Although the subject of mining and its environmental impacts are very wide to be covered in this review, concerns about the impact of phosphate mining and processing typically emphasis on its potential effects on water pollution, air pollution, and human health were accessed. We reviewed published information at different stages of mining; current mines, closed old mines and reclaimed mines and at different complexity of mining; surface mining, underground mining and sea-bed phosphorite mining. Information was analyzed to understand the association of toxic metals and radioactive elements in the phosphate rocks and to trace the transfer pathways of toxic metals and radioactive elements from the phosphate rocks to the environment. According to the reviewed results the major environmental impacts of phosphate mining and processing on the water resources were: impacts on the hydrology by phosphate industry water usage and landscape changes, and impacts on water quality by discharges of industry wastewater into the waterways. Dust was a common air quality problem throughout all mining activities; fluoride emissions and radon gas emission were also serious problems. Toxic metals and radioactive elements of significant human health problems were Pb, Cd, Hg, Cr, As, U Th and Ra. Most researches agreed that 226 Ra is considered as one of the most toxic radionuclide. The nuclide is of further importance as the parent nuclide of the gaseous 222 Rn which, along with its solid decay products, constitutes a significant source of radiation exposure. Scientific researches on mine water drainage and phosphate mining relationship may help to understand the environmental impacts associated with water resource and water quality.
Natural hydrological processes have been changed under the combined influences of climate change and intensive human activities in the Huangbaihe River Basin, where large-scale phosphate mining has been taking place. Therefore, evaluating the impact of climate change and intensive human activities on runoff variation and detecting the main driving factor leading to the variation are important for more efficient water resource management and more sustainable development of the regional economy. Despite numerous studies having been performed on this topic, little research focused on the impact of mining on runoff variation. The non-parametric Mann-Kendall (MK) trend test and accumulative anomaly methods were applied to identifying basic trends and change points of the hydro-meteorological elements over the period from 1978 to 2016. Then, the Soil Water and Assessment Tool (SWAT) and the Slope Changing Ratio of Accumulative Quantity (SCRAQ) were both used to quantify the contributions of climate change and anthropogenic activities on runoff variation. In this step, the runoff data were restored to their natural state before the construction of Xuanmiaoguan (XMG) dam. Due to the lack of locally observed evapotranspiration data, Global Land Evaporation Amsterdam Model and an empirical equation applied to obtain the evapotranspiration data. The results revealed that the change points are in 1985 and 2006. Therefore, the total period was divided into three periods, that is, the baseline period Ta (1978–1984), change period Tb (1985–2005) and change period Tc (2006–2016). Compared with the baseline period Ta, climate change dominates the runoff variation in the period Tb and is responsible for 60.5 and 74.4% of runoff variation, while human activities contribute the most to runoff variation for the period Tc (79.3 and 86.1%). Furthermore, an analysis of the underlying mechanism of underground phosphate mining indicates that mining can affect overland flow and baseflow simultaneously. This study can provide some information in determining the contributions of climate change and human activities in intensive phosphate mined basins and areas lack of evapotranspiration data.
Accurate prediction of runoffs plays an important role in managing water resources in river basins. Conducting runoff prediction is a complex and nonlinear process, therefore suitable for applying tools like Artificial Neural Networks (ANN). A typical algorithm for training the ANN is Back Propagation (BP). But there are two disadvantages for the BP algorithm: slow in convergence, and prone to fall into local extreme points. Therefore, we applied so called GA-ANN hybrid algorithms to predict runoffs. The data from Qing jiang river in between 1989 and 1991 are used as training data set, and 1992 are as testing data set. The historical 3 days daily runoff data were used to predict the upcoming (4th) day's runoff. The precision of the prediction is examined by DC and RMAE. The result showed that, the DC value of GA-ANN hybrid algorithms has increased for 0.12% compared to the traditional BP algorithm, and the RMAE value has been reduced for 1.69%. And in addition, the GA-ANN network is more stable than the traditional BP network.
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