This study is a continuation of previous research work conducted by the author on the stability of man-made slope constructed in UPNM campus. This paper presents the effects of ground water level (GWL) fluctuation on slope stability by using numerical simulation program, SlopeW. Ground water rises were simulated from 5m below the ground until 10m above the ground. Soil samples were taken from the site and tested in laboratory and then were incorporated into the program. It was found that the stability of the slope decreased with an increase of GWL. The critical slip surface formed by each case study is categorised as toe failure with circular and non-circular shapes.
Herein, biochar was prepared using rice straw, and it served as the peroxymonosulfate (PMS) activator to degrade naphthalene (NAP). The results showed that pyrolysis temperature has played an important role in regulating biochar structure and properties. The biochar prepared at 900°C (BC900) had the best activation capacity and could remove NAP in a wide range of initial pH (5-11). In the system of BC900/PMS, multi-reactive species were produced, in which 1O2 and electron transfer mainly contributed to NAP degradation. In addition, the interference of complex groundwater components on the NAP removal rate must get attention. Cl- had a significant promotional effect but risked the formation of chlorinated disinfection by-products. HCO3-, CO32-, and humic acid (HA) had an inhibitory effect; surfactants had compatibility problems with the BC900/PMS system, which could lead to unproductive consumption of PMS. Significantly, the BC900/PMS system showed satisfactory remediation performance in spiked natural groundwater and soil, and it could solve the problem of persistent groundwater contamination caused by NAP desorption from the soil. Besides, the degradation pathway of NAP was proposed, and the BC900/PMS system could degrade NAP into low or nontoxic products. These suggest that the BC900/PMS system has promising applications in in-situ groundwater remediation.
Groundwater, the world's most abundant source of freshwater, is rapidly depleting in many regions due to a variety of factors. Accurate forecasting of groundwater level (GWL) is essential for effective management of this vital resource, but it remains a complex and challenging task. In recent years, there has been a notable increase in the use of machine learning (ML) techniques to model GWL, with many studies reporting exceptional results. In this paper, we present a comprehensive review of 142 relevant articles indexed by the Web of Science from 2017 to 2023, focusing on key ML models, including artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), support vector regression (SVR), evolutionary computing (EC), deep learning (DL), ensemble learning (EN), and hybrid-modeling (HM). We also discussed key modeling concepts such as dataset size, data splitting, input variable selection, forecasting time-step, performance metrics (PM), study zones, and aquifers, highlighting best practices for optimal GWL forecasting with ML. This review provides valuable insights and recommendations for researchers and water management agencies working in the field of groundwater management and hydrology.
Groundwater is one of the alternatives to surface water that can be used for drinking water; however, it normally exists with high iron and manganese content. In this study, a column study was conducted to observe the elimination of iron (Fe) and manganese (Mn) in the groundwater under different retention times by using zeolite immobilized with iron-oxidizing bacteria (IOB). Rossellomorea sp., representing an IOB, was found from the isolation process and was further cultured in the laboratory for immobilization into the natural zeolite as replacement materials for the sand filter. When the zeolite assisted with the Rossellomorea sp. was used, the elimination of Fe and Mn were 99.34% and 88.92%, respectively, compared to the removal of Fe and Mn, which were 93.62% and 93.73%, respectively, for media without immobilization. The presence of Rossellomorea sp. enhances the Fe oxidation, resulting in high removal of Fe. The Thomas and Yoon-Nelson models were performed in both raw zeolite and zeolite with IOB. The total coliform (most probable number [MPN]) increased from 70.8 to 307.6 MPN/100 mL because of the Rossellomorea sp. present that promotes the growth of coliform bacteria. In conclusion, the immobilization of zeolite with IOB is a potential technique to extract the Fe and Mn in the groundwater. PRACTITIONER POINTS: Zeolite incorporated with Rossellomorea sp. has higher removal performance of Fe, whereas the removal of Mn reduced compared to the raw zeolite. The presence of Rossellomorea sp. enhances the oxidation of ferrous iron and improves the removal of Fe in the groundwater because the ferric iron is the priority ion to be exchanged. The removal of UV254 increase when Rossellomorea sp. present in the zeolite because the Rossellomorea sp. consume the natural organic matter as carbon source.
