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Relevant bibliographies by topics / Mining, water quality

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Academic literature on the topic 'Mining, water quality'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 February 2022

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Journal articles on the topic "Mining, water quality"

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Mhlongo, S'phamandla, Paul T. Mativenga, and Annlizé Marnewick. "Water quality in a mining and water-stressed region." Journal of Cleaner Production 171 (January 2018): 446–56. http://dx.doi.org/10.1016/j.jclepro.2017.10.030.

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Khasanova, R. F., Ya T. Suyundukov, I. N. Semenova, and Yu S. Rafikova. "QUALITY OF DRINKING WATER IN MINING AREAS." Bulletin of Nizhnevartovsk State University, no. 2 (June 15, 2019): 104–9. http://dx.doi.org/10.36906/2311-4444/19-2/13.

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The paper presents the results of a drinking water quality study in towns located in the mining areas of the Republic of Bashkortostan, The Russian Federation. The objects of the study were underground water supply sources and water distribution networks of the towns of Uchaly, Sibay, and Baimak. In total, 30 water wells were examined, and five water samples were collected from the water distribution network in each town. The water quality indicators were pH, solid residue, total hardness, copper content, zinc content, iron content, and manganese content. The water quality in water distribution networks corresponded to the permissible limits according to environmental and sanitary regulations, except for the increased iron contentprobably due to corrosion of water supply pipelines. The water quality in non-centralized water supply (wells) in some areas failed to meet the sanitary standards. Priority indicators of water pollution were increased hardness and mineralization, high content of iron and manganese. To provide the residents with high-quality drinking water, it is proposed to make a complete inspection of centralized and non-centralized water sources not only within the towns, but also in the neighbouring communities. It is necessary to install filtration plants, primarily to reduce the iron content, in roder to bring the water taken from the wells for household and drinking purposes to the standard quality.

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Karimipour, F. "Water Quality Management Using GIS Data Mining." Journal of Environmental Informatics 5, no. 2 (2005): 61–71. http://dx.doi.org/10.3808/jei.200500047.

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Le, Thi Minh Khanh, Hanna Miettinen, Malin Bomberg, Nóra Schreithofer, and Olli Dahl. "Challenges in the Assessment of Mining Process Water Quality." Minerals 10, no. 11 (2020): 940. http://dx.doi.org/10.3390/min10110940.

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The changes in water quality owing to recirculation of water in mineral processing plants can compromise the plant performance as well as maintenance needs. Therefore, mining process water quality assessment is becoming critical. Nevertheless, very few studies have investigated the suitability of the current analysis methodology practiced in certified laboratories for evaluating mining process water quality. This article presents two case studies to highlight the major issues encountered when performing sampling for physicochemical and chemical parameters in process water at two European mine sites using procedures from two certified laboratories. In addition, microorganisms were shown to be abundant in process waters and likely affect the mining water chemistries. However, the protocols used for microbial studies are not optimal for mining process samples, and need to be improved. The results showed difficulties in providing satisfactory results when analyzing control samples. Additionally, the analysis results presented a strong imbalance in TDS and sulfur compounds. Several potential causes associated with the poor quality of the analysis results have been outlined with a specific focus on the preservation methods. A literature review on the degradation of thiosalts suggested that the current preservation procedures are not suitable for preserving sulfur compounds. Moreover, the results indicated that the water matrix strongly influenced the validity of the chosen analysis method. In conclusion, the analysis methods should be customized for the different mining water matrix types in order to ensure the accuracy and reproducibility of the results.

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Ewusi, A., B. Y. Apeani, I. Ahenkorah, and R. S. Nartey. "Mining and Metal Pollution: Assessment of Water Quality in the Tarkwa Mining Area." Ghana Mining Journal 17, no. 2 (2017): 17–31. http://dx.doi.org/10.4314/gm.v17i2.4.

