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.

Rainfall is considered as one of the major components of the hydrological process; it takes significant part in evaluating drought and flooding events. Therefore, it is important to have an accurate model for rainfall forecasting. Recently, several data-driven modeling approaches have been investigated to perform such forecasting tasks as multi-layer perceptron neural networks (MLP-NN). In fact, the rainfall time series modeling involves an important temporal dimension. On the other hand, the classical MLP-NN is a static and has a memoryless network architecture that is effective for complex nonlinear static mapping. This research focuses on investigating the potential of introducing a neural network that could address the temporal relationships of the rainfall series. Two different static neural networks and one dynamic neural network, namely the multi-layer perceptron neural network (MLP-NN), radial basis function neural network (RBFNN) and input delay neural network (IDNN), respectively, have been examined in this study. Those models had been developed for the two time horizons for monthly and weekly rainfall forecasting at Klang River, Malaysia. Data collected over 12 yr (1997–2008) on a weekly basis and 22 yr (1987–2008) on a monthly basis were used to develop and examine the performance of the proposed models. Comprehensive comparison analyses were carried out to evaluate the performance of the proposed static and dynamic neural networks. Results showed that the MLP-NN neural network model is able to follow trends of the actual rainfall, however, not very accurately. RBFNN model achieved better accuracy than the MLP-NN model. Moreover, the forecasting accuracy of the IDNN model was better than that of static network during both training and testing stages, which proves a consistent level of accuracy with seen and unseen data.

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.

The aim of this study was to investigate the effect of applying a six-rhizobacterial consortium and nitrogen phosphate potassium (NPK) fertilizer in inorganic arsenic (arsenate) phytoremediation using Ludwigia octovalvis (Jacq.) Raven plants. The experiment included control L. octovalvis plants and three phytoremediation treatments with L. octovalvis plants, namely an arsenic (As) concentration of 39mgkg−1 only, the addition of a six-rhizobacterial consortium at 2% (v/v) with an As concentration of 39mgkg−1 and the addition NPK fertilizer at 0.02% (w/w) with an As concentration of 39mgkg−1. In the As phytoremediation treatment with the presence of As only, plants showed signs of phytotoxicity such as wilting and senescent leaves. L. octovalvis grew well until the end of exposure (Day 42) in the phytoremediation treatment with 2% rhizobacteria consortium or with 0.02% NPK fertilizer addition. The As phytoremediation treatment with 2% rhizobacteria showed the highest removal percentages of bioavailable As (89%), total extractable As (78%) and As in the leachate of the reed beds (98%) resulting in lower As uptake by L. octovalvis than in the other two phytoremediation treatments. This indicates that the bioremediation process played a role in the treatment with 2% rhizobacteria. The fresh weight of L. octovalvis biomass in treatment with 0.02% NPK fertilizer increased by almost five-fold compared with the As only treatment. The effectiveness of phytoremediation in terms of As uptake at Day 42 reached 49.8% in the phytoremediation treatment with 0.02% NPK fertilizer addition. It was the highest than in the other two phytoremediation treatments. In conclusion, NPK fertilizer addition gave the best results in As uptake using L. octovalvis plants in terms of the effectiveness of phytoremediation

Slope stability has been identified as a major obstacle to construction in the rapidly developing countries of Indonesia and Malaysia. In these countries, slope failure has been identified as one of the most commonly occurring natural disasters, leading to financial losses and deaths. Slope failure is often related to prolonged rainfall events where rainfall infiltration increases pore water pressure, reducing soil strength. This failure mechanism will accelerate with the existence of cracks, which are usually caused by differential settling, drying and shrinking of soil, and associated construction activities, among other causes. The existence of cracks on slopes usually provides an easy pathway for rainfall infiltration into soil, allowing rain to infiltrate to deeper layers than in the absence of cracks. The moisture content in deep layers is therefore higher in cracked slopes than in slopes without cracks. To address this issue, we investigated the effects of cracks on slope stability when subjected to rainfall. The influence of crack location, depth, size, and direction on pore water pressure distribution and slope stability was studied by imposing different rainfall intensities. Analysis of seepage and stability were conducted using the GEO STUDIO 2007 softwares SEEP/W and SLOPE/W. Results suggested that pore water pressure and slope stability were influenced by the existence of cracks. Analysis showed that slope factors of safety decreased sharply when cracks were located adjacent to the slope crest, as compared to when cracks were located in the middle of the slope. Furthermore, slope factors of safety decreased with increasing crack depth. Pore water pressure and slope factors of safety decreased further when slopes were subjected to small rainfall intensities for long periods, as compared to when slopes were subjected to high rainfall intensities for short periods. The present study shows that study of cracks should be an integral part of the slope stability analysis.