Influence of pH on lead uptake, chlorophyll and nitrogen content of Nasturtium officinale R. Br. and Mentha aquatica L

In the present study, effects of pH (5.0, 7.0 and 9.0) and lead (1, 5, 10, 25, 50 and 100 microg mL(-1)) were investigated on uptake of lead, content of chlorophyll and nitrogen in Nasturtium officinale and Mentha aquatica. Total chlorophyll and nitrogen contents were adversely affected from Pb2+ concentrations dose dependently at each pH. The macrophytes were adversely affected by pH 5.0 or more than 9.0. After 12-days Pb2+ treatment, results showed that lead accumulation of macrophyte tissues was variable. According to the parts of the macrophytes, Pb2+ amounts were generally found at all tested pH levels and the metal concentrations for M. aquatica in the following order: root > stem > leaf and for N. officinale root > leaf > stem. Pb2+ concentrations in plant tissues (root, stem, leaf) in relation to pH were generally found for both macrophytes in following order: 7.0 > 9.0 > 5.0. Despite of the fact that high Pb2+ accumulation was observed in root tissues of the macrophytes, low metal accumulation was measured in the above-ground parts indicating low root-leaf translocation. The study indicated that uptake rate of Pb2+ and its toxicity on Chlorophyll and nitrogen contents in the macrophytes were dependent upon pH value of solutions.     

Does More Storage Give California More Water?

Increasing reservoir storage is commonly proposed to mitigate increasing water demand and provide drought reserves, especially in semiarid regions such as California. This paper examines the value of expanding surface reservoir capacity in California using hydroeconomic modeling for historical conditions, a future warm‐dry climate, and California's recently adopted policy to end groundwater overdraft. Results show expanding surface storage capacity rarely provides sizable economic value in most of California. On average, expanding facilities north of California's Delta provides some benefit in 92% of 82 years modeled under historical conditions and in 61% of years modeled in a warm‐dry climate. South of California's Delta, expanding storage capacity provides no benefits in 14% of years modeled under historical conditions and 99% of years modeled with a warm‐dry climate. Results vary across facilities between and within regions. The limited benefit of surface storage capacity expansion to statewide water supply should be considered in planning California's water infrastructure.     

Dimethoate-induced oxidative stress and DNA damage in Oncorhynchus mykiss

The present study was conducted in order to investigate pro-oxidant activity of dimethoate in liver and brain tissues following sublethal pesticide exposure for 5, 15 and 30 d by using SOD, GPx, CAT enzyme activities and lipid peroxidation as biomarkers as well as DNA damaging potential via detecting% Tail DNA, Tail moment and Olive tail moment as endpoints in erythrocytes of Oncorhynchus mykiss in an in vitro experiment. Antioxidant enzyme activities were found to elicit two staged response which was an initial induction followed by a sharp inhibition in liver tissue while a sustained increase in GPx activity and slight stimulation in SOD activity were detected in brain tissue. Lipid peroxidation showed an ascending pattern throughout the exposure period in both tissues and a decreasing trend was determined in tissue protein levels which was proved to be positively correlated with duration. Similar findings were obtained from outcomes preferred to quantify DNA damage and TM was decided to reflect the extent of damage more sensitively because of determined positive correlation with concentrations applied. Considering these results, it can be concluded that oxidative stress condition evoked by dimethoate could not be responded effectively and genotoxic nature of pesticide was proven by determined clastogenic effect possibly via being an alkylation agent or stimulating the production of reactive species.     

