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Identification of the extent of artisanal coal Mining and related acid mine water hazards Using remote sensing and field sampling: A case study in Jaiñtia Hills of North-eastern India

Blahwar, Bantehsonglang (2010) Identification of the extent of artisanal coal Mining and related acid mine water hazards Using remote sensing and field sampling: A case study in Jaiñtia Hills of North-eastern India.

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Abstract:Coal is being mined in several districts in the state of Meghalaya, India with the highest production coming from the Jaiñtia Hills district. Due to Constitutional rights allowing the indigenous tribal population to have the complete ownership of their land, including its subsurface resources, coal mining in the State has been carried out by many individuals at a cottage scale level. Coal is extracted using an artisanal method of underground mining which is called as “rat-hole” mining. The mining of coal in this district has brought about several environmental changes including degradation in surface water quality due to acid mine drainage (AMD) as reported in literature. AMD is caused by the oxidation and hydrolysis of metal sulphides, particularly pyrites, found in coal, deposit over-burden and in the waste dumps releasing large quantities of sulphates and protons, thereby lowering the pH of the water bodies. Highly acidic water leaches metals from surrounding rocks and soils adding to the deterioration of water quality in the mining areas. Metals stay in solution till the pH rises further downstream in a water course through dilution from incoming non-polluted tributaries and they precipitate out or get adsorbed by water colloidal particles and stream bed sediments. This creates another environmental problem with stream beds coated with amorphous iron oxide/hydroxide precipitates and high metal concentrations in the sediments which make the streams toxic. The main objectives of this study are: (1) to use remote sensing to identify and map the extent of artisanal coal mines in the Umiurem-Umtarang watershed in the central Jaiñtia Hills district, (2) to carry out an object based classification for semi-automatic identification and mapping of the coal mines, (3) to determine the hydro-chemical characteristics of AMD in surface water by field survey, sampling and analysis, (4) to assess the presence of undesirably high heavy metal concentrations in water samples and sediments in the watershed, and (5) to map the spatial variability of concentrations of different pollutants along the streams in the watershed. Visual analysis of the merged CARTOSAT-1 and RESOURCESAT-1 (IRS-P6) LISS-IV image shows high density of coal mines in the southern portion of the study area comprising of Tura sandstones of Jaiñtia Group. This map, when compared with that interpreted from a pan-sharpened QuickBird (PAN – spatial resolution 0.61m at nadir and multi-spectral – spatial resolution 2.44m at nadir) image, yielded an accuracy of 46% by polygon count and 59% by area comparison. The lower accuracy is primarily because of the lower spatial resolution of the merged CARTOSAT-1/RESOURCESAT-1 image whereby clusters of many mines seen on the QuickBird image appeared as a single big mine. Further, several older mines with darker overburden signatures due to overgrowth merged with the background vegetation or barren land and their identification was missed out in the visual interpretation of merged CARTOSAT-1/RESOURCESAT-1 image. An algorithm is developed to semi-automatically classify the coal mines through an object oriented classification (OOC) approach using the Definiens Developer 7 software. OOC results are found to be much superior over pixel-based supervised classification using Maximum Likelihood Classifier (MXL) since OOC approach considers spectral, textural and shape characters of each segmented image object for classification. While MXL produced inconclusive results, the OOC approach produced an overall accuracy of 67% when compared with the visually interpreted merged CARTOSAT-1/RESOURCESAT-1 image. The error of omission is 33% but the error of commission is quite high at 54%. The high error of commission is mainly because of the misclassification of many ii objects of roads and settlements as they had similar spectral, textural and shape (due to quadtree method of segmentation) characteristics as that of coal mines. These were the main reasons for a high error of commission. The OOC algorithm developed on CARTOSAT-1/RESOURCESAT-1 data is also tested on pan-sharpened QuickBird data in a small subset of the study area. The overall accuracy of OOC based output on QuickBird data is found as 72% with commission and omission errors of 15% and 28% respectively. Field sampling and analysis of streamwater samples during the monsoon and post-monsoon periods reveal that at many locations in proximity to coal mines, water quality is affected by AMD. While water is generally of acidic nature (pH ranging between 2.74 and 7.4) throughout the study area because of intense leaching, the effect of AMD is observed in the southern portion of the watershed where there is high density of coal mines, as seen through remote sensing data. It is also observed that at locations affected by AMD (characterized by low pH and high acidity) streamwaters also contain high concentration of sulphate, iron and manganese. Concentrations of heavy metals in streamwaters and sediments, on the other hand, have not reached alarming levels in the study area. These results show that oxidation and dissolution of pyrites associated with coal and overburden material is the primary reason for AMD in the study area. It is also concluded that the coal/coal bearing formations do not contain appreciable amounts of trace metals in the sulphide minerals. The evidence of AMD is also seen in the form of presence of iron oxide/hydroxide precipitates on the stream beds downstream of coal mining activity. Such precipitates can also be seen through pan-sharpened QuickBird image (true color composite) and color ratio composites of Landsat ETM+. Heavy metals, barring iron and manganese, are found to be within permissible limits in water samples. In the stream sediments, except for cadmium, they are also found to be less than the Threshold Effect Concentrations (TEC’s), below which harmful effects on sediment-dwelling organisms are not expected. However, in no cases are the heavy metals or other metals higher than the Probable Effect Concentrations (PEC’s), above which harmful effects on sediment-dwelling organisms are expected to occur frequently. The predicted incidence of toxicity in the stream sediments during monsoon varies from 9% to 23% and during post-monsoon varies from 4% to 32%, indicating low impact on sediment-dwelling organisms.
Item Type:Essay (Master)
Faculty:ITC: Faculty of Geo-information Science and Earth Observation
Programme:Geoinformation Science and Earth Observation MSc (75014)
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