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The quality of water in mining communities is uncertain since metals associated with acid mine drainage are known to saturate these waters. Previous studies in Tarkwa, an area noted for gold and manganese extraction, have reported large concentrations of aluminium, arsenic, cadmium, copper, lead, manganese and mercury in water samples. This research aimed at investigating the chemistry of groundwater with special focus on the contamination status of trace elements. It also compared levels of metal concentration with those that were determined in previous research works, to identify changes that might have occurred. Thirty-eight water samples from boreholes, hand-dug wells and streams, within the Tarkwa area were obtained and analysed. Results show that 90 % of water in the area is acidic and Eh was determined to be positive, depicting oxidizing conditions. Mean groundwater temperature was 28.9 ºC. Thirty-two samples had either temperature or pH values falling outside the range recommended by the World Health Organisation (WHO). Thirty samples had at least one metal concentration exceeding the WHO guideline values. Among the list of elements that exceeded the guideline, arsenic, manganese, nitrate, nitrite and iron were the most predominant. The dominant ions in the samples were sodium and bicarbonate. High concentrations of Fe and SO42- in some parts of the study area point to the influence of acid mine drainage (AMD). Comparisons of results of metal concentrations with findings from previous research in the area showed a reduction in concentration. Hydrochemical modelling with PhreeqC attributed this reduction to sorption processes. Comparison of levels of metal concentration in the different water supply facilities (borehole, hand-dug well and stream) showed no significant variations. Keywords: Water Quality, Drinking Water, Hydrochemical Modelling, Heavy Metals

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Rahmawati, Laily Agustina, Norma Afiati, and Thomas Triadi Putranto. "River Water Quality Based on Macrozoobentic Bioindicators in the Wonocolo Traditional Oil Mining Area." Jurnal Ilmu Lingkungan 19, no. 1 (2021): 29–35. http://dx.doi.org/10.14710/jil.19.1.29-35.

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Many studies declared traditional oil mining in Wonocolo caused pollution, including river pollution. During Covid-19 Pandemic, traditional oil mining in Wonocolo has been interrupted because world oil prices decreased. This made selling price of crude oil in Wonocolo declined. This made traditional oil mining decreased because oil wells were temporarily closed. The decrease in traditional oil mining might affect river water quality in Wonocolo. In a prior study, the researcher had investigated water quality of Bungsu and Kragsaan River in Wonocolo, based on physicochemical parameters. The river had improved quality during Covid-19 Pandemic, seen from the decrease in the content of several chemical pollutants. Through this study, the researcher examined macrozoobentos community structure as a bio indicator of water quality, like assessing water quality of Bungsu and Kragsaan River based on biological indicators. This study used observation method by determining sample points purposively. Sample of macrozoobentos was analyzed using biodiversity index of Shannon-Wiener, species evennes index, and dominance index. Results of study showed Bungsu River had low biodiversity (H’ index 0.000 – 1.040), distressed community at B-1 and B-3 but stable at B-2, like high dominance at B-1 and B-3 but low at B-2. Kragsaan River also had low biodiversity (H’ index 0.000 – 1.010), unstable community at K-1 and K-3 like distressed at K-2, and low dominance at K-2 and K-3 but medium at K-1. Based on H’ index, Bungsu River was in the heavily polluted category at B-1 and B-3 and the medium polluted category at B-2. Meanwhile, Kragsaan River was in the heavily polluted category at K-1 and K-2 and the medium polluted category at K-3. This means although decreased levels of chemical pollutants at the sampling locations meant an increase in quality of water body, river ecosystem had not been able to rejuvenate condition during Covid-19 Pandemic.

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Kolli, Kamakshaiah, and R. Seshadri. "Ground Water Quality Assessment using Data Mining Techniques." International Journal of Computer Applications 76, no. 15 (2013): 39–45. http://dx.doi.org/10.5120/13324-0885.

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Muzenda, C., and I. Pumure. "Effects of Nickel Mining Activities on Water Quality." Journal American Society of Mining and Reclamation 2002, no. 1 (2002): 190–93. http://dx.doi.org/10.21000/jasmr02010190.

