Pollution indices of selected metals in tea (Camellia sinensis L.) growing soils of the Upper Assam region divulge a non-trifling menace of National Highway.

The study investigated the influence of a National Highway (NH) traversing tea estates (TEs) on heavy metal (HM) contamination in the top soils of Upper Assam, India. The dispersion and accumulation of six HMs, viz. cadmium (Cd), copper (Cu), iron (Fe), manganese (Mn), nickel (Ni), and zinc (Zn), within tea-growing soils were assessed using diverse indices: contamination factor (CF), degree of contamination (DC), enrichment factor (EF), geo-accumulation index (Igeo), modified degree of contamination (MDC), Nemerow pollution index (PINemerow), pollution load index (PLI), potential ecological risk factor (Eri), and potential ecological risk index (RI). The order of HM prevalence was Fe > Mn > Zn > Ni > Cu > Cd. Elevated Cd levels near the NH prompted immediate attention, while Cd and Zn showed moderate pollution in CF, EF, and RI. The remaining metals posed minimal individual risk (Eri< 40), resulting in an overall contamination range of "nil to shallow," signifying slight contamination from the studied metals. From MDC values for investigated metals, it was found to be "zero to very low degree of contamination" at all locations except the vicinity of NH. Soil pollution, as determined by PLI, indicated unpolluted soils in both districts, yet PINemerow values indicated slight pollution. The statistical analysis revealed that there is a significant decrease in most of the indices of HM as the distance from NH increases. The application of multivariate statistical techniques namely Principal Component Analysis and Cluster Analysis showed the presence of three distinct homogenous groups of distances based on different indices. This investigation underscores NH-associated anthropogenic effects on TE soil quality due to HM deposition, warranting proactive mitigation measures.

The Level of Selected Metals in Made Tea and Tea Infusion from the Roadside Tea Plants and Health Risk Assessment.

The effects of human activities are becoming clearer every year, with multiple reports of struggling and eroded ecosystems resulting in new threats of plant and animal extinctions throughout the world. It has been speculated that roadside tea-growing soils impact on metal dynamics from soil to tea plants and subsequently to tea infusion which may be threatened by increasingly unpredictable and dangerous surroundings. Furthermore, heavy metals released from vehicles on the national highway (NH) could be a source of metal contamination in roadside tea soils and tea plants. This study was articulated to realize the effect of NH on a buildup of selected metals (Cu, Cd, Fe, Mn, Ni, and Zn) in made tea along with repeated tea infusion. In general, metal concentration was found significantly higher in made tea prepared from the young shoots collected from the vicinity of NH. The results also showed that distance from the NH and infusion process significantly influenced to content of the analysed metal in tea infusions. The mean average daily intake (ADI) and hazard quotient (HQ) values of analysed tea samples were found in the orderMn˃Fe˃Zn˃Cu˃Ni˃Cd and Mn˃Cu˃Zn˃Fe˃Ni˃Cd, respectively. The HQ values of all analysed metals were found << 1, indicating that ingestion of tea infusion with analysed heavy metals should not cause a danger to human health. However, this study further demonstrates the consumption of tea infusion prepared from made tea around the vicinity of NH may contribute to a significantly higher quantity of metal intake in the human body. From the hierarchical cluster analysis, it has been observed that there are three homogenous groups of analysed heavy metals.

Change in the air due to the coronavirus outbreak in four major cities of India: What do the statistics say?

