45 research outputs found
Fetal therapy for spina bifida in sheep model using tissue engineering
No full textContains fulltext :
92574.pdf (Publisher’s version ) (Open Access)Radboud Universiteit Nijmegen, 14 februari 2012Promotores : Feitz, W.F.J., Berg, P.P. van den, Lotgering, F.K. Co-promotores : Kuppevelt, T. van, Geutjes, P.J.148 p
Great Artesian Basin - Cadna-owie Hooray Aquifer - pH
No full textMaintenance and Update Frequency: notPlannedStatement: SOURCE DATA:
Data was obtained from a variety of sources, as listed below:
1. Water quality data from the Queensland groundwater database, Department of Environment and Resource Management
2. Geological Society of Queensland water chemistry database (1970s to 1980s). Muller, PJ, Dale, NM (1985) Storage System for Groundwater Data Held by the Geological Survey of Queensland. GSQ Record 1985/47. Queensland.
3. Geoscience Australia GAB hydrochemistry dataset 1973-1997. Published in Radke BM, Ferguson J, Cresswell RG, Ransley TR and Habermehl MA (2000) Hydrochemistry and implied hydrodynamics of the Cadna-owie - Hooray Aquifer, Great Artesian Basin, Australia. Canberra, Bureau of Rural Sciences: xiv, 229p.
4. Feitz, A.J., Ransley, T.R., Dunsmore, R., Kuske, T.J., Hodgkinson, J., Preda, M., Spulak, R., Dixon, O. & Draper, J., 2014. Geoscience Australia and Geological Survey of Queensland Surat and Bowen Basins Groundwater Surveys Hydrochemistry Dataset (2009-2011). Geoscience Australia, Canberra Australia
5. Water quality data from the Office of Groundwater Impact Assessment, Department of Natural Resources and Mines, Queensland Government
6. Geoscience Australia (2010) Hydrogeochemical collection. A compilation of quality controlled groundwater data taken from well completion reports from QLD and NSW.
7. Water quality data from the Office of Groundwater Impact Assessment, Department of Natural Resources and Mines, Queensland Government
BOUNDARIES:
Data covers the extent of the Cadna-owie-Hooray Aquifer and Equivalents as defined in Great Artesian Basin - Cadna-owie-Hooray Aquifer and Equivalents - Thickness and Extent dataset (Available from www.ga.gov.au using catalogue number 81678)
METHOD:
Groundwater chemistry data was compiled from the data sources listed above. Data was imported into ESRI ArcGIS (ArcMap 10) as data point sets and used to create a predicted values surface using an ordinary kriging method within the Geostatistical Analyst extension. A log transform was applied to the Alkalinity, TDS, Na, SO4, Mg, Ca, K, F, Cl, Cl36 data prior to kriging. No transform was applied to the 13C, 18O, 2H, pH data prior to kriging. The geostatistical model was optimized using cross validation. The search neighbourhood was extended to a 1 degree radius, comprising of 4 sectors (N, S, E and W) with a minimum and maximum of 3 and 8 neighbours, respectively, per sector. The predicted values surface was exported to a vector format (Shapefile) and clipped to the aquifer boundaries.Data used to produce the predicted pH map for the Cadna-owie - Hooray Aquifer in the Hydrogeological Atlas of the Great Artesian Basin (Ransley et.al., 2015).<br/><br/>There are four layers in the Cadna-owie - Hooray Aquifer pH map data<br/><br/>A. Location of hydrochemistry samples (Point data, Shapefile)<br/>B. Predicted Concentration (Filled contours , Shapefile)<br/>C. Predicted Concentration Contours (Contours, Shapefile)<br/>D. Prediction Standard Error (Filled contours , Shapefile)<br/><br/>The predicted values provide a regional based estimate and may be associated with considerable error. It is recommended that the predicted values are read together with the predicted error map, which provides an estimate of the absolute standard error associated with the predicted values at any point within the map.<br/><br/>The predicted standard error map provides an absolute standard error associated with the predicted values at any point within the map. Please note this is not a relative error map and the concentration of a parameter needs to be considered when interpreting the map. Predicted standard error values are low where the concentration is low and there is a high density of samples. Predicted standard errors values can be high where the concentration is high and there is moderate variability between nearby samples or where there is a paucity of data.<br/><br/>Coordinate system is Lambert conformal conic GDA 1994, with central meridian 134 degrees longitude, standard parallels at -18 and -36 degrees latitude.<br/><br/>The Cadna-owie - Hooray Aquifer pH map is one of 14 hydrochemistry maps for the Cadna-owie - Hooray Aquifer and 24 hydrochemistry maps in the Hydrogeological Atlas of the Great Artesian Basin (Ransley et.al., 2014). <br/><br/>This dataset and associated metadata can be obtained from www.ga.gov.au, using catalogue number 81696<br/> <br/>References:<br/>Hitchon, B. and Brulotte, M. (1994): Culling criteria for 'standard' formation water analyses; Applied Geochemistry, v. 9, p. 637-645<br/><br/>Ransley, T., Radke, B., Feitz, A., Kellett, J., Owens, R., Bell, J. and Stewart, G., 2015. Hydrogeological Atlas of the Great Artesian Basin. Geoscience Australia. Canberra. [available from www.ga.gov.au using catalogue number 79790
Great Artesian Basin - Coreena Wallumbilla Aquifer - Alkalinity
No full textMaintenance and Update Frequency: notPlannedStatement: SOURCE DATA:
Data was obtained from a variety of sources, as listed below:
1. Water quality data from the Queensland groundwater database, Department of Environment and Resource Management
2. Geological Society of Queensland water chemistry database (1970s to 1980s). Muller, PJ, Dale, NM (1985) Storage System for Groundwater Data Held by the Geological Survey of Queensland. GSQ Record 1985/47. Queensland.
