Biosorption of lead ion by chemically-modified biomass of marine brown algae Laminaria japonica

In this paper, marine brown algae Laminaria japonica was chemically-modified by crosslinking with epichlorohydrin (EC(1), EC(2)), or oxidizing by potassium permanganate (PC), or only washed by distilled water (DW). They were used for equilibrium sorption uptake studies with lead. As can be seen from the experimental results that biosorption equilibriums were rapidly established in about 2h. The lead adsorption was strictly pH dependent, and maximum removal of lead on biosorbents were observed at pH 5.3. The effects solid/liquid ratio on lead biosorption was also investigated. The maximum lead uptakes were 1.67 mmol g(-1), 1.62 mmol g(-1), 1.54 mmol g(-1) and 1.21 mmol g(-1), respectively for EC(1), EC(2), PC and DW. The order of maximum lead uptakes for different pretreated and raw alga was EC(1)>EC(2)>PC>DW. A comparison of different isotherm models revealed that the combination of Langmuir and Freundlich (L-F) isotherm model fitted the experimental data best.     

2-chlorophenol induced ROS generation in fish Carassius auratus based on the EPR method

In the present study, a secondary spin trapping technique was used followed by electron paramagnetic resonance (EPR) analysis, to study the potential of reactive oxygen species (ROS) production after fish (Carassius auratus) were injected i.p. with different doses (50, 100, 200, 250, 500mgkg(-1)) of 2-chlorophenol (2-CP). The ROS signal intensity of the EPR spectrum showed a significant increase (p<0.05, compared with the control) when the 2-CP dose was as low as 50mgkg(-1). There is a good relationship between the 2-CP administered doses and ROS generation. Based on the hyperfine splitting constants and shape of the EPR spectrum, the ROS which was generated in fish liver after intraperitoneal (i.p.) injection of 2-CP was identified as ()OH. SOD and CAT activities were found to be induced at lower doses of 2-CP. GSH levels fell below the control level following all treatments with 2-CP, and GSSG levels changed along with those of GSH. These observations indicated that the fish experienced oxidative stress. The strong positive correlation (r=0.966, p<0.005) between ()OH radical and lipid peroxidation suggested that lipid peroxidation was possibly induced by ()OH. The phase II detoxification enzyme glutathione-S-transferase (GST) may play an important role in 2-CP metabolism or excretion and, consequently, reduce ROS production. This study provides strong evidence that level of ROS is significantly increased in 2-CP stressed fish, and ROS may serve as a potential biomarker to indicate 2-CP contamination.     

A parametric transfer function methodology for analyzing reactive transport in nonuniform flow

We analyze reactive transport during in-situ bioremediation in a nonuniform flow field, involving multiple extraction and injection wells, by the method of transfer functions. Gamma distributions are used as parametric models of the transfer functions. Apparent parameters of classical transport models may be estimated from those of the gamma distributions by matching temporal moments. We demonstrate the method by application to measured data taken at a field experiment on bioremediation conducted in a multiple-well system in Oak Ridge, TN. Breakthrough curves (BTCs) of a conservative tracer (bromide) and a reactive compound (ethanol) are measured at multi-level sampling (MLS) wells and in extraction wells. The BTCs of both compounds are jointly analyzed to estimate the first-order degradation rate of ethanol. To quantify the tracer loss, we compare the approaches of using a scaling factor and a first-order decay term. Results show that by including a scaling factor both gamma distributions and inverse-Gaussian distributions (transfer functions according to the advection-dispersion equation) are suitable to approximate the transfer functions and estimate the reactive rate coefficients for both MLS and extraction wells. However, using a first-order decay term for tracer loss fails to describe the BTCs at the extraction well, which is affected by the nonuniform distribution of travel paths.     

