Bunkyo, Mito, Ibaraki 310‑8512,
Japan. 2Institute for Integrated Radiation and Nuclear Science, Kyoto University, 2 Asashiro‑Nishi, Kumatori,
Sennan, Osaka 590‑0494, Japan. 3Meteorological Research Institute, 1‑1 Nagamine, Tsukuba, Ibaraki 305‑0052,
Japan. 4Faculty of Engineering and Design, Kagawa University, 2217‑20 Hayashi‑cho, Takamatsu,
Kagawa 761‑0396, Japan. 5Forage Crop Protection Group, Division of Livestock Feeding and Management, Central
Region Agricultural Research Center, National Agriculture and Food Research Organization, 768 Senbonmatsu,
Nasushiobara, Tochigi 329‑2793, Japan. 6Center for Research in Isotopes and Environmental Dynamics, Universityof Tsukuba, 1‑1‑1 Tennodai, Tsukuba, Ibaraki 305‑8577, Japan. 7Present address: Institute for Integrated Radiation
and Nuclear Science, Kyoto University and College of Science, Ibaraki University, Formerly at Center for Researchin Isotopes and Environmental Dynamics, University of Tsukuba, Tsukuba, Japan. 8Present address: MeteorologicalResearch Institute and Formerly at College of Science, Ibaraki University, Ibaraki, Japan. *email: kazuyuki.kita.iu@
vc.ibaraki.ac.jp; igarashi.yasuhito.4e@kyoto‑u.ac.jp
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Figure 1.  Observation site locations along with a land-cover map of the eastern part of Fukushima Prefecture
before the F1NPP accident. Triangle, FDNPP (F1NPP); circle, Kawamata site; square, Namie site. The map was
created by commands in GDAL version3.0.4 (open source under an X/MIT style Open Source License) andMicrosoft PowerPoint 2018 for Mac and by using data from the High-Resolution Land Use and Land Covermap published by the Japan Aerospace Exploration Agency Earth Observation Research Center ALOS/ALOS-2
Science Project and the Earth Observation Priority Research: Ecosystem Research Group. The contour line
shows the deposition density of 137Cs originating from the F1NPP accident (MBq ­m−2) at the end of May ­201268.
Permission to use the data was granted. Deciduous forest is a mixture of various broadleaved trees, excluding
evergreens. Coniferous forest excludes deciduous needleleaved trees, which are rare in the region.
(possibly formed during the rupture p­ rocess12) can be liberated into the air. Radiocaesium (belonging to thesame chemical family as potassium) can be involved in active bioecological circulation processes and can returnto the atmosphere with bioaerosol r­ elease13,14, which is likely to be partially induced by rain.
We carried out atmospheric observations of radiocaesium (134Cs and 137Cs) initially originating from the
Fukushima Dai-ichi Nuclear Power Plant (F1NPP or FDNPP) accident in March 2­ 01115 to determine its concentrations, the processes involved in its aerosolization and the corresponding ­carrier13,14,16. Although the initial
primary emission surge from the F1NPP site by the accident decreased circa the fall of ­201117,18, radiocaesium
has been detected continuously in the atmosphere since 2011. The source of these continuous atmosphericradiocaesium levels is considered to be resuspension (i.e., secondary emissions from polluted s­ urfaces19); notably, the measured radiocaesium concentrations in the range of 1­ 0–1 to ­10–5 Bq m−3 (Supplementary Information Figure S1) have not reached a level with certain health impacts (see Annex in Igarashi et al.13). In a typicalmountainous village area in Fukushima (see Fig. 1 and Supplementary Photographs 1 and 2), we attempted to
identify the key resuspension processes and carriers of radiocaesium in the ­atmosphere13,14,20. A Chernobyl­study21 described radioactive particle resuspension processes, such as wind uplift of the dust from contaminated surfaces, human activity and forest fires (e.g., ref.22,23). The Japanese summer is characterized by highrainfall and humid air, which may be unfavourable for both fugitive dust and general aerosol suspension due
to wind uplift and forest fires. Furthermore, there is no evidence that photochemical reactions produce a burstof radiocaesium-bearing aerosols. We assume no emission/liberation of volatile organic Cs compounds underenvironmental temperatures (if any salt forms) from biota, as Cs is an alkaline metal. Our previous conclusion
is that in cold seasons, a typical major driver of resuspension is the uplift of contaminated soil dust by ­gusts16,20,
while in warm seasons, the major factors are bioaerosols, including contaminated fungal ­spores13,14 and cedar­pollen24. Suspension of contaminated pollen was reported 6 years in Germany after the Chernobyl nuclear power
plant ­accident25. Deposited radiocaesium was absorbed and strongly fixed by soil minerals, and a limited portion was taken up by vegetation. The time lapse from the accident suggests that the radiocaesium in pollen was
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Total* sampling spanCs activity conc. in
air**

137

Sample number(n)(yyyy/mm/dd)(Bq ­m−3)(Bq ­m−3)134

Sampling site
Namie (deciduous
forest)Kawamata (coniferous
forest)Conditions
With rain
Without rain
With rain
Without rain

6–4

1.21 (0.87–1.67) × 10–3

2014/06/06–2014/07/18 0.19 (0.10–0.26) × 10

–40.50 (0.28–0.70) × 10–3

2014/06/06–2014/08/01 0.76 (0.38–1.18) × 10

–4

2.04 (1.10–3.68) × 10–4

2014/06/06–2014/08/01 0.52 (0.39–0.84) × 10

–4

1.48 (1.17–2.34) × 10–4

2014/06/06–2014/07/18 4.79 (3.47–6.27) × 10

67
7

Cs activity conc. in
air**

Table 1.  Summary of radiocaesium average concentrations in the air of the mountainous village area in thecontaminated restricted zone of Fukushima Prefecture for samples with and without rain in the summerof 2014. *Real sampling durations were dependent on the rain sensor response, and several samples werecollected during the span. **Minimum and maximum data are shown in parentheses.

related to water-soluble radiocaesium in the upper soil layer. Fungi are also a well-known bio-concentrator ofradiocaesium (e.g., Ref.26–28). We refer to these biologically/ecologically mediated atmospheric phenomena as
bioecological resuspension of radiocaesium.
The radiocaesium concentration in the atmosphere over the polluted mountainous village area investigatedin this study is several times higher in summer than in w
­ inter13,14. Carbon with a biological origin in filter
samples had a good correlation with radiocaesium concentrations, and there were sometimes close to 1 million
bioaerosols per m­ 3 in s­ ummer13. In addition, based on a combination of optical and electron microscopy, stateof-the-art DNA analysis, and radiological measurement, it was confirmed that fungal spores, one of the major
components of ­bioaerosols29,30, were possibly the major host bioaerosol of radiocaesium (e.g., Ref.31) during­summer13. A 3-D transport model study also revealed the significance of the secondary emission of radiocaesium
from the forest during ­summer20. The seasonal trend of enhanced radiocaesium concentrations in summer has
not changed significantly up to the present (Supplementary Figure S1). Here, we conducted specially designed
sampling in a forest area in Fukushima Prefecture according to the weather, with the goal of determining thedetailed radiocaesium emission mechanisms during the warm season. Our findings described below confirm
that the polluted forest is the radiocaesium resuspension ­source13, 14, 20 and provide details on the rain-induced
emission mechanism of radiocaesium-bearing aerosols during the Japanese wet summer. It is shown that rain
may induce bursts of radiocaesium-bearing aerosols (coarse bioaerosols mostly of macroconidia) inside bothdeciduous forests and coniferous forests.

Results

In 2014 and 2016 in Fukushima Prefecture, the amount of rain was higher than usual in the rainy season (fromlate spring to early summer), with a few to several hundred mm of rain in each month (see Supplementary
Figures S2 and S3, respectively). At the end of June 2014, a temperate cyclone (on June 29, not a typhoon) developed and brought heavy rain to northern and northeastern Japan. Additionally, in August 2016, three typhoons(Chanthu, Minduleand and Lionrock on August 16–17, 22–23 and 29–30, respectively) brought large volumesof precipitation. We conducted atmospheric sampling under both rainy and nonrainy conditions at two heavily
contaminated forest sites, the Namie site and the Kawamata site (Fig. 1 and Supplementary Photographs 1 and2), which are dominated by deciduous trees and coniferous trees, respectively. The data for 2014 are presented
in Table 1 and Fig. 2. More details of the high-volume aerosol (HV) sampling results are given in Supplementary
Table S1. On average, the sampling time lengths of the nonrainy periods in 2014 were approximately 2.6 times
longer than those of the rainy periods in both the deciduous and coniferous forests. We found that the concentrations of 137Cs in the deciduous forest atmosphere with rain (average 1.21 × 10–3 (± 2.61 × 10–4) Bq m
­ −3) were 2.42
–4
–4
−3
times higher than those without rain (average 5.00 × 10 (± 1.89 × 10 ) Bq m­ ) (Fig. 2a) on average. This difference was significant, with a p value of 0.0082 for a significance level of 1% using the paired t-test. Furthermore,this trend occurred in every consecutive sampling period. In the coniferous forests, this trend was observed inhalf of the sampling cases (Fig. 2b); on average, the 137Cs concentration during the rainy period was 1.37 times
higher than that during the nonrainy period. The average difference was only significant with a p value of 0.25,
giving a significance level of 25%; thus, this difference was not as clear as that in the other case. The weighted
average of the radiocaesium concentration, as shown below, was also applied to the results for the deciduous
forest to determine whether the difference was robust.
(Ri × Fi / Ftotal )iwhere ­Ri is the individual 137Cs concentration, F­ i is the individual sampled air volume and ­Ftotal represents thetotal sampled air volume. The 137Cs concentration was higher during the rainy period (1.11 × 10–3 (± 1.00 × 10–4)
Bq ­m−3) than during the nonrainy period (4.68 × 10–4 (± 2.61 × 10–5) Bq ­m−3), indicating that the difference wasrobust.
Considering that, among bioaerosols, fungal spores are major 137Cs carriers in Fukushima forest ­areas13,14, thedifferent results for deciduous and coniferous forests could be caused by differences in the fungal populations or
fungal phyla between the two types of f­ orests32. Deciduous forests may be richer in fungal activity than coniferous­forests33. Previous authors studied litter decomposition in coniferous and deciduous forests using the litter bag
method. Their results suggested that the decomposition of litter is faster in deciduous forests than in coniferous
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Figure 2.  Atmospheric 137Cs concentration inside the contaminated forest in Fukushima Prefecture, Japan,
during the summer of 2014. Rainy/nonrainy sampling was carried out from June 6 to August 2, 2014. The
sampling period was shorter in the deciduous forest than in the coniferous forest. Samples collected during
rain periods are shown in blue, whereas those collected during periods without rain are shown in red.
Horizontal error bars indicate the whole duration of the sampling, while the vertical bars exhibit errors in theactivity measurement. The top (a) and bottom (b) panels show the data from the Namie deciduous (n = 6) and
Kawamata coniferous (n = 7) forests, respectively. In the deciduous forest (a), the 137Cs concentrations are always
higher during the rainy period than during the nonrainy period. On the other hand, in the coniferous forest (b),the 137Cs concentrations tended to be higher during the rainy period than during the nonrainy period, except in
two observation spans of June 6–13 and June 29–July 4. Caesium-137 data and sampling details are summarizedin Supplementary Table S1.
forests (Castanopsis eyrei) and that the species richness of fungi in deciduous forests (Pinus massoniana) is greaterthan that from coniferous forests, as indicated by the Shannon–Weaver diversity i­ ndex33.
To determine the relationship between fungal particles in the air and the 137Cs activity concentration, we
performed coloured fungal spore counting (Supplementary Figures S4 to S7) at the Namie site during the warm
season in 2016; the results are shown in Fig. 3 (detailed information is given in Supplementary Table S2). Some ofthe data for nonrainy periods published (n = 6) in Igarashi et al.13 were re-evaluated using the present spore counting method. The data set (total n = 14) is a composite of those obtained at a forest site (F) and at a bare ground site
(G; school ground) near the forest (Supplementary Photograph 1). Regression curves were obtained by assumingthat 137Cs was carried only by fungal spores in order that the curves pass through the origin. Although there is
uncertainty in the spore counts (see “Discussion” section), when the curves pass through the origin, fitted curves
are evident, which suggests that the spore count has significance. The obtained linear relationship between theactivity concentration of 137Cs (Y) and the fungal spore number concentration (X) in a unit volume of air during

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Figure 3.  Relationship between the coloured fungal particle number concentration and 137Cs activity
concentration in the air at the Namie site (inside the forest (F) and the bare ground (G)) during the warm
season in 2016. Sampling data are expressed as mm/dd (e.g., m­ 1/d1-m2/d2). Six of the present plotted data for thenonrainy period that had been published in Igarashi et al.13 were re-evaluated using the present spore counting
method (see the text and Supplementary materials). The collection duration for nonrainy samples was 24 h in
the daytime or nighttime (G:8/31–9/2, G:9/4–6 and G:9/24–26) of the dates shown next to each data point. For
instance, daytime data of G:10/1–2 indicate that the sampling was performed from 6:00 to 18:00 on October
1 and October 2 for a total of 24 h. On the other hand, the collection duration for rainy samples encompassed
several weeks due to the small percentage of the whole sampling period represented by rain. Here, regression
curves were obtained by assuming that 137Cs was carried only by fungal spores; thus, the curves should pass theorigin. Caesium-137 data and sampling details are summarized in Supplementary Table S2.
the nonrainy period is expressed as Y = 0.541 × 10–8 × X. The slope of the regression curve gives the 137Cs content
in a single fungal spore, confirming the previous hypothesis that fungal spores carry ­radiocaesium13. On the other
hand, the data for rainy periods exhibit the relationship of Y = 1.67 × 10–8 × X. Notably, the slope is approximately
3 times larger during the rainy period than during the nonrainy period.
This finding indicates two possibilities: (1) during the rainy period, spores with a relatively high Cs concentration are dominant or (2) the spores suspended during the rainy period have larger volumes than thosesuspended during the nonrainy period, although the Cs concentrations of the spores are similar during both
periods. Figure 4 shows a comparison of typical optical microscopic photographs of HV filter samples collectedduring rainy and nonrainy periods at the bare ground site. Notably, the rainy and nonrainy collection durations
differed significantly (the duration was approximately 7 times longer during rainy conditions; see the explanationof Fig. 4), resulting in differences in the particle number concentrations in the filter samples. However, Fig. 4
shows that long and coarse elliptical particles (some exceeding 20 μm), which may be macroconidia (based on
size and morphology, see the Methods section), were significant components of the rainy samples.
To address the abovementioned hypothesis, we investigated whether there were changes in the size distribution of bioaerosol particles between rainy and nonrainy periods. Figure 5a shows the average size distributionof bioaerosols (projection area) for periods with and without rain, while Fig. 5b presents the normalized distribution (Supplementary Figure S8 presents the individual data). One pixel represents approximately 0.008 μm2(Supplementary Figure S5 for reference). In Fig. 5a,b, the bin width W is set as follows:

W = log10 (Area(i)) − log10 (Area(i − 1)) = 0.05
where Area(i) and Area(i−1) express the ith and (i−1)th bins’ highest
edges, respectively. Therefore, the summation of the normalized size distribution of dN/dlogArea is dN/(W)/ N, yielding 10 instead of unity (Fig. 5b).
Figure 5a indicates that the average total number of fungal spores suspended in rainy periods was significantly
less than that in nonrainy periods (with a ratio of 0.34). However, Fig. 5b shows that the portion of particles
larger than approximately 15 μm2 was higher in rainy periods than in nonrainy periods (1.75 times; proportionin nonrainy periods: 0.19, proportion in rainy periods: 0.3) and that more particles finer than approximately
3 μm2 were suspended in nonrainy periods than in rainy periods (1.24 times; proportion in nonrainy periods:
0.57, proportion in rainy periods: 0.46). Considering the results shown in Figs. 4, 5, 6, different types of bioaerosols (undoubtedly fungal spores) are emitted under rainy conditions than under fine weather conditions.
The larger fungal spores released during rainy weather are macroconidia (often with multiple septa) according
to the l­iterature34–37 and based on size and morphology. Although we need more evidence to support theseresults (see the Discussion section), the coarse elliptical particles resemble the conidia of graminicolous fungi,
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Figure 4.  Comparison of typical optical microscopy photographs of HV filter samples (left: collected during
the rainy period of September 2 to October 5, 2016, total volume of 9,094 m­ 3; right: collected during thenonrainy period of September 5 to September 6, 2016, total volume of 1,296 m­ 3). Samples from the rainy
period display many coarse club- and oval-shaped particles, with some hypha-like materials. These areconsidered macroconidia. A portion of the particles exhibit sizes greater than 20 μm (red bar). On the other
hand, the sample from the fine period displays many small dot-like particles of a few μm or smaller in size.
Note that no size cut-off was applied during the sampling. The total pixel size of the photograph was originally2,728 × 2,198 = 5,996,144.
such as Bipolaris, Exserohilum and Drechslera, as described in the abovementioned NARO e­ ncyclopaedia34.
Photographs of fungal particles appearing in Fig. 6 validate our conclusion; beyond a 15 μm2 projection size
range, macroconidia are evident.

Conclusions

Compared to nonrainy conditions, rain induces the release of approximately twice as much radiocaesium-bearing
coarse bioaerosols (especially with a projection size of > 15 μm2) into the atmosphere in a polluted temperate
forest range in Japan, although the total number concentration of bioaerosols is reduced to approximately onethird under rainy conditions. Macroconidia particles (e.g., ref.34) may represent the coarse bioaerosol particles
based on the analysis of size and morphology (see Fig. 6 and the Discussion section, too). Therefore, one of themechanisms behind the summer maxima in radiocaesium over the polluted ­forest13,14,20 was revealed to be rainsplash (e.g., ref.11,38–40). As Fukushima forests are ordinary temperate forests, the finding has many implications
for forest ecology, meteorology, climate, public health, agriculture, and other fields (see the Discussion section) in
which fungal spores play significant roles. However, there are limitations to the present study; we investigated theincrease in bioaerosols on the basis of only radiocaesium and coloured spores, while other bioaerosol components
such as o­ rganics41,42 were not studied in detail. In future research, sampling and measurements with increased
temporal resolution (e.g., hourly) using a real-time monitoring tool, for instance, UV-APS or WIBS (e.g., ref.43),
should be applied, and a more detailed analysis of other bioaerosol components is needed. To understand thefull range of rain-induced bioaerosol emission phenomena in forest areas, we need more observational research.

Discussion

Considering the projection sizes of the coloured fungal spores shown in Supplementary Figure S5, we determined
the typical volume ratio of fungal spores suspended in the air for periods with and without rain. For example, innonrainy periods, characteristic fungal spores exhibited a spherical size of 1,400 pixels (area size: approximately
11 μm2). Using the circle area equation of πr2, r is approximately 1.9 μm. In contrast, the typical size for spores
during rainy periods was larger than 4,600 pixels (approximately 38 μm2, as displayed in Supplementary Figure S5). We found that the aspect ratios of these particles were 1–1.8 and 2.6–3.4 for typical nonrainy and rainy
periods, respectively. We calculated the average single-particle volumes by considering the rotating body of eachparticle with a rotation axis along the minimum particle diameter. The single-particle volumes were 4.28 × 106µm3 and 11.5 × 106 µm3 for typical nonrainy and rainy periods, respectively. The ratio between the volumes ofrainy and nonrainy periods was 2.69. This number is close to the ratio between the slopes during periods withand without rain for the activity concentration of 137Cs (Y) and the coloured fungal spore number concentration(X) in the air (Fig. 3), i.e., 1.67/0.541≃3.1. Each slope represented the relationship between 137Cs air concentration(Y), the coloured fungal spore number concentration (X), the 137Cs volume concentration (C) and the typicalfungal spore volume (V) during the rainy and nonrainy periods as follows:

Y = C × V × X.

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Figure 5.  (a) Averaged (number concentrations per unit air volume (Y)) and (b) normalized (dividing by thesum of the total number (Y)) size distributions of fungal particles collected on the HV filters (n = 6 and 8 forrain and nonrain, respectively) obtained in 2016 using image analysis. In total, 4,672 and 3,764 particles werecounted for nonrainy and rainy samples, respectively. The bin size of the horizontal axis (X; dlog Area) is 0.05
on the scale of the base 10 logarithm. Analysed optical microscopic images were taken from the same filter
samples as those shown in Fig. 2. The size of each fungal particle is expressed in terms of the projected area. One
pixel corresponds to approximately 0.008 μm2. Particles beyond the size of approximately 120 μm2 (more than
15,000 pixels) were cut-off to avoid overlapping images of particles. The scale of the typical bioaerosol sizes is the
projection area shown in Supplementary Fig. 3. (a,b), respectively, reveal that the total number concentrations ofcoloured fungal spores decreased during rainy periods compared to during nonrainy periods (0.34) and that the
portion of large spores (larger than approximately 15 µm2) increased from 0.19 (nonrainy period) to 0.31 (rainy
period), an increase of 1.75 times.
Therefore, we assumed the simplest case, in which only spherical spores were suspended in nonrainy periods,
only spheroid (prolate) spores were suspended in rainy periods, and C remained the same. In this case, the slope
ratio represents the ratio between the fungal particle volumes of the rainy and nonrainy periods. The abovementioned approximate calculation could indicate that the assumptions are close to reality. The results in Fig. 5ashow a decreasing number of fungal spores suspended in the air (the total number concentrations were lower
during rainy periods than during nonrainy periods; 0.34), while Fig. 5b suggests that larger fungal spores are
suspended in the air during rainy periods than during nonrainy periods (the proportion of large spores > 15 µm2
was higher; 1.75). We concluded, as indicated in Fig. 6, that the conidia types of fungal spore, which are larger
in size but have a similar Cs concentration as typical fungal spores, become predominant in the air when it rainsin Fukushima forests. Therefore, in this study, we provide evidence that increases in bioaerosol concentrations
occur due to rain in Japanese forest areas. Notably, this rain-induced bioaerosol phenomenon was once thought
to occur only in specific forests, such as tropical rain f­ orests44, boreal f­ orests5 and semidry f­ orests2, and has neverbeen considered in Japan, as the country is located in the temperate climate zone.
There are three possible major mechanisms for aerosol emissions due to rain:(1)

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Figure 6.  Typical examples of fungal spore particles on the HV filter samples taken at the Namie site during the2016 summer, which are plotted along 4 projection size bins based on experimental/convenient classification.
The size distribution plot is from Fig. 5b. Arrows indicate the particles concerned, and the bar length is 20 μm.
Fungal spore particles are sorted according to the projection area. Beyond the 15 μm2 range, macroconidia were
dominant, as shown in this figure.

as a small water mass. Air bubbles are generated inside this water mass from the Earth’s surface and thenrise. When the tiny air bubbles rupture at the rain drop surface, very tiny droplets are ejected and resultin aerosol ­generation3,7. Additionally, microbubbles burst inside a raindrop touching the Earth’s surface.
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(2)

(3)

The emitted aerosols can transfer material that was within/on the Earth’s surface into the atmosphere; forexample, this transfer explains why the smell of soil occurs during r­ ain3,7. However, dry bare surfaces are
scarce in the forest areas of Fukushima Prefecture, so this mechanism is not applicable in the present case.
Fungi disperse spores using rain and high ­humidity45–48. Active spore dispersion could be a possible major
process of bioaerosol releases in response to rain. The phyla Basidiomycota and Ascomycota in the kingdom Fungi are classified according to their different spore dispersion systems. They utilize atmosphericwater in a highly dedicated structure and emit spores into the air; then, the spores become entrained due
to turbulence over the Earth’s surface. This is a plausible process that provides fungal spores (especiallybasidiospores) to the atmosphere. In our present observation, however, we could not see this effect clearly;
the total number spore concentration decreased to approximately one-third during rain, as depicted in
Figs. 3 and 5.
It has been known for a long time (for instance, ref.49,50) that phytopathogens (e.g., rust fungi, which
belong to Ascomycota) proliferate by rain ­splash35,36,38,40. Recently, high-speed video imaging technology
has been applied, and the associated physical mechanisms have been ­studied11,39,51. Pathogen-bearing tiny
droplets are dispersed by rain drop impacts on vegetation leaves. A more recent s­ tudy51 revealed that rain
drop impacts induce the formation of small air vortexes, effectively liberating dry spores from leaves into
the air. These studies have revealed the role of splashing in the spread of fungal pathogenic spores (mostly
mould and anamorphic Ascomycota). Moreover, rain drop impacts deliver mechanical force onto thesurface materials covering the leaves, branches and trunk of trees, etc., thereby liberating any surficial­materials11,39. In addition, canopy interception losses of r­ ainfall52 may play a role in the hydrological andbiogeochemical cycles in forested a­ reas11,39,40,53, in which a quantity of rainwater is intercepted by the forest
canopy and thus does not reach the forest floor (throughfall). This interception varies between 15 and 45%
in coniferous f­ orests54. Possible mechanisms are described in the literature (e.g., ref.53,55,56). One explanation
could be that water splashes e­ vaporate39, which thus could leave aerosols. We hypothesize that rain splash
evaporation might add more 137Cs to the atmosphere. Related to this, it would be interesting to know fromwhich vertical region of the forest the emissions mostly occur: canopy or ground. The maximum heightof the canopy of the Fukushima forest is 20–25 m. However, currently, we do not have the detailed dataon the height distributions of bioaerosols necessary to form a conclusion. This will be a future task to beaddressed. We consider litter to be important, as described later, so emissions could mainly occur from theground.

As concluded above, rain can induce emissions of larger fungal spores (macroconidia, often with multiple
septa) carrying radiocaesium. Igarashi et al.13 reported that the spores and debris suspended during summer overthe Fukushima typical mountainous village areas belonged to the phyla Ascomycota and Basidiomycota. They
also noted that “rainwater samples exhibited larger proportions of Ascomycota, represented by the orders Capnodiales, Pleosporales, Dothidiales, Helotiales, Diaporthales, Hypocreales, and Xylariales, than did air samples”.
Their findings naturally suggest that rain splash also contains spore (conidia) of these orders. Furthermore, thisassumption leads to the hypothesis that conidia particles may arise from mould species covering not only living
trees but also contaminated litter. Litter should be covered with more mould than the living leaves of trees. Thishypothesis is the most plausible for cases in which rain drops impact contaminated forest areas.
To confirm the conidia and ascospore hypothesis, we isolated and incubated fungal strains (Supplementary
Figure S9) and used DNA analysis to identify the fungi. As shown in Table 2, 45 strains of fungi (4 of which were
unidentified) were isolated from the four HV filter samples collected during rain in the summer of 2016 (datafrom the samples are shown in Fig. 3). Six strains, including Trametes versicolor, were derived from Basidiomycota, while the other 39 strains (87% of isolated strains) were identified as filamentous fungi derived from Ascomycota (see Table 2). These experimental results indicated that ascospores are more dominant than basidiosporesin the typical mountainous village area in Fukushima during rain. In other words, the fungal spore sources inrainy weather seem to be different from those during fine weather (though the atmosphere has high humidity).
However, we do not have clear evidence that mould (Ascomycota) in general bioconcentrates radiocaesium,
which mushroom fungi (mostly Basidiomycota) are known to d
­ o26–28. Another major uncertainty of the present
study is related to the use of optical microscopy for fungal spore counting. In the present case, we counted only
coloured spores (on the order of ­104 to ­105 grains per ­m3), although we tried to count faintly coloured spores
as often as possible. As mentioned in the Methods section, the spore counting method itself involves errors ofapproximately 10%. However, the present spore counting method gives an average that is approximately 3 times
higher than the average of the previous counting method of Igarashi et al.13. As described in a previous r­ eport13,“the total fungal spore number concentration, including both coloured and colourless ones, might be about oneorder of magnitude larger” (­ 105 to 1­ 06 grains per m­ 3), as shown in Fig. 3 in the r­ eport13. Optical microscopy withfluorescent staining may miss dark-coloured spores, while coloured spore counting disregards transparent spores.
Presently, fungal spore counting is methodology dependent, which is clearly a major source of uncertainties
and limitations. However, these uncertainties and limitations do not subtract from the conclusions that fungal
spores are carriers of radiocaesium and that rain induces the emission of bioaerosols. Certainly, more quantitative evaluations are necessary, and therefore, the application of sequential automated bioaerosol counting, such
as UV-APS57 or W
­ IBS58, to reveal if any correlation exists among bioaerosol counts, radiocaesium and weather
parameters is another attractive challenge.
We add that the number of pollen particles suspended in the air was not significant during summer, as
reported in ref.13,14. Pollen particles can contain a considerable amount of 137Cs24; if significant numbers ofthese particles had been mixed with the other bioaerosols, the concentration of 137Cs would have increased. As
explained in Igarashi et al.13, the major bioaerosols serving as radiocaesium carriers in summer are fungal spores,

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Sample collection

Isolated and identified fungi (order level)

Forest site in Namie during Aug. 11 to Sep. 2

Forest site in Namie during Sep. 2 to Oct. 5

Cladosporium sphaerospermum

Ascomycota

Penicillium sp.

Ascomycota

Bjerkandera adusta

Basidiomycota

Talaromyces sp.

Ascomycota

Thanatephorus cucumeris

Basidiomycota

Cephalotheca sulfurea

Ascomycota

Acremonium sp.

Ascomycota

Daedalea dickinsii

Basidiomycota

Toxicocladosporium irritans

Ascomycota

Pseudocercosporella fraxini

Ascomycota

Toxicocladosporium irritans

Ascomycota

Thanatephorus cucumeris

Basidiomycota

Tilletiopsis sp.

Basidiomycota

Other

Bare ground site in Namie during Aug. 11 to Sep. 2

Bare ground site in Namie during Sep. 2 to Oct. 5

Penicillium sp.

Ascomycota

Cladosporium sp.

Ascomycota

Trametes versicolor

Basidiomycota

Cladosporium sp.

Ascomycota

Oidiodendron sp.

Ascomycota

Fibulomyces mutabilis

Basidiomycota

Fusicolla sp.

Ascomycota

Toxicocladosporium irritans

Ascomycota

Pestalotiopsis microspore

Ascomycota

Fusicolla sp.

Ascomycota

Arthrinium phaeospermum

Ascomycota

Xylomelasma sp.

Ascomycota

Pestalotiopsis microspora

Ascomycota

Talaromyces purpureogenus

Ascomycota

Xylomelasma sp.

Ascomycota

Fusarium merismoides

Ascomycota

Valsaria insitiva

Ascomycota

Sordariomycetidae sp.

Ascomycota

Pestalotiopsis neglecta

Ascomycota

Pestalotiopsis microspora

Ascomycota

Pestalotiopsis microspora

Ascomycota

Hypoxylon sp.

Ascomycota

Arthrinium phaeospermum

Ascomycota

Penicillium sp.

Ascomycota

Xylariaceae sp.

Ascomycota

Hypoxylon sp.

Ascomycota

Xylomelasma sp.

Ascomycota

Sordariales sp.

Ascomycota

OthersTable 2.  Summary of isolated and identified fungi from the HV filter samples collected during the rainy
period in the summer of 2016 at the Namie site. The identified fungi are attributed to the phyla Ascomycota or
Basidiomycota. The 4 unidentified strains are expressed as other/others.

not pollen. Kinase et al.14 manually counted the relative numbers of “pollen” and “bacteria” (note that the latterincluded “spores”), representing typical bioaerosols in the warm season, using scanning electron microscopy(SEM) images and concluded that the “pollen” concentration was indeed smaller than 1/10 of the “bacteria”
concentration.
Thus, one of the possible mechanisms of radiocaesium resuspension from the polluted forest environment
during the wet and warm periods was revealed in this study. In other words, radiocaesium can be used as a tracer
to reveal unknown processes related to bioaerosol emissions from forest environments. Although the atmosphericradiocaesium activity concentration is decreasing (Supplementary Figure S1), radiocaesium can be measured
more easily and precisely than bioaerosols, as described here. We estimated the apparent half-life of 137Cs in air
at the Namie site, and we found that at least 19 years will be necessary until the 137Cs concentration decreases
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below the limit of detection. Radiocaesium will certainly disappear in the future in the study region, and the current radiocaesium concentration level ­(10–3 to 1­ 0–5 ­Bqm–3) in air can indeed help us to clarify the radiocaesium
resuspension process, in which bioaerosols are certainly involved. However, to model rain-induced bioaerosol
emissions, further research is necessary. In addition, we disregarded the possibility of bacterial suspension into
the air by rain ­splash59, although bacteria might also carry radiocaesium. Thus, to further reveal the radiocaesium
cycle within the contaminated forest environment, we need to conduct additional research. In particular, weneed more sophisticated definitions and measurement methodologies not only for bioaerosol counting but also
emission/deposition flux observations.
No previous studies have reported the resuspension of radiocaesium by bioaerosols, namely, fungal spores,
during summer in a forest, except for studies in which the present authors were ­involved13,14,20. We searched
for any prior similar bioaerosol/primary biological aerosol particle (PBAP) study in Japan, but no studies haveaddressed rain and its relevance to the PBAP number concentration. Furthermore, even though a study on secondary organic aerosol (SOA) generation from isoprene and terpene derived from vegetation was carried ­out60–62,
primary material outflow from forest ecosystems has received almost no attention. Because two-thirds of thecountry is covered by forest, we strongly feel that there is a need for a full-scale study on bioaerosol and/or organic
matter emissions in response to rain in Japan, and the results may be applicable for all temperate mixed forests
worldwide. Additionally, the emitted fungal spores released during rain are primarily mould spores, so an allergy
pandemic (e.g., ref.63) and agricultural pathogen epidemic (e.g., ref.11,38,39,51) in the rainy season might occur.
It has been discussed whether fungal spores can influence the weather or climate (e.g., ref.64), which is also
an underlying motivation of the present research. We are collecting fungal fruits and obtaining spores not only
from Fukushima but also from Tsukuba, Ibaraki and are trying to analyse their ice nuclei (IN) activity. Although
the results are very preliminary, an example of a basidiospore is presented in Supplementary Figure S10. The ice
nucleation onset was − 18 °C for the present case. Atmospheric IN in a pine forest (Colorado, United States) were
measured in the summer of ­20112,4,64, and the results revealed that bioaerosol and IN concentrations increased
during and after rain events. These studies also found that typical IN were ­basidiospores4,64, although the bioaerosols released due to rain/high humidity varied. Huffman et al.2 thus noted the possibility that ascospore are also
potential IN. The rain-induced spore species were different from the ones in the current study, a possible resultof differences in the ecosystems or the effects of the particle size cut-off of the sampling methods. We applied
no size cut-off in the HV filter sampling, which might have resulted in the observation of coarser bioaerosols inthis study than in other studies. Very recently, in 2019, Iwata et al.65 published a study stating that rain enhances
the IN number (working > − 22 °C) in the air and that some of the IN seemed to be fungal spores based on
observations on the coast of the Sea of Japan. They applied an impactor with a 50% cut-off diameter of 1.1 µm
for sample c­ ollection65; thus, they might have observed different types of bioaerosols than we did. However, thereport is agreement that fungal spores, compared to other IN materials, function at high temperatures of a few
degrees below 0 °C to − 15 °C (e.g., ref.66). Fungal spores might also work as especially large cloud condensation
nuclei (CCN), referred to as a giant CCN (GCCN)46. GCCN can form large droplets within a shorter period oftime than small CCN, thereby removing water from the air column efficiently and contributing to enhanced
precipitation strength. Macroconidia have a larger size than other PBAPs and might thus work more efficiently
as GCCN than other PBAPs. However, we need to confirm these hypotheses in the future.

