Biomedical subjects

Michael H Ramsey

Publications and source records attributed to Michael H Ramsey.

12 recordsLinked to original sources

PubMed2006

Effect of scale of Cd heterogeneity and timing of exposure on the Cd uptake and shoot biomass, of plants with a contrasting root morphology.

A pot experiment was conducted to investigate the influence of spatial heterogeneity of Cd distribution in soil on shoot biomass, shoot metal concentration and total shoot Cd uptake by lettuce (Lactuca sativa, variety Tom Thumb) and Indian mustard (Brassica juncea). Five different soil treatments had similar overall concentration of Cd per pot, but different scales of heterogeneity and also timing of plant exposure during the growth cycle. The presence and scale of heterogeneity and timing of exposure were found to have significant effects on shoot biomass for both plants (with one exception). The mean values of Cd mass taken up were significantly affected by the presence of heterogeneity and timing only for lettuce. Only the scale of heterogeneity affected the uptake of Cd by Indian mustard, presumably because of its larger root system (approximately 18 cm, compared with approximately 5 cm for lettuce). These findings have important implications for phytoremediation, and for human health risk assessment where leafy vegetables are grown in situations with highly elevated Cd concentrations.

Analysis of Variance↗

PubMed2005

Spatial contaminant heterogeneity: quantification with scale of measurement at contrasting sites.

Material within the terrestrial environment is rarely homogeneously distributed, either spatially or temporally. One consequence of heterogeneity is that uncertainty is usually generated in measurements that are taken with the aim of characterising the environment. For example, a measurement of analyte concentration within soil taken from one sampling location on contaminated land can vary substantially when compared against another sample taken at effectively the same nominal location. The measurement uncertainty arising from the heterogeneity can substantially limit the reliability of the interpretations made upon environmental investigations. The sampling uncertainty usually outweighs the analytical uncertainty from the laboratory, often by a factor of 20 or more. One approach to reducing the uncertainty is to design a more suitable sampling strategy. This might be achieved by predicting the degree of heterogeneity prior to the investigation, but this is often difficult to achieve accurately. Another approach, which was investigated here, is to actually characterise the heterogeneity prior to the main investigation using rapid and inexpensive technology, such as in situ measurement techniques. In situ portable X-ray fluorescence (PXRF) and X-ray microprobe (XMP) techniques were employed to test the feasibility of this approach. Two contrasting contaminated land sites were chosen to characterise the two-dimensional spatial heterogeneity of heavy metal contamination in topsoil at a range of scales (50 m to 0.001 m). The spatial heterogeneity of contaminants, expressed as relative standard deviations, was found to differ between the two sites by a factor of two, largely due to the mode of deposition of pollution. The study also indicated that the heterogeneity did not change systematically with the scale of measurement between sampling locations at either site.

England↗

PubMed2005

Optimising uncertainty in physical sample preparation.

Uncertainty associated with the result of a measurement can be dominated by the physical sample preparation stage of the measurement process. In view of this, the Optimised Uncertainty (OU) methodology has been further developed to allow the optimisation of the uncertainty from this source, in addition to that from the primary sampling and the subsequent chemical analysis. This new methodology for the optimisation of physical sample preparation uncertainty (u(prep), estimated as s(prep)) is applied for the first time, to a case study of myclobutanil in retail strawberries. An increase in expenditure (+7865%) on the preparatory process was advised in order to reduce the s(prep) by the 69% recommended. This reduction is desirable given the predicted overall saving, under optimised conditions, of 33,000 pounds Sterling per batch. This new methodology has been shown to provide guidance on the appropriate distribution of resources between the three principle stages of a measurement process, including physical sample preparation.

Data Interpretation, Statistical↗

PubMed2005

Two-stage application of the optimised uncertainty method: a practical assessment.

Uncertainty estimates from routine sampling and analytical procedures can be assessed as being fit for purpose using the optimised uncertainty (OU) method. The OU method recommends an optimal level of uncertainty that should be reached in order to minimise the expected financial loss, given a misclassification of a batch as a result of the uncertainty. Sampling theory can used as a predictive tool when a change in sampling uncertainty is recommended by the OU method. The OU methodology has been applied iteratively for the first time using a case study of wholesale butter and the determination of five quality indicators (moisture, fat, solids-not-fat (SNF), peroxide value (PV) and free fatty acid (FFA)). The sampling uncertainty (s(samp)) was found to be sub-optimal for moisture and PV determination, for 3-fold composite samples. A revised sampling protocol was devised using Gy's sampling theory. It was predicted that an increase in sample mass would reduce the sampling uncertainty to the optimal level, resulting in a saving in expectation of loss of over pounds 2000 per 20 tonne batch, when compared to current methods. Application of the optimal protocol did not however, achieve the desired reduction in s(samp) due to limitations in sampling theory. The OU methodology proved to be a useful tool in identifying broad weaknesses within a routine protocol and assessing fitness for purpose. However, the successful routine application of sampling theory, as part of the optimisation process, requires substantial prior knowledge of the sampling target.

