Anaerobic digestion of agro-industrial waste enriched with bovine bone meal for improved biogas production and biofertilization

The increase in animal and agro-industrial production must be accompanied by the development of appropriate waste and by-product management strategies. Anaerobic digestion is a promising approach to recycle these wastes and reintegrate them into the economic production cycle of biogas and biofertilizer. In order to improve the performance of the anaerobic mesophilic digestion of abundant agro-industrial wastes constituted by potato peel (PP), and poultry waste (PW) and study the contribution of bovine bone meal (BB) as additive rich in phosphorus, which can help to neutralize the acidity of the substrate. The 10-point simplex-centroid design and the isoresponse surfaces strategy were used. This study demonstrated that in mesophilic bio-digestion, the using bovine bones in admixture with agroindustrial residue provided for the proper balance of chemical components required for proliferation of microbiological agent of bioconversion, which also resulted in an increase in biogas production capacity. The best formula was so composed by 66.67% bovine bone, 16.67% potatoes peel, and 16.67% poultry waste. The stability was achieved here after only 12 days. The digestate generated from it was fulfilled with the microbiological and chemical requirements for safety defined by the NF U44-551 standard. Germination test revealed that this optimal produced digestate, did not hinder growth, in fact, almost 85% of seed was germinated. Finally, fertilization experiments prove that this digestate can boost the growth of bell pepper (Capsicum annuum).     

Co-development of an integrated assessment framework to evaluate the effectiveness and impact of selected nature-based water treatment technologies in Sri Lanka,The Philippines,and Vietnam

Water quality is a critical challenge in Asia in the context of growing industrialization, urbanization, and climate change. Nature-based solutions (NbS) could play an important role in reducing urban water pollution, while generating multiple co-benefits that could make cities more liveable and resilient. In this regard, a number of pilot and demonstration projects have been set up to explore their potential across cities in Asia. Their effectiveness and impacts, however, have not been adequately documented, thus how they can be sustained, replicated and up-scaled remain poorly understood. This study aims to contribute to addressing this challenge by co-developing an integrated assessment framework and employing it to understand how existing evaluations of NbS in the region can be improved. It focuses specifically on a set of nature-based solutions that have been employed for water treatment across six cities in Southeast Asia (two in each Sri Lanka, the Philippines, and Vietnam), namely, floating wetlands, constructed wetlands and maturation ponds. The study also suggests specific methodologies for capturing a set of core indicators considered relevant for assessing the effectiveness and capturing the multi-faceted impacts of the examined NbS.     

AI-based runoff simulation based on remote sensing observations: A case study of two river basins in the United States and Canada

Data-driven techniques are used extensively for hydrologic time-series prediction. We created various data-driven models (DDMs) based on machine learning: long short-term memory (LSTM), support vector regression (SVR), extreme learning machines, and an artificial neural network with backpropagation, to define the optimal approach to predicting streamflow time series in the Carson River (California, USA) and Montmorency (Canada) catchments. The moderate resolution imaging spectroradiometer (MODIS) snow-coverage dataset was applied to improve the streamflow estimate. In addition to the DDMs, the conceptual snowmelt runoff model was applied to simulate and forecast daily streamflow. The four main predictor variables, namely snow-coverage (S-C), precipitation (P), maximum temperature (T_max), and minimum temperature (T_min), and their corresponding values for each river basin, were obtained from National Climatic Data Center and National Snow and Ice Data Center to develop the model. The most relevant predictor variable was chosen using the support vector machine-recursive feature elimination feature selection approach. The results show that incorporating the MODIS snow-coverage dataset improves the models' prediction accuracies in the snowmelt-dominated basin. SVR and LSTM exhibited the best performances (root mean square error = 8.63 and 9.80) using monthly and daily snowmelt time series, respectively. In summary, machine learning is a reliable method to forecast runoff as it can be employed in global climate forecasts that require high-volume data processing.     

