The authors gratefully acknowledge Michelle Gonzales, Kenny Kidd, Brad Osbon, and all other personnel that conducted the laboratory procedures for these data. The authors are thankful for assistance from Andrew Scott in developing software coding to conduct model-fitting procedures. The authors also appreciate the funding from the U.S. Department of Agriculture National Institute of Food and Agriculture, Sustainable Agriculture and Research &amp; Education, Sun Grant South Central region, and the National Council of Air and Stream Improvement that made possible the studies from which representative results provided in this paper were drawn.

1.実験エリア内および実験的ユニット20内の条件のサンプルの代表を取得するために土壌を収集<ol><li>テクスチャ、嵩密度、pHは、有機地平線の深さ、および/または栄養素の濃度を含む、このような斜面や土壌特性などのサイトの特性の違いを識別します。プロット内の植生タイプの違いを識別します。サイトの特性が予め指定された相対誤差を達成するために必要なサンプルの数を推定するための変動係数の既知の又は公表さ推定を使用。 </li><li>サイトおよび実験装置の条件に基づいたパターンで、オーガまたは他の収集装置を使用して、サンプル土壌。 <ol><li>均質な条件については、それぞれの実験装置内でランダムサンプリングパターンを使用します。 <ol><li>実験的ユニット内またはジグザグパターンでは完全にランダムな位置のいずれかでサンプルポイントを割り当てます。 </li><li>各ランダム点で、またはジグザグパタパタに割り当てられたポイントでサンプル土壌n個。土壌サンプルを掘削するためにオーガーまたは他の収集装置を使用する前に、鉱質土壌の表面から有機物を払いのけます。 注：SFI方法はOAからと13以下の土壌層位を使用して開発されました。 Oaの上記視野のSFI法を用いて試験することができるならば、さらなる試験が必要です。 </li><li>単一の容器に実験単位以内収集されたすべてのサンプルを組み合わせて、物理的に各実験装置用の複合サンプルを作成するために、容器内の個々のサンプルを混ぜます。 </li></ol></li><li>はるかに一般的である異種の条件については、それぞれの実験ユニット内の系統的なサンプリングパターンを使用します。 <ol><li>横断中のサンプル点の間の距離は、実験単位内の変動性を表すために必要な距離よりも小さくなるように各実験装置の中央に横断沿ってサンプル土壌。 </li><li>各EX内の複数のトランセクトに沿ってサンプルの土壌変動の複数のソースを持つ比較的大きな実験単位または実験単位のグリッドパターンを形成perimentalユニット。 </li><li>単一の容器に各トランセクトに沿って収集したすべてのサンプルを組み合わせて、物理的に各トランセクト用の複合サンプルを作成するために、容器内の個々のサンプルを混ぜます。 </li></ol></li></ol></li></ol> 2. SFIアッセイのための土壌を準備<ol><li>アイスパックに入れサンプルは、現場で収集した後、クーラーすぐに満たされました。 </li><li>施設に到着するとれる試料は、試料調製及びSFI手順が実施されるまで、4℃の冷蔵庫に分析箇所サンプルまで格納されます。 </li><li> 6.4ミリメートルX 6.4ミリメートルメッシュのふるいを通してふるい土壌サンプル。サンプル間の汚染を防ぐために、各サンプル間の水とメッシュを清掃してください。 </li><li>各サンプルについて、3 100グラムのサブサンプルを測定し、250ミリリットルのビーカーに100グラムのサブサンプルを配置します。 EACをカバーパラフィルムで時間ビーカー、25℃で10日間カウンターにそれらを残します。 </li></ol> 3.オーブン乾燥重量決意のための副試料を取ります<ol><li>土壌サンプルの10日のプレインキュベーションの終わりに、各試料からパラフィルムを除去します。 </li><li>アルミボートの重さの重量を記録します。計量ボート内のすべてのサンプルと場所から土壌1グラムを取ります。 </li><li>湿った土の重量を記録し、ボートの重量を量ります。 </li><li> 105℃のオーブンで土壌とボートの重量を量る置きます。サンプルは、48時間後に、典型的には、一定重量、計量ボートや土壌のレコードの重みに到達した後。 </li><li>しっとりと乾燥土壌の重量を得るために計量ボート内の湿った土壌や乾燥土壌を撮影した重量からボート重量を量る引きます。湿った土の重量で乾燥土壌の重量を割ることによって湿った土壌比：乾燥を求めよ。 </li></ol> 4.土壌サンプルを燻蒸<ol><li> （MOR少なくとも二つの底に湿ったペーパータオルを置きeは磁器プレートと10.5 Lガラス真空デシケーター）サンプルの数に応じて必要であり得ます。 </li><li>すべてのサンプルについて、3つの別個のガラスバイアルに土壌30gの重量を量ります。 5章で説明した培養容器のデザインが使用されている場合、40ミリメートルの開口部内に収まるように土壌の40グラムを保持するのに十分な大きさと十分に狭いバイアルを使用してください。 </li><li>各30グラムの土壌のサブサンプルを識別するための標識テープを使用している場合は燻蒸は、インクが低下するため、鉛筆を使用しています。 </li><li>燻蒸や燻蒸を行いません真空デシケーター中に1サブサンプル用の真空デシケーターに各土壌サンプルのための3つの30グラムのサブサンプルのうちの2つを配置します。 </li><li> 100ミリリットルのビーカーでは、ビーカーの底をカバーするのに十分な沸騰石の層を配置します。 </li><li>沸騰石の層で100ミリリットルのビーカーにエタノールフリーのクロロホルム（CHCl 3を）の50ミリリットルを注ぎます。 30グラムの土壌を詰めデシケーターの中央に沸騰石、CHCl 3で100ミリリットルのビーカーを置きサブサンプル。ヒュームフードの下で、この工程を行います。 </li><li>ヒュームフードの下では、土壌サンプルあたりのサブサンプルの2セットを燻蒸するための CHCl 3を沸かすために真空を使用しています。 <ol><li>真空管と真空デシケーターに真空を接続します。真空を起動し、CHCl 3が沸騰し始めると見ます。 </li><li> CHCl 3を 30秒間沸騰し、空気が戻ってデシケーターに流れることを可能にするためにデシケーターから真空管を切断することができます。このステップでは、土壌試料へのCHCl 3ガス進入を促進します。二回繰り返します。 </li><li>それは2分間沸騰することを可能にする、のCHCl 3最後の第4沸騰を実行します。 </li><li>デシケーター内で真空が維持されるように真空がまだ実行中で、真空デシケーターにシールを閉じます。真空の電源をオフにして、デシケーターから真空チューブを外します。 </li></ol></li><li>デシケーターの蓋を置き、真空ストッパーを封止することにより、非燻蒸サンプルを含むデシケーターを密閉します。 P24時間（例えば、キャビネットなど）暗くなった地域にデシケーター（燻蒸および非燻蒸）をひもで締めます。非燻蒸サンプルを含むデシケーターにサブセクション4.7の真空手順を繰り返さないでください。 </li></ol> 5.土壌サンプルのインキュベーションのためのコンテナを組み立て<ol><li>中央にドリル穴でサイズ10ゴム栓を介して長さ15cmのガラス棒を押してください。