3. Geoscience Australia GAB hydrochemistry dataset 1973-1997. Published in Radke BM, Ferguson J, Cresswell RG, Ransley TR and Habermehl MA (2000) Hydrochemistry and implied hydrodynamics of the Cadna-owie - Hooray Aquifer, Great Artesian Basin, Australia. Canberra, Bureau of Rural Sciences: xiv, 229p.
4. Feitz, A.J., Ransley, T.R., Dunsmore, R., Kuske, T.J., Hodgkinson, J., Preda, M., Spulak, R., Dixon, O. & Draper, J., 2014. Geoscience Australia and Geological Survey of Queensland Surat and Bowen Basins Groundwater Surveys Hydrochemistry Dataset (2009-2011). Geoscience Australia, Canberra Australia
5. Water quality data from the Office of Groundwater Impact Assessment, Department of Natural Resources and Mines, Queensland Government
6. Geoscience Australia (2010) Hydrogeochemical collection. A compilation of quality controlled groundwater data taken from well completion reports from QLD and NSW.
7. Water quality data from the Office of Groundwater Impact Assessment, Department of Natural Resources and Mines, Queensland Government
BOUNDARIES:
Data covers the extent of the Rolling Downs Aquitard as defined in Great Artesian Basin - Rolling Downs Aquitard - Thickness and Extent dataset (Available from www.ga.gov.au using catalogue number 81677).
METHOD:
Groundwater chemistry data was compiled from the data sources listed above. Data was imported into ESRI ArcGIS (ArcMap 10) as data point sets and used to create a predicted values surface using an ordinary kriging method within the Geostatistical Analyst extension. A log transform was applied to the Alkalinity, TDS, Na, SO4, Mg, Ca, K, F, Cl, Cl36 data prior to kriging. No transform was applied to the 13C, 18O, 2H, pH data prior to kriging. The geostatistical model was optimized using cross validation. The search neighbourhood was extended to a 1 degree radius, comprising of 4 sectors (N, S, E and W) with a minimum and maximum of 3 and 8 neighbours, respectively, per sector. The predicted values surface was exported to a vector format (Shapefile) and clipped to the aquifer boundaries.Data used to produce the predicted Alkalinity map for the Wallumbilla - Rolling Downs Group aquifers in the Hydrogeological Atlas of the Great Artesian Basin (Ransley et.al., 2015).<br/><br/>There are four layers in the Wallumbilla - Rolling Downs Group aquifers Alkalinity map data<br/><br/>A. Location of hydrochemistry samples (Point data, Shapefile)<br/>B. Predicted Concentration (Filled contours , Shapefile)<br/>C. Predicted Concentration Contours (Contours, Shapefile)<br/>D. Prediction Standard Error (Filled contours , Shapefile)<br/><br/>The predicted values provide a regional based estimate and may be associated with considerable error. It is recommended that the predicted values are read together with the predicted error map, which provides an estimate of the absolute standard error associated with the predicted values at any point within the map.<br/><br/>The predicted standard error map provides an absolute standard error associated with the predicted values at any point within the map. Please note this is not a relative error map and the concentration of a parameter needs to be considered when interpreting the map. Predicted standard error values are low where the concentration is low and there is a high density of samples. Predicted standard errors values can be high where the concentration is high and there is moderate variability between nearby samples or where there is a paucity of data.<br/><br/>Concentrations are Alkalinity as CaCO3 mg/L.<br/><br/>Coordinate system is Lambert conformal conic GDA 1994, with central meridian 134 degrees longitude, standard parallels at -18 and -36 degrees latitude.<br/><br/>The Wallumbilla - Rolling Downs Group aquifers Alkalinity map is one of two hydrochemistry maps for the Wallumbilla - Rolling Downs Group aquifers and 24 hydrochemistry maps in the Hydrogeological Atlas of the Great Artesian Basin (Ransley et. al, 2014). <br/><br/>This dataset and associated metadata can be obtained from www.ga.gov.au, using catalogue number 81690<br/> <br/>References: <br/>Hitchon, B. and Brulotte, M. (1994): Culling criteria for 'standard' formation water analyses; Applied Geochemistry, v. 9, p. 637-645<br/><br/>Ransley, T., Radke, B., Feitz, A., Kellett, J., Owens, R., Bell, J. and Stewart, G., 2015. Hydrogeological Atlas of the Great Artesian Basin. Geoscience Australia. Canberra. [available from www.ga.gov.au using catalogue number 79790
