Gas chromatographic separation and determination of chloroanthracene isomers on fly ash

An efficient, reproducible and rapid fused silica capillary column gas chromatographic method, in splitless mode, for analysing of the three chloroanthracene isomers is described. The method affords efficient separation and good linearity for each isomer in the range of 0.2 -200 μg/ml. The method can also be used in separation and determination of another thirty three aromatic and their halosubstituted compounds. This paper also presents the applications of the method in analysing of complex products mixture from reaction of the selected aromatic compounds with hydrogen chloride on fly ash surface from municipal incinerator.     

Mitigation of hydrogen fluoride aerosols by dry powders

Fluid bed tests were used to determine overall reaction rates for eight different dry powders of low hazard and toxicity before and after reaction with HF. In flow chamber tests, a representative oxide, hydroxide and carbonate were used to measure the efficiency of mitigation of an HF aerosol cloud. The results show that dry powders provide a valid alternative to water spray mitigation. Powder efficiencies are higher than water efficiencies at constant weight ratio.

At present, no attempt has been made to provide any technical designs or layouts for powder mitigation systems. However, the technology for smaller systems is available through manufacturers of dry powder chemical fire extinguisher equipment. When these powders are kept dry and under nitrogen atmosphere, a shelf life of several years can be expected.     

Time series analysis of coal mine accident experience

This study investigates several forecasting techniques that can be useful to mine safety managers for studying mine accident rate behavior. Three time series models were studied for extrapolation of accident rates. These models are applied to historical accident incidence data from a coal mine. Further, a method is presented for evaluating the three models for the selection of an appropriate model. For this particular mine application, it is concluded that the more complex Box-Jenkins ARMA model as well as first order autoregressive model do not give better results than the simple exponential smoothing model. However, when the random variations or autocorrelations in the accident experience rates between periods are different, the models may predict differently. As such, specific models must be developed for each mine on the basis of statistical analysis of the mine accident experience data over time. Moreover, the importance of incorporating human judgement to interpret the results of statistical forecasting cannot be overemphasized. Integration of policy or operating changes, which may impact mine safety performance, with statistical forecasting techniques is essential to arrive at a realistic prediction of future performance.     

Stochastic Transfer Function Model and Neural Networks to Estimate Soil Moisture1

Abstract: A practical methodology is proposed to estimate the three‐dimensional variability of soil moisture based on a stochastic transfer function model, which is an approximation of the Richard’s equation. Satellite, radar and in situ observations are the major sources of information to develop a model that represents the dynamic water content in the soil. The soil‐moisture observations were collected from 17 stations located in Puerto Rico (PR), and a sequential quadratic programming algorithm was used to estimate the parameters of the transfer function (TF) at each station. Soil texture information, terrain elevation, vegetation index, surface temperature, and accumulated rainfall for every grid cell were input into a self‐organized artificial neural network to identify similarities on terrain spatial variability and to determine the TF that best resembles the properties of a particular grid point. Soil moisture observed at 20 cm depth, soil texture, and cumulative rainfall were also used to train a feedforward artificial neural network to estimate soil moisture at 5, 10, 50, and 100 cm depth. A validation procedure was implemented to measure the horizontal and vertical estimation accuracy of soil moisture. Validation results from spatial and temporal variation of volumetric water content (vwc) showed that the proposed algorithm estimated soil moisture with a root mean squared error (RMSE) of 2.31% vwc, and the vertical profile shows a RMSE of 2.50% vwc. The algorithm estimates soil moisture in an hourly basis at 1 km spatial resolution, and up to 1 m depth, and was successfully applied under PR climate conditions.     

Green defaults: Information presentation and pro-environmental behaviour

There is inconsistency in many people's choice of electricity. When asked, they say they prefer a ‘green’ (i.e., environmentally friendly) source for this energy. Yet, although green electricity is available in many markets, people do not generally buy it. Why not? Motivated by behavioural decision research, we argue that the format of information presentation drastically affects the choice of electricity. Specifically, we hypothesise that people use the kind of electricity that is offered to them as the default. We present two natural studies and two experiments in the laboratory that support this hypothesis. In the two real-world situations, there was a green default, and most people used it. In the first laboratory experiment, more participants chose the green utility when it was the default than when ‘grey’ electricity was the default. In the second laboratory experiment, participants asked for more money to give up green electricity than they were willing to pay for it. We argue that changing defaults can be used to promote pro-environmental behaviour. Potential policy-making applications of this work are discussed.     