ロッド径はぴったりの穴を通り抜けるのに十分であるべきです。 </li><li>燻蒸および非燻蒸サブサンプルの識別に対応する識別情報と0.5 L半透明の広口ポリプロピレン瓶にラベルを付けます。 </li></ol>ヒュームフードの下でデシケーターから6.避難クロロホルム<ol><li>デシケーターに空気の流れを可能にするために、真空デシケーターにストッパーを開きます。デシケーターから蓋を外し、デシケーターからサンプルや湿ったタオルを取ります。 </li><li>土壌サンプルからのCHCl 3ガスを排気するために真空を使用してください。 <ol><li&gt;はデシケーターに蓋を置きます。真空チューブを真空にデシケーターを接続します。 </li><li>真空ポンプの電源をオンにし、ポンプは5分間実行することができます。デシケーターに空気の流れを可能にするために、デシケーターから真空チューブを外します。 </li><li>繰り返しステップ6.3.2 4回。 </li></ol></li></ol> 7. 10日間のインキュベーションを実施するインキュベーション容器（図1）に各土壌サブサンプルを移動<ol><li>ピペット培養容器に脱イオン水1ml。ゴムバンドを使用して、サイズ10のストッパーから伸びるガラス棒に空のガラスバイアルを接続します。ガラスバイアルの開放端は、ストッパのベースに直面している必要があります。ガラスバイアルは、流体の40ミリリットルまで保持するのに十分な大きさであるべきです。 </li><li>培養容器に30グラムの土壌のサブサンプルを含むバイアルを置きます。 </li><li> inoculuとして（燻蒸および非燻蒸）、それに対応するサブサンプルのそれぞれにオリジナルの土壌サンプルから非くん蒸土壌1グラムを追加メートル。 </li><li>ピペットストッパー/ガラス棒に接続されたガラスバイアルに2 M NaOHを1ミリリットル。培養容器の上部にストッパー/ガラス棒を押してください。パラフィルムで培養容器の上部をカバーしています。 </li><li>何の土壌を含まない培養容器を作成します。 3〜5個の無土壌の培養容器を組み立てます。 注：無土壌コンテナのサンプルを滴定するために使用される酸は、サブセクション9.3に説明されたインキュベーション時間、中のCO 2鉱化作用の決意に不可欠です。このように、複数の無土壌の容器はすべてのサンプルについて、CO 2鉱化作用の計算に誤りを作成し、無土壌培養容器の誤った取り扱いや滴定に対する保護手段として作成されます。無土壌容器から試料を滴定するために使用される酸は、値の近くなければなりません。無土壌コンテナサンプル間で高度に異種の酸値が正しくない可能性がサンプル処理または滴定の結果です。 <ol&gt; <li>培養容器を組み立てるためにセクション5の手順に従ってください。 </li><li> 7.1と7.4の手順に従ってください。 </li></ol></li><li> 25℃で暗くなった記憶領域の全ての培養容器を置きます。 10日間の記憶領域内の全ての培養容器のままにしておきます。 </li></ol> 8.インキュベーション期間中に微生物の呼吸によって生成されるCO 2を定量化するために、各サブサンプルに滴定を行います<ol><li>培養容器から2 M NaOHを含むガラスバイアルを取り外します。 </li><li>ピペット2 M NaOHを含むガラスバイアルに1 MのBaCl 2を2ml。 </li><li> （C 20 H 14 O 4）ピペットや薬スポイトからのBaCl 2およびNaOHの混合物を含むガラスバイアルにフェノールフタレインの一滴を追加します。ガラス瓶に磁気攪拌棒を置き、撹拌プレート上ガラスバイアルを置きます。 </li><li>攪拌プレートが作動すると、ゆっくりと再までビュレット0.1 N塩酸を追加ガラスバイアル中の混合物のd着色が明確になります。 </li><li>ガラスバイアル中の混合物の着色を変更するために必要な塩酸の量を記録します。 </li></ol>まず燻蒸インキュベーションサイクル16,21,22の間に収集されたデータから微生物バイオマスCを決定<ol><li>ステップ3.8で得られた湿潤重量比：乾燥することにより、その湿潤重量を乗じて、各サブサンプルの土壌の乾燥重量を決定します。 </li><li>無土壌の培養容器を滴定するために使用される塩酸の平均量を決定します。 </li><li>式を用いて10日間のインキュベーション中のCO 2鉱化を計算します。 <img alt="式（1）" src="/files/ftp_upload/54614/54614eq1.jpg" /> 10日間のインキュベーション中のどこにCO 2 = CO 2鉱化 NS =酸は、無土壌の培養容器内のサンプルを滴定するために使用しました S =酸は、培養容器内の土壌を含まれるサンプルを滴定するために使用しました M番目の=モル濃度電子塩酸 E = 6、当量 培養容器に含まれる土壌のW =乾燥重量</li><li>式を用いて微生物バイオマスCを計算します。 <img alt="式（2）" src="/files/ftp_upload/54614/54614eq2.jpg" /> ここBIOC =微生物バイオマスC 燻蒸された土壌のサブサンプルからF = CO 2鉱化 非燻蒸した土壌のサブサンプルからNF = CO 2鉱化 K =微生物バイオマスCの鉱化の割合は、CO 2に<ol><li>土壌または公表値22と予備試験における14 Cの鉱化作用の直接測定のいずれかによって、Kの値を決定します。 0.45の値は、一般的に、このアッセイの23 Kのために使用されます。 </li></ol></li><li>最初の燻蒸インキュベーションサイクルで燻蒸された土壌のサブサンプル用のセクション4-8 7回繰り返すことによって、順次燻蒸およびインキュベーションのサイクルを実行します。 </li></ol> 10. Determ伊根不安定Cと八燻蒸およびインキュベーションサイクルのコース上でのCO 2石灰を使用して潜在的なCの回転率<ol><li>各燻蒸後の試料に加え、土壌接種のための補正係数を決定するには、次の式を使用します。 <img alt="式3" src="/files/ftp_upload/54614/54614eq3.jpg" /> ICは、接種材料のための補正係数=どこ C &#39;= CO 2の量非燻蒸サブサンプルから最初の10日間のインキュベーション中 最初の燻蒸インキュベーションサイクルにおける燻蒸土壌への接種土壌のR =重量比 CのT =インキュベーションサイクル（1、2 ... 8）、例えばT = 1 C T-1 = 0という</li><li>各サブサンプル用の各インキュベーションの間に放出されたCO 2を推定するために、次の式を使用します。 <img alt="式4" src="/files/ftp_upload/54614/54614eq4.jpg" /> Ct = CO 2は、インキュベーション中に放出場所 NS =酸試料を滴定するために使用しました無土壌培養容器内 S =酸は、培養容器内の土壌を含まれるサンプルを滴定するために使用しました ICは（ステップ10.1において決定された）接種材料のための補正係数= E = 6、当量 培養容器に含まれる土壌のW =乾燥重量</li><li>非線形回帰を使用して不安定な有機Cを導出します。 <ol><li>サンプル用の各サンプル識別子に含まれるスプレッドシートを整理し、インキュベーション中に放出され、インキュベーションサイクル数（1、2 ... 8）、およびCO 2は、（ステップ10.2で得られます）。 </li><li>非線形回帰することのできるソフトウェアを使用して、データセットに次のようなモデルに適合： <img alt="式（5）" src="/files/ftp_upload/54614/54614eq5.jpg" /> CSUMは8インキュベーションサイクル中に放出されたCO 2の合計を=場所 LOC =土壌不安定な有機C K =潜在的な回転時間 T =インキュベーションサイクル（1、2 ... 8） </li></ol></li><li>潜在的な売上高のトンを変換します10日間のインキュベーションサイクルを10によるkの逆数を乗じて日にステップ10.3.2からIME。 </li></ol>

