Biosorption of arsenic from aqueous solution using dye waste

The purpose of this study is to examine on removal of arsenic from water by biosorption through potential application of herbal dye wastes. Four different flower dye residues (after extraction of natural dye) viz. Hibiscus rosasinensis, Rosa rosa, Tagetes erecta, and Canna indica were utilized successfully for the removal of arsenic from aqueous solution. Batch studies were carried out for various parameters viz. pH, sorbent dose, contact time, initial metal ion concentration, and temperature. Data were utilized for isothermal, kinetic, and thermodynamic studies. Scanning electron microscopy (SEM), energy-dispersive x-ray spectroscopy (EDAX), and Fourier transform infrared (FTIR) analyses of biomass were performed. The results showed that 1 g/100 ml for 5.0–5.5 h contact time at pH 6.0–7.5 with agitation rate 150 rpm provided 98, 96, 92, and 85 % maximum absorption of arsenic by R. rosa, H. rosasinensis, T. erecta, and C. indica, respectively, at initial concentration of 500 ppb. Data followed Langmuir isotherm showing sorption to be monolayer on heterogeneous surface of biosorbent. Negative values of ΔG° indicated spontaneous nature, whereas ΔH° indicates exothermic nature of system followed by pseudo-first-order adsorption kinetics. FTIR results showed apparent changes in functional group regions after metal chelation. SEM and EDAX analyses showed the changes in surface morphology of all test biosorbents. Herbal dye wastes, used as biosorbent, exhibited significant (85–98 %) removal of arsenic from aqueous solution. Hence, these biosorbents are cost-effective, easily available, eco-friendly, and comparatively more effective than other biosorbents already in use. These may be used to remove arsenic and other toxic metals from water.     

Treatment of low-strength soluble wastewater using an anaerobic baffled reactor (ABR)

Treatment of low-strength soluble wastewater (COD approximately 500 mg/L) was studied using an eight chambered anaerobic baffled reactor (ABR). At pseudo steady-state (PSS), the average total and soluble COD values (COD(T) and COD(S)) at 8h hydraulic retention time (HRT) were found to be around 50 and 40 mg/L, respectively, while at 10h HRT average COD(T) and COD(S) values were of the order of 47 and 37 mg/L, respectively. COD and BOD (3 day, 27 degrees C) removal averaged more than 90%. Effluent conformed to Indian standards laid down for BOD (less than 30 mg/L). Reactor effluent characteristics exhibited very low values of standard deviation indicating excellent reactor stability at PSS in terms of effluent characteristics. Based on mass balance calculations, more than 60% of raw wastewater COD was estimated to be recovered as CH(4) in the gas phase. Compartment-wise profiles indicated that most of the BOD and COD got reduced in the initial compartments only. Sudden drop in pH (7.8-6.7) and formation of volatile fatty acids (VFA) (53-85 mg/L) were observed in the first compartment due to acidogenesis and acetogenesis. The pH increased and VFA concentration decreased longitudinally down the reactor. Residence time distribution (RTD) studies revealed that the flow pattern in the ABR was neither completely plug-flow nor perfectly mixed. Observations from scanning electron micrographs (SEM) suggest that distinct phase separation takes place in an ABR.     

Using conditional inference forests to identify the factors affecting crash severity on arterial corridors

Introduction

The study aims at identifying traffic/highway design/driver-vehicle information significantly related with fatal/severe crashes on urban arterials for different crash types. Since the data used in this study are observational (i.e., collected outside the purview of a designed experiment), an information discovery approach is adopted for this study.

Method

Random Forests, which are ensembles of individual trees grown by CART (Classification and Regression Tree) algorithm, are applied in numerous applications for this purpose. Specifically, conditional inference forests have been implemented. In each tree of the conditional inference forest, splits are based on how good the association is. Chi-square test statistics are used to measure the association. Apart from identifying the variables that improve classification accuracy, the methodology also clearly identifies the variables that are neutral to accuracy, and also those that decrease it.

Results

The methodology is quite insightful in identifying the variables of interest in the database (e.g., alcohol/ drug use and higher posted speed limits contribute to severe crashes). Failure to use safety equipment by all passengers and presence of driver/passenger in the vulnerable age group (more than 55 years or less than 3 years) increased the severity of injuries given a crash had occurred. A new variable, ‘element’ has been used in this study, which assigns crashes to segments, intersections, or access points based on the information from site location, traffic control, and presence of signals.

