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.     

Analysis of pollutant levels in central Hong Kong applying neural network method with particle swarm optimization

Air pollution has emerged as an imminent issue in modernsociety. Prediction of pollutant levels is an importantresearch topic in atmospheric environment today. For fulfillingsuch prediction, the use of neural network (NN), and inparticular the multi-layer perceptrons, has presented to be acost-effective technique superior to traditional statisticalmethods. But their training, usually with back-propagation (BP)algorithm or other gradient algorithms, is often with certaindrawbacks, such as: 1) very slow convergence, and 2) easilygetting stuck in a local minimum. In this paper, a newlydeveloped method, particle swarm optimization (PSO) model, isadopted to train perceptrons, to predict pollutant levels, andas a result, a PSO-based neural network approach is presented. The approach is demonstrated to be feasible and effective bypredicting some real air-quality problems.     

Application of cryofocusing hydride generation and atomic fluorescence detection for dissolved mercury species determination in natural water samples

The concentration levels of mercury (Hg) species in natural water samples are usually low. Consequently, accurate analysis with low detection limits is still a major problem. In this work, a method was applied for the simultaneous direct determination of dissolved mercury species in water samples by on-line hydride generation (HG), cryogenic trapping (CT), gas chromatography (GC) and detection by atomic fluorescence spectrometry (AFS). The suitability of the method for real samples with different organic matter and chloride contents was evaluated by recovery experiments in synthetic and natural spiked water samples. The HG method was compared with other current available methods for mercury analysis with respect to the different fraction of mercury analysed, i.e. 'reactive', 'reducible' or total. HG derivatization and SnCl2 reduction (with and without previous oxidation with BrCl) were applied to synthetic and natural (spiked and non-spiked) water samples. The influence of chloride and dissolved organic matter concentrations was studied. The results suggest that the HG procedure is suitable for the simultaneous determination of Hg2+ and MeHg+ in surface water samples. Inorganic mercury analysed by HG (i.e. reducible) is close to the total inorganic mercury.     

Mercury and trace elements in cloud water and precipitation collected on Mt. Mansfield,Vermont

The lack of high quality measurements of Hg and trace elements in cloud and fog water led to the design of a new collector for clean sequential sampling of cloud and fog water. Cloud water was collected during nine non-precipitating cloud events on Mt. Mansfield, VT in the northeastern USA between August 1 and October 31, 1998. Sequential samples were collected during six of these events. Mercury cloud water concentrations ranged from 7.5 to 71.8 ng l(-1), with a mean of 24.8 ng l(-1). Liquid water content explained about 60% of the variability in Hg cloud concentrations. Highest Hg cloud water concentrations were found to be associated with transport from the Mid-Atlantic and Ohio River Valley, and lowest concentrations with transport from the north of Mt. Mansfield out of Canada. Twenty-nine event precipitation samples were collected during the ten-week cloud sampling period near the base of Mt. Mansfield as part of a long-term deposition study. The Hg concentrations of cloud water were similar to, but higher on average (median of 12.5 ng l(-1)) than Hg precipitation concentrations (median of 10.5 ng l(-1)). Cloud and precipitation samples were analyzed for fifteen trace elements including Mg, Cu, Zn, As, Cd and Pb by ICP-MS. Mean concentrations were higher in cloud water than precipitation for elements with predominately anthropogenic, but not crustal origin in samples from the same source region. One possible explanation is greater in-cloud scavenging of crustal elements in precipitating than non-precipitating clouds, and greater below-cloud scavenging of crustal than anthropogenic aerosols.     

Laboratory-polluted soils: a methodological approach to establish equilibrium conditions for different metal chemical forms in soils

An approach based on the number of extractions performed over time was applied to study the time necessary to attain equilibrium conditions between the different chemical forms present in two natural soils laboratory-loaded with heavy metals (Cu(II), Pb(II), Zn(II)). The influence of some soil parameters, such as pH value, cation exchange capacity and organic matter content, on the equilibrium time was evidenced both by the different nature of the soils and by the treatment of soils with paper mill sludge. Equilibrium conditions were determined by studying the metal partitioning in the soil on a case by case basis. The behaviour of the three metals was not the same, and the conditions of equilibrium among the different chemical forms were the result of the amount of heavy metals added as well as of the nature of the soil.     

Arsenic speciation in human hair: a new perspective for epidemiological assessment in chronic arsenicism

