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Perkins 1 Graham Perkins Writing sample for Shaw Group Effects of Coal Mining Activity on Downstream Water Quality in the Coal River Watershed and Surrounding Areas Abstract In the US, the demand for a cheap, plentiful electricity source has led to the ever-‐increasing extraction of coal. In the Coal River Watershed (CRW), WV, this extraction has wide-‐ranging environmental implications. Areas to be mined are stripped of forest and vegetation and then fractured to allow companies’ access to the underlying coal seams. The mechanization of coal has increased dramatically over the past decade as it allows coal extractors to increase profit margins by extracting coal more efficiently by shifting away from manual labor. Mechanization has allowed both areas of greater size and coal at greater depths to be effectively exploited. This process causes abrupt habitat loss in the immediate area of the mine, but also has perhaps more subtle but significant ramifications for downstream aquatic systems. Mining increases the rate of nutrient runoff into streams as well as releases compounds not naturally found in the surface environment at significant levels, into the watershed. Elevated levels of nutrients and toxins that are released constrain growth of aquatic organisms and significantly alter downstream habitat, dramatically changing the organic and inorganic composition of streams. This study focuses on two particular features of mining, valley fills (VFs) and refuse fills (RFs). VFs are created when the excess spoil, the rock and soil that is removed from the surface to allow miners to access coal seams, is stored in nearby valleys, a process that often completely buries streams. RFs are catchment ponds for the nutrient-‐heavy and toxic runoff that is created as a result of severe ground disturbance. RFs merely act to slow the release of this runoff. The research group took samples of water throughout the CRW, to determine whether mining activity has a material effect on quality of water downstream. The data revealed that, indeed, areas downstream of VF sites tended to have heightened levels of total dissolved solids (TDS), an important indicator for overall environmental health and a stress factor for organisms inhabiting streams under undisturbed conditions. Introduction There has been a dramatic increase in surface and subsurface mining in the US over the past 30 years that has been largely driven by increases in electricity demand and consequently the demand for cheap fuel to supply this electricity. Coal is cheap to extract, abundant, portable and poses little risk of inadvertent combustion. There has been an increase in total mining due to these resource needs and an increase specifically in surface mining due to changes in technology as mechanization of mining requires coal-‐extracting firms to find ways to increase profit margins. Firms are able to exploit more quickly and effectively coal reserves of greater size and coal located at greater depths. This study features the Coal River Watershed (CRW), one of the largest coal mining areas in the United States. Due to high concentrations of higher quality coal seams (generally bituminous), much of this area has been exploited. The sulfur content Perkins 2 of the coal in this region is under 1.5 percent and the ash content of the coal is under six percent (West Virginia Department of Environmental Protection—via class handout). The CRW features coal that contains between about 70 to 85 percent fixed carbon of medium and low volatility (WVDEP). These factors are extremely important because they indicate what by products the coal will leave once burned and the subsequent costs that a power plant will have to endure in order to stay within environmental regulatory constraints. Companies buying coal prefer coal that is of higher carbon content (the active ingredient in combustion for electricity) and which contains fewer pollutants that will have to be mitigated within the power plant. As a result, coal with a higher carbon content more valuable. Coal mining has a profound effect on the environment in an area. Coal mining can be classified into a few categories: strip-‐mining, shallow underground seam mining and deep underground seam mining. Strip mining involves removing the layer of overburden (the layer(s) above the coal seam to be extracted) and performing the extraction. Underground seam mining uses the same technique, however, due to the depth and resulting greater quantities of overburden to be removed, is more problematic to the surrounding environment. In all cases, the areas to be mined are cleared of forests and explosives are used to fracture the rock layers that are located between the surface and the coal seam. “Excess rock (mine “spoil”) is pushed into adjacent valleys, where it buries existing streams” (Palmer et al. 2010). This is known as valley fill (VF) and is one major contributor to environmental degradation in the CRW. Mining activity is especially problematic because the deciduous forest environment of this region supports a high degree of biodiversity and a number of endemic species. “Burial of headwater streams by valley fills causes permanent