The removal of natural organic matter (NOM) using a continuous flow fixed bed granular activated carbon (GAC) column was studied and the results were then fitted with the Adams-Bohart, Bed-Depth-Service-Time and Clarks models. The GAC, KI-6070 and KI-8085 used in the study had external surface areas of 277 m2/g and 547 m2/g, respectively. Adsorption of NOM by the GAC was complex and involved more than one rate-limiting step. The critical bed depths for KI-6070 and KI-8085 were 0.24 m and 0.3 m, respectively. The Clark model was more effective in simulating the absorbent breakthrough process as compared to the Adams-Bohart model. The lower empty bed contact time (EBCT) i.e. 15 minutes gave a better fit to the Clark Model as compared to EBCT of 20 and 30 minutes.
The sewer is an integral part of the urban wastewater system: the sewer, the wastewater treatment plant and the local receiving waters. The sewer is a reactor for microbial changes of the wastewater during transport, affecting the quality of the wastewater and thereby the successive treatment processes or receiving water impacts during combined sewer overflows. This paper presents the results of studies on anoxic processes, namely denitrification, in the bulk water phase of wastewater as it occurs in sewers. Experiments conducted on 12 different wastewater samples have shown that the denitrification process in the bulk wastewater can be simplified by the reduction of nitrate to nitrogen with significant accumulation of nitrite in the water phase. Utilization of nitrate was observed not to be limited by nitrate for concentrations above 5 gNO3-N/m3. The denitrification rates, under conditions of excess substrate and electron acceptor, were found to be in the range of 0.8-2.0 g NO3-N/(m3h). A discussion on the interaction of the sewer processes and the effects on a downstream located wastewater treatment plant (WWTP) is provided.
The objective of this study was to assess the feasibility of the Sequencing Batch Reactor (SBR) system for implementation in Malaysia. Theoretical, field, laboratory investigations, and modelling simulations have been carried out. The results of the study indicated that the SBR system was robust, relatively cost-effective, and efficient under Malaysian conditions. However, the SBR system requires highly skilled operators and continuous monitoring. This paper also attempted to identify operating conditions for the SBR system, which optimise both the removal efficiencies and the removal rates. The removal efficiencies could reach 90-96% for COD, up to 92% for TN, and 95% for SS. An approach to estimate a full operational cycle time, to estimate the de-sludging rate, and to control the biomass in the sludge has also been developed. About 4 hours react time was obtained, as 2.25 hours of nitrification with aerated slow fill and 1.75 hour of denitrification with HAc addition as an additional carbon source. Inefficient settling was one of the problems that affect the SBR effluent quality. The settling time was one hour for achieving Standard B (effluent quality) and 2 hours for Standard A.
In this work, a regression model obtained from response surface methodology (RSM) was proposed for the electrocoagulation (EC) treatment of textile wastewater. The Reactive Black 5 dye (RB5) was used as a model dye to evaluate the performance of the model design. The effect of initial solution pH, applied current and treatment time on RB5 removal was investigated. The total number of experiments designed by RSM amounted to 27 runs, including three repeated experimental runs at the central point. The accuracy of the model was evaluated by the F-test, coefficient of determination (R(2)), adjusted R(2) and standard deviation. The optimum conditions for RB5 removal were as follows: initial pH of 6.63, current of 0.075 A, electrolyte dose of 0.11 g/L and EC time of 50.3 min. The predicted RB5 removal was 83.3% and the percentage error between experimental and predicted results was only 3-5%. The obtained data confirm that the proposed model can be used for accurate prediction of RB5 removal. The value of the zeta potential increased with treatment time, and the X-ray diffraction pattern shows that iron complexes were found in the sludge.
The influences of climate on the retention capability of green roof have been widely discussed in existing literature. However, knowledge on how the retention capability of green roof is affected by the tropical climate is limited. This paper highlights the retention performance of the green roof situated in Kuching under hot-humid tropical climatic conditions. Using the green roof water balance modelling approach, this study simulated the hourly runoff generated from a virtual green roof from November 2012 to October 2013 based on past meteorological data. The result showed that the overall retention performance was satisfactory with a mean retention rate of 72.5% from 380 analysed rainfall events but reduced to 12.0% only for the events that potentially trigger the occurrence of flash flood. By performing the Spearman rank's correlation analysis, it was found that the rainfall depth and mean rainfall intensity, individually, had a strong negative correlation with event retention rate, suggesting that the retention rate increases with decreased rainfall depth. The expected direct relationship between retention rate and antecedent dry weather period was found to be event size dependent.
