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The Influence of Large Scale Phosphate Mining on the Water Quality of the Huangbaihe River Basin in China: Dominant Pollutants and Spatial Distributions

Einfluss großmaßstäblichen Phosphatbergbaus auf die Wasserqualität des Huangbaihe Flussgebiets in China: Hauptschadstoffe und räumliche Verbreitung

Influencia de la minería a gran escala de fosfato en la calidad del agua de la cuenca del río Huangbaihe en China: contaminantes predominantes y distribuciones espaciales

大规模磷矿开采对中国黄柏河流域水质的影响:主要污染物及空间分布

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Abstract

Large scale phosphate mining in the Huangbaihe River Basin, China has reduced the self-purification capacity of the basin’s fresh water. Three years (2014–2016) of monitoring data and chemometric analysis were used to identify the dominant pollutants and define their spatial distribution in the basin. Principal component analysis was applied to determine the contribution of the individual pollutants. Total phosphorus (TP) 53%, water temperature (TEMP) 27%, and total nitrogen (TN) 20% proved to be the dominant problems. A discriminant functions (DF) model was developed to classify the study area into high, moderate, and low pollution zones. The DF coefficients were applied to analyze the correlation between DF and the measured parameters and it was found that TP, TN, and TEMP were positively correlated with the DF, indicating that these parameters were the most important. Finally, the results were compared with the locations of the mining activities, which revealed that TP is higher in the upper sub-basins, Xuanmiaoguan and Tianfumiao, where most of the high pollution zones are located and more than 78% of the areas are affected by the phosphate mines. It is concluded that the phosphate mining is the major source of pollution and TP is the dominant pollutant responsible for the total water quality variation in the river basin. More effective management measures have to be taken to reduce phosphorus runoff into the reservoir watersheds.

Zusammenfassung

Großmaßstäblicher Phosphatbergbau im Huangbaihe Becken, China hat zur Herabsetzung des Selbstreinigungsvermögens des Frischwassers im Flusseinzugsgebiet geführt. Zur Identifizierung der Hauptschadstoffe und deren räumlicher Verteilung im Flussgebiet wurde eine dreijährige Zeitreihe (2014-2016) von Monitoringdaten in Kombination mit einer chemometrischen Analyse herangezogen. Mittels Hauptkomponentenanalyse wurde der Beitrag einzelner Schadstoffe bestimmt. Als vorherrschende Probleme erwiesen sich Gesamtphosphor (TP, 53%), Wassertemperatur (TEMP, 27%) und Gesamtstickstoff (TN, 20%). Zwecks Unterteilung des Untersuchungsgebiets in Zonen mit starker, mäßiger und geringer Kontamination wurde ein Diskriminanzfunktions-(DF-)modell entwickelt. Der DF-Koeffizient wurde zur Untersuchung des Zusammenhangs von DF und den Messparametern verwendet, und es zeigte sich, dass TP, TN und TEMP positiv mit DF korreliert waren und diese Parameter somit als die dominierenden angesehen werden können. Schließlich wurden die Ergebnisse mit der Lage der Bergbaustandorte abgeglichen, wobei sich zeigte, dass TP die höchsten Werte in den oberen Teileinzugsgebieten Xuanmiaoguan und Tianfumiao aufwies, wo sich auch die Zonen starker Belastung und mehr als 78 % der von Phosphatbergbau betroffenen Flächen befinden. Es wird geschlussfolgert, dass der Phosphatbergbau der hauptsächliche Kontaminationsverursacher ist und dass TP der für die Schwankung der Wassergüte im Flussgebiet wichtigste Schadstoff ist. Effektivere Steuerungsmaßnahmen sind nötig zur Verringerung des Phosphoreintrags in die Gewässer.

Resumen

La minería de extracción de fosfato a gran escala en la cuenca del río Huangbaihe, China, ha reducido la capacidad de autolimpieza del agua dulce de la cuenca. Se monitoreó durante tres años (2014-2016) los datos y análisis quimiométricos para identificar los contaminantes predominantes y definir su distribución espacial en la cuenca. El análisis de componentes principales se aplicó para determinar la contribución de los contaminantes individuales. El fósforo total (TP) 53%, la temperatura del agua (TEMP) 27% y el nitrógeno total (TN) 20% resultaron ser los problemas más importantes. Se desarrolló un modelo de funciones discriminantes (FD) para clasificar el área de estudio en zonas de alta, moderada y baja contaminación. Los coeficientes del DF se aplicaron para analizar la correlación entre el DF y los parámetros medidos y se encontró que TP, TN y TEMP correlacionaron positivamente con el DF, lo que indica que estos parámetros eran efectivamente los más importantes. Finalmente, los resultados se compararon con la ubicación de las actividades mineras, que revelaron que el TP es mayor en las subcuencas superiores, Xuanmiaoguan y Tianfumiao, donde se localizan la mayoría de las zonas de alta contaminación y más del 78% de las áreas afectadas por las minas de fosfato. Se concluye que la extracción de fosfato es la principal fuente de contaminación y TP es el contaminante predominante responsable de la variación total de la calidad del agua en la cuenca del río. Se deben tomar medidas de manejo más efectivas para reducir la escorrentía de fósforo en las cuencas hidrográficas del embalse.

摘要

黄柏河流域(中国)大规模磷矿开采已经减弱流域淡水的自净能力。利用三年监测数据(2014-2016)及化学计量学方法识别水体主要污染物和分析污染物空间分布。应用主成分分析法确定单一污染物的贡献率,主要污染问题表现为总磷(TP)53%、水温(TEMP) 27%和总氮(TN)20%。建立了判别函数(DF)模型并将研究区分为高、中和低污染区。判别函数系数反映判别公式(DF)与测量参数之间的关系,发现总磷(TP)、总氮(TN)和水温(TEMP)与判别函数(DF)呈正相关,说明这些参数对判别至关重要。最后,通过与采矿活动地点对比,发现玄庙观、天福庙等上游水体总磷(TP)更高,上游集中了多数高污染区且78%以上范围都为磷矿开采所影响。由本研究可以看出,磷矿开采为主要污染源,总磷(TP)是流域水质变化的主要污染物。建议采取更有效的管理措施减少含磷径流汇入各下游水库。

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Acknowledgements

This research has been supported by the Non-profit Industry Financial Program of Ministry of Water Resources of China (no. 201301066), the National Natural Science Foundation of China (40701024, 41101511, 51409152), and Hubei Provincial Collaborative Innovation Center for Water Security.

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Correspondence to Xiaohua Dong.

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Reta, G.L., Dong, X., Su, B. et al. The Influence of Large Scale Phosphate Mining on the Water Quality of the Huangbaihe River Basin in China: Dominant Pollutants and Spatial Distributions. Mine Water Environ 38, 366–377 (2019). https://doi.org/10.1007/s10230-019-00604-6

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