loss of ecosystems that play critical roles in ecological processes such as nutrient cycling and production of organic matter for downstream food webs; these small Appalachian streams also support abundant aquatic organisms, including many endemic species” (Palmer et al. 2010). Evidence demonstrates that once an area has been deforested and mined, recolonization is ineffective at bringing back endemic species of plants and animals (Palmer et al. 2010). As a result, natural habitat is permanently lost. In addition, due to disturbances upstream, downstream organic and inorganic composition change (Palmer et al. 2010). “Many studies show that when more than 5 to 10% of a watershed’s area is affected by anthropogenic activities, stream biodiversity and water quality suffer” (Palmer et al. 2010). Much of the area sampled has similar or higher levels of disturbance. Once a stream becomes disturbed by a valley fill, the hydrology and environment is permanently altered. Vegetation removal, removal of topsoil and overlying rock formations, alterations in topography “and soil compaction from use of heavy machinery reduce infiltration capacity and promote runoff by overland flow” (Palmer et al. 2010). In a precipitation event, vegetation and topsoil aid in reducing the velocity of raindrops and allow for percolation into the soil, so that the time from impact with the ground to the time when the individual drop reaches a water body is maximized. Post-‐
reclamation, the velocity of the raindrops dramatically increases as much more of the rain enters the water channel more quickly and much less percolates down into the soil. Perkins 3 “This leads to greater storm runoff and increased frequency and magnitude of downstream flooding” (Palmer et al. 2010). It also increases runoff of nutrients and harmful compounds released as a result of the significant ground disturbance. Water that passes through valley fill areas contains “a variety of solutes toxic or damaging to biota” (Palmer et al 2010). Areas below valley fills that have been sampled “are characterized by increases in pH, electrical conductivity and total dissolved solids due to elevated concentrations of sulfate (SO4), calcium, magnesium, and bicarbonate ions” (Palmer et al 2010). Palmer et al. 2010 found “that significant linear increases in the concentrations of metals, as well as decreases in multiple measures of biological health, were associated with increases in stream water SO4 in streams below mined sites” (Palmer et al. 2010). Therefore the increased runoff not only contains a higher-‐
than-‐average nutrient lode, but more toxic levels of materials that occur as a result of the activity. It appears that the loss of downstream biodiversity sustained as a result of mining activity does not recover over time. Increases in conductivity of water, SO4 and “other pollutants associated with mine runoff, can directly cause environmental degradation, including disruption of water and ion balance in aquatic biota” (Palmer et al. 2010). “Elevated SO4 can exacerbate nutrient pollution of downstream rivers and reservoirs by increasing nitrogen and phosphorus availability through internal eutrophication” (Palmer et al. 2010). Study has demonstrated that these levels remain elevated, even well after mining activity has ceased (Timpano 2010). This reduces the amount of available oxygen in the water and constrains the growth of aquatic organisms. The removal of overburden may even cause permanent disruption in downstream ecosystems (Timpano 2010). Because the mine area is simply recovered with rock and other material fractured during the stripping process, it is much more difficult for vegetation to recolonize the area. In one site examined, the rock spoil that had been added as part of the reclamation effort was planted with a strain of extremely hearty grass that, according to Larry Gibson of Friends of the Mountains, could colonize anywhere. Three years after the reclamation, the grass had had apparent difficulty colonizing the area as it was only thinly growing and there were no other plant species evidently growing in the area. This results in the reclaimed area sequestering much less CO2 than a similarly untouched tract of forest. Because it is extremely difficult for woody trees to regrow in an area which features rocky soil with little or no topsoil availability, it is sufficient to say that regrowth of woody vegetation could take decades or even centuries. “The US Clean Water Act and its implementing regulations state that burying streams with materials discharged from mining should be avoided” (Palmer et al. 2010). In many cases, it appears that there is a disconnect between the laws which govern MTR and strip mining and the actual effects associated with mining activity. “Mitigation must render nonsignificant the impacts that mining activities have on the structure and function of aquatic ecosystems” (Palmer et al. 2010). Previous studies such as Palmer et al. 2010 have found that, indeed, these activities significantly affect the downstream ecosystems even long after mining activity in an area has ceased and the area has been Perkins 4 reclaimed. Additional regulations imposed by the Surface Mining Control and