Methods

We have used two forest sites in a mountainous village area in the range of the evacuated zone (the administrative border is not shown) in Fukushima Prefecture: one is in Namie town and the other is in Kawamata town, as
depicted in Fig. 1. This figure was created by using data from the High-Resolution Land Use and Land Cover map(JAXA ­EORC67), and the 137Cs contour line was drawn based on the data of Torii et al.68 The sampling points arealso described in detail ­elsewhere13,14,16,20. The environment of the sampling sites is displayed in Supplementary
Photographs 1 and 2 for reference. The Namie site is located approximately 30 km northwest of the F1NPP, anddeciduous trees are dominant, although some red pine trees are present. This site is on a small hill, and the school
athletic grounds (bare soil originally, though gradually covered by glasses with a few small pine trees) was withina few tens of metres. Decontamination work was later carried out within a range of 1 km (see ref.14), althoughmost of the forest remained contaminated. The Kawamata site is approximately 6 km northwest of the Namiesite, and the level of radioactive contamination is lower than that of the Namie site because the contaminationby the radioactive plumes in 2011 was relatively lower. This site was an artificial conifer plantation (cedar forest)
on a small hill. The contamination level of 134Cs and 137Cs was at approximately a few MBq m­ -2 at both sites in
2012, as evidenced by the contour in Fig. 1.
High-volume aerosol samplers (HV; Sibata HV 1000F and R, Tokyo, Japan) were employed to collect theresuspended 137Cs with carrier aerosols (see Supplementary Photographs). No size cut-off was used for the sampling. One of the two HV samplers was automated to work for an hour after a sensor (Climatec, Tokyo, Japan)
detected rain, while the other HV worked when the HV sampler for rain was not in operation. The automatic
switch equipment was composed of a rain sensor (CPR-PPS-03), a logger (C-CR800-4 M), a 2-channel relay
control driver (C-CPC-2), an alternating current (AC) relay, a power supply, a lightning arrester (C-PT10), USBRS232C conversion cables, etc. When the sensor detected rain drops larger than 0.5 mmϕ, HV sampling started,which continued for one hour. Therefore, we could compare 137Cs concentrations between periods with and without rain. The filters were made of silica fibre (Advantech QR100 or Pal flex 2,500 QAT-UP; 203 mm × 254 mm),which were treated in a furnace at 400 °C before use. The sampling was performed approximately 1.5 m above
the ground from June 6 to August 1, 2014 (see Table 1 and Supplementary Table S1) at the Kawamata (rainy plus
nonrainy samples, n = 14) and Namie (same as above, n = 12) sites (Supplementary Photographs 1 and 2). In thesummer of 2016, a sampling campaign was conducted (same as above, n = 14) only at Namie from August 11 to

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October 29 (see Supplementary Table S2). After sampling, the filters were wrapped by aluminium foil and thenpacked in a plastic sealing bag at the site and taken back to the laboratory. At the laboratory, they were kept atroom temperature mostly with desiccation in a sealed plastic case, and a portion of approximately 2% of thefilter area was punched out as circles (usually 8 pieces in total 16%) and used for the bioaerosol (fungal spore)counting (2 pieces kept at room temp.) and future chemical analysis (6 pieces kept in a refrigerator). Some of
the latter punched-out samples were subjected to DNA analysis. The rest of the HV filters (84%) were subjected
to radioactivity measurements.
The activity of 137Cs in the HV filter samples was obtained by γ-ray spectrometry with an intrinsic germanium semiconductor detector (coaxial type from Ortec EG&G, Eurisys or Canberra, all from Tokyo, Japan)
coupled with a computed multichannel analyser (Oxford-Tennelec Multiport or Seiko EG&G MCA7600, bothfrom Tokyo, Japan). The detection limits of the measurement of 134Cs and 137Cs at the Meteorological ResearchInstitute (MRI) were approximately 9 and 10 mBq per sample, respectively, for approximately 1­ 05 s. The temporalchange in 137Cs air concentrations derived from the F1NPP accident at the Namie site is shown in Supplementary
Figure S1.
The fungal spore counts were performed using optical microscopy (OM). The OM instrument was an Axio
Imager M2m (Carl Zeiss, Tokyo, Japan), and photographs were captured at 50 times magnification in reflection
mode. A portion of the HV filter samples was placed directly on a slide glass and subjected to OM observation.
The OM photograph was taken by a CCD camera (6 M pixels, Zeiss Axiocam 506 colour) equipped with selfadjustment functions for white balance and exposure time. Five sections of the OM photograph that minimized
overlap and maximized the number of spore images were chosen, avoiding lumpy surfaces and pollen. To countcoloured fungal spores digitally to the best extent possible, we defined a coloured particle as a particle darker
than the filter fibre or as a particle with a different colour than the filter fibre. For these reasons, the original
photograph was digitized by adjusting (a) contrast and brightness and (b) chroma saturation, and then coloured
particles were selected. During the image retouching process with the free software ­ImageJ69, the “Brightness/
Contrast” and “Color Threshold” functions were used. The “Brightness” and “Contrast” setting were adjusted
during the “Brightness/Contrast” process, and “Saturation” on the “Colour Threshold” palette was adjusted toobtain clear and distinct images. The obtained images were converted into binary images, and low levels of noise
were removed using the median filter for two pixels. Two binary images were combined, and a final binary image(edge detected) of coloured fungal spores was obtained. In this procedure, the overlapped image was manually
separated into single particles. Additionally, the particle hang on the frame was removed. The “Analyse particle”
command was finally applied, and this automated counting procedure provided statistics on the coloured fungal
spores. An example of the image analysis procedure is given in Supplementary Figure S4. For spore detection,
the minimum spore size was set to approximately 0.4 µm2 (50 pixels), and the maximum spore size was set toapproximately 124 µm2 (15,000 pixels). This corresponds to an equivalent diameter range of 0.73–12.6 µm. A typical size analysis of the coloured bioaerosol is shown in Supplementary Figure S5. The current counting method
resulted in more coloured fungal spore counts than the previous method of Igarashi et al.13. The differences in
the particle counts are shown in Supplementary Figure S6. In the figure, the present counting method yielded 1.8
times more coloured spores than the previous m­ ethod13, while on average, approximately 3 times more particles
were identified. The present method identified a higher number of faintly coloured and small fungal spores. Thisis a methodological limitation, which should be solved in future studies.
Figure 5 was thus created based on the counting mentioned above. For the data plot, 584 ± 284 (n = 8; 1 s.d.)and 627 ± 316 (n = 6) particles on average were counted for nonrainy and rainy samples collected in 2016, respectively. Converting these values into totals yields 4,672 and 3,764 particles for nonrainy and rainy samples, respectively, which seem statistically significant. Additionally, the error in the spore counting was estimated on the basisof 3 factors: (1) the reproducibility of the counting and (2) and (3) the size measurement. (1) The same opticalphotograph (sample number NHVR-281029 Photo#6) was analysed 10 times for total spore counts, and theresulting average and standard deviation were 89.9 ± 11.1 fungal particles (relative error = 12.3%), so the fungal
spore counting involves an error of approximately 10%. (2) A given scale printed on the photograph (20 µm)
was measured 10 times and the average and standard deviation were obtained (average = 220.1 ± 0.43 pixels(relative error = 0.19%)). (3) Three coarse particles were measured for size 10 times (see also Supplementary
Figure S7). Two spores with lengths of 13.7 and 17.4 µm yielded areas of 5,515 ± 218 pixels (relative error = 3.9%)
and 9,581 ± 230 pixels (relative error = 2.4%), respectively. The largest spore was out of the current measurement
range, which certainly represents a limitation of the present counting method. In total, simple summation of theerrors in scale measurement and replication yields an error of less than 10%, so fungal spore counting is expected
to involve an error of approximately 10%. The data reveal the current limitations of the methodology employed.
The samples subjected to DNA analysis were collected by HV filtering during rain in August and September
2016 in the deciduous forest and over bare ground (Namie). A piece of the quartz fibre filter was subjected to
culturing on threefold diluted Gellan gum powder (2%, wt/vol) (plant tissue grade; Wako, Osaka, Japan) at 28 °C
for a week, and a single colony was picked for further incubation. Supplementary Figure S9 displays examplesof the incubated samples, indicating that they were well-isolated single species. Genomic DNA was extracted
from the individual incubated samples according to the method described by Lee and ­Taylor70. Polymerase
chain reaction (PCR) was performed as described by White et al.71 using primers for internal transcribed spacers(ITSs; ITS1 and ITS4) with Taq DNA polymerase (Takara Bio Inc., Kusatsu, Shiga, Japan). The PCR products
were purified and then sequenced using an Applied Biosystems 3730xl DNA Analyzer (Applied Biosystems,
Foster City, CA, USA). Sequencing reactions were performed employing ABI PRISM Big Dye Terminator, v
3.1 (Applied Biosystems) using the primer ITS1. Sequence data of the ITS regions were downloaded from the
DNA Data Bank of Japan and the European Molecular Biology Laboratory/genomic data bank (DDBJ/EMBL/
GenBank) databases, and the Basic Local Alignment Search Tool (BLAST) was utilized to search for regions ofsimilarity between biological sequences.
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Meteorological conditions can influence bioaerosol species, some of which carry 137Cs, and the number
concentrations of bioaerosols. Therefore, precipitation data in 2014 (Supplementary Figure S2) were obtained
from the AMeDAS (Automated Meteorological Data Acquisition System) Japan Meteorological Agency weather
station at Tsushima (37°33.6´ N, 140°45.2´ E, altitude 400 m), which is approximately 6.2 and 1.2 km from the
Kawamata and Namie sites, respectively. Additionally, we obtained data from an automated weather station(AWS) at the Namie bare ground site in 2016; these data are summarized in Supplementary Figure S3. The maintypes of measurements of the AWS are as follows: precipitation (Takeda Keiki Kougyou, TKF-1), wind speed(three-cup anemometer, R. M. Young, Model 3,102, and sonic anemometer, R. M. Young, Model 81,000), airtemperature, and humidity (Vaisala Corp., HMP155D), with data recorded by a data logger (Campbell ScientificInc., CR1000-4 M). Details are also given e­ lsewhere13,14,16.

Data availability

The data that support the findings of this study are available upon request. Please contact the corresponding
authors.
Received: 6 December 2019; Accepted: 7 August 2020

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Acknowledgements

We thank Ms. K. Inukai, Ms. K. Kamioka, Mr. T. Kimura and Mr. K. Hama (Atox Co. Ltd.) for assistance withthe activity measurements and Ms. K. Yanagida (Meteorological Research Institute; MRI) for logistics. We areappreciative to Ms. M. Kitamura (Institute for Integrated Radiation and Nuclear Science, Kyoto University) for
editing the manuscript and redrawing the figures. We acknowledge Drs. Y. Zaizen and M. Kajino (MRI) for
scientific discussions. We thank Dr. K. Ninomiya and Prof. Shinohara (Osaka University) for providing radiocaesium activity data for some HV samples. We acknowledge Mr. T. Kanari (Green Blue Co. Ltd.) for sustaining
the sample exchange and the members and students of the College of Science and College of Engineering, Ibaraki
University, for the maintenance and special installation of tools in the field and data collection. Ms. A. Hashimoto(Ibaraki University) is acknowledged for her help in data management. We acknowledge Dr. K. Hosaka (National
Science Museum) for fungal fruit hunting in the Tsukuba Botanical Garden. A preliminary experiment on theIN activity of fungal spores was carried out by Mr. K. Sasaki, Ibaraki University. The scientific discussion with
Dr. K. Hosaka and Prof. T. Maki, Kanazawa University (currently at Kindai University), facilitated our work.
We also express our thanks to the local governments of Kawamata town and Namie town and to the Fukushima
prefectural government, which kindly provided our monitoring opportunities. A weather-dependent switch for
HV sampling was manufactured by Climatec, Japan, who helped with many observations in Fukushima Prefecture, to which the authors’ thanks are due. We especially thank Mr. Hisanori Tanaka of Climatec. This work
was supported financially by Grants-in-Aid for Scientific Research on Innovative Areas to the A01–2 researchteam of the “Interdisciplinary Study on Environmental Transfer of Radionuclides from the Fukushima Daiichi
NPP Accident (ISET-R)” (grant numbers 24110003; PI: Prof. Yuichi Onda, University of Tsukuba, one of thepresent authors) and by the Ministry of Education, Culture, Sports, Science and Japanese Radioactivity Survey
by Technology and Nuclear Regulation Authority, Japan, at the MRI. This research was also funded by Grantsin-Aid for Scientific Research (A) (No. 17H01478), (B) (Nos. 17H01873 and 18H03385) and (C) (No. 26340049).

Author contributions

K.K. and Y.I. made equal contributions to this work. They supervised the present project and planned the observations and data analysis. Y.O. suggested an original idea for observations during rain and possible significanceof rain splash as a mechanism. K.K., T.K., and N.H. performed observations at the study site and carried out theexperimental analysis. K.K. showed his leadership in the rain observation campaign, without which this study
would not been completed. M.I. contributed meteorological observations. Y.I., K.K., and T.K. carried out radioactivity measurements. K.A. performed optical/electron microscopy analyses with T.K. and N.H. M.K. carried out
the isolation and identification of fungal species. T.T.S. depicted the sampling location map. Y.I., T.K., K.K., N.H.
and M.K. wrote the manuscript, and all authors contributed ideas for the paper and reviewed the manuscript.

Competing interests

The authors declare no competing financial interests.

Additional information

Supplementary information is available for this paper at https​://doi.org/10.1038/s4159​8-020-72029​-z.
Correspondence and requests for materials should be addressed to K.K. or Y.I.
Reprints and permissions information is available at www.nature.com/reprints.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps andinstitutional affiliations.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International
License, which permits use, sharing, adaptation, distribution and reproduction in any medium or
format, as long as you give appropriate credit to the original author(s) and the source, provide a link to theCreative Commons licence, and indicate if changes were made. The images or other third party material in thisarticle are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to thematerial. If material is not included in the article’s Creative Commons licence and your intended use is not
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© The Author(s) 2020

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Reproducibility of Surface Wind and Tracer Transport Simulations over Complex Terrain
Using 5-, 3-, and 1-km-Grid Models
TSUYOSHI THOMAS SEKIYAMA
Meteorological Research Institute, Tsukuba, JapanMIZUO KAJINO
Meteorological Research Institute, and Faculty of Life and Environmental Sciences, University of Tsukuba, Tsukuba, Japan(Manuscript received 1 October 2019, in final form 23 March 2020)
ABSTRACTThe reproducibility of surface wind and tracer transport simulations from high-resolution weather andtransport models was studied over complex terrain in wintertime in Japan. The horizontal grid spacing wasvaried (5-, 3-, and 1-km grids), and radioactive cesium (Cs-137) from the Fukushima nuclear power plant wasused as a tracer. Fukushima has complex terrain, such as mountains and valleys. The model results werevalidated by observations collected from the national networks of the automated meteorological data acquisition system and the hourly air pollution sampling system. The reproducibility depended on the model
resolution, topographic complexity, and synoptic weather conditions. Higher model resolution led to higher
reproducibility of surface winds, especially in mountainous areas when the Siberian winter monsoon was
disturbed. In contrast, the model improvement was negligible or nonexistent over plain/coastal areas when
the synoptic field was steady. The statistical scores of the tracer transport simulations often deteriorated as aresult of small errors in the plume locations. However, the higher-resolution models advantageously performed better transport simulations in the mountainous areas because of the lower numerical diffusion andhigher reproducibility of the mass flux. The reproducibility of the tracer distribution in the valley of the
Fukushima mountainous region was dramatically improved with increasing model resolution. In the range ofmesoscale model resolutions (commonly 1–10 km), it was concluded that a higher-resolution model is definitely recommended for tracer transport simulations over mountainous terrain.

1. Introduction
The time scale of atmospheric tracer transport simulations ranges from seconds to decades. The spatial scalealso ranges from microscale to global. Global orsynoptic-scale tracer simulations often assume thatthe tracer species are well mixed or continuously
distributed in the atmosphere. The assumption isjustified in treating carbon dioxide, ozone, or water
vapor, which are universally present with widespreadsources and sinks. However, the assumption often
fails when the time scale is short (e.g., minutes or

Supplemental information related to this paper is available atthe Journals Online website: https://doi.org/10.1175/JAMC-D-190241.s1.
Corresponding author: Tsuyoshi Thomas Sekiyama, tsekiyam@
mri-jma.go.jp

hours), the spatial scale is limited (e.g., mesoscale orless), and the background concentration is extremely
low (e.g., point source pollution). In that case, thetracer distributions are not continuous, and theirplumes have sharply outlined edges with concentration jumps of more than several orders of magnitude.
Modeling these sharp-edged plumes in the troposphereis crucial for urban pollution predictions or environmental emergency responses (World MeteorologicalOrganization 2006). However, model performance of
sharp-edged plumes is lower than that of continuous
distributions because small errors in the plume location
lead to large uncertainties in the concentration variations. Generally, the plume location is strongly influenced by the accuracy of wind fields (Angevine et al.
2014; Sekiyama et al. 2017), in which the acceptable
range of wind velocity errors is narrower than usually
expected. Especially in the planetary boundary layer(PBL), the wind field becomes complicated over complex

DOI: 10.1175/JAMC-D-19-0241.1
Ó 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright
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terrain, which impairs the reproducibility of surface wind
and plume transport simulations (Srinivas et al. 2016).
Consequently, the topographic model resolution has alarge impact on the accuracy of plume transport simulations over complex terrain (Sekiyama et al. 2015).
Therefore, plume transport simulations are more challenging than continuous transport simulations.
Meanwhile, the model validation of PBL wind andplume simulations is not straightforward because it requires widespread and frequent observations over complex terrain, including rural or mountainous areas, forboth wind velocity and tracer concentration. Therefore,
the reproducibility of PBL wind and plume simulations
over complex terrain has not been investigated in detail.
Although some previous studies addressed the model
resolution dependence of tracer transport simulations,such as inert gases, nitrogen oxides, sulfur dioxide, carbon
dioxide, and ozone, using high-resolution models (e.g.,
Nachamkin et al. 2007; Tie et al. 2010; Pillai et al. 2011;
Cécé et al. 2016; Dingwell et al. 2016; Feng et al. 2016;
Tang et al. 2019), they validated the tracer distributions
at only a few selected observatories over complex terrain.
They did not conduct plume verifications that required
many concentration/wind observations. Although Sugiyama
et al. (2012), Katata et al. (2012a,b), and Sekiyama et al.
(2015) performed high-resolution simulations for radionuclide plumes over complex terrain, they did not
validate the plume concentrations and motions focusing on model resolution.
Sekiyama et al. (2015) conducted plume simulations ofradioactive cesium (Cs-137) emitted by the Fukushima
Daiichi Nuclear Power Plant (FDNPP) accident after the 2011 Tohoku great earthquake in Japan. The
Japanese archipelago consists of steep mountains, narrow plains/basins, and small peninsulas/islands; thatis, the terrain is highly complex (Fig. 1). In particular,
Fukushima is one of the mountainous regions in Japan,
and the FDNPP is located on the coastline of the Pacific
Ocean and only a few kilometers from mountains ashigh as 1000 m. Fukushima also has 2000-m mountainpeaks and a narrow basin between the 2000-m mountainpeaks and the 1000-m mountains behind the FDNPP. In
contrast, Tokyo, more than 200 km south-southwest of
Fukushima, is located in the largest plain in Japan.
Nevertheless, the largest Japanese plain, or the Kanto
Plain, is only 100–150 km in diameter and is surroundedby 1000–2000-m mountains. The Cs-137 that originatedfrom Fukushima is an ideal tracer over the complex
terrain (cf. Bieringer et al. 2013); therefore, Sekiyama
et al. (2015) proceeded with their tracer model experiments. However, Sekiyama et al. (2015) could not obtainenough Cs-137 concentration data to validate the plume
simulations. Therefore, they validated only 1-month

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FIG. 1. Observation stations used in this study for (a) surface Cs137 concentrations provided by the SPM sampling network and(b) surface winds provided by the AMeDAS network. Capitalletters P and T indicate the locations of the Fukushima Daiichi
Nuclear Power Plant and Tokyo City, respectively. Brown shades
indicate the elevations. Blue, gray, and red triangles represent
coastal, interior, and mountainous stations, respectively.

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accumulated deposition distributions without windcomparison.
Since then, the Cs-137 concentrations at more than
100 monitoring stations have been retrieved hourly by
Tsuruta et al. (2014), Oura et al. (2015), and Tsuruta
et al. (2018). These authors analyzed filter tapes forambient suspended particulate matter (SPM) samplingin and near Fukushima and Tokyo. In addition, highquality meteorological variables are universally measured
in Japan by the Japanese government even immediately
after the Tohoku great earthquake and during the FDNPP
accident (cf. Sekiyama et al. 2017). These widespread andfrequent observations for both surface winds and Cs-137
concentrations are used in this study to overcome the difficulty with plume transport model validation over complex terrain. This study must be the first attempt toinvestigate the reproducibility of both surface winds andplume concentrations over complex terrain and its dependence on the model resolution using high-resolution(5, 3, and 1 km) meteorological analyses and models.
The details of the observational data are described in
section 2a. Then, the meteorological analyses used
for the tracer transport simulations are explained in
section 2b. The transport model descriptions are alsopresented in section 2b. The results and discussion arepresented in section 3, and the conclusions are given in
section 4.

2. Data and models
a. Observational data
1) SURFACE CS-137 OBSERVATIONS
Tsuruta et al. (2014) developed a method to retrieve
hourly averaged surface Cs-137 concentrations using
SPM sampling filter tapes with a theoretical detection
limit of 0.1 Bq m23. The SPM sampling network is nationwide and deployed by national and local governments. Some of the local governments provided the
SPM sampling filter tapes for researchers to retrieve
the Cs-137 concentrations during the FDNPP accident.
Tsuruta et al. (2014) and Oura et al. (2015) released
Cs-137 concentration data for 99 SPM sampling stations
in eastern Japan. The air intakes are usually installed
at a height of several meters at each sampling station. In
addition, the Japan Atomic Energy Agency (JAEA)
was operationally measuring hourly Cs-137 concentrations on the premises of their Tokai facilities (36.468N,
140.608E) located between Tokyo and Fukushima(Ohkura et al. 2012). These locations are illustrated inFig. 1a for both the SPM sampling stations and Tokaifacilities. As illustrated in Fig. 1a, the sampling stations are mainly distributed around the FDNPP (P in939

the figure) and Tokyo (T in the figure). While the terrain
surrounding the FDNPP is complex with mountains,valleys, and basins, the terrain surrounding Tokyo isrelatively flat (the Kanto Plain). These Cs-137 observations are used for model validation when the value islarger than the practical detection limit (approximately
1.0 Bq m23).

2) SURFACE WIND OBSERVATIONS
To acquire widespread and frequent surface wind observations for model validation, we used a dataset fromthe automated meteorological data acquisition system(AMeDAS) managed by the Japan Meteorological
Agency (JMA) of the Japanese government. The
AMeDAS is a land surface observation network thatcomprises more than 1000 stations throughout Japan
with an average interval of 10–20 km. Anemometers
are usually installed at a height of 10 m at each
AMeDAS station (hereinafter, we call the 10-mheight winds U10 or V10). Although a few of theAMeDAS stations stopped working immediately after the earthquake, almost every station was operational even during the FDNPP accident. In this study,
hourly AMeDAS data (5-min averages on the hour)
were used. To restrict the wind field validation to thearea with Cs-137 observations, the AMeDAS datain this study were limited within southern Tohoku(around the FDNPP), the Kanto Plain (around Tokyo),and the mountainous vicinity of these areas, as shownin Fig. 1b.
In general, AMeDAS stations are located in populated areas, that is, coasts, plains, and basins. However,
fortunately, some of the stations are installed in mountainous locations. Thus, in this study, we categorized theAMeDAS stations into three location types, as shown in
Fig. 1b: coastal, interior, and mountainous stations,which have approximately 40, 80, and 40 stations, respectively. The mountainous stations were selected only
if they are surrounded on all four sides by steep terrain
and located more than 350 m above sea level (MSL).
Some stations were classified as interior locations since
they are between steep slopes that shape valleys.
We calculate the statistics classifying wind observation
locations into these three categories (mountainous,
interior, and coastal winds) and present them in thefollowing sections.

b. Meteorological analyses and models
1) METEOROLOGICAL ANALYSES
Prior to the tracer transport simulation, we prepared
meteorological analyses with three different horizontal
resolutions (5-, 3-, and 1-km grids) to drive a tracer

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FIG. 2. Domains for the 5-, 3-, and 1-km meteorological analyses.
Grid lines are drawn every 100 grids for not only the 5-km analysis
but also the 3- and 1-km analyses.

transport model. The 5-km-grid analysis was made fromthe operational mesoscale gridpoint value (hereinafter,
meso GPV) dataset that is produced by the JMA anddistributed to the public as a part of weather information.
The 3-km-grid analysis was performed by Sekiyama et al.
(2017). The 1-km-grid analysis was uniquely performed
for this study. All of these analyses were calculated by
the same weather forecast model using the unified
observations.
The 5-km operational meso GPVs are calculated by
the JMA nonhydrostatic-model four-dimensional variational data assimilation (JNoVA) system (Honda et al.
2005), whose domain covers East Asia (Fig. 2). The
JNoVA system consists of the JMA nonhydrostatic regional weather forecast model (JMA-NHM; Saito et al.
2006, 2007) and a four-dimensional variational (4DVar)data assimilation module. The lateral boundary conditions are obtained from the JMA operational global
analysis with an approximately 20-km horizontal resolution (World Meteorological Organization 2011). The
JNoVA system assimilates meteorological observations
quality controlled by the JMA; these data include land
surface pressure, sea surface winds, and observations
from radiosondes, pilot balloons, wind profilers, aircrafts, ships, buoys, radars, and satellites. Although the
5-km operational meso GPV dataset has been archived
with a 3-h time interval, the tracer transport simulation
requires meteorological analysis stored at higher time
resolution. Therefore, we conducted a 5-km-grid JMANHM run every 3 h with the same model parameters as

VOLUME 59

the operational JNoVA settings using the 3-hourly 5-km
operational meso GPVs as the initial and boundary
conditions to obtain new hourly meteorological variables.
The 3-km-grid analysis was provided by another data
assimilation system (Kunii 2014), which consists of the
JMA-NHM and the four-dimensional local ensemble
transform Kalman filter (4D-LETKF; cf. Miyoshi andAranami 2006). Hereinafter, we call this system NHMLETKF. This data assimilation system has been applied to
Fukushima nuclear pollutant simulations (Sekiyama et al.
2015, 2017; Sekiyama and Iwasaki 2018; Iwasaki et al.
2019). Sekiyama et al. (2017) calculated the 3-km-grid
analysis using the same JMA-NHM configurations as the
5-km-grid analysis or the operational meso GPVs except
for the model resolution and the convective parameterization (not activated for the 3-km-grid JMA-NHM). The
model domain covered only eastern Japan, of which lateralboundary conditions were obtained from the JMA operational global analysis same as the operational meso GPVs
(Fig. 2). Sekiyama et al. (2017) assimilated the JNoVA
observations except for radar precipitation and satellite
radiance (but included satellite-observed wind velocities).
Additionally, AMeDAS surface wind observations wereassimilated for the 3-km-grid analysis.
The 1-km-grid analysis was calculated by the NHMLETKF with the same observations and configurations as
the 3-km-grid analysis (Sekiyama et al. 2017), except forthe model domain and resolution. The lateral boundary
conditions and perturbations were obtained from theoutputs of the 3-km-grid analysis (Sekiyama et al. 2017),
which implemented a one-way nested data assimilation
scheme (Kunii 2014). In the same way as the 3-km-grid
analysis (Sekiyama et al. 2017), the LETKF was driven by
20 ensemble members with a covariance localization of
50 km in the horizontal dimension, 0.1 natural-logarithmpressure coordinate in the vertical dimension, and 3 h in
the time dimension, employing an adaptive inflation
scheme (Miyoshi 2011; Miyoshi and Kunii 2012). The
model domain consisted of 461 3 509 horizontal grid
points on the Lambert conformal projection and 60 vertical levels up to approximately 22 km in the terrainfollowing hybrid vertical coordinates, which included
11 levels below 1 km above ground level (the lowermost
level is at 20 m above the ground), the same as the 5- and
3-km-grid analyses.
Terrain features in the model domains of the 5-, 3-,
and 1-km-grid JMA-NHMs were generated from global
elevation data with a horizontal grid spacing of 30 arc s(GTOPO30) from the U.S. Geological Survey (https://doi.org/10.5066/F7DF6PQS), which is approximately
700 m in the east–west direction and 900 m in thesouth–north direction. The terrains were numericallysmoothed in the models of all resolutions to satisfy the

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FIG. 3. Fukushima topography depicted by the 5-, 3-, and 1-km grid resolutions. The filled black circle is the location of the FDNPP.
Arrows and green lines indicate the surface (10-m height) wind vectors of the analyses and their convergence zones, respectively, at 1500
Japanese standard time (JST) 12 Mar 2011. The arrows for the 1-km-grid wind field are shown at every third grid point (53-km spacing).
Thin black lines depict the coastline and a prefectural borderline.

maximum slope of 150& (’ 8.68). This smoothing
procedure was done to avoid numerical instability due
to steep slopes. Fukushima’s complex topography isdepicted by the 5-, 3-, and 1-km-grid analyses in Fig. 3.
Fine structures of small valleys and ridges are well reproduced by the 1-km-grid analysis, while the 5-kmgrid analysis fails to detect them and barely reproduces
the rough shapes of mountains and basins.
As described above, the 3-km-grid analysis (Sekiyama
et al. 2017) and 1-km-grid analysis are perfectly consistentusing completely the same model (JMA-NHM), data assimilation scheme (LETKF), observation dataset, andconfigurations except for the model resolution setup. Notethat the 5-km-grid analysis is calculated by a different data
assimilation scheme (4DVar) assimilating a slightly different observation dataset, although the same forecast
model is used. However, previous studies have confirmedthat the 4DVar and the LETKF generally provide comparable performances as meteorological data assimilation
schemes (cf. Kalnay et al. 2007). Furthermore, all three ofthese analyses are confirmed to reproduce very close
structures of synoptic-scale disturbances because theyshare the same global analysis as lateral boundary conditions. The lack of satellite radiance data assimilation does
not have a large impact on the 3- and 1-km-grid wind fields,
probably due to their small domain sizes. In addition, we
find that the 3-km analysis often shows intermediate behavior between the 5- and 1-km analyses in the lower
troposphere. For example, the location of the surface windconvergence zone undergoes a gradual transition fromwest to east with increasing resolution in the case of Fig. 3.

2) OFFLINE TRANSPORT MODEL
The tracer (Cs-137) transport simulations were performed by an offline Eulerian regional air quality
model, which was driven by the 5-, 3-, and 1-km-gridmeteorological analyses mentioned above. The offline
Eulerian regional air quality model was originallydeveloped by Kajino et al. (2012, 2019a) for nonradioactive aerosol simulations and has subsequently
been used for Fukushima nuclear pollutant simulations by Adachi et al. (2013), Sekiyama et al. (2015,2017), Kajino et al. (2016, 2018, 2019b), Inomata et al.
(2018), Kitayama et al. (2018), Mathieu et al. (2018),
Sato et al. (2018), Sekiyama and Iwasaki (2018), andIwasaki et al. (2019).
In this model, the radionuclide Cs-137 was assumed to
be well mixed with sulfate aerosol particles when it wastransported in the atmosphere. The aerosol particle size
was assumed to be log-normally distributed with anumber equivalent geometric mean dry diameter 5
0.5 mm, a geometric standard deviation 5 1.6, a particle density 5 1.83 g cm23, and a hygroscopicity 5 0.4
assuming a sulfate-organic mixture. Compared with
Kajino et al. (2012), simplified aerosol dynamics was
implemented by assuming a constant particle size distribution, in which nucleation, condensation, and coagulation were not considered. However, the model still
simulated the important nature of the aerosol dynamics,
such as dry deposition and in-cloud/below-cloud scavenging (i.e., wet deposition) processes, based on theprescribed size distribution as described in Sekiyama
et al. (2015).
The meteorological analysis was taken into the offline
Eulerian model every 1 h and linearly interpolated
to suitable time intervals (30, 24, and 8 s for the 5-, 3-,
and 1-km-grid models, respectively). The vertical model
coordinate was converted from 60 layers in the NHMLETKF analyses (expanded from the surface to approximately 22 km) to 20 layers (expanded from thesurface to approximately 10 km) to reduce the computational burden of calculations within the stratosphere.

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The vertical resolution in the PBL was mostly unchanged. The 5- and 3-km-grid simulations shared thesame model domain equivalent to that of the 3-km-gridmeteorological analysis. The 1-km-grid simulation covered a smaller domain equivalent to the 1-km-grid meteorological analysis, as shown in Fig. 2. The Cs-137
emission scenario was provided by Katata et al. (2015),
which was based on a source term estimation method(cf. Bieringer et al. 2013, 2017). Aerosol particles with
Cs-137 were injected into a grid cell above the FDNPP
at heights of 20–150 m above ground level based on atime-varying emission scenario, which was the same forall simulations. Each tracer transport simulation was
performed from 11 to 31 March 2011.
In addition, a test simulation was performed to separate the effects of atmospheric resolution and topographic resolution. The additional simulation was run
by the 1-km-grid-spacing model in the same way as the
1-km-grid simulation but driven by the 5-km-grid meteorological analysis as used in the 5-km-grid simulation.
The 5-km-grid meteorological analysis was interpolated
at 1-km intervals in the additional simulation, in which
the numerical resolution was finer, but the terrain elevation and land use remained coarse. The additional
simulation result was used for discrimination between
the benefits of a highly resolved atmosphere and a highly
resolved terrain. Hereinafter, the additional simulation
is called the ‘‘5-to-1-km-grid’’ run.

3. Results and discussion
a. Plume classification

FIG. 4. Taylor diagrams for a comparison of surface wind analyses
with AMeDAS observations categorized as (a) outer-sea plumeperiods, (b) interior plume periods, and (c) coastal plume periods.
Open triangles, squares, and circles indicate mountainous, interior,and coastal locations, respectively, for the surface wind observations.
Blue, green, and red colors indicate 5-, 3-, and 1-km-grid analyses,
respectively. Black filled circles indicate observations.

Major leakage of radioactive substances by the
Fukushima nuclear accident lasted for three weeks
from the day after the great earthquake (Hatamura
et al. 2012). Most of the radioactive plumes during thethree weeks flowed offshore (eastward) to the Pacific
Ocean with the Siberian winter monsoon. The time
windows of onshore (westward) plumes were limited
to less than 50 h in total during the three weeks(Nakajima et al. 2017). Tsuruta et al. (2014) classified
the onshore Cs-137 propagations into nine plumes(P1–P9) based on the time, location, and direction
detected by their SPM sampling filter-tape observations. Sekiyama and Iwasaki (2018) clearly illustrated
these nine plumes, as shown in the online supplemental material, using time- and column-integrated
mass flux analysis. The plume numbers (P1–P9) in thisstudy are unchanged from Tsuruta et al. (2014).
We categorize these nine plumes into three groups:
outer-sea plumes (P1, P5, and P6), interior plumes(P2, P3, and P8), and coastal plumes (P4, P7, and P9),
as shown in the online supplemental material. The

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outer-sea plumes were detected only at a few SPM
sampling stations along the coastline very close to the
FDNPP. Therefore, the prevailing wind direction
during the periods of the outer-sea plumes was westerly, which appears to be the typical Siberian wintermonsoon with the Siberian high and the Aleutian
low, as indicated in weather charts. The areas of
Fukushima and Tokyo were under the stable condition of high pressure systems during these periods. In
contrast, cyclones were moving through the area of
Fukushima and Tokyo during the periods of the interior and coastal plumes. During the interior plumeperiods, cyclones were centered on Fukushima orTokyo and completely disturbed the Siberian wintermonsoon over the area. This information means thatthe radioactive plumes never intruded inland unless
low pressure disturbances passed over Japan in wintertime. The statistics of the surface wind and Cs-137
concentration are determined using these three categories (outer-sea, interior, and coastal plumes) in thefollowing sections.

b. Surface winds
Figure 4 shows Taylor diagrams (Taylor 2001) for
a comparison of the surface wind analyses to theAMeDAS 10-m wind observations. The analyses are
snapshots on the hour, while the observations are
5-min averages on the hour. The zonal winds (U10) andmeridional winds (V10) were collectively processedto calculate the Pearson correlation coefficients andstandard deviations because the same tendencies wereconsistently maintained for the U10/V10 combined andseparate statistics. The wind analyses at 0000, 0300,
0600, 0900, 1200, 1500, 1800, and 2100 UTC are theinitial fields, but those at other hours are the forecast
fields because the analyses were calculated with a 3-h
assimilation time window. Meanwhile, the AMeDAS
10-m wind observations were used for the assimilationof the 3- and 1-km-grid analyses. Although the influenceof the AMeDAS wind data assimilation on the analyses
is minute (Sekiyama et al. 2017), the Taylor diagrams
were drawn by a comparison of 1-h forecast fields, thatis, the analyses at 0100, 0400, 0700, 1000, 1300, 1600,
1900, and 2200 UTC, to avoid a direct comparison of theinitial fields with the assimilated observations.
Taylor diagrams indicate how observations and model
results compare in terms of their correlation, root-meansquare error (RMSE), and standard deviations. Patternsimilarities between the observed (yi) and modeled (xi)fields can be quantified by the Pearson correlation coefficient r. Amplitude similarities can be quantified bytheir standard deviations sx and sy. Now define thecentered RMSE (CRMSE):(

1 N
CRMSE 5
å [(x 2 X) 2 (yi 2 Y)]2
N i51 i)1/2,

where X (or Y) indicates the sample mean of xi (or yi)
and N is the number of samples. The CRMSE indicatesthe model errors and tends to zero when the patterns
and amplitudes of the two fields are very similar. Taylor(2001) indicated that these quantities (r, sx, sy, and
CRMSE) are related by(CRMSE)2 5 s2x 1 s2y 2 2sx sy r .
In Taylor diagrams, the standard deviations sx and sy
are represented by the radial distances from the origin.
The mark of observation data is always located on the xaxis with the value of sy since the autocorrelation coefficient is 1 at all times. The Pearson correlation coefficient r between observations and model results isshown by the azimuthal position. Then, the distance
between the observation mark and the model resultmark shows the value of the CRMSE because of the lawof cosines when the azimuth is represented by cos21r.
Taylor diagrams are often drawn with normalized standard deviations (NSD), in which sx, sy, and CRMSE arenormalized to the standard deviation of observations sy(as shown in Fig. 4). This normalization allows multiple
data plots with different locations and/or time periods.
Nevertheless, the distance between the observation (r 5 1;
NSD 5 1) and the model result indicates the relativemodel error.
Figure 4 illustrates that the model errors in surface
winds, or the distances from the observation (r 5 1;
NSD 5 1), are always in the order of ‘‘mountainous
location . interior location . coastal location’’ for any
model resolution (5-, 3-, and 1-km grids) or plume category (Figs. 4a–c). The spread of the CRMSEs is muchsmaller for the outer-sea plume periods (Fig. 4a) thanfor the interior/coastal plume periods (Figs. 4b,c). This
result indicates that the topographic dependence of themodel performance is small when the atmosphere isstable under high pressure systems with the prevailing
Siberian winter monsoon. Interestingly, the accuracy ofwinds at coastal stations (indicated by open circles) islower for Siberian winter monsoon conditions (Fig. 4a)than for cyclonic conditions (Fig. 4b), while the accuracyof winds at mountain stations (indicated by open triangles) demonstrates the opposite behavior.
In terms of the model-resolution dependence, thedifferences are completely negligible among the 5-, 3-,
and 1-km-grid models even for mountainous locations
when the Siberian winter monsoon prevails under highpressure systems (Fig. 4a). This finding is contrary tocommon expectations that a higher resolution leads to a

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better performance. However, the 1-km grid resolution
demonstrates the best performance among the threemodel resolutions during the interior plume periods(Fig. 4b), in which the Siberian winter monsoon overJapan is completely disturbed by cyclones. The model
errors are consistently in the order of ‘‘5-km grid . 3-km
grid . 1-km grid’’ for any location (mountainous, interior, and coastal areas) as expected. Although the 1-km
grid resolution is constantly superior to the 5- and 3-km
grid resolutions during the interior plume periods, thedifference is particularly large for mountainous locations. This result is reasonable given that the complexityof the terrain features is remarkable in mountainous
areas. The complexity results in a large discrepancy inmodeled terrains between different resolutions. The
different terrains yield different dynamics (5wind fields)in the models. On the other hand, the superiority of the
1-km grid resolution is not very noticeable during thecoastal plume periods (Fig. 4c). Although the 1-km-gridmodel performs better than the others in the mountainous and interior locations (indicated by open triangles
and squares, respectively), the 3-km-grid model is superior in the coastal location (indicated by open circles).
Note that the improvement of the plume transport performance is significant even when the surface wind performance improves only slightly (Nachamkin et al. 2007;
Sekiyama et al. 2017) because the transport error isaccumulated along the route. Therefore, we expect the
1-km-grid model to produce superior transport simulations in the next section.

c. Cs-137 plumes
Figure 5 shows a Taylor diagram for a comparison of
the modeled Cs-137 concentrations to the SPM sampling
filter-tape observations. The outer-sea plumes are excluded from the statistics since only a few observations
were obtained over land during the outer-sea plumeperiods. Station locations are not classified as in thecomparison of the surface wind validation because 1) the
number of Cs-137 observation stations is fewer than thatof the AMeDAS, 2) only a few ‘‘mountainous’’ stations
observed Cs-137, and 3) almost no concentration record
is available in the interior areas during the coastal plumeperiods. The comparison is performed by using timeintegrated concentrations at each station to avoid adeterioration of statistical scores caused by small differences in plume arrival times. The time integral intervals are 6 h since all interior plumes (plumes 2, 3,
and 8) and coastal plumes (plumes 4, 7, and 9) lasted
for 6 h by definition, as indicated by Tsuruta et al.
(2014), Oura et al. (2015), Nakajima et al. (2017), andSekiyama and Iwasaki (2018) (also see the online
supplemental material).

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FIG. 5. Taylor diagram for a comparison of Cs-137 transportmodel results with SPM sampling filter-tape observations. Coloredfilled squares and circles indicate statistics during interior andcoastal plume periods, respectively. Blue, green, and red colors
indicate 5-, 3-, and 1-km-grid model results, respectively. A black
filled circle indicates observations.

