Response surface optimization of a dynamic dye adsorption process: a case study of crystal violet adsorption onto NaOH-modified rice husk

The adsorption of crystal violet from aqueous solution by NaOH-modified rice husk was investigated in a laboratory-scale fixed-bed column. A two-level three factor (2³) full factorial central composite design with the help of Design Expert Version 7.1.6 (Stat Ease, USA) was used for optimisation of the dynamic dye adsorption process and evaluation of interaction effects of different operating parameters: initial dye concentration (100–200 mg L^?1), flow rate (10–30 mL min^?1) and bed height (5–25 cm). A correlation coefficient (R ²) value of 0.999, model F value of 1,936.59 and its low p value (<0.0001) along with lower value of coefficient of variation (1.38 %) indicated the fitness of the response surface quadratic model developed during the present study. Numerical optimisation applying desirability function was used to identify the optimum conditions for a targeted breakthrough time of 12 h. The optimum conditions were found to be initial solution pH?=?8.00, initial dye concentration?=?100 mg L^?1, flow rate?=?22.88 mL min^?1 and bed height?=?18.75 cm. A confirmatory experiment was performed to evaluate the accuracy of the optimised procedure. Under the optimised conditions, breakthrough appeared after 12.2 h and the column efficiency was determined as 99 %. The Thomas model showed excellent fit to the dynamic dye adsorption data obtained from the confirmatory experiment. Thereby, it was concluded that the current investigation gives valuable insights for designing and establishing a continuous wastewater treatment plant.     

Elucidation of lead-induced oxidative stress in Talinum triangulare roots by analysis of antioxidant responses and DNA damage at cellular level

Hydroponic experiments were performed with Talinum triangulare (Jacq.) Willd. focusing the root cellular biochemistry with special emphasis on DNA damage, structural, and elemental analyses in Pb(NO₃)₂ exposed with 0, 0.25, 0.5, 0.75, 1.0, and 1.25 mM for 7 days. Lead (Pb) increased reactive oxygen species production, lipid peroxidation, protein oxidation, cell death, and DNA damage and decreased the protein content in a dose-dependent manner. Likewise, a dose-dependent induction of antioxidative enzymes superoxide dismutase and catalase by Pb was evident. Ascorbate peroxidase on the other hand responded biphasically to Pb treatments by showing induction at low (0.25 and 0.50) and repression at high (0.75–1.25 mM) concentrations. The estimation of proline content also indicated a similar biphasic trend. Scanning electron microscope and energy-dispersive X-ray spectroscopy analysis showed that 1.25 mM Pb treatment resulted in ultrastructural modifications in roots and stem tissue that was marked by the change in the elemental profile. The findings pointed to the role of oxidative stress in the underlying Pb phytotoxicity and genotoxicity in T. triangulare.     

Attenuation of divalent toxic metal ions using natural sericitic pyrophyllite

The present study investigated the effectiveness of an inexpensive and ecofriendly alumino silicate clay mineral, sericitic pyrophyllite, as an adsorbent for the possible application in the removal of some divalent toxic metal cations such as Pb(2+), Cu(2+)and Zn(2+) from aqueous systems. Batch scale equilibrium adsorption studies were carried out for a wide range of initial concentration from 24.1 to 2410mumolL(-1) for lead, 78.65 to 7865mumolL(-1) for copper and 76.45 to 7645mumolL(-1) for zinc solutions. The removal of Pb(2+) was almost complete at low concentration (maximum lead removal capacity, LRC, 32mg of lead/g of pyrophyllite) with 10gL(-1) of adsorbent in a 30min equilibration time. The effects of temperature on adsorption of heavy metal ions were studied. The applicability of the Langmuir, Freundlich and Dubinin-Radushkevich adsorption models in each case of lead, copper and zinc adsorption was examined separately at different temperatures. The adsorption process was found to be endothermic and the Freundlich adsorption model was found to represent the data at different temperatures more suitably.     

Fire risk evaluation using multicriteria analysis—a case study

Forest fires are one of the major causes of ecological disturbance and environmental concerns in tropical deciduous forests of south India. In this study, we use fuzzy set theory integrated with decision-making algorithm in a Geographic Information Systems (GIS) framework to map forest fire risk. Fuzzy set theory implements classes or groupings of data with boundaries that are not sharply defined (i.e., fuzzy) and consists of a rule base, membership functions, and an inference procedure. We used satellite remote sensing datasets in conjunction with topographic, vegetation, climate, and socioeconomic datasets to infer the causative factors of fires. Spatial-level data on these biophysical and socioeconomic parameters have been aggregated at the district level and have been organized in a GIS framework. A participatory multicriteria decision-making approach involving Analytical Hierarchy Process has been designed to arrive at a decision matrix that identified the important causative factors of fires. These expert judgments were then integrated using spatial fuzzy decision-making algorithm to map the forest fire risk. Results from this study were quite useful in identifying potential “hotspots” of fire risk, where forest fire protection measures can be taken in advance. Further, this study also demonstrates the potential of multicriteria analysis integrated with GIS as an effective tool in assessing “where and when” forest fires will most likely occur.     

