10 resultados para River monitoring network

em University of Queensland eSpace - Australia


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Two water quality monitoring strategies designed to sample hydrophobic organic contaminants have been applied and evaluated across an expected concentration gradient in PAHs in the Moreton region. Semipermeable membrane devices (SPMDs) that sequester contaminants via passive diffusion across a membrane were used to evaluate the concentration of PAHs at four and five sites in spring and summer 2001/2002, respectively. In addition, induction of hepatic cytochrome P4501, EROD activity, in yellowfin bream, Acanthopagrus australis, captured in the vicinity of SPMD sampling sites following deployment in summer was used as a biomarker of exposure to PAHs and related chemicals. SPMDs identified a clear and reproducible gradient in PAH contamination with levels increasing from east to west in Moreton Bay and upstream in the Brisbane River. The highest PAH concentrations expressed as B(a)P-toxicity equivalents (TEQs) were found in urban areas, which were also furthest upstream and experienced the least flushing. Cytochrome P4501 induction in A. australis was similar at all sites. The absence of clear trends in EROD activity may be attributable to factors not measured in this study or variable residency time of A. australis in contaminated areas. It is also possible that fish in the Moreton region are displaying enzymatic adaptation, which has been reported previously for fish subjected to chronic exposure to organic contaminants. These potential interferences complicate interpretation of EROD activity from feral biota. It is, therefore, suggested that future monitoring combine the two methods by applying passive sampler extracts to in vitro EROD assays. (C) 2004 Elsevier Ltd. All rights reserved.

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Identifying water wastage in forms of leaks in a water distribution network of any city becomes essential as droughts are presenting serious threats to few major cities. In this paper, we propose a deployment of sensor network for monitoring water flow in any water distribution network. We cover the issues related with designing such a dedicated sensor network by considering types of sensors required, sensors' functionality, data collection, and providing computation serving as leak detection mechanism. The main focus of this paper is on appropriate network segmentation that provides the base for hierarchical approach to pipes' failure detection. We show a method for sensors allocation to the network in order to facilitate effective pipes monitoring. In general, the identified computational problem belongs to hard problems. The paper shows a heuristic method to build effective hierarchy of the network segmentation.

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The Thames Estuary, UK, and the Brisbane River, Australia, are comparable in size and catchment area. Both are representative of the large and growing number of the world's estuaries associated with major cities. Principle differences between the two systems relate to climate and human population pressures. In order to assess the potential phytotoxic impact of herbicide residues in the estuaries, surface waters were analysed with a PAM fluorometry-based bioassay that employs the photosynthetic efficiency (photosystem II quantum yield) of laboratory cultured microalgae, as an endpoint measure of phytotoxicity. In addition, surface waters were chemically analysed for a limited number of herbicides. Diuron atrazine and simazine were detected in both systems at comparable concentrations. In contrast, bioassay results revealed that whilst detected herbicides accounted for the observed phytotoxicity of Brisbane River extracts with great accuracy, they consistently explained only around 50% of the phytotoxicity induced by Thames Estuary extracts. Unaccounted for phytotoxicity in Thames surface waters is indicative of unidentified phytotoxins. The greatest phytotoxic response was measured at Charing Cross, Thames Estuary, and corresponded to a diuron equivalent concentration of 180 ng L-1. The study employs relative potencies (REP) of PSII impacting herbicides and demonstrates that chemical analysis alone is prone to omission of valuable information. Results of the study provide support for the incorporation of bioassays into routine monitoring programs where bioassay data may be used to predict and verify chemical contamination data, alert to unidentified compounds and provide the user with information regarding cumulative toxicity of complex mixtures. (c) 2005 Elsevier B.V. All rights reserved.

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This paper reports on the development of an artificial neural network (ANN) method to detect laminar defects following the pattern matching approach utilizing dynamic measurement. Although structural health monitoring (SHM) using ANN has attracted much attention in the last decade, the problem of how to select the optimal class of ANN models has not been investigated in great depth. It turns out that the lack of a rigorous ANN design methodology is one of the main reasons for the delay in the successful application of the promising technique in SHM. In this paper, a Bayesian method is applied in the selection of the optimal class of ANN models for a given set of input/target training data. The ANN design method is demonstrated for the case of the detection and characterisation of laminar defects in carbon fibre-reinforced beams using flexural vibration data for beams with and without non-symmetric delamination damage.

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Government agencies responsible for riparian environments are assessing the combined utility of field survey and remote sensing for mapping and monitoring indicators of riparian zone condition. The objective of this work was to compare the Tropical Rapid Appraisal of Riparian Condition (TRARC) method to a satellite image based approach. TRARC was developed for rapid assessment of the environmental condition of savanna riparian zones. The comparison assessed mapping accuracy, representativeness of TRARC assessment, cost-effectiveness, and suitability for multi-temporal analysis. Two multi-spectral QuickBird images captured in 2004 and 2005 and coincident field data covering sections of the Daly River in the Northern Territory, Australia were used in this work. Both field and image data were processed to map riparian health indicators (RHIs) including percentage canopy cover, organic litter, canopy continuity, stream bank stability, and extent of tree clearing. Spectral vegetation indices, image segmentation and supervised classification were used to produce RHI maps. QuickBird image data were used to examine if the spatial distribution of TRARC transects provided a representative sample of ground based RHI measurements. Results showed that TRARC transects were required to cover at least 3% of the study area to obtain a representative sample. The mapping accuracy and costs of the image based approach were compared to those of the ground based TRARC approach. Results proved that TRARC was more cost-effective at smaller scales (1-100km), while image based assessment becomes more feasible at regional scales (100-1000km). Finally, the ability to use both the image and field based approaches for multi-temporal analysis of RHIs was assessed. Change detection analysis demonstrated that image data can provide detailed information on gradual change, while the TRARC method was only able to identify more gross scale changes. In conclusion, results from both methods were considered to complement each other if used at appropriate spatial scales.