Financial support for this research was provided by the Strategic Environmental Research and Development Program (SERDP ER-2338; Andrea Leeson, Program Manager). Michael Pound, Naval Facilities Engineering Command, Southwest provided logistical and site support for the project. Brian White, Erika Thompson and Richard Wong (CBI Federal Services, Inc) provided on-site logistical support, historical site perspective and relevant reports. Todd Wiedemeier (T.H. Wiedemeier &amp; Associates) provided documentation, discussion and historical site perspectives.

1. Preparation and Field Installation
<ol>
	<li>Procure necessary field equipment; pumps, power (batteries, solar, transformers, etc.), tubing, well caps, fittings, sample vials and bottles, probes (pH, Eh, etc.) and low-voltage pumps.</li>
	<li>Seal battery-powered air pumps. Drill a hole into the pump housing (size 53) and route a short piece (3-5&#34;) of 1/16&#34; plastic gas-impermeable tubing (for instance PFA).
	<ol>
		<li>Seal all exterior portions of the pump (around the lower rubber housing) with marine sealant followed by a coat of silicone sealant.</li>
		<li>Pressure tests the pumps by gently blowing in the housing tubing while blocking the outflow. Check for leak visually. 
		NOTE: Light pressure should hold if there is no air leak (Fig. 1).</li>
	</ol>
	</li>
	<li>Install monitoring wells if necessary (in this study existing wells were used &#8212; screened across the vadose:groundwater interface)18. 
	NOTE: One well must be a background well in a location representative of the contaminated site &#8212; but with no known petroleum-based contamination.</li>
	<li>Obtain preliminary groundwater modeling data if they do not exist (hydraulic conductivity, aquifer porosity, soil density, specific yield, hydraulic gradient, etc.) as described18. Use this data to develop a Zone of Influence (ZOI) model (estimate of the CO2 capture zone). Prepare the ZOI model as described in the supplementary materials18.</li>
	<li>Prepare CO2 traps by weighing out ~25 g NaOH and transfer to a 100 ml serum bottle. Cap the serum bottle with a septum and crimp tightly. Prepare a trap for each collection well (Fig. 2), plus a field blank.</li>
	<li>Conduct initial groundwater sampling as needed to obtain initial pH, dissolved inorganic carbon (DIC) concentrations and cation concentrations10,18. Fill a 40 ml volatile organic analysis (VOA) vial, from the bottom to a convex meniscus with groundwater (sample via bailer, peristaltic pump, or vacuum line), add 5 drops of saturated CuSO4 solution19, cap tightly (must use septum caps) with as little headspace as possible.
	<ol>
		<li>Take additional vials for other analyses (contaminant concentrations, for instance). Use an unpreserved vial for pH measurement if a meter is not available in the field. Refrigerate and transport to the laboratory.</li>
	</ol>
	</li>
	<li>Route the power lines (able to carry ~ 1 amp) along the ground or other convenient means to each well. Affix a modified pump (see 1.2) and make sure the pump is operational (should be able to hear it working). 
	NOTE: The pumps can accommodate 12 V but use a lower voltage to save power (Fig. 3).</li>
	<li>Cap wells with modified gas-tight well caps.
	<ol>
		<li>To prepare caps, drill two holes (drill size 53) through caps in order to tightly fit 1/16&#34; gas lines. Route two gas lines through the cap.</li>
		<li>Pull one line so that it rests in proximity to the groundwater table (Fig. 4). Affix a heavy stainless nut on the end in order weigh down the line.</li>
		<li>Route the other line just below the cap (this will be the gas return line). Coat the sealing surfaces and threads with ample vacuum grease to inhibit any air exchange. Tighten on the well cap.</li>
		<li>Route the lower tubing into the pump inlet. Route a gas line from the pump to the CO2 trap (NaOH) using a #16 gauge needle through the septum. Route a return line from the trap (using a second #16 needle) to the gas line ending just below the cap.</li>
		<li>Start the pump by supplying power and collect at least 30 well volumes (this depends on the volume of the well head-space. It can be calculated with the well radius (r) and estimated distance to the groundwater table (l), i.e., &#960;r2l). Discard the initial traps (to clear the headspace).</li>
		<li>Remove and replace with fresh traps before collecting experimental CO2 by pulling out the needles from each bottle&#39;s septum and putting them in the new bottle&#39;s septum. Note time and date for pump turn-on.</li>
	</ol>
	</li>
</ol>
2. Initial Sample Analysis
<ol>
	<li>To measure DIC by coulometry20:
	<ol>
		<li>Transfer triplicate 1 ml subsamples to 40 ml serum vials capped with septa. Acidify subsamples with 1 ml 80% H3PO4. Sparge with a CO2-free air stream.</li>
		<li>Dry the evolved CO2 gas stream and scrub in line with sequential Mg(ClO4)2 and silica gel (230-400 mesh, 60 &#197;) traps. Bubble the gas stream into a coulometric cell where a colorimetric assay is used to quantify CO2. Use certified reference materials to calibrate measurements21.</li>
	</ol>
	</li>
	<li>Measure pH using a standard calibrated pH meter. Measure pH on-site or on preserved samples.</li>
	<li>Measure dissolved cations by ion chromatography:
	<ol>
		<li>Pipet 5 ml unpreserved groundwater samples to autosampler vials. Cap vials and place in an autosampler coupled to an ion chromatograph.</li>
		<li>Use a cation-specific column for the analysis10,18. Use 20 mM methanesulfonic acid as eluent and chromatographic flow set to ~0.7 ml min-1.</li>
		<li>Dilute a stock solution of 6 cation standards (containing Mg, Ca, Na, and K as a minimum) 0.5:4.5, 1:4, 2:3, 3:2, and 4:1 using purified water. Run these standards at the beginning of the analysis and after each 25 unknown samples. Run each sample three times (in triplicate). Create a standard curve by plotting cation concentration versus peak area and generating a linear regression. Analyze field samples accordingly10,18.</li>
	</ol>
	</li>
</ol>
3. Measure CO2 Production and Mineralization Rate On-site
<ol>
	<li>After approximately two weeks to two months (will vary most likely from site to site based on in situ microbial metabolism rates), shut off power to pumps by unplugging them.
	<ol>
		<li>For recirculated gas traps, remove needles and replace with a &#34;fresh&#34; CO2 trap. Traps are stable for long-term storage if sealed (c.f. Fig. 3).</li>
