Understanding the extent of contamination, sources and various carcinogenic and non-carcinogenic risks associated with different heavy metals in soil-crop systems is crucial for the prevention of heavy metal pollution. A survey was undertaken to determine heavy metal concentrations and degree of pollution in soil-crop systems (rice, wheat, and corn) using various indices such as pollution factor (CF), geo-accumulation index (Igeo), enrichment coefficients and transfer coefficient, and to determine the source of heavy metals pollution in the Wanjiang Economic Zone, Anhui Province, China. A total of 308 pairs of soil-crop samples were collected in this study, comprising 245 pairs of soil-rice samples, 53 pairs of soil-wheat samples, and 10 pairs of soil-corn samples. The concentrations of cadmium (Cd) and nickel (Ni) in the soil of the study area exceeded the national limitation of heavy metals in the soil of China (GB 15618-2018, Soil Environmental Quality: Risk Control Standard for Soil Contamination of Agricultural Land. Ministry of Environmental Protection of China. Beijing. China). The concentrations of copper (Cu), zinc (Zn) and lead (Pb) were also above the national limits to a lesser extent. All eight heavy metals (Cd, Cu, Ni, Pb Zn, arsenic (As), chromium (Cr), and mercury (Hg)) exceeded the background values in the study area. The enrichment coefficients of rice, wheat and maize to Cd, Cu and Zn were higher than those to other elements. On the basis of Igeo, it can be indicated that the rhizosphere soil of rice was slightly polluted by Cd and Hg, while the concentrations of the other heavy metals were below the safety limits. The CF and pollution load index (PLI) indicated that the soil in the study area was heavily contaminated with heavy metals. A principal component analysis identified different sources of soil heavy metal pollution, that is, Cu, Pb, Zn and Cd from industrial sources, Cr and Ni from natural sources, and As and Hg from agricultural sources. The carcinogenic risk of heavy metals was related to the intake of crops. Residents in the study area ingest rice, wheat, and corn on a daily basis. On the basis this study, it is suggested that local governments should pay attention to the carcinogenic risk of heavy metals in rice.
Water gushing in mines is one of the most threatening geological disasters in the process of coal mine production, so the key to prevent water gushing disaster in mines is to quickly and effectively identify the water-gushing sources (WGS). In view of the problem that it is difficult to effectively identify the WGS from adjacent limestone aquifer by conventional technical approaches, the Group A coal seams in Panji-2 coal mine, a typical coal mine in Huainan coalfield, was selected as the object of investigation in this study. A total of 60 water samples from two adjacent aquifers (Taiyuan formation limestone aquifer and Ordovician dolomitic-limestone aquifer) were systematically collected by using underground hydrological long-distance observation hole. On the basis of analyzing the hydro-chemical properties of the water samples, the hydrochemistry types of each aquifer were obtained. The results indicate that there is a certain hydraulic connection between Taiyuan formation limestone water (TLW) and Ordovician limestone water (OLW). In order to characterize and trace the similarities and differences between the two types of water sources, the concentration of strontium (γSr2+) and its isotope value (87Sr/86Sr, δ87Sr) were first determined, and it was found that γSr2+ had a significant discrimination between the two types of water sources, and accounted for the major contribution rate in the results of principal component analysis (PCA). Therefore, in conjunction to PCA, four identification models of WGS based on strontium isotope were established successively (Fisher discrimination analysis model (δ87Sr-F model), Distance discrimination analysis model (δ87Sr-D model), BP neural network analysis model (δ87Sr-B model) and Grey relational analysis model (δ87Sr-G model)). First, the stability and reliability of four models were trained according to the data of 40 water samples, and then the trained model was used to identify the source of the remaining 20 water samples. From the results, the δ87Sr-B model has the best discrimination effect, and its accuracy rate reaches 95%. In the actual production process of Panji-2 coal mine in the future, it is suggested to adopt the δ87Sr-B model to carry out the theoretical model of WGS identification of Group A coal seams floor, which provides a guarantee for coal mine safety production.
Heavy metals in freshwater lake sediments often exist in various chemical forms. However, the investigation and evaluation of heavy-metal elements in the sediments of the study area have not been reported, and there is a lack of objective understanding of the concentration level of heavy-metal elements. Therefore, this study is the first to report the concentrations, sources, and potential ecological risks of heavy metals in the sediments of Chengdong Lake and Chengxi Lake in Huoqiu County, Anhui Province, China. The spatial distribution, pollution characteristics, potential pollution sources, and ecological risks of heavy metals in the sediments of Chengxi Lake and Chengdong Lake of Huoqiu City in the middle section of Huaihe River in Anhui Province, China have not been reported. In this study, the sediment samples of the two Lakes were collected systematically, and the concentrations of heavy metals (As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn) were determined. The potential sources of heavy-metal elements in sediments were quantitatively analyzed according to the principal component analysis–absolute principal component fraction–multiple linear regression (PCA–APCS–MLR) receptor model. Descriptive statistics data showed that the enrichment degree of heavy metals in Chengxi Lake was higher than that in Chengdong Lake. The geo-accumulation index (Igeo) and pollution load index (PLI) indicated that there was moderate pollution for Cu, As, Hg, Ni, and Zn. The calculation results of the potential ecological risk index (Er) of the two lakes indicated that Cd (Er,max = 92.22, n = 60) and Hg (Er,max = 64.39, n = 60) showed a certain potential ecological risk in a small amount of sediment, while other heavy metals were classified as low risk. The mean sediment quality guideline quotient indicated that there was a moderate degree of potential adverse biological toxicity in lake sediments. Spatially, the seriously polluted contamination zones were the central position of Chengxi Lake and the northeast end of Chengdong Lake. The PCA–APCS–MLR receptor model revealed that Cr, Ni, Cu, and Zn were mainly from natural sources while Cd, As, Hg, and Pb elements were mainly from industrial sources and pesticide sources.
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