Short-term streamflow prediction is essential for managing flood early warning and water resources systems. Although numerical models are widely used for this purpose, they require various types of data and experience to operate the model and often tedious calibration processes. Under the digital revolution, the application of data-driven approaches to predict streamflow has increased in recent decades. In this work, multiple linear regression (MLR) and random forest (RF) models with three different input combinations are developed and assessed for multi-step ahead short-term streamflow predictions, using 14 years of hydrological datasets from the Kulim River catchment, Malaysia. Introducing more precedent streamflow events as predictor improves the performance of these data-driven models, especially in predicting peak streamflow during the high-flow event. The RF model (Nash-Sutcliffe efficiency (NSE): 0.599-0.962) outperforms the MLR model (NSE: 0.584-0.963) in terms of overall prediction accuracy. However, with the increasing lead-time length, the models' overall prediction accuracy on the arrival time and magnitude of peak streamflow decrease. These findings demonstrate the potential of decision tree-based models, such as RF, for short-term streamflow prediction and offer insights into enhancing the accuracy of these data-driven models.
In this study, the interactive effects of feed flow rate (QF) and up-flow velocity (V up) on the performance of an up-flow anaerobic sludge fixed film (UASFF) reactor treating palm oil mill effluent (POME) were investigated. Long-term performance of the UASFF reactor was first examined with raw POME at a hydraulic loading rate (HRT) of 3 d and an influent COD concentration of 44300 mg/l. Extreme reactor instability was observed after 25 d. Raw POME was then chemically pretreated and used as feed. Anaerobic digestion of pretreated POME was modeled and analyzed with two operating variables, i.e. feed flow rate and up-flow velocity. Experiments were conducted based on a central composite face-centered design (CCFD) and analyzed using response surface methodology (RSM). The region of exploration for digestion of the pretreated POME was taken as the area enclosed by the feed flow rate (1.01, 7.63 l/d) and up-flow velocity (0.2, 3 m/h) boundaries. Twelve dependent parameters were either directly measured or calculated as response. These parameters were total COD (TCOD) removal, soluble COD (SCOD) removal, effluent pH, effluent total volatile fatty acid (TVFA), effluent bicarbonate alkalinity (BA), effluent total suspended solids (TSS), CH4 percentage in biogas, methane yield (Y M), specific methanogenic activity (SMA), food-to-sludge ratio (F/M), sludge height in the UASB portion and solid retention time (SRT). The optimum conditions for POME treatment were found to be 2.45 l/d and 0.75 m/h for QF and V up, respectively (corresponding to HRT of 1.5 d and recycle ratio of 23.4:1). The present study provides valuable information about interrelations of quality and process parameters at different values of the operating variables.
A modeling procedure that predicts trihalomethane (THM) formation from field sampling at the treatment plant and along its distribution system using Tampin district, Negeri Sembilan and Sabak Bernam district, Selangor as sources of data were studied and developed. Using Pearson method of correlation, the organic matter measured as TOC showed a positive correlation with formation of THM (r=0.380,P=0.0001 for Tampin and r=0.478,P=0.0001 for Sabak Bernam). Similar positive correlation was also obtained for pH in both districts with Tampin (r=0.362,P=0.0010) and Sabak Bernam (r=0.215,P=0.0010). Chlorine dosage was also found to have low correlation with formation of THM for the two districts with Tampin (r=0.233,P=0.0230) and Sabak Bernam (r=0.505,P=0.0001). Distance from treatment plant was found to have correlation with formation of THM for Tampin district with r=0.353 and P=0.0010. Other parameters such as turbidity, ammonia, temperature and residue chlorine were found to have no correlation with formation of THM. Linear and non-linear models were developed for these two districts. The results obtained were validated using three different sets of field data obtained from own source and district of Seremban (Pantai and Sg. Terip), Negeri Sembilan. Validation results indicated that there was significant difference in the predictive and determined values of THM when two sets of data from districts of Seremban were used with an exception of field data of Sg. Terip for non-linear model developed for district of Tampin. It was found that a non-linear model is slightly better than linear model in terms of percentage prediction errors. The models developed were site specific and the predictive capabilities in the distribution systems vary with different environmental conditions.