The existing knowledge regarding seawater intrusion and particularly upconing, in which both problems are linked to pumping, entirely relies on theoretical assumptions. Therefore, in this paper, an attempt is made to capture the effects of pumping on seawater intrusion and upconing using 2D resistivity measurement. For this work, two positions, one perpendicular and the other parallel to the sea, were chosen as profile line for resistivity measurement in the coastal area near the pumping wells of Kapas Island, Malaysia. Subsequently, water was pumped out of two pumping wells simultaneously for about five straight hours. Then, immediately after the pumping stopped, resistivity measurements were taken along the two stationed profile lines. This was followed by additional measurements after four and eight hours. The results showed an upconing with low resistivity of about 1-10 Ωm just beneath the pumping well along the first profile line that was taken just after the pumping stopped. The resistivity image also shows an intrusion of saline water (water enriched with diluted salt) from the sea coming towards the pumping well with resistivity values ranging between 10 and 25 Ωm. The subsequent measurements show the recovery of freshwater in the aquifer and how the saline water is gradually diluted or pushed out of the aquifer. Similarly the line parallel to the sea (L2) reveals almost the same result as the first line. However, in the second and third measurements, there were some significant variations which were contrary to the expectation that the freshwater may completely flush out the saline water from the aquifer. These two time series lines show that as the areas with the lowest resistivity (1 Ωm) shrink with time, the low resistivity (10 Ωm) tends to take over almost the entire area implying that the freshwater-saltwater equilibrium zone has already been altered. These results have clearly enhanced our current understanding and add more scientific weight to the theoretical assumptions on the effects of pumping on seawater intrusion and upconing.
Hydrogeochemical investigations had been carried out at the Amol-Babol Plain in the north of Iran. Geochemical processes and factors controlling the groundwater chemistry are identified based on the combination of classic geochemical methods with geographic information system (GIS) and geostatistical techniques. The results of the ionic ratios and Gibbs plots show that water rock interaction mechanisms, followed by cation exchange, and dissolution of carbonate and silicate minerals have influenced the groundwater chemistry in the study area. The hydrogeochemical characteristics of groundwater show a shift from low mineralized Ca-HCO3, Ca-Na-HCO3, and Ca-Cl water types to high mineralized Na-Cl water type. Three classes, namely, C1, C2, and C3, have been classified using cluster analysis. The spatial distribution maps of Na(+)/Cl(-), Mg(2+)/Ca(2+), and Cl(-)/HCO3 (-) ratios and electrical conductivity values indicate that the carbonate and weathering of silicate minerals played a significant role in the groundwater chemistry on the southern and western sides of the plain. However, salinization process had increased due to the influence of the evaporation-precipitation process towards the north-eastern side of the study area.
Natural, inorganic arsenic contamination of groundwater threatens the health of more than 100 million people worldwide, including residents of the densely populated river deltas of South and Southeast Asia. Contaminated groundwater from tube wells in Cambodia was discovered in 2001 leading to the detection of the first cases of arsenicosis in 2006. The most affected area was the Kandal Province. The main objective of this study was to determine the prevalence of arsenicosis in Cambodia based on acceptable criteria, and to investigate the use of hair arsenic as a biomarker not only for arsenicosis-related signs but also for associated symptoms. A cross-sectional epidemiological study of 616 respondents from 3 purposely selected provinces within the Mekong River basin of Cambodia was conducted. The Kandal Province was chosen as a high arsenic-contaminated area, while the Kratie Province and Kampong Cham Province were chosen as moderate and low arsenic-contaminated areas, respectively. The most prevalent sign of arsenicosis was hypomelanosis with a prevalence of 14.5% among all respondents and 32.4% among respondents with a hair arsenic level of ≥1 μg/g. This was followed by hyperkeratosis, hyperpigmentation and mee's lines. Results also suggest a 1.0 μg/g hair arsenic level to be a practical cut off point for an indication of an arsenic contaminated individual. This hair arsenic level, together with the presence of one or more of the classical signs of arsenicosis, seems to be a practical criteria for a confirmed diagnosis. Based on these criteria, the overall prevalence of arsenicosis for all provinces was found to be 16.1%, with Kandal Province recording the highest prevalence of 35.5%. This prevalence is comparatively high when compared to that of other affected countries. The association between arsenicosis and the use of Chinese traditional medicine also needs further investigation.
