MinE 422: Subsidence Resources: Blodgett and Kuipers, 2002. Underground Hard-Rock mining: Subsidence and hydrologic environmental impacts Bauer. 2008. Planned Coal mine Subsidence in Illinois • • • • Cause of Subsidence Effects Modeling, monitoring and mitigation Examples Background: Locations Affected • Anywhere there is underground mining! – United kingdom, China, South Africa, Australia, United States • Most research done on coal mining – Specifically longwall and room and pillar mining • Thinking is that underground mining has less environmental impact, but subsidence effects the land above and the water below Subsidence feature from collapse of one of the deeper lead and zinc mines in Cherokee County. Subsidence due to block caving at the Ridgeway Mine, South Carolina What causes subsidence • Compaction of natural sediments/weak rock – – – – – – Ground water dewatering Withdrawal of petroleum and geothermal fluids Mining of coal/limestone/salt Melting of permafrost Crustal deformation Mining of metallic ores • “subsidence is an inevitable consequence of underground mining – it may be small and localized or extend over large areas, it may be immediate or delayed for many years (SME, 1992). Drilling rid being retrieved from collapsed mineshaft, Glasgow, 1995 Physical hazards identified by the AGS • Including subsidence from underground coal mining (Cardiff just north of Edmonton) Definition of Subsidence • Two general types of subsidence: – Continuous and Discontinuous • Continuous or trough type subsidence is associated with extraction of thin, flat-dipping orebodies overlain by weak sedimentary strata Surface Subsidence Profile Continuous • Fairly easy to predict – Happens almost immediately • Angle of draw – Depends on geology – Seam depth • Rate of drop – Entire mass of rock moves downward as a single block, rather than gradually – Some bending and flexing as bedding surfaces shift Subsidence Mechanics Width of Subsidence • Usually predicted empirically • Subcritical width – Panel is narrow, causes less than the maximum level of subsidence • Critical width – Pannel is wider, only the center has the maximum level of subsidence • Supercritical width – Maximum level of subsidence has a flat profile in center of the surface trough – Angle of draw remains the same, width increases Discontinuous • Discontinuous type subsidence is associated with different mining methods and geological features. + crown hole + plug subsidence + chimney caving + solution cavities, block caving Hard rock mining • Most (nearly all) research on coal mining • Some hardrock mines are bedded (sedimentary) but most in igneous or metamorphic rock – Different (more complex) geology – Faults/folds/hydrothermally altered rocks complicate subsidence prediction From, Anatomy of a mine: From Prospect to Production Planned Subsidence • Longwall mining or block caving – Subsidence occurs almost immediately – In Illinoi, horizontal distance to subsidence is about 50% of the mine depth (eastern United States, about 100% of the mine depth) – Some residual subsidence (say about 5% of mine height) occurs years (~1-3) after mining – Initial subsidence is higher • Room/pillar or stope, etc – May occur over 10-100 years – Difficult to predict – Land use issues Floor Failure • Coal mining in Illinois (room and pillar) • Hanging wall very stable • Floor made up of weak claystone Some factors that affect subsidence • After Blodgett and Kuipers (2002) Somewhat related, uplift • What about the opposite effect • Uplift is more recent, mining/subsidence has been happening for years, but fluid injection for only a few decades (~50 years) • SAGD or EOR • Increase pressure and the surface bulges, eventual decline and subsequent subsidence • Magnitude: – Up to tens of centimeters over a few months • Eventually subsidence will occur as pressure normalizes after extraction (unless you inject more water than you removed oil/water) Uplift • • Passive seismic and surface monitoring of geomechanical deformation associated with steam injection S.C. MAXWELL, J. DU, and J. SHEMETA, Pinnacle Technologies, Calgary, Canada surface uplift … • • • • Total overpresurized reservoir, 125m by 75m area affected Shallow operation (<100m depth) Stopped production in some nearby wells More info: http://www.ercb.ca/docs/Documents/reports/ERCB_StaffReport_JoslynSteamRelease_2010-02.pdf Prediction Prediction Methods • Trough subsidence fairly straightforward • Methods include – Empirical, profile functions, influence functions and numerical – United kingdom national coal board graphical method • Figure 16.18 (p. 458) gives maximum (central) subsidence, S, as a fraction of the extraction thickness, m, for a given depth, h, and panel width, W. • The subsidence profile can next be determined using Figure 16.20. • Need to modify for you location, i.e. use in Wyoming overestimates subsidence Prediction Methods • Different subsidence profiles Prediction method • Influence Functions – Still need to calibrate the coefficients – Can do some theoretically Prediction method • Numerical models – Physical simulation, program of interest would be “FLAC” – Finite element, boundary element and distinct element methods – Need geo-mechanical parameters above mine area • Modulus of elasticity and Poisson’s Ratio (could use geostatistics …) – Often assume only elastic behavior (not very realistic) Prediction method • Summary – Empirical methods are very localized and difficult to extrapolate in new areas • i.e. tried to use the UK system in the US and double the subsidence was predicted. – Empirical methods focused on coal mining and specific to a particular geology – Profile functions work best in rectangular situations – Influence functions and numerical methods difficult to infer the necessary parameterization • Faults, bedding, rock type profiles, soil/rock parameters etc. – Accurately predicting geology (faults/layers/rock strength, etc) for numerical models is difficult Subsidence monitoring • Space based imagery (InSAR technology) • Interferometric Synthetic Aperture Radar • Ground survey Subsidence monitoring • Interferometric Synthetic Aperture Radar (InSAR) • Subsidence still occurring over abandoned coal mines – Bellevue shut down in 1961 Deformation pattern of the lower part of Frank Slide. The white stripes indicate underground coal mines; the red line outlines the Frank Slide boundary, and the black rectangle is the reference area. Figure 4. Crowsnest Pass. In conjunction with studies being