Certainly, the 1-km grid resolution (indicated by redmarks in Fig. 5) demonstrates a good performance
among the three model resolutions during both the interior and coastal plume periods. However, the correlations are evidently weak for all resolutions and plumeperiods. In addition, the model performance is unexpectedly in the order of ‘‘3-km grid , 5-km grid , 1-km
grid’’ for both categories. The normalized standard deviations range more widely (0.35–1.25) than those of thesurface wind analyses (0.8–1.2). This contrast is partly
caused by the wet deposition of aerosols, which leads tolarge discrepancies in the aerosol concentration when
the precipitation location and timing are differently
simulated. The precipitation and foggy areas are notexactly identical among the three resolution models,
which leads to large discrepancies in the deposition location. The authors have previously performed some
Fukushima simulations with and without deposition
processes (Sato et al. 2018; Iwasaki et al. 2019). The
impact of wet deposition on the simulations appeared
large in the northern part of the Kanto Plain probably
because of drizzle or fog on 15 March 2011 over the
Kanto Plain. However, unfortunately, no observations
for Cs-137 were obtained in the northern part of the
Kanto Plain (Fig. 1a) because the local governments had
disposed of the SPM sampling filter tapes. In addition,
rainy areas (especially on 21 March 2011) were mainly

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located in the ocean or coastal regions, where the model
resolution effect is quite small. Therefore, the impact ofwet deposition on the statistics cannot be evaluatedadequately in this study. Perhaps, transport error accumulation is also the cause of the contrast because
transport errors are accumulated along the route withwind errors (Nachamkin et al. 2007; Sekiyama et al.
2017). The 5-to-1-km-grid simulation statistics were
nearly identical to those of the 5-km-grid simulation(not shown), which implies that the impact of topographic resolution on plume simulation is much larger
than that of atmospheric resolution.
Scatter diagrams are exhibited in Fig. 6 for the comparison between Cs-137 observations and model results
to clarify the situation of the interior plume statistics
shown by the abovementioned Taylor diagram. The plotis partly magnified (from the right panels to the left
panels in Fig. 6) since most of the samples range within
100 h Bq m23. The distributions for both the 3- and
1-km-grid models are clearly split into two clusters,
while the 5-km-grid model evidently results in a
widely scattered distribution, that is, a weak correlation. In Fig. 6b, cluster (p) indicates good performance by the 3-km-grid model with a high correlation
and a moderate slope angle. This cluster extends to ahigher-concentration area, as shown by (p0 ). However,cluster (q) is almost parallel to the x axis with a weakcorrelation, in which the modeled concentrations scarcely
fluctuate even when high concentrations are detected ateach station in actuality. The combination of clusters(p) and (q) results in the underestimation (i.e., NSD , 1)of the 3-km-grid model for the interior plumes shown in
Fig. 5. Likewise, cluster (r) for the 1-km-grid model also
shows a high correlation (Fig. 6c), although it overestimates the concentrations. This cluster smoothly extends
to a higher-concentration area, as shown by (r0 ). Cluster(r0 ) seems better than cluster (p0 ) in terms of the correlation. Meanwhile, cluster (s) underestimates with a
gradual slope. The combination of clusters (r) and (s)results in a plausibly good amplitude (NSD ’ 1), as
shown in Fig. 5. The problem is that these split distributions for the 3- and 1-km-grid models make their overallcorrelations extremely lower even if each cluster is notwidespread. Clusters (q) and (s) are formed by plumes
that are slightly shifted from their real locations (as shown
later), which is hardly avoidable in simulations of sharply
edged plumes.
In contrast, in the case of the coastal plumes (Fig. 7),
the differences are very small among the 5-, 3-, and1-km-grid models, although the 1-km-grid model performs slightly better. The data points are almost evenly
scattered, and no cluster is formed except for ones on
the x axis. The reason for no plots on the y axis is that945

the observations have a detection limit. The transportmodels have only small dependence on the model
resolution for the coastal plumes since the coastal
winds originally differ just slightly between the model
resolutions at all times, as shown in Fig. 4 (indicated byopen circles). Both the plume locations and Cs-137
observations are mostly located in the coastal areas forthis plume category. Therefore, most of the plots are
not sampled from complex terrain in Fig. 7. Although
the coastal winds show a good performance (r 5 0.7 ; 0.9,
NSD ’ 1), the transport simulations perform muchworse due to small errors in the plume locations andprecipitation areas. Small errors in the plume locations
lead to deterioration of the correlation. Small errors in
the precipitation lead to deterioration of the amplitude
similarity.
Figures 8–10 illustrate the geographical distributionsof the interior plumes over the Fukushima mountainous
region, which is a typical example of a complex terrain,
to detail the cluster formation described above in the
scatter diagrams. At first glance, the plume shape of
the 1-km-grid simulation appears sharper than thoseof the 5- and 3-km resolutions. The plume edges of the
5- and 3-km-grid simulations are blurred and broadened
due to the strong numerical diffusion (or discretization
error). The plume shape of the 5-to-1-km-grid simulation is very similar to that of the 5-km-grid simulation,
but the plume edge diffusion is smaller in the 5-to-1-kmgrid simulation. This indicates that the benefit of a highly
resolved terrain is much larger than that of a highly resolved atmosphere. Moreover, the 5-km-grid plumes not
only are numerically broadened but also are not blocked
by the mountain range, or the Ou Mountains, and do
not follow the shapes of valleys. Plume 8 (Fig. 8) of the
1-km-grid simulation coincides fairly well with observations, whereas that of the 5-km-grid simulation completely fails to move south along the central Fukushima
Valley that is located between the mountain range(1000–2000 m MSL) and the Abukuma Highlands (500–1000 m MSL). These results indicate that the mountain
range is not well reproduced in the 5-km-grid model. In
the case of plume 8, the mass flux (shown by arrowheads) is evidently directed southward along the central
Fukushima Valley in the 1-km-grid simulation, directed
incorrectly northward in the 5-km and 5-to-1-km-gridsimulations, and directed moderately southward in the
3-km-grid simulation. The mass flux along the valleystrongly depends on the topographic resolution because
the mountains/highlands seriously lower down using
low-resolution terrains.
In contrast, in the case of plume 3 (Fig. 9), the highconcentration tail of the plume is successfully blocked
by the mountains and does not intrude into the central

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FIG. 6. Scatter diagrams for a comparison between Cs-137 observations and transport model results at eachstation for the interior plume category for the (a) 5-, (b) 3-, and (c) 1-km-grid models. The concentrations are
6-hourly time-integrated at each station. (left) Magnified views of a portion (range: 0–100 h Bq m23) of the (right)
full plots (range: 0–800 h Bq m23). The symbols p, p0 , q, r, r0 , or s indicate each cluster of data points. The black lines
with these symbols approximately indicate cluster locations and slopes, which are not regression lines.

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FIG. 7. As in Fig. 6, but for the coastal plume category.

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Fukushima Valley in the 1-km-grid simulation. However,
the tails intrude broadly into the valley in the 5- and 3-kmgrid simulations in contradiction to the observation results. This discrepancy is probably caused by the modeledheight of the Abukuma Highlands that is properly reproduced by the 1-km-grid terrain but failed by the 5- and
3-km-grid terrains. The FDNPP is located at a narrowcoastal area between the Abukuma Highlands and thePacific Ocean. Therefore, the reproducibility of the
Abukuma Highlands is crucial for the plume simulation. Meanwhile, the wide diffusion at the plume edges
sometimes avoids large concentration errors. For example, in the case of plume 2 (Fig. 10), only the 1-km-gridsimulation fails to reproduce the high-concentration observations in the central Fukushima Valley. This error
results in cluster formation adjacent to the x axis, such as
cluster (s). Numerical diffusion (or discretization error)
improves the plume correlation in some situations. For
example, plume 2 is broadened and extended to thecentral Fukushima Valley passing over the Abukuma
Highlands in the 5- and 3-km-grid simulations. This is also
caused by the lower altitude of the Abukuma Highlandsin the lower-resolution simulations. Although the real
plume distribution is unknown here, a large discrepancy
is avoided because of this numerical diffusion, which
probably results in good scores for the 5-km-grid simulation in Fig. 5. In both cases of plumes 2 and 3, the plume
distributions of the 5-to-1-km-grid simulation are close to
those of the 5-km-grid simulation because of the sameterrain resolution. However, striped patterns often appear only in the 5-to-1-km-grid plumes, especially near
the emission source. This is probably caused by spacing
imbalance between wind fields and a transport resolution
or by small diffusion of the 5-to-1-km-grid simulation
relative to the 5-km-grid simulation.
In contrast with the Fukushima region, no large differences in the plume distribution among the threemodel resolutions (5-, 3-, and 1-km grids) are observedin the Kanto Plain or the Tokyo region (not shown).
Unfortunately, these modeled plumes are slightly
shifted from the actual distributions; therefore, thesamples from the Kanto Plain compose cluster (q) or(s) close to the x axis in the scatter diagrams. Similar
to the interior plumes arriving in the Kanto Plain, thecoastal plumes do not show any noticeable differencesamong the three model resolutions since most of thecoastal plumes are observed only in the Kanto Plainfar from mountainous regions. We maintained the
FIG. 8. Geographical distributions of the 6-h time-integrated
surface Cs-137 concentration over the Fukushima region for thetime period of plume 8. Shaded colors indicate the (a) 5-, (b) 3-,(c) 1-, and (d) 5-to-1-km-grid model results. Color circles indicate
SPM sampling filter-tape observations. Arrowheads indicate thedirection of the 6-h time-integrated mass flux (Sekiyama andIwasaki 2018) at the ground surface in the models. The open triangle is the location of the FDNPP.

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FIG. 9. As in Fig. 8, but for plume 3.

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FIG. 10. As in Fig. 8, but for plume 2.

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same vertical resolution and physics schemes for all
the 5-, 3-, and 1-km-grid models in this study. The
nuclear accident occurred in the cold winter season,
when the PBL is relatively stable. In addition, verticalplume distribution strongly depends on modeled physics
schemes for vertical diffusion, not the meteorologicalanalysis (Iwasaki et al. 2019). Therefore, we could not
find any noticeable differences in the boundary layer
depth, static stability, and vertical plume distribution
among the three model resolutions.

4. Conclusions
The surface wind and plume transport simulations inthis study revealed that the model performance depends
on the horizontal resolution, topographic complexity,
and synoptic weather conditions. Higher model resolution surely led to higher reproducibility of surface winds
in mountainous areas only when the synoptic weather
conditions were disturbed. No resolution dependence
was observed when the synoptic field was steady at leastin the areas and periods we investigated in this study.
Only a small dependence on the model resolution
appeared in the coastal and plain areas. The concentration correlations tended to deteriorate due to small
errors in the plume locations. However, the higherresolution models could advantageously perform bettertransport simulations in the Fukushima mountainous
region because of the lower numerical diffusion andhigher accuracy of the mass flux. The model performanceof the plume distribution in the valley of the Fukushima
mountainous region was dramatically improved in the order of the model resolution (1, 3, and 5 km). The improvement was caused by the benefit of a highly resolved
terrain, not a highly resolved atmosphere. The spatial
representabilities of the observations for the surface winds
and tracer concentrations are probably less than 1 3 1 km2
over complex terrain under disturbed synoptic conditions
and more than 5 3 5 km2 otherwise. Mesoscale numerical weather prediction models are operated worldwide with horizontal resolutions of 1–10 km as of 2019
(World Meteorological Organization 2017). In thisrange of mesoscale model resolution, we conclude thata higher-resolution model is definitely recommended
for tracer transport simulations over complex terrain.
Acknowledgments. This work was supported by theEnvironment Research and Technology Development
Fund (1-1802) at the Environmental Restoration andConservation Agency and by JSPS KAKENHI Grants
JP17K00533, JP16H02946, and JP19H01155.
Data availability statement: The 3- and 1-km-grid datafor both the meteorological and concentration analyses

VOLUME 59

are available upon request to the corresponding author(T. T. Sekiyama 2017, 2019, unpublished data). The 5-kmgrid data for the concentration analysis are also available
upon request to T. T. Sekiyama (2019, unpublished data).
The 5-km-grid data for the meteorological analysis andthe 20-km-grid data for the global analysis are provided
operationally by the Japan Meteorological Agency via theJapan Meteorological Business Support Center (2019, updated daily, unpublished data), which are free for research
purpose. The surface wind data can be obtained fromthe website of the Japan Meteorological Agency (2019,
updated daily, unpublished data, http://www.jma.go.jp/
jma/indexe.html, accessed 23 March 2020). The Cs-137
concentration data were obtained from Tsuruta et al. (2014,
unpublished data) and Oura et al. (2015, unpublished data)in text-file format upon request. Only the Cs-137 concentration data at Tokai (Ohkura et al. 2012) are downloadable in PDF format (https://jopss.jaea.go.jp/pdfdata/
JAEA-Data-Code-2012-010.pdf, accessed 23 March 2020).
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OPEN

Received: 18 January 2018
Accepted: 10 December 2018
Published: xx xx xxxx

Fungal spore involvement in theresuspension of radiocaesium insummerYasuhito Igarashi1,2,3,11, Kazuyuki Kita2, Teruya Maki4, Takeshi Kinase1,2,12, Naho Hayashi2,
Kentaro Hosaka5, Kouji Adachi1, Mizuo Kajino 1, Masahide Ishizuka 6,
Tsuyoshi Thomas Sekiyama1, Yuji Zaizen1, Chisato Takenaka7, Kazuhiko Ninomiya8,
Hiroshi Okochi9 & Atsuyuki Sorimachi10
We observed the atmospheric resuspension of radiocaesium, derived from the Fukushima Dai-ichi
Nuclear Power Plant accident, at Namie, a heavily contaminated area of Fukushima, since 2012. During
the survey periods from 2012 to 2015, the activity concentrations of radiocaesium in air ranged fromapproximately 10−5 to 10−2 Bq per m3 and were higher in the warm season than in the cold season.
Electron microscopy showed that the particles collected on filters in summer were predominantly of
biological origin (bioaerosols), with which the observed radiocaesium activity concentration varied.
We conducted an additional aerosol analysis based on fluorescent optical microscopic observation andhigh-throughput DNA sequencing technique to identify bioaerosols at Namie in 2015 summer. The
concentrations of bioaerosols fluctuated the order of 106 particles per m3, and the phyla Basidiomycota
and Ascomycota (true Fungi) accounted for approximately two-thirds of the bioaerosols. Moreover,the fungal spore concentration in air was positively correlated with the radiocaesium concentration at
Namie in summer 2016. The bioaerosol emissions from Japanese mixed forests in the temperate zone
predominately included fungal cells, which are known to accumulate radiocaesium, and should beconsidered an important scientific issue that must be addressed.
Several years have passed since the March 2011 accident at the Fukushima Dai-ichi Nuclear Power Plant (FDNPP)
operated by Tokyo Electric Power Company. Approximately 71% of Fukushima Prefecture is covered by forest(see Supplementary Fig. S1), and 44% of the forested area was contaminated with at least 10–30 kBq m−2 of 137Cs(corresponding to 1 mSv y−1 of excess exposure) by the accident1. The forest contamination by the FDNPP accident was most serious to the northwest2,3. This heavily contaminated (>0.5 MBq m−2 of 137Cs) forest area consistsof 428 km2 (approximately 3% of the total area of Fukushima Prefecture; ca. 14,000 km2)3. Since the accident, theradiological contamination of the forested area by 134Cs and 137Cs (radiocaesium) has decreased mainly due toradioactive decay, and not by erosion or other environmental mechanisms2. Therefore, the forest ecosystem is alarge radiocaesium reservoir1,3 and a potential secondary source of atmospheric radiocaesium4. The Chernobyl
1

Meteorological Research Institute, 1-1 Nagamine, Tsukuba, Ibaraki, 305-0052, Japan. 2Graduate School of Scienceand Engineering, Ibaraki University, 2-1-1 Bunkyo, Mito, Ibaraki, 310-8512, Japan. 3Center for Research in Isotopes
and Environmental Dynamics, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8577, Japan. 4Institute of
Science and Engineering, Kanazawa University, Kakumamachi, Kanazawa, Ishikawa, 920-1192, Japan. 5Departmentof Botany, National Museum of Nature and Science, 4-1-1 Amakubo, Tsukuba, Ibaraki, 305-0005, Japan. 6Faculty ofEngineering and Design, Kagawa University, 2217-20 Hayashi-cho, Takamatsu, Kagawa, 761-0396, Japan. 7Graduate
School of Bioagricultural Sciences, Nagoya University, Furo-cho, Nagoya, 464-8601, Japan. 8Graduate Schoolof Science, Osaka University, 1-1, Machikaneyama, Toyonaka, Osaka, 560-0043, Japan. 9Research Institute for
Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku, Tokyo, 169-8555, Japan. 10Fukushima Medical
University, 1 Hikariga-oka, Fukushima, 960-1295, Japan. 11Present address: Center for Research in Isotopes andEnvironmental Dynamics, University of Tsukuba and Graduate School of Science and Engineering, Ibaraki University,and formerly at Meteorological Research Institute, Ibaraki, Japan. 12Present address: Meteorological ResearchInstitute and formerly at College of Science, Ibaraki University, Ibaraki, Japan. Correspondence and requests formaterials should be addressed to Y.I. (email: yasuhito.igarashi.sci50@vc.ibaraki.ac.jp) or K.K. (email: kazuyuki.kita.
iu@vc.ibaraki.ac.jp)
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study5 listed three mechanisms of secondary radioactive aerosol emissions (resuspension); (1) wind-blown suspension, (2) suspension due to human activities involving the contaminated fugitive dust; and (3) forest fires.
Although resuspension sometimes refers only to (1), herein, we use the term in a more comprehensive sense.
Notably, the Fukushima contamination exhibits bioecological resuspension from the contaminated forest, a new
type of resuspension.
We measured radiocaesium resuspension6 in the atmosphere at Kawamata and Namie, Fukushima Prefecture,
after the accident7,8. In this area, which is 30 to 35 km northwest of the FDNPP and surrounded by heavily contaminated forest, as defined above, the effects of the primary emission of radiocaesium from the FDNPP likely
ceased in fall 20119,10; then, from 2012–2015, the radiocaesium activity concentration in the air slowly decreased,
although seasonal fluctuations were observed, with increases during the warm season and decreases during thecold season (Supplementary Fig. S2). At Namie, the average summer concentration (June–August 2013–2014)
was approximately 6 times the average winter concentration (December–February 2013–2014). This seasonalpattern is the opposite that observed in urban areas8–11, but emission inventory calculations with an aerosol transport model have shown that direct/delayed primary emissions from the FDNPP cannot explain the seasonal
fluctuations in 20134. Monthly radiocaesium activity concentrations (September 2012 to December 2014) at asite in Namie close to that used in this study were previously reported12. The study showed summer maxima forboth the 137Cs concentration and the coarse particulate fraction (>1.1 μm) that support our radiocaesium record.
However, the study attributed the seasonal trends to changes in the prevailing local wind direction and the distribution of surface contamination.
Optical microscopic observations suggested that the radiocaesium host particles in summer were fugitive
dust (numerous coarse particles); their presence was initially attributed to the fact that no aerosol size cutoff
was applied during high-volume (HV) aerosol sampling, but the radiocaesium host particles were subsequently
shown to be of biological origin8 (also see Supplementary Fig. S3). Biological origin particles, which include
microorganisms, pollen, animal debris, and plant debris, are defined as bioaerosols. The bioaerosol concentrations and the 137Cs activity concentration determined by the filter samples from Kawamata and Namie were both
high in the warm season and low in the cold season, and these results suggested that bioaerosols may play animportant role in radiocaesium resuspension during the warm season8. The previous work also suggested8 a correlation between the 137Cs concentration and air temperature from August to September. A 3D aerosol transportmodel with soil dust resuspension7 and forest ecosystem emission schemes was employed to analyse the sourceand budget of radiocaesium in the air and showed that the resuspension of contaminated dust from the bare soil
could not explain the summertime atmospheric radiocaesium level4.
In this study, we examined the bioecological resuspension of radiocaesium and the composition of the bioaerosols that serve as host particles at Namie in August and September 2015. Fungi are known to accumulate
radiocaesium, which they incorporate as analogue of potassium13,14, and a very high radiocaesium concentration(629 Bq g−1 dry weight) was reported in fungal spores15. Therefore, we hypothesized that contaminated fungal
spores may primarily account for the increased resuspension of radiocaesium during the summer. Here, we present novel data on the bioaerosols and the radiocaesium contamination of fungal spores and examine the associated relationships.

Results

At Namie, the activity level of 137Cs in the air (based on HV aerosol sampling from 19 August to 25 September2015) varied from approximately 100 to 600 μBq m−3, and this variation was coincident with that of the carbon
content based on scanning electron microscopy coupled with energy-dispersive X-ray spectrometry (SEM-EDS)(Fig. 1). Due to the absence of heavy industrial and urban activities near the observation site, we inferred fromthis correlation that organic particles (bioaerosols) carry radiocaesium originating from the FDNPP accident.
We observed aerosol particles in bioaerosol samples collected during the sampling days using a fluorescentoptical microscope observation with 4,6-diamidino-2-phenylindole (DAPI) staining. The fluorescent aerosol(FA) could be classified according to their fluorescence colour and morphology (Fig. 2). In general, the mostabundant FAs were yellow particles (diameter <5 μm; indicating fungal cells/debris), blue particles (microbial
particles), and particles identified as sporangia or ascospores. In particular, numerous particles with multiple
septa, which are most likely fungal spores of the phylum Ascomycota, were observed. Only small amounts ofwhite FA (<5 μm in size) and black aerosols, identified as mineral particles and black carbon, respectively, wereobserved. The total concentrations of FAs ranged from 1.7 × 105 to 7.9 × 105 particles m−3 (Fig. 3). Fewer yellow
particles were observed in September than in August (Figs 3 and 4), possibly because of a seasonal change in thebioaerosol source or rainy weather on the sampling days in September (Supplementary Fig. S4 and Table S1).
The total FA concentration differed little between forest and adjacent bare soil observation sites. The bioaerosol
concentration ranged from 2 to 8 × 105 particles m−3, of which 30 to 65% were of fungal origin.
High-throughput DNA sequencing analysis (Fig. 4 and Supplementary Tables S3 and S4) revealed that thesequences of the phylum Basidiomycota accounted for more than 80% in the total sequences of all aerosol samples, regardless of the land cover (forest or bare soil) at the observation site. In August, the members of the order
Polyporales in Basidiomycota composed dominant communities in the forest, whereas Russulales sequences was
dominantly detected in September. At the bare soil location, the members of the orders Agaricales, Boletales,
Russulales, and Hymenochaetales in Basidiomycota were dominant in August. In September, rainwater samples
exhibited larger proportions of Ascomycota, represented by the orders Capnodiales, Pleosporales, Dothidiales,
Helotiales, Diaporthales, Hypocreales, and Xylariales, than did air samples. Ascomycota is the most species-rich
phylum of Kingdom Fungi, and it includes numerous taxa with a prominent anamorphic (mould) stage during
their life cycle16. Therefore, these results suggest that moulds were abundant in the observed environment.
We compared the number of coloured fungal spores (colourless spores were not counted) countable by optical microscopy (without DAPI staining) and the 137Cs activity in aerosol samples collected by an HV sampler
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Figure 1. (a) Time series and (b) scatter plot of 137Cs activity concentrations and the average carbon content(area-averaged relative percentage) in August and September 2015. Carbon data were obtained by scanningelectron microscopy coupled with energy-dispersive X-ray spectroscopy. Error bars indicate the measurement
error (1σ). The good correlation between the two parameters suggests that organic particles (bioaerosols) are
carriers of radiocaesium.
at Namie in summer 2016 (Fig. 5). Sampling was conducted over 24 hours of daytime or nighttime (see theexplanation of Fig. 5). Weather information on the 2016 sampling days is given in Supplementary Fig. S5 andTable S2. Although the data show considerable scatter, the correlation is relatively good in Fig. 5. The spore number concentration reached 5 × 104 m−3, and the average 137Cs activity per fungal spore (grain), which is the slopeof the correlation curve, was approximately 1.7 × 10−8 Bq/grain; this value is near the median of the estimatedrange (2.8 × 10−9 to 2.6 × 10−7) (Supplementary Information and Tables S5–S7). Some uncertainty (one order ofdifference) was associated with the spore number counting, as colourless spores were neglected (see the following
discussion). Despite the uncertainty, the estimated and observed 137Cs activities in a single fungal spore were generally in good agreeance, which suggested that fungal spores are likely a significant atmospheric source of radiocaesium derived from the FDNPP accident, especially in late summer in the heavily contaminated forest area.
The monthly distribution of fungal specimens (fruiting bodies) collected from 2012–2015 at the Tsukuba
Botanical Garden (36.10°N, 140.11°E, approximately 170 km southwest of FDNPP; area of ~140,000 m2; Fig. 6)
supports our data on the fungal spore content of aerosols. The largest number of specimens was collected inJuly (all years), and the second largest number was collected in October (2012 and 2013) September (2014), andJune (2015). In each year, the number of fruiting bodies collected was high from June–October, although fewer
were collected in August. Additionally, relatively few fruiting bodies were collected from winter to early spring(December to March).

Discussion

Recently, it was reported that in a temperate forest in Wakayama, Japan, approximately 3.5° latitude south ofNamie in August 2010, fungal spores accounted for 45% of organic carbon aerosol at nighttime and 22% in thedaytime, whereas biogenic volatile organic compound oxidation products accounted for 15% of organic carbon
at nighttime and 19% during the daytime17. The results support our inference that in the forest at Namie, fungal
spores rather than other bioaerosols are the major source of radiocaesium in the air in summer. The taxonomic
composition varied even over the short observation period (Fig. 4), perhaps reflecting the seasonality of the fungal groups or the occurrence of rain. However, some members of Basidiomycota and Ascomycota would be themajor carriers of radiocaesium at Namie.
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Concentrations of particles (105 particles/m3)

Figure 2. Fluorescent micrographs of DAPI-stained particles (indicated by red arrows) in the bioaerosol
samples collected at Namie site in August and September 2015 (a). The aggregated particles observed as yellow
particles (b,c), yellow and blue particles (d), blue particles (e), black (indicated by the white arrow) and white(indicated by the red arrow) particles (f), white particles (g) and spores form particles that are likely ascospores(h,i). The bars indicate a length of 10 μm. The assignment results were used to construct Fig. 3. In the photo,white and yellow particles may not exhibit the colours seen by the naked eye on the microscopic screen.

10

Yellow (≥5µm)

8Yellow (<5µm)
White (≥ 5µm)

6White (<5µm)

4Bacteria
Spore form

2Black carbon
0
ForestGroundForestGroundForestGroundForestGroundTotal

10:40 ~ 16:31 10:22 ~ 16:45 10:40 ~ 16:07 10:27 ~ 15:47 10:06 ~ 16:07 9:56 ~ 16:13 10:21 ~ 15:18 10:03 ~ 15:31

19 AugustCloudy/Fine

28 AugustRain until morningthen cloudy

7 September

Drizzle all day long

17 September
Small rain
(10 AM to 9 PM)

Figure 3. Number concentrations of DAPI-stained particles observed in the air samples collected from theforested and bare soil areas at Namie on 19 and 28 August and 7 and 17 September 2015, and the weather
conditions on each sampling day. Particles have been classified by their colour and morphology: yellow
particles ≥5 µm, organic aggregates; yellow particles <5 µm, organic particles/fungal spores; white particles≥5 µm, mineral particles; white particles <5 µm, microbial particles; bacteria particles, bacteria; and black
carbon particles, soot (so-called black carbon). Spore forms (orange bars), likely ascospores, were identified bymorphology (see Fig. 2h,i).

A single sample of shiitake mushroom (Lentinula edodes) spores obtained in the northwestern evacuation
area in 2014 was contaminated with 122 Bq g−1 dry weight of 134Cs and 629 Bq g−1 dry weight of 137Cs15. These
concentrations are 1.9–9.0 and 2.5–10.9 times, respectively, higher than those in the fruiting bodies, suggesting
radiocaesium bioaccumulation in fungal spores. It is probable that other fungi in the heavily contaminated area
have similar radiocaesium activity levels in their spores.
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Order level category

100
80
60

Ascomycota
othersPleosporales

Boletales

Dothideales

Agaricales

Dothideomycetes
otherEurotiales

Russulales

Eurotiomycetes
othersRhytismatales

40

Helotiales
Leotiomycetes
unclassified
Leotiomycetes
othersDiaporthales

20
0Capnodiales

8/19 8/28 9/7 8/19 9/7 9/17 9/8 9/13 9/17

ForestGroundPrecipitation

Hymenochaetales
Cantharellales
Polyporales
Agaricomycetes
othersDacrymycetales
Septobasidiales
Tremellales

Hypocreales

Filobasidiales

Xylariales

Basidiomycota
othersSordariomycetes
othersAscomycota
unclassified

OthersFigure 4. Results of the metagenomic analysis showing the relative abundance of identified fungal taxa in thesamples collected at the forested and bare soil sites in Namie and contained in rain water collected at the site
during summer 2015. Ascomycota are indicated in blue font, Basidiomycota in red font. Sampling dates are
expressed as mm/dd. The compositional differences among sampling dates may reflect seasonal and weather
differences.
0.00125
Sep. 24 - 26

Cs (Bq m-3)

0.001

137

0.00075
Aug. 31 - Sep. 2

0.0005

Sep. 4 - 6
Oct. 1 - 2

0.00025

Aug. 24 - 25
Sep. 5 - 6

0010000 20000 30000 40000 50000
Colored particle density (grain/m-3)

60000

Figure 5. The relationship between the concentration of coloured fungal spores (countable without DAPI
staining by optical microscopy) and the 137Cs activity in air at the Namie site in summer 2016. The sampling
duration was 24 hours of daytime or nighttime (circled data; e.g., daytime data from Aug. 24–25 indicates thatsampling was performed from 6:00 to 18:00 on August 24 and 25, a total of 24 hours). Despite the large scatter,
the spore number and 137Cs concentration exhibited a positive correlation (rank correlation; significant at
8% based on a t-test). The slope of the fitted curve (1.66 × 10−8 Bq/grain) corresponds to the lower range of
estimated values (see Supplementary Tables S5–S7).
These data and various other assumptions were used for the estimation that, on average, the 137Cs activity
per fungal spore (Supplementary Tables S5–S7) ranges from 2.8 × 10−9 to 2.6 × 10−7 Bq/grain (see Methods andSupplementary Information). We should also note that the 137Cs concentration frequency distribution in fungi isvery long tailed14. Using the 137Cs activity in shiitake mushroom spores (629 Bq g−1 dry)15, the weight of a single
basidiospore (spore produced by Basidiomycota; 33 pg) and the weight of a single ascospore (spore produced byAscomycota; 65 pg) reported in the literature18, we estimated 137Cs activity values of 2 × 10−7 and 4 × 10−7 Bq/
spore, respectively. Considering the decrease in the 137Cs air concentration in each year (Supplementary Fig. S2),
similarly, the 137Cs activity in a single spore would become lower annually. In our data, the slope of the relationship between the number of coloured fungal spores and the 137Cs concentration (Fig. 5), approximately 1.7 × 10−8
Bq/grain, is one order of magnitude lower than the abovementioned value, although it remains in the estimatedrange (Supplementary Tables S5–S7). The total fungal spore concentration, including both coloured and colourless spores, might be approximately one order of magnitude larger based on the data shown in Fig. 3. We haveno reason to assume that coloured and colourless fungal spores have different mechanisms of emission, and theyshould move through the air in a similar manner and to similar extents. In this case, the 137Cs activity in a spore(the slope of Fig. 5) might be on the order of 10−9 Bq/grain as a mixture of coloured and colourless spores, which
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Figure 6. Monthly distribution of fungal specimens (both Basidiomycota and Ascomycota) collected at the
Tsukuba Botanical Garden (Tsukuba, Japan) from 2012–2015 and the average values.

is also within the estimated range. These results strongly support fungal spore involvement in the resuspension ofradiocaesium in the forested area at Namie during summer (bioecological resuspension).
Using a 3D aerosol transport model, the radiocaesium resuspension flux at Namie in summer 2013 was estimated4 to be approximately 22 mBq m−2 h−1. Spores with a radiocaesium content of 2.8 × 10−9 to 2.6 × 10−7 Bq/
grain must be released from the forest at a rate of 2.2 × 101 to 2.4 × 103 grains m−2 s−1 to produce this 137Cs flux.
These values are similar to or an order of magnitude larger than the maximum spore emission rate from the forest(387 grains m−2 s−1; Table 2 of ref.19). These findings suggest that fungal spores in Japan potentially have extensive
environmental impacts, though internal radiation exposure via radiocaesium inhalation should be negligible (seethe Appendix in the Supplementary Information).
The high-throughput DNA sequencing analysis showed that not only macroscopic fruiting bodies (i.e., mushrooms, mostly Basidiomycota) but also moulds (mostly Ascomycota), especially during precipitation periods,
could provide major sources of bioaerosols (Fig. 4). Many species of Ascomycota are known to be plant pathogens
or endophytes (fungi living inside plant tissues), and hyphae and spores on the tissue surfaces may concentrateradiocaesium and emit it into the air when the spores are launched. In the rain samples collected in September,
Ascomycota accounted for as much as approximately 65% of the fungal groups, suggesting that the emission
mechanism may be weather dependent (dry or wet).
It has been reported that the fungal spore count in air is high in summer and low in winter at several places
around the world20,21. A review22 also noted seasonal differences in the atmospheric fungal aerosol concentration.
These findings are consistent with our results from the Tsukuba Botanical Garden (Fig. 6) for a temperate forest
in Japan. We did not calculate the biomass of mushroom fruiting bodies because only the number of specimens,
each with a varying number of fruiting bodies, was recorded. Although the number of specimens can only indirectly indicate the mushroom biomass, these data are nonetheless consistent with the findings based on independent observations, such as the high-throughput DNA sequencing analysis targeting gDNA extracted directlyfrom forest bioaerosol samples (Fig. 4) and the fluorescence microscopic observation of aerosol particles (Fig. 2).
These seasonal cycles were demonstrated using the global model23.
Although no intensive fungal survey has been conducted in Namie area, and no intensive metagenomic analyses have been conducted in the Tsukuba Botanical Garden, both areas share similar climatic pattern and vegetation type (dominated by Quercus serrata and Q. acutissima of Fagaceae family). It is therefore mycologicallyunrealistic to assume that fungal flora between Namie and Tsukuba are dramatically different. Species composition between two areas may slightly differ, but we can empirically assume that family- and genus-level compositions, and seasonal patterns of fruiting, between Namie and Tsukuba are almost identical. Several pieces of
direct and indirect evidence support this assumption. For example, all major orders of mushrooms detected bymetagenomic analyses in Namie area (depicted in Fig. 4) have been reported from the Tsukuba Botanical Garden.
In addition, all mushroom species, though sampling effort is limited, collected as the form of fruit bodies from
Namie area during the 2017–2018 season (ca. 40 specimens) have been identified as genera and/or species that arealso present in the Tsukuba Botanical Garden. Also, fluorescence microscopic observation indicated the airborne
fungal spores and bacterial cells of Namie are similar to those in Tsukuba Botanical Garden site (Supplementary
Fig. S6). Supplementary Fig. S7 also demonstrate similarities of bioaerosols over Namie and Tsukuba during summer rainy period. Besides, literature reports match our findings and suggest that radiocaesium activity associatedwith the movement of fungal spores is high in summer and low in winter. In addition, the high humidity andrainy conditions of the Japanese summer may favour the emission of fungal spores into the air24–28.
In addition to fungal spores, one possible source of radiocaesium in the air is contaminated cedar pollen.
At Namie, radiocaesium activity concentrations up to approximately 253 Bq g−1 dry weight were observedin cedar pollen from November 2011 to January 201229–31, but by 2015, they had decreased to no more than
25.4 Bq g−1. Therefore, in recent years, cedar pollen has likely played a limited role in radiocaesium resuspension.
Furthermore, in Japan, cedar pollen is emitted from late February to early May32; therefore, it would not havebeen a source of the radiocaesium at Namie in summer.
Considering other possible secondary bioecological sources of radiocaesium in the forest environment, radiocaesium contamination in pollen and bee honey was reported in Munich, Germany, following the Chernobyl
accident (surface 137Cs contamination, 17.4 kBq m−2 in early May 1986)33. The highest 137Cs concentration in
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pollen (>1 Bq g−1) was recorded in May 1986, but this level rapidly decreased to approximately 0.2 Bq g−1 by July
1986. By considering the surface contamination level of 1.5 MBq m−2 at Namie12 and assuming that the pollen
contamination would be proportional to the surface contamination level, a pollen contamination level of up to
20 Bq g−1 can be estimated. In northern Italy during the early 2000s, the 137Cs effective half life in honey was 1.25
years on average34. If the half life in pollen is similar to that in honey, then after 4 years, the concentration would
be reduced to 1/10 of the original level. Therefore, the level of radiocaesium contamination in pollen in the heavily contaminated areas of Fukushima Prefecture would have been approximately 2 Bq g−1. In addition, we detected
no appreciable pollen, such as during the counting of bioaerosol fluorescent particles, because the sampling season (August and September) did not coincide with the flower bloom season. A previous work8 counted relative
numbers of bioaerosols in air (“pollen” and “bacteria” categories, the latter including “spores”) in the warm season
using scanning electron microscopy (SEM), and the results indicated that the “pollen” concentration was 1/10of the “bacteria” concentration or less (Figure 12 in ref.8). Nevertheless, the pollen contribution to radiocaesium
resuspension should still be considered because of the large size of pollen grains (≥30 μm20). Thus, even a small
number of pollen grains might carry a detectable amount of radiocaesium.
Although no heavy radiocaesium contamination of pollen other than cedar has been reported in Japan,
the suspension of pollen lasts until June, except for pollen from gramineous plants (Poaceae), ragweed, wormwood, and Japanese hop emitted from August to October based on an allergy study35. Furthermore, a significant
amount of pollen was not found in the present DAPI-stained FA analysis or direct optical microscope observations (see Supplementary Information). Radiocaesium transfer in forest and aquatic ecosystems was examinedin Fukushima Prefecture, and 137Cs accumulation was found to occur in the following order: litter > detritivores > fungi > predators > plants > herbivores36. This result suggests that any plants in the forest can accumulate
radiocaesium as fungi. Previous work suggested that contaminated pollen grains may have contributed to anincrease in the radiocaesium concentration in the air at Namie in May and June 20158. During this early summer peak period, the radiocaesium concentration correlated with wind speed, which suggests a wind-blownsource, such as pollen or fungal spores. In the future, year-round changes in the bioaerosol composition at Namieshould be examined. In addition to mushrooms and moulds, lichens (mostly Ascomycota), algae, mosses, andbryophytes also produce microscopic spores, and lichens37 and mosses38 are known to amass radiocaesium.
Other spore-producing organisms may also be candidate sources of bioecological radiocaesium resuspension.
Furthermore, bacteria can accumulate radiocaesium39,40. Currently, we cannot exclude these other possible bioecological sources of radiocaesium resuspension.
Primary bioaerosols, including fungal spores, suspended in the atmospheric environment can have impacts on
air quality19,22,41, agriculture25, and human health42,43. In addition, bioaerosols often act44–48 as ice-forming nuclei(IN) and cloud condensation nuclei (CCN). Thus, bioaerosols can have an appreciable effect on climate22,41,48.
Previous reports of high fungal spore fluxes (1,000 or more spores m−2 s−1) have been limited to tropical andsubtropical rainforest regions22, but the present findings suggest that even temperate-zone forests, such as thosefound in eastern Japan, can provide large sources of fungal spores and other bioaerosols. Our results are supported by those of a different study49, which demonstrated that the diversity of some groups of fungi (e.g., ectomycorrhizal mushrooms) in temperate and boreal areas equals or even exceeds those in tropical regions. The
bioaerosols emission inventory in temperate forests should be investigated worldwide, as should the bioaerosol
activity as IN and CCN in different regions. Furthermore, radiocaesium, as a useful chemical tracer, resuspension
studies should also focus on the origins of other organic aerosols, such as humic-like substances and water-solubleorganic compounds possibly sourced from primary bioaerosols.