	</ol>
	</li>
	<li>When ready for analysis, dissolve any remaining unspent (solid) NaOH and transfer the entire liquid contents to a volumetric device to obtain the dilution volume. Determine the full volume (for instance 200 ml to fully dissolve the remaining NaOH) and transfer subsamples (5-10 ml) to 40 ml vials with septa.
	<ol>
		<li>Acidify by injecting 50% (v/v) phosphoric acid, sparge and analyze the resultant gas stream by coulometry (see 2.1).</li>
		<li>Manually calculate the CO2 collection rate by scaling the subsample to the entire volume and to the time of collection (i.e., X g CO2 per day). Subtract the field blank CO2 content. For instance, if the fully-dissolved NaOH is 200 ml, multiply a 10 ml subsample by 20 to reflect the total CO2 concentration. 
		NOTE: If that sample represented 14 days of collection, the collection rate would be the scaled CO2 concentration divided by 14 days. Plot the CO2 collection rate against the initial DIC concentration. If there is no correlation, collection rate is not a sole function of equilibrium kinetics.</li>
		<li>In order to account for equilibrium kinetics, manually subtract the lowest collection rate from the collection rate of all other wells during the sampling period. 
		NOTE: For instance, if the lowest collection rate was 0.0001 mg d-1, make the conservative assumption that this represents solely equilibrium collection and subtract that value for all other collection rates to obtain the CO2 production rate due to degradation. The scaled rate is the organic carbon mineralization rate (conservative as the lowest rate might include some contaminant mineralization).</li>
	</ol>
	</li>
	<li>Analyze the remaining CO2 by Accelerator Mass Spectrometry (AMS) to determine the radiocarbon content22. Use approximately 1 mg carbon for this analysis. Scale the collection time(s) to collect sufficient CO2. Subtract the radiocarbon content in the field blank by mass balance (radiocarbon measurement scaled to the amount CO2 in field blank). 
	NOTE: For the described test site, 2 week collections were more than adequate to obtain 1 mg carbon.</li>
</ol>
4. Model a Zone of Influence to Estimate the Soil Volume Sampled for CO2
<ol>
	<li>Use MT3DMS23 coupled with MODFLOW-200524 via the ModelMuse interface25 to simulate CO2 diffusion and equilibrium associated with the well screen (Video 1). The resolution of the model is 0.09 m 0.09 m which is approximately equal to the cross section of the well and considered reasonable for the ZOI estimation.
	<ol>
		<li>Download and install MODFLOW-2005 (http://water.usgs.gov/ogw/modflow/MODFLOW.html#downloads), MT3DMS (http://hydro.geo.ua.edu/mt3d/), and ModelMuse (http://water.usgs.gov/nrp/gwsoftware/ModelMuse/ModelMuse.html).</li>
		<li>Configure ModelMuse with MODFLOW program location. To do so, click &#34;Model&#34; menu, then select &#34;MOFLOW Program Locations&#8230;&#34;, then point the program to the MODFLOW-2005 program installation directory: /bin/mf2005.exe. Under this same dialog, configure ModelMuse with MT3DMS program location (installation directory: /bin/mt3dms5b.exe).</li>
		<li>Configure MODFLOW Packages and Programs (within the ModelMuse). To do so, select the &#34;Model&#34; menu, then &#34;MODFLOW Packages and Programs&#8230;.&#34;. Under &#34;Flow,&#34; select LPF: &#34;Layer Property Flow Package.&#34;</li>
		<li>Under &#34;Boundary conditions,&#34; Select &#34;Specified head,&#34; then select CHD: Time-Variant Specified-Head package.&#34; Select &#34;MT3DMS.&#34; Select &#34;BTN: Basic Transport package.&#34; Set the mobile species to CO2.</li>
		<li>Configure MODFLOW Options within ModelMuse. To do so, select &#34;Model&#34; menu, then MODFLOW Options. Under the &#34;Options&#34; tab, set model units (meters, hours, g (grams)).</li>
		<li>Configure MODFLOW Time by selecting &#34;Model&#34; menu, then &#34;MODFLOW Time.&#34; Using a 360 length Stress period will have the simulation run for 15 days.</li>
		<li>Configure MODFLOW Data Sets by selecting &#34;Data&#34; menu, select &#34;Data Sets.&#34; Enter data from site of interest: Hydrology (K values in 3 dimensions, Modflow Initial Head, Modflow Specified Head); MT3DMS: (Diffusion Coefficient CO2, Initial Concentration CO2, Longitudinal Dispersivity).</li>
		<li>Edit Global Variables. Select &#34;Data&#34; menu, select &#34;Global Variables.&#34; Enter the CO2 collection rate (from site) and the initial CO2 concentration.</li>
		<li>Run simulation. Press the green arrow on the top icon bar to start the simulation. Save input files when prompted. Simulation will run. After run, export MT3DMS Input files: Select &#34;File&#34; menu, then &#34;Export,&#34; then MT3DMS Input Files. Simulation will compile and export data.</li>
		<li>Observe and output model results. Click the visualize icon on icon bar. Select the simulation. Output ZOI boundary values in X, Y and Z axis 
		NOTE: This model represents a Zone of Influence for the CO2 collection (the full model development is described in report form available from the supporting materials)18. The ZOI, which is defined as the volume of aquifer that has a CO2 concentration of 95% or less, appears to be symmetric about the hydraulic gradient, which suggests relatively small effect of advection process with the small hydraulic gradient during the dry season. Further analysis indicates that the volume of aquifer with any CO2 depletion (i.e., &#60; 99%) extents downgradient significantly longer.</li>
	</ol>
	</li>
</ol>
5. Couple Radiocarbon Content with CO2 Production Rate and Scale to Volume (with ZOI)
<ol>
	<li>Convert radiocarbon ages (if necessary) to per mil notation using standard formulae22. Use the background well radiocarbon value as a known (&#916;14CNOM) in equation (1). &#916;14Cpetroleum is know (-1000). Use the individual well value for &#916;14CO2. Solve for fractionpetroleum. 
	(1)&#916;14CO2 = (&#916;14Cpetroleum x fractionpetroleum) + [&#916;14CNOM x (1 - fractionpetroleum)]</li>
	<li>Multiply the fraction petroleum by the CO2 mineralization rate (3.1) to determine the contaminant mineralization rate (i.e., 50% x 1.0 mg d-1 = 0.5 mg contaminant carbon d-1).</li>
	<li>Divide the contaminant mineralization rate by the ZOI volume calculated in (4) to determine contaminant mass mineralized per unit time per unit volume (i.e., 0.05 mg C m-3 d-1).</li>
</ol>