Small islands are susceptible to anthropogenic and natural activities, especially in respect of their freshwater supply. The freshwater supply in small islands may be threatened by the encroachment of seawater into freshwater aquifers, usually caused by over pumping. This study focused on the hydrochemistry of the Kapas Island aquifer, which controls the groundwater composition. Groundwater samples were taken from six constructed boreholes for the analysis and measurement of its in-situ and major ions. The experimental results show a positive and significant correlation between Na-Cl (r=0.907; p<0.01), which can be defined as the effect of salinization. The mechanisms involved in groundwater chemistry changes were ion exchange and mineralization. These processes can be demonstrated using Piper's diagram in which the water type has shifted into a Na-HCO(3) water type from a Ca-HCO(3) water type. Saturation indices have been calculated in order to determine the saturation condition related to dissolution or the precipitation state of the aquifer bedrock. About 76% of collected data (n=108) were found to be in the dissolution process of carbonate minerals. Moreover, the correlation between total CEC and Ca shows a positive and strong relationship (r=0.995; p<0.01). This indicates that the major mineral component in Kapas Island is Ca ion, which contributes to the groundwater chemical composition. The output of this research explains the chemical mechanism attributed to the groundwater condition of the Kapas Island aquifer.
To understand the variations in the decomposability of tropical peat soil following deforestation for an oil palm plantation, a field incubation experiment was conducted in Sarawak, Malaysia. Peat soils collected from three types of primary forest, namely Mixed Peat Swamp (MPS; Gonystylus-Dactylocladus-Neoscrotechinia association), Alan Batu (ABt; Shorea albida-Gonstylus-Strenonurus association), and Alan Bunga (ABg; Shorea albida association), were packed in polyvinyl chloride pipes and installed in an oil palm plantation. Carbon dioxide (CO2) and methane (CH4) fluxes from soil were monthly measured for 3years. Environmental variables including soil temperature, soil moisture content, and groundwater table were also monitored. The pH, loss on ignition, and total carbon (C) content were similar among the three soils, while total N content was larger in the MPS than in the ABg soils. Based on13C nuclear magnetic resonance (NMR) spectroscopy, C composition of the MPS and ABg soils was characterized by the largest proportion of C present as alkyl C and O-alkyl C, respectively. The C composition of the ABt soil was intermediate between the MPS and ABg soils. The CO2fluxes from the three soils ranged from 78 to 625mgCm-2h-1with a negative correlation to groundwater level. The CH4fluxes ranged from -67 to 653μgCm-2h-1. Both total CO2and CH4fluxes were larger in the order ABg>ABt>MPS (P<0.05). Annual rate of peat decomposition as was estimated from cumulative C loss differed up to 2 times, and the rate constant in exponential decay model was 0.033y-1for the MPS soil and 0.066y-1for the ABg soil. The field incubation results of the three forest peat soils seem to reflect the difference in the labile organic matter content, represented by polysaccharides.
The aim of the current study was to produce groundwater spring potential maps using novel ensemble weights-of-evidence (WoE) with logistic regression (LR) and functional tree (FT) models. First, a total of 66 springs were identified by field surveys, out of which 70% of the spring locations were used for training the models and 30% of the spring locations were employed for the validation process. Second, a total of 14 affecting factors including aspect, altitude, slope, plan curvature, profile curvature, stream power index (SPI), topographic wetness index (TWI), sediment transport index (STI), lithology, normalized difference vegetation index (NDVI), land use, soil, distance to roads, and distance to streams was used to analyze the spatial relationship between these affecting factors and spring occurrences. Multicollinearity analysis and feature selection of the correlation attribute evaluation (CAE) method were employed to optimize the affecting factors. Subsequently, the novel ensembles of the WoE, LR, and FT models were constructed using the training dataset. Finally, the receiver operating characteristic (ROC) curves, standard error, confidence interval (CI) at 95%, and significance level P were employed to validate and compare the performance of three models. Overall, all three models performed well for groundwater spring potential evaluation. The prediction capability of the FT model, with the highest AUC values, the smallest standard errors, the narrowest CIs, and the smallest P values for the training and validation datasets, is better compared to those of other models. The groundwater spring potential maps can be adopted for the management of water resources and land use by planners and engineers.