The onset of the novel Coronavirus (COVID-19) has impacted all sectors of society. To avoid the rapid spread of this virus, the Government of India imposed a nationwide lockdown in four phases. Lockdown, due to COVID-19 pandemic, resulted a decline in pollution in India in general and in dense cities in particular. Data on key air quality indicators were collected, imputed, and compiled for the period 1st August 2018 to 31st May 2020 for India's four megacities, namely Delhi, Mumbai, Kolkata, and Hyderabad. Autoregressive integrated moving average (ARIMA) model and machine learning technique e.g. Artificial Neural Network (ANN) with the inclusion of lockdown dummy in both the models have been applied to examine the impact of anthropogenic activity on air quality parameters. The number of indicators having significant lockdown dummy are six (PM2.5, PM10, NOx, CO, benzene, and AQI), five (PM2.5, PM10, NOx, SO2 and benzene), five (PM10, NOx, CO, benzene and AQI) and three (PM2.5, PM10, and AQI) for Delhi, Kolkata, Mumbai and Hyderabad respectively. It was also observed that the prediction accuracy significantly improved when a lockdown dummy was incorporated. The highest reduction in Mean Absolute Percentage Error (MAPE) is found for CO in Hyderabad (28.98%) followed by the NOx in Delhi (28.55%). Overall, it can be concluded that there is a significant decline in the value of air quality parameters in the lockdown period as compared to the same time phase in the previous year. Insights from the COVID-19 pandemic will help to achieve significant improvement in ambient air quality while keeping economic growth in mind.

Estimation of Heritability under Correlated Errors Using the Full-Sib Model.

In plant and animal breeding, sometimes observations are not independently distributed. There may exist a correlated relationship between the observations. In the presence of highly correlated observations, the classical premise of independence between observations is violated. Plant and animal breeders are particularly interested to study the genetic components for different important traits. In general, for estimating heritability, a random component in the model must adhere to specific assumptions, such as random components, including errors, having a normal distribution, and being identically independently distributed. However, in many real-world situations, all of the assumptions are not fulfilled. In this study, correlated error structures are considered errors that are associated to estimate heritability for the full-sib model. The number of immediately preceding observations in an autoregressive series that are used to predict the value at the current observation is defined as the order of the autoregressive models. First-order and second-order autoregressive models i.e., AR(1) and AR(2) error structures, have been considered. In the case of the full-sib model, theoretical derivation of Expected Mean sum square (EMS) considering AR(1) structure has been obtained. A numerical explanation is provided for the derived EMS considering AR(1) structure. The predicted mean squares error (MSE) is obtained after including the AR(1) error structures in the model, and heritability is estimated using the resulting equations. It is noticed that correlated errors have a major influence on heritability estimation. Different correlation patterns, such as AR(1) and AR(2), can be inferred to change heritability estimates and MSE values. To attain better results, several combinations are offered for various scenarios.

COVID-19 and prices of pulses in Major markets of India: Impact of nationwide lockdown.

The COVID-19 pandemic has impacted almost all the sectors including agriculture in the country. The present paper investigates the impact of COVID-19 induced lockdown on both wholesale and retail prices of major pulses in India. The daily wholesale and retail price data on five major pulses namely Lentil, Moong, Arhar, Urad and Gram are collected for five major markets in India namely Delhi, Mumbai, Kolkata, Chennai and Hyderabad during the period January, 2019 to September, 2020 from Ministry of Consumer Affairs, Food & Public Distribution, Government of India. The Government of India declared nationwide lockdown since March, 24, to May, 31, 2020 in different phases in order to restrict the spread of the infection due to COVID-19. To see the impact of lockdown on price and price volatility, time series model namely Autoregressive integrated moving average (ARIMA) model with error following Generalized autoregressive conditional heteroscedastic (GARCH) model incorporating exogenous variable as lockdown dummy in both mean as well variance equations. It is observed that in almost all the markets, lockdown has significant impact on price of the pulses whereas in few cases, it has significant impact on price volatility.

Machine learning techniques for forecasting agricultural prices: A case of brinjal in Odisha, India.

