Muhammad Mukhlisin

A Review on Heavy Metals (As, Pb, and Hg) Uptake by Plants through Phytoremediation

Heavy metals are among the most important sorts of contaminant in the environment. Several methods already used to clean up the environment from these kinds of contaminants, but most of them are costly and difficult to get optimum results. Currently, phytoremediation is an effective and affordable technological solution used to extract or remove inactive metals and metal pollutants from contaminated soil and water. This technology is environmental friendly and potentially cost effective. This paper aims to compile some information about heavy metals of arsenic, lead, and mercury (As, Pb, and Hg) sources, effects and their treatment. It also reviews deeply about phytoremediation technology, including the heavy metal uptake mechanisms and several research studies associated about the topics. Additionally, it describes several sources and the effects of As, Pb, and Hg on the environment, the advantages of this kind of technology for reducing them, and also heavy metal uptake mechanisms in phytoremediation technology as well as the factors affecting the uptake mechanisms. Some recommended plants which are commonly used in phytoremediation and their capability to reduce the contaminant are also reported.

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.

Numerical model of antecedent rainfall effect on slope stability at a hillslope of weathered granitic soil formation

Modeling rainwater infiltration in slopes is vital to the analysis of slope failure induced by heavy rainfall. Although the significance of rainwater infiltration in causing landslides is widely recognized, there have been different conclusions as to the relative roles of antecedent rainfall to slope failure. In this study, a numerical model was developed to estimate the effect of antecedent rainfall on an unsaturated slope, the formation of a saturated zone, and the change in slope stability under weak rainfall and rainstorm event. Results showed that under a rainstorm event, slope failure occurred at comparably similar time although the antecedent rainfall drainage periods prior to the major rainfall were different (i.e., 24-h, 48-h and 96-h). However, under weak rainfall condition, differences of the antecedent rainfall drainage periods have significant effect on development of pore-water pressure. A higher initial soil moisture conditions caused faster increase in pore water pressure and thus decreasing the safety factor of the slope eventually increasing likelihood of slope failure.

Regularized versus non-regularized neural network model for prediction of saturated soil-water content on weathered granite soil formation

Modeling unsaturated water flow in soil requires knowledge of the hydraulic properties of soil. However, correlation between soil hydraulic properties such as the relationship between saturated soil-water content θs and saturated soil hydraulic conductivity ks as function of soil depth is in stochastic pattern. On the other hand, soil-water profile process is also believed to be highly non-linear, time varying, spatially distributed, and not easily described by simple models. In this study, the potential of implementing artificial neural network (ANN) model was proposed and investigated to map the soil-water profile in terms of ks and θs with respect to the soil depth d. A regularized neural network (NN) model is developed to overcome the drawbacks of conventional prediction techniques. The use of regularized NN advantaged avoid over-fitting of training data, which was observed as a limitation of classical ANN models. Site experimental data sets on the hydraulic properties of weathered granite soils were collected. These data sets include the observed values of saturated and unsaturated hydraulic conductivities, saturated water contents, and retention curves. The proposed ANN model was examined utilizing 49 records of data collected from field experiments. The results showed that the regularized ANN model has the ability to detect and extract the stochastic behavior of saturated soil-water content with relatively high accuracy.

Phytotoxicity of wastewater containing lead (Pb) effects scirpus grossus

Phytoremediation is an environment-friendly and cost-effective method to clean the environment of heavy metal contamination. A prolonged phytotoxicity test was conducted in a single exposure. Scirpus grossus plants were grown in sand to which the diluted Pb(NO3)2 was added, with the variation of concentration were 0, 100, 200, 400, 600, and 800 mg/L. It was found that Scirpus grossus plants can tolerate Pb at concentrations of up to 400 mg/L. The withering was observed on day-7 for Pb concentrations of 400 mg/L and above. 100% of the plants withered with a Pb concentration of 600 mg/L on day 65. The Pb concentration in water medium decreased while in plant tissues increased. Adsorption of Pb solution ranged between 2 to 6% for concentrations of 100 to 800 mg/L. The Bioaccumulation Coefficient and Translocation Factor of Scirpus grossus were found greater than 1, indicating that this species is a hyperaccumulator plant.

New Approach: Integrated Risk-Stochastic Dynamic Model for Dam and Reservoir Optimization

Multiple studies have developed management models to identify optimal operating policies for reservoirs in the last four decades. In an uncertain environment, in which climatic factors such as stream flow are stochastic, the economic returns from reservoir releases that are based on policy are uncertain. Furthermore, the consequences of reservoir release are not fully realized until it occurs. Rather than explicitly recognizing the full spectrum of consequences that are possible within an uncertain environment, the existing optimization models have focused on addressing these uncertainties by identifying the release policies that optimize the summative metric of the risks that are associated with release decisions. This technique has limitations for representing risks that are associated with release policy decisions. In fact, the approach of these techniques may conflict with the actual attitudes of the decision-makers regarding the risk aspects of release policies. The risk aspects of these decisions affect the design and operation of multi-purpose reservoirs. A method is needed to completely represent and evaluate potential consequences that are associated with release decisions. In this study, these techniques were reviewed from the stochastic model and risk analysis perspectives. Therefore, previously developed optimization models for operating dams and reservoirs were reviewed based on their advantages and disadvantages. Specifically, optimal release decisions that use the stochastic variable impacts and the levels of risk that are associated with decisions were evaluated regarding model performance. In addition, a new approach was introduced to develop an optimization model that is capable of replicating the manner in which reservoir release decision risks are perceived and interpreted. This model is based on the Neural Network (NN) theory and enables a more complete representation of the risk function that occurs from particular reservoir release decisions.

Effect of rock fragments on pore water pressure and slope stability at a hillslope

This study investigates the effects of rock fragments on pore water pressure and safety factor of a slope. The study was conducted using computer software SEEP/W and SLOPE/W for a slope with and without rock fragment in order to get value for the pore water pressure and safety factor of the slope. The study also been carried out on the rock fragment characteristics such as position (top, middle and bottom) and size (large, medium and small) to find out the effects of rock fragments on hydrologic response and slope stability. The results show that the rock fragments obstruct water flow on the slope and increase the pore water pressure. However, higher pore water pressure can cause the slope to become less stable. These indicate that the safety factor for the slope with rock fragment is lower than slope without the rock fragment. The results also showed that pore water pressure and seepage flow response are affected by different position of the rock fragment. At a lower position of the rock fragments, the pore water pressure is the highest. Therefore, the safety factors are smaller compared to other rock positions. On the other hand, the results show that the pore water pressure and safety factor of the slope are less influenced by the size of rock fragments.

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 R 2 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.

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 models 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.