Methods

Atmospheric radiocaesium observations have been conducted in the contaminated area of Fukushima Prefecturesince July 2011 (Supplementary Fig. S1). All sites are within 45 km to the northwest of the FDNPP and are inside
the Planned Evacuation Area of 2011. Samples were collected using an HV aerosol sampler. The sampling locations and observations are described elsewhere in detail4,7,8. The activities of radiocaesium were measured at theMeteorological Research Institute (MRI) and at Osaka University by γ-ray spectrometry, following a proceduredescribed elsewhere8. The morphology and elemental composition of aerosols collected on the filters were examined using SEM coupled with an energy-dispersive X-ray spectrometer (EDS), as well as a digital optical microscope (OM) with a data analyser.
Bioaerosols were sampled on sterilized polycarbonate filters at Namie from August—September 2015.
Bioaerosols suspended in a few rain water samples were also collected on the filter by extracting a few tens of
ml of the water by syringe. Bioaerosols on the filters were washed off with 1.5 mL of sterilized ultra-pure watercontaining 0.9% (w/v) of NaCl and shaken, and the solution samples were pelleted via centrifugation at 20,000 G.
Genomic deoxyribonucleic acid (gDNA) was extracted using the combination of a phenol-chloroform extraction
and the cell degradation by lysozyme, protease and sodium lauryl sulphate (SDS)50. Fragments of the internaltranscribed spacer (ITS) region (approximately 400 base pairs; bps) were amplified from the extracted gDNA bypolymerase chain reaction (PCR) using universal fungal primers ITS1-F -KYO1 (5′- Seq A - CTH GGT CAT TTAGAG GAA STA A -3′) and ITS2- KYO2 (5′- Seq B - TTY RCT RCG TTC TTC ATC -3′)51 for the ITS region. The
first PCR fragments were amplified again using the second PCR primers, which targeted the additional sequencesof the first PCR primers and included 8 tag nucleotides, such as Seq A and Seq B, designed for sample identification barcoding. Thermal cycling conditions were employed from a previous investigation50. PCR amplicons wereused for high-throughput sequencing with a MiSeq Genome Sequencer (Illumina, CA, USA). The paired-end
sequences with a read length of 461 bp were grouped based on the tag sequences of each sample. In the PCR
analysis steps, negative controls (no template and template from unused filters) contained no fragments of ITS
amplicons exhibiting the absence of contamination during the process. After the forward and reverse paired-end
reads in the raw sequencing database were merged, the irregularly merged reads (lengths outside the 200–500 bp
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range or exceeding 6 photopolymers) and the error sequences with low Q-scores were removed. The remaining
sequences were clustered into phylotypes using QIIME (Quantitative Insights Into Microbial Ecology; ver. 1.8.0)
software with a minimum coverage of 99% and a minimum identity of 97%. The fungal compositions of the phylotypes were analysed using the Basic Local Alignment Search Tool (BLAST) to compare their sequences withreferences from the DNA Data Bank of Japan. Supplementary Tables S3 and S4 give numbers of ITS sequencesclassified into phylum and order, respectively. All sequences have been deposited in the DDBJ database (accession
number of the submission is DRA007277).
We estimated the radiocaesium activity of a single fungal spore at Namie by assuming that the radiocaesium
activity in fungi is proportional to the level of surface contamination. Potassium-40 concentration in fungi isoften measured with the 137Cs activity, K content in fungi and 40K activity in the unit mass of K are known, andthis approach could be employed to estimate the 137Cs content in a single fungal spore (Supplementary Tables S5–
S7). In the calculation, we applied no decay correction for 137Cs due to its small effect on the estimation results.
In the first and second approaches, fungal spores were assumed to be droplet and wooden particles, as shown in
Supplementary Tables S5 and S6. The third approach (Supplementary Table S7) directly used the transfer factor
in a forest. The estimates obtained by the three approaches overlap (approach 1, 8.1 × 10−9 to 7.8 × 10−8 Bq/
grain; approach 2, 2.8 × 10−9 to 1.5 × 10−7 Bq/grain; and approach 3, 3.3 × 10−9 to 2.6 × 10−7 Bq/grain), whichsuggests that they are plausible and that the 137Cs content in a single fungal spore at Namie ranges from 10−9
to 10−7 Bq. The range of estimates mostly results from (1) the size (volume) difference of the basidiospore andascospore fungal spores and (2) the difference in the 137Cs/40K activity ratios of fungi based on the level of surface
contamination.
Monthly fungal fruiting body abundance levels were retrieved from a mushroom survey project at the
Tsukuba Botanical Garden (Tsukuba, Ibaraki, Japan). The survey was conducted every week from 2012–2015.
Fruiting bodies of both Basidiomycota and Ascomycota of visible size were surveyed and collected weekly fromforested areas of the garden by 3 to 30 investigators. Here, a specimen is defined as one or more fruiting bodies ofthe same species growing in the same vegetation type (section) in the garden. On the same day, multiple specimens of the same species could be collected if they were found in different section of the garden. The total numberof mushroom specimens collected each month, regardless of species, was counted, and the monthly average from2012–2015 was calculated.
The Supplementary Information gives additional details of the above methods.

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Acknowledgements

We thank Ms. K. Inukai, Ms. K. Kamioka and Mr. T. Kimura (Atox Co. Ltd.) for assistance with the activity
measurements; Ms. K. Yanagida (Meteorological Research Institute; MRI) for logistics help; and Ms. Y. Kamiya(MRI) for help with the manuscript editing. We also acknowledge Mr. T. Kanari (Green Blue Co. Ltd.) for
maintaining the measurement sites and Mr. H. Demizu and other members and students of the College of
Science and College of Engineering, Ibaraki University, for the maintenance of the tools, special installations in
the field and data collection. We also express our thanks to the local governments of Kawamata and Namie andto the Fukushima prefectural government, who kindly provided the monitoring opportunity. Y.I. is grateful to
Dr. K. Tagami, National Institute of Radiological Sciences, for discussion of the transfer factor in forests. Ms. M.
Nara helped finishing the figure to fulfil the QC requirement. This work was financially supported by Grantsin-Aid for Scientific Research in Innovative Areas granted to the A01–2, A03–6 and A04–8 research teams of
the “Interdisciplinary Study on Environmental Transfer of Radionuclides from the Fukushima Dai-ichi NPP
Accident (ISET-R)” (grant numbers 24110003, 24110007, and 24110009, respectively; PI: Professor Yuichi Onda,
University of Tsukuba) and by the Ministry of Education, Culture, Sports, Science and Japanese Radioactivity
Survey by Technology and Nuclear Regulation Authority, Japan at the MRI. This research was also funded by
Grants-in-Aid for Scientific Research (A) (No. 17H01478), (B) (No. 24340115, 26304003 and 17H01873) and(C) (No. 17K00533 and 26340049). We would like to give our special thanks to the ISET-R PI, Prof. Onda, andhis staff.

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Author Contributions

Y.I. and K.K. supervised the project and planned the observations and data analysis. K.K., T.M., T.K., N.H. and
M.I. performed observations at the site and conducted the experimental analysis. T.M. was in charge of thebioaerosol sampling and analysis. K.H. conducted mushroom survey at Tsukuba Bitanical Garden and analyze
fruit body production data. M.I. contributed meteorological observations. Y.I., Y.Z., K.K., T.K. and K.N. collectedradioactivity measurements. K.A. performed electron microscopic analysis with T.K. and N.H. M.K. and M.I.
performed the aerosol transport modelling and provided the data. T.T.S. drew the land use map. C.T., H.O. and
A.S. helped with the data analysis from the viewpoint of forest ecology, organics in the air, and aerosol fluxes,
respectively. C.T. also estimated the 137Cs burden of a fungal spore. A.S. helped with the radiation dose estimation.
Y.I., T.M., K.H. and K.K. wrote the manuscript, and all authors contributed ideas for the paper and reviewed themanuscript.

Additional Information

Supplementary information accompanies this paper at https://doi.org/10.1038/s41598-018-37698-x.
Competing Interests: The authors declare no competing interests.
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Scientific Reports |(2019) 9:1954 | https://doi.org/10.1038/s41598-018-37698-x

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Atmospheric Environment 214 (2019) 116830

Contents lists available at ScienceDirect

Atmospheric Environment
journal homepage: www.elsevier.com/locate/atmosenv

Intercomparison of numerical atmospheric dispersion prediction models for
emergency response to emissions of radionuclides with limited source
information in the Fukushima Dai-ichi nuclear power plant accidentTToshiki Iwasakia, Tsuyoshi Thomas Sekiyamab,*, Teruyuki Nakajimac, Akira Watanabed,
Yasushi Suzukie, Hiroaki Kondoe,f, Yu Morinog, Hiroaki Teradah, Haruyasu Nagaih,
Masayuki Takigawai, Hiromi Yamazawaj, Denis Quélok, Anne MathieukaGraduate School of Science, Tohoku University, Aoba-ku, Sendai, 980-8578, Japan
Meteorological Research Institute, Tsukuba, 305-0052, Japan
Earth Observation Research Center, Japan Aerospace Exploration Agency, Tsukuba, 305-8505, JapandFaculty of Symbiotic Systems Science, Fukushima University, Fukushima, 960-1296, Japane
Japan Weather Association, Toshima-ku, Tokyo, 170-6055, Japanf
Environmental Management Research Institute, National Institute of Advanced Industrial Science and Technology, Tsukuba, 305-8569, Japang
National Institute for Environmental Studies, Tsukuba, 305-8506, JapanhJapan Atomic Energy Agency, Tokai, Ibaraki, 319-1195, Japani
Japan Agency for Marine-Earth Science and Technology, Kanazawa-ku, Yokohama, 236-0001, Japanj
Graduate School of Engineering, Nagoya University, Chikusa-ku, Nagoya, 464-8603, Japank
Institute for Radiological Protection and Nuclear Safety, 92262, Fontenay-aux-Roses Cedex, Francebc

G R A P H I C A L A B S T R A C T

A R T I C LE I N FO

A B S T R A C T

Keywords:
Environmental emergency response
Numerical atmospheric dispersion prediction
Model intercomparison
Fukushima nuclear accident

The utilization of numerical atmospheric dispersion prediction (NDP) models for assisting the emergency response to emission of radionuclides has been recommended by a working group of the Meteorological Society ofJapan. This paper veriﬁes the feasibility of the recommendation through NDP model intercomparison withlimited emission source information for the case of the Fukushima Dai-ichi Nuclear Power Plant accident causedby the Great East Japan Earthquake in 2011. According to the recommendation of the working group, the NDP
models are run under the assumption of a constant rate of emission during the whole forecast period. This is theworst-case scenario when limited source information is available. Generally, no information is provided on thetemporal variability and strength of the emissions, while the source location is known. Surface air radionuclide
forecasts are utilized for providing warnings of the risk of inhaling radioactive substances suspended in the lowlevel atmosphere, whereas column-integrated radionuclide forecasts are utilized for estimating the potential
maximum wet deposition of radioactive materials on the ground due to precipitation. The NDP model shortrange forecasts were validated with observational data for three locations, at the times when the most serious*

Corresponding author.
E-mail address: tsekiyam@mri-jma.go.jp (T.T. Sekiyama).

https://doi.org/10.1016/j.atmosenv.2019.116830
Received 26 December 2018; Received in revised form 9 July 2019; Accepted 11 July 2019
Available online 12 July 2019
1352-2310/ © 2019 Elsevier Ltd. All rights reserved.

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T. Iwasaki, et al.

contamination events occurred at each of the three monitoring stations. The NDP models successfully predicted
the risk of surface air contamination and/or ground surface contamination caused by wet deposition in thesecases. Particularly, the NDP model forecasts allow us to disseminate warnings at eﬀective lead times before
exposure to radiation. The diﬀerent NDP models gradually deviate their forecasts as the lead time progresses.
The deviations may indicate the magnitude of forecast errors. Thus, the use of multi-model forecasts is of greater
beneﬁt than the single model forecasts, because forecast error information is suggested.

1. Introduction

should be run assuming a constant emission rate during an environmental emergency (cf. World Meteorological Organization, 2010) withlimited source information. Note that this assumption corresponds tothe worst-case scenario, since breaks in the emission tend to reduce thesize of the area at risk of contamination. If evacuation orders are issued
under the assumption of a constant rate of emission, then the aﬀected
people are expected to be safe under any emission scenario. Of course,
if a reliable emission scenario is available in real time, it should be used
for the NPD model forecasts.
We brieﬂy explain the NDP model products to be disseminated tothe public. One of the most serious forms of radiation exposure is the
direct inhalation of radioactive substances suspended in the low-levelatmosphere. To mitigate the inhalation of suspended substances, weshould utilize a geographical map of radioactive substance density inthe low-level atmosphere (hereafter called the surface air contamination). When surface air contamination is expected, we can mitigate theradiation exposure by advising people to stay indoors.
Another exposure process arises through the deposition of radioactive substances resulting in internal exposure through the intake ofcontaminated water/food and the external exposure to radiation fromthe environment. Precipitation deposits almost all substances below
rainmaking clouds on the ground. Sometimes the ground surface isheavily contaminated, even though the radioactive substance density issmall in the surface atmosphere. NDP models can directly forecast theamount of wet deposition using the output variables of precipitation
and radiative substance density. However, we do not recommend theutilization of model-derived wet deposition, since it has the risk offailing to issue a warning when the model fails to forecast precipitation.
In brief, wet deposition forecasts cannot be used to predict the worstcase scenario. Instead, we recommend the use of the column-integrated
amount of radioactive substances to estimate the maximum wet deposition (hereafter called the column-integrated contamination). We
note that there is a possibility that the precipitation is contaminated at
locations where NDP forecasts indicate high column-integrated contamination. Unless it rains at these locations, the warning associatedwith serious wet deposition may be withdrawn. If it does rain at theselocations, we can eﬀectively survey the contamination area by limiting
only to locations where the actual precipitation coincides with highcolumn-integrated contamination. In fact, the wet deposition can result
in serious contamination over a much greater area than the UPZ of
30 km away from the emission source. We can quickly estimate therange of the contaminated area with the help of the NDP forecasts.
NDP model forecasts contain uncertainty arising from imperfectionsin the atmospheric initial conditions and NDP models. When utilizing
the model forecasts, both for the surface air contamination and columnintegrated contamination, we ought to assume the contaminated areaand period to be larger and longer than in the NDP model forecasts byconsidering the uncertainty of the forecasts. In addition, the model
uncertainty could be estimated or reduced by multi-model forecasts. In
this study, we perform multi-model prediction experiments to clarify
the advantages of the model prediction for environmental emergencies
when the emission source information is limited.

1.1. Discontinuation of the use of NDP model forecasts for aiding emergency
response
The Fukushima Dai-Ichi Nuclear Power Plant (FDNPP) was damaged during the Great East Japan Earthquake and subsequent tsunami
on 11th March 2011, and released a huge amount of radionuclides tothe atmosphere through explosions, ventilations and leaks from abroken reactor pressure vessel (Chino et al., 2011). The released
radionuclides were transferred by the wind and deposited on the
ground. The wet deposition seriously contaminated land, forests,
houses and water, even at locations outside the urgent protective action
planning zone (UPZ) of 30 km from the emission source (MEXT, 2011).
On that occasion, however, the public was not provided with suﬃcient
information on the contamination to properly mitigate their exposure
to radiation.
The World Meteorological Organization (2006) has recommended
the use of computer-based atmospheric dispersion simulations for environmental emergency response activities, including nuclear accidents. A numerical atmospheric dispersion prediction (NDP) model ofradioactive substances, named the System for Prediction of Environmental Emergency Dose Information (SPEEDI), was developed by theJapan Atomic Energy Research Institute as a government-commissioned
project for environmental emergencies caused by the release of radioactive substances (Imai et al., 1985). In the FDNPP accident, however,forecasts by SPEEDI were not utilized for the mitigation of radiation
exposure. The reason for this was that the time series of radionuclide
emission intensity could not be obtained. Information on the emission
intensities would normally have been provided to SPEEDI by the
Emergency Response Support System (ERSS). At that time, the ERSS did
not work, since its power supply was broken by the earthquake(Hatamura et al., 2011, 2012). As a result, evacuation orders were issued only by considering the distance from the emission source. In
2014, three years after the accident, the Nuclear Regulation Authorityof Japan (NRA) decided to discontinue the use of SPEEDI for environmental emergencies, because of uncertainties both in the temporalvariation of the emission intensity and in the weather forecasts (NRA,2014). The Meteorological Society of Japan (MSJ) disagreed with thedecision by the NRA and issued a proposal on the use of NPD model
forecasts of radioactive substances accidentally released from nuclear
power related facilities, based on the recommendations of a report by aworking group of the MSJ (2015).
1.2. Recommendation on utilization of NDP model forecasts
The working group of the MSJ recommended the following approach to utilize NDP models in environmental emergencies for the
mitigation of radiation exposure. Basically, NDP model forecasts should
be provided to avoid the risk of “failure to notice” rather than to avoid
that of “false alarm” by considering the worst-case scenario, because
the accidental release of radioactive substances causes a risk of serious
radiation exposure to many people. NPD model forecasts for the worstcase scenario can help us choose a safer time and place of evacuationand greatly reduce exposure to radiation.
Generally, it is hard to obtain the time series of emission intensity
just after an accident. If these data are not available, NDP models
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2. Experiments

Nakajima et al. (2017). The JAEA dispersion calculation module was a
Lagrangian model, and was included in the Worldwide version of the
System for the Prediction of Environmental Emergency Dose Information (WSPEEDI) used for planning the response to environmental
emergencies (Terada and Chino, 2008; Katata et al., 2015).
Both the MRI and IRSN models adopted a Eulerian scheme formaterial transport and shared the same meteorological forecasts thatwere prepared by MRI using the JMA non-hydrostatic weather forecast
model and local ensemble transform Kalman ﬁlter (JMANHM-LETKF)
data assimilation system (Kunii, 2014; Sekiyama et al., 2015, 2017)with JMA's operational meteorological observations, including thenear-surface wind velocities. The JMANHM-LETKF data assimilation
system was implemented with a 3-km horizontal resolution, where thelateral boundary conditions were obtained from the JMA operational
global deterministic and ensemble forecasts. The NDP conﬁguration
details of the MRI model were described in Sekiyama et al. (2017) and
Kajino et al. (2018), in which the model performance was quantitatively examined. The JMANHM-LETKF meteorological ﬁeld was also
used to drive the oﬄine NDP model of IRSN. The dispersion calculation
for the IRSN model was detailed in Mathieu et al. (2012, 2018), Saunieret al. (2013), Groëll et al. (2014), and Quérel et al. (2015). For Eulerian
NPD models (NIES, MRI, and IRSN models), the time series of contamination at each monitoring station was extracted by the linear interpolation of the nearest grid cells. For a Lagrangian NPD model (JAEA
model), the gridded output was calculated from the particle density andthen the time series was extracted by the linear interpolation of thenearest grid cells.

2.1. NDP model forecast intercomparison
We conduct an intercomparison of the NDP models to validate theNDP model performance with the observation data for the FDNPP accident, and to conﬁrm the suitability of the recommendations by the
MSJ working group. In this experiment, the reliability of NDP models is
assessed on the basis of the forecast consistency among models. Multimodel ensemble approaches are used to provide information on theforecast reliability for tropical cyclone track forecasts (Goerss, 2000).
Information on the forecast reliability is useful for decision-making
regarding evacuation during extreme events. Here, we consider theeﬀectiveness of multi-model ensemble forecasts of NPD models.
After the Chernobyl nuclear accident in 1986, many types of NDP
models have been developed to forecast the atmospheric dispersion ofradioactive substances during environmental emergencies (e.g.,
Ehrhardt, 1997; Brandt et al., 2002; Hoe et al., 2009). After the FDNPP
accident, many model intercomparison experiments have been conducted (e.g., Draxler et al., 2015; Kristiansen et al., 2016; Kitayama
et al., 2018; Sato et al., 2018). However, they were all reanalysis modelstudies. To the best knowledge of the authors, there is no study dealing
with forecast (or hindcast) model intercomparions for the FDNPP accident. Our intercomparison experiment was focused on short-range
forecasts (from several hours to several days), during which the serious
contamination occurred. In the FDNPP accident, radionuclides werespread over several hundred kilometers from the source over the courseof about 2 days. Thus, we compared NDP models, which have domainsof about 800 km × 800 km around the source position and a horizontal
resolution of about 3 km. Passive tracers were released from the lowermost layer at FDNPP at a constant rate of 1 Bq/h. We need only relative concentration values when the outer edge of radioactive plumes
has to be detected. This is because the background concentration ofanthropogenic nuclear products is almost zero. The plume edge has ajump of concentration values by 1010–1020 times in model simulations
even though the constant emission rate is a unit amount (1 Bq/h). It is
exactly the reason why WMO recommends assuming an emission rate of
1 Bq/h (World Meteorological Organization, 2010). The deﬁnition ofplume outer edges is 10−13 × Bq/m3 or 10−11 × Bq/m2 in this study
because this deﬁnition makes a jump of concentration between theinside and outside of plumes larger than ten digits to detect the edge
line of potential contaminated areas.
The forecast period was 30 h, because lateral boundary conditions
for NDP models could be taken from the stored JMA's operational
mesoscale forecasts that extend to up to 33 h. The forecast products tobe compared were the geographical distributions of the surface air
contamination and the column-integrated amount. For the worst-casescenario, no dry/wet deposition was included in the NDP models, sothat the maximum potential contamination was provided by the forecasts. While the output time interval of the NDP models is generally
variable, we used hourly outputs for the model validation.
In this study, four NDP models participated in the short-range
forecast experiment for radioactive substances. These models weredeveloped by the National Institute for Environmental Studies (NIES) of
Japan, the Japan Atomic Energy Agency (JAEA), the MeteorologicalResearch Institute (MRI) of Japan, and the French Institute forRadiological Protection and Nuclear Safety (IRSN). The model domains
were set to cover eastern Japan with an approximately 3-km horizontal
resolution. The dynamical frameworks of the NIES and JAEA models
were based on the weather research and forecast model (WRF) version
3 (Skamarock et al., 2008), and the initial and lateral boundary conditions were obtained from the Japan Meteorological Agency (JMA)
operational mesoscale analysis and forecast, respectively. The dispersion calculation of the NIES model was performed by the Community
Multiscale Air Quality (CMAQ) Eulerian model version 4.6 (Byun andSchere, 2006) as described in detail in Morino et al. (2013) and2.2. Monitoring data for validation
Three cases were chosen to assess the performance of NDP model
forecasts. Fig. 1 shows the time series of the air dose rate and precipitation intensity at the Iwaki and Fukushima monitoring stations(Working group of MSJ, 2015) for validating the model forecasts. At
Iwaki station, the most serious event occurred in the early morning on
15th March. The air dose rate suddenly increased to more than 20 μSv/
h in the early morning, and then rapidly decreased to less than 2 μSv/haround noon. The high level of radiation mostly came from a radioactive plume in the low-level atmosphere (cloud shine). It did not comefrom deposited substances on the ground (ground shine), because no
precipitation was observed at Iwaki.
At the Fukushima monitoring station, the most serious event occurred in the evening on the 15th of March. Around 1700 JST, the air
dose rate rapidly rose to about 20 μSv/h at the same time of precipitation was observed as shown in Fig. 1. Since the air dose rate did
not rapidly return to the normal value, the radiation is considered tocome from substances deposited on the ground (ground shine). If it had
not rained, the radioactive substances would not have contaminated theground so much. The large amount of wet deposition resulted from thespatiotemporal overlap of the precipitation with the column-integrated
contamination. After the 16th of March, the radiation gradually decreased in accordance with the volatilization and decay of iodine's
isotope, 131I, whose half-life is about 8 days, and partly due to soil inﬁltration of radioactive metal ions.
Kashiwa City in the Kanto area was also contaminated as shown in
Fig. 2 (Working group of MSJ, 2015). At the Kashiwa Campus of the
University of Tokyo, the air dose rate increased between the evening of
the 20th and the morning of the 21st of March, and remained constantafterwards. Precipitation was observed from 0800 JST on the 21st at thenearest Automated Meteorological Data Acquisition System (AMeDAS)
observation station at Abiko. It is likely that precipitation depositedradioactive substances on the ground at that time.
Detailed observations exist for the above-mentioned contamination
events at the Iwaki, Fukushima and Kashiwa stations, and thus these
events were selected as the targets of the model intercomparison experiment. The locations of these stations are illustrated in Fig. 3. The
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Fig. 3. Locations of the Fukushima Dai-ichi Nuclear Power Plant (FDNPP), andthe Fukushima, Iwaki, and Kashiwa monitoring stations. Gray shading indicatesthe ground elevation at intervals of 250 m.

Iwaki station is located about 40 km SSW from the emission source at
FDNPP, the Fukushima station is located about 60 km NW, and theKashiwa station is at about 200 km SSW. In addition, we were able toacquire the ground surface concentration data of radioactive cesium(Oura et al., 2015) in Kashiwa City very close to the Kashiwa station.

Fig. 1. Time series of the hourly air dose rate (red line, μSv/h) and precipitation(black bars) at monitoring stations in Iwaki City (140.88 °E, 37.05 °N, upper
panel) and in Fukushima City (140.47 °E, 37.76 °N, lower panel), hourly fromMarch 14 to 31, 2011, respectively. Precipitation was observed at the Onahama
AMeDAS station, which is about 12 km southeast of the Iwaki station, and at the
Fukushima Local Meteorological Observatory, which is about 1 km from the
Fukushima monitoring station. This ﬁgure was reproduced from an earlier
study (Working group on “Radioactive substances accidentally released from
nuclear power related facilities”, Meteorological Society of Japan, 2015) for
validation of model results. (For interpretation of the references to color in thisﬁgure legend, the reader is referred to the Web version of this article.)

3. Results and discussion
3.1. NDP model forecasts for surface air contamination at Iwaki andKashiwa
At ﬁrst, the NDP model intercomparison was conducted with a focus
on the maximum air dose rate observed at the Iwaki monitoring station.
The NDP models were initialized at 0000 JST on March 15th, whereradioactive substances were assumed to be emitted at a constant ratefrom the same initial time, 0000 JST. As mentioned above, the highlevel of radiation observed at Iwaki is considered to come from theplume suspended in the low level atmosphere (cloud shine). Therefore,
the forecasts of the surface air contamination are validated with theobservations at Iwaki. As shown in Fig. 4, all four models forecast adense plume moving SSW which extended over Iwaki at a forecast timeof 6 h (FT = 6). After that, the surface wind gradually turned clockwise
and the plume moved away from Iwaki. At a forecast time of 30 h
(FT = 30), the models again forecast a low-level plume moving
southward and air contamination around the Iwaki station. These
forecasts are consistent with the monitoring results of a large peak in
the air dose rate in the early morning of the 15th of March, and amoderate peak in the early morning of the 16th (see Fig. 1), although
NDP models have larger diﬀerences in the geographical distributions at
a forecast time of 30 h than at a forecast time of 6 h. Fig. 5 shows thevertical east-west cross section of the radioactive substance density
along a line of the latitude of Iwaki (37.05° N). The vertical extension ofradioactive substances is somewhat diﬀerent among the models, which
may reﬂect the magnitude of the vertical diﬀusion. Nevertheless, thedensity forecasts all show a maximum in the lowermost layer.
Time series of the NDP forecasts consistently indicate the maximum
contamination occurred at the Iwaki station in the early morning of the
15th, as illustrated in Fig. 6a. Strictly speaking, the forecast time of theradioactive substances was about 2 h later than the observed time. Oneof the reasons for this may be that the initialization time of theFig. 2. Time series of the hourly air dose rate (red lines, μSv/h) at the
University of Tokyo Kashiwa Campus and precipitation (black bars, mm/h) at
Abiko AMeDAS station, which is about 8 km from the monitoring station, from0000 JST 17th to 0000 JST 26th, March. The air dose rate was observed with a
NaI(TI) scintillation detector at a height of 1 m. Observations were only made in
the day time. This ﬁgure was reproduced from an earlier study (Working group,
Meteorological Society of Japan, 2015) for validation of model results. (Forinterpretation of the references to color in this ﬁgure legend, the reader is referred to the Web version of this article.)

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T. Iwasaki, et al.

Fig. 4. Geographical distributions of the surface air contamination forecasts (concentration of radioactive substances in the lowermost layer; 10−13 × Bq/m3) fromthe model initialization time of 0000 JST on 15th of March at forecast times of 6, 12, 18, 24 and 30 h, by the NDP models of the NIES, JAEA, MRI and IRSN. A
constant emission rate of 1 Bq/h at FDNPP is assumed in the forecasts. The triangle, diamond, and pentagon indicate the locations of Fukushima City, Iwaki, andKashiwa, respectively.

more uncertainty, depending on the initial time they predicted the air
contamination within an error of 2 h over Iwaki on the morning of the
15th of March. As shown in Fig. 6a, the maximum surface air contamination diﬀered between models, and the forecast spread(≈1.5 × 10−11Bq/m3) was more than 50 percent of the ensemble

emissions in NDP models of 0000 JST was later than the actual emission
time of the substances which arrived at the Iwaki station. In this case,the forecast times of 3–6 h are too short for us to take preventive action.
An additional experiment was conducted to increase the lead time up to
24 h using the MRI model (Fig. 6b). Although the forecasts contained

Fig. 5. East-west and vertical cross sections of the 6-h forecasts of the air contamination along a line of the latitude of Iwaki (37.05° N) from the initialization time of0000 JST on 15th March by the four models (unit; 10−13 × Bq/m3). A constant emission rate of 1 Bq/h at FDNPP is assumed in the forecasts. Open triangles indicate
the longitude of Iwaki (140.9° E).
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T. Iwasaki, et al.

and Kashiwa on the mornings of the 15th and 16th, as a worst-casescenario. If people were informed of the possible risk of the surface air
contamination on the mornings of the 15th and 16th, they could remainindoors until the contaminated surface air had passed. In other words,
the models indicated that the health risk from surface air contamination
would be smaller in other time periods, because the models assumed acontinuous rate of emission after initialization without any emission
breaks.

3.2. NDP model forecasts for wet deposition at Fukushima City
Next, NDP model forecasts are validated with the observations at
the Fukushima monitoring station. As mentioned above, precipitation
caused a large amount of wet deposition at about 1700 JST on the 15thof March, and thereafter radionuclides deposited on the ground continued to emit radiation.
In the worst-case scenario for wet deposition, the precipitation deposits all of the column-integrated radioactive substances on the
ground. Here, we examine whether NDP model forecasts of the columnintegrated contamination correctly predicted the possibility of wet deposition on the ground. Fig. 8 shows NDP model forecasts of thecolumn-integrated amount of radioactive substances initialized at 0000
JST on March 15. Note that these are the same forecasts as shown
previously. The spatial pattern of the vertically integrated amountshown in Fig. 8 is signiﬁcantly diﬀerent from that of the surface air
contamination shown in Fig. 4, reﬂecting the vertical wind shear. At
1800 JST (FT = 18), the area of the contaminated column shown in
Fig. 8 is much greater than the area of contaminated surface air shownin Fig. 4. According to a detailed tracer ﬂux analysis (Sekiyama andIwasaki, 2018), radioactive substances emitted from FDNPP passed
over Fukushima City around 1800 JST through two routes. The ﬁrst
route was that of the substances emitted early in the morning, which
went southward, turned clockwise and then moved northeastward.
These substances gradually rose while moving northeastward, andreached a height of about 3000 m. They were dispersed horizontally
under the strong wind shear. The second route was that of the substances emitted in the afternoon, which went northwestward directly to
Fukushima City. Large diﬀerences in the distribution of potential

Fig. 6. Time series of the surface air contamination forecasts (units; Bq/m3)
together with the observations of air dose rate (unit; μSv/h) at the Iwaki station.
The upper panel (a) shows forecasts from the initialization time of 0000 JST on
15th March by the NDP models at NIES, JAEA, MRI and IRSN, and the lower
panel (b) shows the lagged forecasts by the MRI model. The lines for each
forecast and observation are indicated in the legend. A constant emission rate of
1 Bq/h at FDNPP is assumed in the forecasts.

mean value (≈2.5 × 10−11Bq/m3), suggesting a fair amount of uncertainty in the forecasts. In the time series of the surface air contamination, relatively greater values indicate the periods when there isa high risk of inhalation of dangerous levels of radiation with a valuable
lead time.
The radioactive plume shown in Figs. 5 and 6 ﬂowed southward andarrived at Kashiwa about 7 h later (Fig. 7). The forecast time of theradioactive substances in Kashiwa was about 2 h earlier than the observed time as shown in Fig. 7a. This case involves a longer forecast
lead time and a farther distance from the emission source than the caseof Iwaki. Therefore, the time-lagged ensemble (Fig. 7b) indicates larger
errors of the plume arrival time when the forecast initial time is earlier
than 0000 JST on 15th March. Generally speaking, the uncertainty inplume advection simulations is accumulated along the pathway of theplume; i.e., small diﬀerences in the wind velocity along the pathway
may cause large diﬀerences in the plume location (Sekiyama et al.,2017). Therefore, especially when the forecast initial time is earlier andthe lead time is longer, we ought to prepare for the slightly longer time
window and broader area of air contamination than the model forecasts.
For these events, NDP models indicate the possibility that the surface air would be contaminated with radioactive substances at Iwaki

Fig. 7. Same as Fig. 6, but at the Kashiwa station; the observation is groundsurface Cs-137 concentration (Oura et al., 2015), not air dose rate.
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Fig. 8. Same as Fig. 4 but for the column-integrated contamination forecasts (10−11 × Bq/m2).

models forecasted the low-level air contamination to be lower at Fukushima City than that at Iwaki, under the assumption of a constantemission rate. The risk of internal exposure by inhalation at Fukushima
City might be much less than that at Iwaki 12 h earlier. On the other
hand, the large column-integrated amount indicated the risk of wet
deposition around Fukushima City in the evening. Indeed, as shown in
Fig. 1, the precipitation caused the wet deposition and the groundcontamination at the Fukushima station. Fig. 11 shows that the precipitation started around 1400 JST (FT = 14) and the surface contamination mainly occurred at about 1700 JST (FT = 17). The NDP
models forecasted that the radioactive plume would be widely spreadover Fukushima City at about 1800 JST (FT = 18). Note that previous
studies have indicated that the reproducibility of the northwestward
wind from the nuclear power plant to Fukushima City is not good in theafternoon 15th of March (e.g., Morino et al., 2013; Sekiyama et al.,2015; Nakajima et al., 2017), attributable to the orographic model resolution or boundary conditions. This type of meteorological uncertainty cannot be completely removed by the ensemble forecast or
proper use of NDP models. Nonetheless, in Fig. 11, one ensemble
member (JAEA model) was able to predict the contamination arrival
time more accurately than other members. This indicates the beneﬁt ofcontamination among the four NDP models were not found in Fig. 8 as
far as the short-range (30-h) forecasts are concerned. Stacked bar charts
in Fig. 9b shows the similarity degree of the potential contamination
area among the four NDP models depicted in Fig. 8. If the stacked bar
charts are exclusively colored with “1-Model Only” then the potential
contamination areas are completely non-overlapped among the four
NDP models. In contrast, stacked bar charts with a 100% proportion of“4-Model Ovlp” indicates the perfect match of the four NDP models.
Fig. 9b illustrates a large similarity (but a moderate diﬀerence) among
the four NDP models with a small portion of “1-Model Only” areas. The
forecast similarity in Fig. 9b (related to Fig. 8) is higher than that inFig. 9a (related to Fig. 4) because the distribution of contamination in
Fig. 8 is column-integrated, which is apt to mask the diﬀerence invertical diﬀusion between models, while that in Fig. 4 illustrates only
the surface layer.
Fig. 10 is the vertical cross section of the substance density along aline of the latitude of Fukushima City (37.76° N). The contaminated air
extended up to an altitude of about 3000 m. The upper portions of thecontaminated air were emitted in the early morning, and widely dispersed by the wind shear. The lower portions of relatively denseradioactive substances were mainly emitted in the afternoon. The NDP

7

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Fig. 11. Time series of the column-integrated contamination amount forecasts(unit; Bq/m2) together with the observations of air dose rate (unit; μSv/h) at the
Fukushima station. A constant emission rate of 1 Bq/h at FDNPP is assumed in
the forecasts.

warned not to use rainwater to avoid internal exposure, and check thecontamination of the rainwater. If it does not rain, we can withdraw the
wet deposition warning.

3.3. NDP model forecasts for wet deposition at Kashiwa
Although Kashiwa is located about 200 km SSW of FDNPP, signiﬁcant contamination on the ground was recorded due to the wet
deposition around 0800 JST on the 21st of March as shown in Fig. 2. Allof the NDP model forecasts indicated that the tongue of the radiative
plume passed over Kashiwa at this time as shown in Fig. 12. The
forecasted arrival times of the plume by the NIES and JAEA models
were a little earlier than those by the MRI and IRSN models. This isprobably because these NDP forecasts are coupled with diﬀerent mesoscale numerical weather prediction (NWP) models. The former twomodels implemented the WRF model, whereas the MRI implemented
the JMA non-hydrostatic weather forecast model (JMANHM). The IRSN
model was an oﬀ-line transport model, and was driven by the same
meteorological parameters from the JMANHM. Nonetheless, there areno large diﬀerences in the distribution of contamination among models
as shown in Fig. 9c which indicates a higher similarity than in Fig. 9b.
Similar to Fukushima City, if the above forecasts had been available,
people in Kashiwa would have been able to prepare for the contaminated precipitation. We should survey the ground contamination
due to wet deposition only when precipitation occurs in the forecasted
regions of high column-integrated contamination. It is not necessary tosurvey the ground contamination in the non-precipitation area. Thus,
we can quickly determine which areas are at risk of ground contamination by considering only the areas both experiencing precipitation and predicting column-integrated contamination.

