Soil sampling and analysis for volatile organic compounds

Concerns over data quality have raised many questions related to sampling soils for volatile organic compounds (VOCs). This paper was prepared in response to some of these questions and concerns expressed by Remedial Project Managers (RPMs) and On-Scene Coordinators (OSCs). The following questions are frequently asked:

1. Is there a specific device suggested for sampling soils for VOCs?
2. Are there significant losses of VOCs when transferring a soil sample from a sampling device (e.g., split spoon) into the sample container?
3. What is the best method for getting the sample from the split spoon (or other device) into the sample container?
4. Are there smaller devices such as subcore samplers available for collecting aliquots from the larger core and efficiently transferring the sample into the sample container?
5. Are certain containers better than others for shipping and storing soil samples for VOC analysis?
6. Are there any reliable preservation procedures for reducing VOC losses from soil samples and for extending holding times?

Guidance is provided for selecting the most effective sampling device for collecting samples from soil matrices. The techniques for sample collection, sample handling, containerizing, shipment, and storage described in this paper reduce VOC losses and generally provide more representative samples for volatile organic analyses (VOA) than techniques in current use. For a discussion on the proper use of sampling equipment the reader should refer to other sources (Acker, 1974; U.S. EPA, 1983; U.S. EPA, 1986a). Soil, as referred to in this report, encompasses the mass (surface and subsurface) of unconsolidated mantle of weathered rock and loose material lying above solid rock. Further, a distinction must be made as to what fraction of the unconsolidated material is soil and what fraction is not. The soil component here is defined as all mineral and naturally occurring organic material that is 2 mm or less in size. This is the size normally used to differentiate between soils (consisting of sands, silts, and clays) and gravels. Although numerous sampling situations may be encountered, this paper focuses on three broad categories of sites that might be sampled for VOCs:

1. Open test pit or trench.
2. Surface soils (<5 ft in depth).
3. Subsurface soils (>5 ft in depth).

     

Analysing the sensitivity behaviour of two hydrology models

Recently, the New Morris Method has been presented as an effective sensitivity analysis tool for mathematical models. The New Morris Method estimates the sensitivity of an output parameter to a given set of input parameters (first-order effects) and the extent these parameters interact with each other (second-order effects). This method requires the specification of two parameters (runs and resolution) that control the sampling of the output parameter to determine its sensitivity to various inputs. The criteria for these parameters have been set on the analysis of a well-behaved analytical function (see Cropp and Braddock, Reliab. Eng. Syst. Saf. 78:77–83, 2002), which may not be applicable to other physical models that describe complex processes. This paper will investigate the appropriateness of the criteria from (Cropp and Braddock, 2002) and hence the effectiveness of the New Morris Method to determine the sensitivity behaviour of two hydrologic models: the Soil Erosion and Deposition System and Griffith University Representation of Urban Hydrology. In the first case, this paper will separately analyse the sensitivity of an output parameter on a set of input parameters (first- and second-order effects) for each model and discuss the physical meaning of these sensitivities. This will be followed by an investigation into the sampling criteria by exploring the convergence of the sensitivity behaviour for each model as the sampling of the parameter space is increased. By comparing these trends to the convergence behaviour from Cropp and Braddock (2002), we will determine how well the New Morris Method estimates the sensitivity for each model and whether the sampling criteria are appropriate for these models. It will be shown that the New Morris Method can provide additional insight into the functioning of these models, and that, under a different metric, the sensitivity behaviour of these models does converge confirming the sampling criteria set by Cropp and Braddock.     

The efficacy of protoporphyrin as a predictive biomarker for lead exposure in canvasback ducks: Effect of sample storage time

We used 363 blood samples collected from wild canvasback dueks (Aythya valisineria) at Catahoula Lake, Louisiana, U.S.A. to evaluate the effect of sample storage time on the efficacy of erythrocytic protoporphyrin as an indicator of lead exposure. The protoporphyrin concentration of each sample was determined by hematofluorometry within 5 min of blood collection and after refrigeration at 4 °C for 24 and 48 h. All samples were analyzed for lead by atomic absorption spectrophotometry. Based on a blood lead concentration of 0.2 ppm wet weight as positive evidence for lead exposure, the protoporphyrin technique resulted in overall error rates of 29%, 20%, and 19% and false negative error rates of 47%, 29% and 25% when hematofluorometric determinations were made on blood at 5 min, 24 h, and 48 h, respectively. False positive error rates were less than 10% for all three measurement times. The accuracy of the 24-h erythrocytic protoporphyrin classification of blood samples as positive or negative for lead exposure was significantly greater than the 5-min classification, but no improvement in accuracy was gained when samples were tested at 48 h. The false negative errors were probably due, at least in part, to the lag time between lead exposure and the increase of blood protoporphyrin concentrations. False negatives resulted in an underestimation of the true number of canvasbacks exposed to lead, indicating that hematofluorometry provides a conservative estimate of lead exposure.The U.S. Government's right to retain a non-exclusive, royalty-free licence in and to any copyright is acknowledgedDeceased     

Cadmium and zinc source assessment in the Sepetiba Bay and basin region

Industrial and weathering inputs of Cd and Zn to the Sepetiba Bay and basin were assessed, based on production parameters obtained from local environment and industry authorities, and literature data. The results are compared with similar evaluations from other coastal regions and field data obtained in measuring Zn and Cd transport by rivers, direct run-off and atmospheric deposition in the region. Cadmium and zinc inputs to the bay increased by three orders of magnitude relative to pre-industrial fluxes and presently reach up to 1.6 and 180 tonnes per year for Cd and Zn, respectively, which represents a large input-to-area ratio, and explains the high concentration of these metals previously reported in the estuarine biota and sediments of Sepetiba Bay. Industrial activities, mainly metallurgical and chemical, comprise 94% and 84% of the total Cd and Zn inputs to the Bay. This figure identifies the point sources as being responsible for the environmental contamination and for regional poisoning risks.     

