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Identifying the potential sources of trace metals in water from subsidence area based on positive matrix factorization

机译:基于正矩阵分解,识别沉陷区水中痕量金属的潜在来源

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Water in the subsidence area is a good choice for solving the water shortage in the coal mining area of China. In this study, positive matrix factorization model has been applied for the concentrations of seven kinds of trace metals (Pb, V, Cr, Mn, Co, Ni and Cu) in water from the subsidence area in the Luling coal mine, northern Anhui Province, China for identifying and quantifying their potential sources. Statistical analyses (including coefficients of variation and P -value of Anderson–Darling test) of metal concentrations indicate that multi factors (geological weathering/dissolution, filling of coal gauge and anthropogenic discharge) are responsible for the metal concentrations in the water. Based on the variations of Q values, three sources have been determined by US EPA (US Environmental Protection Agency) positive matrix factorization model: coal gauge filling, geological weathering or dissolution and waste discharge. The contribution degrees of these sources for all of the pools are different, and therefore, different strategies (e.g. clean the waste and the coal gauge around and in the pools) should be applied with different pools.
机译:塌陷区的水是解决中国煤矿开采区缺水的好选择。本研究采用正矩阵分解模型对安徽省ling陵煤矿沉陷区水中7种微量金属(Pb,V,Cr,Mn,Co,Ni和Cu)的浓度进行了分析。 ,以确定和量化其潜在来源。金属浓度的统计分析(包括安德森–达林试验的变异系数和P值)表明,水中的金属浓度是多因素(地质风化/溶解,煤量表的填充和人为排放)造成的。基于Q值的变化,美国EPA(美国环境保护局)正矩阵分解模型已确定了三种来源:煤量表填充,地质风化或溶解以及废物排放。这些资源对所有池的贡献程度不同,因此,对不同池应采用不同的策略(例如清洁池周围和池中的废物和煤量计)。

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