Environmentally relevant exposures of male mice to carbendazim and thiram cause persistent genotoxicity in male mice

Environmental Science and Pollution Research - Carbendazim and thiram are fungicides used in combination to prevent mold destruction of crops. Studies have demonstrated genotoxicity by these...     

Heavy metals in water, sediments and wetland plants in an aquatic ecosystem of tropical industrial region, India

Concentrations of heavy metals (Cu, Cr, Fe, Pb, Zn, Hg, Ni, and Cd) and macronutrients (Mn) were measured in industrial effluents, water, bottom sediments, and wetland plants from a reservoir, Govind Ballabh (G.B.) Pant Sagar, in Singrauli Industrial region, India. The discharge point of a thermal power plant, a coal mine, and chlor-alkali effluent into the G.B. Pant Sagar were selected as sampling sites with one reference site in order to compare the findings. The concentrations of heavy metals in filtered water, sieved sediment samples (0.4-63 microm), and wetland plants were determined with particle-induced X-ray emission. The collected plants were Aponogeton natans, L. Engl. & Krause, Cyperus rotundus, L., Hydrilla verticillata, (L.f.) Royle, Ipomoea aquatica, Forssk., Marsilea quadrifolia, L., Potamogeton pectinatus, L., Eichhornia crassipes, (Mart.) Solms Monogr., Lemna minor, L., Spirodela polyrhiza (L.) Schleid. Linnaea, Azolla pinnata, R.Br., Vallisneria spiralis, L., and Polygonum amphibium, L. In general, metal concentration showed a significant positive correlation between industrial effluent, lake water, and lake sediment (p < 0.01). Likewise, significant positive correlation was recorded with metals concentration in plants and lake ambient, which further indicated the potential of aforesaid set of wetland macrophytes for pollution monitoring.     

Screening of brick-kiln area soil for determination of heavy metal Pb using LIBS

Rapid measurement of heavy metals in soil is an important factor in modeling the effect of industrial pollution on agricultural soil. Conventional methods of heavy metal analysis are relatively slow in terms of measurement/analysis time and sample preparation time with the requirement of skilled manpower. Our results highlight the quantitative analysis of toxic metal lead (Pb), for the first time, in an Indian agricultural soil, in the vicinity of brick-kiln area, Phaphamau, near Allahabad, India, by using a novel technique named as Laser-induced breakdown spectroscopy (LIBS). LIBS spectra of soil has been recorded in the wavelength range from ultraviolet (UV) to infrared region (200-1,100 nm). The suitability of Pb lines for drawing the calibration curve is checked and realized, for the first time, that 220.3 nm, which is observed in the UV region of LIBS spectra, is completely interference free and best suited for the quantification of trace amount of Pb in soil instead of any other Pb lines, because it has best linear regression coefficient and smallest standard deviation of the background signal. In the present work the detection limit for Pb in soil is found to be 45 ppm. Based on the present work the concentration of Pb in agricultural soil of brick-kiln area in Phaphamau is found to be congruent with 570 ppm, which is more than the regulatory standards imposed by US Environmental Protection Agency (400 ppm) for the presence of lead in soil, therefore, it is of great concern to us.     

Predicting dissolved oxygen concentration using kernel regression modeling approaches with nonlinear hydro-chemical data

Kernel function-based regression models were constructed and applied to a nonlinear hydro-chemical dataset pertaining to surface water for predicting the dissolved oxygen levels. Initial features were selected using nonlinear approach. Nonlinearity in the data was tested using BDS statistics, which revealed the data with nonlinear structure. Kernel ridge regression, kernel principal component regression, kernel partial least squares regression, and support vector regression models were developed using the Gaussian kernel function and their generalization and predictive abilities were compared in terms of several statistical parameters. Model parameters were optimized using the cross-validation procedure. The proposed kernel regression methods successfully captured the nonlinear features of the original data by transforming it to a high dimensional feature space using the kernel function. Performance of all the kernel-based modeling methods used here were comparable both in terms of predictive and generalization abilities. Values of the performance criteria parameters suggested for the adequacy of the constructed models to fit the nonlinear data and their good predictive capabilities.     

An eco-sustainable green approach for heavy metals management: two case studies of developing industrial region

Multifaceted issues or paradigm of sustainable development should be appropriately addressed in the discipline of environmental management. Pollution of the biosphere with toxic metals has accelerated dramatically since the beginning of the Industrial Revolution. In present review, comparative assessment of traditional chemical technologies and phytoremediation has been reviewed particularly in the context of cost-effectiveness. The potential of phytoremediation and green chemicals in heavy metals management has been described critically. Further, the review explores our work on phytoremediation as green technology during the last 6 years and hand in hand addresses the various ecological issues, benefits and constraints pertaining to heavy metal pollution of aquatic ecosystems and its phytoremediation as first case study. Second case study demonstrates the possible health implications associated with use of metal contaminated wastewater for irrigation in peri-urban areas of developing world. Our researches revealed wetland plants/macrophytes as ideal bio-system for heavy metals removal in terms of both ecology and economy, when compared with chemical treatments. However, there are several constraints or limitations in the use of aquatic plants for phytoremediation in microcosm as well as mesocosm conditions. On the basis of our past researches, an eco-sustainable model has been proposed in order to resolve the certain constraints imposed in two case studies. In relation to future prospect, phytoremediation technology for enhanced heavy metal accumulation is still in embryonic stage and needs more attention in gene manipulation area. Moreover, harvesting and recycling tools needs more extensive research. A multidisciplinary research effort that integrates the work of natural sciences, environmental engineers and policy makers is essential for greater success of green technologies as a potent tool of heavy metals management.     