Nitrogen (N) transport from land to water is a dominant contributor of N in estuarine waters leading to eutrophication, harmful algal blooms, and hypoxia. Our objectives were to (1) investigate the composition of inorganic and organic N forms, (2) distinguish the sources and biogeochemical mechanisms of nitrate-N (NO3-N) transport using stable isotopes of NO3- and Bayesian mixing model, and (3) determine the dissolved organic N (DON) bioavailability using bioassays in a longitudinal gradient from freshwater to estuarine ecosystem located in the Tampa Bay, Florida, United States. We found that DON was the most dominant N form (mean: 64%, range: 46-83%) followed by particulate organic N (PON, mean: 22%, range: 14-37%), whereas inorganic N forms (NOx-N: 7%, NH4-N: 7%) were 14% of total N in freshwater and estuarine waters. Stable isotope data of NO3- revealed that nitrification was the main contributor (36.4%), followed by soil and organic N sources (25.5%), NO3- fertilizers (22.4%), and NH4+ fertilizers (15.7%). Bioassays showed that 14 to 65% of DON concentrations decreased after 5-days of incubation indicating utilization of DON by microbes in freshwater and estuarine waters. These results suggest that despite low proportion of inorganic N forms, the higher concentrations and bioavailability of DON can be a potential source of N for algae and bacteria leading to water quality degradation in the estuarine waters.
This paper describes the design of an artificial neural network (ANN) model to predict the water quality index (WQI) using land use areas as predictors. Ten-year records of land use statistics and water quality data for Kinta River (Malaysia) were employed in the modeling process. The most accurate WQI predictions were obtained with the network architecture 7-23-1; the back propagation training algorithm; and a learning rate of 0.02. The WQI forecasts of this model had significant (p < 0.01), positive, very high correlation (ρs = 0.882) with the measured WQI values. Sensitivity analysis revealed that the relative importance of the land use classes to WQI predictions followed the order: mining > rubber > forest > logging > urban areas > agriculture > oil palm. These findings show that the ANNs are highly reliable means of relating water quality to land use, thus integrating land use development with river water quality management.
The Bio-Ecological Drainage System (BIOECODS) is a sustainable drainage (SUDS) to demonstrate the 'control at source' approaches for urban stormwater management in Malaysia. It is an environmentally friendly drainage system that was designed to increase infiltration, reduce peak flow at outlet, improve water quality, through different BMPs, such as grass swale, retention pond, etc. A special feature of BIOECODS is ecological swale with on-line subsurface detention. This study attempted to create a model of ecological swale with on-line subsurface conveyance system with InfoWorks SD. The new technique has been used Storm Water Management Model (SWMM) model to describe overland flow routing and Soil Conservation Service Method (SCS) used to model infiltration or subsurface flow. The modeling technique has been proven successful, as the predicted and observed closely match each other, with a mean error of 4.58 to 7.32%. The calibrated model then used to determine the ratio of the flow exchange between the surface and subsurface drainage system. Results from the model showed that the runoff ratio exchange between the surface and subsurface is 60 to 90%.
The increasing requirement for Solid Waste Management (SWM) has become a significant challenge for municipal authorities. A number of integrated systems and methods have introduced to overcome this challenge. Many researchers have aimed to develop an ideal SWM system, including approaches involving software-based routing, Geographic Information Systems (GIS), Radio-frequency Identification (RFID), or sensor intelligent bins. Image processing solutions for the Solid Waste (SW) collection have also been developed; however, during capturing the bin image, it is challenging to position the camera for getting a bin area centralized image. As yet, there is no ideal system which can correctly estimate the amount of SW. This paper briefly discusses an efficient image processing solution to overcome these problems. Dynamic Time Warping (DTW) was used for detecting and cropping the bin area and Gabor wavelet (GW) was introduced for feature extraction of the waste bin image. Image features were used to train the classifier. A Multi-Layer Perceptron (MLP) classifier was used to classify the waste bin level and estimate the amount of waste inside the bin. The area under the Receiver Operating Characteristic (ROC) curves was used to statistically evaluate classifier performance. The results of this developed system are comparable to previous image processing based system. The system demonstration using DTW with GW for feature extraction and an MLP classifier led to promising results with respect to the accuracy of waste level estimation (98.50%). The application can be used to optimize the routing of waste collection based on the estimated bin level.