Toward a green economy: The nexus between economic and environmental factors in MENA countries,ARDL bounds approach

With the transition of the global economy toward a green economy, it is important to analyze the elements that can either support or impede this transformation. Therefore, in this study, we aimed to evaluate the potential of the green economy in the Middle East and North Africa (MENA) countries by analyzing the correlation between economic and environmental factors. The objective of this study is to explore the potential of the green economy in MENA countries by analyzing key factors such as access to clean fuel, GDP, and CO₂ emissions. The study aims to distinguish between long- and short-term effects, assess the presence of a long-run relationship or co-integration between the parameter estimates, and evaluate the progress of MENA countries toward a green economy based on the impact of economic, and environmental factors. Using quarterly and seasonally adjusted data from 2000 to 2018, the auto-regressive distributed lag (ARDL) technique was employed to examine the co-integration of the factors in the long and short terms. Multiple cointegration techniques were also used to determine the feasibility of a green economy by analyzing the relationship between access to clean fuel for technology and cooking, GDP, and CO₂ emissions. The study's findings indicate a clear long- and short-term relationship between the analyzed factors, as confirmed by the error correction model (ECM) which suggests that the variables are cointegrated and potentially relevant. Additionally, the result of the autoregressive distributed lag bound test shows that the green economy variables, GDP, CO₂ emissions, and access to clean fuel, are cointegrated in the long-term.     

Spatial and temporal variability of fluoride concentrations in groundwater resources of Larestan and Gerash regions in Iran from 2003 to 2010

Environmental Geochemistry and Health - There is discrepancy about intervals of fluoride monitoring in groundwater resources by Iranian authorities. Spatial and temporal variability of fluoride in...     

Trihalomethanes formation in water treatment plants and distribution lines: a monitoring and modeling scheme

Monitoring and modeling of the concentrations of trihalomethanes (THMs) within four water treatment plants (WTPs) and distribution lines in Fayoum City, Egypt, were studied. Sampling sites for raw and treated waters were determined by global positioning system and the Arc geographic information system software was used for mapping. THMs were monitored using 25 sampling points located at the plant exit and through the distribution lines up to the plant extremity. Results indicated that the THMs concentration varied significantly but it was very rarely higher than the allowed maximum contaminant level of 100 μg L^?1. However, at the dead zones in the distribution lines, the THMs increased by about 160% compared to in-plant level. Furthermore, the level of THMs in hot months was about 1.3 times higher than in cold ones. The influence of some independent variables such as temperature, pH, ammonia concentration, total organic carbon and color were investigated by Pearson model to find their correlation with the rate of THMs formation in WTPs. The resulted R values ranged from 0.81 to 0.98. Successful application of the model to a selected region on the distribution lines has resulted in a correlation coefficient of 0.98.     

Organochlorines and PCBs in Tilapia zillii from Lake El-Manzala,Egypt

Abstract

The mercury content has been determined in samples of fumarolic gases, phreatic waters, soil and vegetation collected at Vulcano, Aeolian Islands, Italy. Volcanic activity is demonstrated as a source of natural mercury pollution whose extent has been evaluated here by studying the contribution of different components of the surface environment. The possible influences for living organisms are examined.     

Estimation of maximum scour depths at upstream of front and rear piers for two in-line circular columns

Previous investigations indicate that scour around bridge piers is one of the most important factors for the failure of waterway bridges. Hence, it is essential to determine the accurate scour depth around the bridge piers. Most of the previous studies were based on scour around a single pier; however, in practice, new bridges are usually wide and then piers comprise two circular piers aligned in the flow direction that together support the loading of the structure. In this study, the effect on maximum scour depth of the spacing between two piers aligned in the flow direction was investigated experimentally under clear water scour conditions. The results show that the maximum scour depth at upstream of the front pier occurs when the spacing between the two piers is 2.5 times the diameter of the pier. Two semi empirical equations have been developed to predict the maximum scour depth at upstream of both front and rear piers as a function of the spacing between the piers, in terms of a pier-spacing factor. If the new equations for the pier-spacing factor are used with some of the existing equations for scour at a single pier, the predicted scouring depths are in good agreement with observed results. The S/M equation exhibited the best performance among the various equations tested and was recommended for use in prediction of the equilibrium scour depth. The findings of this study can be used to facilitate the positioning of piers when scouring is a design concern.     

Novel Sorbent of Sand Coated with Humic Acid-Iron Oxide Nanoparticles for Elimination of Copper and Cadmium Ions from Contaminated Water

Journal of Polymers and the Environment - Nanoparticles of humic acid and iron oxide were impregnated on the inert sand to produce sorbent for treating groundwater contained of cadmium and copper...