Langat River Research Articles

Multi-hazard, multidimensional disaster risk validation in selangor’s three urban districts, Malaysia

Considering the increasing impact of natural hazard-related disasters in Malaysia, understanding and evaluating integrated disaster risk must consider multi-hazard and multidimensional vulnerability at the local level, particularly in developing industrial nations like Malaysia. Therefore, this study proposes an improved methodology for conducting an integrated disaster risk assessment index (IDRI) mapping to measure disaster risk within local administrative boundaries in Malaysia. In this study, multi-hazard spatial overlapping combined two common hazards in Malaysia, which are floods and landslides. This study used multidimensional vulnerability, which encompasses six dimensions, 16 subdimensions, and 54 indicators. Researchers applied this approach in three urban districts of Selangor, Malaysia: Sepang, Kuala Langat, and Hulu Langat, which are located within the Langat River catchment and consist of 17 subdistricts. Overall, 32.9% of the total study area was found to be at risk, with 4.3% in the very high-risk area. In comparison with the latest flood events in 2021, the IDRI components were highly correlated with disaster impact. In conclusion, the contribution of this study provides a novel perspective on disaster risk assessment by addressing several types of hazards and multidimensional vulnerability, as compared to the previous methodology focusing on a single hazard and a physical vulnerability factor.

Some Aspects of the Biology of Invasive Fish Species from a Langat River Tributary, Selangor, Malaysia

Some Aspects of the Biology of Invasive Fish Species from a Langat River Tributary, Selangor, Malaysia

A practical approach for numerical modeling of a complex and data-limited hydrological system

Abstract By their nature, wetlands represent an ecosystem base for many concurrent heterogeneous interactions, where the mission of numerical modeling requires a wide range of consistent and reliable datasets from various sources, spatially and temporally. Such a mission usually collides with the existence of tremendous missing in time-series dataset(s). In this context, MIKE SHE was used to construct an integrated surface–subsurface flow model for the Paya Indah wetland in Malaysia where huge gaps exist in the historical datasets of water level and flow rate. To calibrate and validate the model to a satisfactory level, a tri-criteria simulation approach was applied to overcome the occasional missing values in these datasets. This goal was accomplished by calibrating the surface water level and channel flow while simultaneously matching the steady-state subsurface portion of the system wherever water table depth data allowed. Quantitatively, the integrated model scored the highest values of R (0.765 − 0.927) and CE (0.748 − 0.828) during the validation. However, large RMSE values were calculated for the flow rate during calibration at SWL2 (outlet; 0.766) and during validation at Langat River (0.780). This bias was attributed to the low or occasional absence of variation in the historical time-series datasets necessary for the simulation process.

River basin change index for integrated river basin management of Langat River, Malaysia

Abstract A river basin change index (RBCI) is introduced as a management tool to evaluate integrated river basin management (IRBM) implementation. The framework comprises water governance (WG), river basin organisation (RBO) and technical indicators. WG indicators encompass effectiveness, efficiency, trust and engagement. RBO indicators examine the management capacity and resources in operation and expansion. The technical indicators measure the outcome of IRBM implementation corresponding to each policy in the IRBM plan. Data sources range from questionnaire surveys, official reports and recorded data from the relevant agencies. The indices are calculated by comparing the current value to a reference value, which may be historical or target performance, or average performance of other river basins. RBCI value in the range of 0.9–1.1 characterises regular year-to-year fluctuation. A lower index value <0.9 indicates negative basin change, which may be due to negligence, retardation or disastrous regress in the basin IRBM in deteriorating order. A higher index value >1.1 indicates positive basin change which may be attributed to notable improvement, outstanding attainment or breakthrough advances in the basin IRBM in successive order. The tool is useful to facilitate long-term continuous improvement process and is presently undergoing pilot-testing for the Langat River basin, Malaysia

Spatial multidimensional vulnerability assessment index in urban area- A case study Selangor, Malaysia