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R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Determination+of+microbial+biomass+carbon+and+nitrogen+in+soil.">Determination of microbial biomass carbon and nitrogen in soil.</a> Advances in nitrogen cycling in agricultural ecosystems. , 368-386 (1988).</li><li>Sparling, G. P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Interference+from+plant+roots+in+the+estimation+of+soil+microbial+ATP,+C,+N,+and+P.">Interference from plant roots in the estimation of soil microbial ATP, C, N, and P.</a> Soil Biol. Biochem. 17, 275-278 (1985).</li><li>Christie, P., Beatte, J. A. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Significance+of+sample+size+in+measurement+of+soil+microbial+biomass+by+the+chloroform+fumigation-incubation+method.">Significance of sample size in measurement of soil microbial biomass by the chloroform fumigation-incubation method.</a> Soil Biol. Biochem. 19, 149-152 (1987).</li><li>McLaughlin, K. K., Hobbie, S. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comparison+of+labile+soil+organic+matter+fractionation+techniques.">Comparison of labile soil organic matter fractionation techniques.</a> Soil Sci. Soc. Am. 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The authors have nothing to disclose.

The SFI method is an effective protocol for detecting differences in soil LOC and potential C turnover rates over a range of management practices (such as fertilization, tillage, vegetation control, and harvest practices) and soil conditions. Soil LOC content and C turnover rate can be used to understand alterations of nutrient cycles. The SFI method also provides measurement of microbial biomass C from the first fumigation-incubation event. The ability to measure soil LOC, C turnover, and microbial biomass C concurrently makes the SFI method a relatively cost-effective analytical method. 
Measuring additional soil parameters along with those collected with the SFI method can provide a broader understanding of the impacts of changes in LOC and its associated variables on nutrient cycling. Total C concentration of the soil provides the ability to determine changes in the size of the soil LOC pool relative to total C29. Including assays of soil enzymes facilitates understanding of microbial utilization of the labile C substrate that leads to changes in soil nutrient forms and quantities. For example, dehydrogenase plays a key function in organic matter degradation and is a useful indicator of overall microbial actitivity30. Urease and phosphatase are examples of enzymes associated with nutrient cycling because they are correlated with microbial transformations of nitrogen and phosphorus, respectively31,32.
There are variations in procedures of the SFI method, and the number of fumigation and incubation cycles is adjustable to different soil conditions. In the experiment in which the SFI method was developed, five or eight cycles were used for the array of tropical and subtropical forest soils tested13. In that experiment, five fumigation-incubation cycles were used for the Oa horizon and deeper mineral soil horizons of an Alfisol soil while eight cycles were used for Ultisol, Oxisol, Inceptisol, and Entisol soils. More recent experiments with the SFI method have used five28 and seven11 fumigation-incubation cycles. The key consideration for the number of cycles is whether CO2 accumulation plateaus over the course of the cycles. The SFI method fails to provide estimates of labile organic C and potential turnover if the negative exponential model does not fit the CO2 accumulation data. For some soil conditions, such a plateau can be reached within five fumigation-incubation cycles whereas for others more cycles are necessary. Preliminary testing of the SFI method on the soil conditions of interest can aid in determining the appropriate number of cycles necessary. Graphing CO2 accumulation trends for all samples after each fumigation-incubation cycle can also aid in determining accuracy of the number of cycles to be used.
Due to its dependence on CO2 accumulation to derive the LOC and C turnover rate, accurate measurements of CO2 evolution during incubation are essential to the SFI method. The long history associated with the use of the chloroform fumigation-incubation method from which the SFI method was developed has provided a number of recommendations to minimize potential errors. For example, the 10-day pre-incubation detailed in this SFI protocol description is recommended to allow the influence of soil disturbance to subside and for living fragments of roots to die in order to better isolate microbial CO2 evolution during the initial incubation period33,34. There are several alternatives for incubation container design, but essential components of design include a vial with NaOH to react with CO2 from microbial respiration, separate containers for the NaOH and soil, a source of moisture to prevent soil from drying during incubation, and seals to prevent contamination from ambient air during the incubation period22. Care must also be taken to avoid breathing near the vial of NaOH, which would partially neutralize the alkali and yield erroneous CO2 measurements, during incubation container assembly22. Sample depth in the incubation container can affect accuracy of CO2 measurements. In a test of soil sample depths within incubation containers, sample depths less than 4 cm had significantly greater inconsistency in CO2 evolution measurements than depths from 4 to 10 cm35. The higher inconsistency from the shallower depths was attributed to greater potential for soil drying during incubation, greater diffusion of O2 into soil, greater diffusion of CO2 out of the soil, and a lower potential for anaerobic microorganisms to contribute to decomposition. Soil pH may be another important consideration for the accuracy of CO2 accumulation measurements. After chloroform fumigation, recolonizing microorganisms during the incubation period are primarily bacterial populations. However, at soil pH values under 4.5, bacterial population development is inhibited. For the chloroform fumigation-incubation method, this effect leads to underestimation of soil microbial biomass C relative to that measured with other methods. Effects of soil pH on values yielded by the SFI method have not yet been tested, but precaution about using the method on low-pH soils may be warranted due to the known effects of pH on chloroform fumigation-incubation results.
The SFI method is one of several methods for measuring LOC, and it is important to distinguish measurements from this method relative to that of others. All LOC methods are an attempt to emulate natural processes of microbial degradation in soil to estimate LOC pool size, but these methods can differ in the portions of the LOC pool they sample and estimate36. In a study of labile organic pools in the Wuyi Mountains of China, LOC was measured with KMnO4 oxidation, aqueous extraction, physical fractionation, and the SFI method37. Measurements from each method were positively correlated with each other except for light fraction carbon as measured by physical fractionation. However, there were differences in the ranges of values derived from each method that were a function of the different components of the LOC pool each method measured. Generally, the magnitude of LOC values derived from each method was physical fractionation &gt; KMnO4 oxidation &gt; SFI &gt; aqueous extraction. Physical fractionation was done as a measure of light fraction C, which is typically comprised of plant residues and animal remains. However, light fraction C can also contain charcoal and woody debris that decomposes relatively slowly; this LOC pool is an intermediate stage between plant residue and stable organic matter. Labile organic C values from KMnO4 oxidation (termed easily oxidized C) were approximately twice as large as those of the SFI method, which was attributed to the procedure sampling fractions of LOC with slower turnover than the SFI and aqueous extraction methods. Labile organic C measurements from aqueous extraction (termed water-soluble organic C) were approximately ten-fold lower than those of the SFI method. Due to microbial degradation of C being central to the SFI method, its LOC values were interpreted as microbial available C. These microbial available C values were greater than those of microbial biomass C measured in the same experiment, indicating that some of the C remaining in the soil after measuring microbial biomass C was available for mineralization by microbes. Greater values for LOC from the SFI method than those of microbial biomass C are consistent with several previous studies13,24,25,26,27. The SFI method has been used in several studies (which represent the majority of reported uses of this method to the authors' knowledge) conducted in an array of locations, vegetation types, and soil conditions11,13,24,25,26,27,28,38. Given the apparent differences in the fractions of LOC pools measured by different methods, future applications of the SFI method should be done in concert with LOC methods that estimate different types of labile C as done in the Wuyi Mountain study36. Distinguishing LOC measured by the SFI method as microbial available C may be warranted to more precisely describe the type of LOC estimated by SFI. Inferences derived from the SFI method could also be improved by refining understanding of the portion of the LOC pool that is estimated by SFI and its roles in soil organic C and nutrient cycling processes.