A comprehensive study was conducted to identify the salinization origins and the major hydrogeochemical processes controlling the salinization and deterioration of the Gaza coastal aquifer system through a combination approaches of statistical and geostatistical techniques, and detailed hydrogeochemical assessments. These analyses were applied on ten physicochemical variables for 219 wells using STATA/SE12 and Surfer softwares. Geostatistical analysis of the groundwater salinity showed that seawater intrusion along the coastline, and saltwater up-coning inland highly influenced the groundwater salinity of the study area. The hierarchical cluster analysis (HCA) technique yielded seven distinct hydrogeochemical signature clusters; (C1&C2: Eocene brackish water invasion, C3 saltwater up-coning, C4 human inputs, C5 seawater intrusion, C6 & C7 rainfall and mixing inputs). Box plot shows a wide variation of most of the ions while Chadha's plot elucidates the predominance of Na-Cl (71.6%) and Ca/Mg-Cl (25%) water types. It is found that, the highest and the lowest levels of salinization and the highest level of nitrate pollution were recorded in the northern area. This result reflects the sensitivity of this area to the human activities and/or natural actions. Around 90.4% of the wells are nitrate polluted. The main source of nitrate pollution is the sewage inputs while the farming inputs are very limited and restricted mostly in the sensitive northern area. Among the hydrogeochemical processes, ion exchange process was the most effective process all over the study area. Carbonate dissolution was common in the study area with the highest level in clusters 6, 7, 4 and 2 in the north while Gypsum dissolution was significant only in cluster 1 in the south and limited in the other clusters. This integrated multi-techniques research should be of benefit for effective utilization and management of the Gaza coastal aquifer system as well as for future work in other similar aquifers systems.
The conversions of forests and grass land to urban and farmland has exerted significant changes on terrestrial ecosystems. However, quantifying how these changes can affect the quality of water resources is still a challenge for hydrologists. Nitrate concentrations can be applied as an indicator to trace the link between land use changes and groundwater quality due to their solubility and easy transport from their source to the groundwater. In this study, 25year records (from 1989 to 2014) of nitrate concentrations are applied to show the impact of land use changes on the quality of groundwater in Northern Kelantan, Malaysia, where large scale deforestation in recent decades has occurred. The results from the integration of time series analysis and geospatial modelling revealed that nitrate (NO3-N) concentrations significantly increased with approximately 8.1% and 3.89% annually in agricultural and residential wells, respectively, over 25years. In 1989 only 1% of the total area had a nitrate value greater than 10mg/L; and this value increased sharply to 48% by 2014. The significant increase in nitrate was only observed in a shallow aquifer with a 3.74% annual nitrate increase. Based on the result of the Autoregressive Integrated Moving Average (ARIMA) model the nitrate contamination is expected to continue to rise by about 2.64% and 3.9% annually until 2030 in agricultural and residential areas. The present study develops techniques for detecting and predicting the impact of land use changes on environmental parameters as an essential step in land and water resource management strategy development.
This study presents a theoretical framework based on power law distribution to identify the vulnerable regions to soil loss in Susu river basin at Cameron Highlands, Malaysia by using the geomorphologic factors from Digital Elevation Model (DEM). Drainage area is used to describe the runoff aggregation structure of the watershed which represents the magnitude of discharge. Stream power is also used to describe the energy expenditure pattern of the watershed. They are fitted to power law distribution by means of the maximum likelihood to estimate the threshold for soil loss. The landscape stability condition is assessed through the mechanism of channel initiation. Two regions in the slope area plot are recognized as the regimes susceptible to soil loss, in that discharge, local slope and energy are sufficient for the initiation of soil movement. The result is further improved by incorporating the Topographic Wetness Index (TWI) aiming to locate vulnerable regions to soil loss under the dynamic saturation process. The final result indicates that the vulnerable regions expand from perennial reaches to ephemeral reaches as saturation process develops. It implies the transition of runoff generation from groundwater in perennial reaches to surface runoff in ephemeral reaches. Identification of soil loss vulnerable regions under the dynamic saturation process helps in planning of the mitigation measures for soil erosion.