Rapid urbanization has increased the risks to and vulnerabilities of urban systems, society, and organizations. In recent years, urban areas have been exposed to multiple hazards such as floods, landslides, storms, and rising sea levels. To reduce exposed elements' risk of and vulnerability to natural hazards, the first requirement is a better understanding of the vulnerable elements as stated in Sendai Framework. Different concepts and approaches can be employed in vulnerability assessment, depending on the scope and context of the study. In this study, the vulnerability concept was based on six dimensions (social, economic, physical, institutional, environmental, and cultural) adapted from the Method for the Improvement of Vulnerability (MOVE) framework. This approach was applied to three industrial urban districts in Selangor, Malaysia: Sepang, Kuala Langat, and Hulu Langat. These are located in the Langat River catchment and consist of 17 mukim (subdistricts). A spatial vulnerability assessment was conducted to determine the areas of very high vulnerability in this study area. A map was subsequently produced that shows the areas classified into five vulnerability categories (very low, low, medium, high, and very high vulnerability). The findings from all the areas studied identified 5.7% in the very high class, 8.9% in the high class, 33.3% in the medium class, 21.6% in the low class, and 30.5% in the very low class. The multidimensional vulnerability assessment used scientific proof to provide information for better understanding to the government, disaster agencies, and local governments so that policy making, and local disaster risk reduction (DRR) strategies can improve.

Assessment of river water quality improvement due to riverbank filtration (RBF) mechanism

Riverbank filtration (RBF) is a natural process where river water is induced to flow through riverbed soils to pumping wells located on the banks. It is a well-established and proven natural water treatment technology by having a series of physical and chemical processes during subsurface passage. This paper aims at the measurement of physical and chemical characteristics of river water and groundwater from riverbank filtration wells at Kg. Jenderam Hilir, Selangor and to assess the effectiveness of riverbank filtration through Water Quality Index (WQI) for both river water and groundwater. The riverbank filtration is able to improve quality of water such as reducing the turbidity to 0 NTU, Chemical Oxygen Demand (COD) to 3.67 mg/l, Suspended Solids (SS) to 6.33 mg/l and several other contents that existed in the river. In addition, the effectiveness of RBF is also proven based on the improvement of Water Quality Index (WQI) for Langat river from Class IV to Class III.

Spatial and temporal evaluation of groundwater hydrochemistry in an active phreatic zone of developed basin in selangor, Malaysia

Groundwater provides freshwater supply for multiple purposes such as drinking water, irrigation, and industrial processes. Due to intensive developments, contaminants from various land use practices infiltrate into the groundwater system and jeopardized the existing quality. Besides, uncontrolled groundwater extraction exacerbate the existing issue due to improper management and uncontrolled extraction; especially in developing areas. This study centralized in Kuala Langat; an urbanized basin with mix of land use practices (residential, industrial, and agricultural) with the aim to observe the impacts of land use activities towards groundwater hydrochemistry on seasonal and temporal scale. A total of 30 groundwater samples were collected along Langat River Basin to assess the physicochemical parameters, concentration of major ions and heavy metals. Application of multivariate and graphical analysis as well as pollution indices were incorporated in this study to evaluate the degree of heavy metals contamination. The order of cations concentration evaluated in pre- and post-monsoon seasons are Na > Ca > K > Mg and Na > Mg > Ca > K, respectively while the order of anions concentration is HCO3>Cl > SO4 for both monsoon seasons. Both calculated HPI and Cd value suggested that agricultural activity is the main contributor for groundwater pollution. Overall, most of the stations are potable to be consumed as stated from HPI and Cd results except for station SSG10. It is suggested that for a better precaution, the groundwater in Langat River Basin shall undergo prior treatment before further consumption.

A multidimensional approach for microplastics monitoring in two major tropical river basins, Malaysia

