The monthly time series of the Total Suspended Solids (TSS) concentrations in Euphrates River at Nasria was analyzed as a time series. The data used for the analysis was the monthly series during (1977-2000).

The series was tested for nonhomogenity and found to be nonhomogeneous. A significant positive jump was observed after 1988. This nonhomogenity was removed using a method suggested by Yevichevich (7). The homogeneous series was then normalized using Box and Cox (2) transformation. The periodic component of the series was fitted using harmonic analyses, and removed from the series to obtain the dependent stochastic component. This component was then modeled using first order autoregressive model (Markovian chain). The above a

Nitrogen dioxide NO₂ is one of the most dangerous contaminant in the air, its toxic gas that cause disturbing respiratory effects, most of it emitted from industrial sources especially from the stack of power plants and oil refineries. In this study Gaussian equations modelled by Matlab program to state the effect of pollutant NO₂ gas on area around Durra refinery, this program also evaluate some elements such as wind and stability and its effect on stacks height. Data used in this study is the amount of fuel oil and fuel gas burn inside refinery at a year 2017. Hourly April month data chosen as a case study because it’s unsteady month. After evaluate emission rate of the all fuel and calculate exit velocity from

In this study, an experimental investigation had conducted for six high strength laced reinforced concrete one-way slabs to discover the behavior of laced structural members after being exposed to fire flame (high temperature). Self-compacted concrete (SCC) had used to achieve easy casting and high strength concrete. All the adopted specimens were identical in their compressive strength of ( , geometric layout 2000 750 150 mm and reinforcement specifics except those of lacing steel content, three ratios of laced steel reinforcement of (0.0021, 0.0040 and 0.0060) were adopted. Three specimens were fired with a steady state temperature of  for two hours duration and then after the specimens were cooled suddenly by spraying water. The

The Weibull distribution is considered one of the Type-I Generalized Extreme Value (GEV) distribution, and it plays a crucial role in modeling extreme events in various fields, such as hydrology, finance, and environmental sciences. Bayesian methods play a strong, decisive role in estimating the parameters of the GEV distribution due to their ability to incorporate prior knowledge and handle small sample sizes effectively. In this research, we compare several shrinkage Bayesian estimation methods based on the squared error and the linear exponential loss functions. They were adopted and compared by the Monte Carlo simulation method. The performance of these methods is assessed based on their accuracy and computational efficiency in estimati