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Muzenda, C., and I. Pumure. "EFFECTS OF NICKEL MINING ACTIVITIES ON WATER QUALITY." Journal American Society of Mining and Reclamation 2002, no. 1 (2002): 290–309. http://dx.doi.org/10.21000/jasmr02010290.

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Wireman, Mike. "Potential Water-Quality Impact of Hard-Rock Mining." Groundwater Monitoring & Remediation 21, no. 3 (2001): 40–51. http://dx.doi.org/10.1111/j.1745-6592.2001.tb00743.x.

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Dissertations / Theses on the topic "Mining, water quality"

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Kirby, Laura Rebecca. "Surface Water Impacts from Active Underground Mining." Thesis, Virginia Tech, 2013. http://hdl.handle.net/10919/23290.

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High extraction mining techniques have produced the need to mitigate and understand ground movements associated with this technology. Tools such as the Surface Deformation Prediction System (SDPS) facilitate sound scientific decision making in the industry and has continually improved since its inception in 1987. The capabilities of SDPS have expanded on an as-needed basis. Currently, the regulatory climate has emphasized the need to understand the impact of underground mining on surface waters, physically and chemically.<br /><br />The SDPS program is used to conduct an analysis of ground movements to assess optimal barrier pillar size for stream protection. Typical analytical and empirical methods used in mine planning were compared against SDPS methods to ensure the validity and advantage to the use of SDPS for this purpose.<br /><br />Finally, underground mining effects on stream chemistry and health were explored by studying the heavily mined and industrialized watershed of Dumps Creek located in Russell County, Virginia. This watershed has been identified as being impaired since the Virginia 303(d) List of Impaired Waters was created in 1994. Currently, there are two pumps staged in the headwaters region of Dumps Creek that help to maintain water levels in an inactive underground mine. The pumping is necessary to control methane levels that rising water could force into an active underground mine that lies stratigraphically above the inactive mine. Water is pumped on an as-needed basis and discharges directly into Dumps Creek. Historic measurements of stream conductivity and benthic health scores were compared to assess whether a correlation exists between the two measurements. These measurements were compared based on regulatory decisions that emphasized that conductivity is a direct indicator of stream health in all watersheds.<br /><br />Scientific contributions associated with this research include: Further developments in the use of SDPS programming in order to account for stream protection on a case by case basis for both mine panel and surface water protection by optimizing barrier pillar size in relation to a nearby stream; the analysis of available and currently obtained water chemistry data in a mining impacted watershed in attempt to further research to appropriately characterize and mitigate specific problems in order to improve stream health; and, assessment of the complexity of water chemistry impacts from underground mining as related to stream health indicators in different chemically dominated watersheds.<br /><br>Master of Science

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Moore, Sara. "An Investigation of How Surface Coal Mining Affects Water Quality." Digital Commons @ East Tennessee State University, 2010. https://dc.etsu.edu/honors/5.

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Surface coal mining has become the ideal method for extracting coal from the Appalachia Mountains. However, surface coal mining generates large amounts of waste which may decrease the water quality in central Appalachia. This research is an attempt to determine whether surface coal mining negatively impacts water quality. This research consists of a literature review in addition to an analysis of data obtained through the Virginia Department of Environmental Quality. This data was analyzed at three separate locations along the Clinch River, VA to determine trends and cycles in pH, temperature, total hardness, and chloride, sulfate and metal concentrations. After analysis of data, it was concluded surface mining did not negatively impact water quality at these three locations. In addition, more research must be done to make a more accurate, concise conclusion between water quality and surface mining.

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Haunch, Simon. "Legacy of historic mining and water quality in a heavily mined Scottish river catchment." Thesis, University of Edinburgh, 2013. http://hdl.handle.net/1842/8938.

