Concentrations of Cd,Ni, Pb,and Cr in the two edible fish species Liza klunzingeri and Sillago sihama collected from Hara biosphere in Iran

The concentrations of four metals (Cd, Ni, Pb, and Cr) were determined in the muscles, gills, and livers of two edible fish species (Liza klunzingeri and Sillago sihama) caught from the Hara biosphere of Southern Iran. In both fish species, metal concentrations and bioaccumulation factors were in the sequence liver?>?gill?>?muscle. Bioaccumulation factors were found to be highest in S. sihama. The metal concentrations were descending in the order of Ni?>?Cr?>?Pb?>?Cd, except for muscle samples from S. sihama showing an inversion of Pb and Cr. There is a significant negative correlation between the concentrations of the metals in each tissue with length, weight, and age, except for muscle in L. klunzingeri. Some metal levels in the muscle exceeded the limits recommended by FAO, WHO, and FEPA.     

SPME-based air sampling method for inhalation exposure assessment studies: case study on perchlorethylene exposure in dry cleaning

Exposure to perchlorethylene, especially for dry cleaning workers and for people living near dry cleaning shops, could lead to several diseases and disorders. This study examines the value of solid-phase microextraction (SPME) for sampling perchlorethylene in the atmosphere of dry cleaning shops. Carboxen/polydimethylsiloxane (CAR/PDMS) in 0.5-cm retracted mode was selected. There were no significant differences between sampling rates at different temperatures (range of 20 to 30 °C) and air velocities (2 to 50 cm/s). On the opposite, relative humidity (RH) had a significant effect on sampling rates. Method reproducibility was realized in the laboratory and field conditions and was 6.2 % and 7 to 11 %, respectively. Repeatability was also determined as 8.9 %. Comparison of the results according to the American Industrial Hygiene Association exposure assessment strategy showed the SPME sampler yields more conservative results in comparison with traditional standard method.     

Characterization of ionic composition of TSP and PM10 during the Middle Eastern Dust (MED) storms in Ahvaz, Iran

Because of the recent frequent observations of major dust storms in southwestern cities in Iran such as Ahvaz, and the importance of the ionic composition of particulate matters regarding their health effects, source apportionment, etc., the present work was conducted aiming at characterizing the ionic composition of total suspended particles (TSP) and particles on the order of ～10?μm or less (PM(10)) during dust storms in Ahvaz in April-September 2010. TSP and PM(10) samples were collected and their ionic compositions were determined using an ion chromatography. Mean concentrations of TSP and PM(10) were 1,481.5 and 1,072.9?μg/m(3), respectively. Particle concentrations during the Middle Eastern Dust (MED) days were up to four times higher than those in normal days. Ionic components contributed to only 9.5% and 11.3% of the total mass of TSP and PM(10), respectively. Crustal ions were most abundant during dust days, while secondary ions were dominant during non-dust days. Ca(2+)/Na(+) and Cl(-)/Na(+) ratios can be considered as the indicators for identification of the MED occurrence. It was found that possible chemical forms of NaCl, (NH(4))(2)SO(4), KCl, K(2)SO(4), CaCl(2), Ca(NO(3))(2), and CaSO(4) may exist in TSP. Correlation between the anionic and cationic components suggests slight anion and cation deficiencies in TSP and PM(10) samples, though the deficiencies were negligible.     

Effect of fertilizer application on soil heavy metal concentration

A large amount of chemicals is annually applied at the agricultural soils as fertilizers and pesticides. Such applications may result in the increase of heavy metals particularly Cd, Pb, and As. The objective of this study was to investigate the variability of chemical applications on Cd, Pb, and As concentrations of wheat-cultivated soils. Consequently, a study area was designed and was divided into four subareas (A, B, C, and D). The soil sampling was carried out in 40 points of cultivated durum wheat during the 2006–2007 periods. The samples were taken to the laboratory to measure their heavy metal concentration, soil texture, pH, electrical conductivity, cationic exchange capacity, organic matter, and carbonate contents. The result indicated that Cd, Pb, and As concentrations were increased in the cultivated soils due to fertilizer application. Although the statistical analysis indicates that these heavy metals increased significantly (P value < 0.05), the lead and arsenic concentrations were increased dramatically compared to Cd concentration. This can be related to overapplication of fertilizers as well as the pesticides that are used to replant plant pests, herbs, and rats.     

