Appendices 5-13 - Nautilus Cares - Nautilus Minerals

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2 MODELLING METHODOLOGY AND DATA 2.1 Single Release Modelling To quantify potential mixing zones for the return water discharge into ambient waters, this study made use of advanced computer simulation software known as CHEMMAP to quantify the mixing, transport and dispersion of the plume water taking account of the release characteristics (discharge rate, discharge temperature, pipe configuration) and the movement and physical conditions within the receiving environment. CHEMMAP has been developed over two decades for the assessment of physical fate, biological impacts, natural resource damages and ecological risks. The algorithms and assumptions of the chemical spill/discharge model have been described previously (French et al. 1996, French McCay and Isaji, 2004; French McCay et al 2004). The fates model processes and database are briefly summarized below. The chemical fates model estimates the distribution of chemical (as mass and concentrations) on the water surface, on shorelines, in the water column and in the sediments. The model is three- dimensional, separately tracking surface floating chemical, entrained droplets or suspended particles of pure chemical, chemical adsorbed to suspended particulates, and dissolved chemical. Processes that are simulated are spreading (floating liquids), transport, dispersion, evaporation- volatilization, entrainment (liquids), dissolution, partitioning, sedimentation, and degradation. The model uses physical-chemical properties to predict the fate of a discharge or chemical spill, including density, vapor pressure, water solubility, environmental degradation rates, adsorbed/dissolved partitioning coefficients (Kow, Kow), viscosity, and surface tension. The discharged mass is initialized at the location and depth of the release, in a state dependant upon the physical-chemical properties of the material. The discharge is modelled using the Lagrangian approach, where multiple sublots, called spillets, of the entire mass (or volume) discharged are tracked as they move in three-dimensional space over time (by addition of the transport vectors due to currents and buoyancy). The currents are those provided by the ADCP data collected by <strong>Nautilus</strong> at the discharge location. Stoke’s Law is used to compute the vertical velocity of the chemical particles or suspended sediment with adsorbed chemical. If rise or settling velocity overcomes turbulent mixing, the particles are assumed to float or settle to the bottom. Settled particles may later resuspend (assumed to occur above 20 cm/sec current speed). Turbulent Page 5 of 24
dispersion is modelled using a random walk scheme (Bear and Verruijt, 1987), with the magnitudes scaled by horizontal and vertical diffusion coefficients (Okubo, 1971). The mixing parameters were conservative estimates of deep water conditions. 2.2 Stochastic Modelling Originally CHEMMAP was designed to simulate specific spill incidents and/or ongoing discharges for evaluating impacts and damages (French et al. 1996). More recently, the model has been set up in a probabilistic stochastic configuration, allowing evaluation of risks of consequences and statistical computations (French McCay and Isaji, 2004). While a few chemical spill models exist that can simulate transport and physical fate of single events (Lunel, 1991; Shen et al., 1995; Rusin et al., 1996), CHEMMAP is unique in being able to evaluate biological impacts, in its stochastic implementation, and in its interconnection with hydrodynamic models, geographical information systems, and its graphical user interface. In the stochastic mode, CHEMMAP can be used to predict the fate of multiple or continuous releases that occur under a random selection of prevailing conditions (also known as stochastic modelling). The stochastic model performs a large number of sample simulations for a given release site, randomly varying the sample time frame, so that the transport, concentration and dilution of each particle representing the plume mass and concentration are subject to a different set of prevailing current conditions and water properties. During each simulation, the model records the grid cells that were contacted by the plume, as well as the amount of time that had elapsed prior to the contact or exposure. Once the stochastic modelling is complete, the results are compiled from each of the sample trajectories to provide a statistical weighting to the likelihood of exposure of grid cells. Stochastic results can be summarised as: 1. The probability, frequency or risk that a grid cell may be exposed to the plume; and 2. The maximum expected (averaged) concentrations of the plume in each grid cell. The stochastic modelling approach provides an objective measure of the possible outcomes of a release, as well as the means of quantifying the likelihood of a given outcome. The most commonly occurring conditions would be selected most often while conditions that are more unusual can also be represented. Page 6 of 24