Animals↗

PubMed2005

Effect of alkaline pH and associated Zn on the concentration and total uptake of Cd by lettuce: comparison with predictions from the CLEA model.

An eight-fold underestimate of the potential Cd exposure to humans via ingestion of lettuce grown in moderately alkaline soil has been measured experimentally. Current models of Cd uptake by leafy vegetables, which are used in risk assessment (e.g. CLEA in UK) predict higher concentration factors in acid than in alkaline soils. Experimental evidence shows that Cd uptake, although it decreases with increasing pH from acid to neutral soils, increases again in alkaline soils, confirming recent finding from other workers. The concentration of Zn in the soil also significantly affects the uptake of Cd, although this is not included in the current prediction models either. The effect of Zn on the uptake of Cd by plants is greater in slightly alkaline soils (pH 7.7) than in slightly acidic or neutral soils. High concentrations of Zn in soil (1000 mg/kg), which are often associated with elevated Cd levels, further increase the Cd concentration factor to values 12 times higher than that predicted by the CLEA model. This is due in part to the effect of the high soil Zn on reducing the above-ground biomass of the plants.

Cadmium↗

PubMed2004

Balancing measurement uncertainty against financial benefits: comparison of in situ and ex situ analysis of contaminated land.

The uncertainty of measurements taken for the purpose of characterizing contaminated land can subsequently cause decision errors, which can produce significant, financial consequences. Given the site-specific costs, such as those associated with the measurements or with site misclassification, the important question addressed is "Are the measurements of acceptable quality for that given objective or fit-for-purpose"? It is often considered by investigators that using a standard operating procedure (SOP) with an approved analytical method will give an acceptable level of uncertainty. This is despite evidence that sampling is usually the predominant source of uncertainty, not the chemical analyses, mainly as a result of the contaminant heterogeneity within sampling locations at a site. One in situ and one ex situ measurement technique were used to represent these two contrasting approaches to characterizing a site contaminated with lead in topsoil. The measurement uncertainty, from both sampling and analyses, was estimated for the two techniques, and its fitness-for-purpose was assessed using the innovative optimized contaminated land investigation (OCLI) method. It is objectively demonstrated by the OCLI method that the in situ method (portable X-ray fluorescence) was three times more cost-effective than the ex situ (AAS) method at characterizing a contaminated site, despite generating higher uncertainty on individual measurements.

Cost-Benefit Analysis↗

PubMed2004

Heterogeneity of cadmium concentration in soil as a source of uncertainty in plant uptake and its implications for human health risk assessment.

The major route of exposure of humans to the toxic element cadmium (Cd) is via the consumption of vegetables homegrown on Cd contaminated soil. It is well known that soil pH is one of the main soil properties controlling bioavailability of Cd in plants. This is acknowledged in human health risk assessment models that incorporate pH dependant concentration factors (CF=plant Cd/soil Cd). However, variation in spatial heterogeneity of nutrients and heavy metals in soil can have a profound effect on plant performance and uptake of nutrients and heavy metals. Here we show for lettuce (variety Crispino) that variation in plant-scale heterogeneity of Cd in soil affects bioavailability and hence CF by a factor of 2. Plant yield is also significantly affected. This has important implications for both human health risk assessment, as variation in CF affects predicted exposure, and for phytoremediation where an optimal combination of plant yield and contaminant accumulation is required.

Biological Availability↗

PubMed2003

Multi-analyte optimisation of uncertainty in infant food analysis.

The Optimised Uncertainty (OU) methodology has been developed to optimise multi-analyte situations. It has then been applied to a retail survey of infant food for trace elements, classifying the food as compliant or non-compliant with the regulatory thresholds or specification limits that are appropriate for each element. The large-scale survey of infant foods was successfully adapted to allow the estimation of uncertainties, from both primary sampling and chemical analysis, for elemental concentrations in infant formula (milk) and wet meals. The analytes included in this investigation comprised both contaminants (Pb and Cd) and elements essential for child development (Zn and Cu). Optimisation of the measurement process for a 'single analyte' demonstrated the potential financial benefits of optimising future surveys for a false compliance scenario. Uncertainty estimates for the measurement of elemental concentrations in infant formula were dominated by uncertainty from the analytical method. Large potential savings (up to pounds 575,000 per batch) are predicted for both Pb and Zn by increasing the expenditure on chemical analysis to the optimal level. In comparison the uncertainty estimates for elemental concentration in wet meals showed a dominance of sampling as a source of uncertainty for Cd and Cu due to the increased heterogeneity. The feasibility of 'multi-analyte' optimisation is demonstrated for the case study of infant milk. Single analyte optimisation of the four analytes for a false compliance scenario indicated a decrease in expectations of financial loss of between 99% and 8%. An overall decrease in the total expectation of financial loss of 99% is indicated following multi-analyte optimisation.

Child, Preschool↗

PubMed2003

Measurement uncertainty from physical sample preparation: estimation including systematic error.

