We appreciate the guidance from Dr. Randy Southard on NaOCl oxidation and various discussions of spectral subtractions with Dr. Fungai F.N.D. Mukome.

1. Prepare Soil for Non-treated DRIFT Spectroscopy and SOM Removal
<ol>
	<li>Sieve the soil to &#60; 2 mm using a stainless-steel mesh (the &#39;fine-earth fraction&#39;). 
	NOTE: This demonstration employs two soils of similar texture but a nearly 3-fold difference in total SOM content (Table 1).</li>
</ol>
2. SOM Removal by Chemical Oxidation: Example of NaOCl
<ol>
	<li>Adjust the pH of 6% w/v NaOCl to pH 9.5 by adding 1 M HCl dropwise to the solution while mixing and measuring with a pH meter. 
	NOTE: Most commercial bleaches (e.g., Clorox) are suitable in quality and concentration (typically 3-7% NaOCl v/v) but will have pH &#62; 12. As NaOCl oxidation of organic matter is pH-dependent, and pH 9.5 is recommended for its use with soil samples19,23, it is necessary to adjust the pH of most commercially available bleaches.</li>
	<li>Add 25 mL NaOCl (6% w/v, pH 9.5) to 4 g soil (sieved, air dried) in a 50-mL conical tube and mix by sonication (600 s, output frequency 20 kHz, power 200 W).</li>
	<li>Incubate the mixture in a hot-water bath (15 min, 80 &#176;C) to increase oxidation rate.</li>
	<li>Centrifuge to obtain a clear supernatant (e.g., 15 min at 4,000 &#215; g for coarser textured soils; room temperature). Manually decant the supernatant into a waste container. 
	NOTE: The concentration of NaOCl in the supernatant (conservatively assuming no oxidation and thus no consumption of NaOCl) is the same as commercially available bleach for household use. Finer textured soils may require longer centrifugation time (e.g., up to an additional 15-30 min) at a given centrifuge speed (e.g., 4,000 &#215; g) to obtain a clear supernatant.</li>
	<li>Repeat steps 2.3 and 2.4 twice for a total of three oxidation steps.</li>
	<li>After the last oxidation step, add 20 mL deionized H2O (dH2O) to the soil and mix for 5 min using a horizontal shaker (120 rpm). Centrifuge for 15 min at 4,000 &#215; g and room temperature. Repeat for a total of three treatments.</li>
	<li>Using a spatula and dH2O from a squirt bottle as needed, extract and wash out the soil pellet from the bottom of the centrifuge tube into a plastic weigh boat (or another container with high surface area). Oven-dry (60 &#176;C maximum, 48 h) to an air-dried state.</li>
	<li>Once the soil sample is dried, quantify total organic carbon content by combustion-gas chromatography using a C/N analyzer24. Calculate SOM removal as the difference in organic carbon concentration before and after oxidation treatment. 
	NOTE: Due to loss of organic matter and soil structure, soil will be prone to crusting, in particular for soils with low sand content. It may be necessary to apply gentle pressure and/or hand grinding to re-homogenize the crusted soil. Soils with inorganic carbon (i.e., carbonates) require additional steps for quantifying organic carbon by combustion-gas chromatography25,26.</li>
</ol>
3. SOM Removal by High-Temperature Combustion
<ol>
	<li>Measure ~1-2 g of soil (sieved, air dried) into a porcelain crucible using a spatula.</li>
	<li>Heat at 550 &#176;C for 3 h using a muffle furnace. 
	NOTE: This is an example method of SOM removal using combustion at a relatively high temperature. Refer to Discussion on alternative procedures (e.g., temperature).</li>
</ol>
4. DRIFT Spectroscopy
NOTE: For this example, the FTIR spectrometer software listed in the Table of Materials will be used.
<ol>
	<li>Acquire spectra of untreated soil and mineral enriched reference sample (treated to remove SOM).
	<ol>
		<li>Prepare the soil samples.
		<ol>
			<li>Dilute the samples (optional).
			<ol>
				<li>Use analytical grade KBr (or other halide salt) dried at 105 &#176;C and stored in a desiccator to remove residual moisture. For soil samples, effective KBr dilutions can be achieved at a range of 1-33%, in contrast to &#60; 1% for pure compounds.</li>
				<li>Mix soil and KBr for a final sample size of 100-400 mg. For example, for a 3% dilution, gently grind 12 mg of dry sample with 60 mg of KBr for 60 s with an agate mortar and pestle. Then, &#39;fold in&#39; 328 mg of KBr to fully homogenize the sample.</li>
