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No description available.

This project used an electronic nose (E-nose) system composed of an array of 14 nonspecific conducting polymer sensors for soil and water diagnostics, based on qualitative microbial volatile production patterns. It tested the feasibility of using soil microbial volatile fingerprints for detecting and monitoring changes in microbial activity in three soils, as a response to key environmental factors such as temperature (16, 25, 37°C), water potential (-0.7, -2.8 MPa), and nutrient (glucose and wheat straw) inputs. It also investigated their potential use for atrazine detection when applied to soil at usual field application rates (2.5 ppm) as well as for monitoring its bioremediation using the white-rot fungus Trametes versicolor (R26), for up to 24 weeks. Furthermore, statistical correlations were investigated between soil volatile profiles and traditional microbial parameters for characterising microbial communities and their metabolic activities such as respiration, dehydrogenase (DHA) and laccase (LAC) activities, bacterial and fungal colony counts and fungal community structure under different soil conditions. Finally, this study explored the potential of microbial volatile production patterns for monitoring the activity and differentiation of two Streptomyces species (S. aureofaciens A253 and S. griseus A26) in potable water and in soil, as well as the production of geosmin in both environments. Data in this research has demonstrated that the production of volatile organic compounds (VOC) in soil is likely to arise from microbial metabolism. The E-nose was able to detect variations in the patterns of volatile production from soil according to treatments, functioning as indicators of shifts in microbial activity and community structure. The potential for discrimination between soil types in relation to environmental factors and nutrient addition has been demonstrated for the first time using principle component analysis (PCA). Significant (p<0.05) correlations were also found between soil volatile patterns (through PC1) and traditional soil microbial parameters. The close relationship (r>0.80) between PC1 and soil respiration was particularly relevant, since it indicates that microbial volatile fingerprints, similarly to respiration, respond quickly to changes in soil conditions. The sensor array was also able to detect Streptomyces activity and differentiation as well as discriminate between bacterial species at different concentrations in potable water and in soil. Using this approach, the presence of geosmin was detected in water at 0.5 ppb (below its human odour threshold detection, OTD) and in soil at 100 ppb (OTD not established). This study has, therefore, demonstrated that an E-nose can be employed as a rapid, sensitive, reproducible and non-invasive tool for characterising changes in soil environmental conditions, as well as for monitoring key soil processes such as organic matter decomposition and atrazine degradation. It also suggests that this approach can complement, and perhaps replace, some of these methods for a quick and routine evaluation of the impact of environmental factors on soil microbial communities. Furthermore, this study showed that an E-nose can also be employed for assessing Streptomyces activity and detecting geosmin production at an early stage in water and soil.

Information on the contribution of amino acids to dissolved organic nitrogen and carbon exported from grassland soil is scarce. Evidence from the literature for other environments, suggests that determination of amino acid patterns of distribution may be a useful method for improved understanding of the interaction of microbial synthesis and degradation of organic N in conjunction with soil physical states. A sample pre-concentration technique and an HPLC methodology were developed that enabled the determination of dissolved free (DFAA) and combined (DCAA) amino acids in natural waters at picomolar concentration. These methods were used to examine the content of amino acids and their distribution patterns in waters from 3 different settings. Firstly, field-sized lysimeters (1 ha) were used to examine dissolved free and combined amino acids in surface runoff and drainage waters from a grassland soil over 3 winter drainage periods. The waters were collected from soils beneath drained and undrained permanent ryegrass swards, receiving 280 kg N haˉ¹yrˉ¹ , permanent ryegrass receiving no mineral N input, and grass/white clover (no mineral N). Total DFAA concentration ranged between 1.9 nM - 6.1 µM and total DCAA concentration ranged between 1.3 - 87 µM. A large library of amino acid distributions was assembled and multivariate pattern analysis techniques were used to determine whether there were distinctive amino acid signatures that could be used as a diagnostics for soil management and condition. Although addition of mineral N fertilizer increased amino acid concentration in waters, there was no detectable effect of fertilizer addition on DFAA distribution patterns. In contrast, both DFAA and DCAA patterns were strongly influenced by soil hydrology alone. However, in the case of DCAA patterns, there was evidence of an interaction between hydrology and fertilizer addition. Secondly, monolith lysimeters were used to determine the DFAA in drainage waters from 4 different grassland soil types, in order to find whether there was evidence of pattern difference with soil texture. Results showed that distribution patterns vary between soil types, and contrary to what might be expected, that clay soils do not necessarily retain basic amino acids. Thirdly, the concentration and patterns of DFAA were determined hourly over a 24 hour period, for a river that received exported soil waters from the field lysimeters mentioned above. Total DFAA concentration correlated with water temperature and NH4+ Compared with exported soil waters, the concentrations of DFAA in river water were several orders of magnitude smaller, although GLY, SER, LYS and MET were in greater relative proportions. Results of the studies show that amino acids have the potential to be used as diagnostics of source, soil condition and management.

No description available.

No description available.

Soil organic matter (SOM) models simplify the complex turnover dynamics of organic matter in soils. Stabilization mechanisms are currently thought to play a dominant role in SOM turnover but they are not explicitly accounted for in most SOM models. One study addresses the implementation of an approach to account for the stabilization mechanism of physical protection in the SOC model RothC using 13C abundance measurements in conjunction with soil size fractionation data. SOM models are increasingly used to support policy decisions on carbon (C) mitigation and credibility of model predictions move into the focus of research. A site scale, Monte Carlo based model uncertainty analysis of a SOM model was carried out. One of the major results was that uncertainty and factor importance depend on the combination of external drivers. A different approach was used with the SOM ECOSSE model to estimate uncertainties in soil organic carbon (SOC) stock changes of mineral and organic soils in Scotland. The average statistical model error from site scale evaluation was transferred to regional scale uncertainty to give an indication of the uncertainty in national scale predictions. National scale simulations were carried out subsequently to quantify SOC stock changes differentiating between organic and mineral soils and land use change types. Organic soils turned out to be most vulnerable to SOC losses in the last decades. The final study of this thesis emplyed the RothC model to simulate possible futures of global SOC stock changes under land use change and ten different climate scenarios. Land use change turned out to be of minor importance. The regionally balance between soil C inputs and decomposition leads to a diverse map of regional C gains and losses with different degrees of certainty.