N:P stoichiometry and transparent exopolymeric particles buffer ecological impacts of nutrients in the Ganga River: a mesocosm study

We investigated the possible drivers of the N:P stoichiometric shift and its relationship with micro-algal production of transparent exopolymeric particles (TEP) along a 35?km gradient of the Ganga River. The objective was to evaluate if the trade-off between N:P stoichiometry and production of TEP helps in maintaining water quality of the river. Mesocosm experiments were conducted to examine N:P-TEP linkages and its role in turbidity removal. In situ measurements did not show Si to be a limiting nutrient (N:Si?Aulacosira granulata and Fragilaria intermedia. Settling efficiency, turbidity removal and sedimentation of TEP, biogenic silica (BSi) and biomass all increased with decreasing N:P ratio proportionately to the amount of TEP produced in the mesocosm. The study demonstrates that trade-off between N:P stoichiometry and the production of TEP generates feedback to buffer the ecological impacts of nutrient pollution.     

Growth responses and metal accumulation capabilities of woody plants during the phytoremediation of tannery sludge

Five woody plants species (i.e. Terminalia arjuna, Prosopis juliflora, Populus alba, Eucalyptus tereticornis and Dendrocalamus strictus) were selected for phytoremediation and grow on tannery sludge dumps of Common Effluent Treatment Plant (CETP), Unnao (Uttar Pradesh), India. Concentration of toxic metals were observed high in the raw tannery sludge i.e. Fe-1667 > Cr-628 > Zn-592 > Pb-427 > Cu-354 > Mn-210 > Cd-125 > Ni-76 mg kg⁻¹ dw, respectively. Besides, physico-chemical properties of the raw sludge represented the toxic nature to human health and may pose numerous risks to local environment. The growth performances of woody plants were assessed in terms of various growth parameters such as height, diameter at breast height (DBH) and canopy area of plants. All the plant species have the capabilities to accumulate substantial amount of toxic metals in their tissues during the remediation. The ratio of accumulated metals in the plants were found in the order Fe > Cr > Mn > Pb > Zn > Cu > Cd > Ni and significant changes in physico-chemical parameters of tannery sludge were observed after treatment. All the woody plants indicated high bioconcentration factor for different metals in the order Fe > Cr > Mn > Ni > Cd > Pb > Zn > Cu. After one year of phytoremediation, the level of toxic metals were removed from tannery sludge up to Cr (70.22)%, Ni (59.21)%, Cd (58.4)%, Fe (49.75)%, Mn (30.95)%, Zn (22.80)%, Cu (20.46)% and Pb (14.05)%, respectively.     

Assessment of competitive ability of two Indian wheat cultivars under ambient O3 at different developmental stages

The concentrations of O₃ are increasing, which may have potential adverse effects on crop yield. This paper deals with assessing the intraspecific variability of two wheat cultivars (PBW 343 and M 533) at different growth stages using open top chambers. Mean O₃ concentrations were 50.2 and 53.2 ppb, and AOT40 values were 9 and 12.1 ppm h, respectively, in 2008–2009 and 2009–2010. Reproductive stage showed higher AOT40 values (6.9 and 9.2 ppm h) compared to vegetative (2.23 and 2.9 ppm h). Critical levels of a 3-month AOT 40 of 3 ppm h led to 6 % yield reduction in two wheat cultivars for two consecutive years. Variations in photosynthesis rate, stomatal conductance (g_s), Fv/Fm ratio, photosynthetic pigments, primary and secondary metabolites, morphological parameters, and yield attributes were measured at vegetative and reproductive stages. Reductions in number of leaves, leaf area, total biomass, root/shoot ratio, RGR, photosynthetic pigments, protein content, and Fv/Fm ratio in PBW 343 were more than M 533 at reproductive stage. Photosynthetic rate did not vary between the cultivars, but g_s was higher in PBW 343 compared to M 533 under ambient O₃. Higher total phenolics and peroxidase activity were recorded in M 533 at reproductive stage conferring higher resistance at latter age. Results of O₃ resistance showed that M 533 was sensitive compared to PBW 343 during vegetative stage but developed more resistance at reproductive stage. PBW 343 with larger leaf area and high g_s is more sensitive than M 533 with smaller leaf area and low g_s. The study suggests that the sensitivity varied with plant growth stage, and the plant showing higher sensitivity during vegetative period developed more resistance during reproductive period due to higher defense mechanism. Though the yield reductions were same in both cultivars under ambient O₃, the mechanism of acquiring the resistance is different between the cultivars.     

Predicting adsorptive removal of chlorophenol from aqueous solution using artificial intelligence based modeling approaches

The research aims to develop artificial intelligence (AI)-based model to predict the adsorptive removal of 2-chlorophenol (CP) in aqueous solution by coconut shell carbon (CSC) using four operational variables (pH of solution, adsorbate concentration, temperature, and contact time), and to investigate their effects on the adsorption process. Accordingly, based on a factorial design, 640 batch experiments were conducted. Nonlinearities in experimental data were checked using Brock–Dechert–Scheimkman (BDS) statistics. Five nonlinear models were constructed to predict the adsorptive removal of CP in aqueous solution by CSC using four variables as input. Performances of the constructed models were evaluated and compared using statistical criteria. BDS statistics revealed strong nonlinearity in experimental data. Performance of all the models constructed here was satisfactory. Radial basis function network (RBFN) and multilayer perceptron network (MLPN) models performed better than generalized regression neural network, support vector machines, and gene expression programming models. Sensitivity analysis revealed that the contact time had highest effect on adsorption followed by the solution pH, temperature, and CP concentration. The study concluded that all the models constructed here were capable of capturing the nonlinearity in data. A better generalization and predictive performance of RBFN and MLPN models suggested that these can be used to predict the adsorption of CP in aqueous solution using CSC.