Heavy metal levels and solid phase speciation in street dusts of Delhi,India

Street dust samples were collected from three different localities (industrial, heavy traffic and rural) situated in the greater Delhi area of India. The samples analyzed for Cd, Zn, Pb, Ni, Cu, and Cr indicated remarkably high levels of Cr, Ni, and Cu in the industrial area, whilst Pb and Cd did not show any discernible variations between the three localities. A multivariate statistical approach (Principal Component Analysis) was used to define the possible origin of metals in dusts. The street dusts were sequentially extracted so that the solid pools of Cd, Zn, Pb, Ni, Cu, Cr could be partitioned into five operationally defined fractions viz. exchangeable, bound to carbonates, bound to Fe-Mn oxides, bound to organic matter and residual. Metal recoveries in sequential extractions were +/- 10% of the independently measured total metal concentrations. Cd was the only metal present appreciably (27.16%) in the exchangeable fraction and Cu was the only metal predominantly associated (44.26%) with organic fraction. Zn (45.64%) and Pb (28.26%) were present mainly in the Fe-Mn oxide fraction and the residual fraction was the most dominant solid phase pool of Cr (88.12%) and Ni (70.94%). Assuming that the mobility and bioavailability are related to the solubility of geochemical forms of the metals and decrease in order of extraction, the apparent mobility and potential metal bioavailability for these highly contaminated street dust samples is: Cd>Zn approximately equal Pb>Ni>Cu>Cr.     

Comparison of drinking water,raw rice and cooking of rice as arsenic exposure routes in three contrasting areas of West Bengal,India

Remediation aimed at reducing human exposure to groundwater arsenic in West Bengal, one of the regions most impacted by this environmental hazard, are currently largely focussed on reducing arsenic in drinking water. Rice and cooking of rice, however, have also been identified as important or potentially important exposure routes. Quantifying the relative importance of these exposure routes is critically required to inform the prioritisation and selection of remediation strategies. The aim of our study, therefore, was to determine the relative contributions of drinking water, rice and cooking of rice to human exposure in three contrasting areas of West Bengal with different overall levels of exposure to arsenic, viz. high (Bhawangola-I Block, Murshidibad District), moderate (Chakdha Block, Nadia District) and low (Khejuri-I Block, Midnapur District). Arsenic exposure from water was highly variable, median exposures being 0.02 μg/kg/d (Midnapur), 0.77 μg/kg/d (Nadia) and 2.03 μg/kg/d (Murshidabad). In contrast arsenic exposure from cooked rice was relatively uniform, with median exposures being 0.30 μg/kg/d (Midnapur), 0.50 μg/kg/d (Nadia) and 0.84 μg/kg/d (Murshidabad). Cooking rice typically resulted in arsenic exposures of lower magnitude, indeed in Midnapur, median exposure from cooking was slightly negative. Water was the dominant route of exposure in Murshidabad, both water and rice were major exposure routes in Nadia, whereas rice was the dominant exposure route in Midnapur. Notwithstanding the differences in balance of exposure routes, median excess lifetime cancer risk for all the blocks were found to exceed the USEPA regulatory threshold target cancer risk level of 10⁻⁴–10⁻⁶. The difference in balance of exposure routes indicate a difference in balance of remediation approaches in the three districts.     

Multivariate statistical analysis of heavy metals in soils of a Pb–Zn mining area, India

Surface soil samples collected from a Pb and Zn mining area in India were subjected to multi-elemental analysis by using inductively coupled plasma-atomic emission spectrometry. Multivariate statistical methods such as principal component analysis and cluster analysis, coupled with correlation coefficient analysis, were used to analyze the data and to apportion the possible sources of elements in soils of a metal mining area. Soils in this area have elevated heavy metal concentrations especially Pb, Zn, Mn, Cu, As, and Tl. Using principal component (PC) analysis, six components were extracted, out of which two PCs explaining 50.12% of total variance are more important. The first principal component with a high contribution of Ag, As, Be, Cd, Co, Cu, Mg, Mn, Ni, Pb, and Zn was deemed to be technogenic/anthropogenic component, and the second principal component, with high loadings for the five discerning variables (Al, Be, Cr, K, Li), was considered as lithogenic component. The third component having strong loadings of Ba, Ca, K, and Na is supposed to have a mixed origin (lithogenic as well as technogenic). Electrical conductivity and total organic matter were not correlated with any element and also have a strong loading in the fifth component which is probably the biomass and ions present in these soils. The findings of the principal component analysis were also substantiated by the cluster analysis. The present study would not only enhance our knowledge regarding the soil pollution status in the study area but would also provide us information to manage the sources of these elements in the study area.     