				<li>Use serial dilutions with KBr to obtain a high final dilution rate ( &#60; 1%). Perform replicate dilutions to ensure reproducibility, especially since diluted samples use 101-102 less soil than neat samples.</li>
			</ol>
			</li>
			<li>Grind untreated and treated soil samples to similar consistency by hand grinding and sieving (e.g., 250 &#181;m using a 60# sieve). 
			NOTE: Compared to hand grinding, greater consistency is facilitated by automation, in particular by ball milling. However, the relatively small amount of soil used in SOM removal (e.g., 1-3 g for ashing due to crucible volume) means that hand grinding may be more practical.</li>
		</ol>
		</li>
		<li>Collect the background spectrum.
		<ol>
			<li>Load a sample of KBr (ground in the same manner as soil samples (see 4.1.1.2) to mimic soil matrix effects) into a sample cup or plate well. 
			NOTE: The &#34;background spectrum&#34; is different from the mineral-enriched reference spectrum (see 4.1.3) used for performing subtractions. The background spectrum will be used by the software to remove atmospheric and other ambient absorbances during collection of spectra on soil samples. All software descriptions are specific to the chosen software and will need to be adapted to other software.</li>
			<li>Purge the spectrometer chamber with CO2- and H2O-scrubbed air (via a purge gas generator) or with N2 gas for greater consistency in collection conditions. For example, collection of spectra under ambient atmosphere may entail small fluctuations in humidity and CO2 that can cause changes in absorbance spectra. 
			NOTE: Newer spectrometers may have mirrors (e.g., gold, SiC) that can potentially reduce humidity effects.</li>
			<li>Collect a background spectrum using the same detector and acquisition parameter settings, including scan number, wavenumber range, and resolution, that will be used to collect spectra of samples.
			<ol>
				<li>Open the drop-down menu for Experiment and select the desired experimental collection method (e.g., acquisition mode). 
				NOTE: In this example using chosen spectrometer (see the Table of Materials), the selected method is iS50 Main Compartment.</li>
				<li>Click the Experimental Setup icon to select spectral acquisition parameters.</li>
				<li>Under the Collect tab, check that the number of scans and resolution are appropriate for experimental objectives; for example, a common setting for DRIFT spectra of neat soils is 128 scans at 4 cm-1 resolution. Click Ok to save changes.</li>
				<li>Click the Collect Background icon to collect a background spectrum. Save the background spectrum for use in the collection of spectra of soils (treated and untreated).</li>
			</ol>
			</li>
		</ol>
		</li>
		<li>Acquire spectra of soil samples. 
		NOTE: Use the same acquisition parameters to collect background and sample (untreated soil, mineral-enriched soil) spectra. Differences among detectors in acquisition time and resolution pose trade-offs that impact collection time and spectral quality. Typical scan numbers for soil spectra range from 128-512 scans. Scan number can be decreased and replicates averaged to obtain a total target scan number. For example, two analytical replicates - the same sample loaded in two separate wells - can be collected using 64 scans each and averaged for a total of 128 scans.
		<ol>
			<li>Load the soil sample. To ensure consistent loading and minimize surface roughness, pour samples into the sample cup (or well) to the point of slightly overfilling above the lip or edge of the cup. Then, surface-smooth the soil in the cup using a flat edge (e.g., razor) such that the height of soil sample in the cup is flush with the lip of the cup. 