炭素（C）と栄養循環や土壌の変化に対する感度におけるその重要な役割に、土壌のLOCは、土壌有機物の質の指標として測定するための重要なパラメータです。大規模な程度に森林や農業生態系は、栄養源として土壌有機物中の栄養素の鉱化作用に依存します。管理活動は、栄養供給1の変更を伴う、土壌有機Cのプールサイズと離職率を変更することができます。土壌有機Cは数年2,3,4に数週間から離職率を持って反抗数千年の離職率を持つC、およびLOC、2つの主要画分で構成されています。土壌に不安定なCは、このような植物リター1,4,5から微生物バイオマスC、低分子量化合物（アミノ酸、単純な炭水化物）植物rhizodepositionから、分解副生成物や浸出水として容易に分解性の基材で構成されています。土壌に不安定なCは、容易に分解可能であるため、それがあります邪魔や土壌6を変える経営慣行や自然現象に非常に敏感。土壌不安定なCは、有機物7の分解における土壌微生物のための一次エネルギー源として機能します。土壌有機C 8の安定な形態の場合よりも大きい程度に、このような、LOCの影響栄養素サイクリングなど。土壌微生物はまた、LOC 9,10,11のプライミング効果によって容易に反抗土壌有機物の分解時に発生する従属栄養呼吸の大部分を担っています。土壌有機Cは約2倍、その大気のC 11のであるため、この呼吸は、グローバルCサイクルにおける実質的な役割を果たしています。 陸上生態系におけるその重要性の結果として、いくつかの方法は、土壌LOCを推定するために開発されてきました。物理的、化学的、および生化学的なこれらの方法は、3つの一般的な分類に描写することができます。デンシトメトリーの分離方法は、物理メタあります重鎖または軽画分にまたは粗微粒子有機C 12,13,14,15に土壌有機C分離で構成されてODS。分離方法は、実行するのが比較的容易であるが、これらの画分は、土壌型鉱物組成に応じて変化するので、それらは多くの場合、植物材料の大きさと密度、土壌凝集一貫13,15、一貫した結果を生じません。分離方法はまた、LOC 15についてのみ定量的情報を生成します。 いくつかの化学的方法は、LOC推定のために用意されています。有機炭素の水性抽出を実行するのが比較的容易であり、方法は、多くの場合、容易に再現可能な結果を​​提供します。しかし、これらの抽出物は、微生物15のために利用可能な基板の全体のスペクトルを伴いません。土壌有機Cの化学的分別のためのいくつかの酸化方法が開発されています。酸化方法は、不安定な有機Cの量と質を特徴付けるという利点を有します、いくつかの方法が有害化学物質との仕事を必要とし、結果15の再現性の方法のうちのばらつきはあるものの。酸加水分解の抽出方法は、LOCの量および質を測定することができる化学的分画手順の別のタイプであるが、この方法の結果は、その生物学的特性13,15の解釈を容易にしません。 土壌LOCの解釈のための生化学的方法が開発されています。 CO 2は、呼吸アッセイにおいて微生物によって放出されるように不安定な有機Cを測定することができます。これらのアッセイは、真の無機化有機物質の推定値を提供するが、典型的には、唯一の最も不安定な化合物は、アッセイ15中に鉱化されています。燻蒸インキュベーション16及び燻蒸抽出17によって測定された土壌微生物バイオマスCはLOCに関する推論を開発するために使用されてきました。しかし、これらの手順は、微生物バイオマスはなく、LOにCの推定値を提供しますC.燻蒸両方の手順は、微生物バイオマスCを決定するために、非燻蒸土壌からの値の減算を含むが、非燻蒸土壌を減算せずに得られた値は、微生物バイオマス18に加えて、Cの不安定な有機画分の測定値を与えることが示唆されています。 LOCを測定するためのシーケンシャル燻蒸インキュベーション（SFI）の手順13土壌微生物バイオマスC測定のための燻蒸インキュベーション手順16から適応生化学的な方法です。 SFI方法はLOCを推定する他の方法に比べいくつかの利点を有します。この方法のための概念的基礎は、LOCは、微生物の成長を支配する微生物分解性Cであり、そのLOCが物理的にアクセス可能と土壌微生物による化学的に分解可能であるということです。フィールド条件下で、微生物の増殖は、典型的には、炭素の可用性、栄養素利用可能性、利用可能な孔空間、及び/又は捕食によって制限されます。これらの要因は、ほぼelimiあります微生物の増殖のために妨げられない条件を作成し、燻蒸によっていますか。いいえ栄養素がメソッドのインキュベーション期間の間に除去されていません。複数の燻蒸およびインキュベーションのサイクルの間に、微生物の増殖は、C量と質（不安定性）13によって制限されるようになります。インキュベーションのサイクル中に蓄積CO 2呼吸は、単純な負の指数モデル11,13,19とのLOCを推定するために使用されます。潜在的なCの代謝回転速度はまた、指数モデルの傾きから得ることができるので、SFI法が同時に濃度およびLOC 11の潜在的な代謝回転速度を推定する他のほとんどのLOC方法に優る利点を有します。このような14 Cなどのトレーサーは13を使用している場合は他の方法については、LOCの潜在的な離職率に関する情報のみを確認することができます。 SFI方法は、このようにLOCとその潜在的な離職率の両方の測定値を得るために、比較的簡単で安価な手法です。