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Mine abandonment and the discharge of contaminated mine water is recognised globally as a major source of surface water and groundwater pollution. Contamination generally arises from the oxidation of sulphide minerals, principally pyrite, by the mining process, and the subsequent chemical reactions can lead to the discharge of mineralised, often acidic, iron, and sulphate rich waters. In many historically mined river catchments, mine water discharge is the main cause of poor water quality. Within the UK, managing the legacy of abandoned mines is one of the principal challenges presented by modern environmental legislation, particularly the EU Water Framework Directive, a challenge that is exacerbated by the diverse and widespread nature of historical mining. The impact and hazard associated with abandoned mining in one of the UK’s most intensively mined regions, the Almond River Catchment, Scotland, was examined via: 1) a detailed GIS mapping and investigation of historical mining processes in the catchment, 2) mine site discharge sampling, 3) detailed site investigations, 4) geochemical modelling of four mine waste sites and 5) analysis of temporal and spatial river water quality in the catchment. The results are then brought together to produce a catchment scale mine water hazard map. Mapping has identified over 300 mine sites in the catchment including coal, oil shale and ironstone mine wastes and flooded coal and oil shale mines. The historical development of oil shale retort methods has been shown to have an impact on potential hazard. Sampling of discharge waters from the different mining activities, in conjunction with detailed mineralogical analysis and geochemical modelling at the four mine waste sites has characterised the main hazards. Ironstone and pyrite bearing coal mine wastes discharge waters with highly elevated Fe and sulphate concentrations, up to 160mgl-1 and 1900mgl-1 respectively, due to extensive pyrite oxidation and acid generating salt dissolution (principally jarosite). Coal mine wastes show variable mineralogy, due to the diverse nature of coal bearing strata, and discharge waters with variable chemistry. Oil Shale mine wastes are generally depleted in pyrite due to historic processing and discharge low sulphate waters with moderately elevated Fe concentrations, up to 5mgl-1. Flooded coal mines discharge sulphate dominant alkaline waters, due to the availability of carbonate minerals in the mine complex, with elevated Fe concentrations, up to 50mgl-1, while flooded oil shale mines discharge waters with moderately elevated Fe concentrations, up to 4mgl-1, due to lower pyrite content in mine strata and reduced availability of oxygen related to mine abandonment age. Once in the surface water environment iron and sulphate display significant concentration-flow dependence: iron increases at high flows due to the re-suspension of river bed iron precipitates (Fe(OH)3); sulphate concentrations decrease with increased flow as a result of dilution. Further examination of iron and sulphate loading at low flows indicates a close correlation of iron and sulphate with mined areas; cumulative low flow load calculations indicate that coal and oil shale mining regions contribute 0.21 and 0.31 g/s of iron, respectively, to the main Almond tributary. Decreases in iron loading on river sections demonstrate the deposition and diffuse storage of iron within the river channel. This river bed iron is re-suspended with increased flow resulting in significant transport of diffuse iron downstream with load values of up to 50 g/s iron. Based on this hazard classification, a catchment scale mine water hazard map has been developed. The map allows the prioritisation of actions for future mine water management.

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Mighanetara, Krongkaew. "Impact of metal mining on the water quality in the Tamar catchment." Thesis, University of Plymouth, 2008. http://hdl.handle.net/10026.1/824.

