Phytoremediation of pollutants in oil-contaminated soils by Alhagi camelorum: evaluation and modeling.

Phytoremediation is a cost-effective and environmentally friendly method, offering a suitable alternative to chemical and physical approaches for the removal of pollutants from soil. This research explored the phytoremediation potential of Alhagi camelorum, a plant species, for total petroleum hydrocarbons (TPHs) and heavy metals (HMs), specifically lead (Pb), chromium (Cr), nickel (Ni), and cadmium (Cd), in oil-contaminated soil. A field-scale study spanning six months was conducted, involving the cultivation of A. camelorum seeds in a nursery and subsequent transplantation of seedlings onto prepared soil plots. Control plots, devoid of any plants, were also incorporated for comparison. Soil samples were analyzed throughout the study period using inductively coupled plasma-optical emission spectroscopy (ICP‒OES) for HMs and gas chromatography‒mass spectrometry (GC‒MS) for TPHs. The results showed that after six months, the average removal percentage was 53.6 ± 2.8% for TPHs and varying percentages observed for the HMs (Pb: 50 ± 2.1%, Cr: 47.6 ± 2.5%, Ni: 48.1 ± 1.6%, and Cd: 45.4 ± 3.5%). The upward trajectory in the population of heterotrophic bacteria and the level of microbial respiration, in contrast to the control plots, suggests that the presence of the plant plays a significant role in promoting soil microbial growth (P < 0.05). Moreover, kinetic rate models were examined to assess the rate of pollutant removal. The coefficient of determination consistently aligned with the first-order kinetic rate model for all the mentioned pollutants (R2 > 0.8). These results collectively suggest that phytoremediation employing A. camelorum can effectively reduce pollutants in oil-contaminated soils.

A high-flux P84 polyimide mixed matrix membranes incorporated with cadmium-based metal organic frameworks for enhanced simultaneous dyes removal: Response surface methodology.

The water sources contaminated by toxic dyes would pose a serious problem for public health. In view of this, the development of a simple yet effective method for removing dyes from industrial effluent has attracted interest from researchers. In the present work, flat sheet mixed matrix membranes (MMMs) with different physiochemical properties were fabricated by blending P84 polyimide with different concentrations of cadmium-based metal organic frameworks (MOF-2(Cd)). The resultant membranes were then used for simultaneous removal of eosin y (EY), sunset yellow (SY) and methylene blue (MB) under various process conditions. The findings indicated that the membranes could achieve high water permeability (117.8-171.4 L/m2.h.bar) and promising rejection for simultaneous dyes removal, recording value of 99.9%, 81.2% and 68.4% for MB, EY and SY, respectively. When 0.2 wt% MOF-2(Cd) was incorporated into the membrane matrix, the membrane separation efficiency was improved by 110.2% and 213.3% for EY and SY removal, respectively when compared with the pristine membrane. In addition, the optimization and modeling of membrane permeate flux and dye rejection was explored using response surface methodology. The actual and model results are in good agreement with R2 of at least 0.9983 for dye rejection and permeate flux. The high flux of the developed MMMs coupled with effective separation of dyes suggests a promising prospect of using P84 polyimide MMMs incorporated with MOF-2(Cd) for water purification.

Optimization and modeling of simultaneous ultrasound-assisted adsorption of ternary dyes using copper oxide nanoparticles immobilized on activated carbon using response surface methodology and artificial neural network.