			NOTE: Due to the interaction of infrared light with a matrix such as soil in diffuse reflectance mode, sample loading can influence DRIFT spectra. Samples should not be tamped or subjected to pressure because packing density can affect absorbance. Finer particle size of samples ensures greater ease of surface smoothing (see 4.1.2.1). Depending on the spectrometer model and the sample density, the mass of sample needed to fill a sample cup will range from 300 to 600 mg. In the case of plate wells, this also depends on the well size. Plates with a greater number of wells will have smaller wells and will therefore require less sample. For example, 96-well plates commonly have a well volume of 360 &#181;L whereas 24-well plates have a well volume of 3.4 mL.</li>
			<li>Collect spectra of untreated and treated soil samples. First check that the background spectrum collected previously (see 4.1.2.3.4) is used. Click Experimental Setup. Under the Collect tab, select Use specified background file and load the background spectrum file. Click Ok to save changes. To commence spectral collection on the soil, click Collect Sample. 
			NOTE: Re-load the same sample in a different well or sample cup to collect the replicate spectra to account for scattering artifacts produced by surface roughness and by variability in matrix density.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Perform spectral subtractions. 
	NOTE: The subtraction factor (SF) weighs the degree to which absorbances in the mineral reference spectrum are subtracted from absorbances at the corresponding wavenumber in the spectrum of the untreated soil. For subtractions focused on improving resolution of organic absorbances to characterize SOM, it is recommended to utilize the entirety of the MIR afforded by most spectrometers (e.g., 4,000 to 650 or 400 cm-1, depending on the detector). The next steps describe an empirical method for determining the SF. All software descriptions are specific to the chosen software and will need to be adapted to other software.
	<ol>
		<li>Zero out peaks by using the subtraction option of the software program to change the subtraction factor (SF) to minimize or reduce a target mineral peaks and/or mineral peaks, and/or to maximize a linear baseline14.</li>
		<li>Simultaneously select the untreated and treated soil spectra and click the Subtract icon (top center of screen);the first spectrum selected (untreated soil) will be the spectrum from which the second spectrum (treated soil) will be subtracted.</li>
		<li>Use the vertical toggle bar or arrows to increase or decrease the SF (left-hand of screen). Observe the changes in the previewed subtraction spectrum.
		<ol>
			<li>Use this iterative feature to determine an appropriate SF as described in Representative Results. The numerical SF value appears in the middle of the toggle bar. To adjust the range of SF values, use the Finer and Coarser buttons.</li>
		</ol>
		</li>
		<li>Click Add (upper right-hand of screen) to load the calculated subtraction spectrum into a window. 
		NOTE: Since the majority of mineral absorbances are not linear with concentration in most (if not all) soil samples, it is usually not feasible to remove all mineral peaks. It is recommended that mineral peaks considered less prone to inversion (e.g., quartz-like Si-O at 2,100-1,780 cm-1)14 be used as the target peak to zero-out by adjusting the SF.</li>
		<li>Record and report methodological details on how the subtraction was performed with sufficient detail to allow independent calculation of the same subtraction spectrum from the untreated soil spectrum, including: (1) the wavenumber region used for subtraction, (2) the SF or range of SFs used, and (3) the (mineral) peak or region targeted for zero-ing out. 
		NOTE: A good test of the reliability of a subtraction is to have it performed anew by the same user and/or independently by a different user using the reported subtraction parameters.</li>
	</ol>
	</li>
	<li>Interpret the spectra.
	<ol>
		<li>Perform spectral interpretation using various resources available to analyze and interpret the resulting subtraction spectra, in particular assignments of absorbances to organic functional groups4. 
		NOTE: Other uses of subtraction spectra include multivariate analysis (e.g., principal component analysis), chemometric prediction of soil analytes27, and even forensic fingerprinting9.</li>
	</ol>
	</li>
</ol>