Reclamation Act establish thresholds for discharge released from mining sites. Though these policies seek to mitigate or minimize the amount of environmental damage caused by mining, research continues to show that aquatic life is highly affected and suffers as a result of mining activity (Palmer et al. 2010). Environments which have vegetation, rock, and soil removed and areas which are covered by mining spoil (valley fills) have been shown to be adversely affected (Timpano 2010). Refuse ponds which act to filter waste water created from mining activity are also an obvious consequence as they generally contain higher concentrations of toxic materials and runoff and are often subject to overtopping in the event of a flood. The CRW is a relatively unpopulated area of West Virginia that features relatively little economic activity as well. It contains vast stretches of undisturbed deciduous forest. Because of the steep topography, there is little farming and due to the expenses associated with clearing and leveling land for commercial centers or industrial activities, much of this area is undeveloped. Based on this information, the water in this area should be quite clean. Therefore, this study seeks to analyze the effect of coal mining on downstream water quality to determine whether mining activity tends to decrease water quality. This study measures water quality in terms of total dissolved solids, turbidity and conductivity. These variables will tend to increase when there is disturbance in the watershed and can act as appropriate metrics for measuring overall water quality. They are also appropriate proxies for determining overall environmental health. Higher levels of TDS have been found to be associated with lower levels of environmental quality and degraded environments (Timpano 2010). This group expects that variance in water quality, especially in areas with high conductivity and high turbidity, will be determined largely by the number of valley fills upstream, the number of refuse fills upstream and the number of underground mining permits in relation to the sample sites. Methods This analysis seeks to gain a better idea of the association or relationship between valley fills, refuse ponds, underground mining permits and the quality of water downstream from these features, measured in conductivity, turbidity, and total dissolved solids in the CRW. The research group sampled areas in an around the CRW. In order to find a meaningful relationship, the group wanted to sample areas varying in type and scale of mining-‐related activity. Samples taken from sites downstream of VFs ranged from containing zero to 34 percent VF in the immediate upstream watershed. This is to say that of all the upstream area that could potentially drain to the point sampled, up to 34 percent of the area was covered by VFs. Samples of points downstream from RFs ranged from containing zero to 2.6 percent of the area upstream that could drain to this point, covered in RF. The study group also looked at distance upstream of sample areas from VF, RF, and underground mining permit (UMP) areas to discern whether these features had a material impact on the area sampled. The researchers also wanted considerable variability with respect to distance of points sampled from specific mining-‐related features. Points sampled from VF areas ranged Perkins 5 from 150m to 54800m. Points sampled from RF ranged from 0m to 61652m (0m meaning the point sampled was on top of a RF). Points sampled from UMP areas ranged from 0m to 38643m (0m meaning the point sampled was on top of a UMP). In order to create a layered map in ArcGIS to display our data, the researchers used a topographic layer of the area that was then overlaid with stream data, valley fill data, refuse fill data and underground mining permit data. All these data were obtained from the West Virginia Department of Environmental Protection. The mining activity layers were updated in April 2011. Furthermore, the researchers uploaded the GPS points collected onto ArcGIS. The group then exported this file to Adobe Illustrator so it could analyze what percent of the area upstream of each of the individual sample points featured VFs and RFs. The researchers used a gradation of greyscale to differentiate between what was simply watershed area, and areas of VF and RF [see Image 1 below]. The watershed area was light grey, the VFs were darker grey and the RFs were black. Based on the content in the histogram, we could determine what percentage of each feature was present. [Image 1 (CRW5)]
