Application of chemometrics to analysis of soil pollutants

This overview focuses on the application of chemometrics techniques for the investigation of soils contaminated by polycyclic aromatic hydrocarbons (PAHs) and metals because these two important and very diverse groups of pollutants are ubiquitous in soils. The salient features of various studies carried out in the micro- and recreational environments of humans, are highlighted in the context of the various multivariate statistical techniques available across discipline boundaries that have been effectively used in soil studies. Particular attention is paid to techniques employed in the geosciences that may be effectively utilized for environmental soil studies; classical multivariate approaches that may be used in isolation or as complementary methods to these are also discussed. Chemometrics techniques widely applied in atmospheric studies for identifying sources of pollutants or for determining the importance of contaminant source contributions to a particular site, have seen little use in soil studies, but may be effectively employed in such investigations. Suitable programs are also available for suggesting mitigating measures in cases of soil contamination, and these are also considered. Specific techniques reviewed include pattern recognition techniques such as Principal Components Analysis (PCA), Fuzzy Clustering (FC) and Cluster Analysis (CA); geostatistical tools include variograms, Geographical Information Systems (GIS), contour mapping and kriging; source identification and contribution estimation methods reviewed include Positive Matrix Factorisation (PMF), and Principal Component Analysis on Absolute Principal Component Scores (PCA/APCS). Mitigating measures to limit or eliminate pollutant sources may be suggested through the use of ranking analysis and multi criteria decision making methods (MCDM). These methods are mainly represented in this review by studies employing the Preference Ranking Organisation Method for Enrichment Evaluation (PROMETHEE) and its associated graphic output, Geometrical Analysis for Interactive Aid (GAIA).

Optimal adaptive sequential spatial sampling of soil using pair-copulas

A spatial sampling design that uses pair-copulas is presented that aims to reduce prediction uncertainty by selecting additional sampling locations based on both the spatial configuration of existing locations and the values of the observations at those locations. The novelty of the approach arises in the use of pair-copulas to estimate uncertainty at unsampled locations. Spatial pair-copulas are able to more accurately capture spatial dependence compared to other types of spatial copula models. Additionally, unlike traditional kriging variance, uncertainty estimates from the pair-copula account for influence from measurement values and not just the configuration of observations. This feature is beneficial, for example, for more accurate identification of soil contamination zones where high contamination measurements are located near measurements of varying contamination. The proposed design methodology is applied to a soil contamination example from the Swiss Jura region. A partial redesign of the original sampling configuration demonstrates the potential of the proposed methodology.

Resistance of microbial populations in DDT-contaminated and uncontaminated soils

One DDT-contaminated soil and two uncontaminated soils were used to enumerate DDT-resistant microbes (bacteria, actinomycetes and fungi) by using soil dilution agar plates in media either with 150 μg DDT ml -1 or without DDT at different temperatures (25, 37 and 55°C). Microbial populations in this study were significantly (p<0.001) affected by DDT in the growth medium. However, the numbers of microbes in long-term contaminated and uncontaminated soils were similar, presumably indicating that DDT-resistant microbes had developed over a long time exposure. The tolerance of isolated soil microbes to DDT varied in the order fungi>actinomycetes>bacteria. Bacteria from contaminated soil were more resistant to DDT than bacteria from uncontaminated soils. Microbes isolated at different temperatures also demonstrated varying degrees of DDT resistance. For example, bacteria and actinomycetes isolated at all incubation temperatures were sensitive to DDT. Conversely fungi isolated at all temperatures were unaffected by DDT.