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The authors declare that they have no competing financial interests

A protocol is described which aims to combine rate measurements, proportion mineralization from contaminant(s) and ZOI to determine overall site contaminant degradation. The critical components are, measuring CO2 production (mineralization when corrected) over time, concurrently collecting the respired CO2 in sufficient quantity (~ 1 mg) for AMS radiocarbon analysis providing amount derived from contaminant degradation, and, creating a ZOI model to relate the captured CO2 to a known volume of soil or groundwater (or both). These three main components are combined to arrive at an overall calculation at each sampling point for amount of contaminant degraded per unit volume per unit time (g m-3 d-1, for instance). Scaling the calculations, through repeated and geographically separated measurements (wells covering a site subsampled over longer time-scales), will allow site managers to estimate spatial and temporal degradation dynamics and respond appropriately to regulators and stakeholders.
The described protocol uses recirculating pumps or long-term deployed passive samplers (a strategy currently under development) to trap out CO2 from well headspace gas. The reason is several fold. Primarily, sufficient CO2 must be collected in order to obtain radiocarbon measurements (~1 mg). Respiration rates can be measured using surface soil:air exchange traps or by using soil respiration instruments (Licor flux chamber for instance). These methods suffer from the need to asynchronously gather sufficient CO2 for radiocarbon analysis &#8212; thus perhaps biasing the measurement. For instance, a flux chamber can be outfitted to measure soil:air CO2 exchange while accounting for influx of atmospheric CO217. Unless respiration rates are high, ample CO2 for radiocarbon measurements may not be trapped. In this case, samples can be taken from large soil gas samples or from groundwater (with DIC)12. Furthermore, measuring CO2 flux at the soil:air surface is subject to influx from the atmosphere lateral to the flux chamber or trap. Sampling well headspace &#34;isolates&#34; the signal to the region of contamination (depending on well installation to some degree) but is suitably removed from atmospheric influx (and atmospherically-generated modern 14CO2). The main difficulty is sampling from the well without having to open it in order to change traps (for temporal sampling).
Using recirculating pumps allows one to sample well headspace and change CO2 traps at regular intervals without having to expose the sample location to atmospheric 14CO2. It also allows one to sample considerable CO2 which can then be analyzed for flux and natural radiocarbon content. The recirculation protocol is not without difficulty. A major problem is delivering ample power to run pumps continuously in the field. For the initial experiment (described here), solar panels provided enough energy to run pumps for each two-week period. Voltage logs showed that after several days, solar power could not keep up with the needed power and pumps were not operational for several hours each day. This was immaterial to the flux modeling and overall collection, but highlights the difficulty in providing ample power to field-deployed hardware. In currently-running collections, power to pumps has been interrupted by ground crews mowing in the monitoring well field. Several power lines have been severed. We are currently evaluating headspace-deployed passive CO2 traps which could be lowered into the well and retrieved at a later date with absorbed CO2. A risk-benefit analysis is underway (the risk mostly derived from having to open the well head and allow in atmospheric 14CO2).
The technique&#39;s main limitations are not being able to distinguish the exact respiration source in mixed contaminant systems and not being able to account for intermediate carbon-based degradation products (i.e., DCE, VC, methane). For instance, at the current site, there was historical fuel hydrocarbon contamination in addition to CH contamination. CHs are almost exclusively made from petroleum feedstocks. At the described site, CH is primarily isolated in the region studied - while some residual petroleum evidently exists to the North. No petroleum was found in wells sampled for this work. However, at a mixed contaminant site, the overall mineralization rate might be difficult to tie to one individual or class of contaminants. Using this method, one can quantify the complete CH degradation (to CO2). If, the contaminant carbon is converted to CH4 (anaerobic conditions), the CH4 may be &#34;lost&#34; if it diffuses away from the ZOI. That carbon will likely be converted to CO2 within oxic portions in the vadose zone. If this does not occur within the ZOI, the reported method will not account for it. In this case, the described method can be considered a conservative estimator, which from a regulatory perspective, is desirable. Additionally, the ZOI modeling is not without uncertainty. Simulations are based on &#34;singular&#34; values such as porosity and bulk density which are measured in subsamples assumed to be homogenous &#8212; but in reality are heterogeneous at the macro- and microscales. A perceived limitation may be the analysis cost for natural abundance radiocarbon (which can be as much as $600 per sample). The definitive nature of the information gathered from radiocarbon makes the cost very low in reality. With several well-chosen samples, one can determine if substantial remediation is occurring. If, for instance the CO2 associated with a contaminant plume is radiocarbon-depleted relative to a background site10. A site with low ambient pH (&#62; ~4.8) and considerable limestone (CaCO3) may be a poor candidate for applying this technique. Ancient carbonate deposits might dissolve in low pH and bias the analysis.
The technique&#39;s significance is considerable, as a sole measurement type (natural abundance radiocarbon) can immediately be used to confirm in situ conversion of contaminant to CO2. This analysis is definitive. Radiocarbon cannot become depleted except through radioactive decay - which is constant despite physical, chemical or biological alteration of any starting material. Static radiocarbon measurements (for instance DI14C) can be made on batch samples and immediately confirm if 14C-depleted CO2 is prevalent at a site (irrefutably indicating contaminant mineralization to CO2). This information alone is incredibly valuable to site managers as without it, they are required to use numerous indirect lines of evidence to infer that contaminant mineralization is occurring. No other single measurement can provide a concrete connection between carbon-based contaminant and the carbon-containing CO2 produced through complete degradation.
Future applications are currently underway in which our group will increase sampling temporal resolution to encompass an entire year. By collecting CO2 and determining the mineralization rate(s) over the spatial extent of the site, we will be able to refine models for contaminant degradation over time. This information is critically needed by site managers in order to most effectively manage contaminated sites. In limited use, regulators at three sites where the technique has been applied have recognized the methods definitive results. This has led to cost savings and helped to guide remedial alternatives.

Environmental cleanup costs are staggering, with numerous contaminated sites in the US and abroad. This makes innovative treatment and monitoring strategies essential to reaching Response Complete (RC) status (e.g., no further action needed) economically. Traditionally, lines of converging evidence have substantiated in situ bioremediation, abiotic contaminant conversion, or other forms of natural attenuation. Lines of evidence cannot be used to absolutely confirm degradation or to gather contaminant degradation rate information under in situ conditions1. Collecting a wide array of data to predict remediation timescale(s) has often been recommended, but linking these data cost-effectively to absolutely confirm remediation has been problematic2-4. Obtaining the most realistic and complete site conceptual model data with as little cost as possible is an ultimate site-management goal. Moreover, regulator and stakeholder demands represent additional drivers for obtaining the most timely, valuable and cost-effective information. Relatively inexpensive methods capable of providing compelling evidence for contaminant turnover rates offer the most value for meeting cleanup goals.
Because very distinct isotopic signatures are available in carbon-based contaminants, carbon isotopes have been recently applied to understanding contaminant attenuation processes at field sites5-13. Stable carbon isotopes can be used to determine if a source is attenuating based on Rayleigh distillation kinetics (c.f. 5,6 for reviews). This methodology, while convenient, may be limited when contaminants are from mixed sources &#8212; or do not represent an isotopically-unique &#34;starting&#34; spill (from which initial stable carbon isotope ratios can be derived). Natural abundance radiocarbon analysis represents an alternative (and perhaps complementary) isotopic strategy for measuring carbon-based contaminant degradation to CO2. Fuels and industrial chemicals derived from petroleum feedstocks will be completely devoid of 14C relative to contemporary (actively cycling) carbon, which contains 14C created by cosmic radiation reactions in the atmosphere. Radiocarbon analysis is not subject to fractionation as is stable carbon isotope analysis, and 14C decay is not significantly impacted by physical, chemical or biological processes. Moreover, the 14C signal &#8212; or lack thereof &#8212; in petroleum-derived materials is evenly distributed throughout the contaminant pool making it a fully miscible tracer. The technique described here relies on the observation that any CO2 generated from a fossil derived contaminant will be devoid of 14C while CO2 generated from microorganisms degrading NOM will contain easily-measurable amounts of 14C. Measuring 14CO2 also allows one to directly link full contaminant degradation (i.e., mineralization) to a harmless end product.
14CO2 analysis has been used to follow fossil fuel-derived contaminant degradation products7-13. This is due to the analytical resolution between end members (fossil and contemporary) which is roughly 1,100 parts per thousand (&#8240;). Generally, accelerator mass spectrometry (AMS) is used to resolve natural abundance radiocarbon. Atmospheric CO2 (~ +200&#8240;) living biomass (~ +150&#8240;) and soil organic matter-derived CO2 (~ -200&#8211;+100&#8240;) are all analytically distinct from fossil-derived CO2 (-1,000&#8240;). This is due to the complete decay of all 14C, which has a half-life of roughly 6,000 years. Fuels and industrial chemicals derived from petroleum feedstocks, which are millions of years removed from active carbon cycling, have a distinct radiocarbon signature (-1,000&#8240; &#8776; 0% modern &#8212; meaning no detection on AMS). The measurement is straightforward and in terms of sample contamination, almost all potential biases are toward the conservative (contaminating the sample with modern CO2). For instance, atmospheric CO2 getting into a sample would increase the radiocarbon isotopic signature and thus cause underestimating the degradation rate.
CO2 evolved from fossil-fuel based contaminant degradation will be radiocarbon-free. At a background site with no contamination, CO2 respired from natural organic matter (NOM) will be age appropriate to the NOM. Within the plume or at the fringes, contaminant-derived CO2 will have 0% modern carbon. CO2 from NOM sources and CO2 derived from fossil sources can be distinguished with a two end-member mixing model11. It is thus possible to estimate the proportion of the entire CO2 pool (respired carbon) attributable to the contaminant. Using solely this proportion, fossil-hydrocarbon or industrial chemical oxidation at field sites has been confirmed7-13. This proportion of contaminant derived CO2 can then be coupled with total CO2 mineralization rate (all CO2 collected per unit time and volume) to determine intrinsic contaminant mineralization rate. Assuming this attenuation rate would continue at given site conditions, one could then estimate time needed for site closure.
Techniques are available for determining soil horizon CO2 fluxes with methods having open- or closed-system designs14. Closed-system flux chambers and gas flux models have been used to determine net respiration in contaminated soils12,13,15-17. In these studies, spatial measurements directly associated with a contaminant plume and with background areas showed enhanced biodegradation of organic contaminants. Various modeling methods were used to scale vertical flux measurements to site volume. The goal of this study was to develop methods for collecting ample CO2 for AMS analysis (~1 mg) without influence from atmospheric CO2 contamination (sealed wells) while using the collection rate to determine contaminant respiration. Finally, modeling a zone of influence (ZOI) to ultimately scale the measurement to 3 dimensions (volume) allowed determining the chlorinated hydrocarbon (CH) conversion on a per unit volume and per unit time basis. The ZOI allows one to determine how much volume the respiration and radiocarbon measurements are taken from. The method consists of trapping evolved CO2 by recirculating well headspace gas through a NaOH trap, measuring the radiocarbon content of the collected CO2, using a two end-member model to apportion the CO2 collected to contaminant origin, then scaling the measurement to a volume calculated by a site-specific groundwater model. The well headspace gas is recirculated so that only equilibrium processes &#34;pull&#34; CO2 from the adjacent ZOI.