Studies on the removal of nickel from aqueous solutions using modified riverbed sand

This paper highlights the utility of riverbed sand (RS) for the treatment of Ni(II) from aqueous solutions. For enhancement of removal efficiency, RS was modified by simple methods. Raw and modified sands were characterized by scanning electron microscope (SEM), Energy Dispersive Spectroscopy (EDS), and Fourier Transform Infrared Spectroscopy (FTIR) to investigate the effect of modifying the surface of RS. For optimization of various important process parameters, batch mode experiments were conducted by choosing specific parameters such as pH (4.0–8.0), adsorbent dose (1.0–2.0 g), and metal ion concentrations (5–15 mg/L). Removal efficiency decreased from 68.76 to 54.09 % by increasing the concentration of Ni(II) in solution from 5 to 15 mg/L. Removal was found to be highly dependent on pH of aqueous solutions and maximum removal was achieved at pH 8.0. The process of removal follows first-order kinetics, and the value of rate constant was found to be 0.048 min^?1 at 5 mg/L and 25 °C. Value of intraparticle diffusion rate constant (k _id) was found to be 0.021 mg/g min^1/2 at 25 °C. Removal of Ni(II) decreased by increasing temperature which confirms exothermic nature of this system. For equilibrium studies, adsorption data was analyzed by Freundlich and Langmuir models. Thermodynamic studies for the present process were performed by determining the values of ΔG°, ΔH°, and ΔS°. Negative value of ?H° further confirms the exothermic nature of the removal process. The results of the present investigation indicate that modified riverbed sand (MRS) has high potential for the removal of Ni(II) from aqueous solutions, and resultant data can serve as baseline data for designing treatment plants at industrial scale.     

Adaptation strategies to combat climate change effect on rice and mustard in Eastern India

The negative impact of climate change on crop production is alarming as the demand for food is expected to increase in coming years, at a rate of about 2 percent a year. Wet season rice (Oryza sativa) followed by mustard (Brassica juncea) is one of the prominent cropping sequences in Eastern India. Descreases in their productivity due to climate change will not only hamper the regional food security but also affect the global economy. Considering the fact, the present study aims to assess the impact of climate change on productivity of wet-season rice and mustard and to evaluate the effectiveness of agronomic adjustment as adaptation options. Crop growth simulation model (CGSM) is a very effective tool to predict the growth and yield of a crop. One CGSM, namely InfoCrop (Generic Crop Model), was calibrated and validated for the said crops for West Bengal State, Eastern India. After validation, the model was used to predict the yield under elevated thermal condition (1 and 3 °C rise over normal temperature). Moreover, the future weather situation as predicted by PRECIS (Providing Regional Climates for Impacts Studies) model was used as weather input of the CGSM and the yield was predicted for ten selected locations of West Bengal for the year 2025 and 2050. It was observed that the average yield reduction of the wet-season rice would be in the tune of about 20.0 % for 2025 and 27.8 % for 2050. The mustard yield of West Bengal may be reduced by 20.0 to 33.9 % for the year 2025 and up to 40 % for 2050. It was concluded that the negative impact of climate change on mustard grown in winter season will be more pronounced compared to wet-season rice. Adjustment of sowing time will be the simplest and effective adaptation option for both rice and mustard. Increased rate of nutrient application can sustain the rice yield under future climate. The older seedling at the time of transplanting of wet-season rice and increased seed rate of mustard were proved less effective.     

Heavy metal uptake by Scirpus Littoralis Schrad. from fly ash dosed and metal spiked soils

Scirpus littoralis is a wetland plant commonly found in Yamuna flood plains of Delhi, India. The ability of Scirpus littoralis to take up and translocate five metals- Mn, Ni, Cu, Zn and Pb from fly ash dosed and metal spiked soils were studied under waterlogged and field conditions for 90 days. Scirpus littoralis accumulated Mn, Ni, Cu, Zn and Pb upto a maximum of 494.92, 56.37, 144.98, 207.95 and 93.08 ppm dry wt., respectively in below ground organs (BO) in 90 days time. The metal content ratios BO/soil (B/S) were higher than shoot/soil ratios (T/S) for all the metals, the highest being for Ni. Metal ratios BO/water (B/W) were also higher than shoot/water (T/W) ratios but the B/W ratio was maximum for Zn. The changes in nutrient status (N, P) in soil water and plants were also studied at interval of 30 days. The Pearson's correlation between metal uptake and N, P uptake were calculated. All the metals except Ni showed negative correlation with nitrogen but they were all non-significant. However, P uptake showed positive correlations with all the metals and all were significant at 1% confidence limit.     