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			<td height="21" style="height:21px;width:259px;">Soil auger sampling kit</td>
			<td style="width:131px;">JMC</td>
			<td style="width:119px;">PN039</td>
			<td style="width:167px;">Several other manufacturers of punch augers are available</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:259px;">Parafilm</td>
			<td style="width:131px;">Curwood</td>
			<td style="width:119px;">PM999</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:259px;">Aluminum weighing boats</td>
			<td style="width:131px;">Fisherbrand</td>
			<td style="width:119px;">08-732-103</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:259px;">General purpose drying oven</td>
			<td style="width:131px;">Fisher Scientific</td>
			<td style="width:119px;">15-103-0511</td>
			<td>Many other manufacturers of general purpose laboratory ovens are available</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:259px;">10.5 L vacuum desiccator</td>
			<td style="width:131px;">Corning</td>
			<td style="width:119px;">3121-250</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:259px;">Glass scintillation vial</td>
			<td style="width:131px;">Wheaton</td>
			<td align="right" style="width:119px;">968560</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:259px;">Glass threaded vials, 41 mL&#160;</td>
			<td style="width:131px;">Fisherbrand</td>
			<td style="width:119px;">03-339-21N</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:259px;">Chloroform, stabilized with amylenes</td>
			<td style="width:131px;">Sigma-Aldrich</td>
			<td style="width:119px;">67-66-3</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:259px;">Boiling chips</td>
			<td style="width:131px;">Fisher Scientific</td>
			<td style="width:119px;">S25201</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:259px;">Glass rod</td>
			<td style="width:131px;">Fisherbrand</td>
			<td style="width:119px;">S63449</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:259px;">Size 10 rubber stopper</td>
			<td style="width:131px;">Fisherbrand</td>
			<td style="width:119px;">14-130P</td>
			<td>Rubber stoppers can be purchased as solid and drilled in center to install glass rod or bought with a hole to insert glass rod</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:259px;">Wide-mouth PPCO bottle, 0.5 L</td>
			<td style="width:131px;">ThermoScientific</td>
			<td align="right" style="width:119px;">3121050016</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:259px;">Sodium hydroxide, reagent grade</td>
			<td style="width:131px;">Sigma-Aldrich</td>
			<td style="width:119px;">S5881</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:259px;">Barium chloride</td>
			<td style="width:131px;">Sigma-Aldrich</td>
			<td align="right" style="width:119px;">202738</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:259px;">Phenolphthalein indicator</td>
			<td style="width:131px;">Fisher Scientific</td>
			<td style="width:119px;">S25466</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:259px;">Hydrochloric acid solution, 0.1 N</td>
			<td style="width:131px;">Fisher Scientific</td>
			<td style="width:119px;">SA54-4</td>
			<td></td>
		</tr>
	</tbody>
</table>

assessment of labile organic carbon in soil using sequential fumigation incubation procedures

管理慣行と環境変化は、土壌養分と炭素循環を変更することができます。土壌不安定な有機炭素、容易に分解Cプールは、外乱に対して非常に敏感です。また、栄養循環の基本である土壌微生物のための主要な基板です。これらの属性に、不安定な有機炭素（LOC）は、土壌の健康の指標パラメータとして同定されています。 LOCの回転率を定量化することも、土壌の栄養循環プロセスの変化を理解するのに役立ちます。シーケンシャル燻蒸培養方法は、土壌LOCと電位C代謝回転速度を推定するために開発されました。この方法は、土壌サンプルを燻蒸や燻蒸インキュベーション一連のサイクルで10日間のインキュベーション期間中にCO 2 -Cの呼吸を定量化する必要があります。不安定な有機Cおよび潜在的なCの離職率は負の指数モデルで蓄積されたCO 2から推定されています。この方法を実施するための手順を説明していますD。