Great Artesian Basin - Cadna-owie Hooray Aquifer - 18O
No full textMaintenance and Update Frequency: notPlannedStatement: SOURCE DATA:
Data was obtained from a variety of sources, as listed below:
1. Geoscience Australia GAB hydrochemistry dataset 1973-1997. Published in Radke BM, Ferguson J, Cresswell RG, Ransley TR and Habermehl MA (2000) Hydrochemistry and implied hydrodynamics of the Cadna-owie - Hooray Aquifer, Great Artesian Basin, Australia. Canberra, Bureau of Rural Sciences: xiv, 229p.
2. Feitz, A.J., Ransley, T.R., Dunsmore, R., Kuske, T.J., Hodgkinson, J., Preda, M., Spulak, R., Dixon, O. & Draper, J., 2014. Geoscience Australia and Geological Survey of Queensland Surat and Bowen Basins Groundwater Surveys Hydrochemistry Dataset (2009-2011). Geoscience Australia, Canberra Australia
BOUNDARIES:
Data covers the extent of the Cadna-owie-Hooray Aquifer and Equivalents as defined in Great Artesian Basin - Cadna-owie-Hooray Aquifer and Equivalents - Thickness and Extent dataset (Available from www.ga.gov.au using catalogue number 81678)
METHOD:
Groundwater chemistry data was compiled from the data sources listed above. Data was imported into ESRI ArcGIS (ArcMap 10) as data point sets and used to create a predicted values surface using an ordinary kriging method within the Geostatistical Analyst extension. A log transform was applied to the Alkalinity, TDS, Na, SO4, Mg, Ca, K, F, Cl, Cl36 data prior to kriging. No transform was applied to the 13C, 18O, 2H, pH data prior to kriging. The geostatistical model was optimized using cross validation. The search neighbourhood was extended to a 1 degree radius, comprising of 4 sectors (N, S, E and W) with a minimum and maximum of 3 and 8 neighbours, respectively, per sector. The predicted values surface was exported to a vector format (Shapefile) and clipped to the aquifer boundaries.Data used to produce the predicted Oxygen-18 map for the Cadna-owie - Hooray Aquifer in the Hydrogeological Atlas of the Great Artesian Basin (Ransley et.al., 2015).<br/><br/>There are four layers in the Cadna-owie - Hooray Aquifer Oxygen-18 map data<br/><br/>A. Location of hydrochemistry samples (Point data, Shapefile)<br/>B. Predicted Concentration (Filled contours , Shapefile)<br/>C. Predicted Concentration Contours (Contours, Shapefile)<br/>D. Prediction Standard Error (Filled contours , Shapefile)<br/><br/>The predicted values provide a regional based estimate and may be associated with considerable error. It is recommended that the predicted values are read together with the predicted error map, which provides an estimate of the absolute standard error associated with the predicted values at any point within the map.<br/><br/>The predicted standard error map provides an absolute standard error associated with the predicted values at any point within the map. Please note this is not a relative error map and the concentration of a parameter needs to be considered when interpreting the map. Predicted standard error values are low where the concentration is low and there is a high density of samples. Predicted standard errors values can be high where the concentration is high and there is moderate variability between nearby samples or where there is a paucity of data.<br/><br/>Oxygen-18 units are 18O SMOW. <br/><br/>Coordinate system is Lambert conformal conic GDA 1994, with central meridian 134 degrees longitude, standard parallels at -18 and -36 degrees latitude.<br/><br/>The Cadna-owie - Hooray Aquifer Oxygen-18 map is one of 14 hydrochemistry maps for the Cadna-owie - Hooray Aquifer and 24 hydrochemistry maps in the Hydrogeological Atlas of the Great Artesian Basin (Ransley et.al., 2014). <br/><br/>This dataset and associated metadata can be obtained from www.ga.gov.au, using catalogue number 81705<br/> <br/>References:<br/>Hitchon, B. and Brulotte, M. (1994): Culling criteria for 'standard' formation water analyses; Applied Geochemistry, v. 9, p. 637-645<br/><br/>Ransley, T., Radke, B., Feitz, A., Kellett, J., Owens, R., Bell, J. and Stewart, G., 2015. Hydrogeological Atlas of the Great Artesian Basin. Geoscience Australia. Canberra. [available from www.ga.gov.au using catalogue number 79790
Effect of Fe(III)-ligand properties on effectiveness of modified photo-Fenton processes