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This study discusses the effects of past mining activities on sediment and water quality in streams and rivers in the Tamar catchment. High trace element concentrations, both in water and sediments, were observed in streams and rivers draining areas associated with abandoned mine sites. Maximum concentrations were observed in the Gunnislake/Calstock mining district, where intense metalliferous mining took place during the 19th century. Mine waste from abandoned mine sites in this area contained up to 6.3% arsenopyrite, 5.8% pyrite, 0.3% chalcopyrite and 24% scorodite. As a result, high concentrations of trace elements of up to 180000 mg kg‾¹ As, 6500 mg kg‾¹ Cu were determined in these wastes. Sequential extraction of the mine waste revealed that in most cases, the oxidisable fraction accounted for large proportions of mobile species, followed by the reducible fraction. The exchangeable fraction was relatively low, except for Cu in samples from fine grained waste heaps, in which significant amounts of secondary minerals, such as Fe oxides/oxyhydroxides and Fe-As-O minerals were observed, suggesting trace elements had the tendency to be retained and recycled within the fine grained waste heaps. The Fe oxides/oxyhydroxides can contain up to 12% As and Fe-As-O minerals can contain up to 25% As and 6% Cu, indicating that the As and Cu associated with Fe oxide phases represent their reducible fraction. The coarse grained waste heaps, with higher permeability and low cohesion characteristics, had a higher potential to produce acid leachate and were more susceptible to erosion than the fine grained waste heaps. Contaminants from abandoned mine sites entered aquatic systems within the catchment, as shown by the high concentrations of trace elements (up to 25000 mg kg‾¹ As, 28000 mg kg‾¹ ' Cu, 32000 mg kg‾¹ Mn, 9200 mg kg‾¹ Pb and 2700 mg kg‾¹ Zn) observed in sediments in water channels draining these mine sites. Some streams and adits draining abandoned mine sites carried acidic waters with pH values frequently below pH 4. Dissolved concentrations up to 560 μg L‾¹ As, 7600 μg L‾¹ Cu, 3800 μg L‾¹ Fe, 5700 μg L‾¹ Mn, 170 pg L‾¹ Pb and 2500 μg L‾¹ Zn, and particulate concentrations up to 1600 μg UI As, 7900 μg L‾¹ Fe, 290 pg U' Ni, 11 pg U' Pb and 91 μg L-¹ Zn were observed in channels draining abandoned mine sites. In total, the annual flux of trace elements from 25 studied streams and adits input ca. 13,000 kg Fe, 4300 kg Mn, 4200 kg Cu, 3600 kg Zn, 1400 kg As, 400 kg Ni, 350 kg Co, 43 kg Pb, and 6.6 kg Cd into the Tamar estuary. Seven important point sources of metals to the River Tamar were identified. The mass balance calculation revealed that over 50% of trace elements were not accounted for by the studied point sources, suggesting an importance of diffuse sources. The inputs of solid and dissolved contaminants from the intensive mining district affect the water and sediment quality of the Tamar estuary, an important ecosystem in southwest England. This work has provided important information on the relative importance of point and diffuse sources, ‾which is essential in the formulation of effective catchment management strategies

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Chelin, Monique Josette. "Water in the coal mining industry : an assessment of water management issues facing the coal mining industry of the Witbank and Middelburg Dam catchments." Pretoria : [s.n.], 2006. http://upetd.up.ac.za/thesis/available/etd-05292006-103231/.

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Demchak, Jennifer L. "Water quality changes of underground mines in northern West Virginia." Morgantown, W. Va. : [West Virginia University Libraries], 2005. https://eidr.wvu.edu/etd/documentdata.eTD?documentid=4384.

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Thesis (Ph. D.)--West Virginia University, 2005.<br>Title from document title page. Document formatted into pages; contains x, 77 p. : ill. (some col.), col. maps. Includes abstract. Includes bibliographical references.

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Volcich, Antony. "A case study of an alternative approach to coal mine site water management West Cliff Colliery NSW /." Access electronically, 2007. http://www.library.uow.edu.au/adt-NWU/public/adt-NWU20080424.154728/index.html.

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Zhinin, Kristy Lynn. "LOCAL PARTICIPATION IN MANAGING WATER QUALITY PROBLEMS FROM ARTISANAL GOLD MINING: THE RIO GALA WATERSHED, ECUADOR." Oxford, Ohio : Miami University, 2008. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=miami1209066059.

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Roz, Evan Phillips. "Water quality modeling and rainfall estimation: a data driven approach." Thesis, University of Iowa, 2011. https://ir.uiowa.edu/etd/1258.