Microplastics (MPs) with the size of 1 μm–5 mm are pollutants of great concern ubiquitously found in the environment. Existing efforts have found that most of the MPs present in the seas mainly originated from land via riverine inputs. Asian rivers are known to be among the top in microplastic emissions. However, field data are scarce, especially in Malaysia. This study presents the distribution and characteristics of MPs in the surface water of two major river basins of Malaysia, namely Langat River (West Coast/Straits of Malacca) and Kelantan River (East Coast/South China Sea). Water samples were collected at 21–22 locations in Kelantan and Langat rivers, covering the river, estuary and sea. MPs were physically classified based on sizes, shapes, colours and surface morphology (SEM-EDS). The average of 179.6 items/L and 1464.8 items/L of MPs had been quantified from Kelantan and Langat rivers, respectively. Fibre (91.90%) was highly recorded at Kelantan, compared to Langat whereby both fibre (59.21%) and fragment (38.87%) were prevalence. Anthropogenic activities and urbanised areas contribute to high microplastic abundance, especially in the Langat River. Micro-FTIR analysis identified 14 polymers in Kelantan River, whereas 20 polymers were found in Langat River. Polypropylene, polyethylene, polyethylene terephthalate, nylon, phenoxy resins, poly(methyl acrylate), poly(methyl methacrylate), polystyrene, polytetrafluoroethylene, polyurethane and rayon were discovered in both rivers, although only polyethylene was significant (>1 ppm) when further analysed using pyrolysis-GC/MS. Correlation analysis and multiple linear regression were used to explain the relationship between water quality and MP abundance, suggesting only turbidity was positively significant to the microplastic occurrence. This comprehensive study is first to suggest a full-scale monitoring protocol for MPs in Malaysian riverine system and is significant in understanding MPs abundance in correlation to in-situ environmental factors. Consequently, this will allow the right authorities to develop mitigation strategies to address riverine plastic pollution in major river basins in Malaysia and the South East Asia.

Comparisons of Various Imputation Methods for Incomplete Water Quality Data: A Case Study of The Langat River, Malaysia

In this study, the ability of numerous statistical and machine learning models to impute water quality data was investigated at three monitoring stations along the Langat River in Malaysia. Inconsistencies in the percentage of missing data between monitoring stations (varying from 20 percent (moderate) to over 50 percent (high)) represent the greatest obstacle of the study. The main objective was to select the best method for imputation and compare whether there are differences between the methods used by the different stations. The paper focuses on different imputation methods such as Multiple Predictive Mean Matching (PMM), Multiple Random Forest Imputation (RF), Multiple Bayesian Linear Regression Imputation (BLR), Multiple Linear Regression (non-Bayesian) Imputation (LRNB), Multiple Classification and Regression Tree (CART), k-nearest neighbours (kNN) and Bootstrap-based Expectation Maximisation (EMB). Remarkably, among all seven imputation techniques, the kNN produces identically reliable results. The imputed data is all rated as ‘very good’ (NSE > 0.75). This was confirmed by the calculation of |PBIAS|<5.30 (all imputed data are‘very good’) and KGE≥0.87 (all imputations are rated as’ good’). Imputation performance improves for all three monitoring stations with an index of agreement, WI ≥ 0.94, despite varying percentages of missing data. According to the findings, the kNN imputation approach outperforms the others and should be prioritised in actual use. Future research with the existing methods could benefit from the addition of geographical data.

The spatial relationship between landslide and land use activities in Langat River Basin: A case study

Landslide is a natural hazard that occurs all over the world and can cause enormous damage to the environment and human life. The developing countries are not spared from dealing with the issue of landslides where land use activities are recognised as one of the most triggering factors of landslide occurrences. In this study, investigation on spatial relationship was performed between landslide occurrences and land use activities at Langat River Basin. Landslide spatial distribution was determined using land use activities for the year 2021. The land use map was generated using supervised classification by Artificial Neural Network (ANN) approach. In addition, the accuracy assessment was applied using Kappa coefficient to validate the land use classification. A total of 70 historical landslide points was used to analyse the spatial relationship between landslide and land use activities. Based on the results, land use category at Langat River Basin was dominated by agriculture with 59.65%. The overall accuracy for land use classification was 90.81% and kappa coefficient was 0.88. Meanwhile, the spatial relationship between landslide occurrence and land use activities was higher in the built-up area with 48.57%. The growing population and economic sector in Langat River Basin were found to be contributing to the increase in built-up areas. Thus, these results are particularly useful in understanding the spatial relationship between landslide occurrence and land use activities where the development area is more vulnerable to facing landslide disasters. These findings will also help the government to achieve the agenda for Sustainable Development Goal (SDG) 2030 as highlighted to mitigate the risk of natural disasters.