米国南東部24,25,26,27で行われた一連の実験で、この論文に記載されているようにSFIの方法が用いられてきました。一緒に、これらの実験は、テーダマツの松（ アカマツタエダ L.）、スイッチグラス（ キビvirgatum L.）、ハコヤナギ含め、植生タイプのさまざまな包含（ ポプラdeltoidesバートラム元マーシュ。）、及び大豆（ ダイズ L. MERR。）。この方法は、受精の間でLOCおよび/または潜在的なCの離職率の差を決定し、すべての試験で練習トリートメントをトリミングに感受性でした。 LOCとの研究（ 図2）のこのシリーズで報告された潜在的な離職率の範囲内の重複がありました。これらの研究で報告されたLOCの範囲の変動が不安定なCおよび潜在的なCの離職率の違いを検出する際にSFI法の感度を強調表示します。テーダマツの松とスイッチグラスの路地作付システムは、グラムを持っていました植生タイプ間のLOCのreatest範囲;この植生タイプの研究は、他の植生タイプに比べて立地条件の広い配列を包含しました。サイトでは、少年から後期の回転に土壌の種類とテーダマツ上木の時代に変化しました。上木年齢のクロノ可能性が高い代表的な結果を得るためにここに提示植生タイプ間で有機物の入力で最大のバリエーションを作成しました。スイッチグラス牧草地植生タイプは、土壌テクスチャの広い配列で検討した、そしてそれはまた、報告された値が比較的高い分散を示しました。大豆の植生タイプは、おそらくその収穫時の土壌に死ん上述と地下部バイオマスの年間沈着に関連したサイトの種類の中では比較的高いLOC値を、持っていました。土壌有機物の主な原因として、比較的扱いにくい酸性の松のゴミを特徴とするテーダマツ植林は、午前最高電位Cの離職率を示しました代表的な結果についてはこちらを選択した植生タイプオング。研究のこのシリーズで報告された値の範囲は、SFI方法13を開発するために使用される土壌の範囲内とSFI法を開発した科学者のいずれかで、中国の亜熱帯林で実施後の実験に見られるものの範囲内であります11,28。 <img alt="図1" src="/files/ftp_upload/54614/54614fig1.jpg" /> 不安定な有機Cおよび潜在的なCの離職率の決意のためにシーケンシャル燻蒸インキュベーション手続きを行うための図1.培養容器。左側にはナルゲンボトルは、NaOHを含むようにストッパロッドから吊り下げバイアル、および土壌の30グラムを含むバイアルを分離示されていますデモ目的のために。右側のコンテナは、土壌およびインキュベーション中に行われることになるように上部に沿ってパラフィルムでナルゲンボトル内に配置されたストッパロッドを持っています。<a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/54614/54614fig1large.jpg" target="_blank">この図の拡大版をご覧になるにはこちらをクリックしてください。</a> <img alt="図2" src="/files/ftp_upload/54614/54614fig2.jpg" /> 米国南東部で異なる土壌と植生状況のシーケンシャル燻蒸培養法で測定した不安定な有機Cおよび潜在的なCの離職率の 図2. 範囲。報告範囲は、以前の研究24,25,26,27から部分的に適合されています。植生タイプは、次のとおりです。（1）テーダマツ農園、（2）テーダマツとバヒアグラス牧草地、（3）テーダマツおよびスイッチグラス路地トリミングシステム、（4）スイッチグラスの牧草地、（5）大豆、及び（6）ハコヤナギ農園。バーは標準偏差を表す。 <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/54614/54614fig2large.jpg" target="_blank">番目の拡大版をご覧になるにはこちらをクリックしてください。</a>図です。

Watch this Scientific Journal Video about Assessment of Labile Organic Carbon in Soil Using Sequential Fumigation Incubation Procedures at JoVE.com

Assessment of Labile Organic Carbon in Soil Using Sequential Fumigation Incubation Procedures

Labile organic carbon (LOC) and the potential carbon turnover rate are sensitive indicators of changes in soil nutrient cycling processes. Details are provided for a method based on fumigating and incubating soil in a series of cycles and using the CO2 accumulated during the incubation periods to estimate these parameters.

シーケンシャル燻蒸インキュベーション・プロシージャを使用した土壌中の不安定な有機炭素の評価

Management practices and environmental changes can alter soil nutrient and carbon cycling. Soil labile organic carbon, a readily decomposable C pool, is ...

assessment-labile-organic-carbon-soil-using-sequential-fumigation

Nanyang Technological University

Research

JoVE Journal

Environment

11.4K ビュー.  Louisiana State University Agricultural Center. Labile organic carbon (LOC) and the potential carbon turnover rate are sensitive indicators of changes in soil nutrient cycling processes. Details are provided for a method based on fumigating and incubating soil in a series of cycles and using the CO2 accumulated during the incubation periods to estimate these parameters.

ビデオ: Assessment of Labile Organic Carbon in Soil Using Sequential Fumigation Incubation Procedures

Biofilm Removal Using Carbon Dioxide Aerosols without Nitrogen Purge

Biofilms can cause serious concerns in many applications. Not only can they cause economic losses, but they can also present a public health hazard. Therefore, it is highly desirable to remove biofilms from surfaces. Many studies on CO2 aerosol cleaning have employed nitrogen purges to increase biofilm removal efficiency by reducing the moisture condensation generated during the cleaning. However, in this study, periodic jets of CO2 aerosols without nitrogen purges were used to remove Pseudomonas putida biofilms from polished stainless steel surfaces. CO2 aerosols are mixtures of solid and gaseous CO2 and are generated when high-pressure CO2 gas is adiabatically expanded through a nozzle. These high-speed aerosols were applied to a biofilm that had been grown for 24 hr. The removal efficiency ranged from 90.36% to 98.29% and was evaluated by measuring the fluorescence intensity of the biofilm as the treatment time was varied from 16 sec to 88 sec. We also performed experiments to compare the removal efficiencies with and without nitrogen purges; the measured biofilm removal efficiencies were not significantly different from each other (t-test, p &gt; 0.55). Therefore, this technique can be used to clean various bio-contaminated surfaces within one minute.

Biofilms on surfaces can be effectively and rapidly removed by using a periodic jet of carbon dioxide aerosols without a nitrogen purge.

バイオフィルム除去窒素パージすることなく、二酸化炭素エアロゾルを使用します

Biofilms can cause serious concerns in many applications. Not only can they cause economic losses, but they can also present a public health hazard. ...

環境学

Measurement of the Rheology of Crude Oil in Equilibrium with CO2 at Reservoir Conditions

A rheometer system to measure the rheology of crude oil in equilibrium with carbon dioxide (CO2) at high temperatures and pressures is described. The system comprises a high-pressure rheometer which is connected to a circulation loop. The rheometer has a rotational flow-through measurement cell with two alternative geometries: coaxial cylinder and double gap. The circulation loop contains a mixer, to bring the crude oil sample into equilibrium with CO2, and a gear pump that transports the mixture from the mixer to the rheometer and recycles it back to the mixer. The CO2 and crude oil are brought to equilibrium by stirring and circulation and the rheology of the saturated mixture is measured by the rheometer. The system is used to measure the rheological properties of Zuata crude oil (and its toluene dilution) in equilibrium with CO2 at elevated pressures up to 220 bar and a temperature of 50 &#176;C. The results show that CO2 addition changes the oil rheology significantly, initially reducing the viscosity as the CO2 pressure is increased and then increasing the viscosity above a threshold pressure. The non-Newtonian response of the crude is also seen to change with the addition of CO2.

Measurement of the Rheology of Crude Oil in Equilibrium with CO2 at Reservoir Conditions

A method for measuring the rheology of crude oil in equilibrium with carbon dioxide at reservoir conditions is presented.

COとの平衡状態における原油のレオロジーの測定<sub&gt; 2</sub&gt;貯水池の条件で

A rheometer system to measure the rheology of crude oil in equilibrium with carbon dioxide (CO2) at high temperatures and pressures is described. The ...

