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In preparation for the Fourth Industrial Revolution (IR 4.0) in Malaysia, the government envisions a path to environmental sustainability and an improvement in air quality. Air quality measurements were initiated in different backgrounds including urban, suburban, industrial and rural to detect any significant changes in air quality parameters. Due to the dynamic nature of the weather, geographical location and anthropogenic sources, many uncertainties must be considered when dealing with air pollution data. In recent years, the Bayesian approach to fitting statistical models has gained more popularity due to its alternative modelling strategy that accounted for uncertainties for all air quality parameters. Therefore, this study aims to evaluate the performance of Bayesian Model Averaging (BMA) in predicting the next-day PM10 concentration in Peninsular Malaysia. A case study utilized seventeen years’ worth of air quality monitoring data from nine (9) monitoring stations located in Peninsular Malaysia, using eight air quality parameters, i.e., PM10, NO2, SO2, CO, O3, temperature, relative humidity and wind speed. The performances of the next-day PM10 prediction were calculated using five models’ performance evaluators, namely Coefficient of Determination (R2), Index of Agreement (IA), Kling-Gupta efficiency (KGE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). The BMA models indicate that relative humidity, wind speed and PM10 contributed the most to the prediction model for the majority of stations with (R2 = 0.752 at Pasir Gudang monitoring station), (R2 = 0.749 at Larkin monitoring station), (R2 = 0.703 at Kota Bharu monitoring station), (R2 = 0.696 at Kangar monitoring station) and (R2 = 0.692 at Jerantut monitoring station), respectively. Furthermore, the BMA models demonstrated a good prediction model performance, with IA ranging from 0.84 to 0.91, R2 ranging from 0.64 to 0.75 and KGE ranging from 0.61 to 0.74 for all monitoring stations. According to the results of the investigation, BMA should be utilised in research and forecasting operations pertaining to environmental issues such as air pollution. From this study, BMA is recommended as one of the prediction tools for forecasting air pollution concentration, especially particulate matter level.

Hydraulic simulation models are critical tools for analysing the hydraulic properties of a river’s system flow. The work focuses on the simulation of a river flow in a Kelantan basin using the one-dimensional (1D) Hydrologic Engineering Center - River Analysis System (HECRAS). In the present study, cross-sections from survey data were utilised into the RAS Mapper provided in HEC-RAS 5.0 to simulate the river flow in the region. This study highlights the modelling methodology with a focus on data collection and its importance during the calibration and validation process. The model was used to discover the expected peak flood levels based on historical flood events. Simulated flows were utilised to examine the potential of the model during the model development procedure. The simulation outcomes reveal that the simulated flows are in excellent agreement during the model development as the obtained R2 value was between 0.95 to 1.0 during both model calibration and validation. This demonstrates the applicability of the HEC-RAS 1D model in simulating precise river flow, especially for flood events.

Abstract. The concentration of six heavy metals such as Cu, Cr, Ni, Cd, Zn and Mn were studied in the soils around Perlis. The aim of the study is to assess the heavy metals contamination distribution due to industrialisation and agricultural activities.

The ability of Pleurotus ostreatus spent mushroom compost for the biosorption of Iron (III) from aqueous solutions was investigated. The study was conducted in batch experiments under varying operating conditions. The optimum Iron (III) biosorption was achieved at an initial pH ranging from 4 to 5, contact time of 10 minutes and initial Iron (III) concentration of 50 mg/L using half-saturation constant of 0.4 g biosorbent dosages. The results indicated that the Iron (III) biosorption onto Pleurotus ostreatus spent mushroom compost were well fitted with the Langmuir isotherm model and a second-pseudo order kinetic model.

The rainfall measurement can be done by using rain gauge and weather radar instruments. However, weather radar does not measure the rainfall depth directly as contrary to rain gauge. Therefore, an empirical relationship between reflectivity (Z) and rainfall rate (R) which is commonly known as reflectivity-rainfall (Z-R) relationship consisting of parameter A and exponential b (Z= A·Rb ), usually used to convert reflectivity data into rainfall rate for a radar. Presently, the Z-R relationship parameters proposed by Marshall and Palmer (1947) used in Malaysia is seem no longer suitable for Malaysia condition. Hence, a new relationship should be developed. The reflectivity data from year 2006 to year 2007 at Alor Setar radar and gauge rainfall data from 14 rain gauge in the Northern of Peninsular Malaysia were calibrated. By using the Traditional Matching Method (TMM), a new parameter was developed for Alor Setar radar which located in Northern Peninsular Malaysia. By minimizing the errors, a Mean Field Bias Correction (MFB) technique was apply to all selected parameter in this study with adjusting the value A and fixed the value b. As a results, new climatological Z-R relationship (Z=14.30R 1.9) was obtained. To justify the new relationship, validation analysis has been performed by using the five statistical measure. It was found that the validation analysis has given the best results of Mean Error (ME), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Bias and Pearson Correlation Coefficient (r) with value of 0.00, 0.00, 7.54, 1.00 and 0.85 respectively. Concluded that, it is suitable for radar rainfall estimation in the Northern Region of Peninsular Malaysia.

Simulation of runoff from a river catchment is a very difficult task and it involves a lot of data which need to be considered. However, the modelling is very essential to forecast the patterns of future runoff by observing and analysing previous patterns of runoff, based on the rainfall. This study presents the evaluation of rainfall-runoff modelling for the long-term runoff series using Artificial Neural Network (ANN) and Support Vector Machine (SVM). Both models are trained and validated using the data series of current and nine (9) antecedent rainfall. During the validation, the SVM model is better in the performance as compared the ANN model, with the R and RMSE values are 0.529-0.711 and 14.27-52.55 mm, respectively. However, the SVM model is underestimated for the peak discharge. Both models have the ability to derive the relationship between the inputs and outputs of the rainfall-runoff process.

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