Taher Abunama - Academia.edu (original) (raw)
Papers by Taher Abunama
Renewable and Sustainable Energy Reviews
IUG Journal of Natural Studies, 2015
In order to investigate the effects of applying treated waste water as a source of irrigation on ... more In order to investigate the effects of applying treated waste water as a source of irrigation on both physical properties and chemical composition of white corn, this study was conducted at a land in the neighborhood of Gaza Waste Water Treatment Plant (GWWTP). The land was divided into three groups of cells; each group was irrigated with one water type. The Irrigation types were: (1) Irrigation with Tap water (TW) The control, (2) Irrigation with Treated Waste Water (TWW) and (3) Alternating Irrigation with both TW and TWW. The results indicated that the second set, as compared to the control, had not recorded significant difference in the physical properties, whereas the third set had recorded lower physical properties and had seen significant difference. On the other hand, the chemical analyses demonstrated high increase in concentration of each of TKN, TP and K in plant’s leaves of the second set. Keywords: Municipal Waste Water, Reuse, Corn, Drip Irrigation, Agriculture, Gaza.
Water shortage and environmental hazards of wastewater have increased the need of wastewater reus... more Water shortage and environmental hazards of wastewater have increased the need of wastewater reuse to be used for agricultural irrigation. Experimental approach was used in order to examine the influence of applying treated wastewater as a source of irrigation on physical properties and chemical composition of white-corn, this experiment was performed on a land with an estimated area about 100 m 2 behind the main wastewater treatment plant. The land was divided into three equal cells with different water irrigation types, fresh water, Treated wastewater, and finally alternating irrigation equally with both fresh water and treated wastewater. Physical and chemical tests for white-corn plant were conducted and then analyzed statistically. As a result of analysis, alternating irrigation with water and treated wastewater do not recorded significant difference in term of the physical properties comparing with water irrigation; nevertheless, the wastewater irrigation showed lower physical...
The applications of leachate pollution index (LPI) as an environmental index to express the overa... more The applications of leachate pollution index (LPI) as an environmental index to express the overall leachate-contaminating ability of landfills are increasing. Majority of these applications wrongly quantify LPI due to the absence of various leachate parameters. The traditional linear weighted aggregation equation used in LPI estimations, result in larger errors as missing parameters increase. In this article, error reduction equations were established to predict LPI values more accurately. Approximately 1,797 unique combinations of leachate weights ( ∑ wi) were randomly assigned to cover the missing parameters and augment the accuracy of the proposed error reduction equations. The upper and lower boundaries of LPI were accurately estimated in each case, starting from 1 missing parameter to 12 missing parameters. Various linear equations were generated, when possible, to ease the calculations. Subsequently, the arithmetic, geometric, and harmonic averages were applied between the LP...
Environmental Science and Pollution Research
The rising water pollution from anthropogenic factors motivates further research in developing wa... more The rising water pollution from anthropogenic factors motivates further research in developing water quality predicting models. The available models have certain limitations due to limited timespan data and the incapability to provide empirical expressions. This study is devoted to model and derive empirical equations for surface water quality of upper Indus river basin using a 30-year dataset with machine learning techniques and then to determine the most reliable model capable to accurately predict river water quality. Total dissolve solids (TDS) and electrical conductivity (EC) were used as dependent variables, whereas eight parameters were used as independent variables with 70 and 30% data for model training and testing, respectively. Various evaluation criteria, i.e., Nash-Sutcliffe efficiency (NSE), root mean square error (RMSE), coefficient of determination (R2), and mean absolute error (MAE), were used to assess the performance of models. The data is also validated with the help of k-fold cross-validation using R2 and RMSE. The results indicated a strong correlation with NSE and R2 both above 0.85 for all the developed models. Gene expression programming (GEP) outperformed both artificial neural network (ANN) and linear and non-linear regression models for TDS and EC. The sensitivity and parametric analyses revealed that bicarbonate is the most sensitive parameter influencing both TDS and EC models. Two equations were derived and formulated to represent the novel results of GEP model to help authorities in the effective monitoring of river water quality.