For this histogram (top right of Image 1), the tallest line is the watershed area, followed by the VF area (the one to the immediate left) and then the RF area (on the far left). For this example, the watershed area is 49.94 percent of the image-‐evaluated, the VF area is 0.4 percent and the RF area is 0.13 percent. In order to calculate the relation of the RF and VF to the actual watershed being sampled (the entire area minus the surrounding white that also registers on the histogram), the group performed a simple calculation. Here is a demonstration for how the group derived percent values from the VF and RF makeup for Image 1. Perkins 6 VF = 0.4/(0.4 + 49.94 + 0.13) = 0.0079*100 (to get percent) = 0.79% RF = 0.13/(0.4 + 49.94 + 0.13) = 0.0013*100 = 0.13% In this study, conductivity was measured using an Oxygen Conductivity Salinity and Temperature meter (model: number 85) made by YSI. To measure pH the group used a pH/Conductivity/TDS/Salinity/Temperature Multi-‐Parameter Tester (model: PCSTestr35) made by Thermo Fisher Scientific. To measure turbidity the group used a colorimeter (model: DR/800) made by Hach. To measure temperature the group used a digital thermometer, a lower precision tool which nonetheless allowed the group to minimize our time spent in a given sample location while attaining a fairly accurate reading. In order to register the points, the group used a GPS unit, creating waypoints at sampled areas. The researchers then plugged these points into the image analysis software, ESRI’s GIS software called ArcMap. [See diagram on page 7] The fieldwork included a group of six, having two people create waypoints so that the GPS location could be registered, three people to measure conductivity and temperature and one to measure pH. By having a group of people performing these various tasks, the research group was able to maximize the number of sites sampled while still allowing for a fairly high degree of accuracy. At each site sampled, each of the group members collected a sample of water using a plastic bottle and tested their individual sample. For each source sampled, the group retained a small amount to be tested in the lab in Lexington, VA. Once back, each group member took three readings of each sample to test for turbidity. The group collected samples in the field on March 2-‐3, 2011, March 5-‐6, 2011 and March 9, 2011. In order to calculate conductivity, the group took three separate measurements and determined the mean and standard deviation. To obtain the percent error the following formula was used: % error = ((100*SD)/mean) > 10% to remove outliers The formula for conductivity is based on a 25°C standard, so our group had to formulate a corrected conductivity measurement that allowed for variance in each sample’s temperature. The central reason why conductivity had to be corrected was that the creeks that we sampled had a rather large range of temperatures. In order to minimize the error associated with this variation and to standardize the data, our group performed the corrected conductivity calculation. To correct for temperature, we used the following formula conductivity (corrected) = [cond (initial) + 2 * (25 degrees Celsius – T (temperature)) = 0.00001 (cond)2 + 0.7547*cond – 7.775 Perkins 7 ¯ˉ
scale is roughly 8km or 5mi Perkins 8 Data Table 1 sample TDS CRW1 CRW2 CRW3 ph VF meters RF meters underground meters median turbidity (NTU) % valley fill % refuse fills 278.78588 0.04 0 7.64 1306 61652.8 1017 2 198.71119 68.450704 0.02 0 0.14 0 7.69 7.54 1491 54829.5 37083 61652.8 1150 1782 1 0 CRW4 142.0244 0.57 0.3 7.75 1575 708 70 1 CRW5 109.75328 0.79 0.26 7.73 2370 415 160 7 CRW6 378.54549 0.68 0.22 8.1 15838 5414 189 7 CRW7 537.64171 23.16 0 8.27 1071 61652.8 1653 1 CRW8 CRW9 382.13061 189.58548 5.32 41.94 0 0 8.16 8.16 2091 279 61652.8 61652.8 409 38643 5 9 CRW10 CRW11 316.63955 315.31634 41.6 24.22 0 0 8.1 8.82 149 270 61652.8 61652.8 0 0 27 9 CRW12 CRW13 275.21012 952.46959 34.15 27.48 0 0 8.01 6.65 450 265 61652.8 61652.8 0 0 4 3 CRW14 752.09637 9.83 0 7.63 2189 61652.8 10000 2 CRW15 CRW16 65.118361 233.78973 0 18.45 0 0 7.65 8.16 54829.5 2421 61652.8 61652.8 243 31 0 1 CRW17 482.95142 4.84 0 8.07 900 61652.8 0 0 CRW18 758.37114 10.74 0 8.05 1700 61652.8 251 4 CRW19 480.86404 1.04 2.6 7.82 7927 3220 677 4 CRW20 CRW21 CRW22 CRW23 291.3071 95.842666 57.258498 54.642592 2 0 0 0 0 0 0 0 7.92 4.5 7.57 7.44 7610 54829.5 54829.5 54829.5 6488 61652.8 61652.8 61652.8 444 38643 1788 38643 4 0 1 7 CRW24 55.969163 0 0 7.18 54829.5 61652.8 38643 0 CRW25 46.89321 0 0 6.86 54829.5 61652.8 38643 1 CRW26 75.76257 0 0 7.12 54829.5 61652.8 38643 4 CRW27 333.84378 2.6 0.61 7.93 41371 15579 5518 0 CRW28 CRW29 CRW30 69.163219 126.38151 276.14257 0 0 2.52 0 0 0.5 7.38 7.42 7.57 54829.5 54829.5 49845 61652.8 3180 56048 2857 210 35130 3 12 13 CRW31 CRW32 CRW33 115.15717 222.44301 234.57447 0.0001 0.04 0.034 0 0 0 7.88 8.23 8.52 42902 4208 4011 44069 61652.8 61652.8 13138 228 168 2 0 0 CRW34 CRW35 CRW36 209.84793 263.76893 185.66458 0.04 0 0 0.63 0 0.93 8.34 8.46 8.43 3139 1800 54829.5 1751 3180 8763 0 265 123 4 2 5 CRW37 186.02842 2.29 1.5 8.52 2127 3607 3334 0 CRW38 28.969315 3.82 1.48 7.95 3491 6282 250 0 CRW39 CRW40 CRW41 246.66414 34.546242 60.284841 3.3 0 3.56 1.27 0 0 8.4 7.74 7.56 3386 54829.5 1730 3348 61652.8 0 698 38643 630 1 2 4 In Figure 1 [below] a fairly linear relationship can be seen between the percent of VF area upstream of the points sampled and the quantity of TDS in the downstream water. Not all points conform to this, however, as some areas with VF have relatively low TDS. This could be because mitigation measures at the upstream site were more effective at Perkins 9 reducing TDS. It is quite likely that different companies use different strategies with respect to valley filling and this could certainly affect downstream TDS. In addition, due to differences in dispersion of rain, there will be