undertaken by the Alberta Geological Survey (AGS) on unstable slopes on Turtle Mountain, site of the Frank Slide, InSAR technology was utilized to map subsidence of the ground overlying the abandoned coal mine workings. By utilizing Radarsat-1 Fine beam data from fall 2004 to summer 2006, many thousands of coherent targets were identified above the mines and a map was produced showing the rates and patterns of deformations Example1 • Fairmont West Virginia – Shallow, ~30m depth – Room and pillar (then pillars removed) Coal Seam Subsidence damage • Mining in the 1800’s, subsidence started in 1980’s, companies are long gone Example 2: Retsof Salt Mine • Roof Failure, 1994 Example 3: Mississippi Delta • ~4000km2 of land lost since 1930 Mitigation Techniques • Post-mining stabilization – Backfilling, grouting (~$300k/acre), blasting • Alteration in the mining techniques – Partial mining (leave area supported by pillars) – Backfilling with waste rock or milled tailings – Only reduces the amount of subsidence • Bridge the area on surface Negative effects of subsidence • Damage to structures/people above – Some construction modifications can be implemented with little success, i.e: • trenches filled with compressible material around building foundations • reinforced foundations • Foundations resting on compacted sand – Caused by the tensile and compressive forces at edges of subsidence, not by the vertical movement of land (horizontal) – Dr. D. Apel: worked on mining without tensile/compressive forces, balance them out … • Destruction of natural vegetation or farm land – Surface land use can be dramatically altered – Changes ground slope, surface drainage, disruption of ground water hydrology, pot holes, etc. Negative effects of subsidence • Hydrologic impacts – Surface water • Subsidence can cause open fractures which allows for surface water to flow into lower strata or open mine workings – Ground water • Fracturing can increase the capacity of an aquifer, ground water level drops as water fills lower void space • Water quality can be affected if ground water is brought in contact with minerals surrounding the mine or the mine workings • Vertical flow rate increased through new pathways/fractures – Water is drained from the surrounding aquifer into the mine cavity, similar to surface mining – Water flow patterns (shallow, intermediate and deep) are altered (increased or decreased) by subsidence • Utilities – need to install flexible piping Negative effects of subsidence • Roads, not usually too difficult to repair Regulations • United states, Surface Mining Control and Reclamation act – Prevent subsidence from causing material damage to the extent technologically and economically feasible – Maximize mine stability – Maintain the value and reasonably foreseeable use of such surface lands, except in those instances where the mining technology used requires planned subsidence in a predictable and controlled manner. – Each state has their own regulations • Canada, MINES AND MINERALS ACT – Coal mining leases section: Without compensation of any nature whatsoever the lessee shall, at all times during the term of a lease of a road allowance and any renewal of it, perform, observe and comply with the orders and directions of the Minister of Infrastructure affecting underground operations and, without restricting the generality of the foregoing, those orders or directions may require any measures that that Minister considers necessary to prevent any subsidence. Review • Subsidence is inevitable when underground mining • Dewatering contributes as well • Greater depths of overburden do not, generally, prevent subsidence, but may delay timeline • Direct relationship between mine extraction height and magnitude of subsidence • Many ‘factors’ influence magnitude of subsidence • Varied effects on water level/ quality/ flow rate/etc • Some mitigation strategies, but none eliminate subsidence • Uplift can also temporarily occur if we are injecting… – Subsidence is likely to follow Subsidence Let’s do an example problem. Given: seam thickness, m= 4 m depth, h = 250 m width, W = 200 m Fig. 16.18 gives S/m = 0.77 Thus S = 0.77 (4 m) = 3.1 m Subsidence Now assume that the face has advanced far enough for full subsidence to occur. Now using Fig. 16.20, noting that w/h = 200 m/250 m = 0.8 Subsidence Using each “S curve,” go to w/h = 0.8 and then read off the value for d/h. Subsidence in terms of S Distance from panel center in terms of depth, d/h 1.00 = 3.10 m 0.95 = 2.95 0.80 = 2.48 0.70 = 2.17 0.60 = 1.86 0.50 = 1.55 0.40 = 1.24 0.20 = 0.62 0.10 = 0.31 0 = 0 0 0.08 0.17 0.21 0.25 0.28 0.33 0.43 0.53 1.10 = = = = = = = = = = center panel 20.0 m 42.5 52.5 62.5 70.0 82.5 107.5 132.5 275.0 Subsidence The following subsidence profile results: Distance from panel center, m 0 25 50 75 1.0 2.0 3.0 Subsidence 100 125 150 175 200 225 250 MinE 422: Erosion Resources: Horst. 2007. Landforming : an environmental approach to hillside development, mine reclamation and watershed restoration. 354p. 2005, Morgan, Soil Erosion and Conservation Johnson, 2003 Design of Erosion Protection for Long-Term Stabilization http://pbadupws.nrc.gov/docs/ML0225/ML022530043.pdf • • • • • Cause of Erosion Effects Modeling Mitigation Dust … Definition of Erosion “Dislodgement and transportation of soil materials through the action of water and wind” Spitz (quote and image) •Can occur in a number of ways: 1.Impact of raindrop dislodges soil particles, splash erosion (~6m/s, 0.6m movement vertically of soil particles) 2.Movement of water over surface dislodges and transports soil, Sheet, Rill, Gully, channel Erosion Details The basic process at a point in the landscape is simple. Rainfall is partitioned into infiltration and run-off. Infiltration is the rate at which the soil absorbs water (mm/hr). Basic Infiltration rate (saturated), initial (dry) Excess water is run-off: schematically it can be represented as: Detaches soil particles Rainfall Run-off Carries soil downhill Infiltration Erosion Processes and Measurements The basic process can be graphically represented below. Each factor is subject to significant spatial and temporal variability. Rate Soil’s infiltration rate (soil’s characteristic) Varies with soil type, characteristic and location Rainfall rate Varies with time, space, season Leads to variable energy of impact detaching soil particles – = rainfall erosivity Run-off Time to runoff Time Rate of soil detachment is affected by the energy (erosivity) of rain