Wilting, especially of the leaves, was observed as an initial symptom of arsenate [As(V)] to Ludwigia octovalvis (Jacq.) P. H. Raven. The plants tolerated As(V) levels of 39 mg kg⁻¹ for as long as 35 days of exposure. After 91 days, the maximum concentration of As uptake in the plant occurred at As(V) concentration of 65 mg kg⁻¹ while As concentration in the stems, roots and leaves were 6139.9 ± 829.5, 1284.5 ± 242.9 and 1126.1 ± 117.2 mg kg⁻¹, respectively. In conclusion, As(V) could cause toxic effects in L. octovalvis and the plants could uptake and accumulate As in plant tissues.
In this study, the removal of arsenic (As) by plant, Ludwigia octovalvis, in a pilot reed bed was optimized. A Box-Behnken design was employed including a comparative analysis of both Response Surface Methodology (RSM) and an Artificial Neural Network (ANN) for the prediction of maximum arsenic removal. The predicted optimum condition using the desirability function of both models was 39 mg kg-1 for the arsenic concentration in soil, an elapsed time of 42 days (the sampling day) and an aeration rate of 0.22 L/min, with the predicted values of arsenic removal by RSM and ANN being 72.6% and 71.4%, respectively. The validation of the predicted optimum point showed an actual arsenic removal of 70.6%. This was achieved with the deviation between the validation value and the predicted values being within 3.49% (RSM) and 1.87% (ANN). The performance evaluation of the RSM and ANN models showed that ANN performs better than RSM with a higher R2 (0.97) close to 1.0 and very small Average Absolute Deviation (AAD) (0.02) and Root Mean Square Error (RMSE) (0.004) values close to zero. Both models were appropriate for the optimization of arsenic removal with ANN demonstrating significantly higher predictive and fitting ability than RSM.
The emergence of the aluminium recycling industry has led to an increase in aluminium-containing wastewater discharge to the environment. Biological treatment of metal is one of the solutions that can be provided as green technology. Screening tests showed that Brochothrix thermosphacta and Vibrio alginolyticus have the potential to remove aluminium from wastewater. Brochothrix thermosphacta removed up to 49.60%, while Vibrio alginolyticus was capable of removing up to 59.72% of 100 mg/L aluminium in acidic conditions. The removal of aluminium by V. alginolyticus was well fitted with pseudo-first-order kinetics (k1 = 0.01796/min), while B. thermosphacta showed pseudo-second-order kinetics (k2 = 0.125612 mg substrate/g adsorbent. hr) in the process of aluminium removal. V. alginolyticus had a higher rate constant under acidic conditions, while B. thermosphacta had a higher rate constant under neutral pH conditions.
Certain rhizobacteria can be applied to remove arsenic in the environment through bioremediation or phytoremediation. This study determines the minimum inhibitory concentration (MIC) of arsenic on identified rhizobacteria that were isolated from the roots of Ludwigia octovalvis (Jacq.) Raven. The arsenic biosorption capability of the was also analyzed. Among the 10 isolated rhizobacteria, five were Gram-positive (Arthrobacter globiformis, Bacillus megaterium, Bacillus cereus, Bacillus pumilus, and Staphylococcus lentus), and five were Gram-negative (Enterobacter asburiae, Sphingomonas paucimobilis, Pantoea spp., Rhizobium rhizogenes, and Rhizobium radiobacter). R. radiobacter showed the highest MIC of >1,500 mg/L of arsenic. All the rhizobacteria were capable of absorbing arsenic, and S. paucimobilis showed the highest arsenic biosorption capability (146.4 ± 23.4 mg/g dry cell weight). Kinetic rate analysis showed that B. cereus followed the pore diffusion model (R2 = 0.86), E. asburiae followed the pseudo-first-order kinetic model (R2 = 0.99), and R. rhizogenes followed the pseudo-second-order kinetic model (R2 = 0.93). The identified rhizobacteria differ in their mechanism of arsenic biosorption, arsenic biosorption capability, and kinetic models in arsenic biosorption.