BACKGROUND: Price forecasting of perishable crop like vegetables has importance implications to the farmers, traders as well as consumers. Timely and accurate forecast of the price helps the farmers switch between the alternative nearby markets to sale their produce and getting good prices. The farmers can use the information to make choices around the timing of marketing. For forecasting price of agricultural commodities, several statistical models have been applied in past but those models have their own limitations in terms of assumptions. METHODS: In recent times, Machine Learning (ML) techniques have been much successful in modeling time series data. Though, numerous empirical studies have shown that ML approaches outperform time series models in forecasting time series, but their application in forecasting vegetables prices in India is scared. In the present investigation, an attempt has been made to explore efficient ML algorithms e.g. Generalized Neural Network (GRNN), Support Vector Regression (SVR), Random Forest (RF) and Gradient Boosting Machine (GBM) for forecasting wholesale price of Brinjal in seventeen major markets of Odisha, India. RESULTS: An empirical comparison of the predictive accuracies of different models with that of the usual stochastic model i.e. Autoregressive integrated moving average (ARIMA) model is carried out and it is observed that ML techniques particularly GRNN performs better in most of the cases. The superiority of the models is established by means of Model Confidence Set (MCS), and other accuracy measures such as Mean Error (ME), Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Prediction Error (MAPE). To this end, Diebold-Mariano test is performed to test for the significant differences in predictive accuracy of different models. CONCLUSIONS: Among the machine learning techniques, GRNN performs better in all the seventeen markets as compared to other techniques. RF performs at par with GRNN in four markets. The accuracies of other techniques such as SVR, GBM and ARIMA are not up to the mark.

Recycling of silicon-rich agro-wastes by their combined application with phosphate solubilizing microbe to solubilize the native soil phosphorus in a sub-tropical Alfisol.

It is imperative to find suitable strategies to utilize the native soil phosphorus (P), as natural rock phosphate deposits are at a verge of depletion. We explored two such cost-effective and eco-friendly strategies for native soil P solubilization: silicon (Si)-rich agro-wastes (as Si source) and phosphate solubilizing microorganism (PSM). An incubation study was conducted in a sub-tropical Alfisol for 90 days at 25 °C under field capacity moisture. A factorial completely randomized design with 3 factors, namely: Si sources (three levels: sugarcane bagasse ash, rice husk ash, and corn cob ash), PSM (two levels: without PSM, and with PSM); and Si doses [three levels: no Si (Si0), 125 (Si125) and 250 (Si250) mg Si kg-1 soil] was followed. The PSM increased solution P and soluble Si level by ∼22.2 and 1.88%, respectively, over no PSM; whereas, Si125 and Si250 increased solution P by ∼60.4 and 77.1%, as well as soluble Si by ∼41.5 and 55.5%, respectively, over Si0. Also, interaction of PSM × Si doses was found significant (P<0.05). Activities of soil enzymes (dehydrogenase, acid phosphatase) and microbial biomass P also increased significantly both with PSM and Si application. Overall, PSM solubilized ∼4.18 mg kg-1 of inorganic P and mineralized ∼5.92 mg kg-1 of organic P; whereas, Si125 and Si250 solubilized ∼3.85 and 5.72 mg kg-1 of inorganic P, and mineralized ∼4.15 and 5.37 mg kg-1 of organic P, respectively. Path analysis revealed that inorganic P majorly contributed to total P solubilization; whereas, soluble and loosely bound, iron bound and aluminium bound P significantly influenced the inorganic P solubilization. Thus, utilization of such wastes as Si sources will not only complement the costly P fertilizers, but also address the waste disposal issue in a sustainable manner.

Sick or rich: Assessing the selected soil properties and fertility status across the tea-growing region of Dooars, West Bengal, India.