some downstream areas that will contain more runoff that has passed through VF area, likely increasing the TDS in that body of water. This study also does not take into account a three-‐dimensional view of the VF as we are only concerned about the X and Y extent and not the Z extent of the VF. Some areas may have much deeper fills than others and this could affect the quality of downstream water. [Figure 1] Percent VF and TDS Total Dissolved Solids (ppm) 1200 1000 800 600 400 200 0 0 5 10 15 20 25 30 35 40 45 %VF This space is intentionally left blank. Perkins 10 In Figure 2 [below] there is a generally negative downward trending linear relationship between the distance from VF and the level of TDS. This makes sense intuitively because one can imagine that as the points sampled get farther away from VF, the fills have less of an effect on water quality as there is more water to dissipate the TDS the further downstream the point is from the VF. There are some points that are very close to VF which have relatively low levels of TDS. This could be due to mitigation techniques specific to VF sites or could be due to less rainfall on the VF site contributing to less TDS downstream. [Figure 2] Total Dissolved Solids (ppm) Distance to VF and TDS 1200 1000 800 600 400 200 0 0 10000 20000 30000 40000 50000 60000 DistVF (m) [Figure 3] Total Dissolved Soilds (ppm) Distance to UM permit and TDS 1200 1000 800 600 400 200 0 0 5000 10000 15000 20000 25000 30000 35000 40000 45000 DistUMpermit (m) In Figure 3, there is a negative linear relationship between distance from UMP and TDS. There, however, is considerable variability at the 0m to 500m range from UMP Perkins 11 which can largely be attributed to the imprecision inherent in this measure. The data feature areas that contain UMP without taking into account whether these areas are active or not. This likely accounts for the large range we are seeing. In general the farther the points sampled are from UMP areas, the lower the TDS. The research group found no material relationship between percent of RF cover and TDS in downstream waters. This is likely because none of the areas that were sampled had significant levels of RF (defined as exceeding five percent). Previous studies have demonstrated that it is difficult to find an association between water quality and RF or VF when the amount of area disturbed falls below five percent (Timpano 2010). There was not a meaningful association found between TDS and distance to RF. There also was not a meaningful association between pH and percent VF, percent RF, distance to VF, distance to RF or distance to UMP (after this notes together as independent variables). Temperature and the independent variables also did not indicate a significant relationship. Turbidity and the independent variables did not demonstrate a meaningful relationship. Discussion and Conclusions The data indicate that VFs have a material effect on downstream water quality in terms of total dissolved solids. Areas of WV that are forested and undisturbed should have an extremely low level of TDS (< 330ppm) because, due to vegetative cover and extensive root systems, the rate at which these solids are released into the water system should be minimal (Palmer et al. 2010). There is also a general lack of source material to be deposited in the waterways, a factor that changes dramatically after an area has been disturbed by mining activity. Since areas sampled had low population densities and low concentrations of industrial and commercial activity, coal mining and its propensity to create waste in terms of RFs and VFs are the main anthropogenic stressors that could be expected to affect water quality. The data demonstrate that, indeed, areas with VFs upstream will tend to have greater concentrations of TDS downstream. TDS can be a fairly good indicator of overall water quality and can be used as a proxy for overall environmental health of aquatic systems. Although it is difficult to isolate TDS from other stressors in coalfield streams, it has been identified as a stressor to aquatic life (Timpano et al. 2010). Obviously the scope of this study was limited both by time and resources. The group had planned to use an ICP-‐OES to bring greater depth to our analysis of water samples, however, unfortunately it was undergoing repairs and could not be ready in time. The group can be relatively certain that the water samples were fairly accurate because of the relative precision of the instruments used. In addition, there are inherent imprecisions when considering having small samples reflect the overall quality of the water system at the individual points tested. The quality of water (i.e. conductivity, TDS) is likely to change depending on rainfall in the upstream watershed as well as other factors associated with runoff from mining and reclamation sites. One can imagine a particular pool of RF leaking or overtopping would bring significant changes to Perkins 12 the readings. A structural change in the VF area such as a dramatic shift of the rocks could release much more material than an otherwise stable VF. Features of the analytical portion of our research also are problematic. The group assessed percent VF and percent RF