and run-off Detached soil particles are then transported downhill by water or rainsplash Rate and velocity are affected by inf rate, slope, surface roughness, surface cover – all affected by management Run-off energy contributes to soil detachment Point processes need to be integrated over time and space so that soil loss can be quantified for a defined area (generally at the bottom). Erosion Processes and Measurement Effect of soil structure & texture Rate Soil’s infiltration rate Rainfall rate Run-off Well structured, Sandy soils = High infiltration rate, low run off rates, low total run off Infiltration Initially moist soil Further reduces curve Time to run-off Time Degraded soil, high silt/clay = Low infiltration rate, high run off rates, high total run off Other factors that affect curves • • • • • Surface roughness Slope Length Type of flow Vegetation Effects of Erosion • Reduce slope stability, causing failure • Reduce capacity of soil to support organic material • Potential desertification Different models for predicting erosion • USLE: Universal Soil Loss Equation – Most common (not necessarily the best) method of prediction – http://www.iwr.msu.edu/rusle/soil_loss.htm or – Can be found on the USDA website – Windows program http://www.ars.usda.gov/Research/docs.htm?docid=6038 or – By hand … – http://www.techtransfer.osmre.gov/nttmainsite/Library/hbmanual/rusle/fro ntmatter.pdf • • • • 11,000 plot years of data, 47 locations 24 states (1930s-1950s) 1965 completed USLE RUSLE (revised) (~1998) RUSLE2 (revised revised) (~2003) (R)USLE explained • Empirical formula stemming from many soil tests in the US • Used world wide now (maybe inappropriate) Soil Loss=R K L S C P R=rainfall-runoff erosivity factor K=soil erodibility factor (particle size and plasticity) L and S = topographic factors slope length and gradient C=ground management, veg cover P=soil conservation practices Limitations of the RUSLE • Assumes linear relationship • Does not consider physical processes (empirical) • Does not consider gully/channel erosion, only sheet and rill – Often we have gully/channel erosion (think spoil piles) – Remember Highvale? • May be hard to accurately infer parameters for new locations • Much tradecraft involved • Based on tests carried out in US, may not be portable Process based models • To name a few – – – – GUEST (most friendly?) WEPP EUROSEM LISEM • Most use some model of transport of material: – – – – – – A=mean annual value (kg/yr) N=number of years Mi in kg is the mass of individual runoff events c(t) suspended sediment concentration (kg/m3) Q(t) runoff rate in m3/s Ti time duration in s • Models differ in how these values are simulated – Need different inputs for different models – Soil properties, rainfall behavior (type of erosion), etc. Process based models • Benefits – More likely to be applicable outside initial application (i.e. USLE) – Inputs may be more inferable as they are related to soil qualities not abstracts – Lower long term experimentation cost (if model is accurate), USLE was very expensive • Drawbacks – CPU time – Complex set up/programs – inputs Comparison of modeling techniques From Yu. 2005, Process-based erosion modeling: Promises and progress. In Forests, Water and People in the Humid Tropics: Past, Present and Future Hydrological Research for Integrated Land and Water Management. 944p. Entering Channels/Streams • Material transported by – Suspension – Bed load-particles bounce along the bottom of the stream • Einstein (2004) • Y is an empirical coefficient that depends on the size and shape of the sediment particles • T0 is the shear at the streambed • Tc is the magnitude of the shear at which transport begins • w is the specific weight of waters • Maybe more useful … Mitigation/prevention strategies • Under the Surface Mining Law ALL surface water flowing off disturbed areas of the mine must be routed through sediment ponds. The ponds are designed to slow the flow of water • Other practices include: – Riprap: – Jute matting – Concrete drainage channels – Rock lined drain – Erosion control structures Mitigation/prevention strategies • Two categories – Sustain soil cover, prevent erosion (erosion controls) • Reduce tendency of particles to become dislodged/suspended – Reduce suspended particles in water (sediment controls) • Once particles have been suspended, reduce velocity of water so particles settle • Goal is to 1. 2. 3. 4. 5. Minimize ground disturbance Manage run-on to critical areas Manage drainage within critical areas Manage ground cover Manage runoff and sediment exiting system 1.Minimize ground disturbance • -prevent removal of natural vegetation (remember the C factor in USLE) • Vegetative buffer strip dissipates runoff energy and reduces soil tendency to become detached: • Only takes one trip with dozer to destroy, plan for areas to be undisturbed 2.Manage run-on to critical areas • Diversion ditch around disturbed areas, reduce suspended solids • Prevent gullies from forming 3.Manage drainage within critical areas • Blanket/mulch/covering to protect important areas • Eliminate/reduce sheet/splash erosion – Thus no gullies • Water should not flow under covering • Trying to minimize quantity of flow area and reduce flow velocity 4.Manage ground cover • Keep natural vegetation if possible or re-vegetate (even if temporary) • remember the C factor in USLE 5.Manage runoff and sediment exiting system • • • • Fence (perpendicular to flow or parallel to contour) Sediment builds up on fence, requires removal Sediment filter Reduce flow velocity • Settling Ponds • Includes rip-rap, etc (previous) • Last line of defense, material is already suspended, need to keep on site 6.Increase infiltration • If more water infiltrates there is less opportunity for erosion • Add layer of rocks. Water seeps into crevasse between and stays there until infiltration • Infiltration trench Reclaimed Slopes • Predicting erosion is critical • Prevent contamination of site water with waste material – Depends on the composition of the material – Changing the slope changes the flora/fauna that will inhabit the area • Traditional: Reclaimed Slopes • Why not be more innovative – We have fast computers for design – We know how fluvial systems work – We have GPS equipment • Natural landforms to – Minimize runoff on upper slopes – Complex slopes, steep where there is little water Traditional • Long slopes without channels, rill/gully erosion – Need artificial lined conduits for flow • Turbid water quality • Minimal diversity for flora/fauna • Does not look good/blend with nature GeoFluv Design • • • • • Complex slopes with modeled water flow Less turbid water run-off Natural slope for wildlife and vegetation Looks good / blends in More info – http://www.landforma.com/images/downloads/LF004_Natural% 20Approach%20to%20Mined%20Land%20Rehabilitation.pdf La Plata mine, New Mexico, Coal • After a 1 in a 100yr storm, no erosion… Wind Erosion • Similar mitigation strategies • Will discuss when we talk about modeling dust Review • Erosion • Overland flow: – Sheet, rill gully erosion – What ever water does not infiltrate (infiltration capacity of soil important) flows on surface • Modeling – USLE – Process based • Mitigation techniques • Natural landforms MinE 422 Acid Rock Drainage Online ‘Gard Guide is a great source of information • • • • Definition Chemistry Factors Mitigation Terminology • • • • • acid rock drainage (ARD) saline drainage (SD) acid mine or acid and metalliferous drainage (AMD) mining influenced water (MIW) neutral mine drainage (NMD). Terminology • All types of drainage caused by oxidation of sulphide minerals • Above pH ~6 is NMD and SD • Sulphate concentration greater than 1000 mg/L is considered saline drainage • MIW includes ARD, NMD and SD • A final term is metal leaching (ML) as these waters often have high levels of metals Occurrence • Can occur naturally • Weathering (oxidization) of pyrite • Mining accelerates the process because – – – – Increase movement of air and water over the sensitive areas Exposes large volumes of material Increases the surface area of the reactive component Creates opportunities for the colonization of the area by microorganisms that catalyze the process History • First occurrence believed to be ~3000BC in the Iberian Pyrite Belt – Effects included heavy metal pollution in rivers, increased erosion and deforestation – Similar effects in Ireland, Great Britain and Austria • Iron age and medieval times saw an increase in mining activity – Effects of ARD very clear by 1556 (Georgius Agricola) • Industrial revolution required vast quantities of coal – Increased ARD • Modern times require even more minerals, but we are now aware of the harmful effects of ARD and can mitigate/prevent it Acid Generation • The occurrence of ARD, NMD or SD depends on the source, the pathway for transportation and the environment • Most common ore sources for ARD are – Volcanogenic massive sulphide deposits (VMS), High sulphidation epithermal deposits, Porphyry copper deposits, Skarn deposits, Coal deposits • Most common ore sources for SD are – Low-sulphide gold-quartz veins and clean skarns • Characteristics and relative abundance of sulphides and neutralizing minerals determine the type of drainage Ficklin Diagram • ARD comparison between various mines • Metals selected on y-axis better diagnostic in differentiating type of drainage Ficklin Diagram Sulphur • • • • • Sulphur is key for AMD, NMD and SD generation Chemistry require is mostly REDOX (reduction oxidation) Sulphides of interest include: Global cycle (before looking at specifics): Acid rain Pyrrhotite Fe1‐xS Bornite Arsenopyrite Cu5FeS4 FeAsS Enargite/famatinite Tennantite/tetrahed rite Realgar Cu3AsS4/Cu3SbS4 (Cu,Fe,Zn)12As4S13/ (Cu,Fe,Zn)12Sb4S13 AsS Orpiment As2S3 Stibnite Sb2S3 Biogeochemical Sulphur cycle The Chemistry 1 • Note, the composition of the drainage (and sulphur content) can vary depending on a large number of factors and can change over the mine life or location of mining: – – – – Ore deposit Mine waste Spatial location (could be isolated or disseminated) Mining type (surface or underground) – Processing methodology (heap leaching, tailings) often concentrates sulphur • Main point: things can change • Overall FeS2 + 7/2O2 + H2O = Fe2+ + 2SO42- + 2H+ [1] The Chemistry 2 • • The general reaction FeS2 + 7/2O2 + H2O = Fe2+ + 2SO42- + 2H+ [1] • Aqueous ferric iron can oxidize pyrite as well, this is 2 or 3 orders of magnitude faster and produces more acid Requires disolved iron (pH < 4.5) FeS2 + 14Fe3+ + 8H2O = 15Fe2+ + 2SO42- + 16H+ [2] • • • Reaction [1] occurs first, lowers the pH then reaction [2] takes over. • A final reaction of importance is what happens to the ferrous iron from [1]: • • Fe2+ + ¼O2 + 2½H2O = Fe(OH)3+ 2H+ [4] Combining [1] and [4], which usually occurs when ph<4.5, twice the acid from [1] is produced All metal sulpides can produce acid, but some do not when oxygen is the oxidant, pyrite (and some other) do • The Chemistry 3 • Microorganisms act as a catalyst • Cause these reactions to occur orders of magnitude faster • Also, causes some reactions to occur that violate thermodynamic expectations • All metal sulphides produce acid with aqueous ferric iron as the oxidant The Chemistry 4 • Effect of temperature The Chemistry 4 • Potential products from the aforementioned reactions include – – – – Acid Sulphur species Total dissolved solids Metals • Which reactions are generated and the environment (recall previous slide) determines if ARD, NMD, or SD occur – Ability of the environment to neutralize acid or dilute other compounds/metals – The oxidizer is very important (oxygen or dissolved iron) The Chemistry 5 • Stage 1 may last for years, may give false results if monitoring at the start of mine life • Acid generation increases rapidly after stage 1. Factors • Factors that affect the rate of sulphur oxidation • Factors that affect the composition of the drainage • Factors that affect the composition of the drainage after the mine Rate of Oxidation can vary … – Sulphide mineral: • Type of mineral • Surface area • Encapsulation • Crystallinity • Morphology • Assemblage – Ambient environment: • pH (low ph allows for increased bacteria activity) • Oxidation-reduction (redox) potential • Temperature • Source of water – Oxidant: • Type of oxidant (remember ferric iron increases by 2-3 orders) • Oxygen (often limits the reaction rate) • Ferric iron • Availability of oxidants – Bacteria Drainage modifiers • The drainage must travel from the mine to the environment • Along which, the composition of the drainage can be modified – – – – – – Runoff Overland flow Discharge into surface waters Transport via surface water Infiltration Movement of mine waters by mine water management • Factors (chemical/physical) – – – – – – pH Redox conditions Chemical composition of drainage Secondary mineral formation Sorption Neutralization reactions •Climate conditions •Precipitation events •Water movement •Temperature Receiving