20 resultados para water monitoring


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The Lake Pertobe wetland system is a semi-natural wetland that has been modified primarily for recreational use. However, this lake system receives stormwater from much of the central business district of Warrnambool City (Victoria, Australia) and serves as a buffer zone between the stormwater system and the Merri River and Merri Marine Sanctuary. This work considers the impact of stormwater inputs on Lake Pertobe and the effectiveness of the lake in protecting the associated marine sanctuary. Sediment contaminants (including heavy metals and polycyclic aromatic hydrocarbons (PAHs)) and water quality parameters within the lake, groundwater and stormwater system were measured. Water quality parameters were highly variable between stormwater drains and rain events. Suspended solids rapidly settled along open drains and shortly after entering the lake. Groundwater inputs increased both salinity and dissolved nitrogen in some stormwater drains. Some evidence of bioaccumulation of metals in the food chain was identified and sediment concentrations of several PAHs were very high. The lake acted as a sink for PAHs and some metals and reductions in Escherichia coli, biological oxygen demand and total phosphorus were observed, affording some protection to the associated marine sanctuary. Nutrient retention was inadequate overall and it was identified that managing the lake primarily as a recreational facility impacted on the effectiveness of stormwater treatment in the system.

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The monitoring of lead (II) ions (Pb(2+)) in water is essential for both human health and the environment. Herein, a simple yet innovative biosensor for Pb(2+) detection is presented. The sensor is developed by the self-assembly of gold nanoparticles (GNPs) core-satellite structure using naturally occurring tripeptide glutathione (GSH) as linker. The addition of Pb(2+) caused a red-to-blue color change and the localized surface plasmon resonance (LSPR) band was shifted to ca. 650nm. The limit of detection (LOD) is found to be 47.6nM (9.9ppb) by UV-vis spectroscopy with high selectivity against other heavy metals. This method offers a new strategy for heavy metal detection using functionalized GNPs.

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Monitoring the abundance and distribution of taxa is essential to assess their contribution to ecosystem processes. For marine taxa that are difficult to study or have long been perceived of little ecological importance, quantitative information is often lacking. This is the case for jellyfish (medusae and other gelatinous plankton). In the present work, 4 years of scyphomedusae by-catch data from the 2007-2010 Irish Sea juvenile gadoid fish survey were analysed with three main objectives: (1) to provide quantitative and spatially-explicit species-specific biomass data, for a region known to have an increasing trend in jellyfish abundance; (2) to investigate whether year-to-year changes in catch-biomass are due to changes in the numbers or in the size of medusa (assessed as the mean mass per individual), and (3) to determine whether inter-annual variation patterns are consistent between species and water masses. Scyphomedusae were present in 97% of samples (N=306). Their overall annual median catch-biomass ranged from 0.19 to 0.92gm-3 (or 8.6 to 42.4gm-2). Aurelia aurita and Cyanea spp. (Cyanea lamarckii and Cyanea capillata) made up 77.7% and 21.5% of the total catch-biomass respectively, but species contributions varied greatly between sub-regions and years. No consistent pattern was detected between the distribution and inter-annual variations of the two genera, and contrasting inter-annual patterns emerged when considering abundance either as biomass or as density. Significantly, A.aurita medusae were heavier in stratified than in mixed waters, which we hypothesize may be linked to differences in timing and yield of primary and secondary productions between water masses. These results show the vulnerability of time-series from bycatch datasets to phenological changes and highlight the importance of taking species- and population-specific distribution patterns into account when integrating jellyfish into ecosystem models.

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Water quality monitoring and prediction are critical for ensuring the sustainability of water resources which are essential for social security, especially for countries with limited land like Singapore. For example, the Singapore government identified water as a new growth sector and committed in 2006 to invest S$ 330 million over the following five years for water research and development [1]. To investigate the water quality evolution numerically, some key water quality parameters at several discrete locations in the reservoir (e.g., dissolved oxygen, chlorophyll, and temperature) and some environmental parameters (e.g., the wind distribution above water surface, air temperature and precipitation) are used as inputs to a three-dimensional hydrodynamics-ecological model, Estuary Lake and Coastal Ocean Model - Computational Aquatic Ecosystem Dynamics Model (ELCOM-CAEDYM) [2]. Based on the calculation in the model, we can obtain the distribution of water quality in the whole reservoir. We can also study the effect of different environmental parameters on the water quality evolution, and finally predict the water quality of the reservoir with a time step of 30 seconds. In this demo, we introduce our data collection system which enables water quality studies with real-time sensor data.

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Monitoring the abundances of prey is important for informing the management of threatened and endangered predators. We evaluated the usefulness of faecal counts and distance sampling for monitoring the abundances of rusa deer Rusa timorensis, feral pig Sus scrofa and water buffalo Bubalus bubalis, the three key prey of the Komodo dragon Varanus komodoensis, at 11 sites on five islands in and around Komodo National Park, eastern Indonesia. We used species-specific global detection functions and cluster sizes (i.e. multiple covariates distance sampling) to estimate densities of rusa deer and feral pig, but there were too few observations to estimate densities of water buffalo. Rusa deer densities varied from from 2.5 to 165.5 deer/km2 with coefficients of variation (CVs) of 15-105%. Feral pig densities varied from 0.0 to 25.2 pigs/km 2 with CVs of 25-106%. There was a positive relationship between estimated faecal densities and estimated population densities for both rusa deer and feral pig: the form of the relationship was non-linear for rusa deer, but there was similar support for linear and non-linear relationships for feral pig. We found that faecal counts were more useful when ungulate densities were too low to estimate densities with distance sampling. Faecal count methods were also easier for field staff to conduct than distance sampling. Because spatial and temporal variation in ungulate density is likely to influence the population dynamics of the Komodo dragon, we recommend that annual monitoring of ungulates in and around Komodo National Park be undertaken using distance sampling and faecal counts. The relationships reported here will also be useful for managers establishing monitoring programmes for feral pig, rusa deer and water buffalo elsewhere in their native and exotic ranges.