ABSTRACT
Background and Objectives: In recent years, associations between air pollution and cardiovascular diseases have been reported around the world. However, a few studies have been conducted in industrial megacities which face air pollution in Iran. This study aimed to determine the association between air pollution and hospital admissions for cardiovascular diseases in Tabriz
Materials and Methods: We applied a case-crossover analysis to compute associations between daily concentrations of air pollutants including NO[2], SO[2], CO, PMi[0] and O[3] and hospital admissions for cardio-vascular diseases. Daily hospital admission data from 2009 to 2011 were collected from five hospitals in Tabriz. Air quality data for the study period were obtained from the six fixed online air quality monitoring stations operated by Tabriz Air Quality Control Center. The daily mean temperature and relative humidity data for the same period were obtained from East Azerbaijan Meteorological Bureau
Results: From the 1.512 cases, the data of 753 cases were used in our analysis. The results of ANNs with importance analysis showed that the most important causes of hospital admissions due to 120 were 03, SO[2], NO and PM[0], for 121 were SO[2], NO and NO[2] and for 120.9 were O[3], SO[2], NO and NO[2]. According to the results, die air pollutants had greater adverse effects on females and older cases
Conclusions: The results of this study showed that gaseous air pollutants including SO[2], NO and O[3] had the greatest effects on hospital admissions for cardiovascular diseases and significant effects on females and older people
Subject(s)
Female , Humans , Male , Air Pollution/adverse effects , Cardiovascular Diseases/etiology , Hospitals, University , Hospitalization/statistics & numerical data , Cross-Over StudiesABSTRACT
Background: The measurement data regarding the influent and effluent of wastewater treatment plant [WWTP] provides a general overview, demonstrating an overall performance of WWTP. Nevertheless, these data do not provide the suitable operational information for the optimization of individual units involved in a WWTP. A full-scale evolution of WWTP was carried out in this study via a reconciled data
Methods: A full-scale evolution of acrylonitrile, butadiene and styrene [ABS] resin manufacturing WWTP was carried out. Data reconciliation technique was employed to fulfil the mass conservation law and also enhance the accuracy of the flow measurements. Daily average values from long-term measurements by the WWTP library along with the results of four sampling runs, were utilized for data reconciliation with further performance evaluation and characterization of WWTP
Results: The full-scale evaluation, based on balanced data showed that removal efficiency based on chemical oxygen demand [COD] and biochemical oxygen demand [BOD5] through the WWTP were 80% and 90%, respectively, from which only 28% of COD and 20% of BOD5 removal had occurred in biological reactor. In addition, the removal efficiency of styrene and acrylonitrile, throughout the plant, was approximately 90%. Estimation results employing Toxchem model showed that 43% of acrylonitrile and 85% of styrene were emitted into the atmosphere above water surfaces
Conclusion: It can be concluded that the volatilization of styrene and acrylonitrile is the main mechanism for their removal along with corresponded COD elimination from the WWTP
ABSTRACT
Background and Objectives: arsenic in drinking water is a well-known hazardous material that its pathogenesis has been reported by different researches. Present study was conducted as an epidemiological study for skin effects of exposure to low concentration of arsenic from drinking water
Materials and Methods: present study was conducted as a cross sectional study in two exposed and unexposed villages to arsenic through drinking water [Sarab county: Razliq and Farkoosh villages, in 2012] and skin effects were studied among 279 persons over 10 years. Drinking water sources of both villages were analyzed chemically with an emphasis on arsenic. For statistical analysis of data, independent sample T-test was used and p value less than 0.05 was considered significant
Results: arsenic concentration in drinking water of the exposed and unexposed villages was 96micro g/l and zero, respectively. 9.7% of studied population had pigmentation [13.8% of exposed population, 6% of unexposed population]. Odds ratio of pigmentation in the exposed village was calculated 2.5 fold higher than unexposed village [CI 95% = 1.082- 5.778]. In the exposed village, keratosis just was observed in three persons [equal to 2.3% of village population] however, in unexposed village there was no keratosis
Conclusion: although there were more pigmentation cases in exposed village compared to unexposed one, however, in comparison with other studies, rate of skin lesions [keratosis and pigmentation] were lower indicating that incidence of skin lesions will be minimal if exposure dose of arsenic through drinking water be less than 100 micro g/l
ABSTRACT
Background: Forecasting of air pollutants has become a popular topic of environmental research today. For this purpose, the artificial neural network [AAN] technique is widely used as a reliable method for forecasting air pollutants in urban areas. On the other hand, the evolutionary polynomial regression [EPR] model has recently been used as a forecasting tool in some environmental issues. In this research, we compared the ability of these models to forecast carbon monoxide [CO] concentrations in the urban area of Tabriz city
Methods: The dataset of CO concentrations measured at the fixed stations operated by the East Azerbaijan Environmental Office along with meteorological data obtained from the East Azerbaijan Meteorological Bureau from March 2007 to March 2013, were used as input for the ANN and EPR models
Results: Based on the results, the performance of ANN is more reliable in comparison with EPR. Using the ANN model, the correlation coefficient values at all monitoring stations were calculated above 0.85. Conversely, the R2 values for these stations were obtained <0.41 using the EPR model
Conclusion: The EPR model could not overcome the nonlinearities of input data. However, the ANN model displayed more accurate results compared to the EPR. Hence, the ANN models are robust tools for predicting air pollutant concentrations