Harnessing the potential yields of evergreen perennial crops like tea (Camellia sinensis L.) essentially requires the application of optimum doses of nutrients based on the soil test reports. In the present study, the soil pH, organic carbon (OC), available potassium as K2O (AK), and available sulphur (AS) of 7300 soil samples from 115 tea estates spread over the Dooars ranging from 88°52'E to 89°86'E longitude and 26°45'N to 27°00'N latitude of West Bengal, India have been documented. About 54% of soil samples were found within the optimum range of soil pH (4.50-5.50) for tea cultivation. The overall range of OC was found from 0.28% to 6.00% of which, 94% of the analyzed samples were within the range of satisfactory to excellent level of OC i.e. (>0.80% to 6.00%). Around 36.3% of soil samples were found to have high AK (>100 mg kg-1) but 37.1% of soils were found to have high AS content (>40 mg kg-1). The nutrient index status of soil pH was low in Dam Dim, Chulsa, Nagrakata, Binnaguri, and Jainti sub-districts. Soils from five sub-districts had a high nutrient index (2.47 to 2.83) for soil organic carbon. However, it existed in the medium index (1.69 and 2.22) for Dalgaon and Kalchini sub-districts. Only Nagrakata sub-district soil samples were in the high nutrient index (2.65) for AK. All analyzed samples showed a medium nutrient index (1.97 to 2.27) for AS. The result indicated that soil pH was significantly negatively correlated with soil OC (-0.336) and AK (-0.174). However, the soil OC was significantly positive correlated with AK (0.258) and AS (0.100). It could be concluded that a balanced fertilizer application would be needed as a part of the soil improvement program through soil chemical tests for sustainable tea cultivation.

Machine learning in the estimation of CRISPR-Cas9 cleavage sites for plant system.

CRISPR-Cas9 system is one of the recent most used genome editing techniques. Despite having a high capacity to alter the precise target genes and genomic regions that the planned guide RNA (or sgRNA) complements, the off-target effect still exists. But there are already machine learning algorithms for people, animals, and a few plant species. In this paper, an effort has been made to create models based on three machine learning-based techniques [namely, artificial neural networks (ANN), support vector machines (SVM), and random forests (RF)] for the prediction of the CRISPR-Cas9 cleavage sites that will be cleaved by a particular sgRNA. The plant dataset was the sole source of inspiration for all of these machine learning-based algorithms. 70% of the on-target and off-target dataset of various plant species that was gathered was used to train the models. The remaining 30% of the data set was used to evaluate the model's performance using a variety of evaluation metrics, including specificity, sensitivity, accuracy, precision, F1 score, F2 score, and AUC. Based on the aforementioned machine learning techniques, eleven models in all were developed. Comparative analysis of these produced models suggests that the model based on the random forest technique performs better. The accuracy of the Random Forest model is 96.27%, while the AUC value was found to be 99.21%. The SVM-Linear, SVM-Polynomial, SVM-Gaussian, and SVM-Sigmoid models were trained, making a total of six ANN-based models (ANN1-Logistic, ANN1-Tanh, ANN1-ReLU, ANN2-Logistic, ANN2-Tanh, and ANN-ReLU) and Support Vector Machine models (SVM-Linear, SVM-Polynomial, SVM-Gaussian However, the overall performance of Random Forest is better among all other ML techniques. ANN1-ReLU and SVM-Linear model performance were shown to be better among Artificial Neural Network and Support Vector Machine-based models, respectively.

Assessment of mobility and environmental risks associated with copper, manganese and zinc in soils of a dumping site around a Ramsar site.

This study reports the environmental fate and ecological hazard of the three heavy metals (HMs), viz. copper (Cu), manganese (Mn) and zinc (Zn) in soil influenced by municipal solid waste (MSW) dumping. The experimental site is situated in the vicinity of Deepor Beel, a Ramsar site located in Guwahati, India. This study assessed (i) the distribution pattern of Cu, Mn and Zn in six geochemical fractionations; (ii) the mobility, bioavailability and toxicity of Cu, Mn and Zn based on eight contamination and ecological indices, and (iii) the impact of Cu, Mn and Zn on soil quality. Altogether, 18 soil samples were collected and analysed from the study site using stratified random sampling. Pollution indices and multivariate statistics were applied on the data to identify the level and source of analysed HMs. Sequential extraction has revealed that the binding strength of Cu, Mn and Zn had a uniform trend. Mobility and potential bioavailability of studied HMs were in the order Mn > Cu > Zn. Analysed HMs were dominantly associated with non-bioavailable fractions. The observed low values of various contamination factors indicated the lesser contamination load posed by these metals. Conversely, their high enrichment factor and geo-accumulation index values indicated the sources of these metals were anthropogenic. Overall, the pollution and ecological indices registered lower contamination. Yet, it would be prudent to adopt efficient MSW management strategies for eliminating any future risk emanating out of this dumping site and posing threat to nearby Deepor Beel and its associated flora and fauna.