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Water is vital to man and its quality it a serious topic of concern. Addressing sustainability issues requires new understanding of water quality and water transport. Past research in hydrology has focused primarily on physics-based models to explain hydrological transport and water quality processes. The widespread use of in situ hydrological instrumentation has provided researchers a wealth of data to use for analysis and therefore use of data mining for data-driven modeling is warranted. In fact, this relatively new field of hydroinformatics makes use of the vast data collection and communication networks that are prevalent in the field of hydrology. In this Thesis, a data-driven approach for analyzing water quality is introduced. Improvements in the data collection of information system allow collection of large volumes of data. Although improvements in data collection systems have given researchers sufficient information about various systems, they must be used in conjunction with novel data-mining algorithms to build models and recognize patterns in large data sets. Since the mid 1990's, data mining has been successful used for model extraction and describing various phenomena of interest.

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Kistner, Madison. "The Effects of Molybdenum Water Concentration on Feedlot Performance, Tissue Mineral Concentration, and Carcass Quality of Feedlot Steers." Thesis, Colorado State University, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10256099.

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<p> Thirty cross-bred steers (initial BW 375 ±37.2, replicate 1; and 535.0 ± 39.4 kg, replicate 2) were utilized to investigate the effects of Mo water concentration on performance, carcass characteristics, and mineral status of feedlot steers fed a growing and finishing diet for 151 and 112 d for replicate 1 and replicate 2, respectively. The experimental design was a randomized complete block design. Steers were blocked by weight and then divided into 2 weight block replicates each consisting of 15 steers. Steers were randomly assigned within block to one of 5 treatments (3 pens/treatment; 1 steer/ pen; 2 replicates/treatment). Water treatments consisted of: 1) 0.0 μg, 2) 160 μg, 3) 320 μg 4) 480 μg Mo/L, and 5) 960 μg of supplemental Mo/L added as Na<sub>2</sub>MoO<sub>4</sub> to the drinking water. Steers were housed in individual pens that contained individual 265 L water tanks for monitoring water intake. Daily water intake was recorded for each steer. Steers were individually weighed on 2 consecutive days at the beginning and end of the experiment and interim weights and jugular blood samples were obtained every 28 d. Liver biopsies were obtained on d0 and 84 from each steers. Steers were transported to a commercial abattoir, slaughtered, and individual carcass data and liver samples were collected. Initial BW was used as a covariate for statistical analysis of the data and significance was determined at <i>P</i> ≤ 0.05. No differences were observed for final BW (<i>P</i> ≤ 0.98). Overall ADG, DMI, feed efficiency and water intake were similar across treatments. Hot carcass weight, dressing percentage, yield grade, LMA, adjusted fat thickness, KPH, and marbling scores were similar across treatments. Liver and plasma Cu, Mo, and Zn concentrations were similar across treatments. These data indicate that water Mo concentration had no impact on performance, mineral status, water intake, and carcass characteristics in feedlot steers fed a high concentrate diet.</p>

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Books on the topic "Mining, water quality"

1

Barnes, R. P. Ground water quality protection, mining and mining facilities in Utah. American Water Resources Association, 1987.

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Laine, A. Water quality in the Hemlo, Ontario, gold mining region. Ministry of the Environment, 1992.

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Laine, A. Water quality in the Hemlo, Ontario, gold mining region. Ministry of the Environment, 1992.

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Laine, A. Water Quality in the Hemlo, Ontario, gold mining region: Report. Queen's Printer for Ontario, 1992.

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Laine, Arnold. Water quality in the Hemlo, Ontario, gold mining region: Report. Ministry of the Environment, 1992.

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Smith, Brenda J. Assessment of water quality in non-coal mining areas of Missouri. Dept. of the Interior, U.S. Geological Survey, 1988.

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McCleskey, R. Blaine. Questa baseline and pre-mining ground-water-quality investigation. 16: Quality assurance and quality control for water analyses. U.S. Department of the Interior, U.S. Geological Survey, 2004.

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Maest, Ann S. Questa baseline and pre-mining ground-water quality investigation. U.S. Dept. of the Interior, U.S. Geological Survey, 2004.

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Maest, Ann S. Questa baseline and pre-mining ground-water quality investigation. U.S. Dept. of the Interior, U.S. Geological Survey, 2004.

