Issue 1

Samples of soil acquired from electrical and electronic waste (e-waste) dumpsite in Kansuwan gwari, Minna, Nigeria were utilized for the isolation of microorganisms utilized as a part of lead (Pb) biosorption studies. The soil specimen was cultured for Saccharomyces cerevisiae and Candida tropicalis. Box Behnken plan was utilized to advance the removal of lead by the organisms. The factors utilized as a part of the Box Behnken plan were temperature, pH and concentration of Pb. Saccharomyces cerevisiae was enhanced by the Box Behnken design, a temperature of 250C, pH of 6 and a lead of 2ppm concentration prompted a 70.8% lead removal. The investigation of variance for Candida tropicalis demonstrated that the reaction had a probability estimation of 0.851 for the regression. This likewise showed the model was not a solid match for the plan since the p-value (0.851) was more prominent than 0.05. S. cerevisiae gave a probability estimation of 0.029 for the regression, which demonstrated that the model was a solid match for the outline since the p-value was less than 0.05. More models may be connected to different fields in microbiology as it diminishes the ideal opportunity for protracted examinations and tends to limit the test to how successfully the outcomes can be accomplished. This study can be connected to the master plan of the effective pH, temperature and different parameters for the clean up of contamination in the environment.

This paper presents an efficient, simple, and fast method for the derivatization, extraction, and preconcentration of several phenolic compounds (phenol, o–, m– and p–cresol, 4–chlorophenol, and 2–nitrophenol) from wastewater samples and analysis of those samples by gas chromatography–flame ionization detection. In this method, initially the phenolic compounds are derivatized with acetic anhydride in an alkaline pH. In the following, the derivatized analytes are extracted into mL–volume of acetonitrile during homogeneous liquid–liquid extraction and further enrichment of the analytes are accomplished by their extraction into µL–volume of 1,1,2–trichloroethane through dispersive liquid–liquid microextraction step. Effective parameters controlling the performance of the proposed method such as type and volume of derivatization agent and catalyst, type and volume of extraction/disperser solvent in homogeneous liquid–liquid extraction, and type and volume of extraction solvent and salt addition in dispersive liquid–liquid microextraction are optimized. Under optimum conditions linear range of the proposed method was obtained 0.7–4000 µg L–1. Limits of detection and quantification were in the ranges of 0.07–0.20 and 0.23–0.70 µg L–1, respectively. Enrichment factors and extraction recoveries were ranged from 220 to 440 and 44 to 88%, respectively.

Particulate matter (PM), classified according to aerodynamic diameter, is one of the harmful pollutants causing health damaging effects. It is considered as cancerogenic by the World Health Organization (WHO) because of the substances found in the chemical composition of PM. In this study, short-term prediction of PM2.5 pollution at 1, 2 and 3 hours was modelled using deep learning methods. Three deep learning algorithms and the combination thereof were evaluated: Long-short term memory units (LSTM), recurrent neural networks (RNN) and gated recurrent unit (GRU). Air Quality Monitoring Stations of the Ministry of Environment and Urbanization of Turkey were utilized to obtain the data. Specifically, meteorological and air pollution data were obtained from a monitoring station located in Keçiören District of Ankara. Several trials were conducted using different combinations of RNN, GRU and LSTM models. Pollutant concentrations and meteorological factors were integrated into the model as input parameters to predict PM2.5 concentration for 1,2 and 3 hours. Best results with R2 of 0.83, 0.7 and 0.63 for 1-, 2-, and 3-hour predictions, respectively, were obtained by using a combination of GRU and RNN models. The results of this study are promising for explaining the effect of different deep learning models on prediction performance.