Quality characteristics of infusion and health consequences: a comparative study between orthodox and CTC green teas.

The present study compares the effects of two green tea processing techniques, viz. orthodox and CTC (curl, tear and crush) on the quality parameters and sensory profiles under the geographical and climatic conditions of Assam, India. The results showed that CTC green tea infusions had 13.3, 7.5, 7.1, 9.8, 5.4, 17.3, 17.1 and 18.6% more total polyphenol, total catechin, (-)-epigallocatechin-3-gallate (EGCG), (-)-epicatechin-3-gallate (ECG), (-)-epigallocatechin (EGC), (-)-epicatechin (EC), water extract and theanine level, respectively than the infusions prepared from orthodox green tea. The sensory evaluation preferred the orthodox over CTC processing mode. Risk assessment with daily consumption of five cups (10 g) of green tea reveals that the EGCG level is free from the risk of hepatotoxicity and caffeine will not inflict any health hazard.

Disentanglement of the secrets of aluminium in acidophilic tea plant (Camellia sinensis L.) influenced by organic and inorganic amendments.

Field experiment was carried out for four years in mature tea (Camellia sinensis L.) growing plot to investigate the impacts of different doses of inorganic and organic fertilizers on aluminium (Al) distribution pattern in soil and different parts of tea plant, leaf pigment concentration, gas exchange parameters, as well as the yield of tea. Results indicated that application of 6 × 103 kg compost ha-1 significantly increased the dry matter yields of tea. Pluckable shoot of tea plant were markedly stimulated in the presence of Al irrespective of treatment imposed. Furthermore, Al induced growth stimulation in tea plant was facilitated by higher photosynthesis rate as well as gas exchange parameters. For the present experiment, Tea Research Association Heavy Metal Contamination Index (TRAHMCI) decreases with increase the fertilizer dose and all the experimental soils were found non-polluted with respect to Al. Localization of Al in the root apex predominantly accumulated in the cortex. The translocation of Al from root to shoot was driven by the gradient in hydrostatic pressure and water potential. In all tea infusions influenced by different treatments, Al concentrations were within the maximum permissible limit of Al in drinking water by Provisional Tolerable Weekly Intake (PTWI) established by the Joint FAO/WHO Expert Committee on Food Additives (JECFA, 2 mg kg-1 bw-1) and the tolerable weekly intake (TWI) established by EFSA (European Food Safety Authority, 1 mg kg-1 bw-1). Application of stepwise multiple regression model indicates that around 75% of the variability in the yield of the crop can be expressed by the selected parameters under study. The Hierarchical cluster analysis reveals that two homogenous groups of treatment can be formed based on all the studied parameters.

Paradigm shift of contamination risk of six heavy metals in tea (Camellia sinensis L.) growing soil: A new approach influenced by inorganic and organic amendments.