Environment • Natural neutralization • Sensitivity of the area to the mine release • Dilution • Buffering ability Regulations • • • • • How to keep that ‘social license to mine’ Have to meet the regulations of the country within which you operate Have to meet the regulations of your own company Should meet the regulations of your ‘home country’ Should consider post-closure management – Note that contamination can occur years afterward if the ‘buffer’ was not reached during mining (unlikely but possible) • Some voluntary standards you could use – International Council on Mining and Metals Principles (ICMM, 2003) – International Cyanide Management Code (International Cyanide Management Institute [ICMI], 2006) – Kimberley Process (Kimberley Process Certification Scheme [KPCS], 2008) – Guidelines For Metal Leaching and Acid Rock Drainage at Mine Sites in British Columbia (Price, 1997) – Minerals Council of Australia – Enduring Value (MCA, 2006) – Mining Association of Canada – Towards Sustainable Mining (MAC, 2007) – International network for acid prevention Regulations • Likely a part of your EIA • Need a long term plan for closure • Some legislation you may want to consider – Mine Environment Neutral Drainage (MEND) initiative – Guidelines for Metal Leaching and Acid Rock Drainage at Mine Sites in British Columbia (Price and Errington, 1998) – Federal Metal Mining Effluent Regulations (MMER) – Guidelines for ARD Prediction in the North (Department of Indian and Northern Development, 1992) Summary • AMD is a large environmental issue in mining (if not the largest) – Can contribute to acid rain • The chemistry is fairly simple • Reaction rates depend on a large number of factors and my be difficult to predict – Source – Pathway – Receiving environment • pH and temperature are major factors • Volume of material and capacity of environment to buffer are also major factors • Must meet regulations Part 2: Characterization of ARD • What information do we need to collect? Multi-disciplinary Predicting ARD • Difficult: – Lab conditions are different – Scale in field is different – Both physical and chemical nature important (perm, access to air, transport mechanism) … Types of data of interest Before Mining: • Geostatistical resource map of total sulphur – Build a potential/risk map of ARD • Remote sensing data … • Exploration drill holes provide valuable hydrogeological data – Depth to water – Quantity of water – Flow conditions, permeability (harder) • Surface sampling for baselines • Surface sampling for H2O transport mechanisms – Rivers – Lakes – Runoff (topography) – easy now, satellite info very available • Static tests (testing smaller samples) and kinetic tests (long term leach testing) Types of data of interest During mining • Identify/confirm ARD sources • Identify environmental impact • Water quality measurement to assess need for treatment • Regulatory guidelines important After mining • Decommission water treatment facilities (if possible) • (long term) monitoring of water quality still important Characterization of the Source Sources of ARD • Potential (common) sources of ARD are – – – – Ore stockpiles Heap leach piles Waste rock facilities Tailings storage facilities Sources of ARD: Surface Mining • Avoid high ARD areas • Predict effect on groundwater • Surrounding rock properties control seepage into groundwater Berkeley pit Berkeley pit • Berkeley pit Superfund site, Montana Sources of ARD: Underground Mining • Caving may cause fractures above Sources of ARD: Waste Piles Sources of ARD: Tailings • Sources: runoff and seepage • Composition of tailings and dyke materials important for discharge composition • Permeability of dyke usually low (for good reason), reduces ARD, but seepage a concern Heap Leach Piles, coal and uranium Heap Leach Piles • Sulphuric acid (copper) or sodium cyanide (gold) heap leach piles are common • Improper lining, leakage or overruns with excessive runoff can cause acidic solutions to enter the surrounding environments Coal • Sulphur in coal and (pyrite) in surrounding rock (above and below) • Underground maybe more of an issue (surface is often strip mining and all of coal is often removed, consider stockpiles) Uranium • Leaching sometimes used • Sulphides sometimes present in waste rock Characterization of the Pathway Pathway • Almost always water • Delineate the watershed boundary – Topographical maps – Which watershed(s) contain the potential to be affected • Assess the groundwater as well (porosity/permeability for flow characterization) • Climate (yes there is a reason for your chapter 1 from 402) – How much rainfall – Temperature – Assess seasonal water influences • Hydrologic characterization – Streams, rivers, lakes, points of discharge (lakes/oceans) • Information needed to assess ARD release, fate and transportation mechanisms • Baseline water quality and quantity important to assess the effect of mining (often high metal content before mining due to mineralization) Characterization of the watershed • Aka the receiving environment The Receiving Environment • Sensitivity of the ecosystem to a change in pH and metal content – Vegetation, aquatic life, terrestrial life, livestock, drinking water/human habitations • Buffering capacity (base minerals) • Dilution capacity (volume of water) Prediction of water quality • The main point of the previous information gathering and site modeling/characterization is to predict water/drainage chemistry • Qualitative methods usually sufficient for drainage that is certainly acid generating or certainly not • Quantitative may be required if unsure • Quantitative may be required to refine mitigation strategies – Test leach properties of samples in the lab – Test leach properties of material under field conditions – Geological, hydrological, chemical and mineralogical characterization of waste materials – Geochemical modeling Pre-mining Phase Prediction • Full geochemical composition of samples is usually sufficient to identify potential ARD issues from: – A single rock type – A particular zone of the deposit – The waste (may require information on ore processing) • Make sure you sample for ARD generating minerals, not just the commodity of interest • Usually sufficient, don’t really need a quantitative ‘amount’ of ARD, just need to be aware of the potential to plan for mitigation/prevention strategies Prefeasibility and feasibility • Now have a mine plan • Can plan for prevention strategies using: – watershed information – Waste