Greenhouse gas emissions estimation from proposed El Fukhary Landfill in the Gaza Strip

Landfills throughout the world are contributing to the global warming problem. This is due to the existence of the most important greenhouse gases (GHG) in landfill gas (LFG); namely, methane (CH4) and carbon dioxide (CO2). The aim of this paper is quantifying the total potential emissions, as well as the variation in production with time of CH4 from a proposed landfill (El Fukhary landfill) in the Gaza Strip, Palestine. Two different methods were adopted in order to quantify the total potential CH4 emissions; the Default methodology based on the intergovernmental panel on climate change (IPCC) 1996 revised guidelines and the Landfill Gas Emissions model (LandGEM V3.02) provided by the United States Environmental Protection Agency (EPA). The second objective of the study has been accomplished using the Triangle gas production model. The results obtained from both Default and LandGEM methods were found to be nearly the same. For 25 years of disposing MSW, El Fukhary landfill expected to have potential CH4 emissions of 1.9542 ± 0.0037 ×109 m3. Triangle model showed that the peak production in term of CH4 would occur in 2043; 28 years beyond the open year. Moreover, the model shows that 50 % of the gas will be produced approximately at the middle of the total duration of gas production. Proper control of Methane emissions from El Fukhary landfill is highly suggested in order to reduce the harmful effects on the environment.

Implications: Although, GHG emissions are extensively discussed in the developed countries throughout the world, it has gained little concern in the developing countries because they are forced most of the time to put environmental concerns at the end of their priority list. The paper shows that developing countries have to start recognizing their fault and change their way of dealing with environmental issues especially GHG emissions (mainly Methane and carbon dioxide). The authors estimated the potential methane emissions from a proposed central landfill that has been approved to be built in Palestine, a country that is classified as a developing country.     

Determination of nephrotoxicity and genotoxic potential of silver nanoparticles in Swiss albino mice

Silver nanoparticles (AgNPs) possess properties that are important for industrial and medical applications. This study is aimed to investigate intra-peritoneal toxicity of AgNPs at 26, 52 or 78 mg/kg/day for 5 days in mice Swiss albino mice. The effects on oxidative stress markers activities of serum superoxide dismutase (SOD) and catalase (CAT), levels of serum glutathione (GSH), apoptosis (TUNEL assay), DNA strand breaks (comet assay) in lymphocytes, as well as histopathological of kidney tissue were determined. AgNPs significantly increased SOD and CAT activities reduced GSH levels. In kidney apoptosis (TUNEL assay) while DNA strand breaks (comet assay) in lymphocytes revealed that AgNPs at concentration 78 mg/kg produced significant apoptosis and DNA damage. AgNPs also produced associated histological renal tissue damage. Evidence suggests that AgNPs-mediated alterations may be attributed to oxidative stress.     

Biodegradation of imidacloprid in liquid media by an isolated wastewater fungus Aspergillus terreus YESM3

In the present study, a new fungal strain capable of imidacloprid degradation was isolated from agricultural wastewater drain. The fungal strain of YESM3 was identified as Aspergillus terreus based on ITS1-5.8S rDNA-ITS2 gene sequence by PCR amplification of a 500 bp sequence. Screening of A. terreus YESM3 to the insecticide imidacloprid tolerance was achieved by growing fungus in Czapek Dox agar for 6 days at 28°C. High values (1.13 and 0.94 cm cm^?1) of tolerance index (TI) were recorded at 25 and 50 mg L^?1 of imidacloprid, respectively in the presence and absence of sucrose. However, at 400 mg L^?1 the fungus did not grow. Effects of the imidacloprid concentration, pH, and inoculum size on the biodegradation percentage were tested using Box–Behnken statistical design and the biodegradation was monitored by HPLC analysis at different time intervals. Box–Behnken results indicated that optimal conditions for biodegradation were at pH 4 and two fungal discs (10 mm diameter) in the presence of 61.2 mg L^?1 of imidacloprid. A. terreus YESM3 strain was capable of degrading 85% of imidacloprid 25 mg L^?1 in Czapek Dox broth medium at pH 4 and 28°C for 6 days under static conditions. In addition, after 20 days of inoculation, biodegradation recorded 96.23% of 25 mg L^?1 imidacloprid. Degradation kinetics showed that the imidacloprid followed the first order kinetics with half-life (t₅₀) of 1.532 day. Intermediate product identified as 6-chloronicotinic acid (6CNA) as one of the major metabolites during degradation of imidacloprid by using HPLC. Thus, A. terreus YESM3 showed a potential to reduce pollution by pesticides and toxicity in the effected environment. However, further studies should be conducted to understand the biodegradation mechanism of this pesticide in liquid media.     

Optimization and characterization of the formation of oil-in-water diazinon nanoemulsions: Modeling and influence of the oil phase,surfactant and sonication