Hydrogeochemical characterization of fluoride rich groundwater of Wailpalli watershed, Nalgonda District, Andhra Pradesh, India

The groundwater of Nalgonda district is well known for its very high fluoride content for the past five decades. Many researchers have contributed their scientific knowledge to unravel causes for fluoride enrichment of groundwater. In the present paper, an attempt has been made to relate the high fluoride content in the groundwater to hydrogeochemical characterization of the water in a fracture hard rock terrain—the Wailpally watershed. Groundwater samples collected from all the major geomorphic units in pre- and post-monsoon seasons were analyzed for its major ion constituents such as Ca^2?+?, Mg^2?+?, Na^?+?, K^?+?, CO $_{3}^{-}$ , HCO $_{3}^{-}$ , Cl^???, SO $_{4}^{-2}$ , NO $_{3}^{-}$ , and F^???. The groundwaters in the watershed have the average fluoride content of 2.79 mg/l in pre-monsoon and 2.83 mg/l in post-monsoon. Fluoride concentration in groundwater does not show perceptible change neither with time nor in space. The ionic dominance pattern is in the order of Na^?+? > Ca^2?+??> Mg^2?+??> K^??? among cations and HCO $_{3}^{-}\:\,>$ Cl^????> SO $_{4}^{-2} >$ NO $_{3}^{-} >$ F^??? among anions in pre-monsoon. In post-monsoon, Mg replaces Ca^2?+? and NO $_{3}^{-}$ takes the place of SO $_{4}^{-2}$ . The Modified Piper diagram reflect that the water belong to Ca^?+?2–Mg^?+?2–HCO $_{3}^{-}$ to Na^?+?–HCO $_{3}^{-}$ facies. Negative chloralkali indices in both the seasons prove that ion exchange between Na^?+? and K^?+? in aquatic solution took place with Ca^?+?2 and Mg^?+?2 of host rock. The interpretation of plots for different major ions and molar ratios suggest that weathering of silicate rocks and water–rock interaction is responsible for major ion chemistry of groundwater in Wailpally watershed. Chemical characteristics and evolution of this fluoride-contaminated groundwater is akin to normal waters of other hard rock terrain; hence, it can be concluded that aquifer material play an important role in the contribution of fluoride in to the accompanying water. High fluoride content in groundwater can be attributed to the continuous water–rock interaction during the process of percolation with fluorite-bearing country rocks under arid, low precipitation, and high evapotranspiration conditions.     

Biophysical and socioeconomic characterization of a water-stressed area and simulating agri-production estimates and land use planning under normal and extreme climatic events: a case study

This study aims in linking the biophysical and socioeconomic data base layers with the technical coefficients or simulation models for agri-production estimates and land use planning under normal and extreme climatic events, and exploring the resource and inputs management options in village Shikohpur, Gurgaon district located in the northwest part of India. The socioeconomic profile of Shikohpur is highly skewed with mostly small and marginal farmers. Though the areas under wheat in Shikohpur are increasing, the productivity is declining or remaining stagnant over the years. Most of the area during kharif season (June-September) remains fallow. Pearl millet based cropping systems (pearl millet-mustard and pearl millet-wheat) are predominant. Soils are mostly loamy sand to sandy loam with average of 70-80% sand content. Organic C content in soil is less than 0.3%, due to high prevailing temperature with little rainfall and also intensive agriculture followed in this region. Though the annual average seasonal rainfall in Gurgaon did not have much variation over the years, occurrence of extreme climate events has increased in the last two decades. The crop intensity is low and the water table is declining. Water and nitrogen production functions were developed for the important crops of the region, for their subsequent use in scheduling of the inputs. InfoCrop, WTGROWS and technical coefficients were used for crop planning and resource management under climate change and its variability, extreme events, limited resource availability and crop intensification. These will help in disseminating necessary agro-advisories to the farmers so that they will be able to manipulate the inputs and agronomic management practices for sustained agricultural production under normal as well as extreme climatic conditions.     

Effect of dust load on the leaf attributes of the tree species growing along the roadside

Dust is considered as one of the most widespread air pollutants. The objective of the study was to analyse the effect of dust load (DL) on the leaf attributes of the four tree species planted along the roadside at a low pollution Banaras Hindu University (BHU) campus and a highly polluted industrial area (Chunar, Mirzapur) of India. The studied leaf attributes were: leaf area, specific leaf area (SLA), relative water content (RWC), leaf nitrogen content (LNC), leaf phosphorus content (LPC), chlorophyll content (Chl), maximum stomatal conductance (Gs_max), maximum photosynthetic rate (A _max) and intrinsic water-use efficiency (WUEi). Results showed significant effect of sites and species for DL and the leaf attributes. Average DL across the four tree species was greater at Chunar, whereas, the average values of leaf attributes were greater at the BHU campus. Maximum DL was observed for Tectona grandis at Chunar site and minimum for Syzygium cumini at BHU campus. Across the two sites, maximum value of SLA, Chl and Gs_max were exhibited by S. cumini, whereas, the greatest value of RWC, LNC, LPC, A _max and WUEi were observed in Anthocephalus cadamba. A. cadamba and S. cumini exhibited 28 and 27 times more dust accumulation, respectively, at the most polluted Chunar site as compared to the BHU campus. They also exhibited less reduction in A _max due to dust deposition as compared to the other two species. Therefore, both these species may be promoted for plantation along the roadside of the sites having greater dust deposition.     