The analysis for arsenic in hair is commonly used in epidemiological studies to assess exposure to this toxic element. However, poor correlation between total arsenic concentration in hair and water sources have been found in previous studies. Exclusive determination of endogenous arsenic in the hair, excluding external contamination has become an analytical challenge. Arsenic speciation in hair appears as a new possibility for analytical assessing in As-exposure studies. This study applied a relative simple method for arsenic speciation in human hair based on water extraction and HPLC-HG-ICP-MS. The concentration of arsenic species in human hair was assessed in chronically As(V)-exposed populations from two villages (Esqui?a and Illapata) of the Atacama Desert, Chile. The arsenic concentrations in drinking water are 0.075 and 1.25 mg L(-1), respectively, where As(V) represented between 92 and 99.5% of the total arsenic of the consumed waters. On average, the total arsenic concentrations in hair from individuals of Esqui?a and Illapata were 0.7 and 6.1 microg g(-1), respectively. Four arsenic species, As(III), DMA(V), MMA(V) and As(V), were detected and quantified in the hair extracts. Assuming the found species in extracts represent the species in hair, more than 98% of the total arsenic in hair corresponded to inorganic As. On average, As(III) concentrations in hair were 0.25 and 3.75 microg g(-1) in Esqui?a and Illapata, respectively; while, the As(V) average concentrations were 0.15 and 0.45 microg g(-1) in Esqui?a and Illapata, respectively. Methylated species represent less than 2% of the extracted As (DMA(V)+ MMA(V)) in both populations. As(III) in hair shows the best correlation with chronic exposure to As(V) in comparison to other species and total arsenic. In fact, concentrations of As(total), As(III) and As(V) in hair samples are correlated with the age of the exposed individuals from Illapata (R= 0.65, 0.69, 0.57, respectively) and with the time of residence in this village (R= 0.54, 0.71 and 0.58, respectively).     

Microbiological characteristics in a zero-valent iron reactive barrier

Zero-valent iron (Fe⁰)-based permeable reactive barriertreatment has been generating great interest for passivegroundwater remediation, yet few studies have paid particularattention to the microbial activity and characteristics withinand in the vicinity of the Fe⁰-barrier matrix. The presentstudy was undertaken to evaluate the microbial population andcommunity composition in the reducing zone of influence byFe⁰ corrosion in the barrier at the Oak Ridge Y-12 Plantsite. Both phospholipid fatty acids and DNA analyses were usedto determine the total microbial population and microbialfunctional groups, including sulfate-reducing bacteria,denitrifying bacteria, and methanogens, in groundwater andsoil/iron core samples. A diverse microbial community wasidentified in the strongly reducing Fe⁰ environment despitea relatively high pH condition within the Fe⁰ barrier (up topH 10). In comparison with those found in the backgroundsoil/groundwater samples, the enhanced microbial populationranged from 1 to 3 orders of magnitude and appeared to increase from upgradient of the barrier to downgradient soil. Inaddition, microbial community composition appeared to change overtime, and the bacterial types of microorganismsincreased consistently as the barrier aged. DNA analysisindicated the presence of sulfate-reducing and denitrifyingbacteria in the barrier and its surrounding soil. However, theactivity of methanogens was found to be relatively low,presumably as a result of the competition by sulfate/metal-reducing bacteria and denitrifying bacteria because of the unlimited availability of sulfate and nitrate in the site groundwater. Results of this study provide evidenceof a diverse microbial population within and in the vicinity ofthe iron barrier, although the important roles of microbial activity, either beneficially or detrimentally, on the longevityand enduring efficiency of the Fe⁰ barriers are yet to be evaluated.     

A national framework for monitoring and reporting on environmental sustainability in Canada

In 1991, a collaborative project to revise the terrestrial component of a national ecological framework was undertaken with a wide range of stakeholders. This spatial framework consists of multiple, nested levels of ecological generalization with linkages to existing federal and provincial scientific databases. The broadest level of generalization is the ecozone. Macroclimate, major vegetation types and subcontinental scale physiographic formations constitute the definitive components of these major ecosystems. Ecozones are subdivided into approximately 200 ecoregions which are based on properties like regional physiography, surficial geology, climate, vegetation, soil, water and fauna. The ecozone and ecoregion levels of the framework have been depicted on a national map coverage at 1:7 500 000 scale. Ecoregions have been subdivided into ecodistricts based primarily on landform, parent material, topography, soils, waterbodies and vegetation at a scale (1:2 000 000) useful for environmental resource management, monitoring and modelling activities. Nested within the ecodistricts are the polygons that make up the Soil Landscapes of Canada series of 1:1 000 000 scale soil maps. The framework is supported by an ARC-INFO GIS at Agriculture Canada. The data model allows linkage to associated databases on climate, land use and socio-economic attributes.     

Development of Ecological Land Classification and mapping in support of forest management in northern Newfoundland,Canada

For the sustainable development of forest land, as recently prescribed by the Canadian Forest Strategy, a land classification project in northern Newfoundland was initiated to support the local forest management activities. The method adopted here is a modification of the Canadian Committee for Ecological Land Classification's (CCELC) system, and it applies various levels of mapping to uniform areas based on geomorphology, soils, vegetation, climate, water, and fauna.In this study, all CCELC levels were mapped; resulting maps were digitized and imported into a Geographic Informations System (GIS). The GIS data base contained the following maps: 1) digital terrain model, 2) bedrock geology, 3) surficial geology, 4) forest inventory, and 5) various levels of the ecological land classification, including Vegetation Types at the lowest level. In addition to the mapping, mensurational data were analyzed to provide stand and stock tables for each of the forest types, including growth curves that could be entered into specific forest growth modelling systems to predict wood supply scenarios based upon different management interventions.