<table border="0" cellpadding="0" cellspacing="0" width="737">
	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:280px;">Air pump; Power Bubbles 12V</td>
			<td style="width:171px;">Marine Metal</td>
			<td style="width:119px;">B-15</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:280px;">Marine Sealant</td>
			<td style="width:171px;">3M</td>
			<td style="width:119px;">5200</td>
			<td>for sealing pumps</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:280px;">Silicone Sealant</td>
			<td style="width:171px;">Dap</td>
			<td style="width:119px;">08641</td>
			<td>for sealing pumps</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:280px;">Tubing for gas recirculation</td>
			<td style="width:171px;">Mazzer</td>
			<td style="width:119px;">EFNPA2</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:280px;">Stopcocks (for gas lines)</td>
			<td style="width:171px;">Cole-Parmer</td>
			<td style="width:119px;">30600-09</td>
			<td>for assembling gas lines</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:280px;">Male luer lock fittings</td>
			<td style="width:171px;">Cole-Parmer</td>
			<td style="width:119px;">WU-45503-00</td>
			<td>for assembling gas lines</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:280px;">Female luer lock fittings</td>
			<td style="width:171px;">Cole-Parmer</td>
			<td style="width:119px;">EW-45500-00</td>
			<td>for assembling gas lines</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:280px;">4&#34; Lockable J-Plug well cap</td>
			<td style="width:171px;">Dean Bennett Supply</td>
			<td style="width:119px;">NSN</td>
			<td>2&#34; if smaller wells</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:280px;">HOBO 4-Channel Pulse Data Logger</td>
			<td style="width:171px;">Onset</td>
			<td style="width:119px;">UX120-017</td>
			<td>Older model no longer available. Use to monitor pump operation</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:280px;">Serum bottles 100 mL (cs/144)</td>
			<td style="width:171px;">Fisher Scientific</td>
			<td style="width:119px;">33111-U</td>
			<td>For CO2 traps</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:280px;">Septa (pk/100)</td>
			<td style="width:171px;">Fisher Scientific</td>
			<td style="width:119px;">27201</td>
			<td>For CO2 traps</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:280px;">Coulometry&#160;</td>
			<td style="width:171px;"></td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:280px;">Anode solution</td>
			<td style="width:171px;">UIC, Inc</td>
			<td style="width:119px;">CM300-001</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:280px;">Cathode solution</td>
			<td style="width:171px;">UIC, Inc</td>
			<td style="width:119px;">CM300-002</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:280px;">For IC analysis</td>
			<td style="width:171px;"></td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:280px;">Dionex Filter Caps 5 ML 250/pk</td>
			<td style="width:171px;">Fisher Scientific</td>
			<td style="width:119px;">NC9253179</td>
			<td>Caps for IC</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:280px;">Dionex 5 mL vials, 250/pk</td>
			<td style="width:171px;">Fisher Scientific</td>
			<td style="width:119px;">NC9253178</td>
			<td>Vials for IC</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:280px;">If using solar power</td>
			<td style="width:171px;"></td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:280px;">Renogy Solar Panel kit(s)</td>
			<td style="width:171px;">Renogy&#160;</td>
			<td>KT2RNG-100D-1</td>
			<td>Bundle provides 200W</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:280px;">VMAX Solar Battery</td>
			<td style="width:171px;">VMAX</td>
			<td style="width:119px;">VMAX800S</td>
			<td>For energy storage</td>
		</tr>
	</tbody>
</table>

measuring carbon-based contaminant mineralization using combined co2 flux and radiocarbon analyses

A method is described which uses the absence of radiocarbon in industrial chemicals and fuels made from petroleum feedstocks which frequently contaminate the environment. This radiocarbon signal &#8212; or rather the absence of signal &#8212; is evenly distributed throughout a contaminant source pool (unlike an added tracer) and is not impacted by biological, chemical or physical processes (e.g., the 14C radioactive decay rate is immutable). If the fossil-derived contaminant is fully degraded to CO2, a harmless end-product, that CO2 will contain no radiocarbon. CO2 derived from natural organic matter (NOM) degradation will reflect the NOM radiocarbon content (usually &#60;30,000 years old). Given a known radiocarbon content for NOM (a site background), a two end-member mixing model can be used to determine the CO2 derived from a fossil source in a given soil gas or groundwater sample. Coupling the percent CO2 derived from the contaminant with the CO2 respiration rate provides an estimate for the total amount of contaminant degraded per unit time. Finally, determining a zone of influence (ZOI) representing the volume from which site CO2 is collected allows determining the contaminant degradation per unit time and volume. Along with estimates for total contaminant mass, this can ultimately be used to calculate time-to-remediate or otherwise used by site managers for decision-making.