Impact

The authors were able to identify roadway locations where severe crashes tend to occur. For example, segments and access points were found to be riskier for single vehicle crashes. Higher skid resistance and k-factor also contributed toward increased severity of injuries in crashes.     

Logistic regression model for prediction of roof fall risks in bord and pillar workings in coal mines: An approach

The roof fall hazards are the major problems in underground coal mines, which are generally unpredictable due to the associated uncertainties arising out of the complexity of geological conditions and variability in mining parameters. During the six year period, 1996–2001, 253 Indian coal miners lost their lives and 401 are seriously injured in 490 different roof fall accidents. In India, 32.7% of the total fatal injuries in coal sectors are due to roof fall in bord and pillar method of workings. This paper attempts to predict the severities of roof fall accidents based on some major contributing parameters using the binary logistic regression model. In total, 128 roof fall accidents for the last few years from five underground coal mines in India having bord and pillar method of working are analyzed for this study. The results revealed that wider gallery width is more prone to major and serious accidents than narrower gallery width. Thin seams are more amenable to major accidents in comparison to thick seams. Unsupported or partially supported roofs have higher risk for contributing major as well as serious accidents. Deep workings are more prone to major accidents as compared to shallow depth workings.     

Assessment of river water quality during snowmelt and base flow periods in two catchment areas with different land use

River water quality was evaluated with respect to eutrophication and land use during spring snowmelt and summer base flow periods in Abashiri (mixed cropland-livestock farming) and Okoppe (grassland-based dairy cattle farming), eastern Hokkaido, Japan. Water from rivers and tributaries was sampled during snowmelt and summer base flow periods in 2005, and river flow was measured. Total N (TN), NO₃–N, and Si concentrations were determined using standard methods. Total catchment and upland areas for each sampling site were determined with ArcGIS hydrology modeling software and 1:25,000-scale digital topographic maps. Specific discharge was significantly higher during snowmelt than during base flow. In both areas, TN concentrations increased, whereas Si concentrations decreased, with increased specific discharge, and were significantly higher during snowmelt. The Si:TN mole ratio decreased to below or close to the threshold value for eutrophication (2.7) in one-third of sites during snowmelt. River NO₃–N concentrations during base flow were significantly and positively correlated with the proportion of upland fields in the catchment in both the Abashiri (r = 0.88, P < 0.001) and Okoppe (r = 0.43, P < 0.01) areas. However, the regression slope, defined as the impact factor (IF) of water quality, was much higher in Abashiri (0.025) than in Okoppe (0.0094). The correlations were also significantly positive during snowmelt in both areas, but IF was four to eight times higher during snowmelt than during base flow. Higher discharge of N from upland fields and grasslands during snowmelt and the resulting eutrophication in estuaries suggest that nutrient discharge during snowmelt should be taken into account when assessing and monitoring the annual loss of nutrients from agricultural fields.     

Comparison between availability of heavy metals in dry and wetland tailing of an abandoned copper tailing pond

Wetland sediments are generally considered as a sink for metals and, in the anoxic zone, may contain very high concentrations of heavy metals in reduced state. A comprehensive study was carried out to compare the differences of total, environmentally available (Env-Av), HOAC, EDTA and DTPA available heavy metal fraction in tailing of the marshy area of a copper tailing pond and the dry tailing. The average concentrations of all the seven metals in the wetland tailing were found higher than dry tailing. Regarding pH, organic carbon, availailable N, P and K also found higher in marshy wetland tailing compare to the dry tailing. This information is needed in order to understand wetland system and to assure that wetlands do not themselves eventually become sources of metal contamination to surrounding areas. But as levels of pollutants increases, the ability of a wetland system to incorporate waste can be impaired and the wetland can become a source of toxicity.     

Effect of pendimethalin and quizalofop on N<Subscript>2</Subscript>-fixing bacteria in relation to availability of nitrogen in a <Emphasis Type="Italic">Typic Haplustept</Emphasis> soil of West Bengal,India

An experiment was conducted under laboratory conditions to investigate the effect of two systemic herbicides viz., pendimethalin and quizalofop, at their recommended field rates (1.0 kg and 50 g active ingredient ha^− 1, respectively) on the growth and activities of non-symbiotic N₂-fixing bacteria in relation to mineralization and availability of nitrogen in a Typic Haplustept soil. Both the herbicides, either singly or in a combination, stimulated the growth and activities of N₂-fixing bacteria resulting in higher mineralization and availability of nitrogen in soil. The single application of quizalofop increased the proliferation of aerobic non-symbiotic N₂-fixing bacteria to the highest extent while that of pendimethalin exerted maximum stimulation to their N₂-fixing capacity in soil. Both the herbicides, either alone or in a combination, did not have any significant difference in the stimulation of total nitrogen content and availability of exchangeable NH₄ ⁺ while the solubility of NO₃ ⁻ was highly manifested when the herbicides were applied separately in soil.     