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./0123/M./T!6 1MP!8T ST!T.M./T 1a$t
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Project director David Gwyther Proj
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Appendix 8 Juvenile Amphipod Whole
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10-day Survival of M. plumulosa Pag
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Centre for Environmental Contaminan
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Centre for Environmental Contaminan
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Appendix 9 Elutriate Testing Report
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Table of Contents 1. Introduction 3
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These elutriate tests were designed
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2. An aliquot(s) of sample after ce
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Table 1: pH, redox (Eh), conductivi
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1440 minutes which is also consiste
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Concentration (ppb) 250.0 200.0 150
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Concentration (ug/kg ore) (log scal
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Zinc sulfide is the most soluble of
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Table 5: Major ions in 0.45 !m filt
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3.5 Comparison to ANZECC/ARMCANZ (2
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5. References ANZECC/ARMCANZ (2000)
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Appendix 2: Phase 1: Metal concentr
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Appendix 4: Phase 1: Normalised dat
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Appendix 6: Phase 2: Quality contro
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Appendix 8: Metal concentrations in
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Page 157 and 158: MODELLING THE DISPERSION AND SETTLE
Page 159 and 160: TABLE OF CONTENTS 1 EXECUTIVE SUMMA
Page 161 and 162: 2 MODELLING METHODOLOGY AND DATA 2.
Page 163 and 164: 2.3 The SSFATE Model Sediment dispe
Page 165 and 166: Earth Mechanics Institute of the Co
Page 167 and 168: 3 RESULTS AND DISCUSSION The calcul
Page 169 and 170: Table 3.1. Summary of areas covered
Page 171 and 172: Figure 3.3b. Sediment bottom thickn
Page 173 and 174: Figure 3.3d. Sediment bottom thickn
Page 175: 4 REFERENCES Swanson J.C., Isaji T.
Page 179 and 180: MODELLING THE DISPERSION OF THE RET
Page 181 and 182: TABLE OF CONTENTS 1 EXECUTIVE SUMMA
Page 183: Given the range of tidal and basin
Page 187 and 188: Figure 2.1. Data from the ADCP at 6
Page 189 and 190: 2.5 Outfall Configuration and Assum
Page 191 and 192: 3 RESULTS AND DISCUSSION 3.1 Diluti
Page 193 and 194: Figure 3.1. Snapshot in time of a
Page 195 and 196: 5.0 km at any time during the disch
Page 197 and 198: Figure 3.2. The expected (or averag
Page 199 and 200: Figure 3.4 A zoomed in comparison o
Page 201 and 202: 5 REFERENCES Bear, J. and Verruijt,
Page 203: Rusin J., Lunel T. and Davies L., 1
Page 207 and 208: Prediction of underwater noise asso
Page 209 and 210: Table of Contents 1 Introduction ..
Page 211 and 212: 1 Introduction This report presents
Page 213 and 214: 2 Methods 2.1! Source modelling Cav
Page 215 and 216: dB re 1uPa @ 1m 200 195 190 185 180
Page 217 and 218: Figure 3. Source location for the s
Page 219 and 220: Depth (m) 0 500 1000 1500 2000 2500
Page 221 and 222: Figure 7. Plots of predicted receiv
Page 223 and 224: Figure 9. Zoomed view of plots of p
Page 225 and 226: Figure 11. Predicted maximum level
Page 227 and 228: for computing a is given in Fisher
Page 229 and 230: ecent studies have indicated that c
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References Fisher, F. H., Simmons,
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Appendix 14 The Potential for Natur
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'leaky' and recent lava flows have
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iii.) Incrementally these 1 m swath
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Appendix 15 Stakeholder Consultatio
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Environmental Impact Statement Solw
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Wewak (22 October 2007) Not recorde
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San Diego (17-18 April 2008) Enviro
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