The authors have nothing to disclose.

The method of removing SOM carries two considerations: 1) the amount of SOM removed, and 2) absorbance artifacts in the resulting mineral reference spectrum. It is fortunately possible&#8212; and arguably necessary&#8212; to identify and quantity these issues in order to avoid biased interpretations of SOM composition from the resulting subtraction spectrum. Ideally, spectral subtractions would employ a mineral-only reference spectrum to yield a spectrum of &#39;pure&#39; SOM. In reality, the resulting subtraction spectr...

Soil organic matter (SOM) is a minor constituent by mass in most soil samples but is implicated in multiple properties and processes underlying soil functions, such as nutrient cycling and carbon sequestration1. Characterizing the composition of SOM is one of several approaches to link SOM formation and turnover with its role(s) in soil functions2,3. One method of characterizing SOM composition is Fourier transform infrared (FTIR) spectroscopy, which offers detection of functional groups that constitute organic matter in soils and other environmental samples (e.g., carboxyl C-O, aliphatic C-H)4. However, the utility of FTIR spectroscopy for revealing SOM functional group composition is challenged by the dominant mineral component for the majority of soils (typically &#62; 95% mass) due to strong inorganic absorbances that challenge or severely limit detection and interpretation of organic absorbances.
Spectral subtractions offer a way to improve FTIR spectroscopic characterization of organic matter in soil samples. Subtracting mineral absorbances from the soil spectrum can be used to enhance absorbances of organic functional groups of interest in the analysis of SOM composition
(Figure 1).
Advantages of spectral subtractions over standard FTIR spectroscopy (i.e., soil spectra) include:
(i) Improved resolution and interpretation of organic absorbance bands compared to normal soil spectra. Though interpretation of organic bands in soil spectra can be performed by assuming that the relative differences in absorbance are due to differences in organic functional groups, this limits comparisons to samples with the same mineralogy and relatively high SOM content, and may be less sensitive to changes in organic bands, even those considered to be relatively mineral-free (e.g. aliphatic C-H stretch)5
(ii) Analysis of soils beyond high SOM samples or organic matter-enriched extracts or fractions
(iii) Highlighting changes induced by experimental treatments from mesocosm to field scales6
Additional applications of spectral subtractions in FTIR analysis of SOM include complementing structural and molecular characterizations (e.g., NMR spectroscopy, mass spectrometry)5,7, identifying the composition of SOM removed by an extraction or destructive fractionation8, and fingerprinting SOM composition for forensic purposes9. This method is applicable to a wide variety of mineral-organic mixtures beyond soils, including sediment10, peat11, and coal12,13.
The potential of spectral subtractions to improve FTIR spectroscopic characterization of SOM is demonstrated using examples of organic matter removal to obtain mineral reference spectra, and then, using these mineral reference spectra, performing and evaluating ideal and non-ideal spectral subtractions. This demonstration focuses on diffuse reflectance infrared Fourier transform (DRIFT) spectra collected in the mid-infrared region (MIR, 4,000 - 400 cm-1), as this is a widespread approach for the analysis of soil samples4.
The two example methods of SOM removal for obtaining a mineral-enriched reference spectrum are (i) high-temperature combustion (&#39;ashing&#39;) and (ii) chemical oxidation, using dilute sodium hypochlorite (NaOCl). It should be noted that these are examples of commonly employed SOM removal methods, rather than prescriptive recommendations. Other methods of SOM removal may offer reduced mineral artifacts and/or enhanced removal rates (e.g., low-temperature ashing)14. High-temperature ashing was one of the first methods used to obtain mineral-enriched reference spectra for performing subtractions, initially for OM-enriched samples derived from soils (e.g., dissolved organic matter, litter)15,16 followed by its application to bulk soil samples17,18. The example chemical oxidation used to remove SOM is based on the method of NaOCl oxidation described by Anderson19. This was originally developed as a pretreatment for removing organic matter in soil samples prior to X-ray diffraction (XRD) analysis, and has been investigated as a potential chemical fractionation sensitive to SOM stabilization20,21. Both high-temperature removal and chemical oxidation using NaOCl can entail soil-specific artifacts and have limitations on spectral interpretation that should be considered when selecting a method of SOM removal14,22.