Groundwater resources constitute the main source of clean fresh water for domestic use and it is essential for food production in the agricultural sector. Groundwater has a vital role for water supply in the Campanian Plain in Italy and hence a future sustainability of the resource is essential for the region. In the current paper novel data mining algorithms including Gaussian Process (GP) were used in a large groundwater quality database to predict nitrate (contaminant) and strontium (potential future increasing) concentrations in groundwater. The results were compared with M5P, random forest (RF) and random tree (RT) algorithms as a benchmark to test the robustness of the modeling process. The dataset includes 246 groundwater quality samples originating from different wells, municipals and agricultural. It was divided for the modeling process into two subgroups by using the 10-fold cross validation technique including 173 samples for model building (training dataset) and 73 samples for model validation (testing dataset). Different water quality variables including T, pH, EC, HCO3-, F-, Cl-, SO42-, Na+, K+, Mg2+, and Ca2+ have been used as an input to the models. At first stage, different input combinations have been constructed based on correlation coefficient and thus the optimal combination was chosen for the modeling phase. Different quantitative criteria alongside with visual comparison approach have been used for evaluating the modeling capability. Results revealed that to obtain reliable results also variables with low correlation should be considered as an input to the models together with those variables showing high correlation coefficients. According to the model evaluation criteria, GP algorithm outperforms all the other models in predicting both nitrate and strontium concentrations followed by RF, M5P and RT, respectively. Result also revealed that model's structure together with the accuracy and structure of the data can have a relevant impact on the model's results.
Although estimating the uncertainty of models used for modelling nitrate contamination of groundwater is essential in groundwater management, it has been generally ignored. This issue motivates this research to explore the predictive uncertainty of machine-learning (ML) models in this field of study using two different residuals uncertainty methods: quantile regression (QR) and uncertainty estimation based on local errors and clustering (UNEEC). Prediction-interval coverage probability (PICP), the most important of the statistical measures of uncertainty, was used to evaluate uncertainty. Additionally, three state-of-the-art ML models including support vector machine (SVM), random forest (RF), and k-nearest neighbor (kNN) were selected to spatially model groundwater nitrate concentrations. The models were calibrated with nitrate concentrations from 80 wells (70% of the data) and then validated with nitrate concentrations from 34 wells (30% of the data). Both uncertainty and predictive performance criteria should be considered when comparing and selecting the best model. Results highlight that the kNN model is the best model because not only did it have the lowest uncertainty based on the PICP statistic in both the QR (0.94) and the UNEEC (in all clusters, 0.85-0.91) methods, but it also had predictive performance statistics (RMSE = 10.63, R2 = 0.71) that were relatively similar to RF (RMSE = 10.41, R2 = 0.72) and higher than SVM (RMSE = 13.28, R2 = 0.58). Determining the uncertainty of ML models used for spatially modelling groundwater-nitrate pollution enables managers to achieve better risk-based decision making and consequently increases the reliability and credibility of groundwater-nitrate predictions.
A reedbed system planted with Phragmites australis was implemented to treat chlorinated hydrocarbon-contaminated groundwater in an industrial plant area. Reedbed commissioning was conducted from July 2016 to November 2016 to treat contaminated groundwater via a pump-and-treat mechanism. Combination of horizontal and vertical reedbed systems was applied to treat 1,2-dichloroethane (1,2 DCA) under four parallel installations. The 2-acre horizontal and vertical reedbed systems were designed to treat approximately 305 m3/day of pumped groundwater. Initial concentration of 1,2 DCA was observed at 0.362 mg/L to 4320 mg/L, and the reedbed system successfully reduced the concentration up to 67.9%. The average outlet concentration was measured to be 2.08 mg/L, which was lower than the site-specific target level of 156 mg/L. Natural attenuation analysis was conducted using first-order decay kinetics, showing an average natural attenuation rate of 0.00372/year. Natural attenuation of 1,2 DCA was observed in shallow monitoring wells, which was indicated by the reduction trend of 1,2 DCA concentration, thereby confirming that the reedbed system worked well to remove 1.2 DCA from contaminated groundwater at the shallow profile.
Contaminated groundwater is a priority issue on the environmental agendas of developed countries. Therefore, there is an obvious need to develop instruments and decision-making mechanisms that allow the estimation of the risk to human health due to the presence of contaminants in soils and groundwater, in a fast and reliable manner. Thus, this study aims to assess whether the spilling of hydraulic fracturing fluids prior to injection has a potential risk to groundwater quality in the Kern County Sub-basin, California, by identifying the hydrological factors and solute transport characteristics that control these risks while taking into consideration the temperature rises due to climate change. The approach uses the concept of the groundwater pollution risk based on comparing the concentration of pollutants within the water table by using a predetermined permissible level. The current average annual temperature and that by the end of the 21st century was used to estimate the diffusion of benzene through three types of soil by using HYDRUS-1D software. The software was used to predict the contaminant concentration profile of benzene in the water table with special reference to the impact of surface temperatures. The results showed that an expected rise of the surface temperature by 4.3 °C led to an increase in the concentration of benzene by 2.3 μg/l in sandy loam soil, 6.8 μg/l in silt loam soil, and finally, 2.6 μg/l in loam soil. The results show that climate change can substantially affect soil properties and their chemical constituents, which then play a major role in absorbing pollutants.