The present study provides several contamination and ecological risk indices for selected metals (Cd, Cr, Cu, Mn, Ni and Zn) in tea (Camellia sinensis L.; cv. S.3A/3) growing soil influenced by lower to higher doses of inorganic and organic amendments. While ecological risk indices were applied, it was observed that same treatment showed different risk levels but contamination risk status did not vary significantly. All the indices showed significant correlation with heavy metals' concentration in young shoots of tea plants. As the indices characterized experimental soils with different extents of contamination, it would be important to standardize the indices with long term experiments followed by generation of new index. Therefore, we formulated a new contamination index named as Tea Research Association Heavy Metal Contamination Index (TRAHMCI) for tea growing soils. TRAHMCI is based on the probable change of metal status in soil with progress of growth of tea plant. This could be useful to negate discrepancies arised from use of various existing metal contamination indices in tea growing soils amended with different doses of fertilizers. TRAHMCI was formulated based on individual contamination factor using statistical technique and applied to the present dataset which provided a more holistic understanding of overall tea growing soil behavior. The limitation of the developed TRAHMCI index is that, the index had not been validated for other crops in our study not to claim its effective use for crops other than tea. As already mentioned, this new index had been formulated by taking tea as the test crop with above mentioned six heavy metal contents in young shoot and made tea.

Micronutrients (B, Co, Cu, Fe, Mn, Mo, and Zn) content in made tea (Camellia sinensis L.) and tea infusion with health prospect: A critical review.

Tea (Camellia sinensis L.) is a perennial acidophilic crop, and known to be a nonalcoholic stimulating beverage that is most widely consumed after water. The aim of this review paper is to provide a detailed documentation of selected micronutrient contents, viz. boron (B), cobalt (Co), copper (Cu), iron (Fe), manganese (Mn), molybdenum (Mo), and zinc (Zn) in made tea and tea infusion. Available data from the literature were used to calculate human health aspect associated with the consumption of tea infusion. A wide range of micronutrients reported in both made tea and tea infusion could be the major sources of micronutrients for human. The content of B, Co, Cu, Fe, Mn, Mo, and Zn in made tea are ranged from 3.04 to 58.44 µg g-1, below detectable limit (BDL) to 122.4 µg g-1, BDL to 602 µg g-1, 0.275 to 13,040 µg g-1, 0.004 to 15,866 µg g-1, 0.04 to 570.80 µg g-1 and 0.01 to 1120 µg g-1, respectively. Only 3.2 µg L-1 to 7.25 mg L-1, 0.01 µg L-1 to 7 mg L-1, 3.80 µg L-1 to 6.13 mg L-1, 135.59 µg L-1 -11.05 mg L-1, 0.05 µg L-1 to 1980.34 mg L-1, 0.012 to 3.78 µg L-1, and 1.12 µg L-1 to 2.32 µg L-1 of B, Co, Cu, Fe, Mn, Mo, and Zn, respectively, are found in tea infusion which are lower than the prescribed limit of micronutrients in drinking water by World Health Organization. Furthermore, micronutrient contents in tea infusion depend on infusion procedure as well as on the instrument used for analysis. The proportion of micronutrients found in different tea types are 1.0-88.9% for B, 10-60% for Co, 2.0-97.8% for Cu, 67.8-89.9% for Fe, 71.0-87.4% for Mn, 13.3-34% for Mo, and 34.9-83% for Zn. From the results, it can also be concluded that consumption of three cups of tea infusion per day does not have any adverse effect on human health with respect to the referred micronutrients rather got beneficial effects to human.

Comparative Assessment of Copper, Iron, and Zinc Contents in Selected Indian (Assam) and South African (Thohoyandou) Tea (Camellia sinensis L.) Samples and Their Infusion: A Quest for Health Risks to Consumer.

The current study aims to assess the infusion pattern of three important micronutrients namely copper (Cu), iron (Fe), and zinc (Zn) contents from black tea samples produced in Assam (India) and Thohoyandou (South Africa). Average daily intakes and hazardous quotient were reported for these micronutrients. Total content for Cu, Fe, and Zn varied from 2.25 to 48.82 mg kg-1, 14.75 to 148.18 mg kg-1, and 28.48 to 106.68 mg kg-1, respectively. The average contents of each of the three micronutrients were higher in tea leaves samples collected from South Africa than those from India while the contents in tea infusions in Indian samples were higher than in South African tea samples. Results of this study revealed that the consumption of 600 mL tea infusion produced from 24 g of made tea per day may be beneficial to human in terms of these micronutrients content. Application of nonparametric tests revealed that most of the data sets do not satisfy the normality assumptions. Hence, the use of both parametric and nonparametric statistical analysis that subsequently revealed significant differences in elemental contents among Indian and South African tea.