<table border="0" cellpadding="0" cellspacing="0" width="1093">
	<tbody>
		<tr height="19">
			<td height="19" style="height:19px;width:263px;">Nicolet iS50 spectrometer</td>
			<td style="width:180px;">Thermo Fisher Scientific</td>
			<td style="width:128px;">912A0760</td>
			<td style="width:523px;">infrared spectrometer used to collect spectra</td>
		</tr>
		<tr height="19">
			<td height="19" style="height:19px;width:263px;">EasiDiff</td>
			<td style="width:180px;">Pike Technologies</td>
			<td style="width:128px;">042-1040</td>
			<td>high throughput sample holder</td>
		</tr>
		<tr height="19">
			<td height="19" style="height:19px;width:263px;">OMNIC</td>
			<td style="width:180px;">Thermo Fisher Scientific</td>
			<td style="width:128px;">INQSOF018</td>
			<td>software used to perform subtractions</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:263px;">6% v/v sodium hypochlorite</td>
			<td style="width:180px;">Clorox</td>
			<td style="width:128px;">n/a</td>
			<td>generic store-bought bleach for oxidative removal of soil organic matter</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:263px;">Type 47900 Furnace</td>
			<td style="width:180px;">VWR International</td>
			<td style="width:128px;">30609-748</td>
			<td>muffle furnace for ashing soils to removal soil organic matter</td>
		</tr>
		<tr height="23">
			<td height="23" style="height:23px;width:263px;">VWR Gooch Crucibles, Porcelain&#160;</td>
			<td style="width:180px;">VWR International</td>
			<td style="width:128px;">89038-038</td>
			<td>crucibles for ashing</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:263px;">VWR Tube 50 mL Sterile CS500&#160;</td>
			<td style="width:180px;">VWR International</td>
			<td style="width:128px;">89004-364</td>
			<td>for sodium hypochlorite</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:263px;">Forced air oven</td>
			<td style="width:180px;">VWR International</td>
			<td>89511-414</td>
			<td>for drying soils after oxidation and water washes</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:263px;">VersaStar pH meter</td>
			<td style="width:180px;">Fisher Scientific</td>
			<td>13 645 573</td>
			<td>for measuring pH of oxidation solution</td>
		</tr>
	</tbody>
</table>

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.

The method of SOM removal has practical as well as theoretical implications for the interpretation of subtraction spectra. For example, mineral alterations from high temperature ashing can manifest as losses or appearances of peaks and/or as shifted or broadened peaks in the mineral reference spectrum. These spectral artifacts are prone to occur in regions of overlap with organic bands at 1,600-900 cm-1,22 compromising interpretation of organic bands. Co...

Watch this Scientific Journal Video about Improving Infrared Spectroscopy Characterization of Soil Organic Matter with Spectral Subtractions at JoVE.com

Improving Infrared Spectroscopy Characterization of Soil Organic Matter with Spectral Subtractions

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.

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

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12.3K Views.  University of Illinois Urbana-Champaign. 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.

Video: Improving Infrared Spectroscopy Characterization of Soil Organic Matter with Spectral Subtractions

Design and Operation of a Continuous 13C and 15N Labeling Chamber for Uniform or Differential, Metabolic and Structural, Plant Isotope Labeling

Tracing rare stable isotopes from plant material through the ecosystem provides the most sensitive information about ecosystem processes; from CO2 fluxes and soil organic matter formation to small-scale stable-isotope biomarker probing. Coupling multiple stable isotopes such as 13C with 15N, 18O or&#160;2H has the potential to reveal even more information about complex stoichiometric relationships during biogeochemical transformations. Isotope labeled plant material has been used in various studies of litter decomposition and soil organic matter formation1-4. From these and other studies, however, it has become apparent that structural components of plant material behave differently than metabolic components (i.e. leachable low molecular weight compounds) in terms of microbial utilization and long-term carbon storage5-7. The ability to study structural and metabolic components separately provides a powerful new tool for advancing the forefront of ecosystem biogeochemical studies. Here we describe a method for producing 13C and 15N labeled plant material that is either uniformly labeled throughout the plant or differentially labeled in structural and metabolic plant components.
Here, we present the construction and operation of a continuous 13C and 15N labeling chamber that can be modified to meet various research needs. Uniformly labeled plant material is produced by continuous labeling from seedling to harvest, while differential labeling is achieved by removing the growing plants from the chamber weeks prior to harvest. Representative results from growing Andropogon gerardii Kaw demonstrate the system&#39;s ability to efficiently label plant material at the targeted levels. Through this method we have produced plant material with a 4.4 atom%13C and 6.7 atom%15N uniform plant label, or material that is differentially labeled by up to 1.29 atom%13C and 0.56 atom%15N in its metabolic and structural components (hot water extractable and hot water residual components, respectively). Challenges lie in maintaining proper temperature, humidity, CO2 concentration,&#160;and light levels in an airtight 13C-CO2 atmosphere for successful plant production. This chamber description represents a useful research tool to effectively produce uniformly or differentially multi-isotope labeled plant material for use in experiments on ecosystem biogeochemical cycling.