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Maest, Ann S. Questa baseline and pre-mining ground-water quality investigation. U.S. Dept. of the Interior, U.S. Geological Survey, 2004.

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Book chapters on the topic "Mining, water quality"

1

Tumane, Rajani, Shubhangi Pingle, Aruna Jawade, and Kirtikumar Randive. "Managing Water Quality in Mining Areas: Changing Paradigm of Sustainability." In Innovations in Sustainable Mining. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73796-2_12.

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Klapper, Helmut. "Mining Lakes: Generation, Loading and Water Quality Control." In Remediation of Abandoned Surface Coal Mining Sites. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-662-04734-7_4.

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Singh, Sanika, Sudeshna Chakraborty, and Saurabh Mukherjee. "Water Quality Examining Using Techniques of Data Mining." In Lecture Notes in Mechanical Engineering. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5463-6_10.

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Feng, Jun, Qinghan Yu, and Yirui Wu. "Time-Varying Water Quality Analysis with Semantical Mining Technology." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-48513-9_29.

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Ashraf, Muhammad Aqeel, Maliha Sarfraz, Rizwana Naureen, and Mohamedreza Gharibreza. "Metallic Elements in the Mining Areas: Water Quality Assessment." In Environmental Impacts of Metallic Elements. Springer Singapore, 2015. http://dx.doi.org/10.1007/978-981-287-293-7_3.

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Borisov, V. N., S. V. Alexeev, and V. A. Pleshevenkova. "The diamond mining quarries of East Siberia as a factor affecting surficial water quality." In Water-Rock Interaction. Routledge, 2021. http://dx.doi.org/10.1201/9780203734049-215.

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Wan, Dingsheng, Haoyun Wu, Shijin Li, and Fang Cheng. "Application of Data Mining in Relationship between Water Quantity and Water Quality." In Artificial Intelligence and Computational Intelligence. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23881-9_53.

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Al-Barmani, Zahraa, and Samaher Al-Janabi. "Intelligent Data Mining Techniques to Verification of Water Quality Index." In Hybrid Intelligent Systems. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73050-5_58.

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Blockeel, Hendrik, Sašo Džeroski, and Jasna Grbović. "Simultaneous Prediction of Multiple Chemical Parameters of River Water Quality with TILDE." In Principles of Data Mining and Knowledge Discovery. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-540-48247-5_4.

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Katzur, J., and F. Liebner. "Effects of Superficial Tertiary Dump Substrates and Recultivation Variants on Acid Output, Salt Leaching and Development of Seepage Water Quality." In Acidic Mining Lakes. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-71954-7_13.

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Conference papers on the topic "Mining, water quality"

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Seixas, A. J., S. L. P. L. Beatriz, and F. F. E. Nelson. "Mining spatial and temporal data to classify water quality: a case study." In DATA MINING 2008. WIT Press, 2008. http://dx.doi.org/10.2495/data080091.

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Zhu, Changjun, and Qinghua Liu. "Evaluation of Water Quality Using Grey Clustering." In 2009 Second International Workshop on Knowledge Discovery and Data Mining (WKDD). IEEE, 2009. http://dx.doi.org/10.1109/wkdd.2009.28.

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Dutta, Prasun, and Rituparna Chaki. "A survey of data mining applications in water quality management." In the CUBE International Information Technology Conference. ACM Press, 2012. http://dx.doi.org/10.1145/2381716.2381805.

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Pohrebennyk, Volodymyr. "EVALUATION OF SURFACE WATER QUALITY IN MINING AND CHEMICAL INDUSTRY." In 17th International Multidisciplinary Scientific GeoConference SGEM2017. Stef92 Technology, 2017. http://dx.doi.org/10.5593/sgem2017/51/s20.056.

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Liang, Bin, Dammika Vitanage, Corinna Doolan, et al. "Predicting Water Quality for the Woronora Delivery Network with Sparse Samples." In 2019 IEEE International Conference on Data Mining (ICDM). IEEE, 2019. http://dx.doi.org/10.1109/icdm.2019.00149.