Journal of Environmental Management
International Journal of Environment and Waste Management
Sains Malaysiana
This study investigates the seasonal and spatial water quality patterns along a tropical river th... more This study investigates the seasonal and spatial water quality patterns along a tropical river that continuously receives various pollution sources. Multivariate analysis was used to study the spatial and temporal variations of the water quality parameters and to determine the origin of the pollution sources. Three regions (low, moderate, and high pollution levels) were determined based on cluster analysis. The stepwise DA mode proposed six parameters (pH, EC, COD, NO3, TC, and Fe) with 75% correct assignations as the most significant water quality parameters to present the spatial variations. In the temporal discrimination, forward stepwise mode analysis showed eight parameters (EC, TUR, BOD,COD, AN, NO3, Cu, and Cr) with 92% correct assignations, while five parameters (EC, AN, Al, Cu, and Cr) affording 89% correct assignations in backward stepwise mode analysis. Principal component analysis and factor analysis were used to investigate the origins of each water quality parameter ba...
Sustainability, 2021
Modeling surface water quality using soft computing techniques is essential for the effective man... more Modeling surface water quality using soft computing techniques is essential for the effective management of scarce water resources and environmental protection. The development of accurate predictive models with significant input parameters and inconsistent datasets is still a challenge. Therefore, further research is needed to improve the performance of the predictive models. This study presents a methodology for dataset pre-processing and input optimization for reducing the modeling complexity. The objective of this study was achieved by employing a two-sided detection approach for outlier removal and an exhaustive search method for selecting essential modeling inputs. Thereafter, the adaptive neuro-fuzzy inference system (ANFIS) was applied for modeling electrical conductivity (EC) and total dissolved solids (TDS) in the upper Indus River. A larger dataset of a 30-year historical period, measured monthly, was utilized in the modeling process. The prediction capacity of the develo...
Water
The Gaza coastal aquifer (GCA) is the only source of water for about two million citizens living ... more The Gaza coastal aquifer (GCA) is the only source of water for about two million citizens living in Gaza Strip, Palestine. The groundwater quality in GCA has deteriorated rapidly due to many factors. The most crucial factor is the excess pumping due to the high population density. The objective of this article was to evaluate the influence of excess pumping on GCA’s salinity using 10-year predicted future scenarios based on artificial neural networks (ANNs). The ANN-based model was generated to predict the GCA’s salinity for three future scenarios that were designed based on different pumping rates. The results showed that when the pumping rate remains at the present conditions, salinity will increase rapidly in most GCA areas, and the availability of fresh water will decrease in disquieting rates by 2030. Only about 8% of the overall GCA’s area is expected to stay within 500 mg/L of the chloride concentration. Results also indicate that salinity would be improved slightly if the pu...
Environmental Science and Pollution Research
Leachate is one of the main surface water pollution sources in Selangor State (SS), Malaysia. The... more Leachate is one of the main surface water pollution sources in Selangor State (SS), Malaysia. The prediction of leachate amounts is elementary in sustainable waste management and leachate treatment processes, before discharging to surrounding environment. In developing countries, the accurate evaluation of leachate generation rates has often considered a challenge due to the lack of reliable data and high measurement costs. Leachate generation is related to several factors, including meteorological data, waste generation rates, and landfill design conditions. The high variations in these factors lead to complicating leachate modeling processes. This study aims at identifying the key elements contributing to leachate production and developing various AI-based models to predict leachate generation rates. These models included Artificial Neural Network (ANN)-Multi-linear perceptron (MLP) with single and double hidden layers, and support vector machine (SVM) regression time series algorithms. Various performance measures were applied to evaluate the developed model’s accuracy. In this study, input optimization process showed that three inputs were acceptable for modeling the leachate generation rates, namely dumped waste quantity, rainfall level, and emanated gases. The initial performance analysis showed that ANN-MLP2 model—which applies two hidden layers—achieved the best performance, then followed by ANN-MLP1 model—which applies one hidden layer and three inputs—while SVM model gave the lowest performance. Ranges and frequency of relative error (RE%) also demonstrate that ANN-MLP models outperformed SVM models. Furthermore, low and peak flow criterion (LFC and PFC) assessment of leachate inflow values in ANN-MLP model with two hidden layers made more accurate values than other models. Since minimizing data collection and processing efforts as well as minimizing modeling complexity are critical in the hydrological modeling process, the applied input optimization process and the developed models in this study were able to provide a good performance in the modeling of leachate generation efficiently.