Design and Operation of a Continuous 13C and 15N Labeling Chamber for Uniform or Differential, Metabolic and Structural, Plant Isotope Labeling

This method explains how to build and operate a continuous 13C and 15N isotope labeling chamber for uniform or differential plant tissue labeling. Representative results from metabolic and structural labeling of Andropogon gerardii are discussed.

Tracing rare stable isotopes from plant material through the ecosystem provides the most sensitive information about ecosystem processes; from CO2 fluxes ...

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

Management practices and environmental changes can alter soil nutrient and carbon cycling. Soil labile organic carbon, a readily decomposable C pool, is highly sensitive to disturbance. It is also the primary substrate for soil microorganisms, which is fundamental to nutrient cycling. Due to these attributes, labile organic carbon (LOC) has been identified as an indicator parameter for soil health. Quantifying the turnover rate of LOC also aids in understanding changes in soil nutrient cycling processes. A sequential fumigation incubation method has been developed to estimate soil LOC and potential C turnover rate. The method requires fumigating soil samples and quantifying CO2-C respired during a 10 day incubation period over a series of fumigation-incubation cycles. Labile organic C and potential C turnover rate are then extrapolated from accumulated CO2 with a negative exponential model. Procedures for conducting this method are described.

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.

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

Understanding Dissolved Organic Matter Biogeochemistry Through In Situ Nutrient Manipulations in Stream Ecosystems

Dissolved organic matter (DOM) is a highly diverse mixture of molecules providing one of the largest sources of energy and nutrients to stream ecosystems. Yet the in situ study of DOM is difficult as the molecular complexity of the DOM pool cannot be easily reproduced for experimental purposes. Nutrient additions to streams however, have been shown to repeatedly alter the in situ and ambient DOM pool. Here we demonstrate an easily replicable field-based method for manipulating the ambient pool of DOM at the ecosystem scale. During nutrient pulse experiments changes in the concentration of both dissolved organic carbon and dissolved organic nitrogen can be examined across a wide-range of nutrient concentrations. This method allows researchers to examine the controls on the DOM pool and make inferences regarding the role and function that certain fractions of the DOM pool play within ecosystems. We advocate the use of this method as a technique to help develop a deeper understanding of DOM biogeochemistry and how it interacts with nutrients. With further development this method may help elucidate the dynamics of DOM in other ecosystems.

Understanding Dissolved Organic Matter Biogeochemistry Through In Situ Nutrient Manipulations in Stream Ecosystems

Dissolved organic matter provides an important source of energy and nutrients to stream ecosystems. Here we demonstrate a field-based method to manipulate the ambient pool of dissolved organic matter in situ through easily replicable nutrient pulses.

Dissolved organic matter (DOM) is a highly diverse mixture of molecules providing one of the largest sources of energy and nutrients to stream ecosystems. ...

