Approaches to describe the exposure of non-target aquatic organisms to agricultural pesticides can be limited by insufficient knowledge of the environmental conditions where the compounds are used. This study analysed information from national and regional datasets gathered in the UK describing the morphological and physico-chemical properties of rivers, streams, ponds and ditches. An aggregation approach was adopted whereby the landscape was divided into 12 hydrogeological classes for agricultural areas and a 13 th class that comprised non-agricultural land. The data describe major differences in the abundance, dimensions and chemistry of water bodies in the different landscapes. There is almost an order of magnitude difference in the total input of pesticide per unit area between the different landscapes. Ditches are shown to be most proximate to arable land, streams and rivers intermediate and ponds the least proximate. Results of the study have implications for the development of standard scenarios for use in protective screening steps within the risk assessment. Data can be used to produce more realistic estimates of the exposure to pesticides and to examine how that exposure varies across the landscape.
A novel technique is described for the identification and quantification of environmental pollutants based on toxicity fingerprinting with a metabolic lux-marked bacterial biosensor. This method involved characterizing the toxicity-based responses of the biosensor to seven calibration pollutants as acute temporal-dose response fingerprints. An algorithm is described to allow comparisons of responses of an unknown pollutant to be made against the calibration data. This is based on predicting pollutant concentration at each of six different time points over the course of a 5-min assay. If the prediction is consistent between the unknown pollutant and a calibration pollutant at the 95% test level, this is considered to be a positive identification. All seven calibration pollutants could be successfully distinguished from each other with this technique. Environmental samples, individually spiked with single concentrations of pollutants, were compared in this way against the calibration pollutants. An 83% identification success was achieved, with no false positives at the 95% test level. This is a simple and rapid technique that potentially can be applied to monitoring of industrial wastewater or as a screening tool for regulators.
A novel technique is described for the identification and quantification of environmental pollutants based on toxicity fingerprinting with a metabolic lux-marked bacterial biosensor. This method involved characterizing the toxicity-based responses of the biosensor to seven calibration pollutants as acute temporal-dose response fingerprints. An algorithm is described to allow comparisons of responses of an unknown pollutant to be made against the calibration data. This is based on predicting pollutant concentration at each of six different time points over the course of a 5-min assay. If the prediction is consistent between the unknown pollutant and a calibration pollutant at the 95% test level, this is considered to be a positive identification. All seven calibration pollutants could be successfully distinguished from each other with this technique. Environmental samples, individually spiked with single concentrations of pollutants, were compared in this way against the calibration pollutants. An 83% identification success was achieved, with no false positives at the 95% test level. This is a simple and rapid technique that potentially can be applied to monitoring of industrial wastewater or as a screening tool for regulators.
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