No full textThis paper examines a modified photo-Fenton (UV/Fe oxalate/H2O2 process. The degradation of oxalate in this system in the absence of Reactive Red 235 was studied using both experimentation and kinetic modelling. The degradation of Reactive Red 235 in this system was also studied. Light intensity and solution pH had large effects on the degradation of both oxalate and Reactive Red 235, with the effect of pH not due simply to speciation changes. The most important properties of the oxalate ligand in the UV/Fe oxalate/H2O2 process are that it forms Fe(III)-oxalato complexes that are easily photolysed and also is relatively unreactive with OH• radicals.</jats:p
Steroid estrogens in ocean sediments
No full textThis paper gives results from a study measuring the abundance of steroid hormones in ocean sediments in the proximity of a deep ocean sewage outfall. The outfall is discharge point for an enhanced primary sewage treatment plant and sediment samples were taken adjacent and 7 km from the outfall. All samples contained steroid estrogens at nanogram per gram levels with higher concentrations at the 7 km sampling site. The concentration of estrone ranged from (0.16–1.17 ng/g), 17β-estradiol (0.22–2.48 ng/g) and the synthetic 17α-ethinylestradiol (<0.05–0.5 ng/g). The values detected correspond with estimates based on the proportion of estrogens sorbed to particles in the effluent and the expected proportion of particles originating from sewage in the ocean sediments. The results suggest that estrogens associated with the particulate fraction aggregate on contact with high ionic strength seawater and settle to the seafloor after discharge through deep ocean outfalls
Australian human and eco-toxicity life cycle impact assessment characterisation factors and normalisation data for 2002/2003
No full textContains fulltext :
35106.pdf (Publisher’s version ) (Open Access
The role of soil flux and soil gas monitoring in the characterisation of a CO2 surface leak: A case study in Qinghai, China
No full textAbstractFollowing the drilling of a shallow natural CO2 reservoir at the Qinghai research site, west of Haidong, China, it was discovered that CO2 was continuously leaking from the wellbore due to well-failure. The site has become a useful research facility in China for studying CO2 leakage and monitoring technologies for application to geological storage sites of CO2. During an eight day period in 2014, soil gas and soil flux surveys were conducted to characterise the distribution, magnitude and likely source of the leaking CO2.Two different sampling patterns were utilised during soil flux surveys. A regular sampling grid was used to spatially map out the two high-flux zones which were located 20–50m away from the wellhead. An irregular sampling grid, with higher sampling density in the high-flux zones, allowed for more accurate mapping of the leak distribution and estimation of total field emission rate using cubic interpolation. The total CO2 emission rate for the site was estimated at 649-1015kgCO2/d and there appeared to be some degree of spatial correlation between observed CO2 fluxes and elevated surface H2O fluxes.Sixteen soil gas wells were installed across the field to test the real-time application of Romanak et al.’s (2012) process-based approach for soil gas measurements (using ratios of major soil gas components to identify the CO2 source) using a portable multi-gas analyser. Results clearly identified CO2 as being derived from one exogenous source, and are consistent with gas samples collected for laboratory analysis. Carbon-13 isotopes in the centre of each leak zone (−0.21‰ and −0.22‰) indicate the underlying CO2 is likely sourced from the thermal decomposition of marine carbonates.Surface soil mineralisation (predominantly calcite) can be used to infer prior distribution of the CO2 hotspots and as a consequence highlighted plume migration of 20m in 11 years. The broadening of the affected area beyond the wellbore at the Qinghai research site markedly increases the area that needs surveying at sufficient density to detect a leak. This challenges the role of soil gas and soil flux in a CCS monitoring and verification program for leak detection, suggesting that these techniques may be better applied for characterising the source and emission rate of a CO2 leak, respectively