The present study examines simultaneous adsorption of ternary dyes such as rose bengal (RB), safranin O (SO) and malachite green (MG) from aqueous media on copper oxide nanoparticles immobilized on activated carbon (CuO-NPs-AC) in a batch system. To forecast and optimize the adsorption, artificial neural network (ANN) and response surface methodology (RSM) were utilized. The effect of various factors, e.g. dye concentration, sonication time, adsorbent dosage and pH on the adsorption process were evaluated through five level six factor central composite design (CCD) using RSM. Maximum removal efficiency of MG, SO and RB dyes were seen 94.26%, 71% and 76% under optimal operating conditions. The suggested quadratic models revealed good fit with the actual data. To testing the data, the coefficients of determination (R2) of 0.9976, 0.9971 and 0.9952 and Fisher F-values of 2048.92, 1660.95 and 926.84 were obtained for MG, SO and RB dyes, respectively. The same data were utilized to construct the ANN models. The results revealed that both models yielded high R2 values, while the RSM models were slightly more accurate in predictions as compared to ANN models for MG, SO and RB dyes removal. The equilibrium data followed the Langmuir isotherm model, although the rate of the adsorption process well fitted to pseudo-second-order kinetics. The maximum adsorption capacity of the CuO-NPs-AC for MG, SO and RB were found to be 212.79, 149.25 and 172.42 mg/g, respectively.

Using Image Processing in the Proposed Drowsiness Detection System Design.

BACKGROUND: Drowsiness is one of the underlying causes of driving accidents, which contribute, to many road fatalities annually. Although numerous methods have been developed to detect the level of drowsiness, techniques based on image processing are quicker and more accurate in comparison with the other methods. The aim of this study was to use image-processing techniques to detect the levels of drowsiness in a driving simulator. METHODS: This study was conducted on five suburban drivers using a driving simulator based on virtual reality laboratory of Khaje-Nasir Toosi University of Technology in 2015 Tehran, Iran. The facial expressions, as well as location of the eyes, were detected by Violla-Jones algorithm. Criteria for detecting drivers' levels of drowsiness by eyes tracking included eye blink duration blink frequency and PERCLOS that was used to confirm the results. RESULTS: Eye closure duration and blink frequency have a direct ratio of drivers' levels of drowsiness. The mean of squares of errors for data trained by the network and data into the network for testing, were 0.0623 and 0.0700, respectively. Meanwhile, the percentage of accuracy of detecting system was 93. CONCLUSION: The results showed several dynamic changes of the eyes during the periods of drowsiness. The present study proposes a fast and accurate method for detecting the levels of drivers' drowsiness by considering the dynamic changes of the eyes.

Removal of Reactive Black 5 dye from aqueous solutions by coupled electrocoagulation and bio-adsorbent process.

BACKGROUND: Textile wastewater contains a significant amount of bio dye compounds including Reactive Black 5 (RB5) as an Azo dye that causes serious damage to the environment and aquatic life in receiving water resources. It is estimated that approximately 15% of the dyestuff is discharged into water bodies and 50% of those are Azo dyes. OBJECTIVE: The aim of this study was to examine the effectiveness of combining electrocoagulation and bio-adsorption methods using the active carbons from Kenya tea waste to remove RB5 from aquatic solutions. METHODS: Experiments were done in wastewater and the Wastewater laboratory of Gonabad University of Medical Sciences in 2017. Initially, iron electrodes were installed in a 1 L glass container and connected to electrical power with primary dye concentration of 500 mg/l, EC 1000-3000 µs/cm, adsorbent dosage of 0.2-1.0 mg/l, pH 2-9 and electrical potential 30 V. After that, the samples were collected at twenty-minute intervals until the eightieth minute. Performance of dye removal was measured using spectrophotometer techniques (597 nm). Finally, the data were analyzed by descriptive statistics using Microsoft Excel 2017 version. RESULTS: The findings of this study demonstrated that the highest simultaneous removal performance (96%) was obtained by pH 6, 80-minute reaction time, and EC 3000 µs/cm. With the increasing of reaction time, the energy consumption, electrodes wear, pH, final temperature of effluent, and removal efficiency would increase. Adsorption process could play a small role in the removal of the dye, while it was very effective in improving the performance of the electrocoagulation process. CONCLUSION: A combination of the electrocoagulation and adsorption processes can be adopted as an efficient and economical way to treat effluents contaminated with dyestuff.

On the chemical nature of precipitation in a populated Middle Eastern Region (Ahvaz, Iran) with diverse sources.