At the test site, historical CH contamination has been highest within the central well cluster (MW-25-MW-30) and near Sherman Road (Fig. 5). In 1983, large portions of contamination were removed from the landfill site (North of the test site) and additional excavation occurred in 2001. CH concentrations have decreased after source removal particularly near the former pits (Sherman Road), but a persistent plume continues to exist in the central well cluster region. Seasonal rains are known to transiently increase CH concentrations and residual contamination desorbs for soils27. Soils in the area are primarily former dredge sands. A possible interference with the described method could exist if ancient carbonate rocks are present, and groundwater pH is very low (&#60;~5). This could lead to carbonate dissolution and an ancient signal in CO2 generated. No significant CaCO3 are known in the area, nonetheless, cations and pH were measured and subjected to regression and principal components analyses (PCA). The primary concern was that low pH might promote calcium carbonate (CaCO3) dissolution, which could bias radiocarbon analysis (ancient carbonate rocks could provide ancient CO2 if dissolved by acidic waters). Na+ content was marginally higher at the Southern side of the site (closest to the ocean), but no values were in a range indicating significant seawater intrusion. Calcium ion concentrations ranged from 8.0 to 58 mg L-1. Carbonate dissolution was not indicated when relating calcium ion concentration to pH (r2 &#60; 0.3). PCA bi-plots did not indicate strong loadings with any variable. Between-well differences also did not indicate carbonate dissolution (Fig. 6). This conformational analysis should be considered critical when adapting the methodology to new sites &#8212; particularly those with regional geology indicating significant carbonate rock formations.
CO2 production rates ranged from 0 to 34 mg CO2 d-1. CO2 production was lowest in the central well cluster in the region where historical contamination was highest (Fig. 5). CO2 production in well MW-01 (background well &#8212; not shown, but ~500 meters Northwest of the main well cluster) was the very high at 31 mg CO2 d-1). Duplicate respiration analyses had standard errors ranging from 0.03 to 6% CO2 and averaged less than 1% (0.98). The two, 2-week periods dry season measurements were averaged for subsequent calculations. Respiration measurements did not vary considerably between individual 2-week periods. Between period respiration standard error ranged from &#60;1 to 51% but averaged 13% (Table 1). Respiration averaging allowed calculating a single CH volume removed during a one-month period. The background well (MW-01) had a radiocarbon age of 1,280 years before present (ybp) or 85 percent modern (pMC)&#160;&#8212; within a common range for aged soil organic matter26. This well&#39;s value was used as background for the isotopic mixing model. Again, because sampling was limited to one-month total, two back-to-back periods during the same season were used to &#34;represent&#34; the dry season&#160;&#8212; generally thought to be the most stagnant conditions and thus conservative for extrapolated estimates. As with DIC production rates, radiocarbon measurements were similar between individual 2-week periods. The standard error between periods ranged from 0.25 to 18% and averaged 6%. CO2 radiocarbon ages ranged from ~34 to 85 pMC or ~1,340 to 8,700 ybp (Table 1). MW-27 and MW-32, suspected of being compromised by pump leaking had modern radiocarbon values and were thus confirmed as compromised. These samples were not included in further analysis.
Previous reports were used for groundwater hydraulic and CO2 solute properties to develop the ZOI model26,27 (Table 2). Weather data (2007, 2011 and 2012) from the CIMIS San Diego station (Station ID 184) were used to estimate the aquifer recharge rate. Tidal data over the same time period from the NOAA San Diego Station (Station ID: 9410170) were used to define boundary conditions. The model calibration assumed a steady hydraulic gradient and constant CO2 collection rates. Supplemental simulations varying average CO2 collection rates and initial background CO2 coupled with a 10% hydraulic gradient increase aided in parameterizing the model. A supplemental simulation using the average CO2 collection rate showed an approximately 46% increase in the estimated background CO2 (i.e., increased from 6.5 to 9.5 g m-3) if the collection rate changed from 0.00530 (+10%) to 0.00434 g hr-1 (-10%) over the 2-week collection period (Table 3). Assumptions for the ZOI model included negligible CO2 production attributable to CH degradation during the collection period and the uniform initial CO2 distribution to develop the final simulation (Fig. 7). The CO2 reaction rate may be underestimated for the study site.
Using CO2 production rate, CO2 attributable to CH degradation, and estimates from the ZOI model, the mass CH removal at each well per unit time was calculated. Data from Table 1 was used with the two end-member mixing model (eq (1)) to solve for fpet at each well. Because the site is only known to CH contamination and no other CO2 source was found within or near the site, CH degradation is assumed as the main contribute of CO2. The fpet ranged from 1 to 60% over the site (Table 4). The proportion was converted to carbon basis and multiplied by the CO2 production rate to calculate CH degradation rate (Table 4). Using the ZOI volume (Table 3), contaminant degradation rate per unit time and volume was determined (Table 4). This value ranged between 0 to 32 mg C m-3 d-1 (Table 4). CH degradation was lowest in regions of highest historical CH contamination (MW-25 - MW-30). At wells near the site periphery (near Sherman Road), the highest CH degradation was measured. CO2 production was higher in these areas, while fpet indicated significant CH turnover (Fig. 8).
<img alt="Figure 1" src="/files/ftp_upload/53233/53233fig1.jpg" /> 
Figure 1. Sealing and preparing recirculation pumps. Sealing recirculation pumps for field deployment.
<img alt="Figure 2" src="/files/ftp_upload/53233/53233fig2.jpg" /> 
Figure 2. NaOH traps prepared for field deployment. 120 ml serum bottles with NaOH trap added and crimp sealed.
<img alt="Figure 3" src="/files/ftp_upload/53233/53233fig3.jpg" /> 
Figure 3. Field setup. Wire routed to outfitted wells (left), trap deployed at a well (upper right), and solar power distribution system (lower right). Wells are outfitted in the field with collection systems (including wiring, power distribution, and pump/traps).
<img alt="Figure 4" src="/files/ftp_upload/53233/53233fig4.jpg" /> 
Figure 4. Modified well caps showing gas recirculation lines. This figure shows well caps modified with gas inlet and return lines.
<img alt="Figure 5" src="/files/ftp_upload/53233/53233fig5.jpg" /> 
Figure 5. Historical chlorinated hydrocarbon contamination (&#181;g L-1). This figure shows the historical chlorinated hydrocarbon contamination at the test site.
<img alt="Figure 6" src="/files/ftp_upload/53233/53233fig6.jpg" /> 