The objective of this study was to investigate the performance of employing persulfate reagent in the advanced oxidation of ozone to treat stabilized landfill leachate in an ozone reactor. A central composite design (CCD) with response surface methodology (RSM) was applied to evaluate the relationships between operating variables, such as ozone and persulfate dosages, pH, and reaction time, to identify the optimum operating conditions. Quadratic models for the following four responses proved to be significant with very low probabilities (<0.0001): COD, color, NH3-N, and ozone consumption (OC). The obtained optimum conditions included a reaction time of 210 min, 30 g/m(3) ozone, 1g/1g COD0/S2O8(2-) ratio, and pH 10. The experimental results were corresponded well with predicted models (COD, color, and NH3-N removal rates of 72%, 96%, and 76%, respectively, and 0.60 (kg O3/kg COD OC). The results obtained in the stabilized leachate treatment were compared with those from other treatment processes, such as ozone only and persulfate S2O8(2-) only, to evaluate its effectiveness. The combined method (i.e., O3/S2O8(2-)) achieved higher removal efficiencies for COD, color, and NH3-N compared with other studied applications. Furthermore, the new method is more efficient than ozone/Fenton in advanced oxidation process in the treatment of the same studied leachate.
This paper deals with a system of integration of Radio Frequency Identification (RFID) and communication technologies for solid waste bin and truck monitoring system. RFID, GPS, GPRS and GIS along with camera technologies have been integrated and developed the bin and truck intelligent monitoring system. A new kind of integrated theoretical framework, hardware architecture and interface algorithm has been introduced between the technologies for the successful implementation of the proposed system. In this system, bin and truck database have been developed such a way that the information of bin and truck ID, date and time of waste collection, bin status, amount of waste and bin and truck GPS coordinates etc. are complied and stored for monitoring and management activities. The results showed that the real-time image processing, histogram analysis, waste estimation and other bin information have been displayed in the GUI of the monitoring system. The real-time test and experimental results showed that the performance of the developed system was stable and satisfied the monitoring system with high practicability and validity.
This paper presents a forecasting study of municipal solid waste generation (MSWG) rate and potential of its recyclable components in Kuala Lumpur (KL), the capital city of Malaysia. The generation rates and composition of solid wastes of various classes such as street cleansing, landscape and garden, industrial and constructional, institutional, residential and commercial are analyzed. The past and present trends are studied and extrapolated for the coming years using Microsoft office 2003 Excel spreadsheet assuming a linear behavior. The study shows that increased solid waste generation of KL is alarming. For instance, the amount of daily residential SWG is found to be about 1.62 kg/capita; with the national average at 0.8-0.9 kg/capita and is expected to be increasing linearly, reaching to 2.23 kg/capita by 2024. This figure seems reasonable for an urban developing area like KL city. It is also found that, food (organic) waste is the major recyclable component followed by mix paper and mix plastics. Along with estimated population growth and their business activities, it has been observed that the city is still lacking in terms of efficient waste treatment technology, sufficient fund, public awareness, maintaining the established norms of industrial waste treatment etc. Hence it is recommended that the concerned authority (DBKL) shall view this issue seriously.
Solid waste prediction is crucial for sustainable solid waste management. The collection of accurate waste data records is challenging in developing countries. Solid waste generation is usually correlated with economic, demographic and social factors. However, these factors are not constant due to population and economic growth. The objective of this research is to minimize the land requirements for solid waste disposal for implementation of the Malaysian vision of waste disposal options. This goal has been previously achieved by integrating the solid waste forecasting model, waste composition and the Malaysian vision. The modified adaptive neural fuzzy inference system (MANFIS) was employed to develop a solid waste prediction model and search for the optimum input factors. The performance of the model was evaluated using the root mean square error (RMSE) and the coefficient of determination (R(2)). The model validation results are as follows: RMSE for training=0.2678, RMSE for testing=3.9860 and R(2)=0.99. Implementation of the Malaysian vision for waste disposal options can minimize the land requirements for waste disposal by up to 43%.