EDDS and EDTA-enhanced phytoextraction of metals from artificially contaminated soil and residual effects of chelant compounds

The potential of 18 different plants to be used in the chemically enhanced phytoextraction of Cu, Pb, Zn and Cd was assessed using pot experiments. Chrysanthemum coronarium L. was the species most sensitive to the application of EDTA, and had the highest enhancement of Cu and Pb concentrations in its shoots. Compared with EDTA, EDDS was more effective in enhancing the concentration of Cu in the shoots of Chrysanthemum coronarium L. and Zea mays L. grown on multi-metal contaminated soils. The EDTA-treated soil still had a significant ability to enhance the concentrations of Cu and Pb in the shoots of Zea mays L. six months after the chelant treatment. However, the EDDS-treated soil did not have any effect in enhancing the concentrations of metals in the shoots of Zea mays L. in the second crop test. The results may indicate that EDDS biodegrades more rapidly than EDTA in soil and is better in limiting potential metal leaching.     

Dynamic modeling of chemical fate and transport in multimedia environments at watershed scale-I: theoretical considerations and model implementation

A geo-referenced environmental fate model was developed for analyzing unsteady-state dispersion and distribution of chemicals in multimedia environmental systems. Chemical transport processes were formulated in seven environmental compartments of air, canopy, surface soil, root-zone soil, vadose-zone soil, surface water, and sediment. The model assumed that the compartments were completely mixed and chemical equilibrium was established instantaneously between the sub-compartments within each compartment. A fugacity approach was utilized to formulate the mechanisms of diffusion, advection, physical interfacial transport, and transformation reactions. The governing equations of chemical mass balances in the environmental compartments were solved simultaneously to reflect the interactions between the compartments. A geographic information system (GIS) database and geospatial analysis were integrated into the chemical transport simulation to provide spatially explicit estimations of model parameters at watershed scale. Temporal variations of the environmental properties and source emissions were also considered in the parameter estimations. The outputs of the model included time-dependent chemical concentrations in each compartment and its sub-compartments, and inter-media mass fluxes between adjacent compartments at daily time steps.     

Retrieving leaf area index for coniferous forest in Xingguo County, China with Landsat ETM+ images

Spatial distributions of the leaf area index (LAI) needed for carbon cycle modeling in Xingguo County, China were estimated based on correlations between the field-measurements and vegetation indices (VIs). After making geometric and atmospheric corrections to two Landsat ETM+ images, one in January 2000 and the other in May 2003, three VIs (SR, NDVI, and RSR) were derived, and their separate correlations with ground LAI measurements were established. The correlation with RSR was the highest among the three VIs. The retrieved LAI values for January 2000 were lower than those for May 2003 because of a small seasonal variation in the coniferous forests (predominantly masson pine) and the decrease in the understorey vegetation during winter.     

PM2.5 concentration estimation using convolutional neural network and gradient boosting machine

Surface monitoring, vertical atmospheric column observation, and simulation using chemical transportation models are three dominant approaches for perception of fine particles with diameters less than 2.5 micrometers (PM_2.5) concentration. Here we explored an image-based methodology with a deep learning approach and machine learning approach to extend the ability on PM_2.5 perception. Using 6976 images combined with daily weather conditions and hourly time data in Shanghai (2016), trained by hourly surface monitoring concentrations, an end-to-end model consisting of convolutional neural network and gradient boosting machine (GBM) was constructed. The mean absolute error, the root-mean-square error and the R-squared for PM_2.5 concentration estimation using our proposed method is 3.56, 10.02, and 0.85 respectively. The transferability analysis showed that networks trained in Shanghai, fine-tuned with only 10% of images in other locations, achieved performances similar to ones from trained on data from target locations themselves. The sensitivity of different regions in the image to PM_2.5 concentration was also quantified through the analysis of feature importance in GBM. All the required inputs in this study are commonly available, which greatly improved the accessibility of PM_2.5 concentration for placed and period with no surface observation. And this study makes an exploratory attempt on pollution monitoring using graph theory and deep learning approach.