Chemosphere
From a holistic perspective, this review is the first to comprehensively assess and characterise ... more From a holistic perspective, this review is the first to comprehensively assess and characterise leachate quality from waste disposal facilities (WDFs), landfills and dumpsites, located in 61 countries worldwide. A continent wise grouping approach was adopted to identify the variability of leachate quality and polluting abilities in light of leachate pollution index (LPI). The literature data on leachate quality included 428 samples, with eighteen leachate parameters, classified under, organic, inorganic, and heavy metals. Statistically significant differences in LPI were found between different continents and WDFs demographic data, i.e., type, status, age, rainfall, etc. A negative correlation was found between pH and the majority of studied parameters, especially for heavy metals such as Pb, Zn, As, Hg, Cy, as the decrease in pH intensifies heavy metals' solubility. Based on the studied worldwide leachate data and WDFs age, an LPI rating was identified, where high, intermediate, and low contaminated leachate are typically classified with having an average of 26.5, 23.6 and 17.5, respectively. The provided database in this review could be of great importance in establishing a more comprehensive global databank by including other countries- and site-specific factors that are vital in enhancing the accuracy of LPI and formatting a more representative leachate diagnosis index.
Renewable and Sustainable Energy Reviews
IUG Journal of Natural Studies, 2015
In order to investigate the effects of applying treated waste water as a source of irrigation on ... more In order to investigate the effects of applying treated waste water as a source of irrigation on both physical properties and chemical composition of white corn, this study was conducted at a land in the neighborhood of Gaza Waste Water Treatment Plant (GWWTP). The land was divided into three groups of cells; each group was irrigated with one water type. The Irrigation types were: (1) Irrigation with Tap water (TW) The control, (2) Irrigation with Treated Waste Water (TWW) and (3) Alternating Irrigation with both TW and TWW. The results indicated that the second set, as compared to the control, had not recorded significant difference in the physical properties, whereas the third set had recorded lower physical properties and had seen significant difference. On the other hand, the chemical analyses demonstrated high increase in concentration of each of TKN, TP and K in plant’s leaves of the second set. Keywords: Municipal Waste Water, Reuse, Corn, Drip Irrigation, Agriculture, Gaza.
Water shortage and environmental hazards of wastewater have increased the need of wastewater reus... more Water shortage and environmental hazards of wastewater have increased the need of wastewater reuse to be used for agricultural irrigation. Experimental approach was used in order to examine the influence of applying treated wastewater as a source of irrigation on physical properties and chemical composition of white-corn, this experiment was performed on a land with an estimated area about 100 m 2 behind the main wastewater treatment plant. The land was divided into three equal cells with different water irrigation types, fresh water, Treated wastewater, and finally alternating irrigation equally with both fresh water and treated wastewater. Physical and chemical tests for white-corn plant were conducted and then analyzed statistically. As a result of analysis, alternating irrigation with water and treated wastewater do not recorded significant difference in term of the physical properties comparing with water irrigation; nevertheless, the wastewater irrigation showed lower physical...