DDT resistance and transformation by different microbial strains isolated from DDT-contaminated soils and compost materials

Bioremediation is a potential option to treat 1, 1, 1-trichloro-2, 2 bis (4-chlorophenyl) ethane (DDT) contaminated sites. In areas where suitable microbes are not present, the use of DDT resistant microbial inoculants may be necessary. It is vital that such inoculants do not produce recalcitrant breakdown products e.g. 1, 1-dichloro-2, 2-bis (4-chlorophenyl) ethylene (DDE). Therefore, this work aimed to screen DDT-contaminated soil and compost materials for the presence of DDT-resistant microbes for use as potential inoculants. Four compost amended soils, contaminated with different concentrations of DDT, were used to isolate DDT-resistant microbes in media containing 150 mg I -1 DDT at three temperatures (25, 37 and 55°C). In all soils, bacteria were more sensitive to DDT than actinomycetes and fungi. Bacteria isolated at 55°C from any source were the most DDT sensitive. However DDT-resistant bacterial strains showed more promise in degrading DDT than isolated fungal strains, as 1, 1-dichloro 2, 2-bis (4-chlorophenyl) ethane (DDD) was a major bacterial transformation product, while fungi tended to produce more DDE. Further studies on selected bacterial isolates found that the most promising bacterial strain (Bacillus sp. BHD-4) could remove 51% of DDT from liquid culture after 7 days growth. Of the amount transformed, 6% was found as DDD and 3% as DDE suggesting that further transformation of DDT and its metabolites occurred.

Influence of hydrocarbon contamination on clay soil microstructure

Microstructural (fabric, forces and composition) changes due to hydrocarbon contamination in a clay soil were studied using Scanning Electron Microscope (micro-fabric analysis), Atomic Force Microscope (forces measurement) and sedimentation bench test (particle size measurements). The non-polluted and polluted glacial till from north-eastern Poland (area of a fuel terminal) were used for the study. Electrostatic repelling forces for the polluted sample were much lower than for the non-polluted sample. In comparison to non-polluted sample, the polluted sample exhibited lower electric charge, attractive forces on approach and strong adhesion on retrieve. The results of the sedimentation tests indicate that clay particles form larger aggregates and settle out of the suspension rapidly in diesel oil. In non-polluted soil, the fabric is strongly aggregated – densely packed, dominate the face-to-face and edge-to-edge types of contacts, clay film tightly adheres to the surface of larger grains and interparticle pores are more common. In polluted soil, the clay matrix is less aggregated – loosely packed, dominate the edge-to-face types of contacts and inter-micro-aggregate pores are more frequent. Substantial differences were observed in the morphometric and geometrical parameters of pore space. The polluted soil micro-fabric proved to be more isotropic and less oriented than in non-polluted soil. The polluted soil, in which electrostatic forces were suppressed by hydrocarbon interaction, displays more open porosity and larger voids than non-polluted soil, which is characterized by occurrence of the strong electrostatic interaction between clay particles.

Raman spectroscopy of some complex arsenate minerals—implications for soil remediation

The application of spectroscopy to the study of contaminants in soils is important. Among the many contaminants is arsenic, which is highly labile and may leach to non-contaminated areas. Minerals of arsenate may form depending upon the availability of specific cations for example calcium and iron. Such minerals include carminite, pharmacosiderite and talmessite. Each of these arsenate minerals can be identified by its characteristic Raman spectrum enabling identification.

Raman spectroscopy of the copper chloride minerals nantokite, eriochalcite and claringbullite - implications for copper corrosion

The application of Raman spectroscopy to the study of the copper chloride minerals nantokite, eriochalcite and claringbullite has enabled the vibrational modes for the CuCl, CuOH and CuOH2 to be determined. Nantokite is characterised by bands at 205 and 155 cm-1 attributed to the transverse and longitudinal optic vibrations. Nantokite also has an intense band at 463 cm-1, eriochalcite at 405 and 390 cm-1 and claringbullite at 511 cm-1. These bands are attributed to CuO stretching modes. Water librational bands at around 672 cm-1 for eriochalcite have been identified and hydroxyl deformation modes of claringbullite at 970, 906 and 815 cm-1 are observed. Spectra of the three minerals are so characteristically different that the minerals are readily identified by Raman spectroscopy. The minerals are often determined in copper corrosion products by X-ray diffraction. Raman spectroscopy offers a rapid, in-situ technique for the identification of these corrosion products.