Maximizing diesel removal from contaminated sand using Scirpus mucronatus and assessment of rhizobacteria addition effect.

Phytoremediation is one of the green technologies that is friendly to nature, utilizes fewer chemicals, and exhibits good performance. In this study, phytoremediation was used to treat diesel-contaminated sand using a local aquatic plant species, Scirpus mucronatus, by analyzing the amount of total petroleum hydrocarbons (TPHs). Optimization of diesel removal was performed according to Response Surface Methodology (RSM) using Box-Behnken Design (BBD) under pilot-scale conditions. The quadratic model showed the best fit to describe the obtained data. Actual vs. predicted values from BBD showed a total of 9.1 % error for the concentration of TPH in sand and 0 % error for the concentration of TPH in plants. Maximum TPH removal of 42.3 ± 2.1 % was obtained under optimized conditions at a diesel initial concentration of 50 mg/kg, an aeration rate of 0.48 L/min, and a retention time of 72 days. The addition of two species of rhizobacteria (Bacillus subtilis and Bacillus licheniformis) at optimum conditions increased the TPH removal to 51.9 ± 2.6 %. The obtained model and optimum condition can be adopted to treat diesel-contaminated sand within the same TPH range (50-3000 mg/kg) in sand.

Lead (Pb) removal from contaminated water using constructed wetland planted with Scirpus grossus: Optimization using response surface methodology (RSM) and assessment of rhizobacterial addition.

Lead (Pb) is one of the toxic heavy metals that pollute the environment as a result of industrial activities. This study aims to optimize Pb removal from water by using horizontal free surface flow constructed wetland (HFSFCW) planted with Scirpus grossus. Optimization was conducted using response surface methodology (RSM) under Box-Behnken design with the operational parameters of initial Pb concentration, retention time, and aeration. Optimization results showed that 37 mg/L of initial Pb concentration, 32 days of retention time, and no aeration were the optimum conditions for Pb removal by using the systems. Validation test was run under two different conditions, namely, non-bioaugmented and bioaugmented with rhizobacteria (Bacillus cereus, B. pumilus, B. subtilis, Brevibacillus choshinensis, and Rhodococcus rhodochrous). Results of the validation test showed that Pb removal in water achieved 99.99% efficiency with 0.2% error from the RSM prediction, while the adsorption of Pb by plants reached 5160.18 mg/kg with 10.6% error from the RSM prediction. The bioaugmentation of the five rhizobacterial species showed a slight improvement in Pb removal from water and Pb adsorption by plants. However, no significant improvement was achieved (p < 0.05). Overall results suggested that operating the HFSFCW under optimum conditions with no bioaugmentation might be a feasible choice for the treatment of Pb-contaminated water.

Arsenic Resistance and Biosorption by Isolated Rhizobacteria from the Roots of Ludwigia octovalvis.

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 (R 2 = 0.86), E. asburiae followed the pseudo-first-order kinetic model (R 2 = 0.99), and R. rhizogenes followed the pseudo-second-order kinetic model (R 2 = 0.93). The identified rhizobacteria differ in their mechanism of arsenic biosorption, arsenic biosorption capability, and kinetic models in arsenic biosorption.

Performance evaluation of four-parameter models of the soil-water characteristic curve.

Soil-water characteristic curves (SWCCs) are important in terms of groundwater recharge, agriculture, and soil chemistry. These relationships are also of considerable value in geotechnical and geoenvironmental engineering. Their measurement, however, is difficult, expensive, and time-consuming. Many empirical models have been developed to describe the SWCC. Statistical assessment of soil-water characteristic curve models found that exponential-based model equations were the most difficult to fit and generally provided the poorest fit to the soil-water characteristic data. In this paper, an exponential-based model is devised to describe the SWCC. The modified equation is similar to those previously reported by Gardner (1956) but includes exponential variable. Verification was performed with 24 independent data sets for a wide range of soil textures. Prediction results were compared with the most widely used models to assess the model's performance. It was proven that the exponential-based equation of the modified model provided greater flexibility and a better fit to data on various types of soil.

Accuracy enhancement for forecasting water levels of reservoirs and river streams using a multiple-input-pattern fuzzification approach.

Water level forecasting is an essential topic in water management affecting reservoir operations and decision making. Recently, modern methods utilizing artificial intelligence, fuzzy logic, and combinations of these techniques have been used in hydrological applications because of their considerable ability to map an input-output pattern without requiring prior knowledge of the criteria influencing the forecasting procedure. The artificial neurofuzzy interface system (ANFIS) is one of the most accurate models used in water resource management. Because the membership functions (MFs) possess the characteristics of smoothness and mathematical components, each set of input data is able to yield the best result using a certain type of MF in the ANFIS models. The objective of this study is to define the different ANFIS model by applying different types of MFs for each type of input to forecast the water level in two case studies, the Klang Gates Dam and Rantau Panjang station on the Johor river in Malaysia, to compare the traditional ANFIS model with the new introduced one in two different situations, reservoir and stream, showing the new approach outweigh rather than the traditional one in both case studies. This objective is accomplished by evaluating the model fitness and performance in daily forecasting.

Performance evaluation of volumetric water content and relative permittivity models.

In recent years many models have been proposed for measuring soil water content (θ) based on the permittivity (ε) value. Permittivity is one of the properties used to determine θ in measurements using the electromagnetic method. This method is widely used due to quite substantial differences in values of ε for air, soil, and water, as it allows the θ value to be measured accurately. The performance of six proposed models with one parameter (i.e., permittivity) and five proposed models with two or more parameters (i.e., permittivity, porosity, and dry bulk density of soil) is discussed and evaluated. Secondary data obtained from previous studies are used for comparison to calibrate and evaluate the models. The results show that the models with one parameter proposed by Roth et al. (1992) and Topp et al. (1980) have the greatest R² data errors, while for the model with two parameters, the model proposed by Malicki et al. (1996) agrees very well with the data compared with other models.

Phytoremediation of wastewater containing lead (Pb) in pilot reed bed using Scirpus grossus.

Phytoremediation is a technology to clean the environment from heavy metals contamination. The objectives of this study are to threat Pb contaminated wastewater by using phytoremediation technology and to determine if the plant can be mention as hyperaccumulator. Fifty plants of Scirpus grossus were grown in sand medium and 600 L spiked water in various Pb concentration (10, 30 and 50 mg/L) was exposed. The experiment was conducted with single exposure method, sampling time on day-1, day-14, day-28, day-42, day-70, and day-98. The analysis of Pb concentration in water, sand medium and inside the plant tissue was conducted by ICP-OES. Water samples were filtered and Pb concentration were directly analyzed, Pb in sand samples were extracted by EDTA method before analyzed, and Pb in plant tissues were extracted by wet digestion method and analyzed. The results showed that on day-28, Pb concentration in water decreased 100%, 99.9%, 99.7%, and the highest Pb uptake by plant were 1343, 4909, 3236 mg/kg for the treatment of 10, 30, and 50 mg/L respectively. The highest BC and TF were 485,261 on day-42 and 2.5295 on day-70 of treatment 30 mg/L, it can be mentioned that Scirpus grossus is a hyperaccumulator.

Arsenic toxicity on Ludwigia octovalvis in spiked sand.

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.