A Lipid Extraction and Analysis Method for Characterizing Soil Microbes in Experiments with Many Samples

Microbial communities are important drivers and regulators of ecosystem processes. To understand how management of ecosystems may affect microbial communities, a relatively precise but effort-intensive technique to assay microbial community composition is phospholipid fatty acid (PLFA) analysis. PLFA was developed to analyze phospholipid biomarkers, which can be used as indicators of microbial biomass and the composition of broad functional groups of fungi and bacteria. It has commonly been used to compare soils under alternative plant communities, ecology, and management regimes. The PLFA method has been shown to be sensitive to detecting shifts in microbial community composition.
An alternative method, fatty acid methyl ester extraction and analysis (MIDI-FA) was developed for rapid extraction of total lipids, without separation of the phospholipid fraction, from pure cultures as a microbial identification technique. This method is rapid but is less suited for soil samples because it lacks an initial step separating soil particles and begins instead with a saponification reaction that likely produces artifacts from the background organic matter in the soil. 
This article describes a method that increases throughput while balancing effort and accuracy for extraction of lipids from the cell membranes of microorganisms for use in characterizing both total lipids and the relative abundance of indicator lipids to determine soil microbial community structure in studies with many samples. The method combines the accuracy achieved through PLFA profiling by extracting and concentrating soil lipids as a first step, and a reduction in effort by saponifying the organic material extracted and processing with the MIDI-FA method as a second step.

The article describes a method that increases throughput while balancing effort and accuracy for extraction of lipids from the cell membranes of microorganisms for use in characterizing both total lipids and the relative abundance of indicator lipids to determine soil microbial community structure in studies with many samples.

Microbial communities are important drivers and regulators of ecosystem processes. To understand how management of ecosystems may affect microbial ...

Production and Measurement of Organic Particulate Matter in a Flow Tube Reactor

Organic particulate matter (PM) is increasingly recognized as important to the Earth's climate system as well as public health in urban regions, and the production of synthetic PM for laboratory studies have become a widespread necessity. Herein, experimental protocols demonstrate approaches to produce aerosolized organic PM by &#945;-pinene ozonolysis in a flow tube reactor. Methods are described for measuring the size distributions and morphology of the aerosol particles. The video demonstrates basic operations of the flow tube reactor and related instrumentation. The first part of the video shows the procedure for preparing gas-phase reactants, ozonolysis, and production of organic PM. The second part of the video shows the procedures for determining the properties of the produced particle population. The particle number-diameter distributions show different stages of particle growth, namely condensation, coagulation, or a combination of both, depending on reaction conditions. The particle morphology is characterized by an aerosol particle mass analyzer (APM) and a scanning electron microscope (SEM). The results confirm the existence of non-spherical particles that have grown from coagulation for specific reaction conditions. The experimental results also indicate that the flow tube reactor can be used to study the physical and chemical properties of organic PM for relatively high concentrations and short time frames.

This paper describes the operation procedure for the flow tube reactor and related data collection. It shows the protocols for setting the experiments, recording data and generating the number-diameter distribution as well as the particle mass information, which gives useful information about chemical and physical properties of the organic aerosols.

Organic particulate matter (PM) is increasingly recognized as important to the Earth's climate system as well as public health in urban regions, and the ...

Production and Measurement of Organic Particulate Matter in the Harvard Environmental Chamber

The production and the evolution of atmospheric organic particulate matter (PM) are insufficiently understood for accurate simulations of atmospheric chemistry and climate. The complex production mechanisms and reaction pathways make this a challenging research topic. To address these issues, an environmental chamber, providing enough residence time and close-to-ambient concentrations of precursors for secondary organic materials, is needed. The Harvard Environmental Chamber (HEC) was built to serve this need, simulating the production of gas and particle phase species from volatile organic compounds (VOCs). The HEC has a volume of 4.7 m3 and a mean residence time of 3.4 h under typical operating conditions. It is operated as a completely mixed flow reactor (CMFR), providing the possibility of indefinite steady-state operation across days for sample collection and data analysis. The operation procedures are described in detail in this article. Several types of instrumentation are used to characterize the produced gas and particles. A High-Resolution Time-of-Fight Aerosol Mass Spectrometer (HR-ToF-AMS) is used to characterize particles. A Proton-Transfer-Reaction Mass Spectrometer (PTR-MS) is used for gaseous analysis. Example results are presented to show the use of the environmental chamber in a wide variety of applications related to the physicochemical properties and reaction mechanisms of organic atmospheric particulate matter.

