Source identification of polycyclic aromatic hydrocarbons (PAHs) in sediment samples from the northern part of the Persian Gulf,Iran

Samples of surface sediments from the Iranian coast of the Persian Gulf were examined to determine the levels and sources of 15 polycyclic aromatic hydrocarbons (PAHs). Samples were collected from 30 sampling sites and analyzed for PAHs by gas chromatography–mass spectrometry (GC-MS). Total concentrations of PAHs ranged from 93 to 4,077 ng g^?1 dry weight. The PAH composition from 30 sampling sites was dominated by four-ring PAH compounds. Molecular indices based on the ratios of PAH concentrations were used to differentiate PAHs from pyrolitic to petrogenic and mixed origins. The results suggested that the main sources of PAHs in sediments from the studied region were mixed pyrolitic and petrogenic. Furthermore, benthic organisms in most of the investigated areas were not at ecotoxicological risk, according to the results from the effects range low (ERL)/effects range median (ERM) techniques suggested by the US Sediment Quality Guidelines (SQGs).     

Hydride generation coupled to microfunnel-assisted headspace liquid-phase microextraction for the determination of arsenic with UV–Vis spectrophotometry

In this research, a microfunnel-assisted headspace liquid-phase microextraction technique has been used in combination with hydride generation to determine arsenic (As) by UV–Vis spectrophotometry. The method is based on the reduction of As to arsine (AsH₃) in acidic media by sodium tetrahydroborate (NaBH₄) followed by its subsequent reaction with silver diethyldithiocarbamate (AgDDC) to give an absorbing complex at 510 nm. The complexing reagent (AgDDC) has been dissolved in a 1:1 (by the volume ratio) mixture of chloroform/chlorobenzene microdroplet and exposed to the generated gaseous arsine via a reversed microfunnel in the headspace of the sample solution. Several operating parameters affecting the performance of the method have been examined and optimized. Acetonitrile solvent has been added to the working samples as a sensitivity enhancement agent. Under the optimized operating conditions, the detection limit has been measured to be 0.2 ng mL^?1 (based on 3s_b/m criterion, n _b?=?8), and the calibration curve was linear in the range of 0.5–12 ng mL^?1. The relative standard deviation for eight replicate measurements was 1.9 %. Also, the effects of several potential interferences have been studied. The accuracy of the method was validated through the analysis of JR-1 geological standard reference material. The method has been successfully applied for the determination of arsenic in raw and spiked soft drink and water samples with the recoveries that ranged from 91 to 106 %.     

Field application of SPME as a novel tool for occupational exposure assessment with inhalational anesthetics

Occupational exposure to inhalational anesthetics occurs routinely in operating rooms. It could induce serious health hazards and diseases. This exposure assessment is a crucial step in determining risks. In this study, a pen-shaped holder for solid-phase microextraction (SPME) sampler was successfully applied as a time-weighted average sampling tool for workshift exposure assessment of operation room staff to halothane. It proved to be very convenient for use in occupational environments such as operation rooms. Samples were analyzed by a gas chromatography-mass spectrometry. The validity of the SPME method was checked in real-world conditions with Occupational Safety and Health Administration (OSHA) 103 standard method for the determination of inhalational anesthetics. A good agreement between OSHA 103 and SPME methods was obtained and results demonstrated no statistically significant differences in anesthetic concentrations determined by the two analytical methods (p?≥?0.05). It is concluded that SPME in retracted mode could successfully be applied in occupational exposure assessment purposes.     

Effect of fishing vessels on trace metal contamination in sediments of three harbors along Iranian Oman Sea coast

In this study, the levels of natural and anthropogenic metal contamination (aluminum (Al), iron (Fe), manganese (Mn), arsenic (As), cadmium (Cd), chromium (Cr), copper (Cu), nickel (Ni), lead (Pb), and zinc (Zn)) in surface sediments of three harbors along the Iranian coast of Gulf of Oman were examined and reported for the first time. Effect of grain size, mineralogy, normalization technique using Fe concentrations, and different sediment quality guidelines were discussed. Data from the harbors were compared with other harbors worldwide. Sediments inside the harbors are characterized by moderate and high levels of pollution by trace metals Cu (12–287 μg/g), Pb (11–1,780 μg/g), Zn (38–547 μg/g), Cr (70–2,370 μg/g), and Ni (31–116 μg/g). However, As and Cd did not show considerable pollution inside and outside the harbors. Considering that there is no industrial activity around the study harbors, the major sources of contamination in the harbors are repairing, fueling, greasing, and painting of fishing ships and boats. Mineralogy of sediments in the study area as well as trace metal concentration in reference samples taken from onshore geological units confirmed that natural inputs of Cd, Cu, Pb, and Zn in the harbors are very low while most of Ni and Cr contamination in the study area comes from erosion of ophiolitic mélange units at the North of Makran mountains.     

