جستجوی مقالات مرتبط با کلیدواژه « مسجد سلیمان » در نشریات گروه « پزشکی »

Street dust reflects one of the most important indicators of pollution in the urban environment. Most studies carried out in the world on heavy metal pollution are related to cities and metropolitans¡ while less attention has been paid to towns. Masjed-e- Soleiman¡ the most oil-rich town is located in the north eastern part of Khuzestan Province in Iran. This town contains geogenic pollutants such as gas and oil sources¡ oil and gas activities¡ oil and gas combustion¡ industries¡ and heavy traffic¡ which make it important from the viewpoint of environmental impact assessment.
For monitoring and management of pollution¡ 11 samples of street dust were collected and prepared from different stations within the town during the dry season (June 2013). Each sample was analyzed using ICP-OES instrument.
Possible sources of the heavy metals were identified by multivariate analyses such as Principal Component Analysis (PCA) and Cluster Analysis (CA). In addition¡ factors such as the Index of Geo-accumulation (Igeo)¡ Pollution Index (PI)¡ Integration Pollution Index (IPI)¡ and Enrichment Factor (EF) were assessed to determine the degree of heavy metals pollution in street dust of the Masjed-e- Soleiman Town. The results of PCA and CA indicated that the region is mostly affected by oil pollution (both natural and combustion of fossil fuels)¡ traffic¡ and industrial activities (PI revealed high levels of Pb¡ Cd¡ Cu¡ and Zn pollution in samples and the average level of Ni¡ As¡ and V pollution in other samples). Igeo indicated the moderate to severe pollution of some samples to Pb¡ Cd¡ Cu¡ and Zn while no to moderate pollution against Ni¡ As¡ and V in other samples. EF for Pb¡ Cd¡ Cu¡ and Zn showed a significant pollution in some samples and other samples represented the least pollution against Ni¡ As¡ and V.
Results showed that the concentrations of heavy metals in dust samples were higher than control samples in Masjed-e- Soleiman. Thus¡ the classification of pollution in street dust in Masjed-e- Soleiman is as follows: IPI> EF> Igeo ≈ PI.

Objective (s): To identify the predictive model of road traffic injuries by a time series model. This was an analytical study involving patients attending central hospital of Masjed –Soleyman (a city in Ahvaz Province), Iran during 24 months, from March 2008 to March 2010. Time series carried out to determine monthly occurrence of road traffic injuries in order to build a statistical model on data. The ARMA Box Jenkins linear model was used and an Autoregressive model was selected. Out of all 1210 injured patients referring to the hospital, 78.5% (n=950) were men. The mean age of patients was 30.57±17.08 years. The majority of injured (35.1%) were 20-29 years old. Autoregressive model was appropriate to describe our data over time. The result indicated the road traffic injuries had an increased pattern by 7 times as compare with previous month on average. The frequency of road traffic injuries is high in men and young age adolescents. The pattern is increasing and there is need for serious public health considerations.