This paper describes operation procedures for the Harvard Environmental Chamber (HEC) and related instrumentation for measuring gaseous and particle species. The environmental chamber is used to produce and study secondary organic species produced from the organic precursors, especially related to atmospheric organic particulate matter.

The production and the evolution of atmospheric organic particulate matter (PM) are insufficiently understood for accurate simulations of atmospheric ...

Characterization of Aquatic Biofilms with Flow Cytometry

Biofilms are dynamic consortia of microorganism that play a key role in freshwater ecosystems. By changing their community structure, biofilms respond quickly to environmental changes and can be thus used as indicators of water quality. Currently, biofilm assessment is mostly based on integrative and functional endpoints, such as photosynthetic or respiratory activity, which do not provide information on the biofilm community structure. Flow cytometry and computational visualization offer an alternative, sensitive, and easy-to-use method for assessment of the community composition, particularly of the photoautotrophic part of freshwater biofilms. It requires only basic sample preparation, after which the entire sample is run through the flow cytometer. The single-cell optical and fluorescent information is used for computational visualization and biological interpretation. Its main advantages over other methods are the speed of analysis and the high-information-content nature. Flow cytometry provides information on several cellular and biofilm traits in a single measurement: particle size, density, pigment content, abiotic content in the biofilm, and coarse taxonomic information. However, it does not provide information on biofilm composition on the species level. We see high potential in the use of the method for environmental monitoring of aquatic ecosystems and as an initial biofilm evaluation step that informs downstream detailed investigations by complementary and more detailed methods.

Flow cytometry in combination with visual clustering offers an easy-to-use and fast method for studying aquatic biofilms. It can be used for biofilm characterization, detection of changes in biofilm community structure, and detection of abiotic particles embedded in the biofilm.

Biofilms are dynamic consortia of microorganism that play a key role in freshwater ecosystems. By changing their community structure, biofilms respond ...

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.

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.

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

Measurements of CO2 Fluxes at Non-Ideal Eddy Covariance Sites

This protocol is an example of utilizing the eddy covariance (EC) technique to investigate spatially and temporally averaged net CO2 fluxes (net ecosystem production, NEP), in non-typical ecosystems, on a currently reforested windthrow area in Poland. After a tornado event, a relatively narrow &#8220;corridor&#8221; was created within surviving forest stands, which complicates such kind of experiments. The application of other measuring techniques, such as the chamber method, is even more difficult under these circumstances, because especially at the beginning, fallen trees and in general great heterogeneity of the site provide a challenging platform to perform flux measurements and then to properly upscale obtained results. In comparison with standard EC measurements carried out in untouched forests, the case of windthrow areas requires special consideration when it comes to the site location and data analysis in order to ensure their representativeness. Therefore, here we present a protocol of real-time, continuous CO2 flux measurements at a dynamically changing, non-ideal EC site, which includes (1) site location and instrumentation setup, (2) flux computation, (3) rigorous data filtering and quality control, and (4) gap filling and net fluxes partitioning into CO2 respiration and absorption. The main advantage of the described methodology is that it provides a detailed description of the experimental setup and measurement performance from scratch, which can be applied to other spatially limited ecosystems. It can also be viewed as a list of recommendations on how to deal with unconventional site operation, providing a description for non-specialists. Obtained quality-checked, gap filled, half-hour values of net CO2, as well as absorption and respiration fluxes, can be finally aggregated into daily, monthly, seasonal or annual totals.

Measurements of CO2 Fluxes at Non-Ideal Eddy Covariance Sites

The presented protocol uses the eddy covariance method at non-typical locations, applicable to all types of short-canopy ecosystems with limited area, on a currently reforested windthrow site in Poland. Details of measuring site setup rules, flux calculations and quality control, and final result analysis, are described.

This protocol is an example of utilizing the eddy covariance (EC) technique to investigate spatially and temporally averaged net CO2 fluxes (net ecosystem ...