Geostatistical Evaluation of Spatial Variation Related to Groundwater Quality Database: Case Study for Arak Plain Aquifer,Iran

Geostatistical methods are one of the advanced techniques to interpolate groundwater quality data. Geostatistical interpolation techniques employ both the mathematical and the statistical properties of the measured points. Compiling the data distribution on spatial and temporal domain is of crucial importance in order to evaluate its quality and safety. The main purpose of this paper is to assess groundwater quality of Arak plain, Iran, by an unbiased interpolated method so called Kriging. Therefore, seven quality variables of Arak plain aquifer including TDS, SAR, EC, Na⁺, TH, Cl^?, and SO₄ ^2? have been analyzed, studied, and interpreted statistically and geostatistically. Utilized data in this study were collected from 97 water well samples in Arak plain, in 2012. After normalizing data, variogram as a geostatistical tool for defining spatial regression was calculated and experimental variograms have been plotted by GS⁺ software, then the best theoretical model was fitted to each variogram based on minimum RSS error. Cross validation was used to determine the accuracy of the estimated data. The uncertainty of the method could be well assessed via this method since the method not only gave the average error (around 0 in this study) but also gave the standard deviation of the estimations. Therefore, more than 3800 points were estimated by ordinary Kriging algorithm in places which have not been sampled. Finally, estimation maps of groundwater quality were prepared and map of estimation variance, EV, has been presented to assess the quality of estimation in each estimated point. Results showed that the Kriging method is more accurate than the traditional interpolation algorithms not honoring the spatial properties of the database.     

Elemental contamination in urban parks of Rawalpindi/Islamabad—a source identification and pollution level assessment study

The soil quality of urban parks is of vital importance as the children playing in the parks get themselves easily contaminated. A study was conducted to assess the level of elemental pollution caused by constant urbanization and industrialization, in various parks of Islamabad and Rawalpindi. The soil samples, collected from 14 urban parks of Islamabad and Rawalpindi areas, were analysed for their elemental concentrations. In each sample, 32 elements were quantified using semi-absolute k (0)-instrumental neutron activation analysis and flame atomic absorption spectrophotometry. The quality of analysis was assured by analysing the International Atomic Energy Agency (IAEA) reference material IAEA-S7 (soil). The pollution level was assessed by enrichment factor, pollution load index and our suggested indicator called "average toxic element concentration". The elemental concentrations measured in the parks of two cities were compared by t test. Four sources of different elements in the soils were identified by employing principal component analysis and cluster analysis. The results of multivariate techniques grouped all parks into four classes. The use of enrichment factor indicated the presence of Ni at slightly higher level in all parks while the pollution load index revealed that the parks of Rawalpindi were relatively more polluted as compared to that of Islamabad. The hot spot areas of elemental concentration were closely related to high traffic conditions.     

Heavy metal concentrations in water and sediments in Tasik Chini, a freshwater lake, Malaysia

The purpose of this paper are to determine the concentration of heavy metals namely cadmium (Cd), copper (Cu) and lead (Pb) in water and sediment; and to investigate the effect of sediment pH and sediment organic matter on concentration of cadmium, copper and lead in sediment at oxidation fraction. For this purpose the concentration of heavy metals were measured in water and sediments at 15 sites from Tasik Chini, Peninsular Malaysia. The sequential extraction procedure used in this study was based on defined fractions: exchangeable, acid reduction, oxidation, and residual. The concentration of heavy metals in residual fraction was higher than the other fractions. Among the non-residual fractions, the concentration of heavy metals in organic matter fraction was much higher than other fractions collected from all sampling sites. The pH of the sediment in all sites was acidic. The mean pH ranges from 4.8 to 5.5 with the higher value observed at site 15. Results of organic matter analysis showed that the percentage of organic matter present in sediment samples varies throughout the lake and all sites of sediments were relatively rich in organic matter ranging from 13.0% to 34.2%. The highest mean percentage of organic matter was measured at sampling site 15, with value of 31.78%.     

Spatiotemporal variation of groundwater quality using integrated multivariate statistical and geostatistical approaches in Amol–Babol Plain,Iran

In recent years, groundwater quality has become a global concern due to its effect on human life and natural ecosystems. To assess the groundwater quality in the Amol–Babol Plain, a total of 308 water samples were collected during wet and dry seasons in 2009. The samples were analysed for their physico-chemical and biological constituents. Multivariate statistical analysis and geostatistical techniques were applied to assess the spatial and temporal variabilities of groundwater quality and to identify the main factors and sources of contamination. Principal component analysis (PCA) revealed that seven factors explained around 75 % of the total variance, which highlighted salinity, hardness and biological pollution as the dominant factors affecting the groundwater quality in the Plain. Two-way analysis of variance (ANOVA) was conducted on the dataset to evaluate the spatio-temporal variation. The results showed that there were no significant temporal variations between the two seasons, which explained the similarity between six component factors in dry and wet seasons based on the PCA results. There are also significant spatial differences (p?>?0.05) of the parameters under study, including salinity, potassium, sulphate and dissolved oxygen in the plain. The least significant difference (LSD) test revealed that groundwater salinity in the eastern region is significantly different to the central and western side of the study area. Finally, multivariate analysis and geostatistical techniques were combined as an effective method for demonstrating the spatial structure of multivariate spatial data. It was concluded that multiple natural processes and anthropogenic activities were the main sources of groundwater salinization, hardness and microbiological contamination of the study area.