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The Zuluhong quartz-vein-type polymetallic deposit, located in the Alatau area of Western Tianshan, China, is a particular and typical tungsten deposit associated with copper. This paper presents major and trace element analyses of magmatic and altered (i.e, chloritized) biotite from the deposit, in order to identify the source of the magmas and characterize the mineralization physical-chemical condition. Magmatic biotite is Fe-rich and has high Rb/Ba ratios (0.27–9.14), indicative of extensive differentiation of granite. Moreover, magmatic biotite has total rare earth element (∑REE) contents that are 5–10% of the whole-rock contents, shows slight light REE depletion, and negative Ce anomalies. Magmatic biotite is enriched in some large-ion lithophile elements (LILE; e.g., Rb and K) and depleted in some high-field-strength elements (HFSE; e.g., Th and Nb). These geochemical features, coupled with geological evidence, indicate that the Zuluhong intrusion is a highly fractionated I-type granite derived from lower crustal melting. During ore formation, magmatic biotite was partially to totally altered to chlorite due to interaction with ore-forming fluids. The temperature and oxygen fugacity decreased during alteration. The mineralization in the Zuluhong polymetallic deposit can be divided into at least two stages. In the early stage, quartz-vein-type wolframite mineralization formed from Si- and volatile-rich fluids that were derived from fractionated granitic magma. In the later stage, W–Cu ores formed as metal sulfides were dominated by chalcopyrite. The later ore-forming fluids experienced a decrease in temperature and oxygen fugacity as they reacted (i.e, chloritization and lesser silicification) with reducing wall rocks around the contact zone of the intrusion. [ABSTRACT FROM AUTHOR]

To fulfill sustainability principles, a three-site field experiment was conducted to screen suitably mixed passivators from lime + biochar (L + C, 9000 kgha−1 with a rate of 1:1) and lime + biochar + sepiolite (L + C + S, 9000 kg ha−1 with a rate of 1:1:1), in Yuecheng (YC), Zhuji (ZJ), and Fuyang (FY), where there are typical contaminated soils, in South China. Treated with passivators in soil, DTPA-extractable Cd, Cr and Pb in soil were decreased by 9.87–26.3%, 37.2–67.5%, and 19.0–54.2%, respectively; Cd, Cr, and Pb in rice were decreased by 85.9–91.5%, 40.0–76.5%, and 16.4–45.4%, respectively; and these were followed by slightly higher efficacy of L + C + S than L + C. The differences between L + C and L + C + S mainly lie in soil microbial communities, enzymes, and fertility. In YC, treatment with L + C + S increased microbial carbon and activities of urease (EC3.5.1.5) and phosphatase (EC3.1.3.1) by 21.0%, 85.5%, and 22.3%; while treatment with L + C decreased microbial carbon and activities of phosphatase and sucrose (EC3.2.1.26) by 1.31%, 34.9%, and 43.4%, respectively. Moreover, the treatment of FY soils with L + C + S increased microbial carbon and activities of urease, phosphatase, and sucrase by 35.4%, 41.6%, 27.9%, and 7.37%; and L + C treatment only increased the microbial carbon and the activity of phosphatase by 3.14% and 30.3%, respectively. Furthermore, the organic matter and available nitrogen were also increased by 8.8–19.0% and 7.4–14.6% with L + C + S treatments, respectively. These suggested that the combination of L + C + S stimulated the growth of soil microbial communities and increased the activity of soil enzymes. Therefore, the L + C + S strategy can be a practical and effective measure for safe rice production as it was more suitable for the remediation of heavy metals in our experimental sites. [ABSTRACT FROM AUTHOR]

Understanding the spatiotemporal patterns of water quality is crucial because it provides essential information for water pollution control. The spatiotemporal variations in water quality for the Nanxi River in the Taihu watershed of China were evaluated by a water quality index (WQI) and multivariate statistical techniques; additionally, the potential sources of contamination were identified. The data set included 22 water quality parameters collected during the monitoring period from 2015 to 2020 for 14 monitoring stations. WQI assessment revealed that approximately 85% of monitoring stations were classified as "medium-low" water quality, and most showed continuous improvement in water quality. Cluster analysis divided the 14 monitoring stations into three clusters (low contamination, medium contamination and high contamination). Discriminant analysis identified pH, petroleum, volatile phenol, chemical oxygen demand, total phosphorus, F, S, fecal coliform, SO4, Cl, NO3-N, total hardness, NO2-N and NH3 as important parameters affecting spatial variations. Factor analysis identified four potential contamination source types: nutrient, organics, feces and oil. This study demonstrated the usefulness of multivariate statistical techniques in assessing large data sets, identifying contamination source types, and better understanding spatiotemporal variations in water quality to restore and protect water resources. [ABSTRACT FROM AUTHOR]