Determination of cobalt by air-assisted liquid–liquid microextraction

Abstract

A rapid and selective technique for extraction, preconcentration and determination of trace amounts of cobalt in water and pharmaceutical samples by air-assisted liquid–liquid microextraction combined with flame atomic absorption spectrometry is proposed. 1-Nitroso-2-naphthol is used as a complexing agent and 1-octanol as an extraction solvent. Parameters relevant for analytical effectivity, i.e. pH of sample solution, concentration of complexing agent, volume of extraction solvent, and number of extraction cycles are optimized using a Box–Behnken design. At optimum conditions, a dynamic linear range of 5–600?µg L^?1 is obtained, with a limit of detection of 1.2?µg L^?1. The method is used for determination of Co(II) in environmental water and pharmaceutical samples.     

Investigation of Great Egret (Casmerodius albus) breeding success in Hara Biosphere Reserve of Iran

Study of Great Egret breeding success was carried out for the first time in Hara Biosphere Reserve of Iran. Since Great Egret is considered as wading bird as well as wetland-dependent species which is located on top of the food chain in this ecosystem, its breeding study is an appropriate means for evaluating food supply fluctuations and environmental threatening factors by comparison of different years. On the other hand, Great Egret is considered a suitable indicator to examination of biological changes, impact of pollutions, and other effective human activities on Hara Biosphere Reserve. Therefore, read-ahead is required for area management planning in order to maintain the health of mangrove ecosystem and control threatening factors of the sensitive biodiversity of area. The results indicate that the average breeding success of Great Egret in different stages of hatching success, fledging success, and breeding success were equal to 0.54, 0.61, and 0.50 in 2008 and 0.61, 0.59, and 0.42 in 2009, respectively.     

Evaluation of a novel hollow fiber membrane technique for collection of 1,1-dimethylhydrazine in air

The purpose of this study was to develop a novel one-step method for the time-weighted average determination of 1,1-dimethylhydrazine (UDMH) in the air followed by spectrophotometric detection. For this reason, 0.1% hydrochloric acid as the absorbent was used in hollow fiber (HF) membrane for sampling of UDMH from an atmospheric standard chamber. Response surface methodology (RSM) with central composite design (CCD) was used to optimize the sampling parameters, such as flow rate and sampling time. Moreover, several analytical parameters including breakthrough (BT) volume, storage time, and carryover effect of the proposed HF were investigated. The results showed that optimal sampling rate was 9.90 mL/min. In order to validate the proposed method, it was compared with the National Institute for Occupational Safety and Health (NIOSH) 3515 method, which showed good compatibility between the two methods. Intra- and inter-day repeatability values of the HF method were in the range 0.082–0.1 and 0.091–0.12, respectively, and the limits of detection (LODs) and limits of quantitation (LOQs) were 0.002 and 0.006 ng/mL, respectively. The storage time of the proposed HF was 7 days at 2 °C. These results demonstrated that the one-step HF membrane offered a high sensitivity for sampling of UDMH in air.     

Positive environmental effects of the coronavirus 2020 episode: a review

Environment, Development and Sustainability - The outbreak of COVID-19 has made a global catastrophic situation that caused 1,039,406 deaths out of 35,347,404 infections, and it will also cause...     

A critical review of state-of-the-art electrocoagulation technique applied to COD-rich industrial wastewaters

Environmental Science and Pollution Research - Electrocoagulation (EC) is one of the emerging technologies in groundwater and wastewater treatment as it combines the benefits of coagulation,...     

Application of Artificial Neural Networks to Predict Total Dissolved Solids at the Karaj Dam

We applied multilayer perceptron (MLP) and radial basis function (RBF) neural networks using data from two water quality monitoring stations at the Karaj Dam in Iran. Input data were calcium ions (Ca²⁺), magnesium ions (Mg²⁺), sodium ions (Na⁺), chloride ions (Cl^?), sulfate (), and pH, and the output data were total dissolved solids (TDS). An MLP with one hidden layer containing eight neurons was selected for the upstream water quality station using normalized input data. We developed a second MLP neural network for the downstream station with one hidden layer containing 10 neurons in the hidden layer using normalized input data. Considering applying normalized input data and one hidden layer, the coefficient of determination (R ²) and index of agreement (IA) between the observed and the predicted data for the upstream and downstream monitoring stations using the MLP neural networks were 0.985, 0.84, 0.99, and 0.92, respectively. The RBF neural network with 100 neurons in its hidden layer reached the minimum errors between the observed and the predicted results in upstream and downstream stations. The R ² between observed and predicted data for upstream and downstream monitoring stations for the RBF was 0.999 and 0.998, respectively. Data normalization improved the performance of the MLP neural networks. Sensitivity analysis indicated that magnesium is the most effective water quality parameter for predicting TDS, and sulfate is the second most effective water quality parameter affecting TDS prediction at the Karaj Dam.