Figure 6. PCA bi-plot showing no co-correlation between dissolved cations and pH. This figure shows a bi-plot of the PCA scores and loadings created from hydrogeological data (pH and cations) for the test site.
<img alt="Figure 7" src="/files/ftp_upload/53233/53233fig7.jpg" /> 
Figure 7. Calibrated ZOI model for the average CO2 collection rate (0.0048 g m-3). The calibrated background CO2 concentration was 6.5 g m-3, and the ZOI threshold concentration was 6.18 g m-3 (solid black line). Longitudinal and transverse diameters of the ZOI were 2.28 m and 0.72 m, respectively. Depth of the ZOI was 0.12 m. Modified from 18. This figure shows a graphical representation of the ZOI model in 3 dimensions.
<img alt="Figure 8" src="/files/ftp_upload/53233/53233fig8.jpg" /> 
Figure 8. Contaminant degradation rate per unit time per unit area. Modified from 18. This is the interpolated degradation rate for CH over the study site over the time period sampled.
<img alt="Video 1" src="/files/ftp_upload/53233/53233video1.jpg" /> 
Video 1. Development of ZOI using MT3DMS23 - MODFLOW simulation (<a href="http://cloudflare.jove.com/files/ftp_upload/53233/Video_1_ZOI_simuation.mp4" target="_blank">right click to download</a>). Download, install, initialize and create simulation for the ZOI.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Well</td>
			<td>&#948;13C 
			(&#8240;VPDB)</td>
			<td>&#916;14C 
			(&#8240;)</td>
			<td>Conventional Age 
			(ybp)</td>
			<td>Percent Modern C 
			(pMC)</td>
		</tr>
		<tr>
			<td>MW-01</td>
			<td>-34</td>
			<td>-147</td>
			<td>1280</td>
			<td>85</td>
		</tr>
		<tr>
			<td>MW-21</td>
			<td>-28</td>
			<td>-663</td>
			<td>8730</td>
			<td>34</td>
		</tr>
		<tr>
			<td>MW-25</td>
			<td>-23</td>
			<td>-153</td>
			<td>1340</td>
			<td>85</td>
		</tr>
		<tr>
			<td>MW-26</td>
			<td>-25</td>
			<td>-298</td>
			<td>2845</td>
			<td>70</td>
		</tr>
		<tr>
			<td>MW-27</td>
			<td>-18</td>
			<td>N.D.*</td>
			<td>N.D.*</td>
			<td>N.D.*</td>
		</tr>
		<tr>
			<td>MW-28</td>
			<td>-25</td>
			<td>-190</td>
			<td>1695</td>
			<td>81</td>
		</tr>
		<tr>
			<td>MW-30</td>
			<td>-35</td>
			<td>-254</td>
			<td>2365</td>
			<td>75</td>
		</tr>
		<tr>
			<td>MW-32</td>
			<td>-20</td>
			<td>N.D.*</td>
			<td>N.D.*</td>
			<td>N.D.*</td>
		</tr>
		<tr>
			<td>MW-34</td>
			<td>-32</td>
			<td>-283</td>
			<td>2670</td>
			<td>72</td>
		</tr>
		<tr>
			<td>MW-35</td>
			<td>-25</td>
			<td>-598</td>
			<td>7320</td>
			<td>40</td>
		</tr>
		<tr>
			<td>MW-38</td>
			<td>-32</td>
			<td>-354</td>
			<td>3515</td>
			<td>65</td>
		</tr>
		<tr>
			<td>MW-41</td>
			<td>-28</td>
			<td>-232</td>
			<td>2125</td>
			<td>77</td>
		</tr>
		<tr>
			<td>MW-42</td>
			<td>-23</td>
			<td>-482</td>
			<td>5280</td>
			<td>52</td>
		</tr>
		<tr>
			<td colspan="5">*N.D. No data - pump leaking</td>
		</tr>
	</tbody>
</table>
Table 1. CO2 isotope measurements and conversions. CO2 Stable isotope and radiocarbon measurements and conversions to units used in the manuscript.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td colspan="2">Parameter</td>
			<td>Units</td>
			<td>Value</td>
		</tr>
		<tr>
			<td colspan="2">Hydrology</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td rowspan="2"></td>
			<td rowspan="2">Hydraulic Conductivity</td>
			<td rowspan="2">ml hr-1</td>
			<td>0.44 (aquifer)</td>
		</tr>
		<tr>
			<td>10 (well)</td>
		</tr>
		<tr>
			<td rowspan="2"></td>
			<td rowspan="2">Porosity (aquifer)</td>
			<td rowspan="2"></td>
			<td>0.48 (aquifer)</td>
		</tr>
		<tr>
			<td>0.99 (well)</td>
		</tr>
		<tr>
			<td></td>
			<td>Bulk Density</td>
			<td>g cm-3</td>
			<td>1.4</td>
		</tr>
		<tr>
			<td></td>
			<td>Specific Yield</td>
			<td>cm3 cm-3</td>
			<td>0.2</td>
		</tr>
		<tr>
			<td></td>
			<td>Hydraulic Gradient</td>
			<td>m m-1</td>
			<td>0.015</td>
		</tr>
		<tr>
			<td colspan="2">CO2 Solute Transport</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td></td>
			<td>Diffusion Coefficient</td>
			<td>m2 hr-1</td>
			<td>5.77 x 10-5</td>
		</tr>
		<tr>
			<td rowspan="2"></td>
			<td>Longitudinal</td>
			<td rowspan="2">m</td>
			<td rowspan="2">6.1</td>
		</tr>
		<tr>
			<td>Dispersivity</td>
		</tr>
		<tr>
			<td rowspan="2"></td>
			<td>Horizontal Transverse</td>
			<td rowspan="2">m</td>
			<td rowspan="2">0.61</td>
		</tr>
		<tr>
			<td>Dispersivity</td>
		</tr>
		<tr>
			<td rowspan="2"></td>
			<td>Vertical Transverse</td>
			<td rowspan="2">m</td>
			<td rowspan="2">0.061</td>
		</tr>
		<tr>
			<td>Dispersivity</td>
		</tr>
		<tr>
			<td></td>
			<td>Soil Gas CO2</td>
			<td>%</td>
			<td>0.56</td>
		</tr>
	</tbody>
</table>
Table 2. ZOI model parameters. Parameters used in the ZOI model and simulations.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td rowspan="2">Collection Rate Level</td>
			<td rowspan="2">Collection Rate</td>
			<td rowspan="2">Background Concentration</td>
			<td colspan="4">ZOI Size</td>
		</tr>
		<tr>
			<td>Longitudinal</td>
			<td>Transverse</td>
			<td>Depth</td>
			<td>Volume</td>
		</tr>
		<tr>
			<td></td>
			<td>(g/hr)</td>
			<td>(g/m3)</td>
			<td>(m)</td>
			<td></td>
			<td></td>
			<td>(m3)</td>
		</tr>
		<tr>
			<td>Maximum&#160;</td>
			<td>0.0131</td>
			<td>17.6</td>
			<td>2.47</td>
			<td>0.77</td>
			<td>0.13</td>
			<td>0.193</td>
		</tr>
		<tr>
			<td>Average</td>
			<td>0.0048</td>
			<td>6.5</td>
			<td>2.28</td>
			<td>0.72</td>
			<td>0.12</td>
			<td>0.176</td>
		</tr>
		<tr>
			<td>Minimum</td>
			<td>0.0003</td>
			<td>4</td>
			<td>2.16</td>
			<td>0.68</td>
			<td>0.11</td>
			<td>0.149</td>
		</tr>
	</tbody>
</table>
Table 3. ZOI model outputs. Model outputs for the ZOI. This table describes the three-dimensional volume for the ZOI.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Well</td>
			<td>fpet 
			(%)</td>
			<td>Contaminant degradation rate 
			(mg C d-1 &#177;10%)</td>
			<td>Contaminant degradation per unit time and&#160;volume 
			(mg C m-3 d-1 &#177;15%)</td>
		</tr>
		<tr>
			<td>MW-01</td>
			<td>0</td>
			<td>N.A.</td>
			<td>N.A.</td>
		</tr>
		<tr>
			<td>MW-21</td>
			<td>60</td>
			<td>5.6</td>
			<td>32</td>
		</tr>
		<tr>
			<td>MW-25&#165;</td>
			<td>1</td>
			<td>0</td>
			<td>0</td>
		</tr>
		<tr>
			<td>MW-26</td>
			<td>18</td>
			<td>0.18</td>
			<td>1</td>
		</tr>
		<tr>
			<td>MW-28</td>
			<td>5</td>
			<td>0.017</td>
			<td>0.098</td>
		</tr>
		<tr>
			<td>MW-30</td>
			<td>12</td>
			<td>0.34</td>
			<td>1.9</td>
		</tr>
		<tr>
			<td>MW-34</td>
			<td>16</td>
			<td>0.1</td>
			<td>0.58</td>
		</tr>
		<tr>
			<td>MW-35</td>
			<td>53</td>
			<td>3.6</td>
			<td>20</td>
		</tr>
		<tr>
			<td>MW-38</td>
			<td>24</td>
			<td>1.4</td>
			<td>8.1</td>
		</tr>
		<tr>
			<td>MW-41</td>
			<td>10</td>
			<td>0.44</td>
			<td>2.5</td>
		</tr>
		<tr>
			<td>MW-42</td>
			<td>39</td>
			<td>1.7</td>
			<td>9.8</td>
		</tr>
		<tr>
			<td colspan="4">N.A. Not applicable &#8211; MW-01 used as the background (e.g., no contamination)</td>
		</tr>
		<tr>
			<td colspan="4">&#165;Assumed to be purely equilibrium-driven (e.g., no respiration)</td>
		</tr>
	</tbody>
</table>
Table 4. Scaled contaminant degradation estimates. Estimates for contaminant degradation per unit time and unit volume for sampled wells.