Fig. 9. Overlapped and distinct plume areas (km2) between the four NDP
models at forecast times (FT) of 6, 12, 18, 24, and 30 h within the latitude 35.5°
N – 39.0° N and longitude 138.0° E − 142.0° E. In the legend, 4-Model Ovlp, 3Model Ovlp, 2-Model Ovlp, and 1-Model Only indicate the area where all thefour NDP model plumes are overlapped, the total area where three out of thefour NDP model plumes are overlapped, the total area where two out of the four
NDP model plumes are overlapped, and the total area where only one plumeexists (= non-overlapped with any others), respectively. (a) Statistics related toFig. 4 with the plume edge deﬁnition of 10−13 × Bq/m3, (b) related to Fig. 8
with the plume edge deﬁnition of 10−11 × Bq/m2, and (c) related to Fig. 12
with the plume edge deﬁnition of 10−11 × Bq/m2.

the model ensemble forecast.
If the column-integrated contamination forecasts had been available, we could have prepared for contaminated precipitation in theregion of high column-integrated contamination. If it rains in the region
where a large column-integrated amount is forecasted, residents can be

Fig. 10. Same as Fig. 5 but for the 18-h forecast along a line of the latitude of Fukushima City (37.76° N). Open triangles indicate the longitude of Fukushima City
(140.5° E).
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T. Iwasaki, et al.

Fig. 12. Same as Fig. 8 but for the initialization time of 0600 JST on 20th March.

4. Conclusions

unobserved contamination because of coarse spatial distributions when
there are insuﬃcient stations available. For example, precipitation cansigniﬁcantly contaminate the ground surface even far away from theemission source, where it is diﬃcult to deploy observatories enough to
depict the whole contaminated area. Emergency systems should beestablished to eﬀectively survey the contamination due to wet deposition on the ground over wide areas. The utilization of the NDP models
helps us to reduce the radiation exposure by considering the match of
the predicted plume with the observed precipitation. For determining
preventive actions, we should use information from both the observations and forecasts (World Meteorological Organization, 2006).

4.1. How to utilize NDP models for environmental emergencies
All four NDP models succeeded in forecasting the three-dimensional
distribution on short time scales for the three locations considered here.
The NDP models have the potential to provide useful information to the
public on the surface air contamination and column-integrated contamination even if the emission inventory information is not available.
The surface air contamination forecasts can be utilized for mitigating
the inhalation of radioactive substances, and the column-integrated
contamination forecasts for mitigating the radiation exposure associated with the wet deposition. Note that the column-integrated contamination is the worst-case scenario for wet-deposition. As with all
natural hazard forecasts, the greatest beneﬁt of using forecasts is theincreased lead time at which warnings can be provided, giving moretime to prepare.
Accurate observational data are also valuable for preparing preventive measures against nuclear disasters. Emergency systems based
only on observations, however, may not be able to provide timely information. Also, the use of observations only incurs the risk of

4.2. Recommendation
The reason why the NRA discontinued the use of NDP model forecasts for environmental emergencies was that both the time-dependent
emission scenarios and weather forecasts contain considerable uncertainties. According to them, model forecasts can hardly be used
quantitatively, for example, compared with the threshold density values, to issue evacuation orders. Thus, we recommend using model
forecasts for qualitative indications of the worst-case scenario, which
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T. Iwasaki, et al.

References

can help us choose a safer time and place of evacuation and greatly
reduce exposure to radiation. The assumption of emission rate constancy provides the worst-case scenario when limited source information is available. Under the constant emission scenario, it does notmatter what absolute values of emission are used in NDP models because the hazardous plumes can be discriminated with a huge gap ofconcentration in the model simulations.
On the other hand, NDP model forecasts also have uncertainties
arising from the forecasts of dynamic and thermodynamic ﬁelds. If
these meteorological ﬁelds are inaccurate, then the location of safe
areas may not be safe even though the NDP model forecasts suggest it
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Draxler et al., 2015; Kristiansen et al., 2016; Kitayama et al., 2018; Satoet al., 2018) but also for a forecast (or hindcast) purpose. The uncertainty of numerical weather forecasts still remains especially forground surface wind because the wind in the planetary boundary layer(PBL) strongly depends on the orographic reproducibility in the model.
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Declaration of competing interest
The authors declare that they have no known competing ﬁnancial
interests or personal relationships that could have appeared to inﬂuence the work reported in this paper.
Acknowledgements
We acknowledge the committee members of the working group on“Radioactive substances accidentally released from nuclear power related facilities” for their cooperation in drafting the MSJ's proposal andtheir encouragement throughout this study. We are also indebted to Dr.
Tetsuji Yamada for valuable comments on this manuscript. This work
was supported by activity of the Core Research Cluster of Disaster
Science in Tohoku University. This work was supported by theEnvironment Research and Technology Development Fund (5-1501 and1-1802) at the Environmental Restoration and Conservation Agency
and partly by JSPS KAKENHI Grant Number JP17K00533. The model
data used in this paper are available upon request to the corresponding
author.
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11

RESEARCH ARTICLE
10.1029/2018JD028998
Key Points:• Sensitivity to meteorological models
was assessed for the ﬁrst time for the
Fukushima nuclear accident study• Modeled deposition rates were
underestimated, while precipitation
values and source term werereasonable• In‐cloud scavenging was the mostdominant mechanism for radio‐Cs
deposition, followed by dry and fog
depositions

Correspondence to:
M. Kajino,kajino@mri‐jma.go.jp

Citation:
Kajino, M., Sekiyama, T. T., Igarashi,Y., Katata, G., Sawada, M., Adachi, K.,
et al. (2019). Deposition and dispersionof radio‐cesium released due to the
Fukushima nuclear accident:
Sensitivity to meteorological models
and physical modules. Journal ofGeophysical Research: Atmospheres,
124, 1823–1845. https://doi.org/
10.1029/2018JD028998
Received 18 MAY 2018
Accepted 10 JAN 2019
Accepted article online 15 JAN 2019
Published online 2 FEB 2019
Author Contributions:Conceptualization: Mizuo Kajino,
Tsuyoshi Thomas Sekiyama, Yasuhito
Igarashi, Genki Katata
Data curation: Mizuo Kajino, Kouji
Adachi, Yuji Zaizen, Haruo Tsuruta
Formal analysis: Mizuo Kajino
Funding acquisition: Yasuhito
Igarashi, Teruyuki Nakajima
Investigation: Mizuo Kajino
Methodology: Mizuo Kajino, Tsuyoshi
Thomas Sekiyama, Genki Katata
Resources: Mizuo Kajino, Kouji
Adachi, Yuji Zaizen, Haruo Tsuruta
Supervision: Mizuo Kajino, Yasuhito
Igarashi, Genki Katata, Kouji Adachi,
Yuji Zaizen, Haruo Tsuruta, Teruyuki
Nakajima
Validation: Mizuo Kajino
Visualization: Mizuo Kajino, Morihiro
SawadaWriting ‐ original draft: Mizuo
Kajino©2019. American Geophysical Union.
All Rights Reserved.

KAJINO ET AL.

Deposition and Dispersion of Radio‐Cesium Released
Due to the Fukushima Nuclear Accident: Sensitivity
to Meteorological Models and Physical Modules
Mizuo Kajino1,2 , Tsuyoshi Thomas Sekiyama1 , Yasuhito Igarashi1,3,4, Genki Katata5,
Morihiro Sawada6, Kouji Adachi1 , Yuji Zaizen1, Haruo Tsuruta7, and Teruyuki Nakajima8
1

Meteorological Research Institute, Japan Meteorological Agency, Tsukuba, Japan, 2Faculty of Life and Environmental
Sciences, University of Tsukuba, Tsukuba, Japan, 3Institute for Integrated Radiation and Nuclear Science, Kyoto
University, Kumatori, Japan, 4College of Science, Ibaraki University, Mito, Japan, 5Institute for Global Change Adaptation
Science, Ibaraki University, Mito, Japan, 6Graduate School of Life and Environmental Sciences, University of Tsukuba,
Tsukuba, Japan, 7Remote Sensing Technology Center of Japan, Tokyo, Japan, 8Earth Observation Research Center, Japan
Aerospace Exploration Agency, Tsukuba, Japan

Abstract To assess the uncertainty of meteorological simulations in the transport and deposition ofradio‐Cs release associated with the Fukushima Daiichi Nuclear Power Station accident in Japan, amultiple meteorological model and module ensemble analysis with a single chemical transport model(CTM) was conducted. Although several multimodel ensemble studies have previously been performed, thecurrent type (i.e., one CTM with several meteorological ﬁelds) was applied for the ﬁrst time and represents
a useful way to evaluate the uncertainty of each component of CTM. The current analysis concluded thatthe underestimation of the deposition efﬁciency of CTM was the reason for the underestimation ofsimulated radio‐Cs deposition, whereas the simulated dispersion and precipitation and estimated source
term were all reasonable: all of the simulations underestimated the deposition amount, whereas someunderestimated but others overestimated the simulated precipitation and radio‐Cs concentrations. The
CTM simulation performed using the meteorological ensemble mean ﬁeld was successful in reducing
variance, and they gave reasonable results. The simulated deposition using the meteorological ensemble
was better than others because the ensemble mean enlarged the light precipitation areas and because theland contamination was mainly caused by light precipitation. The current ensemble study indicated thatin‐cloud scavenging was the most dominant mechanism of radio‐Cs deposition, followed by dry deposition
and fog deposition over the entire land area. In some deposition regions, fog deposition was dominant,exceeding 80%, depending on the simulations. The simulated concentrations and depositions varied bymore than twofold, depending on the selection of the meteorological ﬁeld.
1. Introduction
The Fukushima Daiichi Nuclear Power Station (FDNPS) accident released ﬁssion products to theenvironment in March 2011 and contaminated land ecosystems through the air. The aircraft‐measured
deposition of 137Cs over the land was approximately 3 PBq (NRA [Nuclear Regulation Authority], 2012;
Torii et al., 2012), excluding the area within 3 km of FDNPS because no measurements were available fromthis area (no‐ﬂy zone). Due to the huge amount of land deposition, together with the longer half lives of137
Cs (30.1 years) and 134Cs (2.07 years), there still remain habitation‐restricted zones in Fukushima
prefecture. In addition, once deposited to the land, radio‐Cs only circulates within local land ecosystems,
and its migration out of the local ecosystem has been found to be not very rapid: The resuspension rate fromthe land to the atmosphere was lower than 0.1% per year during the year 2013 (Kajino et al., 2016), and thedischarge rate from the land to the river was 0.73–3.7% per year from August 2012 to September 2013
(Iwagami et al., 2016). Therefore, a better understanding of the dispersion and deposition mechanisms ofprimary‐emitted radio‐Cs is critically needed.
Thanks to the substantial efforts to reveal the atmospheric behavior and budget of radio‐Cs by using ﬁeld
observations and numerical models, knowledge has been accumulated (Kajino, Sekiyama, et al., 2018;
Mathieu et al., 2018). The major isotopes of radio‐Cs emitted to the air were 134Cs and 137Cs, with almost
equal activity. The total primary deposition (3 PBq) amounted to 20% of the estimated released amount,

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Journal of Geophysical Research: Atmospheres

10.1029/2018JD028998

15 PBq (Katata et al., 2015); thus, most of the 137Cs was transported and deposited outside Japanese territory.
Transport events of 137Cs toward land in Japan occurred several times in March 2011 (Nakajima et al., 2017;
Tsuruta et al., 2014), which were associated with some proportions of immediate resuspension from theground (Igarashi et al., 2015). Wet deposition processes played key roles in ground surface contaminationin Japan (e.g., Morino et al., 2013; Quérel et al., 2015). This occurred because the sizes of the major proportions of Cs‐bearing particles were as small as 1 μm or less in aerodynamic diameter (Doi et al., 2013;
Kaneyasu et al., 2012; Masson et al., 2013); thus, the dry deposition velocity of radio‐Cs was not very large.
Nevertheless, there is a relatively large uncertainty in the source term estimation of 137Cs (10–50 PBq;
Mathieu et al., 2018). The atmospheric behavior of radio‐Cs is much simpler compared to that of radio‐I:
Radio‐Cs exists only in the aerosol phase in the air, whereas radio‐I exists in the aerosol phase, as inorganic
gas, and as organic gas. However, the contributions of deposition processes have varied between models (e.g.,
SCJ [Science Coucil of Japan], 2014). To quantify the uncertainty in the atmospheric budget estimation and to
identify the source of the uncertainty, several multimodel intercomparison studies (Draxler et al., 2015;
Kitayama et al., 2018; Kristiansen et al., 2016; SCJ, 2014) and sensitivity analyses of model parameters (sometimes referred to as intracomparisons; Girard et al., 2014, 2016; Groëll et al., 2014; Leadbetter et al., 2015;
Morino et al., 2013; Quérel et al., 2015) have been conducted. Each method has its pros and cons. The intercomparison of different source terms, meteorological ﬁelds, and transport models (Kitayama et al., 2018; SCJ,
2014) can quantify the overall uncertainty in the system but cannot quantify the uncertainty of each component. In contrast, the intercomparison of different transport models with a common conﬁguration (such as
the common source term, Draxler et al., 2015; Kristiansen et al., 2016) can quantify the uncertainty of eachcomponent but cannot quantify the overall uncertainty. A model intercomparison study can quantify themagnitude of the uncertainty but cannot adequately identify the reason for the uncertainty. On the other
hand, an intracomparison study can identify the reason for an uncertainty but only for a single model case,even though the results could vary substantially depending on models. Consequently, the accumulation ofknowledge using several combinations of model intercomparison and intracomparison studies is important.
Thus far, one kind of combination of model comparison (or sensitivity analysis) studies has been missing: asingle transport model applied to multiple meteorological ﬁelds. Sekiyama et al. (2015) performed an ensemble dispersion simulation using a square root Kalman ﬁlter to assess the uncertainties in meteorologicalsimulations. Their method can quantify the uncertainty in the initial and boundary conditions of meteorological ﬁelds, but it cannot quantify the uncertainty in the simulation of each meteorological process, such as
cloud microphysics, solar and terrestrial radiation, boundary layer turbulence, and surface ﬂuxes. The purpose of the present study is to quantify the differences in the simulated concentration and deposition of 137Cs
due to the selection of meteorological simulations and to identify the reason for the discrepancies between
simulations and observations. Also investigated are the contributions of each process to 137Cs deposition,such as dry deposition, in‐cloud scavenging, below‐cloud scavenging, and fog deposition over different
regions and between different meteorological simulations.

2. Materials and Methods
2.1. Transport Model
The regional‐scale Eulerian transport model NHM‐Chem (Kajino, Deushi, et al., 2018; Kajino et al., 2019)has been used for simulations of the transport and deposition of radionuclides. NHM‐Chem is a chemicaltransport model (CTM) coupled with a meteorological model, that is, the Japan Meteorological Agency's(JMA) Nonhydrostatic Model (NHM; Saito et al., 2007). The CTM part of NHM‐Chem solves for troposphericphotochemical reactions and aerosol dynamics processes, but a simpliﬁed version was built and applied forthis study. A previous version of the CTM part was called the Regional Air Quality Model 2 (RAQM2; Kajino
et al., 2012), and its simpliﬁed version (Adachi et al., 2013) was also applied to previous studies of the atmospheric simulations of the radionuclides released in association with the FDNPS accident (Adachi et al.,2013; Sekiyama et al., 2015, 2017).
Since the model applied to this study was modiﬁed for the transport of radionuclides, it is brieﬂy described
here, with a focus on its differences from the models of Kajino, Deushi, et al. (2018) and Adachi et al. (2013).
This model considers nine tracers, that is, gas‐phase 131I, aerosol‐phase 131I, 132Te, 137Cs, 134Cs, and 133Xe,and the three moments (number, surface area, and volume concentrations) of aerosols, which carry the
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radionuclides released from FDNPS. Thus, all nuclides are assumed to be carried by the same type of aerosols, and the carrier aerosols are assumed to have no interaction with other environmental aerosols or condensable gases. Here note that an aerosol “type” is the same as an aerosol category of Kajino, Deushi, et al.
(2018), which has some size distribution but has a uniform chemical composition, density, and shape.
(Diesel exhaust, brake wearing, sea‐salt, mineral dust, or pollen is an example of a “type,” deﬁned). As
the chemical compositions are not explicitly treated in the current model, a prescribed hygroscopicity wasapplied to the type of carrier aerosols to calculate its hygroscopic growth and cloud condensation nuclei(CCN) activation. A log‐normal size distribution was assumed for the population of carrier aerosols.
Because three log‐normal parameters are used to identify the size distribution, that is, the number, geometric mean diameter, and geometric standard deviation, the three log‐normal parameters can be ﬁxed by
the simulated three moments. The algorithms of advection, turbulent diffusion, dry deposition, fog deposition, gravitational settling, in‐cloud and below‐cloud scavenging processes were the same as those used by
Kajino, Deushi, et al. (2018). Photochemical reactions and thus aerosol microphysical processes, such as
new particle formation, condensation/evaporation, and coagulation, were not considered in the simulation,
but changes in the size distributions during transport due to deposition processes were considered in thesimulation. (Changes in the size distributions during transport were not considered in Adachi et al.,2013.) The ice nuclei activation was not considered in this study, as it is not the major process in terms of
the removal of aerosol mass, and the CCN activation was substantially simpliﬁed. The CCN activation fraction was prescribed as unity, as indicated by Kaneyasu et al. (2012): Sulfate is a carrier of radio‐Cs. The unit
activation fraction was assumed for all cloud types, including fog and stratiform and convective clouds.
Although there existed other types of Cs‐bearing particles, found and named as Cs‐ball by Adachi et al.
(2013), which are totally different in size and hygroscopicity (super micron and nonhygroscopic) than sulfate
particles (submicron and hygroscopic), the Cs‐ball was not considered in the current simulation, following
almost all previous simulation studies, except those of Adachi et al. (2013) and Kajino, Sekiyama, et al.
(2018). The impact of Cs‐ball on dispersion and deposition will be presented and quantitatively discussed
in our next paper. Two options are available for the below‐cloud scavenging processes in NHM‐Chem,
namely, the conventional method and size‐resolved method (Kajino, Deushi, et al., 2018). The latter wasused for the current simulation, as it is time consuming but considers other processes, such as thermophoresis, diffusiophoresis, and electrostatic forces, which the conventional method does not take into account.
The ofﬂine‐coupled NHM‐Chem was used for this study. An ofﬂine model comprises an interface connecting the meteorology model part and the CTM part, which is converting the meteorological model output into
the CTM input. An advantage of ofﬂine coupling is that various meteorological simulation results can beused for the input of CTM, only by preparing the interfaces between them. On the other hand, hard coding
is needed to embed the CTM program into the meteorological model for online coupling.
2.2. Meteorological Simulations and Ensembles
As listed in Table 1, transport simulations using combinations of several meteorological models and simulation settings were compared in this study. Figure 1 shows the purpose of the comparison of meteorologicalsimulations and the method used to produce the ensemble mean of the meteorological ﬁeld (#9). This ﬁgure
also shows the method used to produce the ensemble mean of the chemical ﬁeld (#8).
We used two meteorological models, NHM (ver. 3.5) and the Weather Research and Forecasting (WRF)model (Skamarock et al., 2008; ver. 3.5.1) for the meteorological simulations. Both simulations #1 and #2 inTable 1 used NHM, but for #1, NHM was driven using the JMA's Meso‐Regional Objective Analysis(MANAL) as the initial and boundary conditions, while for #2, the data assimilation system of NHM, using
the local ensemble transform Kalman ﬁlter (LETKF), which was developed by Kunii (2013) and named
NHM‐LETKF, was used. For #1, the spectral nudging method (Nakano et al., 2012) was used to constrain
the model forecast by the MANAL analysis. Spectral nudging was applied above a height of 7 km for thelarge‐scale wave component (wavelength > 1,000 km) of horizontal momentums and potential temperature,with a weighting factor of 0.06. For #2, the simulation of NHM‐LETKF itself is the reanalysis.
NHM was developed for the purpose of operational weather forecasting, and only a few combinations ofphysics modules (i.e., cloud microphysics, radiation, turbulence, and land surface processes) are available.
Although the combination of modules was limited, each module has been extensively developed to achieve
the best performance of weather prediction in Japan. On the other hand, the Advanced Research WRF was
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Table 1
Meteorological Models and Physical Modules Compared in the Study

1
2
3
4
5
6
7
9
a

Name

Analysis

Data assimilation

NHM
NHM‐LETKFg
WRF ‐WSM6‐MYJ
WRF‐WDM6‐MYJ
WRF‐MORR‐MYJ
WRF‐MORR‐MYNN25
WRF‐MORR‐MYNN30m
Met_EnsMean

MANAL
e
—
MANAL
MANAL
MANAL
MANAL
MANAL
‐

b

Spectral nudging
f
LETKF
Grid nudging
Grid nudging
Grid nudging
Grid nudging
Grid nudging‐acPlanetaryboundary layer

Cloud microphysicsaSix‐category double momentaSix‐category double moment
h
WSM6
j
WDM6
k
MORR
MORR
MORR
‐

d

MYNN30
MYNN30
i
MYJ
MYJ
MYJ
l
MYNN25
MYNN30‐b

NHM: JMA's Nonhydrostatic Model (Saito et al., 2007; JMA [Japanese Meteorological Agency], 2008). MANAL:cd
JMA Meso‐Regional Objective Analysis. Nakano et al. (2012). MYNN30: Mellor‐Yamada‐Nakanishi‐Niino levelef
3.0 scheme (Nakanishi & Niino, 2006). This is a meteorological analysis. LETKF: Local ensemble transformg
Kalman ﬁlter (Kunii, 2013; Sekiyama et al., 2017). WRF: Weather Research and Forecast model (Skamarock et al.,hi2008). WSM6: six‐category single moment cloud microphysics scheme (Lin et al., 1983). MYJ: Mellor‐Yamada‐jJanjic scheme (Janjic, 2002). WDM6: six‐category double moment cloud microphysics scheme (Lim & Hong,kl2010). MORR: Morrison's double moment cloud microphysics scheme (Morrison et al., 2009). MYNN25: Mello‐m
Met_EnsMean: Meteorological model
Yamada‐Nakanishi‐Niino level 2.5 scheme (Nakanishi & Niino, 2006).
ensemble mean ([1] + [2] + ([3] + [4] + ([5] + [6] + [7])/3)/3)/3.

developed for research purposes; thus, a number of physics modules have been implemented and canbe compared.
From #3 to #7, in order to evaluate the sensitivity of the physics modules, we conducted multimodule simulations of WRF using three different cloud microphysics modules (Lin et al., 1983; Lim & Hong, 2010;
Morrison et al., 2009, denoted as WSM6, WDM6, and MORR, respectively) and three different planetary
boundary layer (PBL) physics modules (Janjic, 2002, and level 2.5 and level 3 of Nakanishi & Niino, 2006,
denoted as MYJ, MYNN25, and MYNN30, respectively). Instead of comparing all nine combinations, ﬁve
were selected for comparison, using MORR‐MYJ as a control run: the combination of WSM6‐MYJ,
WDM6‐MYJ, and MORR‐MYJ was selected for the comparison of cloud microphysics modules, and the
combination of MORR‐MYJ, MORR‐MYNN25, and MORR‐MYNN30 was selected for the comparison of

Figure 1. Meteorological models, meteorological model ensembles, and chemical transport simulations together with its
ensemble, used and compared in the study. NHM = Nonhydrostatic Model; LETKF = local ensemble transform Kalmanﬁlter; WRF = Weather Research and Forecasting; CTM = chemical transport model; PBL = planetary boundary layer.

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Figure 2. (a) Model domain and topographic height. (b) Deposition area deﬁned in the study.

PBL modules, as shown in Figure 1a. MANAL was also used as the initial and boundary conditions, as well
as for the grid nudging. Grid nudging was applied for horizontal wind speed, temperature, and water vapor,with a nudging factor of 0.0003, only above the PBL.
Instead of equally averaging the seven meteorological simulations, the following averaging was performed to
produce the meteorological ensemble mean (#9, Met_EnsMean): Met_EnsMean is an average of NHM,
NHM‐LETKF, and WRF_EnsMean; WRF_EnsMean is an average of WRF‐WSM6‐MYJ, WRF‐WDM6‐
MYJ, and WRF‐MORR_EnsMean; and WRF‐MORR_EnsMean is an average of WRF‐MORR‐MYJ,
WRF_MORR‐MYNN25, and WRF_MORR‐MYNN30. The ensemble mean of the seven transport simulations, #8 CTM_EnsMean, was obtained in the same manner, as is shown in Figure 1b. The unweighted average is signiﬁcantly biased toward the meteorological ﬁelds produced by the WRF simulations because all of
the WRF simulations were predicted by a common dynamic core and constrained to MANAL. Even though
the NHM simulation was also constrained to MANAL, the simulations of NHM were signiﬁcantly different
from those performed by WRFs, because the dynamic cores of the models and their methods of constraint
were different (i.e., grid nudging vs. spectral nudging). Although the dynamic cores of NHM and NHM‐LETKF were similar, the two simulations were signiﬁcantly different due to the differences in their initialand boundary conditions. This feature is obviously found in the surface concentrations, as shown later inFigure 4 (see the differences in mean bias [MB] and the shapes of scatter diagrams between NHM, NHM‐LETKF and WRFs).
Overall, we used eight meteorological model variations and nine chemical model variations for the current
comparison study.
2.3. Simulation Settings
As shown in Figure 2a, the simulation domain and grid resolution are the same as those used in our previous
studies (Adachi et al., 2013; Sekiyama et al., 2015, 2017). There are 213 × 257 horizontal grid cells with a 3‐km grid resolution on the Lambert conformal coordinate. There are 48 vertical layers reaching up to approximately 22 km above sea level (ASL) for NHM and 27 layers reaching up to 100 hPa for WRF, which are
reduced to 19 layers reaching up to 10 km ASL for the CTM on the terrain‐following coordinate with vertically stretched grids (with more grids at lower levels to resolve the dynamics within the PBL). The latest version of the Japan Atomic Energy Agency's (JAEA) emission scenario, as established by Katata et al. (2015),
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was selected for all simulations. The meteorological simulation was conducted from 00 UTC on March 11 to
00 UTC on 1 April, with a spin‐up period of 20 hr: Katata et al.'s (2015) emission was initiated at 20 UTC on
11 March.
In the current simulation, as mentioned earlier, all of the radio‐Cs nuclides were carried by sulfate aerosols,
as inferred by Kaneyasu et al. (2012) and assumed by all simulations except for those of Adachi et al. (2013)
and Kajino, Sekiyama, et al. (2018). In terms of the physical parameters of Cs‐carrying sulfate during theemission (although sulfate is clearly “not” directly emitted from the reactor, it is assumed that the emitted
Cs was mixed with environmental sulfate aerosols immediately after its emission from the reactor), we used
a number‐equivalent geometric mean dry diameter of 102 nm, a geometric standard deviation of 1.6, a particle density of 1.83 g/cm3, and a hygroscopicity κ of 0.4 (see Adachi et al., 2013).
2.4. Observation Data
The JMA Radar/rain gauge‐Analyzed Precipitation (RAP) data were used for the validation of the simulated
precipitation amount. Since the RAP data provide 1‐hr precipitation data with 30‐min resolutions startingfrom 0 and 30 min, only data starting from 0 min were used in the study. The aircraft measurement dataof NRA (2012) were interpolated to the model domain and used for the validation of the simulated deposition amount of 137Cs. The simulated surface concentrations of 137Cs were validated by the hourly observation data of 99 stations obtained by Tsuruta et al. (2014) and Oura et al. (2015).
2.5. Deposition Areas
The deposition areas used for the process analysis of this study are shown in Figure 2b. The deposition areas
were selected where the grid mean (3‐km) deposition of the aircraft measurement (NRA, 2012) exceeded
10 kBq/m2, as shown later in Figure 5. These areas were divided into nine subareas based on the geography(mainly mountains), prefectures, and possible deposition processes (or events), which are also summarized
in Morino et al. (2013) and Mathieu et al. (2018). The observed total deposition amount of 137Cs over theentire land area of Japan (denoted as Area 0) was 2.59 PBq. Hamadori (Area 1) is a coastal area of
Fukushima prefecture where FDNPS is located and bordered by the Abukuma Highlands, which are moderately high mountains (~1,000 m). The largest amount of deposition, 1.4 PBq, occurred in this area, whichamounted to 53.4% of the total land deposition (as shown later in Table 3). Nakadori (Area 2), which islocated next to Hamadori, is a valley between the Abukuma Highlands and the Ou Mountains, a high mountain chain (1,000~2,000 m). The second‐largest amount of deposition occurred in this area, 0.44 PBq, which
is 17.1% of the total land deposition. The major amounts of deposition in Hamadori and Nakadori werecaused by light precipitation, which was lower than the detection limit of rain‐gauge measurement(0.5 mm) or dry deposition (Mathieu et al., 2018). A total of 70% of deposition occurred in these two areas.
Aizu (Area 3), which is located next to Nakadori, is located west of the dividing Ou Mountains; thus, it
was less contaminated compared to the rest of Fukushima prefecture (0.076 PBq, 2.9%). South Miyagi(Area 4; 0.047 PBq, 1.8%) and Iwate‐Miyagi (Area 5; 0.038 PBq, 1.4%) are located in the northern region of
Fukushima prefecture. The Iwate‐Miyagi area is isolated; thus, its contamination is presumed to haveoccurred due to air‐aloft processes, that is, in‐cloud scavenging. In Tochigi (Area 6) and Gumma (Area 7)
prefectures, contamination occurred mainly over mountainous regions, and the importance of fog deposition (or cloud deposition) was inferred by Hososhima and Kaneyasu (2015), Katata et al. (2015), Sanada
et al. (2018), and this study. The deposition of 137Cs over both areas totaled 0.21 PBq, which represented
7.9% of the total land deposition. (Area 8) Iwaki‐Ibaraki and (Area 9) Ibaraki‐Chiba are located in the southern region of Fukushima prefecture; their 137Cs depositions were 0.11 PBq (4.3%) and 0.044 (1.7%), respectively. The deposition amount for the remaining land areas, that is, areas with <10 kBq/m2, or Area 0 minus
the sum of Areas 1 to 9, was 0.24 PBq (9.4%).

3. Results
3.1. Comparison of Precipitation Fields
Throughout the paper, we only focus on 137Cs because the atmospheric behavior and radioactivity of 134Cs
should be almost the same as those of 137Cs for the current analysis. Certainly, the slight difference in theactivities of 134Cs and 137Cs was essentially important for the analysis of their origins, as conducted bySatou et al. (2018). Thus, the simulation results of all radio‐Cs can be obtained by doubling the results of 137Cs.
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Figure 3. (upper panel) Horizontal distributions of observed and simulated cumulative precipitation from 11 to 31 March. (middle and lower panel) Scattergrams
between observation and simulations for the whole period (middle) and for the afternoon (12–24 LT) of 15 March, when the most serious contamination occurred
over the land of Japan. The correlation coefﬁcient R, observed areal average, and mean bias (MB) values are embedded in the panels. NHM = Nonhydrostatic
Model; LETKF = local ensemble transform Kalman ﬁlter; WRF = Weather Research and Forecasting.

Figure 3 shows the observed and simulated horizontal distributions of the cumulative precipitation from 11
to 31 March over the model domain in the upper panels. The middle and lower panels show the scattergram
between the observed and simulated precipitation for the whole period and that during the afternoon of 15
March, respectively. The RAP data below the detection limit (0.5 mm/hr) were not added to the cumulative
amount. The horizontal average of the RAP data (with a resolution of approximately 1 km) was made tomatch the model grids (Δx = 3 km) in order to directly compare the simulated and observed values.
Due to the horizontal averaging, there are observed values below 0.5 mm in the scattergrams. Note that
10 grids from the lateral boundaries of the model domain were excluded from the comparison scattergrams. The observed areal average (obs. ave. = 55.0 mm), correlation coefﬁcient R, and mean bias (MB)are embedded in the panels. Note that R is the linear‐linear correlation despite the log‐log plot of thescattergrams. Additionally, the RAP has a larger uncertainty for light precipitation due to the detection
limits of the small droplet size (such as drizzle) for the C‐band radar and the precipitation amount of
the rain gauge (0.5 mm).
In terms of the cumulative precipitation from 11 to 31 March, WRF‐WDM6 predicted the largest precipitation (MB = 12.3 mm), especially over the mountainous regions located at the center of the Japan
Archipelago, where the cumulative precipitation exceeded 300 mm. WRF‐WSM6 also overestimated precipitation, but to a lower degree (MB = 3.4). Despite the common dynamic core used, WRF‐MORR did notshow any overestimation over the mountainous regions and underestimated the values for the whole region(MB = −7.6). These data indicate that the selection of cloud microphysics modules is essentially important
for the prediction of precipitation. They also indicate that the precipitation was well predicted by the WRF
ensembles because the observed values fell between the minimum and maximum estimates, that is, theobserved values were within the uncertainty of the cloud microphysics modules. Both NHM models
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Figure 4. Horizontal distributions of observed and simulated cumulative surface concentrations from 13 to 23 March and scattergrams between observations andsimulations. The correlation coefﬁcient R, observed areal average, and mean bias (MB) values are embedded in the panels. The observation data in each model gridwere averaged and used for the comparison. NHM = Nonhydrostatic Model; LETKF = local ensemble transform Kalman ﬁlter; WRF = Weather Research andForecasting; CTM = chemical transport model.

underestimated the observations, but the underestimation of NHM‐LETKF (MB = −18.2) was greater thanthat of NHM (MB = −5.2) due to the different treatments of their lateral boundary conditions. The lateralboundary of NHM contained hydrometeors, while that of NHM‐LETKF did not (it only contains water
vapor). Although 10 grids from the lateral boundaries were excluded in this comparison, the lateral
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Figure 5. Horizontal distributions of observed and simulated cumulative deposition ofCs from 11 to 31 March and scattergrams between observations andsimulations. The gross deposition amounts, correlation coefﬁcient R, observed areal average, and mean bias (MB) values are embedded in the panels.
NHM = Nonhydrostatic Model; LETKF = local ensemble transform Kalman ﬁlter; WRF = Weather Research and Forecasting; CTM = chemical transport model.

boundary conditions affected this analysis. Nevertheless, the artifact of the lateral boundary conditions isminor because the major deposition areas (e.g., Figure 2b) are well centered in the model domain. It isalso noted here that the radioactive plumes were blocked by the high mountain chains; thus, theoverestimation of precipitation over mountainous areas predicted by WRF‐WSM6 and WRF‐WDM6

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should not deteriorate the model performances in the prediction of the wet deposition of 137Cs. The correlation coefﬁcients were not very different between the models, as they ranged from 0.43 (NHM) to 0.54 (WRF‐
WDM6). Although they are not shown here, the differences in the PBL turbulence schemes (WRF‐MORR‐
MYJ, WRF‐MYNN25, and WRF‐MYNN30) were not signiﬁcant for the predictions of precipitation (R: 0.48–
0.48, MB: −9.8 to −7.6). Again, the precipitation was well predicted by the current meteorological model
ensembles because the observed values fell between the minimum (NHM‐LETKF) and maximum
estimates (WRF‐WDM6).
The simulated precipitation of Met_EnsMean was successful in reducing variance, yielding a higher R (0.50)
value than the lowest one of NHM (0.43) and a lower absolute value of MB (7.0) than those of NHM‐LETKF(18.2) and WRF‐WDM6 (12.3).
In terms of the cumulative precipitation on the afternoon of 15 March, a similar conclusion was inferred asin the case of the entire simulation period: overestimation by WRF‐WDM6 (MB = 1.97) and WSM6
(MB = 1.94) and underestimation by NHM‐LETKF (−0.92). The precipitation was well predicted by the current meteorological model ensembles because the observed values fell within the minimum (NHM‐LETKF)
and maximum estimates (WRF‐WDM6). Here NHM, the operational forecast model of the JMA, performed
best in this case (yielding the highest R, lowest absolute value of MB, and narrowest aggregation of plots).
The Met_EnsMean was successful, yielding a high R (0.80) close to the highest values (0.82 of NHM and
WRF‐WSM6) and showing the lowest absolute value of MB (0.40).
3.2. Comparison of 137Cs Surface Concentration Fields
Figure 4 shows the observed and simulated horizontal distributions of the cumulative concentration(Bq·m−3·hr) from 13 to 23 March. The observation data were averaged to the model 3‐km grids and thencompared with the simulated data. Still, however, the values in this comparison are biased toward thedata of areas, where the observation sites are densely situated, that is, Tokyo and the surrounding areas.
The observed areal average (obs. ave. = 379 Bq·m−3·hr), correlation coefﬁcient R, and mean bias (MB)values are embedded in the panels. The WRF simulations overestimated the observed values(MB = 158–178 Bq·m−3·hr), the NHM simulations underestimated them (MB = −140.7), and the
NHM‐LETKF yielded the best MB value (13.4). Same as in the precipitation prediction, the surface activity concentration was also well predicted by the current meteorological model ensembles because theobserved values fell within the minimum (NHM) and maximum (WRF‐WDM6) estimates.
Because all the models used a common horizontal diffusion scheme, the differences in model performance
were mainly due to vertical diffusion and horizontal advection. Since both the NHM and NHM‐LETKF also
employ the same Mellor‐Yamada‐type PBL scheme (MYNN30) as the WRF simulations, the differences inthe simulated surface air concentrations due to differences in vertical diffusivities were not signiﬁcant.
Within the PBL, all of the simulations were not assimilated to the meteorological analysis data, except for
NHM‐LETKF: the surface wind data of the JMA's Automated Meteorological Data Acquisition System atmore than 200 stations in the model domain were assimilated to the simulation (Sekiyama et al., 2017).
This partly explains why NHM‐LETKF‐Chem performed best in the quantitative simulation of the surface
activity concentrations of 137Cs. The correlation coefﬁcients obtained in all simulations were very similar
to each other (R = 0.70–0.81). The CTM_EnsMean was successful in reducing variance, yielding a higher
R (0.74) value than the lowest one of WRF‐MORR (0.70) and showing the lowest absolute value of MB(3.2) compared to any other simulations.
The CTM simulation performed by Met_EnsMean (denoted as Met_EnsMean) was shown to perform well inthe simulation of concentrations, showing the highest R (0.81) value and lower absolute value of MB (66.7)
compared to the largest one of WRF‐WDM6 (178.4). It is not surprising that the performance of
CTM_EnsMean was good, but it is not necessary that the performance of Met_EnsMean‐Chem was good.
This is one of the major implications of the current study.
The simulated concentration of WRF‐WDM6 was the largest, although the simulated precipitation of thismodel was also the largest. This appears to be contradictory, as precipitation removes aerosols. However, thisresult is still not surprising because below‐cloud scavenging was not a controlling factor of the prediction of
surface concentration, as is discussed extensively in sections 4.1 and 4.2.
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Figure 6. (a) The correlation coefﬁcient R and normalized mean bias (NB) values of the cumulative
Cs deposition
between the observed and simulated values by the nine models over the entire land area of Japan (Area 0). (b, c) Sameas (a) but for (b) R and (c) NB over the nine deposition areas as deﬁned in Figure 1.