The applications of leachate pollution index (LPI) as an environmental index to express the overa... more The applications of leachate pollution index (LPI) as an environmental index to express the overall leachate-contaminating ability of landfills are increasing. Majority of these applications wrongly quantify LPI due to the absence of various leachate parameters. The traditional linear weighted aggregation equation used in LPI estimations, result in larger errors as missing parameters increase. In this article, error reduction equations were established to predict LPI values more accurately. Approximately 1,797 unique combinations of leachate weights ( ∑ wi) were randomly assigned to cover the missing parameters and augment the accuracy of the proposed error reduction equations. The upper and lower boundaries of LPI were accurately estimated in each case, starting from 1 missing parameter to 12 missing parameters. Various linear equations were generated, when possible, to ease the calculations. Subsequently, the arithmetic, geometric, and harmonic averages were applied between the LP...
Environmental Science and Pollution Research
The rising water pollution from anthropogenic factors motivates further research in developing wa... more The rising water pollution from anthropogenic factors motivates further research in developing water quality predicting models. The available models have certain limitations due to limited timespan data and the incapability to provide empirical expressions. This study is devoted to model and derive empirical equations for surface water quality of upper Indus river basin using a 30-year dataset with machine learning techniques and then to determine the most reliable model capable to accurately predict river water quality. Total dissolve solids (TDS) and electrical conductivity (EC) were used as dependent variables, whereas eight parameters were used as independent variables with 70 and 30% data for model training and testing, respectively. Various evaluation criteria, i.e., Nash-Sutcliffe efficiency (NSE), root mean square error (RMSE), coefficient of determination (R2), and mean absolute error (MAE), were used to assess the performance of models. The data is also validated with the help of k-fold cross-validation using R2 and RMSE. The results indicated a strong correlation with NSE and R2 both above 0.85 for all the developed models. Gene expression programming (GEP) outperformed both artificial neural network (ANN) and linear and non-linear regression models for TDS and EC. The sensitivity and parametric analyses revealed that bicarbonate is the most sensitive parameter influencing both TDS and EC models. Two equations were derived and formulated to represent the novel results of GEP model to help authorities in the effective monitoring of river water quality.
Journal of Environmental Management
International Journal of Environment and Waste Management
Sains Malaysiana
This study investigates the seasonal and spatial water quality patterns along a tropical river th... more This study investigates the seasonal and spatial water quality patterns along a tropical river that continuously receives various pollution sources. Multivariate analysis was used to study the spatial and temporal variations of the water quality parameters and to determine the origin of the pollution sources. Three regions (low, moderate, and high pollution levels) were determined based on cluster analysis. The stepwise DA mode proposed six parameters (pH, EC, COD, NO3, TC, and Fe) with 75% correct assignations as the most significant water quality parameters to present the spatial variations. In the temporal discrimination, forward stepwise mode analysis showed eight parameters (EC, TUR, BOD,COD, AN, NO3, Cu, and Cr) with 92% correct assignations, while five parameters (EC, AN, Al, Cu, and Cr) affording 89% correct assignations in backward stepwise mode analysis. Principal component analysis and factor analysis were used to investigate the origins of each water quality parameter ba...
Sustainability, 2021
Modeling surface water quality using soft computing techniques is essential for the effective man... more Modeling surface water quality using soft computing techniques is essential for the effective management of scarce water resources and environmental protection. The development of accurate predictive models with significant input parameters and inconsistent datasets is still a challenge. Therefore, further research is needed to improve the performance of the predictive models. This study presents a methodology for dataset pre-processing and input optimization for reducing the modeling complexity. The objective of this study was achieved by employing a two-sided detection approach for outlier removal and an exhaustive search method for selecting essential modeling inputs. Thereafter, the adaptive neuro-fuzzy inference system (ANFIS) was applied for modeling electrical conductivity (EC) and total dissolved solids (TDS) in the upper Indus River. A larger dataset of a 30-year historical period, measured monthly, was utilized in the modeling process. The prediction capacity of the develo...
