Glyphosate (GLY) is a major herbicide used throughout the world, and its continuous application has become an environmental issue. Adsorption is an important mechanism for removing organic contaminant in water. The present study characterized cow dung (CD) and rice husk ash (RHA), and determined the adsorption-desorption of GLY and its metabolite, aminomethylphoshonic acid (AMPA), on to them. The results revealed that both CD and RHA were alkaline and had no or low content of arsenic, cadmium, chromium and lead. The CD had lower surface area (13.104 mg2g−1) than RHA (21.500 m2g−1). The CD contained amines, phenol, ethers and carboxylic functional groups, while in addition to carboxylic and ether, RHA contains siloxane. Both CD and RHA had high affinities for GLY and AMPA. The Freundlich sorption coefficient (Kf) on AMPA were 2.915 and 2.660 for CD and RHA, respectively, while the values on GLY were 1.168 and 1.166 (mg g−1) for CD and RHA, respectively. Desorption of GLY only occurred at lower concentrations, while no desorption of AMPA was recorded, indicating their strong adsorption on CD and RHA. Considering their availabilities and affordable prices, both CD and RHA can be recommended as economical adsorbent for the removal of GLY and AMPA.
The need to increase the production and utilization of locally available food and antimicrobial resources has been discussed at different national and international forum. Fresh Neocarya macrophylla fruits were obtained from Birnin Kebbi central market in Kebbi state and it was transported to Biochemistry Department at Usmanu Danfodiyo University Sokoto, Sokoto State, Nigeria. Where the fruit of N. macrophylla was transformed or crushed through stigmasterol extraction with 70% of methanol. The compound, stigmasterol, indicated varying actions of antimicrobial activity against the microbes tested. Susceptibility test result showed inhibition ranging from 23 mm to 30 mm against all the organisms, which are S. aureus (24 mm), Salmonella Typhimurium (23 mm), P. aeruginosa (26 mm), E. coli (28 mm), Streptococcus pyogenes (25 mm), B. subitilis (23 mm), A. niger (29 mm), C. albicans (24 mm), and C. kruseii (23 mm). Creating zone of inhibition. The ash content was 6.70±0.05., moisture 14.23%±0.10., lipids 6.70% ±0.05., fibre 10.15%± 0.57., crude protein 1.015%±0.127., carbohydrate 51.33%±1.025. The pulp also contains low concentrations of magnesium (2.843±0.025) and very low concentrations of iron (0.0856±0.002), manganese (0.0122±0.048), copper (0.0087±0.002), and zinc (0.0024±0.001), which are important micro elements required by body for proper functioning. The result obtained indicate that Neocarya macrophylla fruit pulp of pharmaceutical and medical significances that are useful in Combating antibiotics resistance infections, nutritional rich in terms of minerals and carbohydrate composition.
In order to model and forecast exchange rates in both developed and emerging countries, majority of time series analysts have employed various technical and fundamental approaches, the forecast outcome differs depending on the approach chosen or implemented. In this view, this study is about hybridization of Autoregressive Integrated Moving Average (ARIMA) with Generalized Autoregressive Conditional Heteroscedastic (GARCH) model in forecasting exchange rate using monthly data of the Nigerian Naira against the U.S. Dollar for the period of January 2002 to February 2020. The stationarity of the exchange rate series is examined using unit root test of Augmented Dickey Fuller (ADF) test and Kwaitkowski-Philips-Schmidt-Shin (KPSS) which showed that the series is non stationary. To make the exchange rate series stationary, the data was transformed by first differencing and appropriate ARIMA models were obtained using Box-Jenkins method. ARIMA (0,1,1) and ARIMA(0,1,2) models were selected using AIC criteria and the residuals of these models were found to be serially correlated and heteroscedastic; hence the need for the hybridization of ARIMA with GARCH model. Therefore ARIMA models were hybridized with GARCH(1,1) to form ARIMA(0,1,1)-GARCH(1,1) and ARIMA(0,1,2)-GARCH(1,1). The results of forecast performance indicates that the best model is ARIMA(0,1,1)–GARCH(1,1) which has the lowest Root Means Square Error (RMSE) and Mean Absolute Error( MAE).
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