3.3. Comparison of 137Cs Deposition Fields Over Deposition Regions
Figure 5 shows the observed and simulated horizontal distributions of the cumulative deposition (kBq/m2).
The total land deposition (PBq), observational areal average (obs. ave. = 88.2 kBq/m2), correlation coefﬁcient R, and mean bias (MB) values are embedded in the panels. Note that R represents the linear‐linear correlation between the data above the observational detection limit (10 kBq/m2). The simulated Rs of the 137Cs
depositions (R = 0.46–0.65) were not as large as those obtained in previous studies (R > 0.7–0.8; e.g., Draxler
et al., 2015; Morino et al., 2013), but they were similar to those of simulated precipitation for the whole period (R = 0.43–0.54) and signiﬁcantly lower than those of the cumulative concentrations (R = 0.70–0.81). The
R and normalized bias values (NB; MB divided by the observational mean) over the entire land area (Area 0)
and the nine depositional areas of the nine simulations are shown in Figure 6. All of the values of R and NB
are listed in Tables 2 and 3, respectively.
In contrast to the precipitation and surface concentration simulations, all of the simulations underestimatedthe observations. The reason for this is discussed later in section 4.2.1. As shown in Figure 5, NHM signiﬁcantly underestimated the 137Cs deposition (MB = −70.5), whereas the underestimations of the other simulations were similar to each other: The largest deposition was simulated by NHM‐LETKF (MB = −52.9), andthe smallest deposition after NHM was simulated by WRF‐WDM6 (−60.4). In terms of R, the WRF simulations showed better agreements with the observed 137Cs deposition (R = 0.61–0.65), as they could successfully reproduce the general features of the deposition map: The most contaminated regions extended
toward the northwest direction from the FDNPS (Area 1, 53.4% of total land deposition), the second‐mostcontaminated regions were in the Nakadori valley (Area 2, 17.1%), and the third‐most contaminated regions
occurred over the mountain regions of Tochigi and Gunma prefectures (Area 6 + Area 7, 7.9%), the regions
south of FDNPS and the Kanto plain (Area 8 + Area 9, 6.0%; see Figure 5). In fact, because R was obtained
from the linear‐linear relationship, the R values over the entire land region were determined by the magnitudes of R over Areas 1 and 2 (Figures 6a and 6b). The R of NHM was lowest (0.46) because it did not reproduce the northwest direction of the highest contamination over Area 1 and the deposition amounts over
Nakadori (Area 2) and Tochigi and Gunma (Area 6 + Area 7) were signiﬁcantly underestimated.
Although NHM‐LETKF the showed best quantity over Area 1, it signiﬁcantly underestimated the
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Table 2
Correlation Coefﬁcients (R) Between Simulated and Observed Cumulative Depositions of Radio‐Cesium for Each Deposition Area and Combination of Numerical
Models, as Shown in Figures 6a and 6b
Model/deposition
area

1
NHM

2 NHM‐LETKF

3 WRF‐
WSM6‐MYJ

4 WRF‐
WDM6‐MYJ

5 WRF‐
MORR‐MYJ

6 WRF‐MORR‐
MYNN25

7 WRF‐MORR‐
MYNN308 CTM_
EnsMean

9 Met_
EnsMeanAverageaof R

0 = (entire land)
1 (Hamadori)
2 (Nakadori)
3 (Aizu)
4 (South Miyagi)
5 (Iwate‐Miyagi)
6 (Tochigi)
7 (Gunma)
8 (Iwaki‐Ibaraki)
9 (Ibaraki‐Chiba)b
Average of R

0.46
0.38
0.19
0.21
0.14
−0.09
−0.11
0.07
0.17
−0.18
0.12

0.52
0.45
0.37
0.24
−0.07
−0.06
0.00
0.26
−0.04
−0.39
0.13

0.63
0.57
0.58
0.24
0.16
0.01
−0.13
0.03
0.02
−0.23
0.19

0.65
0.60
0.64
0.24
0.41
−0.02
−0.04
0.03
0.10
−0.18
0.24

0.61
0.56
0.60
0.21
0.06
0.02
−0.14
0.09
0.05
−0.37
0.17

0.58
0.55
0.38
0.28
0.15
−0.07
−0.15
0.17
−0.07
−0.36
0.15

0.53
0.48
0.46
0.28
0.15
−0.05
−0.15
0.20
−0.06
−0.40
0.15

0.58
0.50
0.53
0.25
0.16
−0.08
−0.08
0.14
−0.03
−0.34
0.16

0.63
0.57
0.59
0.27
0.28
−0.17
−0.06
0.36
0.17
−0.10
0.25

0.58
0.52
0.48
0.25
0.16
−0.06
−0.10
0.15
0.03
−0.28

a

Statistically nonsensical but useful measure to compare the mean predictability over different deposition areas.
to compare the mean predictability of different models.

b

Statistically nonsensical but useful measuredeposition amounts over Nakadori (Area 2; Figure 6c). In Tochigi and Gunma prefectures, simulated
depositions were found over the plain areas rather than in the mountain areas, in contrast to theaircraft observation.
The CTM_EnsMean was successful: Its R (0.58) was higher than the lowest one (0.46) predicted by NHM,
and its absolute value of MB (60.2) was lower than the highest one (70.5) predicted by NHM. Because theperformance of NHM was the worst in both aspects (R and MB), the performances of single WRF simulations
were better than or as good as those of the CTM_EnsMean. NHM performed very well in terms of precipitation and NHM‐LETKF performed best in the simulations of concentrations, whereas WRF simulations were
better in terms of the simulations of 137Cs deposition. This appears to be contradictory; the reason for thisdisparity is brieﬂy discussed later in section 4.2.2.
It appears surprising that the Met_EnsMean‐Chem performed signiﬁcantly well in the simulation of 137Cs
deposition: Its R (0.63) was as large as the largest R (0.65) predicted by WRF‐WDM6, and its MB (−47.4)
was the best of all the simulations (−70.5 to −52.9). This is due simply to the enhancement of precipitation
areas by the ensemble mean. Because precipitation areas are spatially sporadic and not continuous, using theensemble mean of different precipitation ﬁelds should enlarge the precipitation area and decrease the spatial
peaks. Contamination was caused by light precipitation after the FDNPS accident; thus, the enhanced light
precipitation areas resulted in the enhancement of the simulated total 137Cs deposition.

4. Discussion
4.1. Dominant Deposition Processes
Figure 7 shows (upper panel) the observed and simulated deposition amounts and (lower panel) contributions of deposition processes simulated by the nine models of the entire land area (Area 0) and the ninedeposition areas. All of the models generally underestimated the observed deposition amounts, except inGunma (Area 7), where the observed value fell within the range of uncertainty in the simulations. The
underestimation was not very signiﬁcant in the southern direction of FDNPS, that is, Area 8 and Area 9.
The simulated values of NHM‐LETKF and WRF‐WDM6 were sufﬁciently close to the observed values for
Area 8 and Area 9, respectively (also see Table 3). The underestimation of the simulation over the entire land
was approximately 1.5 PBq, except in NHM, where it was approximately 2.0 PBq. Except for NHM, out of the
1.5 PBq of underestimation, 1 PBq was found in Hamadori (Area 1), and the other 0.5 PBq originated from
Nakadori (Area 2).
The current ensemble study indicated that in‐cloud scavenging (both solid and liquid precipitations) was themost dominant mechanism of the 137Cs deposition; the next most dominant mechanisms were dry deposition and fog deposition, depending on the simulations and deposition regions. From southern to northern
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KAJINO ET AL.
−0.60
−0.53
−0.86
−0.77
−0.82
−0.78
−0.68
−0.55
−0.07
−0.39
−0.60
0.28 (25.0)

1 NHM

−0.80
−0.72
−0.97
−0.89
−0.81
−0.96
−0.95
−0.87
−0.78
−0.18
−0.79
0.11 (23.1)

−0.64
−0.65
−0.92
−0.55
−0.68
−0.94
−0.78
0.42
−0.50
−0.23
−0.54
0.36 (31.3)

3 WRF‐
WSM6‐MYJ
−0.68
−0.67
−0.91
−0.57
−0.65
−0.95
−0.85
−0.20
−0.60
−0.18
−0.62
0.32 (32.1)

4 WRF‐
WDM6‐
MYJ
−0.64
−0.69
−0.91
−0.56
−0.56
−0.94
−0.76
0.75
−0.45
−0.37
−0.50
0.35 (30.9)

5 WRF‐
MORR‐
MYJ

b

−0.61
−0.62
−0.93
−0.60
−0.52
−0.93
−0.82
0.55
−0.25
−0.36
−0.50
0.28 (25.8)

6 WRF‐MORR‐
MYNN25

Statistically nonsensical but useful measure to compare the mean predictability over different deposition areas.
ity of different models.

a0 (entire land)
1 (Hamadori)
2 (Nakadori)
3 (Aizu)
4 (South Miyagi)
5 (Iwate‐Miyagi)
6 (Tochigi)
7 (Gunma)
8 (Iwaki‐Ibaraki)
9 (Ibaraki‐Chiba)b
Average of NB
Land except areas 1–9(PBq) and contribution
to the simulated entire
land deposition (%)

Model/deposition area

2 NHM‐LETKF

Table 3Same as Table 2 but for Normalized Bias (NB), as Shown in Figures 6b and 6b

−0.68
−0.64
−0.92
−0.74
−0.75
−0.89
−0.81
−0.38
−0.44
−0.34
−0.65
0.24 (26.8)

8 CTM_
EnsMean

−0.54
−0.47
−0.87
−0.67
−0.54
−0.69
−0.61
−0.28
−0.21
−0.10
−0.49
0.31 (25.8)

9 Met_
EnsMean

−0.65
−0.63
−0.91
−0.66
−0.65
−0.89
−0.78
−0.01
−0.40
−0.28

Averageaof NB

0.24 (9.4)

2.6
1.4 (53.4)
0.44 (17.1)
0.076 (2.9)
0.047 (1.8)
0.038 (1.4)
0.088 (3.4)
0.12 (4.5)
0.11 (4.3)
0.044 (1.7)

Statistically nonsensical but useful measure to compare the mean predictabil-

−0.62
−0.64
−0.93
−0.62
−0.52
−0.93
−0.79
0.52
−0.29
−0.40
−0.51
0.27 (25.9)

7 WRF‐MORR‐
MYNN30137

ObservedCs
deposition (PBq)
and contribution
to the whole area(%)

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137

Figure 7. (upper panels) The observed (dashed line) and nine‐model simulated (circles) gross deposition of Cs (PBq) for the entire land area of Japan (denoted as
Area 0) and the nine deposition areas as deﬁned in Figure 1. (lower panels) The simulated contributions of each deposition process to the total deposition: (red)dry deposition, (green) fog deposition, (sky blue/solid) in‐cloud scavenging by liquid precipitation, (sky blue/hatched) in‐cloud scavenging by solid precipitation,(navy/solid) below‐cloud scavenging by liquid precipitation, and (navy/hatched) below‐cloud scavenging by solid precipitation. NHM = Nonhydrostatic
Model; LETKF = local ensemble transform Kalman ﬁlter.

and lower‐ to higher‐altitude areas, the snow (or solid precipitation) contribution increased (from Areas 1 to
4). As indicated by previous studies, fog deposition was the major process in Tochigi (Area 6) and Gunma(Area 7) prefectures. Because the major part of the Japan Archipelago was covered by mountainous forests,
the contribution of fog (or cloud reaching to ground surface of mountains) was ubiquitous, except in Area 9,
which is located over the Kanto plain. Different models gave different answers for Area 5: Some said they
were dry, while others said they were wet. Additionally, some said they were liquid, while others said they
were solid. This deposition area was isolated and located further north of the FDNPS. By assuming isentropictransport, the radioactive plumes that caused deposition over Area 5 could have been aloft; thus, in‐cloudscavenging could have been a major process. Among the deposition processes, in‐cloud scavenging can be
the most difﬁcult to simulate because a number of elemental processes are involved, and each process has
its own uncertainty. The ofﬂine coupling itself has an inherent limitation in the in‐cloud scavenging modeling, as discussed in Kajino, Deushi, et al. (2018); thus, the deposition modeling over Area 5 was also difﬁcult
to simulate using the current ofﬂine coupling framework.

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Figure 8. Time series of observed (open circles) and simulated (lines) LWC at Nikko and Karuizawa. The observed liquid water content (LWC) was estimated from137
3
Cs concentrations by Met_EnsMean above 1 and 0.1 Bq/m ,the observed visibility. The black and gray dots near the top of each panel indicate the simulated
respectively.

The current simulations indicated the importance of fog deposition, which was not considered in previous
modeling studies, except for that of Katata et al. (2015). However, there are signiﬁcant differences in the contributions of fog between the models. Generally, NHM produced less fog deposition. The fog contribution of
NHM over Areas 0 and 1 and that of NHM‐LETKF over Area 2 were negligibly small. The WRF simulations
tended to produce more fog deposition, except for WRF‐WDM6, which produced as little fog deposition as
NHM and NHM‐LETKF. Differences in the selection of the PBL turbulence schemes (MORR‐MYJ and
MORR‐MYNNs) caused signiﬁcant differences in the fog contribution, especially over Area 1. Figure 8
shows the comparisons of the time series of fog water content at the two mountainous sites at Nikko(Tochigi, Area 6) and Karuizawa in Nagao (next to Gunma, Area 7). Both of these sites belong to the
JMA's Automated Meteorological Data Acquisition System, where visibility data were available. The
observed visibility data were converted to fog water content by using the formula of Stoelinga and Warner(1999) and compared with the simulations. The black and gray dots at the tops of the panels indicate thatthe 137Cs concentrations simulated by Met_EnsMean exceeded 1 and 0.1 Bq/m3, respectively. From 15
and 16 March and 20–23 March, the fog occurrence coincided with the transport of the radioactive plumes,
which were well predicted by the simulations. However, the amount of fog water content varied signiﬁcantly
between the simulations. Although NHM and NHM‐LETKF could not predict the fog deposition over Areas
6 and 7 (Figure 5), they actually produced fog but did not coincide with the transport of radioactive plumes(Figures 8a and 8b). On the other hand, the lower fog contribution of WRF‐WDM6 (Figure 7) was probably
due to the signiﬁcant underestimation of the fog water content (Figures 8a–8d). Since the difference between
the MORR‐MYNN25 and MORR‐MYNN30 was small, only MORR‐MYNN30 is shown in Figures 8c and 8d.
These data show that the selection of the PBL schemes sometimes caused signiﬁcant differences in the prediction of fog water content.
There is an argument for the importance of the threshold of the fog water content. In the deposition moduleof Katata et al. (2015) used in this study, as follows:V df ¼ RLUC Ac U 10 ;(1)

where Vdf is the fog deposition velocity and RLUC is the ratio of Vdf for each land use category (LUC) to thatfor coniferous forest and set 1, 0.2, and 0.1 for forest, short vegetation, and smooth surfaces, respectively. Ac
is the function of leaf area index (LAI) or LAI and the canopy height h (see Equation A12 of Katata et al.,2015) and U10 is the 10‐m wind speed. Because h data were not available for the whole model domain, weused the function of LAI as follows:

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Figure 9. (a) Rank histograms of simulations with the seven meteorological models (NHM, NHM‐LETKF, and the ﬁve WRFs) for (black) cumulative precipitation
137
Cs concentration from 13 to 23 March, and (gray)for the afternoon (12–24 LT) of 15 March (data used only observed values above 0.1 mm), (white) cumulative137
Cs deposition from 11 to 31 March. (b) Same as (a) but for simulations with the three meteorological models (NHM, NHM‐LETKF, andcumulative
WRF_EnsMean).

Ac ¼ 0:0095 LAI3 −0:05 LAI2 þ 0:0916 LAI þ 0:0082(2)

Tav (2017) and Tav et al. (2018) indicated that fog deposition can only be detected with fog water mixing
ratios of larger than 10−4 kg/kg under calm wind conditions. Nevertheless, it is not accurate to set the threshold value as 10−4 kg/kg for the simulation because no fog deposition will occur when the simulated fog mixing ratio is slightly lower than the threshold, such as 0.99 × 10−4 kg/kg. Therefore, the threshold was set as
10−5 kg/kg in the current study, thus reﬂecting the possibility of the underestimation of simulated fog by afactor of 10. In addition, in the presence of stronger surface wind, fog deposition could be enhanced, even in
the case of thinner fog. To evaluate the sensitivity of the data to the threshold values, we conducted the simulation by Met_EnsMean with the threshold of 10−4 kg/kg to provide a minimum estimate of the fog deposition. The result is shown in Appendix A. Also, the fog deposition velocity should be proportional to fog
mixing ratio, because the size (and the inertia) of fog droplets is generally larger as the mixing ratio is higher.
This effect was not considered in the simulation.
4.2. Implications From the Multimeteorological Model Ensembles
4.2.1. Underestimation of Deposition Efﬁciencies
The simulated precipitations and cumulative concentrations of 137Cs obtained by the meteorological model
ensembles either overestimated or underestimated the observed values. Their simulated spatial distributions
agreed well with the observations. These indicated that the simulations of wind ﬁelds and precipitations andthe source term used in this study (Katata et al., 2015) were probably realistic. On the other hand, all of thesimulated depositions of 137Cs were underestimated. This feature is readily observed in Figure 9, whichshows the rank histograms of (black) the cumulative precipitation for the afternoon of 15 March, whenthe most serious contamination occurred over the land of Japan (corresponding to the lower panel of
Figure 3), (white) the cumulative concentration of 137Cs (corresponding to Figure 4), and (gray) the cumulative deposition of 137Cs (corresponding to Figure 5). The relative frequencies of the precipitation and concentration data in Figure 9 are enough close to each other, whereas the ensemble showed signiﬁcantunderestimation for the deposition. Because the behaviors of the ﬁve WRF simulations were relatively similar, compared to those of NHM and NHM‐LETKF in Figure 9a, data in Figure 9a were biased toward the ﬁve
WRF simulations. As the result, the relative frequencies of the precipitation and concentration data in
Figure 9b (an ensemble of NHM, NHM‐LETKF, and WRF_EnsMean) were more close to each other thanthose of Figure 9a. Therefore, Figure 9b also shows the validity of the current way of ensemble mean
(CTM_EnsMean and Met_EnsMean): This three‐member ensemble had the average spread in terms of
the precipitation and the concentration data.
Combining these facts, it is safe to presume that the depositional efﬁciencies of CTM were underestimated:without the current multimeteorological model analysis, the reason for this could not be identiﬁed.
Certainly, wrong patterns of simulated precipitation and/or wind ﬁelds could also lead to such a situation.
Therefore, this conclusion is supported not only by the fact that the ensembles showed the average spread
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but also by the fact that they showed good spatial agreements with the observations as presented in sections
3.1 and 3.2.
NHM‐Chem considers four types of deposition mechanisms, that is, dry deposition, in‐cloud scavenging,
below‐cloud scavenging, and fog deposition. Each process contains large uncertainties. The dry deposition
efﬁciency is determined by the 137Cs‐carrying aerosol size distribution and the ground surface conditions.
The below‐cloud scavenging efﬁciency is determined by the size distribution of aerosols and hydrometeors.
The state of hydrometeors is also important because most solid hydrometeors are very nonspherical, andtheir gravitational velocities and collection efﬁciencies are different than those of spheres. In‐cloud scavenging and fog deposition consist of two processes, that is, “CCN activation” and “microphysical deposition.”
CCN activation is determined by the size and hygroscopicity of the carrier aerosols and the supersaturationof the air, both for in‐cloud scavenging and fog deposition (the supersaturation of cloud formation, ~0.1–1%,
is one to 2 orders of magnitude larger than that of fog formation, ~0.01–0.1%). For the microphysical deposition part of in‐cloud scavenging, a number of elemental processes, that is, so‐called cloud microphysical processes, are involved in the evolution of cloud droplets that form liquid or solid hydrometeors and settle to theground. During the deposition process of fog deposition, the mechanism is the same as that of dry deposition:
The deposition efﬁciency is determined by the 137Cs‐carrying fog size distribution and the ground surface
conditions. Uncertainties in all of these parameters can contribute to the underestimation of deposition efﬁciencies; thus, it is difﬁcult to identify the reason for this underestimation.
Nevertheless, several issues need to be investigated to identify the cause of this underestimation and improvethe deposition modeling of radio‐Cs by the current CTM:
1. In terms of the underestimation of dry deposition, Cs‐ball should be considered. The sizes of Cs‐ballrange from one to several hundred micrometers in diameter, such that the dry deposition velocities of
Cs‐ball should be signiﬁcantly larger than those of submicron carrier aerosols, as assumed in the simulation. The consideration of Cs‐ball will certainly improve the underestimation of 137Cs deposition. It isnoted here that an aircraft survey could underestimate the 137Cs deposition of Cs‐ball, depending on
the areal density: Doses from sporadic sources on the ground could be lower than those from homogeneously contaminated surfaces.
2. It has been well accepted and thus studied that the theoretical efﬁciencies of below‐cloud scavenging aresometimes 1 to 2 orders of magnitude smaller than the experimental efﬁciencies for approximately 1‐μm
size ranges (e.g., Wang et al., 2010; Zhang et al., 2013). In addition, radioactive aerosols could be highly
charged, such that their efﬁciencies could be enhanced due to electrostatic forces. Applying empiricalefﬁciencies and/or considering the electric charges due to radioactive decay will certainly improve theunderestimation of 137Cs deposition.
3. There could be another solution to improve deposition modeling: a statistical approach. As performed by
Toyoda et al. (2013), optimizing the four types of deposition efﬁciencies using a Greens' function
approach toward the observed 137Cs deposition with the current sets of the meteorological ensembles
can help identify the cause of underestimation in the deposition modeling of 137Cs.
4. Discrepancy in the deposition schemes can be assessed by using the Cs/Xe ratio. Xe is a noble gas so theCs/Xe ratio in atmospheric concentrations only changes due to dry and wet removal of Cs during thetransport, except radioactive decay. The discrepancy in the deposition schemes caused larger impacts
on concentrations over further downwind regions. Therefore, the current and improved deposition processes should be evaluated by the Cs/Xe ratios analysis over further downwind regions such as North
America and Europe.
4.2.2. Contradictory Performances in Meteorological and Deposition Modeling
As mentioned in section 3.3, NHM performed very well in terms of precipitation and NHM‐LETKF performed best in terms of simulated concentrations, whereas WRF simulations were generally better in termsof the simulation of 137Cs deposition. These results appear to be contradictory. One reason for this appears tobe the inconsistency in in‐cloud scavenging modeling. In‐cloud scavenging is one of the major processes of137
Cs deposition. In‐cloud scavenging involves the vertical motion of air, cloud formation and evolution, andprecipitation, but the simulated vertical proﬁles of 137Cs and hydrometeors were not evaluated. If the simulated vertical proﬁles of 137Cs and hydrometeors by the NHM and NHM‐LETKF meteorological ﬁelds were
farther from reality than those simulated by the WRF ﬁelds, it would be natural that the performances of thedeposition modeling of the WRF simulations would be better than those of NHM and NHM‐LETKF. The

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observed vertical proﬁles of 137Cs are limited. Fukushima University initiated radioactivity sonde observations from April 2011 (the data are available at http://www.sss.fukushima‐u.ac.jp/sonde_data/, last access:7 April 2018). Although most land surface contamination observations were made in March, the model performance of the simulated vertical distribution of 137Cs can be evaluated using the radioactivity sonde data.
The better performance in the simulation of precipitation does not necessarily ensure a better performancein the simulation of hydrometeors. The simulated spatial distributions of hydrometeors should be evaluatedby radar observations for the further analysis of in‐cloud scavenging modeling.
There is another difﬁculty in evaluating the in‐cloud scavenging modeling of 137Cs. The majority of land surface contamination was caused by light precipitation (or fog) events (<0.5 mm). Meteorological models andobservations have been designed to study heavy precipitation. The radars used for the operational weather
monitoring networks (C‐band or S‐band) cannot detect such small sizes of droplets (cloud, fog, and drizzle),
and rain gauges cannot detect precipitation until it accumulates to 0.5 mm. The meteorological models used
for the operational weather forecast, which employ “bulk” cloud microphysics modules, were not designed
to predict light precipitation events, and their simulated light precipitation cannot be evaluated by the measurements. The current case study should be revisited in the future to evaluate a number of similar future
light precipitation events in the region under similar pressure patterns as that in March 2011, using different
models and observations that are designed to study light precipitation.

5. Conclusions
Meteorological model and module ensemble analysis with a single CTM (NHM‐Chem) was conducted forthe simulation of the transport and deposition of radio‐Cs released in association with the FDNPS accident
in March 2011 with a horizontal grid resolution of 3 km over the eastern and northern part of the main islandof the Japan Archipelago. A total of eight meteorological ﬁelds were compared: seven from NHM, NHM‐LETKF, ﬁve combinations of modules with WRF (different cloud microphysics and PBL modules), andthe meteorological ensemble mean. Then, we obtained a total of nine CTM simulation results: seven byusing the seven meteorological simulations, the ensemble mean of the seven simulations, and a simulation
using the meteorological ensemble mean. Strictly speaking, the variables of the meteorological ensemble
mean violate the physical consistency, but the current study proved that it was practically applicable. The
three‐member ensemble (NHM, NHM‐LETKF, and WRF_EnsMean) was shown to have average spreadin terms of the precipitation and the surface concentration data.
Whereas several multimodel ensemble studies were performed in previous studies, the current ensemble type(one CTM with several meteorological ﬁelds) was applied here for the ﬁrst time. The current approach was
found to be a useful way to evaluate the uncertainty in each component of CTM. The current analysis could
successfully deduce that the underestimation of the deposition efﬁciencies of CTM was the reason for theunderestimation of simulated radio‐Cs deposition, whereas the simulated precipitation and estimated source
term were all reasonable: All of the simulations underestimated the deposition amount, whereas some underestimated but others overestimated the simulated precipitation and radio‐Cs air concentrations and the simulated spatial distributions of precipitation and concentrations were well correlated with the observations. The
simulated total deposition amounts of 137Cs ranged from 0.46–1.23 PBq, while the observed amount was
2.59 PBq (except in areas 3 km from the FNDPS). The MB values of simulated cumulative precipitation from11 to 31 March ranged from −18.2 to 12.3 mm, and the observed average value was 55.0 mm. The MB valuesof the cumulative 137Cs surface concentrations from 13 to 23 March ranged from −137.7 to 174.2 Bq·m−3·hr,
and the observed average value was 376.12 Bq·m−3·hr. It was also found that the CTM simulation using themeteorological ensemble was successful in reducing variance and they gave reasonable results. The simulated deposition using the meteorological ensemble yielded the largest quantity (1.23 PBq). This occurredbecause the light precipitation areas were enlarged by the ensemble mean of meteorological ﬁelds andbecause the contamination was caused by light precipitation after the FDNPS accident.
Another target of this study was to reveal the dominant processes of the deposition of radio‐Cs over eachdeposition region of the simulation land area and to show how they vary depending on meteorological ﬁelds.
The simulated area was divided into nine areas, namely, Hamadori, Nakadori, Aizu, South Miyagi, Iwate‐
Miyagi, Tochigi, Gunma, Iwaki‐Ibaraki, and Ibaraki‐Chiba. The magnitudes of their contributions varied
depending on the simulations, but the current ensemble study showed that in‐cloud scavenging was the
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Figure A1. Same as Figure 5 but simulated by Met_EnsMean with different thresholds of fog water mixing ratio applied to−5−4
the fog deposition scheme of 10 and 10 (kg/kg), respectively.

most dominant mechanism (40–65%) for the entire land region, followed by dry deposition (15–35%) or fog
deposition (5–35%). The contribution of below‐cloud scavenging was the least signiﬁcant (<5%) for all
simulations. These contributions also varied in the deposition areas. In Fukushima and Miyagi
prefectures, from the coastal to mountain areas and from the southern to northern areas, thecontributions of solid precipitation increased: More than 90% of contributions were liquid over Hamadori,but 70–90% were solid over South Miyagi. Because most Japanese land is covered with mountain forests,
the contribution of fog deposition was ubiquitous everywhere except in Ibaraki‐Chiba, which is located in
the Kanto plain and includes no mountain areas. As suggested by Hososhima and Kaneyasu (2015) andSanada et al. (2018), fog deposition was the major process in Tochigi, as well as in Gunma (30–85%). It
was quite hard for the models to simulate deposition over Iwate‐Miyagi, as the most dominant processes
over the area were totally different between the models.
In inverse modeling, the simulated concentrations and/or depositions are used for the source term estimation (Saunier et al., 2013, 2016; Winiarek et al., 2014; Yumimoto et al., 2016). In the study, we showed thatthe simulated concentrations and depositions could vary by more than twofold, depending on the selectionof the meteorological ﬁeld, which could contribute to the uncertainty in the source term estimation. In the
future, the source term estimation with a single CTM, a single inversion method, and the different meteorological ﬁelds should be made to evaluate the meteorological uncertainty in the estimated source term.
It should be noted here that the radio‐Cs was assumed to be carried by hygroscopic submicron aerosols, as
inferred by Kaneyasu et al. (2012). On the other hand, Adachi et al. (2013) found other types of the carrier
aerosols, named Cs‐ball, which differ signiﬁcantly in terms of their size and hygroscopicity, and thus atmospheric behaviors. Adachi et al. (2013) also performed a sensitivity simulation of the size and hygroscopicityof the carrier aerosols for a limited period using limited knowledge about the production and emission
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mechanisms of Cs‐ball. Since then, the microphysical and chemical properties of Cs‐ball have been extensively studied by Abe et al. (2014), Yamaguchi et al. (2016), Satou et al. (2016), and Higaki et al. (2017),
and much knowledge has been accumulated. Recently, the Cs‐balls found in the environment were successfully linked to the accidental sequences of FDNPS by Satou et al. (2018); thus, it is now necessary to resimulate the atmospheric behavior of Cs‐ball and to show their differences compared to hygroscopic submicronaerosols. The sensitivity of simulated dispersion and deposition with respect to the properties of Cs‐ball will
be assessed in our next paper.

Appendix A: Sensitivity of Fog Deposition to the Threshold of Fog
Water Content
Figures A1 and A2 show the differences in the simulated deposition and contributions of deposition processes obtained by using the different thresholds of fog water content. For simplicity, only the CTM

Figure A2. Same as Figure 7 but simulated by Met_EnsMean with different thresholds of fog water mixing ratio applied to the fog deposition scheme of 10−4
10 (kg/kg), respectively.

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and

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simulation obtained with Met_EnsMean was compared and shown. The simulation performed with athreshold fog water content of 10−4 kg/kg resulted in signiﬁcantly less deposition over the mountainous
regions over Nakadori, Tochigi, and Gunma. There were almost no contributions of fog, except in Tochigi
and Gunma prefectures. As discussed previously, the simulation with 10−4 kg/kg could yield the lowest estimate, but the selection of the threshold value was also shown to be an important factor for the prediction offog deposition. The accurate prediction of fog and its deposition was indispensable for the prediction of
FDNPS‐derived radio‐Cs deposition over the mountainous areas of Japan.
Acknowledgments
This research was mainly supported by
Grants‐in‐Aid for Scientiﬁc Researchon Innovative Areas 24110002 and24110003 of the Ministry of Education,
Culture, Sports, Science andTechnology (MEXT). It was partly
supported by the JapaneseRadioactivity Survey from the NRA,
Japan, and also supported by the Japan
Society of the Promotion of Science(JSPS) and Ministère des Affaires
Étrangères et du Développement
International (MAEDI) under theJapan‐France Integrated Action
Program (SAKURA) and by KAKENHI
Grant JP15K16121, JP15H02811,
JP24340115, and JP16KK0018 of JSPS.
The issues presented in this paper wereextensively discussed with Drs. Anne
Mathieu, Arnaud Quérel, Denis Quélo,
Olivier Saunier, and Damien Didier ofl'Institut de Radioprotection et deSûreté Nucléaire (IRSN). The authors
feel obliged to their useful comments on
the fog deposition mechanism by Dr.
Olivier Masson of IRSN, the uncertainty in aircraft observations by Prof.
Michio Aoyama of Fukushima
University; the radar and precipitation
observation products by Drs. Nobuhiro
Nagumo, Takeshi Iriguchi, Hiroshige
Tsuguti, and Ahoro Adachi of MRI; theoptimization method by Dr. Takahiro
Toyoda of MRI; the effects of electriccharge due to radioactive decay by Dr.
Pascal Lemaitre of IRSN; and the pros
and cons of ensemble analysis by Drs.
Makoto Deushi and Keiichi Kondo of
MRI. The authors are deeply gratefulfor the support of Mr. Yuki Kobayashi
of University of Tsukuba for his analysis of the visibility data. The simulated
and observed data used in all ﬁgures areavailable at https://mri‐2.mri‐jma.go.
jp/owncloud/index.php/s/
CwFDOmEPCOlABbW (last access: 8
January 2019). In terms of the raw
observation data sets, the surface activity concentration data are available atAppendix A of Oura et al. (2015; http://
www.radiochem.org/en/j‐online152.
html; last access: 2 October 2018), theactivity deposition data are available athttps://emdb.jaea.go.jp/emdb/en/portals/b1010301/ (last access: 2 October
2018), and the RAP data are provided by
Japan Meteorological Business Support
Center for a charge.

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$$%ÿÿÿÿÿ$(ÿÿ"ÿ"#%(ÿH@=(%ÿ(%$ÿ@($ÿ$ÿ7(ÿ%ÿ42ÿ+"ÿÿ/"$"ÿ(ÿ#ÿÿ,%(ÿÿ(%,$ÿ'@"(2ÿ"ÿ'ÿ/(ÿÿ($"ÿÿ"ÿ@($ÿ,%ÿÿ"ÿ,%#ÿÿÿÿ4#ÿ"ÿ7)=K=L=(#ÿ+(@ÿ(ÿ%ÿ(,$,(ÿ#ÿ%ÿ7)=K=L=(#ÿ@/%(ÿ%($2ÿÿ/ÿ,%ÿ"ÿ"ÿ@($ÿ$ÿÿÿ'(@",ÿD(ÿ/ÿ$(#Fÿ'(Hÿ$ÿ'ÿ'ÿ/"ÿ$"'$ÿÿÿÿ
$'@(#ÿ""ÿÿÿH%ÿ(ÿ$"(%ÿÿÿÿ4%ÿÿ"ÿ@"#$ÿ%ÿ$"'$ÿ$"($($ÿ%ÿÿ(6$(#ÿ#ÿÿ(ÿ@($ÿ"ÿ@%ÿ(ÿ"ÿ7!;ÿ'(ÿ2ÿ/ÿ$$,%%ÿ"ÿ"ÿ(%$ÿ@($ÿ/(ÿ(%ÿ%ÿ'%ÿÿ"ÿ'@"(ÿÿ"ÿ'ÿÿ/(ÿ6$ÿ(ÿ$ÿ"ÿ($(ÿ(ÿ"ÿ"#%(ÿH@
MNOPQRSTUÿ*,-,"'ÿ.$"ÿ6,$(ÿ!/(ÿ!ÿ$$%Eÿ(ÿ@($Eÿ#$"((ÿ(%ÿL=(#ÿ#Eÿ(%$ÿ'(4. Publication list 1954-2021
論文リスト（英文誌）1954-2021- 15 -

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2021
Abe, Y., Onozaki, S., Nakai, I. et al. Widespread distribution of radiocesium-bearing
microparticles over the greater Kanto Region resulting from the Fukushima nuclear accident.
Prog Earth Planet Sci 8, 13 (2021). https://doi.org/10.1186/s40645-020-00403-6
Kajino, M., Adachi1, K., Igarashi,Y., Satou,Y., Sawada, M., Sekiyama, T,T., Zaizen, Y., Saya,A., Tsuruta, H., and Moriguchi, Y. Deposition and Dispersion of Radio-Cesium Released dueto the Fukushima Nuclear Accident: 2. Sensitivity to Aerosol Microphysical Properties of CsBearing Microparticles (CsMPs) Journal of Geophysical Research: Atmospheres, Volume 126,
Issue 1. doi: 10.1029/2020JD033460
Kajino, M., Watanabe, A., Ishizuka,M., Kita, K., Zaizen, Y., Kinase, T., Hirai, R., Konnai, K.,
Saya, A., Shiroma, Y., Hasegawa, H., Akata, N., Hosoda, M., Tokonami, S. and Igarashi,Y.
Re-assessment of radiocesium resuspension flux from contaminated ground surface in East
Japan. https://doi.org/10.5194/acp-2021-687
Sekiyama, T. T. and M. Kajino (2021) Performance of a 250-m grid Eulerian dispersion
simulation evaluated at two coastal monitoring stations in the vicinity of the Fukushima Daiichi
Nuclear Power Plant, J. Meteor. Soc. Japan, 99, doi:10.2151/jmsj.2021-052
Sekiyama, T, T., Kajino, M., and Kunii, M. Ensemble Dispersion Simulation of a Point-Source
Radioactive Aerosol Using Perturbed Meteorological Fields over Eastern Japan.
Atmosphere 2021, 12(6), 662; https://doi.org/10.3390/atmos12060662
Watanabe, A., Kajino, M., Ninomiya, K., Nagahashi1, Y. and Shinohara, A. Eight-year
variations in atmospheric radiocesium in Fukushima city. Atmospheric Chemistry and Physics.
doi.org/10.5194/acp-2021-591
Ohno, T., Sato, N., Shikimori, J., Ijichi, Y., Fukami, Y., & Igarashi, Y. (2021). Temporal changeof 236U/238U and 235U/238U isotopic ratios in atmospheric deposition in Tokyo and Akita
from 1963 to 1979. Science of The Total Environment, 151292.
2020
Kinase, T., Adachi, K., Sekiyama, T.T. et al. Temporal variations of 90Sr and 137Cs in
atmospheric depositions after the Fukushima Daiichi Nuclear Power Plant accident with longterm observations. Sci Rep 10, 21627 . https://doi.org/10.1038/s41598-020-78312-3
Kita, K., Igarashi, Y., Kinase, T. et al. Rain-induced bioecological resuspension of radiocaesiumin a polluted forest in Japan. Sci Rep 10, 15330. https://doi.org/10.1038/s41598-020-72029-z
Sekiyama, T. T. and Kajino, M., Reproducibility of Surface Wind and Tracer Transport
Simulations over Complex Terrain Using 5-, 3-, and 1-km-Grid Models. Journal of Applied
Meteorology and Climatology. Volume 59. 937-952 doi: https://doi.org/10.1175/JAMC-D-190241.1
2019
Igarashi, Y., K., Kita, T., Maki, T., Kinase, N., Hayashi, K., Hosaka, K., Adachi, M., Kajino,M., Ishizuka, T. T., Sekiyama, Y., Zaizen, C., Takenaka, K., Ninomiya, H., Okochi, A.,
Sorimachi, Fungal spore involvement in the resuspension of radiocaesium in summer. Scientific
Reports 9:1954, 2019.
Iwasaki, T., Sekiyama, T. T., Nakajima, T., Watanabe, A., Suzuki, Y., Kondo, H., Morino, Y.,
Terada, H., Nagai, H., Takigawa, M., Yamazawa, H., Quélo, D., and Mathieu, A.
Intercomparison of numerical atmospheric dispersion prediction models for emergency
response to emissions of radionuclides with limited source information in the Fukushima Daiichi Nuclear Power Plant accident. Atmospheric Environment, Volume 214, 2019, Article
116824
Kajino, M., Sekiyama, T, T., Igarashi, Y., Katata, G., Sawada, M., Adachi, K., Zaizen, Y.,
Tsuruta, H., and Nakajima, T., Deposition and Dispersion of Radio‐Cesium Released Due to the
Fukushima Nuclear Accident: Sensitivity to Meteorological Models and Physical Modules.
Journal of Geophysical Research: Atmospheres, 124, 1823-1845.doi: 10.1029/2018JD028998.