Degradation of tricyclazole: Effect of moisture, soil type, elevated carbon dioxide and Blue Green Algae (BGA).

Pesticide persistence and degradation in soil are influenced by factors like soil characteristics, light, moisture etc. Persistence of tricyclazole was studied under different soil moisture regimes viz., dry, field capacity and submerged in two different soil types viz., Inceptisol and Ultisol from Delhi and Karnataka, respectively. Tricyclazole dissipated faster in submerged (t1/2 160.22-177.05d) followed by field capacity (t1/2 167.17-188.07d) and dry (t1/2 300.91-334.35d) in both the soil types. Half-life of tricyclazole in Delhi field capacity soil amended with Blue Green Algae (BGA), was 150.5d as compared to 167.1d in unamended soil. In Karnataka soil amended with BGA the half-lives were 177.0d compared to 188.0d in unamended soil, indicating that BGA amendment enhanced the rate of dissipation of in both the selected soils. Tricyclazole was found to be stable in water over a pH range of 3-9, the half life in paddy field was 60.20d and 5.47d in paddy soil and paddy water, respectively. Statistical analysis and Duncan's Multiple Range Test (DMRT) revealed significant effect of moisture regime, organic matter and atmospheric CO2 level on dissipation of tricyclazole from soil and pH of water (at 95% confidence level p<0.0001).

Complexation of DTPA and EDTA with Cd2+: stability constants and thermodynamic parameters at the soil-water interface.

Two alkaline soils collected from the surface horizon (0-15 cm) of two agricultural fields Lakshmikantapur (LKP; 22° 06' 03″ N and 88° 18' 19″ E) and Diamond Harbour (DHB; 22° 11' N and 88° 14' E) of West Bengal, India were studied to observe the stability of cadmium (Cd) chelate complexes with diethylenetriaminepentaacetatic acid (DTPA) and ethylenediaminetetraacetic acid (EDTA), removing organic matter (OM). The objective of the present study is "determination of the stability constants and the thermodynamic parameters of Cd-DTPA and Cd-EDTA complexes at different pH and temperatures at the soil-water interface". Complex formation of soil Cd with DTPA and EDTA at the soil-water interface was studied under different ligand-to-metal ratios, pHs and temperatures. Apparent conditional stability constants (log k´) were calculated from the concentrations of Cd chelates and free Cd2+, estimated by solid phase extraction with an ion exchanger. Standard Gibbs energy (ΔG°), standard enthalpy (ΔH°) and standard entropy (ΔS°) of formation were calculated at three different temperatures. The higher stability constants of Cd-DTPA than Cd-EDTA indicated longer persistence of Cd-DTPA at the soil solution interface than Cd-EDTA complex. Increase of ΔG°, ΔH° and ΔS° with progress of temperature revealed that Cd-complex formation was facilitated by temperature. Highly negative ΔG° and positive ΔH° for Cd-complex formation indicated the reaction spontaneous and exothermic. In general, both ligands complexed high percentages of cadmium signalling their role in enhancing remobilization of Cd present in soil and preventing exchange of contaminated Cd from external source with soil mineral matrix; these phenomena may greatly reduce hazard for environment and human health. The result of this study support that DTPA increases solubility and more persistence of Cd in acidic soils within the range of temperature and mole fraction (MF = moles of Cd2+ / sum of the moles of Cd2+ and chelating agent) than that of EDTA due to higher capability of complex formation with Cd2+. Therefore, DTPA enhanced Cd toxicity in acid soils and groundwater. Complex formation in the presence of DTPA at acidic pH decreases with temperature and increases with pH. The higher per cent of Cd complexed in the presence of DTPA revealed that DTPA is a stronger chelating agent than EDTA at acidic pHs. Whereas, the capability of complex formation by EDTA is lower at lower pH but higher at higher pH.

