Determination of polycyclic aromatic hydrocarbons in dry tea

Twenty-eight different tea samples sold in the United States were evaluated using high-performance liquid chromatography (HPLC) with fluorescence detection (FLD) for their contamination with polycyclic aromatic hydrocarbons (PAHs). Many PAHs exhibit carcinogenic, mutagenic, and teratogenic properties and have been related to several kinds of cancer in man and experimental animals. The presence of PAHs in environmental samples such as water, sediments, and particulate air has been extensively studied, but food samples have received little attention. Eighteen PAHs congeners were analyzed, with percentage recovery higher than 85%. Contamination expressed as the sum of the 18 analyzed PAHs was between 101 and 1337 μg/kg on dry mass and the average contents in all of the 28 examined samples was 300 μg/kg on dry mass. Seven of the congeners were found in all samples with wide ranges of concentrations as follows: fluorene (7–48 μg/kg), anthracene (1–31 μg/kg), pyrene (1–970 μg/kg), benzo(a)anthracene (1–18 μg/kg) chrysene (17–365 μg/kg), benzo(a)pyrene (1–29 μg/kg), and indeno(1,2,3-cd)pyrene (4–119 μg/kg). The two most toxic congeners benzo(a)pyrene and dibenzo(a,h)anthracene were found at high concentrations only in Earl Grey Twinnings, Earl Grey Harney& Sons Fine Teas, and Chai Ultra Spice Black Tea Twinnings. Six PAH congeners are considered as suspected carcinogens (U.S.EPA), formed the basis of the estimation of the toxic equivalent (TEQ), Chai Ultra-Spice Black Tea Twinnings had the highest TEQ (110.9) followed by two grey tea samples, Earl Grey Harney & Sons Fine Tea (57.7) and Earl Grey Twinnings (54.5). Decaffeinated grey teas had the lowest TEQs, decaffeinated Earl Grey Bigelow (9.4) and Green Tea Honey Lemon Decaffeinated Lipton (9.6).     

Improving situational awareness through the design of offshore installations

Maintaining the situational awareness of control room operators on offshore installations contributes to the timely diagnosis of conditions and making appropriate decisions. This is particularly important when dealing with events and incidents. Recent initiatives aimed at reducing operators’ exposure to the hazards of working on offshore installations may have a negative impact upon situational awareness within the control room environment.This paper discusses mitigation of the negative impact through the design and operation of the installation and control system; either by improving the general level of situational awareness or by specifically targeting the areas affected by these initiatives.     

Predicting copper concentrations in acid mine drainage: a comparative analysis of five machine learning techniques

Acid mine drainage (AMD) is a global problem that may have serious human health and environmental implications. Laboratory and field tests are commonly used for predicting AMD, however, this is challenging since its formation varies from site-to-site for a number of reasons. Furthermore, these tests are often conducted at small-scale over a short period of time. Subsequently, extrapolation of these results into large-scale setting of mine sites introduce huge uncertainties for decision-makers. This study presents machine learning techniques to develop models to predict AMD quality using historical monitoring data of a mine site. The machine learning techniques explored in this study include artificial neural networks (ANN), support vector machine with polynomial (SVM-Poly) and radial base function (SVM-RBF) kernels, model tree (M5P), and K-nearest neighbors (K-NN). Input variables (physico-chemical parameters) that influence drainage dynamics are identified and used to develop models to predict copper concentrations. For these selected techniques, the predictive accuracy and uncertainty were evaluated based on different statistical measures. The results showed that SVM-Poly performed best, followed by the SVM-RBF, ANN, M5P, and KNN techniques. Overall, this study demonstrates that the machine learning techniques are promising tools for predicting AMD quality.     

Using structural equation modeling to investigate relationships among ecological variables

Structural equation modeling is an advanced multivariate statistical process with which a researcher can construct theoretical concepts, test their measurement reliability, hypothesize and test a theory about their relationships, take into account measurement errors, and consider both direct and indirect effects of variables on one another. Latent variables are theoretical concepts that unite phenomena under a single term, e.g., ecosystem health, environmental condition, and pollution (Bollen, 1989). Latent variables are not measured directly but can be expressed in terms of one or more directly measurable variables called indicators. For some researchers, defining, constructing, and examining the validity of latent variables may be the end task of itself. For others, testing hypothesized relationships of latent variables may be of interest. We analyzed the correlation matrix of eleven environmental variables from the U.S. Environmental Protection Agency's (USEPA) Environmental Monitoring and Assessment Program for Estuaries (EMAP-E) using methods of structural equation modeling. We hypothesized and tested a conceptual model to characterize the interdependencies between four latent variables-sediment contamination, natural variability, biodiversity, and growth potential. In particular, we were interested in measuring the direct, indirect, and total effects of sediment contamination and natural variability on biodiversity and growth potential. The model fit the data well and accounted for 81% of the variability in biodiversity and 69% of the variability in growth potential. It revealed a positive total effect of natural variability on growth potential that otherwise would have been judged negative had we not considered indirect effects. That is, natural variability had a negative direct effect on growth potential of magnitude –0.3251 and a positive indirect effect mediated through biodiversity of magnitude 0.4509, yielding a net positive total effect of 0.1258. Natural variability had a positive direct effect on biodiversity of magnitude 0.5347 and a negative indirect effect mediated through growth potential of magnitude –0.1105 yielding a positive total effects of magnitude 0.4242. Sediment contamination had a negative direct effect on biodiversity of magnitude –0.1956 and a negative indirect effect on growth potential via biodiversity of magnitude –0.067. Biodiversity had a positive effect on growth potential of magnitude 0.8432, and growth potential had a positive effect on biodiversity of magnitude 0.3398. The correlation between biodiversity and growth potential was estimated at 0.7658 and that between sediment contamination and natural variability at –0.3769.     

Effect of 3 years' free-air exposure to elevated ozone on mature Norway spruce (Picea abies (L.) Karst.) needle epicuticular wax physicochemical characteristics

We examined the effect of ozone (O₃) on Norway spruce (Picea abies) needle epicuticular wax over three seasons at the Kranzberg Ozone Fumigation Experiment. Exposure to 2× ambient O₃ ranged from 64.5 to 74.2 μl O₃ l⁻¹ h AOT40, and 117.1 to 123.2 nl O₃ l⁻¹ 4th highest daily maximum 8-h average O₃ concentration. The proportion of current-year needle surface covered by wax tubes, tube aggregates, and plates decreased (P = 0.011) under 2× O₃. Epistomatal chambers had increased deposits of amorphous wax. Proportion of secondary alcohols varied due to year (P = 0.004) and O₃ treatment (P = 0.029). Secondary alcohols were reduced by 9.1% under 2× O₃. Exposure to 2× O₃ increased (P = 0.037) proportions of fatty acids by 29%. Opposing trends in secondary alcohols and fatty acids indicate a direct action of O₃ on wax biosynthesis. These results demonstrate O₃-induced changes in biologically important needle surface characteristics of 50-year-old field-grown trees.