Biological sulfate reduction of a sulfate-rich industrial waste liquor using sulfate reducing bacteria

An industrial waste liquor having high sulfate concentrations was subjected to biological treatment using the sulfate-reducing bacteria (SRB) Desulfovibrio desulfuricans. Toxicity levels of different sulfate, cobalt and nickel concentrations toward growth of the SRB with respect to biological sulfate reduction kinetics was initially established. Optimum sulfate concentration to promote SRB growth amounted to 0.8 - 1 g/L. The strain of D. desulfuricans used in this study initially tolerated up to 4 -5 g/L of sulfate or 50 mg/L of cobalt and nickel, while its tolerance could be further enhanced through adaptation by serial subculturing in the presence of increasing concentrations of sulfate, cobalt and nickel. From the waste liquor, more than 70% of sulfate and 95% of cobalt and nickel could be precipitated as sulfides, using a preadapted strain of D. desulfuricans. Probable mechanisms involving biological sulfide precipitation and metal adsorption onto precipitates and bacterial cells are discussed.

Assessing GCM Convergence for India Using the Variable Convergence Score

General circulation models (GCMs) use transient climate simulations to predict climate conditions in the future. Coarse-grid resolutions and process uncertainties necessitate the use of downscaling models to simulate precipitation. However, in the downscaling models, with multiple GCMs now available, selecting an atmospheric variable from a particular model which is representative of the ensemble mean becomes an important consideration. The variable convergence score (VCS) provides a simple yet meaningful approach to address this issue, providing a mechanism to evaluate variables against each other with respect to the stability they exhibit in future climate simulations. In this study, VCS methodology is applied to 10 atmospheric variables of particular interest in downscaling precipitation over India and also on a regional basis. The nested bias-correction methodology is used to remove the systematic biases in the GCMs simulations, and a single VCS curve is developed for the entire country. The generated VCS curve is expected to assist in quantifying the variable performance across different GCMs, thus reducing the uncertainty in climate impact-assessment studies. The results indicate higher consistency across GCMs for pressure and temperature, and lower consistency for precipitation and related variables. Regional assessments, while broadly consistent with the overall results, indicate low convergence in atmospheric attributes for the Northeastern parts of India.