PCB Congeners and Dechlorination in Sediments of Upper Sheboygan River,Wisconsin

Nine hand-collected sediment cores were obtained for PCB concentration analysis and dating from the Upper Sheboygan River, Wisconsin, USA. The primary PCB Aroclors in the Upper Sheboygan River were 1248 (50%) and 1254 (50%). The total PCB concentrations in the sediments ranged from 0.1–104 ppm (based on dry weight). The PCB data obtained from the sampling were analyzed using a factor analysis (FA) model with non-negative constraints in order to identify PCB sources and congener patterns. The factor loadings obtained from the FA model represent a general dechlorination profile common to most of the higher concentration samples and another associated with contamination from upstream dredging during 1989–1991. Anaerobic dechlorination is occurring in Upper Sheboygan River sediments and is most significant at high concentrations (≥ 20 ppm). Observation of elevated amounts of lower chlorinated congeners such as congeners 4 (2-2), 8 (2-4), 25 (24-3), and 26 (25-3) indicates the presence of anaerobic dechlorination activities. Based on the data from the FA model, anaerobic dechlorination was occurring according to dechlorination activities M and P. All possible dechlorination pathways involving marker congeners were considered. A significant improvement (86–91%) in similarity between the original Aroclor profile and the altered ones was found.     

Knowledge-based versus data-driven fuzzy habitat suitability models for river management

Aquatic habitat suitability models have increasingly received attention due to their wide management applications. Ecological expert knowledge has been frequently incorporated in such models to link environmental conditions to the quantitative habitat suitability of aquatic species. Since the formalisation of problem-specific human expert knowledge is often difficult and tedious, data-driven machine learning techniques may be helpful to extract knowledge from ecological datasets. In this paper, both expert knowledge-based and data-driven fuzzy habitat suitability models were developed and the performance of these models was compared. For the data-driven models, a hill-climbing optimisation algorithm was applied to derive ecological knowledge from the available data. Based on the available ecological expert knowledge and on biological samples from the Zwalm river basin (Belgium), habitat suitability models were generated for the mayfly Baetis rhodani (Pictet 1843). Data-driven models appeared to outperform expert knowledge-based models substantially, while a step-forward model selection procedure indicated that physical habitat variables adequately described the mayfly habitat suitability in the studied area. This study has important implications on the application of expert knowledge in ecological studies, especially if this knowledge is extrapolated to other areas. The results suggest that data-driven models can complement expert knowledge-based approaches and hence improve model reliability.     

Adsorption of microcystin-LR by activated carbon and removal in full scale water treatment

Removal of microcystin toxins from drinking water was evaluated at two full scale treatment plants that employed coagulation-sedimentation, dual media filtration and chlorination combined with either granular activated carbon filtration or powdered activated carbon. The influence of natural organic matter on the adsorption of the cyanobacterial toxin, microcystin-LR, by activatedcarbon was also evaluated in laboratory studies over a range of toxin concentrations similar to those typically observed in raw water at these plants. The sensitive protein phosphatase inhibition bioassay was used to quantify microcystin. Conventional treatment processes combined with activated carbon generally removed more than 80% of microcystin from raw water, but a residual concentration of 0.1-0.5 μg equivalents of microcystin-LR per liter was observed considering both (GAC and PAC) treatment facilities. Most values of residual microcystin-LR were at the low end of this range, but the upper end approaches the guidance level being considered by Health Canada for these toxins in drinking water     

Distribution of Major and Trace Elements in Sediments and Pore Water of Lake Erie

Concentration profiles of major and trace elements were determined in sediment cores from the Central Basin of Lake Erie. The concentrations of trace elements (As, Cd, Cr, Cu, Hg, Mn, Ni, Pb, and Zn) in the sediments were greatest at approximately 18 cm sediment depth, corresponding to the early 1960s. The concentrations of all elements in the sediments have considerably decreased in the last decade. However, the concentrations of Hg, Pb, and Cd are still about 10, 5, and 4 times greater, respec, than those in the pre-industrial sediments. The profiles of dissolved concentrations of the trace ele in pore water were characterized by maxima below the sediment-water interface, dropping off rapidly within the zone of reduced sediments. Concentrations of dissolved trace elements below the sediment-water interface are about one order of magnitude greater than those in the lake water. Conservative estimates of benthic fluxes ranged from 0.04 /μg/cm².y to 194 /μg/cm².y for Cd and Fe, respectively. Upward diffusive remobilization from sediments to lake water is a significant transport process in the Central Basin of Lake Erie and may play an important role in the transport of trace elements from the sediments.     

Thin film coatings which inhibit spin relaxation of polarized potassium atoms

A new technique is being developed at Argonne National Laboratory to produce a beam of polarized deuterium atoms. A part of the apparatus consists of a small cell which contains potassium vapor. The potassium atoms are polarized by circularly polarized light from a high-power cw laser. A port feeds deuterium into the cell and the polarization of the potassium is transferred to the deuterium by spin-exchange scattering. It is important that the potassium and deuterium atoms do not lose polarization by scattering from the walls of the exchange cell. We have tried various coatings of the exchange walls in order to inhibit losses from spin relaxation. Methods used to produce these coatings as well as their success will be described.     

Incidence of foodborne pathogens on European fish

Fresh fish from France, Great Britain and Portugal were examined for the pathogens Aeromonas hydrophila, Clostridium botulinum, Listeria monocytogenes, Salmonella spp., Vibrio parahaemolyticus and Yersinia enterocolitica, whilst fish from Greece were examined for A. hydrophila, Cl. botulinum and V. parahaemolyticus only. Cl. botulinum and Salmonella spp. were not detected from any of the samples examined. L. monocytogenes was detected from trout and Y. enterocolitica from both salmon and trout in Great Britain but neither were detected in samples from France or Portugal. A. hydrophila was detected from all sites, with an overall incidence of 40%, whilst V. parahaemolyticus was detected in samples from Portugal (35%) and Greece (14%) but not in those from Great Britain or France.     

Incorporated ZnO onto nano clinoptilolite particles as the active centers in the photodegradation of phenylhydrazine

ZnO was incorporated into nano particles of clinoptiloite by ion exchanging the zeolite in a zinc nitrate aqueous solution followed by calcinations process. All raw and modified samples were characterized by XRD, FT-IR, DRS, TG-DTG, SEM, BET and TEM. The prepared catalyst was used in the photocatalytic degradation of phenylhydrazine (PHZ) and the best experimental parameters were obtained as: 0.25 g L⁻¹ of the catalyst, 20 ppm pollutant concentration and pH = 8. The degradation extent was monitored by UV–vis spectroscopy and the results were confirmed by HPLC and the chemical oxygen demand (COD).     

Importance of spatially distributed hydrologic variables for land use change modeling

Land use changes have a pronounced impact on hydrology. Vice versa, hydrologic changes affect land use patterns. The objective of this study is to test whether hydrologic variables can explain land use change. We employ a set of spatially distributed hydrologic variables and compare it against a set of commonly used explanatory variables for land use change. The explanatory power of these variables is assessed by using a logistic regression approach to model the spatial distribution of land use changes in a meso-scale Indian catchment. When hydrologic variables are additionally included, the accuracies of the logistic regression models improve, which is indicated by a change in the relative operating characteristic statistic (ROC) by up to 11%. This is mostly due to the complementarity of the two datasets that is reflected in the use of 44% commonly used variables and 56% hydrologic variables in the best models for land use change.