Seasonal monitoring of heavy metals and physicochemical characteristics in a lentic ecosystem of subtropical industrial region, India

Assessment of seasonal changes in surface water quality is an important aspect for evaluating temporal variations of lentic ecosystem (lakes and reservoirs) pollution due to industrial effluent discharge. In this study, nine metals and 15 physicochemical parameters, collected from four sampling sites in a tropical lake receiving the discharge from thermal power plant, coal mine, and chloralkali industry, during the years from 2004 to 2005, were analyzed. For greater efficacy in monitoring of heavy metals, particle-induced X-ray emission has been used during present investigation. Different statistical techniques like analysis of variance, Pearson correlation, principal component analysis, and factor analysis were employed to evaluate the seasonal correlations of physicochemical parameters. Most of the metals and physicochemical parameters monitored in the present study exhibited high spatial and temporal variability. Pertaining to metal pollution, the most polluted site was Belwadah, i.e., waters and sediments had the highest concentration of all the relevant metals. The reference site was characterized by the presence of low concentrations of metals in waters and in sediments. Based on the high metal concentration recorded in lake ambient, drinking, bathing, and irrigation water should not be used by the local people at the effluent discharge points.     

Influence of mercury from fly ash on cattle reared nearby thermal power plant

Cattle grazing nearby coal-fired power stations are exposed to fly ash. The present investigation aims to assess the environmental and health impacts of fly ash containing mercury emitted from thermal power plant. The health effect of fly ash were studied using 20 lactating cattle reared within a 5-km radius of s thermal power plant for the possible effect of fly ash such as the alterations in hematological and biochemical parameters of blood, milk, and urine. Results indicated that the hemoglobin levels (6.65?±?0.40?g/dl) were significantly reduced in all the exposed animals. Biochemical parameters viz., blood urea nitrogen (27.35?±?1.19?mg/dl), serum glutamate oxaloacetate transaminase (43.39?±?3.08?IU/l), albumin, and creatinine were found to be increased, whereas serum glutamate pyruvic transaminase (29.26?±?2.02) and Ca²⁺ were observed to be statistically insignificant in exposed animals. Mercury concentrations estimated in the blood, milk, and urine of exposed (n?=?20) and control (n?=?20) animals were 7.41?±?0.86, 4.75?±?0.57, 2.08?±?0.18, and 1.05?±?0.07, 0.54?±?0.03, 0.20?±?0.02?μg/kg, respectively. The significant increase (P?<?0.01) in the levels of mercury in blood, milk, and urine of exposed animals in comparison to control indicated that the alterations of biochemical parameters in exposed cattle could be due to their long term exposure to fly ash mercury which may have direct or indirect impact on human populations via food chain.     

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.     

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.     

Groundwater Quality Assessment of Tehsil Kheragarh, Agra (India) with Special Reference to Fluoride

Fluoride concentration and other parameters in groundwater from 261 villages in Tehsil Kheragarh of District Agra were assessed and attempts were made to observe the relationship between fluoride and other water quality parameters. Of 658 groundwater samples (collected from separate sources) analysed for fluoride, 27% were in the range of 0–1.0 mg/L, 25% in 1.0–1.5 mg/L, 32% in1.5–3.0 mg/L and 16% above 3.0 mg/L. The highest fluoride concentration recorded was 12.80 mg/L. Significant correlation of fluoride with pH, alkalinity, Na, SiO₂ and PO₄ were observed. Factor analysis was also attempted in order to identify the contributing sources.     

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.     

Effects of thiobencarb in combinations with molinate and chlorpyrifos on selected soil microbial processes

The impact of pesticides, namely thiobencarb (TBC), molinate (MOL) and chlorpyrifos (CPF), on soil microbial processes was studied in two Australian soils. Substrate induced respiration (SIR), substrate induced nitrification (SIN) and phosphatases and chitinase enzymatic activities were assessed during a 30-day microcosm study. The pesticides were applied to soils at recommended rates either alone, or as binary mixtures with TBC. Soil samples were sampled at 5, 15 and 30 days after pesticide treatments. Substrate induced respiration was only transiently affected by pesticides in both soils. In contrast, the process of indigenous nitrification was affected by the presence of pesticides in both soils, especially when the pesticides were applied as binary mixtures. Substrate induced nitrification increased with pesticides in the Griffith soil (except with MOL+TBC after 5 days) whereas SIN values were non-significantly different to the control on the Coleambally soil. The binary mixtures of pesticides with TBC resulted in a decrease in SIN in both soils, but the effects disappeared within 30 days. The enzymatic activities were not consistently affected by pesticides, and varied with the soil and pesticides studied. This study showed that, when applied at recommended application rates, TBC, MOL, and CPF (individually or as binary mixtures), had little or only transitory effects on the functional endpoints studied. However, further investigations are needed to assess the effect on microbial densities and community structure despite the low disturbance to the functions noted in this work.     

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.