stockpile information – Water management plans • May want to initiate some field scale kinetic tests • Start thinking of material balance equations – Estimate impact of environment and strategies • Start thinking of monitoring program – Iterate mitigation strategies Summary • Purpose of site characterization/prediction is – – – – – Water treatment requirements Mitigation methods Assessment of water quality Assessment of impact of drainage on surrounding ecosystem Determination of reclamation bond amounts • Mitigation strategies to come … • Need 4 components to have ARD (exposed sulfides, air, water, bacteria) • ARD occurs if acid production > acid neutralization • ARD causes dissolution of metals • Dissolved Fe is a stronger oxidizing agent than air Extra • Ontario: 20/100 abandoned mines have ARD issues (830 ha) • Quebec: 21/107 abandoned mines have ARD issues (4500 ha) Mitigation, Handling Methods • Goal is to control physical properties (i.e. hydraulic conductivity, surface area, etc) to minimize ARD, some examples include… • Remining – – – – – Excavate UG mines or dumps, often hard to deal with after the fact Apply more appropriate mitigation strategy Cover existing waste pile with benign material Rehandle waste to new storage facility Not always economical • Segregation of potentially acid generating (PAG) material – Keep subaqueous or minimize surface area exposed and water/air infiltration • Coal Mines, typical strategy consists of – Compacting and treating with alkaline materials – Cap with low perm material (i.e. clay) – Cover with non acid generating (NAG) material and topsoil to decrease air/water movement Mitigation, Handling Methods • Compaction – Decreases porosity and permeability • Blending – Mix PAG with neutralizing material – Requires cheap local alkaline material – Need to consider possible flow pathways through material – The degree of mixing is important, heterogeneous material could cause localized ‘hot spots’ – Often use limestone or ash • Permafrost/freezing – Decrease chemical reaction rate to acceptable levels such that environment can absorb pH – i.e. Nanisivik Mine, Nunavut Mitigation, Water Management methods • Diversion of site surface drainage and groundwater • Almost always sufficient H2O to react, goal is to stop or intercept transportation of acid before reaching the environment • Divert flow to treatment/storage areas using ditches/channel, very topography dependent Mitigation, Water Management methods • Divert flow around low perm tailings, water flows around PAG material • Dewatering around affected areas • Flooding – Most effective method, oxygen levels of water 30 times less in water – Risk of partial flooding a serious concern – Seasonal H2O levels change – Must seal mine openings (UG) – Need sufficient depth to prevent wave action Liners and Dry Covers • Dry covers – – – – Earthen Organic Synthetic Reduce air and water flow through PAG – Will not go into depth, see MEND… Summary Costs Summary • Chemistry fairly well understood • For your site need to understand – ARD source – Pathway to surrounding environment – Receiving environment • Apply mitigation strategy appropriate to the above three MinE 422:Prediction of air contaminates from a point source Dispersion of contaminates • Assumptions • Calculations • Impact analysis involves: • Impact Prediction: To forecast the nature, magnitude, extent, and duration of the main impacts. • Impact Evaluation (significance testing): To determine the significance of residual impacts i.e. after taking into account how mitigation will reduce predicted impact. 2 Dispersion of a Contaminant from an Elevated Point Source Plume centerline Z (X, Y, Z) h Ug (Z, stability class) X (X, 0, 0) (X, Y, 0) H h Y ug (z, stability class) = wind velocity (m/sec) H = effective height of stack (m) h = physical height of stack (m) h = plume rise (m) Gaussian Dispersion Model 2 2 Q 1 y 1 z H C( x,y,z ) exp 2u g y z 2 z 2 y where, C(x,y,z) = concentration of contaminant i at location x, y, and z relative to source Q = source strength of contaminant i ug = wind speed at physical height of stack (h) y = standard deviation of the plume’s probability distribution function along the y axis, also referred to as the plume’s dispersion coefficient along the y axis z = standard deviation of the plume’s probability distribution function along the z axis, also referred to as the plume’s dispersion coefficient along the z axis H = effective height of plume ug,2 Z2 ug,1 Z1 P where, ug,2 = wind speed at height Z2 ug,1 = wind speed at height Z1, which is typically at Z1 = 10 m P = function of atmospheric stability and surface roughness Value of Exponent P Stability Category A Rural exponent 0.07 Urban Exponent 0.15 B 0.07 0.15 C 0.10 0.20 D 0.15 0.25 E 0.35 0.30 F 0.55 0.30 Determination of Atmospheric Stability Classes Wind speed, u (m/sec)a <2 2–3 3–5 5–6 >6 Day Solar Radiation Strongb Moderatec Slightd B A A - Bf f B C A-B f C B B-C f D C C-D C D D Night Cloudinesse > 4/8 Cloud < 3/8 Cloud E F D E D D D D aSurface wind speed is measured at 10 m above the ground to clear summer day with sun higher than 60 above the horizon cCorresponds to a summer day with a few broken clouds, or a clear day with sun 35-60 above the horizon dCorresponds to a fall afternoon, a cloudy summer day, or a clear summer day with the sun 15-35 above the horizon eCloudiness is defined as the fraction of sky covered by clouds f For A-B, B-C, or C-D conditions, average the values obtained for each bCorresponds Note: A: very unstable; B: moderately unstable; C: slightly unstable; D: neutral; E: slightly stable; F: stable. Regardless of wind speed, class D should be assumed for overcast condition, day or night Pasquill-Gifford Curves: Dependence of y and z on Stability, Surface Type, and Distance Downwind y z Empirical Constants Used to Determine Values for y and z for Rural Environment y axb where, b = 0.894 z cx d f Stability A B C D E F a 213 156 104 68 50.5 34 c 440.8 106.6 61.0 33.2 22.8 14.35 x 1 km d 1.941 1.149 0.911 0.725 0.678 0.740 f 9.27 3.3 0 -1.7 -1.3 -0.35 c 459.7 108.2 61.0 44.5 55.4 62.6 x 1 km d 2.094 1.098 0.911 0.516 0.305 0.180 SOURCE: D.O. Martin, J. Air Pollu. Control Assoc. 