Hydride generation coupled to microfunnel-assisted headspace liquid-phase microextraction for the determination of arsenic with UV–Vis spectrophotometry

In this research, a microfunnel-assisted headspace liquid-phase microextraction technique has been used in combination with hydride generation to determine arsenic (As) by UV–Vis spectrophotometry. The method is based on the reduction of As to arsine (AsH₃) in acidic media by sodium tetrahydroborate (NaBH₄) followed by its subsequent reaction with silver diethyldithiocarbamate (AgDDC) to give an absorbing complex at 510 nm. The complexing reagent (AgDDC) has been dissolved in a 1:1 (by the volume ratio) mixture of chloroform/chlorobenzene microdroplet and exposed to the generated gaseous arsine via a reversed microfunnel in the headspace of the sample solution. Several operating parameters affecting the performance of the method have been examined and optimized. Acetonitrile solvent has been added to the working samples as a sensitivity enhancement agent. Under the optimized operating conditions, the detection limit has been measured to be 0.2 ng mL^?1 (based on 3s_b/m criterion, n _b?=?8), and the calibration curve was linear in the range of 0.5–12 ng mL^?1. The relative standard deviation for eight replicate measurements was 1.9 %. Also, the effects of several potential interferences have been studied. The accuracy of the method was validated through the analysis of JR-1 geological standard reference material. The method has been successfully applied for the determination of arsenic in raw and spiked soft drink and water samples with the recoveries that ranged from 91 to 106 %.     

Field application of SPME as a novel tool for occupational exposure assessment with inhalational anesthetics

Occupational exposure to inhalational anesthetics occurs routinely in operating rooms. It could induce serious health hazards and diseases. This exposure assessment is a crucial step in determining risks. In this study, a pen-shaped holder for solid-phase microextraction (SPME) sampler was successfully applied as a time-weighted average sampling tool for workshift exposure assessment of operation room staff to halothane. It proved to be very convenient for use in occupational environments such as operation rooms. Samples were analyzed by a gas chromatography-mass spectrometry. The validity of the SPME method was checked in real-world conditions with Occupational Safety and Health Administration (OSHA) 103 standard method for the determination of inhalational anesthetics. A good agreement between OSHA 103 and SPME methods was obtained and results demonstrated no statistically significant differences in anesthetic concentrations determined by the two analytical methods (p?≥?0.05). It is concluded that SPME in retracted mode could successfully be applied in occupational exposure assessment purposes.     

Transition index maps for urban growth simulation: application of artificial neural networks,weight of evidence and fuzzy multi-criteria evaluation

Transition index maps (TIMs) are key products in urban growth simulation models. However, their operationalization is still conflicting. Our aim was to compare the prediction accuracy of three TIM-based spatially explicit land cover change (LCC) models in the mega city of Mumbai, India. These LCC models include two data-driven approaches, namely artificial neural networks (ANNs) and weight of evidence (WOE), and one knowledge-based approach which integrates an analytical hierarchical process with fuzzy membership functions (FAHP). Using the relative operating characteristics (ROC), the performance of these three LCC models were evaluated. The results showed 85%, 75%, and 73% accuracy for the ANN, FAHP, and WOE. The ANN was clearly superior compared to the other LCC models when simulating urban growth for the year 2010; hence, ANN was used to predict urban growth for 2020 and 2030. Projected urban growth maps were assessed using statistical measures, including figure of merit, average spatial distance deviation, producer accuracy, and overall accuracy. Based on our findings, we recomend ANNs as an and accurate method for simulating future patterns of urban growth.     

Green synthesis of 1,3-dihydrobenzimidazol-2-ones from aromatic diamines by microwave in a tetrabutylammonium bromide–ethanol molten salt paste

Environmental Chemistry Letters - The replacement of hazardous solvents by greener alternatives is a major goal in green chemistry. Benzimidazol-2-ones are heterocycles exhibiting a wide range of...     

Cellulose Schiff Base as a Bio-based Polymer Ligand: Extraction,Modification and Metal Adsorption Study

Journal of Polymers and the Environment - Cellulose was initially extracted from Safflower (Carthamus tinctorius L.) stem wastes and used for the preparation of a fully bio-based adsorbent polymer...     

Fish and shrimp waste management at household and market in Bushehr,Iran

Journal of Material Cycles and Waste Management - The aim of this study was to investigate the estimation and management of fish and shrimp wastes in Bushehr province. Two-part questionnaire...     

Sustainable rangeland management using a multi-fuzzy model: how to deal with heterogeneous experts' knowledge

While fuzzy specialists commonly use homogeneous experts' knowledge to construct fuzzy models, it is much more difficult to deal with knowledge elicited from a heterogeneous group of experts. This issue is exemplified in the area of sustainable rangeland management (SRM). One way to deal with the diversity of opinions is to develop a fuzzy system for all experts and to combine all these, the so-called primary systems, into one multi-fuzzy model. To derive each of the primary fuzzy systems, several semi-structured interviews were held in three different areas of the Fars province in Southwest Iran using the knowledge of a group of administrative experts. To obtain the final output of the multi-fuzzy model, we applied different 'voting' methods. The first method simply uses the arithmetic average of the primary outputs as the final output of the multi-fuzzy model. This final output represents an estimation of the right rate of stocking (RRS). We also propose other (un)supervised voting methods. Most importantly, by harmonising the primary outputs such that outliers get less emphasis, we introduce an unsupervised voting method for calculating a weighted estimate of the RRS. This harmonising method is expected to provide a new useful tool for policymakers dealing with heterogenity in experts' opinions: it is especially useful where limited field data are available and one is forced to rely on experts' knowledge only. By constructing the three fuzzy models based on the elicitation of heterogeneous experts' knowledge, our study shows the multidimensional vaguenesses that exist in SRM. Finally, by comparing the final RRS with its common values, this study strongly points to the existence of overgrazing in pastures in the three regions of the Fars province in Southwest Iran.