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2018
Hirose, K., Kikawada, Y., Igarashi, Y., Fujiwara, H., Jugder, D., Matsumoto, Y., Oi, T., andNomura, M. Plutonium, 137Cs and uranium isotopes in Mongolian surface soils, Journal ofEnvironmental
Radioactivity,
166,
97̶̶-103,2017.
http://dx.doi.org/10.1016/j.jenvrad.2016.01.007
Igarashi, Y., Kita., K., Maki, T., Kinase, T., Hyashi, N., Hotaka, K., Adachi, K., Kajino, M.,
Ishizuka, M., Sekiyama, T., T., Zaizen, Y., Takenaka, C., Ninomiya, K., Okochi, H., andSorimachi, A. Fungal spore involvement in the resuspension of radiocaesium in summer,
Scientific Reports, 9:1-10, 2019. 2019. DOI:10.1038/s41598-018-37698-x
Iwagami, S., Tsujimura, M., Onda, Y., Nishino, M., Konuma, R., Abe, Y., Hada, M., Pun, I.,
Sakaguchi, , A., Kondo, H., Yamamoto, M., Miyata, Y., and Igarashi, Y. Temporal changes indissolved 137Cs concentrations in groundwater and stream water in Fukushima after the
Fukushima Dai-ichi Nuclear Power Plant accident. Journal of Environmental Radioactivity, 166,458-465, 2017. http://dx.doi.org/10.1016/j.jenvrad.2015.03.025
Kajino, M.., Sekiyama, T. T., Mathieu, A,. Korsakissok, I., Périllat, R., Quélo, D., Quérel, A.,
Saunier, O., Adachi, K., Girard, S., Maki, T., Yumimoto, K., Didier, D., Masson, O., andIgarashi, Y., Lessons learned from atmospheric modeling studies after the Fukushima nuclear
accident: Ensemble simulations, data assimilation, elemental process modeling, and inverse
modeling, Geochemical Journal, Vol. 52, 2018.
Kinase, T., Kita, K., Igarashi, I., Adachi, K., Ninomiya, K., Shinohara, A., Okochi, H., Ogata,
H., Ishizuka, M., Toyoda, S., Yamada, K., Yoshida, N., Zaizen, Y., Mikami, M,. Demizu, H.,
and Onda, Y., The seasonal variations of atmospheric 134,137Cs activity and possible hostparticles for their resuspension in the contaminated areas of Tsushima and Yamakiya,
Fukushima, Japan, Progress in Earth and Planetary Science, 5:12, 2018.
Satou, Y., Sueki, K., Sasa, K., Yoshikawa, H., Nakama, S., Minowa, H., Abe, Y., Nakai, I., Ono,
T., Adachi, K., and Igarashi, Y., Analysis of two forms of radioactive particles emitted during
the early stages of the Fukushima Dai-ichi Nuclear Power Station accident, Geochemical
Journal, Vol. 52, 137-143, 2018. doi:10.2343/geochemj.2.0514.
Sekiyama, T. T. and T. Iwasaki, Mass flux analysis of 137Cs plumes emitted from the Fukushima
Daiichi Nuclear Power Plant, Tellus B, 70(1), 1–11, doi:10.1080/16000889.2018.1507390, 2018.
2017
Iwagami, S., Tsujimura, M., Onda, Y., Nishino, M., Konuma, R., Abe, Y., Hada, M., Pun, I.,
Sakaguchi, A., Kondo, H., Yamamoto, M., Miyata, Y., and Igarashi, Y., Temporal changes indissolved 137Cs concentrations in groundwater and stream water in Fukushima after the
Fukushima Dai-ichi Nuclear Power Plant accident, Journal of Environmental Radioactivity,166,
458-465, 2017.
Hirose, K., Kikawada, Y., Igarashi, Y., Fujiwara, H., Jugder, D., Matsumoto, Y., Oi, T., Nomura,M., Plutonium, 137Cs and uranium isotopes in Mongolian surface soils, Journal of Environmental
Radioactivity, 166, 97-103, 2017.
Sekiyama, T. T., Kajino, M., and Kunii, K., The Impact of Surface Wind Data Assimilation on
the Predictability of Near-Surface Plume Advection in the Case of the Fukushima Nuclear
Accident, Journal of the Meteorological Society of Japan, Vol. 95, 447-454, 2017. doi:
10.2151/jmsj.2017-025.
2016
Kajino, M., Ishizuka, M., Igarashi, Y., Kita, K., Yoshikawa, C., and Inatsu, M., Long-term
assessment of airborne radiocerium after the Fukushima nuclear accident: re-suspension frombare soil and forest ecosystems, Atmos. Chem. Phys, 16, 13149-13172, 2016.
Satou,Y., Sueki, K., Sasa, K., Adachi, K., and Igarashi, Y., First successful isolation ofradioactive particles from soil near the Fukushima Daiichi Nuclear Power Plant, Anthropocene,
14, 71-76, 2016.
Yamaguchi, N., Mitome, M., Kotone, A-H., Asano, M., Adachi, K., and Kogure, T., Internal
structure of cesium-bearing radioactive microparticles released from Fukushima nuclear power
plant. Scientific Reports. 6, 20548, 2016.

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2015
Igarashi, Y., Kajino, M., Zaizen, Y., Adachi, K., and Mikami, M., Atmospheric radioactivity
over Tsukuba, Japan: a summary of three years of observations after the FDNPP accident,
Progress in Earth and Planetary Science 2:44, 2015. doi: 10.1186/s40645-015-0066-1.
Kajino, M., G-3. NHM-Chem: Sensitivity of Cs deposition to the size and hygroscopicity of Csbearing aerosols, Technical Reports of the Meteorological Research Institute, 76, 132–142,
2015.
Sekiyama, T. T., Kunii, M., Kajino, M., and Shinbori, T., Horizontal Resolution Dependence ofAtmospheric Simulations of the Fukushima Nuclear Accident Using 15-km, 3-km, and 500-m
Grid Models, J. Meteorol. Soc. Jpn., 93B, 49–64, 2015.

2014
Abe, Y., Iizawa, Y., Terada, Y., Adachi, K., Igarashi, Y., and Nakai, I., Detection of uranium
and chemical state analysis of individual radioactive microparticles emitted from the Fukushima
nuclear accident using multiple synchrotron radiation X-ray analyses, Analytical Chemistry, 86,
8521-8525, 2014.
2013
Aoyama, M., Uematsu, M., Tsumune, D., and Hamajima, Y., Surface pathway of radioactive
plume of TEPCO Fukushima NPP1 released 134Cs and 137Cs, Biogeosciences, 10, 30673078, 2013. doi: 10.5194/bg-10-3067-2013.
Tsumune, D., Tsubono, T., Aoyama, M., Uematsu, M., Misumi, K., Maeda, Y., Yoshida, Y.,
and Hayami, H., One-year, regional-scale simulation of 137Cs radioactivity in the ocean
following the Fukushima Daiichi Nuclear Power Plant accident, Biogeosciences, 10, 5601-5617,
2013.
Povinec, P.P., Aoyama, M., Biddulph, D., Breier, R., Buesseler, K., Chang, C., Golser, R., Hou,X., Ješkovský, M., and Jull, A., Cesium, iodine and tritium in NW Pacific waters-a comparisonof the Fukushima impact with global fallout. Biogeosciences, 10, 5481-5496. 2013.
Kumamoto, Y., Murata, A., Kawano, T., and Aoyama, M., Fukushima-derived radiocesium inthe northwestern Pacific Ocean in February 2012, Applied Radiation and Isotopes, 81, 335-339,
2013. doi: 10.1016/j.apradiso.2013.03.085.
Toyama, C., Muramatsu, Y., Igarashi, Y., and Aoyama, M., Atmospheric fallout of 129I in Japan
before the Fukushima accident: Regional and global contributions, 1963-2005, Environmental
Science & Technology, 47(15), 8383-8390, 2013. doi: 10.1021/es401596z.
Adachi, K., Kajino, M., Zaizen, Y., and Igarashi, Y., Emission of spherical cesium-bearing
particles from an early stage of the Fukushima nuclear accident. Scientific Reports, 3, Article
number 2554, 2013. doi:10.1038/srep02554.
2012
Toyama, C., Muramatsu, Y., Uchida, Y., Igarashi, Y., Aoyama, M., and Matsuzaki, H.,
Variations of 129I in the atmospheric fallout of Tokyo, Japan 1963-2003, Journal ofEnvironmental Radioactivity, 113, 116-122, 2012. doi: 10.1016/j.jenvrad.2012.04.014.
Inomata, Y., Aoyama, M., Tsumune, D., Motoi, T., and Nakano, H., Optimum interpolation
analysis of basin-scale 137Cs transport in surface seawater in the North Pacific Ocean, Journalof Environmental Monitoring, 14, 3146-3155, 2012. doi: 10.1039/C2EM30707C.
Aoyama, M., Tsumune, D., Uematsu, M., Kondo, F., and Hamajima, Y., Temporal variation of
134Cs and 137Cs activities in surface water at stations along the coastline near the Fukushima Daiichi Nuclear Power Plant accident site, Japan. Geochem. J, 46, 321-325, 2012.
Aoyama, M., Tsumune, D., and Hamajima, Y., Distribution of 137Cs and 134Cs in the North
Pacific Ocean: impacts of the TEPCO Fukushima-Daiichi NPP accident, Journal ofRadioanalytical and Nuclear Chemistry, 1-5, 2012. doi: 10.1007/s10967-012-2033-2.
Honda, M. C., Aono, T., Aoyama, M., Hamajima, Y., Kawakami, H., Kitamura, M., Masumoto,Y., Miyazawa, Y., Takigawa, M., and Saino, T., Dispersion of artificial caesium-134 and-137
in the western North Pacific one month after the Fukushima accident, Geochem. J, 46, e1-e9,
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Povinec, P. P., Hirose, K., and Aoyama, M., Radiostrontium in the Western North Pacific:
characteristics, behavior, and the Fukushima impact, Environmental Science & Technology, 46,10356-10363, 2012. doi: 10.1021/es301997c.
Tsumune, D., Tsubono, T., Aoyama, M., and Hirose, K., Distribution of oceanic 137Cs from the
Fukushima Dai-ichi Nuclear Power Plant simulated numerically by a regional ocean model,
Journal of Environmental Radioactivity, 111, 100-108, 2012.

2011
Aoyama, M., Fukasawa, M., Hirose, K., Hamajima, Y., Kawano, T., Povinec, P.P., and SanchezCabeza, J. A., Cross Equator transport of 137Cs from North Pacific Ocean to South Pacific Ocean,
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Buesseler, K., Aoyama, M., and Fukasawa, M., Impacts of the Fukushima nuclear power plants
on marine radioactivity. Environmental Science & Technology, 45(23), 9931-9935, 2011. doi:
10.1021/es202816c.
Povinec, P. P., Aoyama, M., Fukasawa, M., Hirose, K., Komura, K., Sanchez-Cabeza, J.A.,
Gastaud, J., Jeskovsky, M., Levy, I., and Sykora, I., 137Cs water profiles in the South Indian
Ocean - an evidence for accumulation of pollutants in the subtropical gyre. Progress in
Oceanography, 89 , 17-30, 2011. doi:10.1016/j.pocean.2010.12.004.
Gastaud, J., Povinec, P.P., Aoyama, M., Hirose, K., Sanchez-Cabeza, J.A., Levy, I., Roos, P.,
Eriksson, M., Bosc, E. and Rezzoug, S., Transport and scavenging of Pu in surface waters of theSouthern Hemisphere Oceans, Progress in Oceanography, 89, 92-100, 2011.
doi:10.1016/j.pocean.2010.12.009.
Hirose, K., Kim, C.S., Yim, S.A., Aoyama, M., Fukasawa, M., Komura, K., Povinec, P.P., andSanchez-Cabeza, J.A., Vertical profiles of plutonium in the central South Pacific, Progress in
Oceanography, 89, 101-107, 2011. doi:10.1016/j.pocean.2010.12.010.
Tsumune, D., Aoyama, M., Hirose, K., Bryan, F., Lindsay, K. and Danabasoglu, G., Transportof 137Cs to the Southern Hemisphere in an Ocean General Circulation Model, Progress in
Oceanography, 89, 38-48, 2011. doi:10.1016/j.pocean.2010.12.006.
Sanchez-Cabeza, J.A., Levy, I., Gastaud, J., Eriksson, M., Osvath, I., Aoyama, M., Povinec, P.P.
and Komura, K., Transport of North Pacific 137Cs labeled waters to the south-eastern Atlantic
Ocean. Progress in Oceanography, 89, 31-37, 2011. doi:10.1016/j.pocean.2010.12.005.
Levy, I., Povinec, P.P., Aoyama, M., Hirose, K., Sanchez-Cabeza, J.A., Comanducci, J-F.,
Gastaud, J., Eriksson, M., Hamajima, Y., Kim, C.S., Komura, K., Osvath, I., Roos, P. and Yim,
S.A., Marine anthropogenic radiotracers in the Southern Hemisphere: new sampling andanalyticalstrategies,
Progressin
Oceanography,
89,
120-133,
2011.
doi:10.1016/j.pocean.2010.12.012.
2010
Inomata,Y., Igarashi, Y., Yoshioka, K., Tanaka, T. Y., and M, Chiba., Temporal variation of
222Rn at the summit of Mt. Fuji associated with the Asian continental outflow, Atmospheric
Environment, 44,3856-3865, 2010.
Nakano, H., Motoi, T., Hirose, K., and Aoyama, M., Analysis of 137Cs concentration in the
Pacific using a Lagrangian approach. Journal of Geophysical Research, 115, C06015, 2010.
doi:10.1029/2009JC005640.
Hirose, K., Igarashi, Y., Aoyama, M., and Inomata, Y., Depositional behaviors of plutonium
and thorium isotopes at Tsukuba and Mt. Haruna in Japan indicate the sources of atmosphericdust. Journal of Environmental Radioactivity, 101,106-112, 2010.
Zhang, Y., Zeng, J., Yamada, M., Wu, F., Igarashi, Y., and Hirose, K., Characterization of
Pu concentration and its isotopic composition in a reference fallout material. Science of the
Total Environment, 408(5), 1139-1144, 2010.

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2009
Aoyama, M., Hamajima, Y., Fukasawa, M., Kawano, T., and Watanabe, S., Ultra low level deep
water 137Cs activity in the South Pacific Ocean, Journal of Radioanalytical and Nuclear
Chemistry, 2009. doi:10.1007/s10967-009-0253-x.
Hirose, K., Aoyama, M., and Povinec, P.P., 239, 240Pu /137Cs ratios in the water column of theNorth Pacific: a proxy of biogeochemical processes, Journal of Environmental Radioactivity,
100, 258-262, 2009.
Igarashi, Y., Inomata, Y., Aoyama, M., Hirose, K., Takahashi, H., Shinoda, Y., Sugimoto, N.,
Shimizu, A., and Chiba, M., Possible change in Asian dust source suggested by atmosphericanthropogenic radionuclides during the 2000s, Atmospheric Environment, 43, 2971-2980, 2009.
Inomata, Y., Aoyama, M., and Hirose, K., Analysis of 50-y record of surface 137Csconcentrations in the global ocean using the HAM-global database, Journal of Environmental
Monitoring, 11(1), 116-125, 2009. doi: 10.1039/b811421h.
Hirose, K., Igarashi, Y., Aoyama, M., and Inomata, Y., Depositional behaviors of plutonium
and thorium isotopes at Tsukuba and Mt.Haruna in Japan indicate the sources of atmospheric106-112,
2009.
dust,
JournalofEnvironmental
Radioactivity,
101(2),
doi:10.1016/j.jenvrad.2009.09.003.
Igarashi Y., Kikawada, Y., Oda, K., Yamauchi, R., Nomura, M., Honda, T., Oi, T., and Hirose,K., Anomalous Uranium Isotope Ratio in Atmospheric Deposits in Japan, Journal of Nuclear
Science and Technology, 46(12), 1094-1098. 2009.
2008
Aoyama, M., Hirose, K., Nemoto, K., Takatsuki, Y., and Tsumune, D., Water masses labeledwith global fallout 137Cs formed by subduction in the North Pacific, Geophysical Research
Letters, 35, L01604, 2008. doi:10.1029/2007GL031964.
Hirose, K., Igarashi, Y., and Aoyama, M., Analysis of the 50-year records of the atmosphericdeposition of long-loved radionuclides in Japan, Applied Radiation and Isotopes, 66, 1675-1678,
2008.
Hirose, K., Aoyama, M., Igarashi, Y., and Komura, K., Improvement of 137Cs analysis in small
Volume seawater samples using the Ogoya underground facility, Journal of Radioanalytical andNuclear Chemistry, 276,3,795-798, 2008.
Aoyama, M., Hirose, K., Nemoto, K., Takatsuki, Y., and Tsumune, D., Water masses labeledwith global fallout 137Cs formed by subduction in the North Pacific, Geophysical Research
Letters, 35, L01604, 2008. doi:10.1029/2007GL031964.
2007
Hirose, K., Aoyama, M., and Kim, C.S., Plutonium in Seawater of the Pacific Ocean, Journal ofRadioanalytical and Nuclear Chemistry, 274 No. 3, 635-638, 2007.
Hirose, K., Igarashi, Y., and Aoyama, M., 50 years records of atmospheric deposition of longlived anthropogenic radionuclides in Japan, International Conference on Environmental
Radioactivity: From Measurements and Assessments to Regulation, IAEA-CN-145, 95-96,
2007.
Hirose, K., Igarashi, Y., and Aoyama, M., Recent trends of plutonium fallout observed in Japan:
Comparison with natural lithogenic radionuclides, thorium isotopes, Journal of Radioanalyticaland Nuclear Chemistry, 273 No.1, 115-118, 2007.
Hirose, K., Aoyama, M., Fukasawa, M., Kim, C. S., Komura, K., Povinec, P. P., and SanchezCabeza, J. A., Plutonium and 137Cs in surface water of the South Pacific Ocean, Science of the
Total Environment, 381, 243-255, 2007.
2006
Yamamoto, M., Sakaguchi, A., Sasaki, K., Hirose, K., Igarashi, Y., and Kim, C.K., Seasonaland spatial variation of atmospheric 210Pb and 7Be deposition: features of the Japan Sea side ofJapan, Journal of Environmental Radioactivity, 86, 110-131, 2006.

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Hirose, K., Aoyama, M., Kim, C.S., Kim, C.K., and Povinec, P.P., Plutonium isotopes inseawater of the North Pacific: effects of close-in fallout, INTERNATIONAL CONFERENCE
ON ISOTOPES AND ENVIRONMENTAL STUDIES. Eds. P. Povinec, J.A. Sanchez-Cabeza.
Vol. 8 of Radioactivity in the Environment, Elsevier, London, 2006.
Aoyama, M., Hirose, K., and Igarashi, Y., Re-construction and updating our understanding on
the global weapons tests 137Cs fallout, Journal of Environmental Monitoring, 8, 431-438, 2006.
Aoyama, M., Fukasawa, M., Hirose, K., Mantoura, R.F.C., Povinec, P.P., Kim, C.S., andKomura, K., Southern Hemisphere Ocean Tracer Study (SHOTS): An overview and preliminary
results, INTERNATIONAL CONFERENCE ON ISOTOPES AND ENVIRONMENTALSTUDIES. Eds. P. Povinec, J.A. Sanchez-Cabeza, Vol. 8 of Radioactivity in the Environment,
Elsevier, London, 2006.Inoue, H.Y., Matsueda, H., Igarashi, Y., Sawa, Y., Wada, A., Nemoto,K., Sartorius, H., and Schlosser, C., Seasonal and long-term variations in atmospheric CO2
and 85Kr in Tsukuba, Central Japan, Journal of the Meteorological Society of Japan, 84/6,
959-968, 2006.

2005
Hirose, K., Aoyama, M., Igarashi, Y., and Komura, K., Extremely low backgroundmeasurements of 137Cs in seawater samples using an underground facility (Ogoya), Journal ofRadioanalytical and Nuclear Chemistry, 263, 349-353, 2005.
Povinec, P.P., Aarkrog, A., Buesseler, K.O., Delfanti, R., Hirose, K., Hong, G.H., Ito, T.,
Livingston, H.D., Nies, H., Noshkin, V.E., Shima, S., and Togawa, O., 90Sr, 137Cs and 239,
240
Pu concentration surface water time series in the Pacific and Indian Oceans- WOMARS
results, Journal of Environmental Radioactivity, 81, 63-87, 2005.
Igarashi, Y., Aoyama, M., Hirose, K., Povinec, P.P., and Yabuki, S., What anthropogenic
radionuclides (90Sr and 137Cs) in atmospheric deposition, surface soils and Aeoliandusts suggest
for dust transport over JAPAN, Water, Air, and Soil Pollution: Focus, 5, 51-69, 2005.
2004
Hirose, K., Kim, C.K., Kim, C.S., Chang, B.W., Igarashi, Y., and Aoyama, M., Wet and dry
depositions of plutonium in Daejeon, Korea, The Science of the Total Environment, 332, 243252, 2004.
Povinec, P.P., Hirose, K., Honda, T., Ito, T., MarianScott, E., and Togawa, O., Spatial
distribution of 3H, 90Sr, 137Cs and 239, 240Pu in surface waters of the Pacific and Indian Oceans GLOMARD database, Journal of Environmental Radioactivity, 76, 113-137, 2004.
Aoyama, M., and Hirose, K., Artificial Radionuclides database in the Pacific Ocean: Ham
database, TheScientificWorldJOURNAL, 4, 200-215, 2004.
Hirota, M., Nemoto, K., Wada, A., Igarashi, Y., Aoyama, M., Matsueda, H., Hirose, K.,
Sartorius, H., Schlosser, C., Schmid, S., Weiss, W., and Fujii, K., Spatial and Temporal
Variations of Atmospheric 85Kr Observed During 1995-2001 in Japan: Estimation of
Atmospheric 85Kr inventory in the Northern Hemisphere, Journal of Radiation Research,
45, 405-413, 2004.
Hirose, K., Chemical Speciation of Thorium in Marine Biogenic Particulate matter, The
Scientific World JOURNAL 4, 67-76, 2004.
2003
Hirose, K., and Aoyama, M., Analysis of 137Cs and 239, 240Pu concentrations in surface waters of
the Pacific Ocean, Deep-Sea Research II, 50, 2675-2700, 2003.
Hirose, K., Aoyama, and Povinec, P.P., Concentration of particulate plutonium in surface anddeep-water, samples collected during the IAEA’97 expedition, Deep-Sea Research II, 50, 26392647, 2003.
Povinec, P.P., Livingston, H.D., Shima, S., Aoyama, M., Gastaud, J., Goroncy, I., Hirose, K.,
Huynh-Ngoc, L., Ikeuchi, Y., Ito, T., Rosa, J.L., Kwong, L.L.W., Lee, S.H., Moriya, H., Mulsow,
S., Oregioni, B., Pettersson, H., and Togawa, O., IAEA’97 expedition to the NW Pacific OceanResults of oceanographic and radionuclide investigations of the water column, Deep-Sea
Research II, 50,2607-2637, 2003.
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Ito, T., Povinec, P.P., Togawa, O., and Hirose, K., Temporal and Spatial variation ofanthropogenic radionuclides in seawater of the Japan Sea, Deep-Sea Research II, 50, 2701-2711,
2003.
5. Tsumune, D., Aoyama, M., and Hirose, K., Behavior of 137Cs and concentrations in the North
Pacific in an ocean general circulation model, Journal of Geophysical Research, 108, C8, 3262,
2003.
6. Hirose, K., and Aoyama, M., Present Background Levels of Surface 137Cs and 239, 240Pu
Concentrations in the Pacific, Journal of Environmental Radioactivity, 69(1-2), 53-60, 2003.
7. Tsumune, D., M. Aoyama, and K. Hirose, Numerical simulation of 137Cs and 239, 240Pu
concentrations by an ocean general circulation model, Journal of Environmental Radioactivity,69(1-2), 61-84, 2003.
8. Aoyama, M., and Hirose, K., Temporal variation of 137Cs water column inventory in the North
Pacific since the 1960s, Journal of Environmental Radioactivity, 69(1-2),107-117, 2003.
9. Hirose, K., Igarashi, Y., Aoyama, M., Kim, C.K., Kim, C.S., Chang, B.W., Recent trends ofplutonium fallout observed in Japan: plutonium as a proxy for desertification, J. Environ. Monit,
5, 302-307, 2003.
10. Igarashi, Y., Aoyama, M., Hirose, K., Miyao, T., Nemoto, K., Tomita, M., and Fujikawa, T.,
Resuspension: Decadal Monitoring Time Series of the Anthropogenic Radioactivity Depositionin Japan, Journal of Radiation Research, 44, 319-328, 2003.
11. Kim, C.K., Kim, C.S., Chang, B.U., Choi, S.W., Chung, C.S., Hong, G.H., Hirose, K., andIgarashi, Y., Plutonium isotopes in seas around the Korean Peninsula, The Science of the Total
Environment, 318, 197-209, 2003.
12. Kim, C.K., Kim, C.S., Chang, B.U., Choi, S.W., Chung, C.S., Hong, G.H., Hirose, K., andPettersson, H.B.L., 240Pu /239Pu atom ratios in the bottom sediments of the NW Pacific
Ocean, Journal of Radioanalytical and Nuclear Chemistry, 258(2), 265-268, 2003.
13. Lee, H.N., Tanaka, T., Chiba, M., and Igarashi, Y., Long range transport of Asian dust from dust
storms and its impact on Japan, Water, Air, and Soil Pollution, 3, 231-243, 2003
14. Hirose, K., Implication of POC/234Th ratios in oceanic particulate matter: an approach to particle
aggregation, Papers of Meteorology and Geophysics, 53, 4, 109-118, 2003.
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2002
Hirose, K., Miyao, T., Aoyama, M., and Igarashi, Y., Plutonium isotopes in the Sea of Japan,
Journal of Radioanalytical and Nuclear Chemistry, 252, 293-299, 2002.
Hirose, K., and Aoyama, M., Chemical speciation of plutonium in seawater, Analytical andBioanalytical Chemistry, 372, 418-420, 2002.
Hong, G.H., Kim, Y.I., Lee, S.H., Cooper, L.W., Choe, S.M., Tkalin, A.V., Lee, T., Kim, S.H.,
Chung, C.S., and Hirose, K., 239+240Pu and 137Cs concentrations for zooplankton and nekton in
the Northwest Pacific and Antarctic Oceans 1993-1996, Marine Pollution Bulletin, 44, 660-665,
2002.
2001
Igarashi, Y., Aoyama, M., Nemoto, K., Hirose, K., Miyao, T., Fushimi, K., Suzuki, M., Yasui,
S., Asai, Y., Aoki, I., Fujii, K., Yamamoto, S., Sartorius, H., and Weiss, W., 85Kr measurement
system for continuous monitoring at Meteorological Research Institute, Japan, Journal ofEnvironmental Monitoring, 3, 688-696, 2001.
Igarashi, Y., Aoyama, M., Hirose, K., Miyao, T., and Yabuki, S., Is it Possible to use 90Sr and137Cs as tracers for the aeolian transport?, Water. Air. And Soil Pollution, 130, 349-354, 2001
Hirose, K., Igarashi, Y., Aoyama, M., and Miyao, T., Long-term trends of plutonium fallout
observed in Japan, In Plutonium in the Environment, 251-266, 2001.
Aoyama, M., Hirose, K., Miyao, T., Igarashi, Y., and Povinec, P.P., 137Cs activity in surface
water in the western North Pacific, J. Radioanal. Nucl. Chem., 248, 3, 789-793, 2001.
Aoyama, M., Hirose, K., Miyao, T., Igarashi, Y., and Povinec, P.P., Temporal variation of 137Cs
inventory in the western North Pacific, J Radioanal Nucl Chem, 248, 3, 785-787, 2001

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Hirose, K., Aoyama, M., Miyao, T., and Igarashi, Y., Plutonium in seawaters on the western
North Pacific, J Radioanal Nucl Chem, 248, 3, 771-776, 2001.
Tsumune. D., Aoyama, M., Hirose, K., Maruyama, K., and Nakashiki, N., Caluculation of
Artificial Radionuclides in the Ocean by an Ocean General Circulation Model, J Radioanal Nucl
Chem, 248, 3, 777-783, 2001.

2000
Shiraishi, K., Kimura, S., Yonehara, H., Takada, J., Ishikawa, M., Igarashi, Y., Aoyama, M.,
Komura, K., and Nakazima, T., Survey of external dose around the JCO facility using sugar
samples and ESR method, Adv. ESR Appl, 16, 9-14, 2000.
Komura, K, Yamamoto, M., Muroyama, T., Murata, Y., Nakanishi, T., Hoshi, M., Takada, J.,
Ishikawa, M., Kitagawa, K., Suga, S., Endo, A., Tozaki, N., Mitsugashira, T., Hara, M.,
Hashimoto, T., Takano, M., Yanagawa, Y., Tsuboi, T., Ichimasa, M., Ichimasa, Y., Imura, H.,
Sasajima, E., Seki, R., Saito, Y., Kondo, M., Kojima, S., Muramatsu, Y., Yoshida, S., Shibata,S., Yonehara, H., Watanabe, Y., Kimura, S., Shiraishi, K., Bannai, T., Sahoo, S.K., Igarashi, Y.,
Aoyama, M., Hirose, K., Uehiro, M., Doi, T., and Matsuzawa, T., The JCO criticality accident
at Tokai-mura, Japan: an overview of the sampling campaign and preliminary results, J. Environ.
Radioactivity, 50, 3-14, 2000.
Igarashi, Y., Miyao, T., Aoyama, M., Hirose, K., Sartorius, H., and Weiss, W., Radioactive noble
gases in the surface air monitored at MRI, Tsukuba, before and after the JCO accident, J.
Environ. Radioactivity, 50, 107-118, 2000.
Miyao, T., Hirose, K., Aoyama, M., and Igarashi, Y., Trace of the recent deep water formationin the Japan Sea deduced from historical 137Cs data, Geophys. Res. Lett., 27, 22, 3731-3734,
2000.
Aoyama, M., Hirose, K., Miyao, T., and Igarashi, Y., Low level 137Cs measurements in deep
seawater samples, Appl. Radiat. Isot, 53, 159-162, 2000.
Igarashi, Y., Sarutorius, H., Miyao, T., Weiss, W., Fushimi, K., Aoyama, M., Hirose, K., andInoue, H.Y., 85Kr and 133Xe monitoring at MRI, Tsukuba and its importance, J. Environ.
Radioactivity, 48, 191-202, 2000.
1999
Hirose, K., Amano, H., Baxter, M.S., Chaykovskaya, E., Chumichev, V.B., Hong, G.H., Isogai,
K., Kim, C.K., Kim, S.H., Miyao, T., Morimoto. T, Nikitin, A., Oda, K., Pettersson, H.B.L.,
Povinec, P.P., Seto, Y., Tkalin, A., Togawa, O., and Veletova, N.K., Anthropogenic
radionuclides in seawater in the East Sea/Japan Sea: Results of the first-stage Japanese-KoreanRussian expedition, J. Environ. Radioactivity, 43, 1-13, 1999.
Igarashi, Y., Aoyama, M., Miyao, T., Hirose, K., Komura, K., and Yamamoto, M., Airconcentration of radiocaesium in Tsukuba, Japan following the release from the Tokai waste
treatment plant: comparisons of observations with predictions, Applied Radiation and Isotopes,
50, 1063-1073, 1999.
Igarashi, Y., Aoyama, M., Miyao, T., Hirose, K., and Tomita, M., Anomalous 90Sr deposition
during the fall, 1995 at MRI, Tsukuba, Japan, J. Radioanal. Nucl. Chem, 239(3), 539-542, 1999.
Ikeuchi, Y., Amano, H., Aoyama, M., Berezhnov, V.I., Chaykovskaya, E., Chumichev, V.B.,
Chung, C.S., Gastaud, J., Hirose, K., Hong, G.H., Kim, C.K., Kim, S.H., Miyao, T., Morimoto,T., Nikitin, A., Oda, K., Pettersson, H.B.L., Povinec, P.P., Tkalin, A., Togawa, O., and Veletova,
N.K., Anthropogenic radionuclides in seawater of the Far Eastern Seas, Sci. Total Environ.,
237/238, 203-212, 1999.
Pettersson, H.B.L., Amano, H., Berezhnov, V.I., Chaykovskaya, E., Chumichev, V.B., Chung,C.S., Gastaud, J., Hirose, K., Hong, G.H., Kim, C. K., Lee, S.H., Morimoto, T., Nikitin, A., Oda,
K., Povinec, P.P., Suzuki, E., Tkalin, A., Togawa, O., Veletova, N.K., Volkov, Y., and Yoshida,K., Anthropogenic radionuclides in sediments in the NW Pacific Ocean and its marginalseas:results of the 1994-1995 Japanese-Korean-Russian expeditions, Sci. Total Environ,
237/238, 213-224, 1999.

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1998
Igarashi, Y., Hirose, K., and Otsuji-Hatori, M., Beryllium-7 Deposition and Its Relation to
Sulfate Deposition, J. Atmos. Chem, 29, 217-231, 1998.
Miyao, T., Hirose, K., Aoyama, M., and Igarashi, Y., Temporal variation of 137Cs and 239, 240Pu
in the sea of Japan, J. Environ. Radioactivity, 40, 239-250, 1998.
1997
Hirose, K., Complexation-scavenging of plutonium in the ocean, Radioprotection - Colloq, 32,
C2-225 - C2-230, 1997.
1996
Igarashi, Y., Otsuji-Hattori, M., and Hirose, K., Recent deposition of 90Sr and 137Cs observed in
Tsukuba, J. Environ. Radioactivity, 31, 157-169, 1996.
Otsuji-Hatori. M., Igarashi, Y., and Hirose, K., Preparation of a Reference Fall out Material for
Activity Measurements, J. Environ Radioactivity, 31, 2, 143-155, 1966.

1995
Aoyama, M., and Hirose, K., The temporal and spatial variation of 137Cs concentration in thewestern North Pacific and its marginal seas during the period from 1979 to 1988, J. Environ.
Radioactivity, 29, 57-74, 1995.
Hirose, K., Geochemical studies on the Chernobyl radioactivity in environmental samples, J.
Radioanal. Nucl. Chem., Articles, 197, 315-335, 1995.
1994
Hirose, K., Takatani, S., and Aoyama, M., Deposition of 90Sr and plutonium isotopes derived
from the Chernobyl accident in Japan, J. Radioanal. Nucl. Chem., Articles, 182, 349-358, 1994.
1993
Hirose, K., and Sugimura, Y., Chemical speciation of particulate 238U, 239, 240Pu and Th isotopes
in seawater, Sci. Total Environ., 130/131, 517-524, 1993.
Hirose, K., Takatani, S., and Aoyama, M., Wet deposition of radionuclides derived from the
Chernobyl accident, J. Atmos. Chem., 17, 61-71, 1993.
Shiraishi, K., Igarashi, Y., Yamamoto, M., and Nakajima, T., Concentrations of thorium anduranium in freshwater samples collected in the former USSR, J. Radioanal. Nucl. Chem.,
185,157-165, 1993.
1992
Aoyama, M., Hirose, K., and Takatani, S., Particle size dependent dry deposition velocity of theChernobyl radioactivity, In Precipitation Scavenging and Atmosphere - Surface
Exchange.Volume 3, The 22e Summers olume: Application and Appraisals. Coordinated by S.E.
Schwartz and W.G.N. Slinn. Hemisphere Publishing Corporation, Washington, 1581-1593,
1992.
Hirose, K., Sugimura, Y., and Aoyama, M., Plutonium and 137Cs in the western North Pacific:
estimation of residence time of plutonium in surface water, Appl. Radiat. Isot, 43, 349-359,
1992.
1991
Aoyama, M., Hirose, K., and Sugimura, Y., The temporal variation of stratospheric fallout
derived from the Chernobyl accident, J. Environ. Radioactivity, 13, 103-115, 1991.
Hirose, K., and Sugimura, Y., Chemical speciation of particulate uranium in seawater, J.
Radioanal. Nucl. Chem., Articles, 149, 83-96, 1991.
1990
Hirose, K., and Sugimura, Y., Plutonium isotopes in the surface air in Japan: effect of Chernobyl
accident, J. Radioanal. Nucl. Chem., Articles, 138, 127-138, 1990.

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Hirose, K., Aoyama, M., and Sugimura, Y., Plutonium and cesium isotopes in river waters inJapan, J. Radioanal. Nucl. Chem., Articles, 141, 191-202, 1990.
Hirose, K., Aoyama, M., and Sugimura, Y., Short and long term effects of Chernobyl
radioactivity on deposition and air concentrations in Japan, IAEA-SM 306/129, 141-149, 1990.