Watch this Scientific Journal Video about Measuring Carbon-based Contaminant Mineralization Using Combined CO2 Flux and Radiocarbon Analyses at JoVE.com

Measuring Carbon-based Contaminant Mineralization Using Combined CO2 Flux and Radiocarbon Analyses

A protocol is described wherein CO2 mineralized from organic contaminant (derived from petroleum feedstocks) biodegradation is trapped, quantified, and analyzed for 14C content. A model is developed to determine CO2 capture zone's spatial extent. Spatial and temporal measurements allow integrating contaminant mineralization rates for predicting remediation extent and time.

Measuring Carbon-based Contaminant Mineralization Using Combined CO2 Flux and Radiocarbon Analyses

A method is described which uses the absence of radiocarbon in industrial chemicals and fuels made from petroleum feedstocks which frequently contaminate ...

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11.5K Views.  US Naval Research Laboratory. A protocol is described wherein CO2 mineralized from organic contaminant (derived from petroleum feedstocks) biodegradation is trapped, quantified, and analyzed for 14C content. A model is developed to determine CO2 capture zone's spatial extent. Spatial and temporal measurements allow integrating contaminant mineralization rates for predicting remediation extent and time. 

Video: Measuring Carbon-based Contaminant Mineralization Using Combined CO2 Flux and Radiocarbon Analyses

Surface Renewal: An Advanced Micrometeorological Method for Measuring and Processing Field-Scale Energy Flux Density Data

Advanced micrometeorological methods have become increasingly important in soil, crop, and environmental sciences. For many scientists without formal training in atmospheric science, these techniques are relatively inaccessible. Surface renewal and other flux measurement methods require an understanding of boundary layer meteorology and extensive training in instrumentation and multiple data management programs. To improve accessibility of these techniques, we describe the underlying theory of surface renewal measurements, demonstrate how to set up a field station for surface renewal with eddy covariance calibration, and utilize our open-source turnkey data logger program to perform flux data acquisition and processing. The new turnkey program returns to the user a simple data table with the corrected fluxes and quality control parameters, and eliminates the need for researchers to shuttle between multiple processing programs to obtain the final flux data. An example of data generated from these measurements demonstrates how crop water use is measured with this technique. The output information is useful to growers for making irrigation decisions in a variety of agricultural ecosystems. These stations are currently deployed in numerous field experiments by researchers in our group and the California Department of Water Resources in the following crops: rice, wine and raisin grape vineyards, alfalfa, almond, walnut, peach, lemon, avocado, and corn.

Surface renewal is a micrometeorological method that is being used increasingly to determine energy fluxes, but its technical complexity makes it inaccessible to a broad audience. We describe the steps needed to set up and calibrate a surface renewal field station, to acquire and process data, and to correctly interpret results.

Advanced micrometeorological methods have become  increasingly important in soil, crop, and environmental sciences.  For many scientists without formal ...

Measuring Fluxes of Mineral Nutrients and Toxicants in Plants with Radioactive Tracers

Unidirectional influx and efflux of nutrients and toxicants, and their resultant net fluxes, are central to the nutrition and toxicology of plants. Radioisotope tracing is a major technique used to measure such fluxes, both within plants, and between plants and their environments. Flux data obtained with radiotracer protocols can help elucidate the capacity, mechanism, regulation, and energetics of transport systems for specific mineral nutrients or toxicants, and can provide insight into compartmentation and turnover rates of subcellular mineral and metabolite pools. Here, we describe two major radioisotope protocols used in plant biology: direct influx (DI) and compartmental analysis by tracer efflux (CATE). We focus on flux measurement of potassium (K+) as a nutrient, and ammonia/ammonium (NH3/NH4+) as a toxicant, in intact seedlings of the model species barley (Hordeum vulgare L.). These protocols can be readily adapted to other experimental systems (e.g., different species, excised plant material, and other nutrients/toxicants). Advantages and limitations of these protocols are discussed.

In planta measurement of nutrient and toxicant fluxes is essential to the study of plant nutrition and toxicity. Here, we cover radiotracer protocols for influx and efflux determination in intact plant roots, using potassium (K+) and ammonia/ammonium (NH3/NH4+) fluxes as examples. Advantages and limitations of such techniques are discussed.

Unidirectional influx and efflux of nutrients and toxicants, and their resultant net fluxes, are central to the nutrition and toxicology of plants. ...

Measurement of Greenhouse Gas Flux from Agricultural Soils Using Static Chambers

Measurement of greenhouse gas (GHG) fluxes between the soil and the atmosphere, in both managed and unmanaged ecosystems, is critical to understanding the biogeochemical drivers of climate change and to the development and evaluation of GHG mitigation strategies based on modulation of landscape management practices. The static chamber-based method described here is based on trapping gases emitted from the soil surface within a chamber and collecting samples from the chamber headspace at regular intervals for analysis by gas chromatography. Change in gas concentration over time is used to calculate flux. This method can be utilized to measure landscape-based flux of carbon dioxide, nitrous oxide, and methane, and to estimate differences between treatments or explore system dynamics over seasons or years. Infrastructure requirements are modest, but a comprehensive experimental design is essential. This method is easily deployed in the field, conforms to established guidelines, and produces data suitable to large-scale GHG emissions studies.