A Comparative Assessment of Sampling Ratios Using Artificial Neural Network (ANN) for Landslide Predictive Model in Langat River Basin, Selangor, Malaysia

Landslides have been classified as the most dangerous threat around the world, causing huge damage to properties and loss of life. Increased human activity in landslide-prone areas has been a major contributor to the risk of landslide occurrences. Therefore, machine learning has been used in landslide studies to develop a landslide predictive model. The main objective of this study is to evaluate the most suitable sampling ratio for the predictive landslide model in the Langat River Basin (LRB) using Artificial Neural Networks (ANNs). The landslide inventory was divided randomly into training and testing datasets using four sampling ratios (50:50, 60:40, 70:30, and 80:20). A total of 12 landslide conditioning factors were considered in this study, including the elevation, slope, aspect, curvature, topography wetness index (TWI), distance to the road, distance to the river, distance to faults, soil, lithology, land use, and rainfall. The evaluation model was performed using certain statistical measures and area under the curve (AUC). Finally, the most suitable predictive model was chosen based on the model validation results using the compound factor (CF) method. Based on the results, the predictive model with an 80:20 ratio indicates a realistic finding and was classified as the first rank among others. The AUC value for the training dataset is 0.931, while the AUC value for the testing dataset is 0.964. These attempts will help a great deal when it comes to choosing the best ratio of training samples to testing samples to create a reliable and complete landslide prediction model for the LRB.

Investigation of Correlation Between Suspended Sediment Discharge and Activity Concentration of Natural Radionuclides in Langat River Basin

Investigation of the correlation between natural radionuclide activity concentrations (RC) in river sediment and suspended sediment discharge (SD) is carried out for Langat river basin over a period of 4 years. Three river stations with suspended sediment discharge rate derived from validated sediment rating equation are paired with three radionuclide observation stations by geographical proximity. Long-term correlation of the quarterly RC observation with maximum SD is generally poor. However, for specific time window, the data trend correlation ranges from moderate to good, indicative of the influence of SD on the transport process of NORM within the river basin. Examination of the seasonal variation shows that Station 2 exhibits good correlation of RC and SD in the months of March and August/September, corresponding to the pre-storm seasons, with R2 value as high as 0.9799. Despite the limitation of the undated RC observation and the use of hindcasted SD data, the present study shows the increase of radionuclide activity concentration of Ra-226, Ra-228 and K-40 in river sediment can be associated with the suspended sediment discharge rate and hence, the river streamflow especially during the pre-storm season.

Invaders of Langat River: Introduction of varied vermiculated sailfin catfish, Pterygoplichthys disjunctivus

Globally and locally, invasive aquatic species pose a major threat towards aquatic ecosystems. Among the most successful of these aquatic invaders in Malaysian freshwater river systems is the vermiculated sailfin catfish or Pterygoplichthys disjunctivus. The study is conducted in Langat River, a subsidiary of the Selangor River basin which flows through the capital and high population density areas of the country. The impact of the species can be seen through its overpopulation and high density in the river due to the absence of natural predators and extremely high fecundity. This study provides the first report on the occurrence of introduced vermiculated sailfin catfish, P. disjunctivus, from Langat River, where the P. pardalis had been previously reported. In this study, random sampling exercises were conducted by using gill nets in a stretch of the Langat River. Out of 24 samples of Pterygoplichthys obtained, 11 were of P. pardalis. This was further confirmed by the morphometric and meristic characters as well as the patterns on the fish. Furthermore, during the sampling, we also described a new body pattern Pterygoplichthys, which does not fit both P. pardalis and P. disjunctivus entirely. The new body pattern of Pterygoplichthys, could be the result of hybridization between both species or variation patterns within the same species. However, a deeper study is necessary to be conducted to confirm this claim.

Modeling of 1D sediment transport in the langat river using quasi-unsteady HEC-RAS

The primary goal of this research was to investigate the sediment transport characteristics of the Langat River. The Langat River is situated in the state of Selangor, Malaysia, and is prone to flooding on a regular basis. The River Analysis System tool from the Hydraulic Engineering Center (HEC-RAS) was used to determine the sediment transport capacity of the river. The sediment transport and hydraulic models were created using elevation and cross-sectional data in HEC-RAS. The sediment transport model employed several approaches to calculate sediment transport capacity. HEC-RAS contains some useful built-in functions which were used in this study to visualize the sediment transport, calibration and calculation of Manning’s roughness coefficients, and forecast changes in bed elevation. The investigation reveals that urban development has resulted in a large rise in riverbed depth, implying that is no difference in flooding pattern in the short period of time. This paper focuses only on bed load sediment. The results of three years of stream flow show that some areas have experienced silt transport and river deposition. The river had changed slightly after utilizing the Laursen-Copeland method in HEC-RAS. The simulation outcome shows the segment that experienced sediment transport at River Stations RS 12926 from Lui, RS 9362 from Langat upper reach, and RS 3730 from Langat Lower Reach are experiencing sediment deposition activities.

