Health risk assessment of PAHs in fruit juice samples marketed in city of Tehran,Iran

Environmental Science and Pollution Research - The objective of the present study was to investigate the level of 16 PAHs in fruit juice samples (orange, apple, peach, pineapple, and mango) with...     

Estimation of soil specific surface area using some mechanical properties of soil by artificial neural networks

Soil specific surface area (SSA) is an important property of soil. Depending on the measurement techniques, determination of the SSA is costly and time consuming. Hence, a limited number of studies have been conducted to predict the SSA from the soil variables. In this study, the soil samples were taken from the literature. Fractal parameters (FP) were calculated by the model of Bird et al. (European Journal of Soil Science 51, 55–63, 2000) used as the input variables to predict the SSA. Some studies have been carried out on the prediction capability of the different parameters using the artificial neural networks (ANNs). The ANNs were further used and 20 models were developed to investigate the value of input variables to predict the SSA. The results showed that the PTF13 (RMSE?=?0.13) and PTF18 (RMSE?=?0.13) with the input variables of particle-size distribution and Atterberg limits revealed better performance than the other PTFs (in the training step). It is because of the fact that free swelling index (FSI) and Atterberg limits were closely correlated to the soil clay mineralogy as one of the important factors controlling the SSA. In general, this results demonstrated that the PTF9 with the variables of sand, clay, plastic limit (PL), liquid limit (LL), and FSI showed the best (RMSE?=?0.37) results in the estimation of the SSA. In conclusion, there was not a strong correlation between the soil mechanical properties and SSA but also ANNs were a suitable method to predict the SSA from the soil variables.     

An observational study of child safety seat use in an international safe community: Tehran,Iran

Objective: Despite strong evidence of the effectiveness of child safety seats in reducing injuries, the use of these devices in some communities is still rare. The purpose of this study was to determine the prevalence of child safety seat use and the factors influencing its use in the safe community of Tehran.

Methods: This roadside observational study was conducted in 2015 and 2,178 personal cars with a child under 12 years aboard were observed on Tehran's streets in regard to use of child safety seats. Other variables such as the gender of the driver, driver's age group, type of street, region of municipality, time of day, and day of the week were also collected.

Results: Prevalence of child safety seat use was 4.3% and was significantly higher among women drivers, on freeways, and in municipal regions 1, 4, and 7.

Conclusions: The prevalence of child safety seat use in Tehran as a most populous member of the international safe community was very low and most children commuted in insecure situations in the vehicle. Therefore, it is proposed that plans should be made to increase the use of child safety seats in international safe communities.     

Determination of mercury and arsenic levels in fish caught in the Beheshtabad River,Chaharmahal and Bakhtiari Province,Iran

Mercury and arsenic pollution has been recognized as a potential environmental and public health problem for over 40 years. The major source of exposure to mercury for humans is the ingestion of fish. This study was conducted with the aim of determining the levels of mercury and arsenic in the muscles of four fish species caught in the Beheshtabad River and comparing the results with the maximum tolerance levels for mercury and arsenic. The samples of 90 fish were used for the determination of both the metals by graphite furnace atomic absorption spectrometry. The results showed that the concentrations ranged from 1.5 to 3.8 µg kg^?1 for mercury and from 35 to 70 µg kg^?1 for arsenic, with means of 2.7 ± 0.5 and 57 ± 12 µg kg^?1, respectively. Both mean levels were lower than the threshold limits acceptable by WHO standards.     

Using Design-Expert to Optimize the Properties of a Polyethersulfone Ultrafiltration Membrane Through the Incorporation of NH2-MIL-53(Fe) and PVP for Maximum Cr(VI) Removal and Flux

Journal of Polymers and the Environment - In this study, an NH2-MIL-53(Fe) functionalized membrane was fabricated for Cr(VI) removal from contaminated groundwater. Design-Expert was employed to...     

Seasonal trend and source identification of polycyclic aromatic hydrocarbons associated with fine particulate matters (PM2.5) in Isfahan City,Iran, using diagnostic ratio and PMF model

Environmental Science and Pollution Research - Particulate matters (PMs) and their associated chemical compounds such as polycyclic aromatic hydrocarbons (PAHs) are important factors to evaluate...     

Biodegradation of low-density polyethylene (LDPE) by isolated fungi in solid waste medium

In this study, biodegradation of low-density polyethylene (LDPE) by isolated landfill-source fungi was evaluated in a controlled solid waste medium. The fungi, including Aspergillus fumigatus, Aspergillus terreus and Fusarium solani, were isolated from samples taken from an aerobic aged municipal landfill in Tehran. These fungi could degrade LDPE via the formation of a biofilm in a submerged medium. In the sterilized solid waste medium, LPDE films were buried for 100 days in a 1-L flask containing 400 g sterile solid waste raw materials at 28 °C. Each fungus was added to a separate flask. The moisture content and pH of the media were maintained at the optimal levels for each fungus. Photo-oxidation (25 days under UV-irradiation) was used as a pretreatment of the LDPE samples. The progress of the process was monitored by measurement of total organic carbon (TOC), pH, temperature and moisture. The results obtained from monitoring the process using isolated fungi under sterile conditions indicate that these fungi are able to grow in solid waste medium. The results of FT-IR and SEM analyses show that A. terreus and A. fumigatus, despite the availability of other organic carbon of materials, could utilize LDPE as carbon source. While there has been much research in the field of LDPE biodegradation under solid conditions, this is the first report of degradation of LDPE by A. fumigatus.     

Land surface temperature estimating in urbanized landscapes using artificial neural networks

Scenario-based land surface temperature (LST) modeling is a powerful tool for adopting proper urban land use planning policies. In this study, using greater Isfahan as a case study, the artificial neural network (ANN) algorithm was utilized to explore the non-linear relationships between urban LST and green cover spatial patterns derived from Landsat 8 OLI imagery. The model was calibrated using two sets of variables: Normalized Difference Built Index (NDBI) and Normalized Difference Vegetation Index (NDVI). Furthermore, Compact Development Scenario (CDS) and Green Development Scenario (GDS) were defined. The results showed that GDS is more successful in mitigating urban LST (mean LST?=?40.93) compared to CDS (mean LST?=?44.88). In addition, urban LST retrieved from the CDS was more accurate in terms of ANOVA significance (sig?=?0.043) than the GDS (sig?=?0.010). The findings of this study suggest that developing green spaces is a key strategy to combat against the risk of LST concerns in urban areas.     

Estimation of Water Demand in Iran Based on SARIMA Models

The generation of synthetic, residential water demands that can reproduce essential statistical features of historical residential water consumption is essential for planning, design, and operation of water resource systems. Most residential water consumption series are seasonal and nonstationary. We employ the seasonal autoregressive integrated moving average (SARIMA) model. We fit this model to monthly residential water consumption in Iran from May 2001 to March 2010. We find that a three-parameter log-logistic distribution fits the model residuals adequately. We forecast values for 1 year ahead using the fitted SARIMA model.