26, no. 2 (1976):145. Note: X is in km, y and z are in m. f -9.6 2.0 0 -13.0 -34.0 -48.6 Example 1 Nitric oxide (NO) is emitted at 110 g/s from a stack with effective height of 100 m. The wind speed at the physical stack height is 5 m/s on an overcast morning. Assume rural environment. a) calculate the ground level centerline concentration 1.0 km from the stack. b) Calculate the concentration at 100 m off the centerline at the same x distance. Class D stability should be used for overcast conditions. for x = 1km, rural environment, and Stability class D, y ,z =? From Pasquill-Gifford curves, y = 69 m and z = 32m 1 y Q C exp 2 ug y z 2 y 2 1 z H 2 exp 2 z 1 0 2 1 0 100 2 110(106 ) C exp - exp 2 32 2(5)(69)(32) 2 69 1 100 2 1586(1)exp 1586(0.0076) 2 32 C 12g / m3 1 y Q C exp 2 ug y z 2 y 2 1 z H 2 exp 2 z b) At y = 100m, everything else being the same, 1 100 2 C 12exp - 2 69 C 12(0.34) g C 4.2 3 m Example 2 Nitric oxide (NO) is emitted at 110 g/s from a stack with effective height of 100 m. The wind speed at the physical stack height is 5 m/s on an overcast morning. Assume rural environment. a) calculate the ground level centerline concentration 2.0 km from the stack. b) Calculate the concentration at 100 m off the centerline at the same x distance. Class D stability should be used for overcast conditions. b = 0.894, for x = 2km and Stability class D, a = 68, c= 44.5, d = 0.516, f =-13 y ,z =? y axb 68(20.894 ) 126m z cxd f 44.5(20.516 ) (13) 51m z > H/2.15 Reflection on earth surface must be considered 2 Q 1 y 1zH C exp 2 ug y z 2 y 2 z 1 y 2 1 z H exp - 2 y 2 z 2 2 1 y 2 C exp 2 ug y z 2 y 1 z H 2 1 z H 2 exp exp 2 z 2 z Q 1 0 2 110(106 ) C exp - 2 (5)(126)(51) 2 126 1 0 100 2 1 0 100 2 exp exp 2 51 2 51 1 100 2 545(1)(2)exp 545(0.293) 2 51 C 159 g / m 3 b) At y = 100m, everything else being the same, 1 100 2 C 159 exp - 2 126 C 159(0.73) g C 116 3 m (18 pts) A coal-fired power plant is proposed to be established in an urban area. The proposed power plant will have a stack with a physical height of 40m. When the plant is operated, it is expected that it will emit SO2 at a rate of 12 kg/min. For a clear summer day, at noon (sun about 90o above horizon), a 10m high meteorological tower adjacent to the proposed power plant reports an average temperature of 25oC, wind speed of 4 m/sec, and atmospheric pressure of 1 atm. At these meteorological conditions, the plume rise will be 10 m, the wind speed at the physical stack height will be 4.9 m/s, the wind speed at the effective stack height will be 5.1 m/s. Answer the following questions and show how you were able to answer each question. (10 pts) Determine the ground-level centerline concentration of SO2 at 2,000 m downwind of the power plant in mg/m3. (5 pts) Determine the ground-level centerline concentration of SO2 at 150 m downwind of the power plant in mg/m3. (3 pts) What is the value (in mg/m3) and location (in m) of the local maximum ground level concentration of SO2 downwind of the facility? Maximum Ground-level Concentration •Graphical and analytical methods are available to determine the maximum ground-level concentrations of contaminants that are emitted from elevated point sources. • The max location exists at ground level along the centerline of the plume (y = z = 0). •graphical technique requires, atmospheric stability class, effective stack height (H), wind speed (ug), and source strength (Q) •Given that information, the distance downwind (xmax) where the maximum concentration occurs and the parameter (Cug/Q)max can be determined • The maximum ground-level concentration of the contaminant (C) can then be determined knowing ug and Q. Maximum Ground-Level Concentration and its Location This maximum concentration can also be determined analytically: C ug exp a b (In (H)) c (In (H))2 d (In (H))3 Q max where, H = effective stack height (m) C ug = (m-2) Q max a,b,c,d = empirical constants Values of Constants Used to Calculate C ug Q max Stability Class A B C D E F a -1.0563 -1.8060 -1.9748 -2.5302 -1.4496 -1.0488 Coefficients b c d -2.7153 0.1261 0 -2.1912 0.0389 0 -1.9980 0 0 -1.5610 -0.0934 0 -2.5910 0.2181 -0.0343 -3.2252 0.4977 -0.0765 Summary •Effect of pollution from a stationary point source can be determined •Concentrations often measured at point source, need to infer downstream •Impact prediction important for regulatory guidelines • many assumptions imbedded in this model, but it is a reasonable starting point •Could use your VBA skills to generate a 3D model with this equation … 25 MinE 422: Modeling Environmental Variables Resources: -Some slides modified from Andrew W. Moore, Carnegie Mellon -Many geostatistics/environment books out there • • • • Applications of Geostatistics What is different about environmental variables Some techniques from machine learning Examples Types of variables of interest • • • • • • Contaminants/pollution Radiation Precipitation Temperature Illness rates (cancer, infection, highly contagious diseases, …) Etc .. • What is different about most of these variables when compared to grade? Physical Setting 1. Underground – Contaminates transported following flow in porous media relationships – Need to infer/measure input contaminate rate – Need to infer rock properties (geostatistics) – Need to model contaminate dispersion 2. Surface – Contaminates in spoil/tailings/surroundings – Similar to modeling common in mining 3. Air – Similar to underground water flow 4. Water, streams – Euclidian distance is wrong, shortest path between two locations is not a straight line 5. Water, open bodies – Complex contaminate diffusion issues 1) Underground Flow in Porous media • A dissolving non-aqueous-phase-liquid (NAPL) contamination scenario: • • • Uncertainty in permeability, source geometry, dissolution rate, decay rate forward modeling to simulate uncertainty in source geometry inverse modeling to simulate uncertainty in flow/transport parameters Single phase flow equation: xi h h θki qsr S s xi t Dissolved mass transport equation: θCs xi t θDij Cs θvi Cs θ R NAPL θCs x j xi R NAPL max 0, k NAPL C seq C s where, ki, h, qsr, C, Dij, vi, kdis, Ceq, and lambda represent hydraulic conductivity, hydraulic head, dispersion coefficient, seepage velocity, NAPL dissolution rate constant, equilibrium concentration, and first-order biodegradation rate constant Flow in Porous media • Flow simulate to predict plume movement 2,3) Surface and air Surface • Estimating variables measured at surface – Contaminate concentrations in soil – Buildup over time, cumulative effects • Identical to estimating grades, etc. Air • Identical to estimating flow underground but physics (equations) are different. • Predicting wind patterns becomes important 4) Water in streams • How to estimate along a (virtually) 1D stream • Could straighten the stream • Could