Water Quality Index Classification Based on Machine Learning: A Case from the Langat River Basin Model

Traditionally, water quality is evaluated using expensive laboratory and statistical procedures, making real-time monitoring ineffective. Poor water quality requires a more practical and cost-effective solution. Water pollution has been a severe issue, hurting water quality in recent years. Therefore, it is crucial to create a model that forecasts water quality to control water pollution and inform consumers in the event of the detection of poor water quality. For effective water quality management, it is essential to accurately estimate the water quality class. Motivated by these considerations, we utilize the benefits of machine learning methods to construct a model capable of predicting the water quality index and water quality class. This study aims to investigate the performance of machine learning models for multiclass classification in the Langat River Basin water quality assessment. Three machine learning models were developed using Artificial Neural Networks (ANN), Decision Trees (DT), and Support Vector Machines (SVM) to classify river water quality. Comparative performance analysis between the three models indicates that the SVM is the best model for predicting river water quality in this study. In addition, there is a statistically significant difference in performance between the SVM, DT, and ANN models at the 0.05 level of confidence. The use of the kernel function, the grid search method, and the multiclass classification technique used in this study significantly impacts the effectiveness of the SVM model. The findings bolster the idea that machine learning models, particularly SVM, can be used to forecast WQI with a high degree of accuracy, hence enhancing water quality management. Consequently, the model based on machine learning lowered the cost and complexity of calculating sub-indices of six water quality parameters and classifying water quality compared to the standard IKA-JAS formula.

Mean Field Bias Correction to Radar QPE as Input to Flood Modeling for Malaysian River Basins

The occurrence of unprecedented flood events has increased in Malaysia recently. To mitigate the impact of the disaster, the National Flood Forecasting and Warning Centre has endeavored to improve the warning system to produce more accurate and reliable early warning to the public. The paper describes the use of radar composites from the radar network in Peninsular Malaysia to produce quantitative precipitation estimates (QPE) as input to the flood model. The processing of the raw radar data and the conversion of rain rate are described. The comparison between radar QPE and gauge rainfall shows that radar QPE underestimates the gauge rainfall, and the results are better at the western parts of Peninsular Malaysia compared to the eastern parts of Peninsular Malaysia. The comparison between Marshall Palmer (MP) and Rosenfeld (RF) conversion equations shows that there is not much difference in performance between the two equations. Both underestimate the rainfall, although RF estimates higher radar QPE for high rainfall intensity. The underestimated radar QPE is improved by calibration process via the Mean Field Bias (MFB) correction technique. The study introduced zoning into smaller regions for the MFB factors derivation. Results indicated that the radar QPE is much improved after the calibration process. Simulation of flood event in December 2021 for the case study of Langat River basin indicates the improvement of correlation coefficient from 0.67 to 0.99 after the calibration process via MFB for smaller zones.

Comparison between Regression Models, Support Vector Machine (SVM), and Artificial Neural Network (ANN) in River Water Quality Prediction

Both anthropogenic and natural sources of pollution are regionally significant. Therefore, in order to monitor and protect the quality of Langat River from deterioration, we use Artificial Intelligence (AI) to model the river water quality. This study has applied several machine learning models (two support vector machines (SVMs), six regression models, and artificial neural network (ANN)) to predict total suspended solids (TSS), total solids (TS), and dissolved solids (DS)) in Langat River, Malaysia. All of the models have been assessed using root mean square error (RMSE), mean square error (MSE) as well as the determination of coefficient (R2). Based on the model performance metrics, the ANN model outperformed all models, while the GPR and SVM models exhibited the characteristic of over-fitting. The remaining machine learning models exhibited fair to poor performances. Although there are a few researches conducted to predict TDS using ANN, however, there are less to no research conducted to predict TS and TSS in Langat River. Therefore, this is the first study to evaluate the water quality (TSS, TS, and DS) of Langat River using the aforementioned models (especially SVM and the six regression models).

