Asymmetric measures of association, closed data, and multivariate analysis

The association between constant-sum variables X_i and X_j expressed as percentages can be calculated as a product-moment correlation between X_i and X_j/(100 – X_i ) and a correlation between X_j and X_i/(100 – X_j ). An asymmetric, square matrix may be formed from these coefficients, and multivariate analysis performed by two methods: singular value decomposition and canonical decomposition. Either analysis avoids problems in the interpretation of correlation coefficients determined from closed arrays, and provides information about dependencies among the variables beyond that obtained from the usual correlation coefficient between X_i and X_j.Two examples show the canonical decomposition to have the greater usefulness.     

Identifying uncertainties in Arctic climate change projections

Wide ranging climate changes are expected in the Arctic by the end of the 21st century, but projections of the size of these changes vary widely across current global climate models. This variation represents a large source of uncertainty in our understanding of the evolution of Arctic climate. Here we systematically quantify and assess the model uncertainty in Arctic climate changes in two CO₂ doubling experiments: a multimodel ensemble (CMIP3) and an ensemble constructed using a single model (HadCM3) with multiple parameter perturbations (THC-QUMP). These two ensembles allow us to assess the contribution that both structural and parameter variations across models make to the total uncertainty and to begin to attribute sources of uncertainty in projected changes. We find that parameter uncertainty is an major source of uncertainty in certain aspects of Arctic climate. But also that uncertainties in the mean climate state in the 20th century, most notably in the northward Atlantic ocean heat transport and Arctic sea ice volume, are a significant source of uncertainty for projections of future Arctic change. We suggest that better observational constraints on these quantities will lead to significant improvements in the precision of projections of future Arctic climate change.     

Challenges and lessons from a wetland LiDAR project: a case study of the Okefenokee Swamp,Georgia, USA

Airborne LiDAR (Light Detection and Ranging) provides opportunities to generate high-quality digital elevation models (DEMs) even in wetland environments. Our project, performed over the Okefenokee Swamp in Georgia during the spring of 2010, shows that several, distinctive factors must be considered to ensure successful wetland LiDAR projects. Some of the challenges include selecting optimal flight times in accordance with weather variability and water levels, having effective and quality control protocols, applying and developing filtering and interpolation algorithms, breaklines in swamps and accounting for data striping and noise. While some of these issues are faced in any airborne LiDAR acquisition, many of these require special consideration in a low-slope wetland environment with water saturated soils, widespread shallow water, and sediments and extensive vegetation. An examination of these issues and how they were handled will help in ensuring the success of future LiDAR acquisitions and, in particular, will advance knowledge of producing quality DEMs in wetland environments.     

Oxidation of fugitive methane in ground water linked to bacterial sulfate reduction

When fugitive methane migrates upward along boreholes of oil and gas wells, it may migrate into shallow ground water or pass through overlying soil to the atmosphere. Prior to this study, there was little information on the fate of fugitive methane that migrates into ground water. In a field study near Lloydminster, Alberta, Canada, we found hydrogeochemical evidence that fugitive methane from an oil well migrated into a shallow aquifer but has been attenuated by dissimilatory bacterial sulfate reduction at low temperature ( approximately 5 degrees C) under anaerobic conditions. Evidence includes spatial and temporal trends in concentrations of methane and sulfate in ground water and associated trends in concentrations of bicarbonate and sulfide. Within 10 m of the oil well, sulfate concentrations were low, and sulfate was enriched in both 34S and 18O. Sulfate concentrations had a strong positive correlation with delta13C values of bicarbonate, and sulfide was depleted in 34S compared to sulfate. These data indicate that bacterial sulfate reduction occurred near the production well. Near the oil well, elevated concentrations of bicarbonate were observed, and the bicarbonate was depleted in 13C. Modeling indicates that the main source of this excess 13C-depleted bicarbonate is oxidized methane. In concert with the sulfate concentration and isotope data, these results support an interpretation that in situ bacterial oxidation of methane has occurred, linked to bacterial sulfate reduction. Bacterial sulfate reduction may play a major role in bioattenuation of fugitive natural gas in ground water in western Canada.     

Discriminating sources of PCB contamination in fish on the coastal shelf off San Diego, California (USA)

Management of coastal ecosystems necessitates the evaluation of pollutant loading based on adequate source discrimination. Monitoring of sediments and fish on the shelf off San Diego has shown that some areas on the shelf are contaminated with polychlorinated biphenyls (PCBs). Here, we present an analysis of PCB contamination in fish on the shelf off San Diego designed to discriminate possible sources. The analysis was complicated by the variability of species available for analysis across the shelf, variable affinities of PCBs among species, and non-detects in the data. We utilized survival regression analysis to account for these complications. We also examined spatial patterns of PCBs in bay and offshore sediments and reviewed more than 20 years of influent and effluent data for local wastewater treatment facilities. We conclude that most PCB contamination in shelf sediments and fish is due to the ongoing practice of dumping contaminated sediments dredged from San Diego Bay.     

Remediation of floating,open water oil spills: Comparative efficacy of commercially available polypropylene sorbent booms

Several complex methods of remediation are applied to open water oil spills. Sorbing the liquid hydrocarbons with polypropylene booms is an effective and less complex means of treating such events. There are, however, a variety of commercially available booms which display different performances in sorbing different viscosity hydrocarbons. There is no acceptable A.S.T.M. protocol to evaluate these booms for performance efficiency in various weather and hydrocarbon viscosity scenarios. The current paper proposes such a protocol and evaluates the most commonly used sorbent products with the new test procedures. Nine specific performance criteria, based on actual field applications, are demonstrated.     

Settlement Prediction of Model Piles Embedded in Sandy Soil Using the Levenberg–Marquardt (LM) Training Algorithm

This investigation aimed to examine the load carrying capacity of model piles embedded in sandy soil and to develop a predictive model to simulate pile settlement using a new artificial neural network (ANN) approach. A series of experimental pile load tests were carried out on model concrete piles, comprised of three piles with slenderness ratios of 12, 17 and 25. This was to provide an initial dataset to establish the ANN model, in attempt at making current, in situ pile-load test methods unnecessary. Evolutionary Levenberg–Marquardt (LM) MATLAB algorithms, enhanced by T-tests and F-tests, were developed and applied in this process. The model piles were embedded in a calibration chamber in three densities of sand; loose, medium and dense. According to the statistical analysis and the relative importance study, pile lengths, applied load, pile flexural rigidity, pile aspects ratio, and sand-pile friction angle were found to play a key role in pile settlement at different contribution levels, following the order: P?>?δ?>?lc/d?>?lc?>?EA. The results revealed that the optimum model of the LM training algorithm can be used to characterize pile settlement with good degree of accuracy. There was also close agreement between the experimental and predicted data with a root mean square error, (RMSE) and correlation coefficient (R) of 0.0025192 and 0.988, respectively.     

The seasonal growth of ice in the Antarctic

Composite pictures of the areal extent of Antarctic sea ice derived from satellite photographs, show that the growth and the rate of growth of the pack ice compare favorably to the values previously estimated on other bases. Anomalous growth patterns are found in the Weddell Sea. Possible causes of this anomaly include surface and subsurface advection of ice crystals. The rate of retrogradation of the pack ice is found to exceed the rate of progradation.