Dissipation of flubendiamide residues in/on cabbage (Brassica oleracea L.)

Residues of fubendiamide and its metabolite desiodo flubendiamide were estimated in cabbage and soil using high-performance liquid chromatography with UV–vis detector. The initial deposits of flubendiamide residues on cabbage were found to be 0.16 and 0.31 μg g^?1 following two applications of flubendiamide 20 WG at 12.5 (standard dose) and 25 (double dose) g a.i. ha^?1 respectively at 10-days interval. The half-life values (t _1/2) of flubendiamide on cabbage ranged from 3.4 to 3.6 days. When flubendiamide applied at both the standard and double dose, no detectable residues were found in cabbage and soil at harvest. Thus, a waiting period of 1.63 days was suggested for the safe consumption of flubendiamide-treated cabbage. These data could provide guidance for the proper and safe use of this pesticide on cabbage crops in India.     

Maternal and cord blood levels of Aldrin and Dieldrin in Delhi population

Aldrin and dieldrin, structurally similar organochlorine pesticides belong to cyclodiene family and were widely used for agriculture and public health program in India. Although the manufacturing, use and import of aldrin and dieldrin have been banned in India since 2003, these pesticides are still persistent in environment and may be associated with adverse neurological and reproductive effects. The aim of this study is to assess the recent exposure level of aldrin and dieldrin and their placental transfer to fetus in normal healthy full-term pregnant women belonging to north Indian population undergoing normal delivery at Obstetrics and Gynecology department of UCMS and GTB hospital, Delhi. Quantitative analysis of aldrin and dieldrin residues in maternal and cord blood samples were carried out by gas chromatography system equipped with electron capture detector. The results of our study clearly revealed that maternal and cord blood levels of aldrin and dieldrin of pregnant women are age and dietary habit dependent. The aldrin level in maternal blood and dieldrin level in cord blood are higher in women in the age group 25–30 years than in women in age group of 19–24 years. Similarly, aldrin level in maternal blood is significantly higher in women with non-vegetarian dietary habit than in women with vegetarian dietary habit. No significant association is found for maternal and cord blood level. The results of the present study clearly demonstrate prenatal uptake of aldrin and dieldrin and provide recent information on the subsequent transplacental transfer.     

Enhancement of nutritional value of fried fish using an artificial intelligence approach

Frying affects the nutritional quality of fish detrimentally. In this study, using Catla catla and mustard oil, experiments were carried out in varying temperatures (140–240 °C), times (5–20 min), and oil amounts (25–100 ml/kg of fish) which established drastic reduction of 44.97% and 99.40% for polyunsaturated fatty acid (PUFA)/saturated fatty acids (SFA) and index of atherogenicity (IA) profile, respectively. Artificial neural network (ANN) was implemented successfully to provide an association between the independent inputs with dependent outputs (values of R² were 0.99 and 0.98; RMSE were 0.038 and 0.046; and performance were 0.038 and 0.067 for PUFA/SFA and IA, respectively) by exhaustive search of various algorithms and activation functions available in literature. ANN model–based meta-heuristic, stochastic optimization formalisms, genetic algorithm (GA) and particle swarm optimization (PSO), were applied to optimize the combination of cooking parameters for improving the nutritional quality of food which improved the nutritional value by maximizing the PUFA/SFA profile up to 63.05% and minimizing the IA profile to 99.64%. Multi-objective genetic algorithm (MOGA) was also employed to tune the inputs by maintaining a balance between the contrasting outputs and enhance the overall food value simultaneously with multi-objective (beneficial for health, economic, and environment-friendly) proposal. MOGA was able to improve the PUFA/SFA profile up to 44.76% and reduce the IA profile to 92.94% concurrently with the reduction of wastage of culinary media and energy consumption, following the optimized cooking condition (118.92 °C, 6.06 min, 40 ml oil/kg of fish).