1988
Aoyama, M., Evidence of stratospheric fallout of caesium isotopes from Chernobyl accident,
Geophys. Res. Lett., 15, 327-330, 1988.
Miyake, Y., Saruhashi, K., Sugimura, Y., Kanazawa, T., and Hirose, K., Contents of 137Cs,
plutonium and americium isotopes in the southern ocean waters, Pap. Met. Geophys, 39, 95113, 1988.
1987
Aoyama, M., Hirose, K., and Sugimura, Y., Deposition of gamma-emitting nuclides in Japanafter the reactor-IV accident at Chernobyl, J. Radioanal. Nucl. Chem., Articles, 116, 291-306,
1987.
Hirose, K., Aoyama, M., Katsuragi, Y., and Sugimura, Y., Annual deposition of Sr-90, Cs-137
and Pu-239, 240 from the 1961-1980 nuclear explosions: a simple model, J. Meteor. Soc. Japan,
65, 259-277, 1987.
1986
Aoyama, M., Hirose, K., Suzuki, Y., and Sugimura, Y., High level radioactive nuclides in Japanin May, Nature, 321, 819-820, 1986.
Hirose, K., Sugimura, Y., and Katsuragi, Y., 90Sr and 239+240Pu in the surface air in Japan: theirconcentrations and size distributions, Pap. Met. Geophys, 37, 255-269, 1986.
Katsuragi, Y., and Aoyama, M., Seasonal variation of Sr-90 fallout in Japan through the end of
1983, Pap. Met. Geophys., 37, 15-36, 1986.
Suzuki, Y., Inoue, H., Katsuragi, Y., and Sugimura, Y., The distribution of 85Krin the air over
the North and South Pacific Ocean Mem., Natl. Inst. Polar Res., Spec. Issue, 40, 462-466, 1986.
1985
Hirose, K., and Sugimura, Y., A new method of plutonium speciation in seawater, J. Radioanal.
Nucl. Chem, Articles, 92, 363-369, 1985.
1984
Hirose, K., and Sugimura, Y., Plutonium in the surface air in Japan, Health Phys., 46, 12811285, 1984.
Inoue, H., Katsuragi, Y., and Shigehara, K., Tritiated water vapor in the surface air at Tokyo,
Pap. Met. Geophys, 35, 11-20, 1984.
1983
Katsuragi, Y., A study of 90Sr fallout in Japan, Pap. Met. Geophys., 33, 277-291, 1983.
Katsuragi, Y., Kawamura, K., and Inoue, H., Tritium fallout in Tokyo, Pap. Met. Geophys, 34,
21-30, 1983.
1982
Inoue, H., and Katsuragi, Y., A study of tritium fallout in Japan, Pap. Met. Geophys, 32, 21-28,
1982.
Katsuragi, Y., Hirose, K., and Sugimura, Y., A study of plutonium fallout in Japan, Pap. Met.
Geophys, 33, 85-93, 1982.
1981
Hirose, K., and Sugimura, Y., Concentration of Uranium and the activity ratio of 234U /238U insurface air-effect of atmospheric burn-up of cosmos-954, Pap. Met. Geophys., 32, 317-322,
1981.
Hirose, K., and Sugimura, Y., Plutonium content of river water in Japan, Pap. Met. Geophys, 32,
301-305, 1981.
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Sugimura, Y., and M. Mayeda, An improved method of analysis of uranium in sea water using
chelating resin, Pap. Met. Geophys, 32, 167-171, 1981.

1980
Sugimura, Y., The uranium content and the activity ratio 234U /238U in sea water in the Pacificocean, Isotope Marine Chemistry, 211-246, 1980.
1979
Sugiura, Y., Saruhashi, K., and Miyake, Y., Evaluation on the disposal of radioactive wastes
into the North Pacific. - the effect of steady flow and up - welling, Proc. The 3rd NEA Seminaron Marine Radioecology, 1979.
1978
Miyake, Y., Nuclear weapons and radioactive pollution of the earth's environment, In, Proc.
Internal Symp. On the damage and after-effects of the atomic bombing of Hiroshima andNagasaki July 21-Aug., 9, 1977, Tokyo, 164-188, 1978.
1976
Miyake, Y., and Saruhashi, K., A critical study on the IAEA definition of high level radioactive
waste unsuitable for dumping at sea, Pap. Met. Geophys. 27, 75-80, 1976.
Miyake, Y., and Saruhashi, K., Disposal of radioactive waste into deep seas, J. Radiat. Res., 17,
42-43, 1976.
Miyake, Y., and Sugimura, Y., The plutonium content in the Pacific Ocean waters, Proc. of
Symp. on Transuranium Nuclides in the Environment, San Francisco, 17-21 Nov. 1975,
USERDA and IAEA, IAEA-SM-199/22, 91-105,1976.
Sugimura, Y., Symposium II plutonium in environment, II-1.Distribution and behavior ofplutonium in the global environments, J. Radiat. Res., 17, 4-5, 1976.
Sugiura, Y., Saruhashi, K., and Miyake, Y., Evaluation on the disposal of radioactive wastes
into the North Pacific, Pap. Met. Geophys, 27, 81-87, 1976.
1975
Miyake, Y., Shimada, T., Kawamura, K., Sugimura, Y., Shigehara, K., and Saruhashi, K.,
Distribution of tritium in the Pacific ocean, Rec. Oceanogr. Works Japan, 13, 17, 1975.
Miyake, Y., Katsuragi, Y., and Sugimura, Y., Plutonium fallout in Tokyo, Pap. Met. Geophys,
26, 1-8, 1975.
Miyake, Y., Sugimura, Y., and Hirao, Y., Uranium, thorium and potassium contents in granitic
and basaltic rocks in Japan, In, The Natural Radiation Environment II, II, Proc. 2nd Intnl. Symp.
On Natural Radiation Environment, Aug. 7-11, 1972, Houston, Texas, USA, ed. J. A. S. Adams,
W. M. Lowder, T. F. Gesell, 535-558,1975.
Saruhashi, K., I-1 The global radioactive contamination due to nuclear weapon testings, J. Radiat.
Res., 16, 47, 1975.
Saruhashi, K., Katsuragi, Y., Kanazawa, T., Sugimura, Y., and Miyake, Y., 90Sr and 137Cs in the
Pacific waters, Rec. Oceanogr. Works Japan, 13, 1-15, 1975.
1973
Miyake, Y., and Saruhashi, K., The radio-carbon model of the ocean, Pap. Met. Geophys, 24,
263-271, 1973.
Miyake, Y., Saruhashi, K., and Sugimura, Y., The isotopic ratio 234U /238U in sea water and its
bearings on the isotopic ratio in river waters, Oceanogr. Works Japan, 12, 23-25, 1973.
Miyake, Y., Sugimura, Y., and Saruhashi, K., Content of plutonium in river water in Japan, Pap.
Met. Geophys, 24, 75-78, 1973.
Miyake, Y., Sugimura, Y., and Yasujima, T., Thorium isotope content in river water in Japan,
Pap. Met. Geophys, 24, 67-73, 1973.

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3.
4.

1972
Miyake, Y., Sugimura, Y., and Uchida, T., A new method of spectrophotometric determinationof uranium in sea water and uranium content with 234U /238U ratio in the Pacific water, Rec.
Oceanogr. Works Japan, 11, 53-63, 1972.
1970
Miyake, Y., Katsuragi, Y., and Sugimura, Y., A study on plutonium fallout, J. Geophys. Res.,75, 2329-2330, 1970.
Miyake, Y., Sugimura, Y., and Mayeda, M., The uranium content and the activity ratio 234U
/238U in marine organisms and sea water in the western North Pacific, J. Oceanogr. Soc. Japan,
26, 123-129, 1970.
1969
Miyake, Y., Radioactive contamination of the ocean, Bull. Jpn. Soc. Fisheries Oceanogr., Nov.,
1969, 1969.
1968
Miyake, Y., and Sugimura, Y., Plutonium content in the western North Pacific waters, Pap. Met.
Geophys, 19, 481-485, 1968.
Miyake, Y., Katsuragi, Y., and Sugimura, Y., Deposition of plutonium in Tokyo through the endof 1966, Pap. Met. Geophys, 19, 267-276, 1968.
1967
Miyake, Y., and Kanazawa, T., Atmospheric ozone and radioactive fallout, Pap. Met. Geophys.,
18, 311-326, 1967.
Miyake, Y., Radioactive contamination of the ocean, J. Radiat. Res., 8, 1, 1967.
Miyake, Y., Sea, radioactivity in, International Dictionary of Geophysics, 1-7, 1967.
1966
Miyake, Y., and Saruhashi, K., On the radio-carbon age of the ocean waters, Pap. Met. Geophys.,
17, 218-223, 1966.
Miyake, Y., and Sugimura, Y., Ratio 234U /238U and the uranium concentration in seawater inthe western North Pacific, J. Geophys. Res., 71, 3083-3087, 1966.
1965
Kuroda, P. K., Miyake, Y., and Nemoto, J., Strontium isotopes Global circulation after the
Chinese nuclear explosion of 14 May 1965, Science, 150, 1289-1290, 1965.
Miyake, Y., Saruhashi, K., Katsuragi K., and Kanazawa, T., Radioactivity of dust and rain - theratio of Cs-137 and Sr-90 in the radioactive fallout, Proc. Internl. Conf. On Cloud Physics,Tokyo,
IAMAP, 395-399, 1965.
Park, K., George, M.J., Miyake, Y., Saruhashi, K., Katsuragi, Y., and Kanazawa, T., Strontium90 and caesium-137 in Columbia river plume, July 1964, Nature, 208, 1084-1085, 1965.
1964
Miyake, Y., and Sugimura, Y., Uranium and radium in the western North Pacific waters, Studies
on Oceanography, Prof. K. Hidaka Sixtieth Birthday Commemorative Volume, Univ. of Tokyo
Press, Tokyo, 274-278, 1964.
Miyake, Y., and Ohtsuka, Y., Beryllium-7 in rain water, Pap. Met. Geophys, 15, 89-92, 1964.
Miyake, Y., A sequential procedure for the radiochemical analysis of marine material, Annex tosafety series 11."Method of surveying and monitoring marine radioactivity, IAEA, Vienna, 26,1964.
Miyake, Y., Atomic weapons and the pollution of the sea, The East, 3, 64-68, 1964.

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4.
5.
6.

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4.

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2.

1.
2.
3.

1.
2.

Miyake, Y., Saruhashi, K., Katsuragi, Y., Kanazawa, T., and Sugimura, Y., Uranium, radium,
thorium, Ionium, strontium 90 and cesium 137 in coastal waters of Japan, In, Recent Researches
in the Fields of Hydrosphere, Atmosphere and Nuclear Geochemistry, published by Editorial
Committee of Sugawara Festival Volume, Maruzen Co. Ltd., 127-141, 1964.
Miyake, Y., Sugimura, Y., and Tsubota, H., Content of uranium, radium, and thorium in riverwaters in Japan, In, The Natural Radiation Environment, ed. By J. A. S. Adams and W. M.
Lowder, Univ. of Chicago Press, Chicago, Rice Univ. Semicentennial Series, 219-225, 1964.
Sugimura, Y., Torii, T., and Murata, S., Uranium distribution in drake passage waters, Nature,
204, 464-465, 1964.
1963
Folsom, T.R., and Saruhashi, K., A comparison of analytical techniques used for determinationof fallout cesium in sea water for oceanographic purpose, J. Radiat. Res., 4, 39-53, 1963.
Miyake, Y., and Tsubota, H., Estimation of the direct contribution of meteoric water to riverwaters by means of fall-out radio-cesium and radiostrontium, "Radioisotopes in Hydrology"
Proc. IAEA Symp., Tokyo. IAEA, Vienna, 425-431, 1963.
Miyake, Y., Artificial radioactivity in the Pacific ocean, IUGG Monograph, 20 Radioactive
traces in oceanography, 1963.
Miyake, Y., Saruhashi, K., Katsuragi, K., and Kanazawa, T., Deposition of Cs-137 and Sr-90 in
Tokyo through the end of July 1963, Pap. Met. Geophys, 13, 180-181, 1963.
Miyake, Y., Saruhashi, K., Katsuragi, Y., Kanazawa, T., and Tsunogai, S., Deposition of 90Sr
and 137Cs in Tokyo through the end of July 1963, Pap. Met. Geophys, 14, 58-65, 1963.
Miyake, Y., Sugiura, Y., and Tsubota, H., II. Contents of uranium, radium, and thorium in riverwaters in Japan, The Natural Radiation Environment, RICE Univ. semicentennial Pub, 1963.

1962
Miyake, Y., Saruhashi, K., Katsuragi, Y., and Kanazawa, T., Seasonal variation of radioactive
fallout, J. Geophys. Res., 67, 189-193, 1962.
Miyake, Y., Saruhashi, K., Katsuragi, Y., and Kanazawa, T., Penetration of 90Sr and 137Cs indeep layers of the Pacific and vertical diffusion rate of deep water, J. Radiat. Res., 3, 141-147,
1962.
Miyake, Y., Saruhashi, K., Katsuragi, Y., and Kanazawa, T., The peak in radioactive fallout inthe temperate zone of the northern hemisphere, J. Radiat. Res., 3,148-152, 1962.
Sugimura, Y., and Sugimura, T., Uranium in recent Japanese sediments, Nature, 194, 568-569,
1962.
1961
Miyake, Y., Saruhashi, K., Katsuragi, Y., and Kanazawa, T., Cesium 137 and strontium 90 insea water, J. Radiat. Res., 2 25-28, 1961.
Miyake, Y., Saruhashi, K., Katsuragi, Y., and Kanazawa, T., Cesium 137 and strontium 90 insea water, Pap. Met. Geophys., 12, 85-88, 1961.
1960
Miyake, Y., and Saruhashi, K., Vertical and horizontal mixing rates of radioactive material in
the ocean, Disposal of Radioactive wastes IAEA Vienna, 167-173, 1960.
Miyake, Y., and Katsuragi, Y., Strontium 90 in western North Pacific surface waters, Pap. Met.
Geophys., 11, 188-190, 1960.
Miyake, Y., Saruhashi, K., Katsuragi, Y., and Kanazawa, T., Radioactive fallout in Japan andits bearings on meteorological conditions, Pap. Met. Geophys., 11, 151-158, 1960.
1959
Miyake, Y., Saruhashi, K., and Katsuragi, Y., The Sr-90 fallout and the air motion, Pap. Met.
Geophys., 9, 172-176, 1959.
Miyake, Y., Special committee on oceanic research, Working Group on Radioactivity in the
Ocean, 1959.

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7.
8.

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1958
Miyake, Y., and Saruhashi, K., Distribution of man-made radio-activity in the North Pacificthrough summer 1955, J. Mar. Res., 17, 383-389, 1958.
Miyake, Y., and Sugiura, Y., The method of measurement of radioactivity in sea water, Pap.
Met. Geophys., 9, 48-50, 1958.
Miyake, Y., Hazards to human health of radioactive dust, Radio Japan, 2, 3-5, 1958.
Miyake, Y., The distribution of artificial radioactivity in the equatorial region in the Pacific in
the summer of 1956, The Proc. 9th Pacific Science Congress, 16, 227, 1958.
1957
Miyake, Y., and Saruhashi, K., The world-wide strontium 90 deposition during the period from1951 to the fall of 1955, Pap. Met. Geophys., 8, 241-243, 1957.
Miyake, Y., The biological effects of nuclear tests - warning by Japanese scientists, Radio Japan,
1, 3-5, 1957.
Miyake, Y., XI. Methods for the measurement of radioactivity in sea water, Annuals ofInternational Geophysical Year, 5, 1957.
Miyake, Y., XII. Radioactivity as a tracer of air motions in the atmosphere, Symp. On
radioactivity, IGY, Utrecht, 360, 1957.
Miyake, Y., Sugiura, Y., Saruhashi, K., and Kanazawa, T., The estimation of the amount of Sr90 deposition and the external infinite gamma does in Japan due to man-made radioactivity, Pap.
Met. Geophys., 8, 222-231, 1957.
1956
Miyake, Y., On the distribution of radioactivity in the North Pacific ocean in 1954-1955, Internl.
Conf. For Peaceful Uses of Atomic Energy in Geneva, August 1955, 13, pp.381-384, 1956.
Miyake, Y., Radioactivity in rain water and air, Proc. Internl. Conf. For Peaceful Uses of Atomic
Energy in Geneva, August 1955, 13, 345-349, 1956.
Sugiura, Y., and Kanazawa, T., On the radioactive fallout collected in Tokyo on 26th November,
1955, Pap. Met. Geophys., 7, 128-135, 1956.
1955
Miyake, Y., and Sugiura, Y., Radiochemical analysis of radio-nuclides in sea water collected
near BIKINI atoll, Pap. Met. Geophys., 6, 90-92, 1955.
Miyake, Y., Effects of atomic explosions on the atmosphere and sea, The research in the effects
and influences of the nuclear bomb text explosions, 1-6, 1955.
Miyake, Y., Kigoshi, K., and Saruhashi, K., Radiochemical analysis of fission products
contained in the soil collected at Tokyo, May, 1954, Pap. Met. Geophys., 6, 1, 93-94, 1955.
Miyake, Y., The artificial radioactivity in rain water observed in Japan, 1954-1955, Research in
the Effects and Influences of the Nuclear Bomb Test Explosions Science, 151-159, 1955.
Miyake, Y., The artificial radioactivity in rain water observed in Japan, from autumn 1954 tospring 1955, Pap. Met. Geophys., 6, 26-32, 1955.
Miyake, Y., Sugiura, Y., and Kameda, K., On the artificial radioactivity in the sea near Japan,
Pap. Met. Geophys., 6, 90-92, 1955.
Miyake, Y., Sugiura, Y., and Kameda, K., On the distribution of radioactivity in the sea around
Bikini atoll in June, 1954, Pap. Met. Geophys., 5, 253-262, 1955.
Miyake, Y., Sugiura, Y., and Kameda, K., Research in the effects and influence of the nuclear
bomb test explosions, Soc. For Promotion of Science, 415-417, 1955.
1954
Miyake, Y., Kigoshi, K., Sugiura, Y., and Saruhashi, K., A study on the productivity in coastal
waters by means of the radio-carbon, Pap. Met. Geophys., 5, 89-94, 1954.
Miyake, Y., The artificial radioactivity in rain water observed in Japan from May to August,
1954, Pap. Met. Geophys, 5, 173-177, 1954.

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5. Publication list (in Japanese) 1954-2019
論文リスト（和文誌）1954-2019- 30 -

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6.

2019
小野﨑 晴佳, 阿部 善也, 中井 泉, 足立 光司, 五十嵐 康人, 大浦 泰嗣, 海老原 充, 宮坂
貴文, 中村 尚, 末木 啓介, 鶴田 治雄, 森口 祐一: 福島第一原子力発電所事故により 1 号
機から放出された放射性エアロゾルの物理・化学的性状の解明, 分析化学, 68, 757-768,
10.2116/bunsekikagaku.68.757, 2019.
2017
足立光司, 福島原子力発電所事故で放出された不溶性放射性粒子の形態と組成, エアロゾル
研究, 32-4,255‒260, 2017.
五十嵐康人, 放射性物質の大気沈着・拡散および陸域からの再浮遊について, Proceedings ofthe 17th Workshop Environmental Radioactivity. KEK Proceedings, 2017-6, 50-55, 2017.
小野貴大, 飯澤勇信, 阿部善也, 中井 泉, 寺田康子, 佐藤志彦, 末木啓介, 足立光司, 五十嵐康人, 福島第一原子力発電所事故により１号機から放出された放射性粒子の放射光マイク
ロビーム X 線分析を用いる化学性状の解明, 分析化学 66（4）, 2017.
梶野瑞王, 大気エアロゾルの環境動態シミュレーション,安全工学, 57-6, 433-441, 2018
木村徹, 五十嵐康人, 財前祐二, ガンマ線スペクトル解析ソフト P-SCAN を用いた高精度な Cs134/Cs137 放射能比の導出, Proceedings of the 17th Workshop Environmental Radioactivity.
KEK Proceedings, 2017-6, 262-266, 2017.

1.

2016
木村徹, 五十嵐康人, 財前祐二, 2016: IAEA-TEL-2015-03 Proficiency Test 試料の測定,
Proceedings of the 17th Workshop Environmental Radioactivity. KEK Proceedings, 2016-8, 228-231.

1.
2.

2015
足立光司, 電子顕微鏡がとらえた放射性粒子, 地球化学, 49,185‒193, 2015.
木村徹, 五十嵐康人,「大気降下物試料の放射能分析の品質管理」，Proceedings of the 16th
Workshop Environmental Radioactivity. KEK Proceedings 2015-4 , 23-27, 2015.

1.

2014
青山道夫, 東電福島原発事故後の海洋での放射能汚染の推移 （With English Abstract）.
YAKUGAKU ZASSHI, Journal of the Pharmaceutical Society of Japan. 2014.

1.

2013
青山道夫, 極低レベル放射能測定：少量表層海水および極深層海水試料への応用.放射線,39(1), 17-20, 2013.

1.
2.
3.
4.

1.
2.

2012
青山道夫, 福島第一原子力発電所から放出された放射性セシウム同位体の北太平洋における総量と分布, 日本原子力学会誌, 54, 780-783, 2012.
青山道夫, 五十嵐康人, 1957 年から始まる気象研究所における環境放射能研究の歴史, 放射線, 38(3), 123-128, 2012.
青山道夫, 海洋に放出された放射性物質の長期地球規模での挙動, 検証！福島第一原発事故-放射性物質の実際と科学者たちの活動の記録（化学 4 月号別冊）. 4, 33-38, 2012.
青山道夫, 五十嵐康人, 広瀬勝己, 月間降下物測定 660 ヶ月が教えること-90Sr, 137Cs および Pu 降下量 1957 年 4 月～2012 年 3 月-. 科学, 82(4), 0442-0457, 2012.
2011
青山道夫, 福島原子力発電所事故による海洋汚染. 理大科学フォーラム, 11(通 巻 329 号),
42-45, 2011.
津旨大輔, 坪野考樹, 青山道夫, 廣瀬勝巳, 福島第一原子力発電所から漏洩した 137Cs の海
洋拡散シミュレーション(With English Abstract). 電力中央研究所報告書, V11002, PP.18, 2011.
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2009
五十嵐康人, 放射性同位体分析, In 岩坂泰信, 西川雅高, 山田丸, 洪天祥（編）, 黄砂, 古今書院, pp81-87, 東京, 2009.
Igarashi, Y., Anthropogenic Radioactivity in Aerosol -A Review Focusing on Studies during the
2000s-, 保健物理, 44(3), 2009.
猪股弥生, 五十嵐康人, 千葉長, 青山道夫, 廣瀬勝己, 再現期間を用いた降水時における高ガンマ線線量率の異常値判定. Radioisotopes, 58, 37-42, 2009.
2007
三浦誓也, 貝森優希, 工藤英嗣, 野呂幸男, 時枝隆之, 廣瀬勝己, 青森県における大気中クリプトン-85 濃度調査, 青森県原子力センター所報, 1, 43-44, 2007.
廣瀬勝己, 青山道夫, Kim、S.A. Kim, 太平洋の海水中のプルトニウムの挙動について, 日本
放射化学会誌 別冊, 8 supplement, 103, 2007.
2006
青山道夫, 大気と海洋深層における核実験起源放射性降下物の超低レベル放射能測定,
ADIOISOTOPES, 55, 429-438, 2006.
小村和久, 青山道夫, 秋山正和, 氏平祐輔, 大田裕二, 大塚良仁, 大西勝基, 櫻井敬久, 篠原
伸夫, Ge 検出器の使用状況に関するアンケート報告, RADIOISOTOPES, 55, 699-707,2006.
2004
廣瀬勝己, 化学トレーサーを利用した海洋変動の研究, 測候時報第 71 巻特別号, 71, S81S86, 2004.
五十嵐康人, 廣瀬勝己, 放射性降下物標準試料の調製を通じた 90Sr, 137Cs,プルトニウムなどの人工放射性核種分析の相互比較と参照値の決定, 放射化分析, No. 17, 39-42, 2004.

1.

2002
五十嵐康人, 90Sr と 137Cs を用いたダスト輸送過程と再飛散, エアロゾル研究, 12, 17(4), 252,
2002.

1.

1986
鈴木款, 井上久幸, 杉村行勇, 大気中の放射性クリプトンの分布, 地球化学, 20, 106-108,
1986.

1.
2.
3.

1.
2.
1.

1978
葛城幸雄, 人工放射性物質, 天気, 25, 第 11 号, 20-23, 1978.
猿橋勝子, 海洋中の放射性物質を指標とした物質の移動・拡散, 水汚染の機構と解析－環境
科学持論－, 日本地球化学編, 東京, 産業図書, 271-287, 1978.
三宅泰雄, 猿橋勝子, 杉村行勇, 葛城幸雄, 金沢照子, 核兵器と地球環境の放射能汚染, 被爆の実相と被爆者の実情 －1977 年 NGO 被爆問題シンポジウム報告書, ISDA－ JNPC 編集,
朝日イブニングニュース社発行, 182-212, 1978.
1976
葛城幸雄, 日本における放射性降下物, 天気, 23, No. 7, 333-345, 1976.
杉村行雄, 環境のなかのプルトニウム, 自然, 3 月号, 1976.
1975
三宅泰雄, 島田利夫, 川村清, 重原好次, 地上大気のトリチウム含量と気象との関係, 日本
地球化学会 “環境問題特別号”, 131-135, 1975.

- 32 -

1.

1974
三宅泰雄, 葛城幸雄, 最近の 90Sr 降下物の起源, 天気, 21, 639-644, 1974.

1.

1971
葛城幸雄, 人工放射能による大気汚染, 気象研究ノート, 107, 258-282, 1971

1.

1970
三宅泰雄, 葛城幸雄, 最近の日本における放射性塵の降下, 天気, 17, 593-598, 1970.

1.
2.

1965
葛城幸雄, 日本における Cs-137 および Sr-90 降下について(I), 天気, 12, 323-328, 1965.
葛城幸雄, 日本における Cs-137 および Sr-90 降下について(II) "成層圏における人工放射性物質の滞留時間の推定", 天気, 12, 377-384, 1965.

1.

1964
三宅泰雄, 葛城幸雄, 金沢照子, 放射性降下物の現状と将来, 科学, 34, 142-148, 1964.

1.

1963
三宅泰雄, 放射性降下物の気象学の側面, 天気, 9, No. 1, 1962.

1962
1. 三宅泰雄, 放射性降下物の気象学的側面, 天気, 9, 1-6, 1962.
2. 三宅泰雄, 猿橋勝子, 放射能による水の汚染, 水利科学, 23, 1-14, 1962.
1.
1.

1.
2.
3.
4.
1.
2.
3.

1958
三宅泰雄, 猿橋勝子, 放射化学と海洋, 科学, 28, 510-513, 1958.
1956
三宅泰雄, 杉浦吉雄, 葛城幸雄, 1955 年 4 月旭川地方に降った放射性の落下塵,気象集誌, 第2 号, 34, 226-230, 1956.
1955
三宅泰雄, 放射能雨の性格, 測候時報, 22, 196-202, 1955.
三宅泰雄, 日本に降った人工放射性雨・雪[II]9 月～12 月, 天文と気象, 21, 1-8, 1955.
三宅泰雄, 杉浦吉雄, 亀田和久, 日本近海の海水放射能について, 昭和 29 年度海洋資源開発
調査報告（黒潮班）, 81-82, 1955.
三宅泰雄, 放射能雨の性格, 測候時報, 22, No. 7, 196-202, 1955.
1954
三宅泰雄, 日本に降った人工放射性雨(1954 年 5-7 月), 天文と気象, 20, 1-8, 1954.
三宅泰雄, 杉浦吉雄,亀田和久, ビキニ海域における人工放射能の分布とその海洋学的考察"
ビキニ灰とそれによる傷害 第三", 科学, 24, 601-605, 1954.
三宅泰雄, 有住直介, 磯野謙治, 田島英三, ビキニの灰とそれによる傷害（つづき）日本に降った放射能雨, 科学, 24, No. 8, 405-409, 1954.

- 33 -

年代・雑誌名不明
1.
2.
3.
4.

三宅泰雄, 放射能とその作用, 遺伝,（1975？）
三宅泰雄, 第 II 編 環境の放射能汚染と化学分析,（1970？）
三宅泰雄, 猿橋勝子, 杉村行勇, 海洋における放射性核種, 海洋化学講座, 第 6 巻, 海洋無機
化学, 第 4 章, 109-170,（1969？）
三宅泰雄, 田島英三, 日米放射能会議, 8-18,（1954？）- 34 -

6. History of the studies at the Meteorological Research Institute

表：研究の歴史- 35 -

研究の歴史
放射能調査研究費以前および、放射能調査研究費での研究
西暦

年度

1954

昭和29年

1955

S30

北太平洋観測（日・米・加）

1956

S31

北太平洋赤道海域観測（日・米・仏）

1957

S32

国際地球観測年事業費

1958

S33

国際地球観測年事業費

1959

予算項目

予算項目
課題名

課題名

課題名

課題名

課題名

放射化学分析(落下塵・降放射能調査研究費 水・海水中の放射性物質の研究)

深海水の循環に関する研究(以下深海水）S34

放射能調査研究費 放射化学分析

深海水

1960

S35

国際原子力機関委託研究 放射能調査研究費 放射化学分析

深海水

1961

S36

国際原子力機関委託研究 放射能調査研究費 放射化学分析

深海水

1962

S37

国際インド洋観測（日・米・放射能調査研究費 放射化学分析
ソ・英・仏）

深海水

1963

S38

国際インド洋観測（日・米・放射能調査研究費 放射化学分析
ソ・英・仏）

深海水

1964

S39

オレゴン州立大学との共同研究

放射能調査研究費 放射化学分析

深海水
海水中における放射廃棄物の化学的挙動の研究(以下廃棄物化学的挙動）1965

S40

放射能調査研究費 放射化学分析

海洋中の放射性廃棄物のモニタリングの測定法
深海水に関する研究（以下海洋廃棄物モニタリング）

1966

S41

放射能調査研究費 放射化学分析

海洋廃棄物モニタリング 深海水

廃棄物化学的挙動

1967

S42

放射能調査研究費 放射化学分析

海洋廃棄物モニタリング 深海水

廃棄物化学的挙動

1968

S43

放射能調査研究費 放射化学分析

海洋廃棄物モニタリング 深海水

廃棄物化学的挙動

1969

S44

放射能調査研究費 放射化学分析

深海水

廃棄物化学的挙動

1970

S45

放射能調査研究費 放射化学分析

深海水

廃棄物化学的挙動

1971

S46

放射能調査研究費 放射化学分析

深海水

廃棄物化学的挙動

課題名課題名

放射能調査研究費以前および、放射能調査研究費での研究
西暦

年度

予算項目

予算項目
課題名

課題名

課題名

課題名

課題名

1972

S47

放射能調査研究費 放射化学分析

放射性固体廃棄物の海洋処分に伴う鉛直拡散に関する研究(以下廃棄物鉛直
拡散）

1973

S48

放射能調査研究費 放射化学分析

廃棄物鉛直拡散課題名課題名

大気中におけるKr-85
およびH-3挙動と蓄積
廃棄物鉛直拡散に関する調査研究(以下Kr-85／H-3）
廃棄物鉛直拡散Kr-85／H-3
廃棄物鉛直拡散Kr-85／H-3数種の放射性核種 原子力施設に由来するの同時測定による深 放射性気体の広域分
海拡散の研究(以下 布に関する調査研究(以下放射性気体）
深海拡散）

1974

S49

放射能調査研究費 放射化学分析

1975
1976

S50
S51

放射能調査研究費 放射化学分析
放射能調査研究費 放射化学分析

1977

S52

放射能調査研究費 放射化学分析

1978

S53

放射能調査研究費 放射化学分析

深海拡散

放射性気体

環境における超ウラン元素の分布と挙動に関する研究（以下超ウラン元素）

1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991

S54
S55
S56
S57
S58
S59
S60
S61
S62
S63
H1
H2
H3

放射能調査研究費
放射能調査研究費
放射能調査研究費
放射能調査研究費
放射能調査研究費
放射能調査研究費
放射能調査研究費
放射能調査研究費
放射能調査研究費
放射能調査研究費
放射能調査研究費
放射能調査研究費
放射能調査研究費

深海拡散
深海拡散
深海拡散
深海拡散
深海拡散
深海拡散
深海拡散
深海拡散
深海拡散
深海拡散
深海拡散
深海拡散
深海拡散

放射性気体
放射性気体
放射性気体
放射性気体
放射性気体
放射性気体
放射性気体
放射性気体
放射性気体
放射性気体
放射性気体
放射性気体
放射性気体

超ウラン元素超ウラン元素超ウラン元素超ウラン元素超ウラン元素超ウラン元素超ウラン元素超ウラン元素超ウラン元素超ウラン元素超ウラン元素超ウラン元素超ウラン元素放射性気体

超ウラン元素

放射性気体
放射性気体
放射性気体

超ウラン元素超ウラン元素超ウラン元素放射化学分析
放射化学分析
放射化学分析
放射化学分析
放射化学分析
放射化学分析
放射化学分析
放射化学分析
放射化学分析
放射化学分析
放射化学分析
放射化学分析
放射化学分析

1992

H4

放射能調査研究費 放射化学分析

海洋における放射性核種の挙動に関する調査研究（以下海洋放射性
核種）

1993
1994
1995

H5
H6
H7

放射能調査研究費 放射化学分析
放射能調査研究費 放射化学分析
放射能調査研究費 放射化学分析

海洋放射性核種
海洋放射性核種
海洋放射性核種

放射能調査研究費以前および、放射能調査研究費での研究
西暦

年度

予算項目

予算項目
課題名

課題名

課題名

課題名

課題名

課題名

1996

H8

海洋環境における放射
大気圏の放射性核種の動性核種の挙動に関する放射能調査研究費 態に関する研究（以下大気研究（以下海洋環境放
圏放射性核種）
射性核種）

1997

H9

放射能調査研究費 大気圏放射性核種

海洋環境放射性核種

1998

H10

放射能調査研究費 大気圏放射性核種

海洋環境放射性核種

1999

H11

放射能調査研究費 大気圏放射性核種

海洋環境放射性核種

2000

H12

放射能調査研究費 大気圏放射性核種

海洋環境放射性核種

2001

H13

海洋環境における放射
大気圏の放射性核種の長
性核種の長期挙動に関
放射能調査研究費 期的動態に関する研究(以する研究(以下海洋環境
下大気圏放射性核種）
放射性核種）

大気中の放射性気体の実態把握に関する研究(以下放射性
気体)

2002

H14

放射能調査研究費 大気圏放射性核種

海洋環境放射性核種

放射性気体

2003

H15

放射能調査研究費 大気圏放射性核種

海洋環境放射性核種

放射性気体

2004

H16

放射能調査研究費 大気圏放射性核種

海洋環境放射性核種

放射性気体

2005

H17

放射能調査研究費 大気圏放射性核種

海洋環境放射性核種

放射性気体

2006

H18

海洋環境における放射放射性降下物の長期変動性核種の長期挙動に関
放射能調査研究費 と再浮遊に関する研究（以する研究(以下海洋環境
下放射性降下物）
放射性核種）

2007

H19

放射能調査研究費 放射性降下物

海洋環境放射性核種

2008

H20

放射能調査研究費 放射性降下物

海洋環境放射性核種

2009

H21

放射能調査研究費 放射性降下物

海洋環境放射性核種

2010

H22

放射能調査研究費 放射性降下物

海洋環境放射性核種課題名

放射能調査研究費以前および、放射能調査研究費での研究
西暦

年度

予算項目

予算項目
課題名

2011

H23(8月から）「大気を通じた人工放射性
核種の陸圏・水圏への沈放射能調査研究費 着およびその後の移行過程の解明研究（以下陸圏
水圏人工放射性核種）」

2012

H24

放射能調査研究費 陸圏水圏人工放射性核種

2013

H25

放射能調査研究費 陸圏水圏人工放射性核種

2014

H26

放射能調査研究費 陸圏水圏人工放射性核種

2015

H27(4月から）「人工放射性核種のバック
グラウンド大気監視と数値
放射能調査研究費
解析に関する研究（以下ＢＧ放射性核種）」

2016

H28

放射能調査研究費 ＢＧ放射性核種

2017

H29

放射能調査研究費 ＢＧ放射性核種

2018

H30

放射能調査研究費 ＢＧ放射性核種

2019

R01

放射能調査研究費 ＢＧ放射性核種

2020

R02（4月から）「人工放射性核種の大気放射能調査研究費 長期変動監視と変動メカニ
ズム解析に関する研究」

2021

R03（４月から）「人工放射性核種の大気放射能調査研究費 長期変動監視に関する研
究」

課題名

課題名

課題名

課題名

課題名

課題名

表紙の図説明
2021 年 3 月までの 90Sr および 137Cs 月間降下量
福島第一原発事故後の大気の放射能汚染の実態把握を継続しています。2011 年3 月の事故以降に確定できた90Sr および137Cs 月間降下量を対数軸で誤差と併せてプロットしています。
134Cs は事故直後には 137Cs とほぼ等量降下していますから、放射性セシウム全体ではこのプロットのほぼ倍量となります。ただし、90Sr、137Cs はどちらもおよそ 30 年の半減期で減衰しますが、134Cs はおよそ 2 年で半減します。図示した誤差は計測の統計誤差で、１シグマです。測定誤差は本来全ての測定値につき表示すべきですが、グラフが見づらくなるため、従来はあえて表示してきませんでした。また、数十年前のデータについては誤差データが伝えられていません。
福島第一原発事故以前に採取され、分析や前処理途上だった試料については、事故の汚染によって実験室環境および測定室環境、測定機器のバックグラウンドなどが大幅に上昇したため、蒸発濃縮工程においても試料の汚染（コンタミネーションと言います）の問題が発生します。そのため、観測値を求めること自体が困難になっていましたが、試料を汚染の水準の低い関西にて分析しこの問題の回避に努めました。しかし、細心の注意と努力にもかかわらず、2010 年の後半および 2011 年初のデータは欠測となってしまいました。
徐々に実験環境の除染や測定機器の入れ替えなどを実施して事故以前のきれいな実験環境を追求し、データを求めるように努めています。また、IAEA が実施した分析相互比較に参画するなどし、その精度の維持管理に努めていますので、現状のデータの信頼性は担保できていると考えています。

- 36 -

7. A caption of the cover art
Monthly depositions of 90Sr and 137Cs before March 2018.
We have been monitoring the deposition amounts for the purpose of understanding the actual conditionof radioactive pollution after the accident of Fukushima Daiichi Nuclear Power Plant (FDNPP). The
monthly deposition amounts of both 90Sr and 137Cs, which were successfully fixed values after the accident
in March 2011, are shown in logarithmic axis with error widths. Since the deposition amounts of 134Cs were
nearly equal to those of 137Cs, the total amounts of radioactive cesium were approximately two times of thevalues in this figure. We would like to note that the half-lives of 90Sr and 137Cs are approximately 30 years,
however, 134Cs decays to half in about 2 years. The error widths are one sigma in statistical error ofmeasurements. Although the error widths of all measurement values were better to be shown, error values
used to be not shown on purpose to avoid busy figure in the former versions. In addition, error data before
several decades are not handed down.
For the analysis of samples collected before the accident of FDNPP and not measured then, significant
increase of background values were severe problem, because environments including laboratories andinstruments were polluted, and it was also problem that samples could have been polluted in the operationsof concentration or other preprocesses (contamination problem). We overcame the difficulty by carrying theanalysis out in western Japan (Kansai area) where the pollution level was low, however, the data during thelatter part of 2010 and the former part of 2011 were lacked in spite of our efforts.
We are making efforts to recover clean working environment as before the accident, by cleaning of theenvironments, exchanging instruments, etc. for the purpose of acquire the correct data. In addition, we
maintain the precision of the data, for example, by participating inter-comparison programs performed by
IAEA, so that, the credibility of the data are assured (please refer to the section: quality control of radioactive
analysis of atmospheric deposition samples ).

- 37 -