Species, tissue and gender-related organochlorine bioaccumulation in white-sided dolphins, pilot whales and their common prey in the northwest Atlantic

Organochlorine concentrations were measured in white-sided dolphins, pilot whales, and their prey from the Gulf of Maine and used to identify species, tissue, and gender differences, and trophic transfer trends, in bioaccumulation. Polychlorinated biphenyl concentrations ([PCB]) in dolphin blubber (13 +/- 7.1 micrograms/g fresh wt.) were twice those in pilot whales, but pesticide concentrations (20 +/- 13 micrograms/g fresh) were similar between species. 4,4'-DDE, trans-non-achlor, Cl6(153) and Cl6(138) concentrations were highest. Skin tissues had more recalcitrant organochlorines than the internal organs. Male dolphins bioaccumulated higher concentrations of nonmetabolizable PCBs and hexachlorocyclohexane (HCH) isomers, whereas pilot whales had no gender-related differences in bioaccumulation. Pilot whales, mackerel, and herring had proportionately higher concentrations of DDTs, whereas [PCB] were higher in dolphins and squid. Although these odontocetes feed at the same trophic level and store a similar suite of contaminants, dolphins bioaccumulated higher and potentially hazardous 4,4'-DDE and PCB concentrations from food in their more geographically restricted range.     

Population explosion in Africa: further implications

A serious problem associated with the population explosion in Africa is the widespread degradation of vegetation and soils. Various factors such as climate, animal overgrazing, overcultivation, fires, and soil erosion contribute to the social and economic impact in some areas. An increase of droughts due to lack of precipitation has damaged grasslands. In addition, increased animal population due to animal disease control, increased water supply, and social custom of measuring wealth in terms of herd size cause near wastelands from overgrazing. Also, overcultivation of commercial crops due to an increasing population has accelerated soil erosion by allowing shorter fallow periods, which causes lower crop yields and soil deterioration. Firing woodlands and grasslands to suppress new woody growth is damaging soil as well as encouraging the growth of coarse grasses that have little value for grazing animals. The barren land due to firing causes soil erosion both by water and wind; a total estimated drift of dust from western Africa is 60 million tons/year. These conditions indicate that deterioration will continue resulting in lower carrying capacities and lower productivity from cultivated land.     

Biochemical alterations in juvenile carp (Cyprinus carpio) exposed to zinc: glutathione reductase as a target

The aim of this study was to investigate biochemical changes in juvenile carp (Cyprinus carpio) exposed to zinc chloride (10, 30 and 100 microM) for a period of 48 h. Zinc exposure caused a concentration-dependent reduction in glutathione reductase (GR) activity in gills, liver and brain. Gill glutathione S-transferase (GST) was reduced when animals were exposed to the highest concentration of 100 microM zinc. The phosphorylation of p38(MAPK) increased in the brain of fish exposed to zinc 100 microM, while phosphorylation of the extracellular signal-regulated protein kinase 1/2 (ERK1/2) and c-Jun N-terminal protein kinase 1/2 (JNK1/2) remained unchanged. Expression of proteins HSP60 and HSP70 were not affected by zinc exposure. Considering the significant concentration-dependent inhibition of GR in all tissues analyzed, this enzyme could be a potential biomarker of exposure to zinc, which has to be confirmed.     

探测液体污染的地质雷达

通过介绍美国中西部一个实验场地下水位之上的石油物质的情况，证明了地质雷达（ＧＰＲ）作为探测地面附近污染的的一种手段重要作用，一些试验表明，ＧＰＲ可以提供测绘渗流层中烃类物质的方法，俄州立大学一个砂质试验坑中的控制测量的结果确实表明，在柴油容器和装有柴油饱和的砂质材料的容器上方丰在的清晰的ＧＰＲ异常。在印第安纳州北部一个汽油泄漏场地，一年中不同时期（夏，秋，冬）测得的ＧＰＲ资料在对比给出了在高度潮湿     

粗网格模式中仍然存在赤道Kelvin波

最近几年，许多科学家一直在用低分辨率的耦合模式研究ＥＮＳＯ现象，一般认为，在粗网格海洋模式中不存在海洋赤道Ｋｅｌｖｉｎ波，这种波也就不能作为模式中存在的ＥＮＳＯ现象的形成机制，在本文中我们将证明在粗网格模式中也存在赤道Ｋｅｌｖｉｎ波。     

MAROS: a decision support system for optimizing monitoring plans

The Monitoring and Remediation Optimization System (MAROS), a decision-support software, was developed to assist in formulating cost-effective ground water long-term monitoring plans. MAROS optimizes an existing ground water monitoring program using both temporal and spatial data analyses to determine the general monitoring system category and the locations and frequency of sampling for future compliance monitoring at the site. The objective of the MAROS optimization is to minimize monitoring locations in the sampling network and reduce sampling frequency without significant loss of information, ensuring adequate future characterization of the contaminant plume. The interpretive trend analysis approach recommends the general monitoring system category for a site based on plume stability and site-specific hydrogeologic information. Plume stability is characterized using primary lines of evidence (i.e., Mann-Kendall analysis and linear regression analysis) based on concentration trends, and secondary lines of evidence based on modeling results and empirical data. The sampling optimization approach, consisting of a two-dimensional spatial sampling reduction method (Delaunay method) and a temporal sampling analysis method (Modified CES method), provides detailed sampling location and frequency results. The Delaunay method is designed to identify and eliminate redundant sampling locations without causing significant information loss in characterizing the plume. The Modified CES method determines the optimal sampling frequency for a sampling location based on the direction, magnitude, and uncertainty in its concentration trend. MAROS addresses a variety of ground water contaminants (fuels, solvents, and metals), allows import of various data formats, and is designed for continual modification of long-term monitoring plans as the plume or site conditions change over time.     

Latin hypercube approach to estimate uncertainty in ground water vulnerability

A methodology is proposed to quantify prediction uncertainty associated with ground water vulnerability models that were developed through an approach that coupled multivariate logistic regression with a geographic information system (GIS). This method uses Latin hypercube sampling (LHS) to illustrate the propagation of input error and estimate uncertainty associated with the logistic regression predictions of ground water vulnerability. Central to the proposed method is the assumption that prediction uncertainty in ground water vulnerability models is a function of input error propagation from uncertainty in the estimated logistic regression model coefficients (model error) and the values of explanatory variables represented in the GIS (data error). Input probability distributions that represent both model and data error sources of uncertainty were simultaneously sampled using a Latin hypercube approach with logistic regression calculations of probability of elevated nonpoint source contaminants in ground water. The resulting probability distribution represents the prediction intervals and associated uncertainty of the ground water vulnerability predictions. The method is illustrated through a ground water vulnerability assessment of the High Plains regional aquifer. Results of the LHS simulations reveal significant prediction uncertainties that vary spatially across the regional aquifer. Additionally, the proposed method enables a spatial deconstruction of the prediction uncertainty that can lead to improved prediction of ground water vulnerability.