Fate of cadmium at the soil-solution interface: a thermodynamic study as influenced by varying pH at South 24 Parganas, West Bengal, India.

A study on the sorption kinetics of Cd from soil solution to soils was conducted to assess the persistence of Cd in soil solution as it is related to the leaching, bioavailability, and potential toxicity of Cd. The kinetics of Cd sorption on two non-contaminated alkaline soils from Canning (22° 18' 48.02″ N and 88° 39' 29.0″ E) and Lakshmikantapur (22° 06' 16.61″ N and 88° 19' 08.66″ E) of South 24 Parganas, West Bengal, India, were studied using conventional batch experiment. The variable soil suspension parameters were pH (4.00, 6.00, 8.18, and 9.00), temperatures (308, 318, and 328 K) and Cd concentrations (5-100 mg L(-1)). The average rate coefficient (kavg) and half-life (t1/2) values indicate that the persistence of Cd in soil solution is influenced by both temperature and soil suspension pH. The concentration of Cd in soil solution decreases with increase of temperature; therefore, Cd sorption on the soil-solution interface is an endothermic one. Higher pH decreases the t 1/2 of Cd in soil solution, indicating that higher pH (alkaline) is not a serious concern in Cd toxicity than lower pH (acidic). Based on the energy of activation (Ea) values, Cd sorption in acidic pH (14.76±0.29 to 64.45±4.50 kJ mol(-1)) is a surface control phenomenon and in alkaline pH (9.33±0.09 to 44.60±2.01 kJ mol(-1)) is a diffusion control phenomenon The enthalpy of activation (ΔH∓) values were found to be between 7.28 and 61.73 kJ mol(-1). Additionally, higher positive energy of activation (ΔG∓) values (46.82±2.01 to 94.47±2.36 kJ mol(-1)) suggested that there is an energy barrier for product formation.

Struvite for composting of agricultural wastes with termite mound: Utilizing the unutilized.

Although, compost is the store house of different plant nutrients, there is a concern for low amount of major nutrients especially nitrogen content in prepared compost. The present study deals with preparation of compost by using agricultural wastes with struvite (MgNH4PO4·6H2O) along with termite mound. Among four composting mixtures, 50kg termite mound and 2.5kg struvite with crop residues (stover of ground nut: 361.65kg; soybean: 354.59kg; potato: 357.67kg and mustard: 373.19kg) and cow dung (84.90kg) formed a good quality compost within 70days of composting having nitrogen, phosphorus and potassium as 21.59, 3.98 and 34.6gkg(-1), respectively. Multivariate analysis of variance revealed significant differences among the composts. The four composts formed two (pit 1, pit 2 and pit 3, pit 4) different groups. Two principal components expressed more than 97% of the total variability. Hierarchical cluster analysis resulted two homogeneous groups of composts.

Optimal extraction and fingerprinting of carotenoids by accelerated solvent extraction and liquid chromatography with tandem mass spectrometry.

Accelerated solvent extraction (ASE) is applied for the extraction of carotenoids from orange carrot and the extraction parameters were optimized. Two carotenoids, lutein and ß-carotene, are selected as the validation process. Hildebrand solubility parameters and dielectric constant of solvents were taken into consideration in selecting solvent mixture. The effects of various experimental parameters, such as temperature, static time, drying agent etc., on the ASE extraction efficiency are investigated systematically. Interactions among the variables were also studied. Furthermore, two carotenoids were analyzed and characterized by LC-ESI MS. The study concluded that Hildebrand solubility parameter approach may be applicable for less polar bioactive molecules like carotenoids. The properties of solvent and extraction temperature are found to be the most important parameters affecting the ASE extraction efficiency of thermolabile natural compounds.