Statistical inferences from measured data on concentrations of naturally occurring radon,thoron, and decay products in Kumaun Himalayan belt

Environmental Science and Pollution Research - Regional averages of radon, thoron, and associated decay product concentration are reported to be higher than their respective global averages in...     

Complete dechlorination of endosulfan and lindane using Mg0/Pd(+4) bimetallic system

A Mg0/Pd(+4) bimetallic system was evaluated to dechlorinate endosulfan and lindane in the aqueous phase. Studies were conducted with endosulfan and lindane separately, with or without acid in a 1:1 (v/v) water:acetone phase. In the absence of any acid, higher degradation of endosulfan and lindane was observed using Mg0/Pd(+4) doses of 10/0.5 and 4/0.1 mg/mL, respectively. Acetone plays an important role in facilitating the dechlorination reaction by increasing the solubilities of pesticides. Dechlorination kinetics for endosulfan and lindane (30 and 50 mg/L [30 and 50 ppm] concentration of each pesticide) were conducted with varying Mg0/Pd(+4) doses, and the time-course profiles were well-fitted into exponential curves. The optimum observed rate constants (k(obs)) for endosulfan and lindane were obtained with Mg0/Pd(+4) doses of 5/0.5 and 4/0.1 mg/mL, respectively. Gas chromatography-mass spectrometry analyses revealed that endosulfan and lindane were dechlorinated completely into their hydrocarbon skeletons-Bicyclo [2,2,1] hepta 2-5 diene and benzene, respectively.     

Application of best subset method for river water quality modeling: A study on Godavari River,India

The utilization of water quality analysis to inform optimal decision-making is imperative to achieve sustainable management of river water quality. A multitude of research works in the past has focused on river water quality modeling. Despite being a precise statistical regression technique that allows for fitting separate models for all potential combinations of predictors and selecting the optimal subset model, the application of best subset method in river water quality modeling is not widely adopted. The current research aims to validate the use of best subset method in evaluating the water quality parameters of the Godavari River, one of the largest rivers in India, by developing regression equations for different combinations of its physicochemical parameters. The study involves in formulating best subset regression equations to estimate the concentrations of river water quality parameters while also identifying and quantifying their variations. A total of 17 water quality parameters are analyzed at 13 monitoring sites using 13 years (1993–2005) of observed data for the monsoon (June–October) period and post-monsoon (November–February) period. The final subset model is selected among model combinations that are developed for each year's dataset through widely used statistical criteria such as R², F value, adjusted R²_a, AIC_c, and RSS. The final best subset model across all parameters exhibits R² values surpassing 0.8, indicating that the models possess the ability to account for over 80% of the variations in the concentrations of dependent parameters. Therefore, the findings demonstrated the appropriateness of this method in evaluating the water quality parameters in extensive rivers. This work is very useful for decision-making and in the management of river water quality for its sustainable use in the study area.