Evaluation of the anthropogenic influx of metal and metalloid contaminants into the Moulay Bousselham lagoon, Morocco, using chemometric methods coupled to geographical information systems

Superficial and cored sediment samples from the Moulay Bousselham lagoon and sub-watershed were analyzed for Al, Fe, Cu, Zn, Pb, Mn, Ni, Cr, As, Hg, and Cd. The temporal and spatial distributions of the main contamination sources of heavy metals were identified and described using chemometric and geographic information system (GIS) methods. Sediments from coastal lagoons near urban and agricultural areas are commonly contaminated with heavy metals, and the concentrations found in surface sediments are significantly higher than those from 50–100 years ago. The concentrations of these elements decrease sharply with depth in the sediment column, and the elements are preferentially enriched in the <2-μm-sized fraction of the sediment. The zones of enhanced risk of heavy metals were detected by means of GIS-based geostatistical modeling. According to sediment pollution indices and statistical analysis, heavy metals (Pb, Cu, Ni, Zn, Cr, and Hg) that pose a risk have become largely enriched in the lagoon sediments during the recent period of agricultural intensification.     

Analytical solution to evaluate salt precipitation during CO2 injection in saline aquifers

Carbon dioxide sequestration in deep saline aquifers is a means of reducing anthropogenic atmospheric emissions of CO₂. Among various mechanisms, CO₂ can be trapped in saline aquifers by dissolution in the formation water. Vaporization of water occurs along with the dissolution of CO₂. Vaporization can cause salt precipitation, which reduces porosity and impairs permeability of the reservoir in the vicinity of the wellbore, and can lead to reduction in injectivity. The amount of salt precipitation and the region in which it occurs may be important in CO₂ storage operations if salt precipitation significantly reduces injectivity. Here we develop an analytical model, as a simple and efficient tool to predict the amount of salt precipitation over time and space. This model is particularly useful at high injection velocities, when viscous forces dominate.First, we develop a model which treats the vaporization of water and dissolution of CO₂ in radial geometry. Next, the model is used to predict salt precipitation. The combined model is then extended to evaluate the effect of salt precipitation on permeability in terms of a time-dependent skin factor. Finally, the analytical model is corroborated by application to a specific problem with an available numerical solution, where a close agreement between the solutions is observed. We use the results to examine the effect of assumptions and approximations made in the development of the analytical solution. For cases studied, salt saturation was a few percent. The loss in injectivity depends on the degree of reduction of formation permeability with increased salt saturation. For permeability-reduction models considered in this work, the loss in injectivity was not severe. However, one limitation of the model is that it neglects capillary and gravity forces, and these forces might increase salt precipitation at the bottom of formation particularly when injection rate is low.     

The use of artificial neural networks and multiple linear regression to predict rate of medical waste generation

Prediction of the amount of hospital waste production will be helpful in the storage, transportation and disposal of hospital waste management. Based on this fact, two predictor models including artificial neural networks (ANNs) and multiple linear regression (MLR) were applied to predict the rate of medical waste generation totally and in different types of sharp, infectious and general. In this study, a 5-fold cross-validation procedure on a database containing total of 50 hospitals of Fars province (Iran) were used to verify the performance of the models. Three performance measures including MAR, RMSE and R² were used to evaluate performance of models. The MLR as a conventional model obtained poor prediction performance measure values. However, MLR distinguished hospital capacity and bed occupancy as more significant parameters. On the other hand, ANNs as a more powerful model, which has not been introduced in predicting rate of medical waste generation, showed high performance measure values, especially 0.99 value of R² confirming the good fit of the data. Such satisfactory results could be attributed to the non-linear nature of ANNs in problem solving which provides the opportunity for relating independent variables to dependent ones non-linearly. In conclusion, the obtained results showed that our ANN-based model approach is very promising and may play a useful role in developing a better cost-effective strategy for waste management in future.     

Introduction of Ionic Liquids as Highly Efficient Plasticizers and Flame Retardants of Cellulose Triacetate Films

Journal of Polymers and the Environment - As a promising biodegradable polymer, cellulose triacetate (CTA) was synthesized and plasticized with ionic liquids to produce flexible biocomposite films...     

Application of polysaccharide-based biopolymers as supports in photocatalytic treatment of water and wastewater: a review

Environmental Chemistry Letters - Rising health issues of Worldwide pollution by fossil fuel products are Fostering the development of safer materials such as biopolymers in many sectors such as...     

Green synthesis of zinc oxide nanoparticles loaded on activated carbon prepared from walnut peel extract for the removal of Eosin Y and Erythrosine B dyes from aqueous solution: experimental approaches,kinetics models,and thermodynamic studies

Environmental Science and Pollution Research - Water contamination due to release of dye containing effluents is one of the environmental problems of serious concern today. The present study...     

The analysis and probabilistic health risk assessment of polycyclic aromatic hydrocarbons in cereal products

Environmental Science and Pollution Research - The analysis and probabilistic health risk assessment of polycyclic aromatic hydrocarbons (PAHs) in cereal products were done by using magnetic...