calculate distance along stream 5) Open water spills • Similar mechanics to predicting underground/air flow but with different physics again – (think BP oil spill) – http://www2.ucar.edu/news/ocean-currents-likely-to-carry-oil-spillalong-atlantic-coast Spatial data • Use some type of interpolator – Poor in extrapolation mode – Often do not have many samples (almost always in 2D from the surface) n Z (u) i Z (u i ) * i 1 n C(u , u ) C(u, u ) , j1 j i j i i 1,..., n 1 c d iw i 1 n i 1 c d w i Interpolation Technique • Inverse distance – Select a power • Kriging – Select a variogram and some search parameters • Trends are a significant aspect of all resource estimation, but can be very important in environmental data Radioactivity around Chernobyl Some important details • Trends • Debiasing • Temporal effects Trends • The full cokriging approach (you may have taken in MinE 310) is impractical in most cases: – – – • Different approximations are considered depending on: – – – – • K(K+1)/2 direct and cross variograms in multiple directions LMC is difficult to fit Full cokriging is not completely implemented in most software Source of secondary data (trends, measurements,…) Scale of the secondary data Number of variables being considered (interested in a few or many) Spacing of the secondary data (exhaustive, isotopic,…) Some methods: 1. 2. 3. 4. 5. 6. 7. LVM ED CCK BU SW CT PF locally varying mean external drift collocated cokriging Bayesian updating stepwise transformation cloud transform p-field simulation Trends: Locally Varying Mean • LVM: – The local mean of the primary variable is created as a trend model and used in simulation – Two different implementations: np y u m u y ( u ) m( u ) * 1 np y u y (u ) 1 m u 1 1 * np – The mean gets a large influence in the first approach – Affect of the mean is mitigated in the second approach – Appropriate for geological trends and smooth secondary data Debiasing • Monitoring stations are expensive, where would be put them? • Need to correct in areas we know are low … Debiasing • Calibrate to some (potentially made up) secondary Modeling Time series data • Simple univariate time series (at a single spatial location) Major Issues • Trends (same as with spatial data) – Consider a well log (same in mining) and a temporal variable • Need to remove the trend from the data – Fit a line and subtract the value – Model residual and add trend back in Major Issues • Seasonal effects – Fit a periodic function (i.e. sine wave) and subtract value like a trend Some interesting machine learning algorithms that are becoming more common • • • • • Neural Networks Support vector machines Genetic algorithms Simulated annealing Clustering • OK a bit of a stretch for the course, but you should know about these … Optimization Artificial Neural Networks (ANN’s) • A data structure to emulate (our current understanding) the mechanism of biological neurons. • Applications in geostats: – Modeling functions (variograms) – Classification of facies/soil/waste/anything – Seismic inversion A Neuron Model • Weights represent signal strength • Multiple signals (inputs) are combined – Sum, product, or other • We change the neuron function by adjusting the weights – learning Biological http://www.utexas.edu Numerical k output f wi xi i 1 Humans versus Computers • Humans: – Highly parallel: 100 billion (1010) neurons and up to 104 connections per neuron – One neuron passes information (fires) in 10-3 seconds • Computers: – Networks composed of <10 to 100’s of nodes – Operate in 10-9 seconds – Can parallelize Types • Feed-forward – – – – Back-propagation (most common) Bidirectional associating memory Adaline Self-organizing • Feedback or recurrent – Elman (finite state machine) – Hopfield (form of associative memory) – Others Basic Network Architecture • Layers – Inputs – Hidden (multiple layers) – Output • Fully or partially connected • Activation Functions – Differentiable – Twice-differentiable – f(x) [0,1] or [-1,1] Sigmoid 1.5 1.0 0.5 0.0 -5 -4 -3 -2 -1 0 1 2 3 4 5 -0.5 Training the network • Basic process: – Randomly initialize network weights – Evaluate error using training data – Calculate gradient/Hessian for all weights E E 2 E 2 E 2E , , , , wij w jk wij 2 w jk 2 wij w jk – Update using gradient descent or Newton method h n yˆ k f w jk g wij xi i 1 j 1 m 1 2 E yˆ k yk m k 1 Neural Networks Example • neural network model for typhoon-rainfall forecasting Support vector machines • Support vector machines – Just a linear classifier denotes +1 w x + b>0 f(x,w,b) = sign(w x + b) denotes -1 How would you classify this data? w x + b<0 Support vector machines • Support vector machines – Just a linear classifier f(x,w,b) = sign(w x + b) denotes +1 denotes -1 How would you classify this data? Support vector machines • Support vector machines – Just a linear classifier f(x,w,b) = sign(w x + b) denotes +1 denotes -1 How would you classify this data? Support vector machines • Support vector machines – Just a linear classifier f(x,w,b) = sign(w x + b) denotes +1 denotes -1 How would you classify this data? Support vector machines • Classifier Margin denotes +1 denotes -1 f(x,w,b) = sign(w x + b) Define the margin of a linear classifier as the width that the boundary could be increased by before hitting a datapoint. Support vector machines • Maximum Margin denotes +1 denotes -1 Support Vectors are those datapoints that the margin pushes up against 1. Maximizing the margin is good accordingf(x,w,b) to intuition = sign(w x + b) 2. Implies that only support vectors are important; other The training examples maximum are ignorable. margin linear 3. Empirically it works well classifier is the linear classifier with the, um, maximum margin. Linear SVM This is the simplest kind of SVM (Called an LSVM) Support vector machines • How to get the actual line: – Simple optimization problem • Can fit in a high dimension, then boundary is nonlinear in low dimension • Can use kernels to weight misclassified data values or to consider soft boundaries: denotes +1 denotes -1 What are the mining/environmental applications Example • Soil type classification and estimation of soil properties using support vector machine • Area near Serbia • Need to classify soil for land use issues Review • General issues in modeling environmental variables – Dealing with spatial-temporal issues – Dealing with complex contaminate transportation mechanics – Trends and debasing • Machine learning techniques
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