Nanoemulsions are particularly suitable as a platform in the development of delivery systems for lipophilic functional agents. Current research describes the formation of oil-in-water (O/W) diazinon nanoemulsions using synthetic and natural additives by adopting a high-energy (ultrasound) emulsification method. The diazinon nanoemulsions were spontaneously formed by adding dropwise a mixture of diazinon, solvent and co-solvent in an aqueous solution containing a surfactant (tween or lecithin) with continuous stirring. The nanoemulsions were then formed by ultra-sonication. The effects of three levels of active ingredient, solvent, co-solvent, surfactant, sonication time and sonication cycle and power were performed by Minitab software to design the experiment. Effects of these factors on droplet size, polydispersity index (PDI), viscosity and pH of nanoemulsions were investigated. The results of the modeling showed that the experimental data could be adequately adapted in a second-order polynomial model with a multiple regression coefficient r² of 0.821 for the prediction of particle size, PDI and viscosity. The long-term and thermodynamic stability of the prepared nanoemulsions were tested. The droplet size and morphology of the nanoemulsions were measured by dynamic light scattering (DLS) and transmission electron microscopy (TEM). On this basis, a water-insoluble insecticide diazinon was incorporated into 26 optimized nanoemulsion systems to demonstrate potential applications in pest control. The results of DLS and TEM measurements showed that most of prepared nanoemulsions had an almost monodisperse droplet size distribution (PDI < 200 nm). Incorporation of diazinon had no significant effect on the size and stability of the nanoemulsions and the formulated nanoemulsion remained stable after four months of storage.     

Survey on awareness and attitudes of secondary school students regarding plastic pollution: implications for environmental education and public health in Sharjah city,UAE

Environmental Science and Pollution Research - Since the industrial revolution in the 1800s, plastic pollution is becoming a global reality. This study aims to assess knowledge and attitude about...     

Comparative assessment of vulnerability of smallholder livestock farmers to climate change in North-West Ghana

ABSTRACT

This study examined the vulnerability of smallholder livestock farmers in North-West Ghana to climate change using data obtained from 200 livestock farmers obtained through the administration of a semi-structured questionnaire. The Livelihood Vulnerability Index (LVI) approach was used to examine the levels of vulnerability. The study compared the vulnerability between the Nandom district and the Lawra district. The empirical results revealed that livestock farmers are more vulnerable to climatic extremes in the Nandom district than the Lawra district. The study highlights the critical role of the government regarding education and construction of water resources, among others.     

Competitive biosorption of lead, cadmium, copper, and arsenic ions using algae

The present study aims to evaluate the competitive biosorption of lead, cadmium, copper, and arsenic ions by using native algae. A series of experiments were carried out in a batch reactor to obtain equilibrium data for adsorption of single, binary, ternary, and quaternary metal solutions. The biosorption of these metals is based on ion exchange mechanism accompanied by the release of light metals such as calcium, magnesium, and sodium. Experimental parameters such as pH, initial metal concentrations, and temperature were studied. The optimum pH found for removal were 5 for Cd²⁺ and As³⁺ and 3 and 4 for Pb²⁺ and Cu²⁺, respectively. Fourier transformation infrared spectroscopy analysis was used to find the effects of functional groups of algae in biosorption process. The results showed that Pb²⁺ made a greater change in the functional groups of algal biomass due to high affinity to this metal. An ion exchange model was found suitable for describing the biosorption process. The affinity constants sequence calculated for single system was K _Pb > K _Cu > K _Cd > K _As; these values reduced in binary, ternary, and quaternary systems. In addition, the experimental data showed that the biosorption of the four metals fitted well the pseudo-second-order kinetics model.     

Comparison of Advection-Diffusion Models and Neural Networks for Prediction of Advanced Water Treatment Effluent

An artificial neural network (ANN) can help in the prediction of advanced water treatment effluent and thus facilitate design practices. In this study, sets of 225 experimental data were obtained from a wastewater treatment process for the removal of phosphorus using oven-dried alum residuals in fixed-bed adsorbers. Five input variables (pH, initial phosphorus concentration, wastewater flow rate, porosity, and time) were used to test the efficiency of phosphorus removal at different times, and ANNs were then used to predict the effluent phosphorus concentration. Results of experiments that were conducted for different values of the input parameters made up the data used to train and test a multilayer perceptron using the back-propagation algorithm of the ANN. Values predicted by the ANN and the experimentally measured values were compared, and the accuracy of the ANN was evaluated. When ANN results were compared to the experimental results, it was concluded that the ANN results were accurate, especially during conditions of high phosphorus concentration. While the ANN model was able to predict the breakthrough point with good accuracy, the conventional advection-diffusion equation was not as accurate. A parametric study conducted to examine the effect of the initial pH and initial phosphorus concentration on the effluent phosphorus concentration at different times showed that lower influent pH values are the most suitable for this advanced treatment system.     

Solid waste forecasting using modified ANFIS modeling

Solid waste prediction is crucial for sustainable solid waste management. Usually, accurate waste generation record is challenge in developing countries which complicates the modelling process. Solid waste generation is related to demographic, economic, and social factors. However, these factors are highly varied due to population and economy growths. The objective of this research is to determine the most influencing demographic and economic factors that affect solid waste generation using systematic approach, and then develop a model to forecast solid waste generation using a modified Adaptive Neural Inference System (MANFIS). The model evaluation was performed using Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and the coefficient of determination (R2). The results show that the best input variables are people age groups 0-14, 15-64, and people above 65 years, and the best model structure is 3 triangular fuzzy membership functions and 27 fuzzy rules. The model has been validated using testing data and the resulted training RMSE, MAE and R2 were 0.2678, 0.045 and 0.99, respectively, while for testing phase RMSE =3.986, MAE = 0.673 and R2 = 0.98.

Implications: To date, a few attempts have been made to predict the annual solid waste generation in developing countries. This paper presents modeling of annual solid waste generation using Modified ANFIS, it is a systematic approach to search for the most influencing factors and then modify the ANFIS structure to simplify the model. The proposed method can be used to forecast the waste generation in such developing countries where accurate reliable data is not always available. Moreover, annual solid waste prediction is essential for sustainable planning.