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Dabrowski, Joel Janek, Ashfaqur Rahman, Andrew George, Stuart Arnold, and John McCulloch. "State Space Models for Forecasting Water Quality Variables." In KDD '18: The 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2018. http://dx.doi.org/10.1145/3219819.3219841.

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Ruijian Zhang, Hairong Zhao, and Yong Piao. "Applying data mining and HPC for water quality assessment and prediction." In 2011 3rd International Conference on Advanced Computer Control (ICACC). IEEE, 2011. http://dx.doi.org/10.1109/icacc.2011.6016460.

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Lowongtrakool, Chaloemchai, and Panida Lorwongtrakool. "IoT Based Water Quality Measurement Using Hybrid Sensors and Data Mining." In 2018 International Conference on Information Technology (InCIT). IEEE, 2018. http://dx.doi.org/10.23919/incit.2018.8584873.

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Xiang, Yunrong, and Liangzhong Jiang. "Water Quality Prediction Using LS-SVM and Particle Swarm Optimization." In 2009 Second International Workshop on Knowledge Discovery and Data Mining (WKDD). IEEE, 2009. http://dx.doi.org/10.1109/wkdd.2009.217.

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FERNANDEZ, NICOLAS, and LUIS A. CAMACHO. "COUPLING HYDROLOGICAL AND WATER QUALITY MODELS FOR ASSESSING COAL MINING IMPACTS ON SURFACE WATER RESOURCES." In 38th IAHR World Congress. The International Association for Hydro-Environment Engineering and Research (IAHR), 2019. http://dx.doi.org/10.3850/38wc092019-1700.

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Reports on the topic "Mining, water quality"

1

Skone, Timothy J. Water use and quality from surface mining of coal. Office of Scientific and Technical Information (OSTI), 2013. http://dx.doi.org/10.2172/1509233.

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Skone, Timothy J. Water use and quality from underground mining of coal. Office of Scientific and Technical Information (OSTI), 2013. http://dx.doi.org/10.2172/1509234.

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3

Vohden, Jim. Hydrologic and water quality investigations related to placer mining in Interior Alaska, summer 1998. Alaska Division of Geological & Geophysical Surveys, 1999. http://dx.doi.org/10.14509/1905.

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Ray, S. R. Hydrologic and water quality investigations related to placer mining in Interior Alaska, summer 1992. Alaska Division of Geological & Geophysical Surveys, 1993. http://dx.doi.org/10.14509/1605.

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Ray, S. R. Hydrologic and water quality investigations related to placer mining in interior Alaska: summer 1989. Alaska Division of Geological & Geophysical Surveys, 1990. http://dx.doi.org/10.14509/1462.

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Ray, S. R. Hydrologic and water quality investigations related to placer mining in interior Alaska; summer 1990. Alaska Division of Geological & Geophysical Surveys, 1991. http://dx.doi.org/10.14509/1485.

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Ray, S. R. Hydrologic and water quality investigations related to placer mining in interior Alaska; summer 1991. Alaska Division of Geological & Geophysical Surveys, 1992. http://dx.doi.org/10.14509/1529.

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8

Mack, S. F., M. A. Moorman, and Linda Harris. Hydrologic and water quality investigations related to placer mining in interior Alaska, summer 1987. Alaska Division of Geological & Geophysical Surveys, 1988. http://dx.doi.org/10.14509/1351.

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Ray, S. R. Hydrologic and water-quality investigations related to placer mining in interior Alaska, summer 1988. Alaska Division of Geological & Geophysical Surveys, 1989. http://dx.doi.org/10.14509/1426.

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Mack, S. F., and M. A. Moorman. Hydrologic investigations of water quality in selected placer-mining areas in interior Alaska, summer 1986. Alaska Division of Geological & Geophysical Surveys, 1988. http://dx.doi.org/10.14509/2455.

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