Water
The Gaza coastal aquifer (GCA) is the only source of water for about two million citizens living ... more The Gaza coastal aquifer (GCA) is the only source of water for about two million citizens living in Gaza Strip, Palestine. The groundwater quality in GCA has deteriorated rapidly due to many factors. The most crucial factor is the excess pumping due to the high population density. The objective of this article was to evaluate the influence of excess pumping on GCA’s salinity using 10-year predicted future scenarios based on artificial neural networks (ANNs). The ANN-based model was generated to predict the GCA’s salinity for three future scenarios that were designed based on different pumping rates. The results showed that when the pumping rate remains at the present conditions, salinity will increase rapidly in most GCA areas, and the availability of fresh water will decrease in disquieting rates by 2030. Only about 8% of the overall GCA’s area is expected to stay within 500 mg/L of the chloride concentration. Results also indicate that salinity would be improved slightly if the pu...
Environmental Science and Pollution Research
Leachate is one of the main surface water pollution sources in Selangor State (SS), Malaysia. The... more Leachate is one of the main surface water pollution sources in Selangor State (SS), Malaysia. The prediction of leachate amounts is elementary in sustainable waste management and leachate treatment processes, before discharging to surrounding environment. In developing countries, the accurate evaluation of leachate generation rates has often considered a challenge due to the lack of reliable data and high measurement costs. Leachate generation is related to several factors, including meteorological data, waste generation rates, and landfill design conditions. The high variations in these factors lead to complicating leachate modeling processes. This study aims at identifying the key elements contributing to leachate production and developing various AI-based models to predict leachate generation rates. These models included Artificial Neural Network (ANN)-Multi-linear perceptron (MLP) with single and double hidden layers, and support vector machine (SVM) regression time series algorithms. Various performance measures were applied to evaluate the developed model’s accuracy. In this study, input optimization process showed that three inputs were acceptable for modeling the leachate generation rates, namely dumped waste quantity, rainfall level, and emanated gases. The initial performance analysis showed that ANN-MLP2 model—which applies two hidden layers—achieved the best performance, then followed by ANN-MLP1 model—which applies one hidden layer and three inputs—while SVM model gave the lowest performance. Ranges and frequency of relative error (RE%) also demonstrate that ANN-MLP models outperformed SVM models. Furthermore, low and peak flow criterion (LFC and PFC) assessment of leachate inflow values in ANN-MLP model with two hidden layers made more accurate values than other models. Since minimizing data collection and processing efforts as well as minimizing modeling complexity are critical in the hydrological modeling process, the applied input optimization process and the developed models in this study were able to provide a good performance in the modeling of leachate generation efficiently.
Chemosphere
From a holistic perspective, this review is the first to comprehensively assess and characterise ... more From a holistic perspective, this review is the first to comprehensively assess and characterise leachate quality from waste disposal facilities (WDFs), landfills and dumpsites, located in 61 countries worldwide. A continent wise grouping approach was adopted to identify the variability of leachate quality and polluting abilities in light of leachate pollution index (LPI). The literature data on leachate quality included 428 samples, with eighteen leachate parameters, classified under, organic, inorganic, and heavy metals. Statistically significant differences in LPI were found between different continents and WDFs demographic data, i.e., type, status, age, rainfall, etc. A negative correlation was found between pH and the majority of studied parameters, especially for heavy metals such as Pb, Zn, As, Hg, Cy, as the decrease in pH intensifies heavy metals' solubility. Based on the studied worldwide leachate data and WDFs age, an LPI rating was identified, where high, intermediate, and low contaminated leachate are typically classified with having an average of 26.5, 23.6 and 17.5, respectively. The provided database in this review could be of great importance in establishing a more comprehensive global databank by including other countries- and site-specific factors that are vital in enhancing the accuracy of LPI and formatting a more representative leachate diagnosis index.