GIS-based statistical model for the prediction of flood hazard susceptibility

At present, flood is the most significant environmental problem in the entire world. In this work, flood susceptibility (FS) analysis has been done in the Dwarkeswar River basin of Bengal basin, India. Fourteen flood causative factors extracted from different datasets like DEM, satellite images, geology, soil and rainfall data have been considered to predict FS. Three heuristic models and one statistical model fuzzy Logic (FL), frequency ratio (FR), multi-criteria decision analysis (MCDA) and logistic regression (LR) have been used. The validating datasets are used to validate these models. The result shows that 68.71%, 68.7%, 60.56% and 48.51% area of the basin is under the moderate to very high FS by the MCDA, FR, FL and LR, respectively. The ROC curve with AUC analysis has shown that the accuracy level of the LR model (AUC?=?0.916) is very much successful to predict the flood. The rest of the models like FL, MCDA and FR (AUC?=?0.893, 0.857 and 0.835, respectively) have lesser accuracy than the LR model. The elevation was the most dominating factor with coefficient value of 19.078 in preparation of the FS according to the LR model. The outcome of this study can be implemented by local and state authority to minimize the flood hazard.

     

Estimating Population Size and Detection Probability of the Lorestan Newt,Neurergus kaiseri (Schmidt, 1952) using an Integrated Method

Russian Journal of Ecology - Robust estimates of species population abundance are necessary for the success of conservation plans and species management strategies. In the present study, our main...     

Differential growth,nutrition, physiology,and gene expression in Melissa officinalis mediated by zinc oxide and elemental selenium nanoparticles

Environmental Science and Pollution Research - Regarding the rapid progress in the production and consumption of nanobased products, this research considered the behavior of Melissa officinalis...     

Alteration of body total antioxidant capacity and thiol molecules in human chronic exposure to aluminum

The physiological role of aluminum (Al) is not yet known. Exposure to Al may cause many human disorders. The aim of this study was to explore how occupational human exposure to Al might affect the body oxidative stress. The relation between Al toxicity and oxidative stress was studied in blood samples obtained from 45 primary Al production workers, with a minimum work history of three years in the age range of 29–52 years. They were evaluated for oxidative stress biomarkers including thiobarbituric acid reactive substances (TBARS) indicator of lipid peroxidation, ferric reducing ability of plasma (FRAP) indicator of total antioxidant capacity, total thiol molecules and Al level in blood. The results showed that workers have significantly higher blood Al levels and concomitant lower blood FRAP and total thiol molecules in comparison to controls. Smokers had lower total thiol molecules than non-smokers. The subjects who had a previous history of disease had lower FRAP levels. It is concluded that Al induces oxidative stress in primary Al production workers. Supplementation of workers with antioxidants may have beneficial effects.     

Application of ANN and ANFIS models for reconstructing missing flow data

Hydrological yearbooks, especially in developing countries, are full of gaps in flow data series. Filling missing records is needed to make feasibility studies, potential assessment, and real-time decision making. In this research project, it was tried to predict the missing data of gauging stations using data from neighboring sites and a relevant architecture of artificial neural networks (ANN) as well as adaptive neuro-fuzzy inference system (ANFIS). To be able to evaluate the results produced by these new techniques, two traditionally used methods including the normal ratio method and the correlation method were also employed. According to the results, although in some cases all four methods presented acceptable predictions, the ANFIS technique presented a superior ability to predict missing flow data especially in arid land stations with variable and heterogeneous data. Comparing the results, ANN was also found as an efficient method to predict the missing data in comparison to the traditional approaches.