TREND ANALYSIS OF RIVER FLOW IN LANGAT RIVER BASIN USING SWAT MODEL

The increase in population and the human activities in this basin area has led to many water shortage problems. This research was carried out to investigate the impacts of land-use changes and urbanization on water discharge in Langat River Basin using ArcGIS. It was applied using the Soil Water Assessment Tool (SWAT). This research aimed to identify how critical land-use changes and the increment of the population in Langat River Basin could impact the water discharge. The rainfall, humidity, solar, wind speed data from 2010 – 2016 were used for modelling purposes. The results obtained from calibrated results were satisfactory, with the value of the coefficient of determination of 0.63 and the value of Nash-Sutcliffe Efficiency (NSE) of 0.69. The precipitation and river flow pattern observed shows decreasing in the river flow from May to September 2016 and increased in November to December 2016. It can be observed that the river flow decreased due to some factors, and this may be a concerning issue as Langat River Basin is used to supply water to people in Klang Valley. This paper has proven that the GIS technology and Arc SWAT model can simulate the streamflow in Langat River Basin and can be further demonstrated to other basins. The model also can be used for further analysis and for in the future.

Development of deep eutectic solvents based ferrofluid for liquid phase microextraction of ofloxacin and sparfloxacin in water samples

A smart material based on ferrofluid deep eutectic solvents graphene oxide magnetite (GO@Fe3O4-DES FF) was successfully synthesised by adding choline chloride-ethylene glycol DES as carrier solvent onto GO@Fe3O4 composite. The morphological, functional group, and magnetic characteristics of the synthesised materials were characterised by elemental analysis, Fourier Transform-infrared spectroscopy, scanning electron microscopy, transmission electron microscopes and, vibrating sample magnetometer. The adsorption performances of ofloxacin (OFL) and sparfloxacin (SPR) were evaluated based on kinetic, isotherm, and thermodynamic models. The GO@Fe3O4-DES FF material was used as ferrofluid sorbent for liquid phase microextraction (LPME) of OFL and SPR prior to HPLC-UV analysis. The effect of several parameters on the peak areas of OFL and SPR including type of desorption solvent, ferrofluid volume, extraction time, desorption solvent volume, desorption time, solution pH, and sample volume were optimized. Under the optimum conditions, good linearity was achieved (1–1500 µg/L) with a coefficient of determination (R2) of 0.9921 and 0.9956 for OFL and SPR, respectively. The LOD values were recorded in the range of 0.0063–0.0058 µg/L. The developed GO@Fe3O4-DES FF LPME method was applied for the determination of OFL and SPR in water samples collected from Langat River Basin, Selangor, with recovery values ranging from 70.3 to 121.5 %. Based on the results obtained, the developed GO@Fe3O4-DES FF LPME method demonstrated excellent sensitivity and efficiency for the extraction of OFL and SPR in water samples. It showed great potential as an alternative method for the extraction of pharmaceutically active contaminants in the aquatic system.

Identification of Water Pollution Sources for Better Langat River Basin Management in Malaysia

The shutdown of drinking water treatment plants (DWTPs) at the Langat River Basin, Malaysia, which provides drinking water to almost one-third population in the basin, is very frequent, especially due to chemical pollution in the river. This study explored the pollution sources in the Langat River based on eight specific water intake points of the respective DWTPs to suggest an integrated river basin management (IRBM). Analysis of Al (250.26 ± 189.24 µg/L), As (1.65 ± 0.93 µg/L), Cd (1.22 ± 0.88 µg/L), Cr (0.47 ± 0.27 µg/L), and Pb (9.99 ± 5.38 µg/L) by ICP-MS following the Chelex® 100 column resin ion exchange method found that the mean concentrations except Al were within the water quality standard of the Ministry of Health (MOH) as well as the Dept. of Environment (DOE) Malaysia. However, the determined water quality index based on physicochemical parameters (2005–2015) at the midstream of Langat River was Class III, which needs substantial treatment before drinking. The linear regression model of Al, As, Cd, and Pb suggests that water quality parameters are significantly influencing the increase or decrease in these metal concentrations. Moreover, the principal component analysis (PCA) and the hierarchical cluster analysis (HCA) also support the regression models that the sources of pollution are both natural and man-made activities, and these pollution sources can be clustered into two categories, i.e., upstream (category 1) and mid to downstream (category 2) in the Langat River. The degraded water quality in the midstream compared to up and downstream of the river is mainly due to human activities apart from the natural weathering of minerals. Therefore, the implementation of policies should be effective at the local level for pollution management, especially via the proactive leadership roles of local government for this transboundary Langat River to benefit from IRBM.