Estimation method to identify scalar point source in turbulent flow based on Taylor’s diffusion theory

We introduce a new approach to diffusion-source estimation for quick identification of the unknown source, based on Taylor’s diffusion theory for turbulent transport of passive scalar from a fixed point source. In order to evaluate the method, we used planar laser-induced fluorescence to measure the concentration field of fluorescent dye in water flowing in a channel. We considered two kinds of datasets: basis data and observed data. The former is used to determine the basis functions characterizing the streamwise dependence of variances for three statistics: the mean concentration, root-mean-square (RMS) of fluctuations in the concentration, and RMS of the temporal gradient of the fluctuating concentration. Consistent with Taylor’s theory, we found that the lateral distribution of each statistic was basically Gaussian, and their standard deviations increased as a function of the square root of the distance from the emitted point. Based on these facts, a basis function can be formulated and expected to be valid for estimation of unknown sources. Source estimation was performed with the observed data, which corresponded to limited available information about the concentration from an unknown point source. We confirmed a good prediction accuracy of the proposed method with an averaged bias as small as the turbulent integral scale. Better precision was achieved by employing several statistics simultaneously. In this case, the standard deviation of the estimated source position was assessed at 14 % of the mean distance between the source and measurement points, after 100 source-estimate trials with different datasets. The methodology tested in this paper is expected to be applicable more general and complex environmental diffusion issues involving anisotropic turbulent dispersion, and space–time variable mainstream systems; but its versatility in such systems is currently under investigation.     

Functional clustering of varved lake sediment to reconstruct past seasonal climate

Annually laminated (varved) lake sediments constitutes excellent environmental archives, and have the potential to play an important role for understanding past seasonal climate with their inherent annual time resolution and within-year seasonal patterns. We propose to use functional data analysis methods to extract the relevant information with respect to climate reconstruction from the rich but complex information in the varves, including the shapes of the seasonal patterns, the varying varve thickness, and the non-linear sediment accumulation rates. In particular we analyze varved sediment from lake Kassjön in northern Sweden, covering the past 6400 years. The properties of each varve reflect to a large extent weather conditions and internal biological processes in the lake the year that the varve was deposited. Functional clustering is used to group the seasonal patterns into different types, that can be associated with different weather conditions. The seasonal patterns were described by penalized splines and clustered by the k-means algorithm, after alignment. The observed (within-year) variability in the data was used to determine the degree of smoothing for the penalized spline approximations. The resulting clusters and their time dynamics show great potential for seasonal climate interpretation, in particular for winter climate changes.     

Analyzing longitudinal circular data by projected normal models: a semi-parametric approach based on finite mixture models

The analysis of circular data has been recently the focus of a wide range of literature, with the general objective of providing reliable parameter estimates in the presence of heterogeneity and/or dependence among observations under a longitudinal setting. In this paper, we extend the variance component model approach to the analysis of longitudinal circular data, defining a mixed effects model for radial projections onto the circle and introducing dependence between projections through a set of correlated random coefficients. Estimation is carried out by numerical integration through an expectation-maximization algorithm without parametric assumptions upon the random coefficients distribution. The resulting model is a finite mixture of projected normal distributions. A simulation study has been carried out to investigate the behavior of the proposed model in a series of empirical situations. The proposed model is computationally parsimonious and, when applied to a real dataset on animal orientation, produces novel results.     

Sonochemical degradation of diclofenac: byproduct assessment,reaction mechanisms and environmental considerations

The study covers a thorough assessment of the overall degradation of diclofenac-Na (DCF) by high-frequency ultrasound, focusing particularly on identification, interpretation, and characterization of the oxidation byproducts and their reaction mechanisms. It was found that sonication of 5 mg L^?1 DCF at near neutral pH rendered complete conversion of the compound, 45 % carbon, 30 % chlorine, and 25 % nitrogen mineralization. Density functional theory (DFT) calculations confirmed the experimentally detected major byproduct 2,6-dichloroaniline, the formation of which was explained by OH? addition to the ipso-position of the amino group. The stability of UV absorption at around 276–280 nm throughout reaction was in agreement with the detected byproduct structures, i.e., the presence of amino/amine groups and phenolic, aniline, benzene, and quinine-type derivatives, which all absorbed at around the same band. Microtox toxicity of the reactor aliquots at early reaction showed that initially the reaction products, specifically 1-(2,6-dichlorophenyl)-2-indoline-one, were very toxic; subsequently toxicity exhibited a fluctuating pattern, and a steady declination towards the “non-toxic” level was observed only after 90 min. Oxygen uptake analysis also revealed the formation of harmful products at early reaction, but the reactor was totally biodegradable upon 1-h sonication.