Speciation of arsenic using solid phase extraction cartridges

Various solid phase extraction (SPE) cartridges were investigated for speciation of arsenite [As(III)], arsenate [As(v)], monomethylarsonic acid (MMA) and dimethylarsinic acid (DMA). Cartridges containing different types of sorbent materials were tested for arsenic retention and elution characteristics. Alumina cartridges were found to completely retain all the four target arsenic species, and are suitable for removal and preconcentration purposes. For speciation analysis, different arsenic species were separated on the basis of their selective retention on and elution from specific cartridges. DMA was retained on a resin-based strong cation exchange cartridge and eluted with 1.0 M HCl. MMA and As(v) were both retained on a silica-based strong anion exchange cartridge and sequentially eluted with 60 mM acetic acid (for MMA) and 1.0 M HCl [for As(v)]. As(III) was not retained on either cartridge and remained in solution. Arsenic species in solution and those eluted from the cartridges were subsequently quantified by using flow injection with hydride generation atomic fluorescence spectrometry (FI-HGAFS) and hydride generation atomic absorption spectrometry (FI-HGAAS). A detection limit of 0.05 microg L(-1) arsenic in water sample was achieved using HGAFS. An application of the method was demonstrated at a drinking water treatment facility. As(III) and As(v) species were determined in water at various stages of treatment. The method is suitable for routine determination of trace levels of arsenic in drinking water to comply with more stringent environmental regulations.     

Multi-objective, decision-based assessment of a water quality monitoring network in a river system

Water quality monitoring network design has historically tended to use experience, intuition and subjective judgement in locating monitoring stations. Better design procedures to optimize monitoring systems need to simultaneously identify significant planning objectives and consider a number of social, economic and environmental constraints. The consideration of multiple objectives may require further decision analysis to determine the preference weights associated with the objectives to aid in the decision-making process. This may require the application of an optimization study to extract such information from decision makers or experts and to evaluate the overall effectiveness of locating strategies. This paper assesses the optimal expansion and relocation strategies of a water quality monitoring network using a two-stage analysis. The first stage focuses on the information retrieval of preference weights with respect to the designated planning objectives. With the aid of a pre-emptive goal programming model, data analysis is applied to obtain the essential information from the questionnaire outputs. The second stage then utilizes a weighted multi-objective optimization approach to search for the optimal locating strategies of the monitoring stations in the river basin. Practical implementation is illustrated by a case study in the Kao-Ping River Basin, south Taiwan.     

Applying mathematical programming to reserve selection

During the last 10–15 years heuristic methods have been developed for problems in optimal reserve selection. Unfortunately, there is no guarantee that heuristics will find optimal solutions. In recognizing this limitation, analysts have formulated reserve selection problems as set covering problems, for which matrix reduction and integer (0/1) programming can be used to find optimal solutions. In this paper we restate the set covering formulation and review solution techniques. A new 0/1 programming model, which is a generalization of the set covering model, is then presented and applied to a hypothetical reserve selection problem. Objectives of minimizing the number of sites selected and maximizing the number of species represented are addressed. Solutions which characterize the tradeoffs between these objectives provide a rich set of information for planners and decision makers. Applications of mathematical programming to related problems in land use planning and forestry are also discussed.     

Optimal and suboptimal control of anaerobic digesters

Anaerobic digester failure due to entry of inhibitors or sudden changes in the feed substrate concentration may be encompassed beneficially by applying optimal control theory. An almost proportional relationship between the dilution rate and the methane production rate leads to a simple suboptimal control law with only minor loss in performance, after the occurrence of the above mentioned events.     

Attributes of reliable long-term landscape-scale studies: Malpractice insurance for landscape ecologists

Monitoring long-term change in forested landscapes is an intimidating challenge with considerable practical, methodological, and theoretical limitations. Current field approaches used to assess vegetation change at the plot-to-stand scales and nationwide forest monitoring programs may not be appropriate at landscape scales. We emphasize that few vegetation monitoring programs (and, thus, study design models) are designed to detect spatial and temporal trends at landscape scales. Based primarily on advice from many sources, and trial and error, we identify 14 attributes of a reliable long-term landscape monitoring program: malpractice insurance for landscape ecologists. The attributes are to: secure long-term funding and commitment; develop flexible goals; refine objectives; pay adequate attention to information management; take an experimental approach to sampling design; obtain peer-review and statistical review of research proposals and publications; avoid bias in selection of long-term plot locations; insure adequate spatial replication; insure adequate temporal replication; synthesize retrospective, experimental, and related studies; blend theoretical and empirical models with the means to validate both; obtain periodic research program evaluation; integrate and synthesize with larger and smaller scale research, inventory, and monitoring programs; and develop an extensive outreach program. Using these 14 attributes as a guide, we describe one approach to assess the potential effect of global change on the vegetation of the Front Range of the Colorado Rockies. This self-evaluation helps identify strengthes and weaknesses in our program, and may serve the same role for other landscape ecologists in other programs.