based on GIS data, using relatively imprecise techniques such as discerning based on the properties of the histogram what percentage of the land was covered by VF and RF. As stated, VFs certainly shift over time and the magnitude of the VF cannot necessarily be completely understood based on a two-‐dimensional GIS shapefile, especially considering varying topography. The effects of RF on the aquatic environment are also inherently difficult to assess because some are much more effective at preventing toxic or harmful runoff from escaping and so there is likely a range of effects for downstream environments depending on the quality of the RF storage pond and external environmental factors. UMPs in our context are also problematic for analysis because we have treated all UMPs equally without regard to sites that are actively being mined or have been mined in the past and areas which are simply prospective mining leases which may have been completely unharmed anthropogenically. This study demonstrates that while producing and burning coal for electricity is cheap in terms of actual prices, the burden that the coal industry imposes on the environment is quite large. Because the policies of the coal industry are allowed to be passed off onto the environment (through habitat destruction and pollution), it has led many to believe that coal is inexpensive, however, as previous studies have found, there are many uncalculated externalities which result from our consumption of electricity generated from coal. Though regulations at both the state and federal level give the appearance of fairly strict control of coal mining and related activity especially with respect to its effect on downstream ecosystems, the reality appears to contradict this assertion. Mining strips land of forests and fractures and disturbs the geology of an area, having wide ranging environmental implications. Toxins that are released from ground disturbance and from actively mining coal become more concentrated as a result of the activity and, currently, mitigation techniques are insufficient for protecting downstream habitat from this disturbance. In general, land disturbance tends to increase nutrient runoff and the nature, intensity, and scale of disturbance associated with mining activity releases a variety of compounds not normally found at the ground-‐
level surface at significant levels, that are “uncovered” as the ground is fractured. VFs create environments inhospitable to plant recolonization and increase runoff due to the steep slopes of the rocks that are featured in “filled in” areas. Even decades after areas are filled in by mining debris, they remain desolate and unsupportive of life. RFs, catchment ponds that are created to minimize harmful runoff resulting from mining activity, tend to concentrate toxic and harmful materials that are released from mining and act simply to slow the release of these materials into streams. They are generally at risk of overtopping from flood events and suffering from unscheduled releases. In the coming years the need for coal to supply electricity in the US will increase and as the easier to access coal seams are exploited, MTR, perhaps the most environmentally damaging type of mining, will increase. The greatest disconnect is that between regulations and the environmental damage that is associated with mining. Perkins 13 Though regulations appear to seek to minimize the negative environmental impact of mining, mining companies have largely been able to skirt the more stringent clauses, likely due to its success at lobbying at the state and federal levels. A general lack of data on the environmental ramifications of coal mining also tends to confound the problem, as it is not necessarily evident what specific effects of mining cause harm to the environment. We know that areas in the immediate vicinity of mining and downstream of mining are at particular risk and have shown tremendous declines in health following mining activity, but it is difficult to tease out what is having the greatest effect on these declines. It might be that all of the disturbance and accelerated runoff of nutrients and toxins associated with mining are harmful especially when we consider that an environment is a self-‐regulating system that does not cope well with quick and dramatic change. It is pivotal that more intense study be undertaken in and around coal mining areas so that effective regulation can be supported and executed. Bibliography Copeland, Claudia. “Mountaintop Mining: Background on Current Controversies” CRS Report for Congress. Updated November 29, 2005. Epstein, Paul R. et al. “Full cost accounting for the life cycle of coal in “Ecological Economics Reviews.” Robert Costanza, Karin Limburg & Ida Kubiszewski, Eds. Ann. N.Y. Acad. Sci. 1219: 73-‐98. Timpano, Anthony J. et al. “ISOLATING EFFECTS OF TOTAL DISSOLVED SOLIDS ON AQUATIC LIFE IN CENTRAL APPALACHIAN COALFIELD STREAMS”. National Meeting of the American Society of Mining and Reclamation, Pittsburgh, PA Bridging Reclamation, Science and the Community. June 5 – 11, 2010. Palmer, M.A. et al. “Mountaintop Mining Consequences”. Science vol. 327 (2010): 148-‐
49. West Virginia Department of Environmental Protection