A methodology is proposed, which employs duplicated primary sampling and subsequent duplicated physical preparation coupled with duplicated chemical analyses. Sample preparation duplicates should be prepared under conditions that represent normal variability in routine laboratory practice. The proposed methodology requires duplicated chemical analysis on a minimum of two of the sample preparation duplicates. Data produced from the hierarchical design is treated with robust analysis of variance (ANOVA) to generate uncertainty estimates, as standard uncertainties ('u' expressed as standard deviation), for primary sampling (ssamp), physical sample preparation (sprep) and chemical analysis (sanal). The ANOVA results allow the contribution of the sample preparation process to the overall uncertainty to be assessed. This methodology has been applied for the first time to a case study of pesticide residues in retail strawberry samples. Duplicated sample preparation was performed under ambient conditions on two consecutive days. Multi-residue analysis (quantification by GC-MS) was undertaken for a range of incurred pesticide residues including those suspected of being susceptible to loss during sample preparation procedures. Sampling and analytical uncertainties dominated at low analyte concentrations. The sample preparation process contributed up to 20% to the total variability and had a relative uncertainty (Uprep%) of up to 66% (for bupirimate at 95% confidence). Estimates of systematic errors during physical sample preparation were also made using spike recovery experiments. Four options for the estimation of measurement uncertainty are discussed, which both include and exclude systematic error arising from sample preparation and chemical analysis. A holistic approach to the combination and subsequent expression of uncertainty is advised.

Calibration↗

PubMed2002

Optimized contaminated land investigation at minimum overall cost to achieve fitness-for-purpose.

A methodology for optimized contaminated land investigation (OCLI) is described that balances the uncertainty of measurements against the cost of taking the measurements and the financial losses that may arise from misclassification of the land. Uncertainty from the sources of both field sampling and chemical analysis is estimated using existing techniques, based on the taking of duplicated samples. The actual costs of sampling and analysis and the expected costs that could arise from either 'false positive' or 'false negative' classification of areas of land were estimated. A loss function was constructed that calculates the expectation of financial loss that will arise for a given uncertainty of measurement. The function shows a clear minimum value of cost at an optimal value of uncertainty. Application of this OCLI technique to two case studies demonstrated this minimum value. Below the optimum value of uncertainty, the costs increased due to higher measurement costs. Above the optimum, the costs increased due to increasing risk of factors such as unnecessary remediation or potential litigation over undetected contamination. Many areas for further development of OCLI are identified, but the technique is demonstrated as a useful new approach to judging fitness-for-purpose of such measurements.

Cost Control↗

PubMed2002

Optimised uncertainty in food analysis: application and comparison between four contrasting 'analyte-commodity' combinations.

The optimised uncertainty (OU) methodology is applied across a range of analyte-commodity combinations. The commodities and respective analytes under investigation were chosen to encompass a range of input factors: measurement costs (sampling and analytical), sampling uncertainties, analytical uncertainties and potential consequence costs which may be incurred as a result of misclassification. Two types of misclassification are identified-false compliance and false non-compliance. These terms can be used across a wide range of foodstuffs that have regulations requiring either minimum compositional requirements, maximum contaminant allowances or compositional specifications. The latter refers to foodstuffs with regulations that state an allowable tolerance around the compositional specification, i.e. the upper specification limit (USL) and the lower specification limit (LSL). The traditional OU methodology has been adapted so that it is applicable in these cases and has been successfully applied in practice. The Newton-Raphson method has been used to determine the optimal uncertainty value for the two case studies in which analyte concentration is assessed against a 'single threshold' regulatory requirement. This numerical method was shown to give a value of the optimal uncertainty that is practically identical to that given by the previously used method of visual inspection. The expectation of financial loss was reduced by an average of 65% over the four commodities by the application of the OU methodology, showing the benefit of the method.

Food Analysis↗

PubMed2002

Portable X-ray fluorescence in the characterisation of arsenic contamination associated with industrial buildings at a heritage arsenic works site near Redruth, Cornwall, UK.

An investigation using in situ analysis by portable X-ray fluorescence (PXRF) has shown that contamination present on industrial buildings at a heritage arsenic works site near Redruth, Cornwall, UK results from the absorption of arsenic by porous and semi-porous building materials that were in contact with arsenic-rich flue gases. Results from a preliminary survey indicate that arsenic remains locked in these materials and is being gradually leached out by weathering processes. This weathering causes general contamination of the adjacent building surfaces averaging 1845 microg g(-1) arsenic, presumably caused by evaporation of leach solutions in contact with air at the surface of the building materials. More extensive crystalline deposits were found under arches protected from dissolution and further dispersion by rain water. These deposits appeared to comprise calcium sulfate (gypsum), associated with on average between 1.2 and 6.8% m/m As. In situ PXRF proved to be highly effective in locating sources of contamination at the site and in providing data that allowed a hypotheses for the origin of this contamination to be formulated and tested in the field.

Arsenic↗