Procedures of Laboratory Fumigation for Pest Control with Nitric Oxide Gas

Nitric oxide (NO) is a newly discovered fumigant for postharvest pest control. This paper provides detailed protocols for conducting NO fumigation on fresh products and procedures for residue analysis and product quality evaluation. An airtight fumigation chamber containing fresh fruit and vegetables is first flushed with nitrogen (N2) to establish an ultralow oxygen (ULO) environment followed by injection of NO. The fumigation chamber is then kept at a low temperature of 2 - 5 &#176;C for a specified time period necessary to kill a target pest to complete a fumigation treatment. At the end of a fumigation treatment, the fumigation chamber is flushed with N2 to dilute NO prior to opening the chamber to ambient air to prevent the reaction between NO and O2, which produces NO2 and may damage delicate fresh products. At different times after NO fumigation, NO2 in headspace and nitrate and nitrite in liquid samples were measured as residues. Product quality was evaluated after 2 weeks of post-treatment cold storage to determine effects of NO fumigation on product quality. Keeping&#160;O2 from reacting with NO is critical to NO fumigation and is an important part of the protocols. Measuring NO levels is challenging and a practical solution is provided. Possible protocol modifications are also suggested for measuring NO levels in the fumigation chambers as well as residues. NO fumigation has the potential to be a practical alternative to methyl bromide fumigation for postharvest pest control on fresh and stored products. This publication is intended to assist other researchers in conducting NO fumigation research for postharvest pest control and accelerating the development of NO fumigation for practical applications.

This paper describes nitric oxide (NO) fumigation protocols for postharvest pest control. Fumigation chambers are flushed with nitrogen (N2) to establish ultralow oxygen conditions before NO is injected. At the end, chambers are flushed with N2 to dilute NO before exposing products to ambient air to prevent exposure to NO2.

窒素酸化物ガスで害虫駆除のため研究所くん蒸処理の手順

Nitric oxide (NO) is a newly discovered fumigant for postharvest pest control. This paper provides detailed protocols for conducting NO fumigation on ...

Improving Infrared Spectroscopy Characterization of Soil Organic Matter with Spectral Subtractions

Soil organic matter (SOM) underlies numerous soil processes and functions. Fourier transform infrared (FTIR) spectroscopy detects infrared-active organic bonds that constitute the organic component of soils. However, the relatively low organic matter content of soils (commonly &#60; 5% by mass) and absorbance overlap of mineral and organic functional groups in the mid-infrared (MIR) region (4,000-400 cm-1) engenders substantial interference by dominant mineral absorbances, challenging or even preventing interpretation of spectra for SOM characterization. Spectral subtractions, a post-hoc mathematical treatment of spectra, can reduce mineral interference and enhance resolution of spectral regions corresponding to organic functional groups by mathematically removing mineral absorbances. This requires a mineral-enriched reference spectrum, which can be empirically obtained for a given soil sample by removing SOM. The mineral-enriched reference spectrum is subtracted from the original (untreated) spectrum of the soil sample to produce a spectrum representing SOM absorbances. Common SOM removal methods include high-temperature combustion (&#39;ashing&#39;) and chemical oxidation. Selection of the SOM removal method carries two considerations: (1) the amount of SOM removed, and (2) absorbance artifacts in the mineral reference spectrum and thus the resulting subtraction spectrum. These potential issues can, and should, be identified and quantified in order to avoid fallacious or biased interpretations of spectra for organic functional group composition of SOM. Following SOM removal, the resulting mineral-enriched sample is used to collect a mineral reference spectrum. Several strategies exist to perform subtractions depending on the experimental goals and sample characteristics, most notably the determination of the subtraction factor. The resulting subtraction spectrum requires careful interpretation based on the aforementioned methodology. For many soil and other environmental samples containing substantial mineral components, subtractions have strong potential to improve FTIR spectroscopic characterization of organic matter composition.

SOM underlies many soil functions and processes, but its characterization by FTIR spectroscopy is often challenged by mineral interferences. The described method can increase the utility of SOM analysis by FTIR spectroscopy by subtracting mineral interferences in soil spectra using empirically obtained mineral reference spectra.

スペクトル減算と土壌有機物の赤外分光特性の向上

Soil organic matter (SOM) underlies numerous soil processes and functions. Fourier transform infrared (FTIR) spectroscopy detects infrared-active organic ...

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

Landscape topography is a critical factor affecting soil formation and plays an important role in determining soil properties on the earth surface, as it regulates the gravity-driven soil movement induced by runoff and tillage activities. The recent application of Light Detection and Ranging (LiDAR) data holds promise for generating high spatial resolution topographic metrics that can be used to investigate soil property variability. In this study, fifteen topographic metrics derived from LiDAR data were used to investigate topographic impacts on redistribution of soil and spatial distribution of soil organic carbon (SOC). Specifically, we explored the use of topographic principal components (TPCs) for characterizing topography metrics and stepwise principal component regression (SPCR) to develop topography-based soil erosion and SOC models at site and watershed scales. Performance of SPCR models was evaluated against stepwise ordinary least square regression (SOLSR) models. Results showed that SPCR models outperformed SOLSR models in predicting soil redistribution rates and SOC density at different spatial scales. Use of TPCs removes potential collinearity between individual input variables, and dimensionality reduction by principal component analysis (PCA) diminishes the risk of overfitting the prediction models. This study proposes a new approach for modeling soil redistribution across various spatial scales. For one application, access to private lands is often limited, and the need to extrapolate findings from representative study sites to larger settings that include private lands can be important.

Landscape processes are critical components of soil formation and play important roles in determining soil properties and spatial structure in landscapes. We propose a new approach using stepwise principal component regression to predict soil redistribution and soil organic carbon across various spatial scales.

主成分の土壌の再分配と土壌の有機炭素をマップに地形モデルをスケール アップ用

Landscape topography is a critical factor affecting soil formation and plays an important role in determining soil properties on the earth surface, as it ...

Single-throughput Complementary High-resolution Analytical Techniques for Characterizing Complex Natural Organic Matter Mixtures

Natural organic matter (NOM) is composed of a highly complex mixture of thousands of organic compounds which, historically, proved difficult to characterize. However, to understand the thermodynamic and kinetic controls on greenhouse gas (carbon dioxide [CO2] and methane [CH4]) production resulting from the decomposition of NOM, a molecular-level characterization coupled with microbial proteome analyses is necessary. Further, climate and environmental changes are expected to perturb natural ecosystems, potentially upsetting complex interactions that influence both the supply of organic matter substrates and the microorganisms performing the transformations. A detailed molecular characterization of the organic matter, microbial proteomics, and the pathways and transformations by which organic matter is decomposed will be necessary to predict the direction and magnitude of the effects of environmental changes. This article describes a methodological throughput for comprehensive metabolite characterization in a single sample by direct injection Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS), gas chromatography mass spectrometry (GC-MS), nuclear magnetic resonance (NMR) spectroscopy, liquid chromatography mass spectrometry (LC-MS), and proteomics analysis. This approach results in a fully-paired dataset which improves statistical confidence for inferring pathways of organic matter decomposition, the resulting CO2 and CH4 production rates, and their responses to environmental perturbation. Herein we present results of applying this method to NOM samples collected from peatlands; however, the protocol is applicable to any NOM sample (e.g., peat, forested soils, marine sediments, etc.).