Selected organochlorine pesticides (OCPs) in surface soils from three major states from the northeastern part of India

Eighty-two surface soil samples were collected from forest, grassland, tea estate, wildlife sanctuary, wetland, and roadside areas from the northeastern states of India, viz., Tripura, Manipur, and Assam. Thirteen different organochlorine pesticides (OCPs) were detected from background soils using gas chromatography electron capture detector. Manipur soils were found to be with higher concentration of dichlorodiphenyltrichloroethanes (DDTs), hexachlorocyclohexanes (HCHs), and endosulfan followed by Tripura and Assam. The spearman correlation coefficient shows significant correlation between HCHs, DDTs, and endosulfan isomers (r ²?>?0.5 and p?<?0.05). Additionally, α-HCH, δ-HCH, o,p′-DDE, and endosulfan-sulfate shows good correlation with total organic carbon in soil (r ²?=?0.5, p?=?0.05), indicating that the soil organic matter could enhance adsorption of these compounds, also demonstrating that the present OCPs in the background soil were from similar source. Further principal component analysis evaluates that most of the higher volatile compounds where clustered together in soil. However, after comparing with different states of Indian soil samples, the concentrations of OCPs in the present study areas are much lower and comparable with background soil across the globe.     

Receptor model-based source apportionment of particulate pollution in Hyderabad, India

Air quality in Hyderabad, India, often exceeds the national ambient air quality standards, especially for particulate matter (PM), which, in 2010, averaged 82.2?±?24.6, 96.2?±?12.1, and 64.3?±?21.2 μg/m³ of PM₁₀, at commercial, industrial, and residential monitoring stations, respectively, exceeding the national ambient standard of 60 μg/m³. In 2005, following an ordinance passed by the Supreme Court of India, a source apportionment study was conducted to quantify source contributions to PM pollution in Hyderabad, using the chemical mass balance (version 8.2) receptor model for 180 ambient samples collected at three stations for PM₁₀ and PM_2.5 size fractions for three seasons. The receptor modeling results indicated that the PM₁₀ pollution is dominated by the direct vehicular exhaust and road dust (more than 60 %). PM_2.5 with higher propensity to enter the human respiratory tracks, has mixed sources of vehicle exhaust, industrial coal combustion, garbage burning, and secondary PM. In order to improve the air quality in the city, these findings demonstrate the need to control emissions from all known sources and particularly focus on the low-hanging fruits like road dust and waste burning, while the technological and institutional advancements in the transport and industrial sectors are bound to enhance efficiencies. Andhra Pradesh Pollution Control Board utilized these results to prepare an air pollution control action plan for the city.     

Statistical approaches for hydrogeochemical characterization of groundwater in West Delhi, India

Parametric statistical approaches, correlations and multiple linear regressions were used to develop models for the interpretation of hydrogeochemical parameters in the Western part of Delhi state, India. The hydrogeochemical parameters indicated that the groundwater quality is not safe for consumption. The water is moderately saline and the salinity level is increasing over time. There is also the problem of nitrate pollution. The correlation between electrical conductivity (EC) and other water quality parameters except potassium (K(+)), nitrate (NO(3)(-)) and bicarbonate (HCO(3)(-)) is significantly positive and Ca(++)+ Mg(++)/Na(+)+ K(+) is significantly negative. In predicting EC, the multiple R(2) values of 0.996 and 0.985 indicate that 99.6% and 98.5% variability in the observed EC could be ascribed to the combined effect of Na(+), HCO(3)(-), Cl(-), SO(4)(--), NO(3)(-) and Ca(++)+ Mg(++) for the year of 2005 and 2006 respectively. Out of 99.6% of the variability in EC in 2005, 51.2% was due to Cl(-) alone, and 8.5%, 12.5%, 6.1%, 14.7% and 6.7% were due to Na(+), HCO(3)(-), SO(4)(--), NO(3)(-) and Ca(++) + Mg(++). Similarly in 2006, out of 98.5% of the variability in EC, 48.5% was due to Cl(-) alone, and 10.4%, 12.7%, 5.3%, 17.2% and 4.4% were due to Na(+), HCO(3)(-), SO(4)(--), NO(3)(-) and Ca(++)+ Mg(++). The analysis shows that a good correlation exists between EC, Cl(-) and SO(4)(--) either individually or in combination with other ions and the multiple regression models can predict EC at 5% level of significance.