Food waste has significant detrimental economic, environmental and social impacts. The magnitude and complexity of the global food waste problem has brought it to the forefront of the environmental agenda; however, there has been little research on the patterns and drivers of food waste generation, especially outside the household. This is partially due to weaknesses in the methodological approaches used to understand such a complex problem. This paper proposes a novel conceptual framework to identify and explain the patterns and drivers of food waste generation in the hospitality sector, with the aim of identifying food waste prevention measures. This conceptual framework integrates data collection and analysis methods from ethnography and grounded theory, complemented with concepts and tools from industrial ecology for the analysis of quantitative data. A case study of food waste generation at a hotel restaurant in Malaysia is used as an example to illustrate how this conceptual framework can be applied. The conceptual framework links the biophysical and economic flows of food provisioning and waste generation, with the social and cultural practices associated with food preparation and consumption. The case study demonstrates that food waste is intrinsically linked to the way we provision and consume food, the material and socio-cultural context of food consumption and food waste generation. Food provisioning, food consumption and food waste generation should be studied together in order to fully understand how, where and most importantly why food waste is generated. This understanding will then enable to draw detailed, case specific food waste prevention plans addressing the material and socio-economic aspects of food waste generation.
This paper attempts to integrate both a forward and reverse supply chain to design a closed-loop supply chain network (CLSC). The problem in the design of a CLSC network is uncertainty in demand, return products and the quality of return products. Scenario analyses are generated to overcome this uncertainty. In contrast to the existing supply chain network design models, a new application of a CLSC network was studied in this paper to reduce waste. A multi-product, multi-tier mixed integer linear model is developed for a CLSC network design. The main objective is to maximize profit and provide waste management decision support in order to minimize pollution. The result shows applicability of the model in the tire industry. The model determines the number and the locations of facilities and the material flows between these facilities.
Composting can potentially remove organic pollutants in sewage sludge. When estimating pollutant removal efficiency, knowledge of estimate uncertainty is important for understanding estimate reliability. In this study the uncertainty (coefficient of variation, CV) in pollutant degradation rate (K1) and relative concentration at 35days of composting (C35/C0) was evaluated. This was done based on recently presented pollutant concentration data, measured under full-scale composting conditions using two different sampling methods for a range of organic pollutants commonly found in sewage sludge. Non-parametric statistical procedures were used to estimate CV values for K1 and C35/C0 for individual pollutants. These were then used to compare the two sampling methods with respect to CV and to determine confidence intervals for average CV. Results showed that sampling method is crucial for reducing uncertainty. The results further indicated that it is possible to achieve CV values for both K1 and C35/C0 of about 15%.
Waste collection is an important part of waste management that involves different issues, including environmental, economic, and social, among others. Waste collection optimization can reduce the waste collection budget and environmental emissions by reducing the collection route distance. This paper presents a modified Backtracking Search Algorithm (BSA) in capacitated vehicle routing problem (CVRP) models with the smart bin concept to find the best optimized waste collection route solutions. The objective function minimizes the sum of the waste collection route distances. The study introduces the concept of the threshold waste level (TWL) of waste bins to reduce the number of bins to be emptied by finding an optimal range, thus minimizing the distance. A scheduling model is also introduced to compare the feasibility of the proposed model with that of the conventional collection system in terms of travel distance, collected waste, fuel consumption, fuel cost, efficiency and CO2 emission. The optimal TWL was found to be between 70% and 75% of the fill level of waste collection nodes and had the maximum tightness value for different problem cases. The obtained results for four days show a 36.80% distance reduction for 91.40% of the total waste collection, which eventually increases the average waste collection efficiency by 36.78% and reduces the fuel consumption, fuel cost and CO2 emission by 50%, 47.77% and 44.68%, respectively. Thus, the proposed optimization model can be considered a viable tool for optimizing waste collection routes to reduce economic costs and environmental impacts.