Assessment of chromium efficacy on germination,root elongation,and coleoptile growth of wheat (Triticum aestivum L.) at different growth periods

The tannery effluents contain a high concentration of chromium (Cr). It drastically reduces the crop yield when used for irrigation purpose. A huge volume of tannery effluents is available as irrigation for crop production. It is negatively affecting germination as well as yield of the crop. The wheat seeds were exposed to five different concentrations of Cr (0, 20, 40, 80, and 100 ppm). In Petri plates, 100 seeds were placed and the germination percent was recorded after 72 hour (h). Root elongation and coleoptile growth were measured at 72, 120, 168, and 240 h. Results showed that the germination percent of the test crop decreased with increasing Cr levels. It decreased by 6, 14, 30, and 37 % under the Cr concentration of 20, 40, 80, and 100 ppm, respectively. The root elongation was more sensitive than the coleoptile growth. The negative correlation was found between Cr levels and root elongation as well as coleoptile growth. These growth parameters were significantly affected up to 80 ppm of Cr level. The wheat growers using tannery effluent as irrigation should be well treated prior to application.     

Assessment of groundwater quality: a fusion of geochemical and geophysical information via Bayesian neural networks

Deplorable quality of groundwater arising from saltwater intrusion, natural leaching and anthropogenic activities is one of the major concerns for the society. Assessment of groundwater quality is, therefore, a primary objective of scientific research. Here, we propose an artificial neural network-based method set in a Bayesian neural network (BNN) framework and employ it to assess groundwater quality. The approach is based on analyzing 36 water samples and inverting up to 85 Schlumberger vertical electrical sounding data. We constructed a priori model by suitably parameterizing geochemical and geophysical data collected from the western part of India. The posterior model (post-inversion) was estimated using the BNN learning procedure and global hybrid Monte Carlo/Markov Chain Monte Carlo optimization scheme. By suitable parameterization of geochemical and geophysical parameters, we simulated 1,500 training samples, out of which 50 % samples were used for training and remaining 50 % were used for validation and testing. We show that the trained model is able to classify validation and test samples with 85 % and 80 % accuracy respectively. Based on cross-correlation analysis and Gibb’s diagram of geochemical attributes, the groundwater qualities of the study area were classified into following three categories: “Very good”, “Good”, and “Unsuitable”. The BNN model-based results suggest that groundwater quality falls mostly in the range of “Good” to “Very good” except for some places near the Arabian Sea. The new modeling results powered by uncertainty and statistical analyses would provide useful constrain, which could be utilized in monitoring and assessment of the groundwater quality.     

Mapping of groundwater potential zones across Ghana using remote sensing, geographic information systems, and spatial modeling

Groundwater development across much of sub-Saharan Africa is constrained by a lack of knowledge on the suitability of aquifers for borehole construction. The main objective of this study was to map groundwater potential at the country-scale for Ghana to identify locations for developing new supplies that could be used for a range of purposes. Groundwater potential zones were delineated using remote sensing and geographical information system (GIS) techniques drawing from a database that includes climate, geology, and satellite data. Subjective scores and weights were assigned to each of seven key spatial data layers and integrated to identify groundwater potential according to five categories ranging from very good to very poor derived from the total percentage score. From this analysis, areas of very good groundwater potential are estimated to cover 689,680 ha (2.9 % of the country), good potential 5,158,955 ha (21.6 %), moderate potential 10,898,140 ha (45.6 %), and poor/very poor potential 7,167,713 ha (30 %). The results were independently tested against borehole yield data (2,650 measurements) which conformed to the anticipated trend between groundwater potential and borehole yield. The satisfactory delineation of groundwater potential zones through spatial modeling suggests that groundwater development should first focus on areas of the highest potential. This study demonstrates the importance of remote sensing and GIS techniques in mapping groundwater potential at the country-scale and suggests that similar methods could be applied across other African countries and regions.