Environmental changes in Sepetiba Bay,SE Brazil

Sepetiba Bay is an example of an aquatic environment that has been severely impacted by human occupation and industrial activities in its basin. Some 400 industries including metallurgical, petrochemical and pyrometallurgical smelters, which emitted pollutants to air, soil and water, were established in Sepetiba Basin during the past 30 years. Apart from these point sources, changes in land use have also resulted in a large remobilization of pollutant deposition on Sepetiba Bay Basin. Studies have pointed out significant changes in sedimentation rates, concentrations of inorganic pollutants (Zn, Cd, Pb and Hg) and more recently, eutrophication, pointing to this area as an example of an impacted coastal zone. Notwithstanding local sources, Sepetiba Bay also suffers environmental impacts caused by diversion of river waters from adjacent basins, with some 30% of the total Hg flux to Sepetiba Bay and a 10-fold increase in water and sediment fluxes resulting from this. Decreasing environmental quality compromises both the large biodiversity and the potential economic uses of Sepetiba Bay, including fisheries and tourism. Monitoring of heavy metal levels in organisms (algae, mollusks, crustaceans and oysters) often shows concentrations well above the limits allowed following Brazilian legislation for food quality. Historical evolution of these concentrations suggests a worsening of the situation. Failure to monitor the effect of land-based activities, including those from other basins artificially associated with Sepetiba Bay has resulted in poor scenario construction and proper management planning.     

Anguilla anguilla L. plasma cortisol, lactate and glucose responses to abietic acid, dehydroabietic acid and retene

Anguilla anguilla L. were exposed to 0, 0.1, 0.3, 0.9 and 2.7 microM abietic (AA), dehydroabietic (DHAA) acids and retene (Re) during 8, 16, 24 and 72 h. The eels plasma cortisol, glucose and lactate were measured. A significant decrease in plasma cortisol was observed at 72 h exposure to 0.9 and 2.7 microM Re. DHAA (0.1 microM) significantly decreased plasma cortisol in eels after 8 and 24 h exposure. However, a significant plasma cortisol increase was found after 16 h, 2.7 microM AA exposure and after 24 h exposure to 0.1 microM and 2.7 microM AA. Furthermore, 72 h exposure to 0.9 microM AA also induced a plasma cortisol increase. A general rise in plasma glucose was detected after all exposure periods to Re. The plasma lactate also increased after 72 h exposure to 2.7 microM AA and after 8 h exposure to 0.1 microM DHAA.     

Assessment of arsenic and heavy metal concentrations in water and sediments of the Rio Grande at El Paso-Juarez metroplex region

The Rio Grande located along the US-Mexico border is affected by anthropogenic activities along its geographical course. Runoff and wind deposition of smelting residues may contribute to the pollution of the Rio Grande in the El Paso-Ciudad Juarez area. Few studies have addressed the presence or impacts of heavy metals or arsenic in this ecosystem. This study reports a survey of heavy metals (Cr, Cu, Cd, Ni, Pb, and Zn) and arsenic (As) in water and sediments of the Rio Grande collected from seven sites in the El Paso-Juarez region. Since water quality influences metal content in water, physical (temperature, flow and conductivity), and chemical (pH, dissolved oxygen, nitrates, alkalinity, and water hardness) parameters were measured at each site. Arsenic and heavy metal levels were determined using Inductively Couple Plasma (ICP) emission spectroscopy following EPA procedures. Zinc and lead were found as both total and dissolved metals in most of the samples, with concentrations of total recoverable metals reaching up to 105 and 70 microg/l, respectively. Most metals were found in sediment samples collected from four of seven sites. The highest Cu concentration (35 mg/l) was found at the American Dam site. Concentrations of metals found through this survey will be used as a reference for future studies in monitoring arsenic, heavy metals, and their impacts in the Rio Grande.