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A CO2 Concentration Gradient Facility for Testing CO2 Enrichment and Soil Effects on Grassland Ecosystem Function

Continuing increases in atmospheric carbon dioxide concentrations (CA) mandate techniques for examining impacts on terrestrial ecosystems. Most experiments examine only two or a few levels of CA concentration and a single soil type, but if CA can be varied as a gradient from subambient to superambient concentrations on multiple soils, we can discern whether past ecosystem responses may continue linearly in the future and whether responses may vary across the landscape. The Lysimeter Carbon Dioxide Gradient Facility applies a 250 to 500 &#181;l L-1 CA gradient to Blackland prairie plant communities established on lysimeters containing clay, silty clay, and sandy soils. The gradient is created as photosynthesis by vegetation enclosed in in temperature-controlled chambers progressively depletes carbon dioxide from air flowing directionally through the chambers. Maintaining proper air flow rate, adequate photosynthetic capacity, and temperature control are critical to overcome the main limitations of the system, which are declining photosynthetic rates and increased water stress during summer. The facility is an economical alternative to other techniques of CA enrichment, successfully discerns the shape of ecosystem responses to subambient to superambient CA enrichment, and can be adapted to test for interactions of carbon dioxide with other greenhouse gases such as methane or ozone.

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The Lysimeter Carbon Dioxide Gradient Facility creates a 250 to 500 &#181;l L-1 linear carbon dioxide gradient in temperature-controlled chambers housing grassland plant communities on clay, silty clay, and sandy soil monoliths. The facility is used to determine how past and future carbon dioxide levels affect grassland carbon cycling.

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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.

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

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The ambrosia beetle, Platypus quercivorus (Murayama), is the vector of a fungal pathogen that causes mass mortality of Fagaceae trees (Japanese oak wilt). Therefore, knowing the dispersal capacity may help inform trapping/tree removal efforts to prevent this disease more effectively. In this study, we measured the flight velocity and duration and estimated the flight distance of the beetle using a newly developed flight mill. The flight mill is low cost, small, and constructed using commonly available items. Both the flight mill arm and its vertical axis comprise a thin needle. A beetle specimen is glued to one tip of the arm using instant glue. The other tip is thick due to being covered with plastic, thus it facilitates the detection of rotations of the arm. The revolution of the arm is detected by a photo sensor mounted on an infrared LED, and is indicated by a change in the output voltage when the arm passed above the LED. The photo sensor is connected to a personal computer and the output voltage data are stored at a sampling rate of 1 kHz. By conducting experiments using this flight mill, we found that P. quercivorus can fly at least 27 km. Because our flight mill comprises cheap and small ordinary items, many flight mills can be prepared and used simultaneously in a small laboratory space. This enables experimenters to obtain a sufficient amount of data within a short period.

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Length and diameter of stolons or rhizomes are usually measured using simple rulers and calipers. This procedure is slow and laborious, so it is often used on a limited number of stolons or rhizomes. For this reason, these traits are limited in their use for morphological characterization of plants. The use of digital image analysis software technology may overcome measurement errors due to human mistakes, which tend to increase as the number and size of samples also increase. The protocol can be used for any kind of crop but is particularly suitable for forage or grasses, where plants are small and numerous. Turf samples consist of aboveground biomass and an upper soil layer to the depth of maximum rhizome development, depending on the species of interest. In studies, samples are washed from the soil, and stolons/rhizomes are cleaned by hand before analysis by digital image analysis software. The samples are further dried in a laboratory heating oven to measure dry weight; therefore, for each sample, the resultant data are total length, total dry weight, and average diameter. Scanned images can be corrected before analysis by excluding visible extraneous parts, such as remaining roots or leaves not removed with the cleaning process. Indeed, these fragments normally have much smaller diameters than stolons or rhizomes, so they can be easily excluded from analysis by fixing the minimum diameter below which objects are not considered. Stolon or rhizome density per unit area can then be calculated based on sample size. The advantage of this method is quick and efficient measurement of the length and average diameter of large sample numbers of stolons or rhizomes.

A software-based image analysis system provides an alternative method to study the morphology of stoloniferous and rhizomatous species. This protocol permits measurement of the length and diameter of stolons and rhizomes and can be applied to samples with a large amount of biomass and to a wide variety of species.

Length and diameter of stolons or rhizomes are usually measured using simple rulers and calipers. This procedure is slow and laborious, so it is often ...

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Combined size and density fractionation (CSDF) is a method to physically separate soil into fractions differing in texture (particle size) and mineralogy (density). The purpose is to isolate fractions with different reactivities towards soil organic matter (SOM), in order to better understand organo-mineral interactions and SOM dynamics.

Combined size and density fractionation (CSDF) is a method used to physically separate soil into fractions differing in particle size and mineralogy. CSDF ...

Identifying Per- and Polyfluorinated Chemical Species with a Combined Targeted and Non-Targeted-Screening High-Resolution Mass Spectrometry Workflow

Historical and emerging per- and polyfluoroalkyl substances (PFASs) have garnered significant interest from the public and government agencies from the local to federal levels. The continuing evolution of PFAS chemistries presents a challenge to the environmental monitoring, where ongoing development of targeted methods necessarily lags the discovery of new chemical compounds. There is a need, therefore, to have forward-looking methodologies that can detect emerging and unexpected compounds, monitor these species over time, and resolve details of their chemical structure to enable future work in human health. To this end, non-targeted analysis by high-resolution mass spectrometry offers a broad base detection approach that can be combined with almost any sample preparation scheme and provides significant capabilities for compound identification after detection. Herein, we describe a solid-phase extraction (SPE) based sample concentration method tuned for shorter chain and more hydrophilic PFAS chemistries, such as per fluorinated ether acids and sulfonates, and describe analysis of samples prepared in this fashion in both targeted and non-targeted modes. Targeted methods provide superior quantification when reference standards are available but are intrinsically limited to expected compounds when performing analysis. In contrast, a non-targeted approach can identify the presence of unexpected compounds and provide some information about their chemical structure. Information about chemical features can be used to correlate compounds across sample locations and track abundance and occurrence over time.

Here, we present a protocol for the sequential targeted quantification and non-targeted analysis of fluorinated compounds in water by mass spectrometry. This methodology provides quantitative levels of known fluorochemical compounds and identifies unknown chemicals in related samples with semi-quantitative estimates of their abundance.

Historical and emerging per- and polyfluoroalkyl substances (PFASs) have garnered significant interest from the public and government agencies from the ...

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This work demonstrates a new method for measuring the stability of enzyme activity by isothermal titration calorimetry (ITC). The peak heat rate observed after a single injection of the substrate solution into an enzyme solution is correlated with enzyme activity. Multiple injections of the substrate into the same enzyme solution over time show the loss of enzyme activity. The assay is autonomous, requiring very little personnel time, and is applicable to most media and enzymes.&#160; &#160;

The thermal stability of enzyme activity is readily measured by isothermal titration calorimetry (ITC). Most protein stability assays currently used measure protein unfolding, but do not provide information about enzymatic activity. ITC enables direct determination of the effect of enzyme modifications on the stability of enzyme activity.

This work demonstrates a new method for measuring the stability of enzyme activity by isothermal titration calorimetry (ITC). The peak heat rate observed ...