This protocol describes a single throughput for complementary analytical and omics techniques culminating in a fully-paired characterization of natural organic matter and microbial proteomics in different ecosystems. This approach permits robust comparisons for identifying metabolic pathways and transformations important for describing greenhouse gas production and predicting responses to environmental change.

シングル スループット複雑な天然有機物の混合物の評価の補完的な高解像度分析の手法

Natural organic matter (NOM) is composed of a highly complex mixture of thousands of organic compounds which, historically, proved difficult to ...

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements

Researchers from myriad fields frequently seek to quantify and classify concentrations of carbonaceous aerosols as organic carbon (OC) or elemental carbon (EC). This is commonly accomplished using thermal-optical OC/EC analyzers (TOAs), which enable measurement via controlled thermal pyrolysis and oxidation under specific temperature protocols and within constrained atmospheres. Several commercial TOAs exist, including a semi-continuous instrument that enables on-line analyses in the field. This instrument employs an in-test calibration procedure that requires relatively frequent calibration. This article details a calibration protocol for this semi-continuous TOA and presents an open-source software tool for data analysis and rigorous Monte Carlo quantification of uncertainties. Notably, the software tool includes novel means to correct for instrument drift and identify and quantify the uncertainty in the OC/EC split point. This is a significant improvement on the uncertainty estimation in the manufacturer&#8217;s software, which ignores split point uncertainty and otherwise uses fixed equations for relative and absolute errors (generally leading to under-estimated uncertainties and often yielding non-physical results as demonstrated in several example data sets). The demonstrated calibration protocol and new software tool enabling accurate quantification of combined uncertainties from calibration, repeatability, and OC/EC split point are shared with the intent of assisting other researchers in achieving better measurements of OC, EC, and total carbon mass in aerosol samples.

This article presents a protocol and software tool for the quantification of uncertainties in the calibration and data analysis of a semi-continuous thermal-optical organic/elemental carbon analyzer.

熱光学有機/元素炭素測定の分割点解析と不確実性定量

Researchers from myriad fields frequently seek to quantify and classify concentrations of carbonaceous aerosols as organic carbon (OC) or elemental carbon ...

Fluorescently Labeled Bacteria as a Tracer to Reveal Novel Pathways of Organic Carbon Flow in Aquatic Ecosystems

Elucidating trophic interactions, such as predation and its effects, is a frequent task for many researchers in ecology. The study of microbial communities has many limitations, and determining a predator, prey, and predatory rates is often difficult. Presented here is an optimized method based on the addition of fluorescently labelled prey as a tracer, which allows for reliable quantitation of the grazing rates in aquatic predatory eukaryotes and estimation of nutrient transfer to higher trophic levels.

Presented here is a protocol for a single-cell, epifluorescence microscopy-based technique to quantify grazing rates in aquatic predatory eukaryotes with high precision and taxonomic resolution.

水生生態系における有機炭素流の新しい経路を明らかにするトレーサーとしての蛍光標識細菌

Elucidating trophic interactions, such as predation and its effects, is a frequent task for many researchers in ecology. The study of microbial ...

Measuring Phosphorus Release in Laboratory Microcosms for Water Quality Assessment

Phosphorus (P) is a critical limiting nutrient in agroecosystems requiring careful management to reduce transport risk to aquatic environments. Routine laboratory measures of P bioavailability are based on chemical extractions performed on dried samples under oxidizing conditions. While useful, these tests are limited with respect to characterizing P release under prolonged water saturation. Labile orthophosphate bound to oxidized iron and other metals can rapidly desorb to solution in reducing environments, increasing P mobilization risk to surface runoff and groundwater. To better quantify P desorption potential and mobility during extended saturation, a laboratory microcosm method was developed based on repeated sampling of porewater and overlying floodwater over time. The method is useful for quantifying P release potential from soils and sediments varying in physicochemical properties and can improve site-specific P mitigation efforts by better characterizing P release risk in hydrologically active areas. Advantages of the method include its ability to simulate in situ dynamics, simplicity, low cost, and flexibility.

Accurate quantification of phosphorus (P) desorption potential in saturated soils and sediments is important for P modeling and transport mitigation efforts. To better account for in situ soil-water redox dynamics and P mobilization under prolonged saturation, a simple approach was developed based on repeated sampling of laboratory microcosms.

水質評価のための実験室用微小宇宙におけるリン放出の測定

Phosphorus (P) is a critical limiting nutrient in agroecosystems requiring careful management to reduce transport risk to aquatic environments. Routine ...

Two-Dimensional Visualization and Quantification of Labile, Inorganic Plant Nutrients and Contaminants in Soil

We describe a method for two-dimensional (2D) visualization and quantification of the distribution of labile (i.e., reversibly adsorbed) inorganic nutrient (e.g., P, Fe, Mn) and contaminant (e.g., As, Cd, Pb) solute species in the soil adjacent to plant roots (the &#8216;rhizosphere&#8217;) at sub-millimeter (~100 &#181;m) spatial resolution. The method combines sink-based solute sampling by the diffusive gradients in thin films (DGT) technique with spatially resolved chemical analysis by laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). The DGT technique is based on thin hydrogels with homogeneously distributed analyte-selective binding phases. The variety of available binding phases allows for the preparation of different DGT gel types following simple gel fabrication procedures. For DGT gel deployment in the rhizosphere, plants are grown in flat, transparent growth containers (rhizotrons), which enable minimal invasive access to a soil-grown root system. After a pre-growth period, DGT gels are applied to selected regions of interest for in situ solute sampling in the rhizosphere. Afterwards, DGT gels are retrieved and prepared for subsequent chemical analysis of the bound solutes using LA-ICP-MS line-scan imaging. Application of internal normalization using 13C and external calibration using matrix-matched gel standards further allows for the quantification of the 2D solute fluxes. This method is unique in its capability to generate quantitative, sub-mm scale 2D images of multi-element solute fluxes in soil-plant environments, exceeding the achievable spatial resolution of other methods for measuring solute gradients in the rhizosphere substantially. We present the application and evaluation of the method for imaging multiple cationic and anionic solute species in the rhizosphere of terrestrial plants and highlight the possibility of combining this method with complementary solute imaging techniques.

This protocol presents a workflow for sub-mm 2D visualization of multiple labile inorganic nutrient and contaminant solute species using diffusive gradients in thin films (DGT) combined with mass spectrometry imaging. Solute sampling and high-resolution chemical analysis are described in detail for quantitative mapping of solutes in the rhizosphere of terrestrial plants.