The identification of nitrogen sources and cycling processes is critical to the management of nitrogen pollution. Here, we used both stable (δ15N-NO3-, δ18O-NO3-, δ15N-NH4+) and radiogenic (222Rn) isotopes together with nitrogen concentrations to evaluate the relative importance of point (i.e. sewage) and diffuse sources (i.e. agricultural-derived NO3- from groundwater, drains and creeks) in driving nitrogen dynamic in a shallow coastal embayment, Port Phillip Bay (PPB) in Victoria, Australia. This study is an exemplar of nitrogen-limited coastal systems around the world where nitrogen contamination is prevalent and where constraining it may be challenging. In addition to surrounding land use, we found that the distributions of NO3- and NH4+ in the bay were closely linked to the presence of drift algae. Highest NO3- and NH4+ concentrations were 315 μmol L-1 and 2140 μmol L-1, respectively. Based on the isotopic signatures of NO3- (δ15N: 0.17 to 21‰; δ18O: 3 to 26‰) and NH4+ (δ15N: 30 to 39‰) in PPB, the high nitrogen concentrations were attributed to three major sources which varied between winter and summer; (1) nitrified sewage effluent and drift algae derived NH4+ mainly during winter, (2) NO3- mixture from atmospheric deposition, drains and creeks predominantly observed during summer and (3) groundwater and sewage derived NO3- during both surveys. The isotopic composition of NO3- also suggested the removal of agriculture-derived NO3- through denitrification was prevalent during transport. This study highlights the role of terrestrial-coastal interactions on nitrogen dynamics and illustrates the importance of submarine groundwater discharge as a prominent pathway of diffuse NO3- inputs. Quantifying the relative contributions of multiple NO3- input pathways, however, require more extensive efforts and is an important avenue for future research.
In many regions around the world, there are issues associated with groundwater resources due to human and natural factors. However, the relation between these factors is difficult to determine due to the large number of parameters and complex processes required. In order to understand the relation between land use allocations, the intrinsic factors of the aquifer, climate change data and groundwater chemistry in the multilayered aquifer system in Malaysia's Northern Kelantan Basin, twenty-two years hydrogeochemical data set was used in this research. The groundwater salinisation in the intermediate aquifer, which mainly extends along the coastal line, was revealed through the hydrogeochemical investigation. Even so, there had been no significant trend detected on groundwater salinity from 1989 to 2011. In contrast to salinity, as seen from the nitrate contaminations there had been significantly increasing trends in the shallow aquifer, particularly in the central part of the study area. Additionally, a strong association between high nitrate values and the areas covered with palm oil cultivations and mixed agricultural have been detected by a multiple correspondence analysis (MCA), which implies that the increasing nitrate concentrations are associated with nitrate loading from the application of N-fertilisers. From the process of groundwater salinisation in the intermediate aquifer, could be seen that it has a strong correlation the aquifer lithology, specifically marine sediments which are influenced by the ancient seawater trapped within the sediments.
The contamination of water bodies from heavy metals, either from natural sources or
anthropogenic sources, has become a major concern to the public. Industrial activities with improper
water treatment, and then leach into the water body, have become contaminated and harmful to
consume. Passive remediation is one of the treatments introduced to counter this problem as it is a low
cost but effective technique. After being widely acknowledged and through research conducted, the
most suitable remediation technique found is the permeable reactive barriers (PRBs). PRB is defined
as an in situ permeable treatment zone filled with reactive materials, designed to intercept and
remediate a contaminant plume under natural hydraulic gradients. There have been many findings
made from PRB which can be used to remove contaminants such as heavy metal, chlorinated solvents,
carbonates and aromatic hydrocarbons. The most crucial criteria in making a successful PRB is the
reactive media used to remove contaminants. The current paper presents an overview of the PRB
selective medias that have been used and also the unresolved issue on the long term performance of
PRB. The overall methodology for the application of PRB at a given site is also discussed in this
paper. This inexpensive but effective technique is crucial as a sustainable technology in order to treat
the drainage before it enters water tables to prevent water pollution and can be used as an alternative
raw water source.