The factors controlling the partitioning of polybrominated diphenyl ethers and polychlorinated biphenyls in the water-column of the Pearl River Estuary in South China

In this study, the distribution and partition of polybrominateddiphenyl ethers (PBDEs) and polychlorinated biphenyls (PCBs) in water from the Pearl River Estuary were investigated. A seasonal dependence for dissolved and particle phase PCB and PBDE levels was observed. A high dissolved phase level occurred in the dry season. The positive correlation between POC content in the particles and phase distribution coefficients (K_p) suggested that POC may play an important role in determining the partition of PBDEs and PCBs. A linear correlation between the observed log K_OC and log K_OW for PBDEs and PCBs was found with slopes less than one. The observed log K_OC values were lower than the predicted log K_OC values for PBDEs (except for BDE28). The log K_OC values were higher than the predicted values for PCBs. A combination of sorption to colloids (10-98% for PBDEs and less than 25% for PCBs) and adsorption to BC was responsible for this observation.     

Spatial analysis of the invasion of lionfish in the western Atlantic and Caribbean

Pterois volitans and Pterois miles, two sub-species of lionfish, have become the first non-native, invasive marine fish established along the United States Atlantic coast and Caribbean. The route and timing of the invasion is poorly understood, however historical sightings and captures have been robustly documented since their introduction. Herein we analyze these records based on spatial location, dates of arrival, and prevailing physical factors at the capture sights. Using a cellular automata model, we examine the relationship between depth, salinity, temperature, and current, finding the latter as the most influential parameter for transport of lionfish to new areas. The model output is a synthetic validated reproduction of the lionfish invasion, upon which predictive simulations in other locations can be based. This predictive model is simple, highly adaptable, relies entirely on publicly available data, and is applicable to other species.     

青海东昆仑西藏大沟金矿床花岗闪长斑岩锆石U-Pb年龄、地球化学及其成矿意义

利用LA-ICP-MS锆石U-Pb定年,对位于东昆仑地区巴颜喀拉成矿带的西藏大沟金矿床进行研究,首次获得与成矿有密切关系的花岗闪长斑岩加权平均年龄为（225.0±1.2）Ma,厘定为晚三叠世;岩石明显富集大离子亲石元素（Rb、Sr、Ba、K）、轻稀土元素和Pb,明显亏损高场强元素（如Nb、Ta、P、Ti）,显示轻稀土元素强烈富集的右倾式稀土配分型式。结合东昆仑地区区域构造演化及金矿成矿年代学资料分析认为,西藏大沟金矿床花岗闪长斑岩形成于挤压向伸展转换阶段,西藏大沟金矿床成矿时间应介于225.0~218.6 Ma,属晚三叠世。      

青海东昆仑拉浪麦钨多金属矿区英云闪长岩锆石U-Pb测年、岩石地球化学及找矿意义

拉浪麦钨多金属矿位于青海东昆仑东段察汗乌苏河地区,是近年新发现的矽卡岩型矿床。本文对矿区与成矿关系密切的英云闪长岩进行了详细的岩相学、年代学和地球化学研究,为东昆仑原特提斯岩浆构造演化提供新的约束。笔者利用LA-ICP-MS锆石U-Pb定年,获得拉浪麦英云闪长岩加权平均年龄为421.5±5.0 Ma(n=21,MSWD=0.34),厘定其形成时代为顶志留世。岩石地球化学研究表明,英云闪长岩为弱过铝质低钾拉斑—钙碱性系列岩石,成因为Ⅰ型花岗岩类型。岩石明显富集大离子亲石元素(Rb、Sr、K)、活泼的不相容元素(如U、Th)和Pb,相对亏损高场强元素(如Nb、Ta、P、Ti)。Rb/Sr、Nb/Ta特征反映出岩石具壳幔混合岩浆的特点。结合东昆仑地区已有年代学资料和区域地质构造演化特征,认为拉浪麦英云闪长岩形成于原特提斯构造旋回的后碰撞阶段。综合分析认为加里东晚期也是东昆仑地区一个重要的多金属成矿期,在今后的找矿工作中应引起足够的重视。     

Detecting genotoxicity using the Comet assay following chronic exposure of Manila clam Tapes semidecussatus to polluted estuarine sediments

Sediments frequently cause damage to biota due to the accumulation of toxic compounds and the bioavailability of sediment-bound contaminants. Damage can be assessed using biomarkers, such as the degree of genotoxic impact following in vivo exposure to pollutants. Genotoxic damage, expressed as single-strand DNA breaks, was measured in cells isolated from haemolymph, gill and digestive gland from the clam Tapes semidecussatus, using the single cell gel electrophoresis (Comet assay). Clams were exposed for three weeks to sediment samples collected from a polluted site and a ‘clean’ reference site.

The level of DNA damage was assessed using an image analysis package and expressed as Tail Moment. Throughout the study, significant differences in DNA damage were recorded for each tissue type between clams exposed to the two sediment samples. We conclude that the Comet assay is a useful tool for the detection of DNA damage in clams chronically exposed to polluted sediments.     

利用人工神经网络由孔隙度预测渗透率

利用人工神经网络，可以由钻孔的孔隙度值预测同深度下的渗透率值。使用的是一种很适合进行预测的反向传播的网络结构。在传统的回归分析法预测渗透率的过程中，通常假定孔隙度和渗透率的关系是已知的。但实际上，这些函数形式即模式方程并不确定。而神经网络法并不事先对这种函数关系作任何假设。在亚拉巴马州南部的Ｂｉｇ Ｅｓｃａｍｂｉａ Ｃｒｅｅｋ（ＢＥＣ）侏罗纪Ｓｍａｃｋｏｖｅｒ碳酸盐岩油田，我们选择了６口井来检验该