Fresh water resources in terms of water quality is a crucial issue worldwide. In Egypt, the Nile River is the main source of fresh water in the country and monitoring its water quality is a major task on governments and research levels. In the present case study, the physical, chemical and algal distribution in Nile River was monitored over two seasons (winter and summer) in 2019. The aims of the study were to check the seasonal variation among the different water parameters and also to check the correlations between those parameters. Water samples were collected from the Nile in Cairo governorate in EGYPT. The different physiochemical and microbiological properties in water samples were assessed. The studied parameters were included: temperature, turbidity, dissolved oxygen, chemical oxygen demand, pH, electric conductivity, total dissolved solids, total hardness, anions and cations. Also, the total algae count, blue-green algae, green algae, diatoms, unicellular and filamentous algae were monitored. The results revealed that during winter season the values recorded for (turbidity, total dissolved solids, pH, total alkalinity, total hardness, dissolved oxygen, chemical oxygen demand as well as nitrate, sulfate, chloride, fluoride ions, calcium and magnesium) were higher than during summer. While other parameters including ammonia, nitrite, silicate, carbon dioxide, phosphate, manganese, iron and residual aluminium were higher in summer compared to winter. The data showed a variation total algal count of 4600 to 6500 unit/ml in winter and varied from 3100 to 4500 unit/ml during summer season with predominance of diatoms. The recorded Pearson’s correlations indicated several significant correlations between tested parameters. In conclusion, although there were several variations in tested water quality parameters though all results were within the permissible limits set by the World Health Organization for drinking water.
Abstract
Bivariate time series modeling and forecasting have become a promising field of applied studies in recent times. For this purpose, the Linear Autoregressive Moving Average with exogenous variable ARMAX model is the most widely used technique over the past few years in modeling and forecasting this type of data. The most important assumptions of this model are linearity and homogenous for random error variance of the appropriate model. In practice, these two assumptions are often violated, so the Generalized Autoregressive Conditional Heteroscedasticity (ARCH) and (GARCH) with exogenous varia
... Show MoreIn this paper, estimation of system reliability of the multi-components in stress-strength model R(s,k) is considered, when the stress and strength are independent random variables and follows the Exponentiated Weibull Distribution (EWD) with known first shape parameter θ and, the second shape parameter α is unknown using different estimation methods. Comparisons among the proposed estimators through Monte Carlo simulation technique were made depend on mean squared error (MSE) criteria