This study reports on the chemical composition of rainwater collected at three ground sites with varying degrees of pollution in Ahvaz, Iran, between January 2014 and February 2015. A total of 24 rainwater samples were analyzed for pH and concentrations of trace elements (Fe, Al, Pb, and Cd) and major ions (Na+, NH4+, Ca2+, Mg2+, HCO3-, NO3-, Cl- and SO42-). Principle Component Analysis (PCA) was used to identify sources of the measured species. The equivalent concentration of the components followed the order of Ca2+ > SO42- > HCO3- > NH4+ > Cl- > Na+ > NO3- > Mg2+. The average pH of the rainwater samples was 6, and only three events exhibited acidic conditions below a pH of 5.6. The lowest and the highest average pH values were observed in the high traffic area (5.96) and industrial area (6.54), respectively. The highest and lowest Ca2+ levels were observed in the industrial and high traffic areas, respectively. Na+, Mg2+, and SO42- exhibited their highest and lowest concentrations in the industrial and high traffic areas, respectively. 70.36% of the total variance was due to anthropogenic species (Ca2+, SO42-, Mg2+, NO3-, Cl-), soil particles (Cl-, Na+, and HCO3-), and biomass burning (NH4+, pH). The results of this study show that local anthropogenic sources and Middle Eastern Dust (MED) storms affect the rainwater chemistry strongly, which the latter stems from the Arabian Peninsula, Kuwait, Iraq, and some parts of Iran.

Study on attitudes of students of Islamic Azad University Tehran Medical Branch toward food safety, 2016.

BACKGROUND: Given the importance of high-quality healthy food for humans, contamination control is the most important concern for healthy staff. AIM: To determine the attitudes of students at Islamic Azad University (Tehran Medical Branch) toward food safety. METHODS: This cross-sectional and analytic-descriptive study was conducted on 326 students of Azad University of Medical Sciences in 2016. A self-made questionnaire consisting of 40 questions was used. The reliability of the questionnaire was confirmed using internal consistency method (Cronbach's alpha coefficient of 0.80). After collecting data, we use descriptive statistical indexes (mean and standard deviation) among demographic variables and the level of knowledge to describe and analyze the data. The participants' attitudes and operation are measured by Spearman tests, and the analytical results are given using SPSS version 20. RESULTS: According to the findings, 55.3, 30 and 14.7 percent of students had high, moderate and low attitude scores toward food safety, respectively. In addition, male and female students had equal attitudes toward food safety, and no significant relationship between sex and attitude was observed a significant difference (p>0.05), but between educational levels (p=0.008) and ages (p=0.001) of students significance was a positive correlation. CONCLUSIONS: Due to the low attitudes score of about half of the students of Islamic Azad University Tehran Medical Branch toward food safety, it can be claimed, food safety training in this community is required.

An evaluation of hospital admission respiratory disease attributed to sulfur dioxide ambient concentration in Ahvaz from 2011 through 2013.

There is no doubt that air pollutants have adverse impacts on human health. The main objective of this study was to evaluate hospital admission respiratory disease (HARD) attributed to sulfur dioxide levels in Ahvaz during three successive years. Data was taken from Iranian Environmental Protection Agency (EPA). The AirQ2,2,3 model is used to quantify the impact of SO2 on inhabitants of Ahvaz and in terms of hospital admission respiratory diseases. This is a kind of statistical model which is based on some epidemiological indices such as relative risk, baseline incidence, and attributable proportion. Sampling was already performed for 24 h in four stations during 2011-2013. Four stations are good representative for residential, high traffic, industry, and background sites which cover the whole area of the Ahvaz city. Regarding to gravimetric scale, raw data of sulfur dioxide was processed using Excel software. Encoding, filtering, and processing were conducted to prepare input file for the Air Q2,2,3 model. After running model, outputs presented in term of hospital admissions respiratory cases. Based on our result, the highest mean and maximum of seasonal and annual levels for sulfur dioxide were observed in 2013. We concluded that obnoxious quality of fuel and some deficiencies in maintenance and operation of industries lead to worse quality of ambient air especially in 2013. Cumulative cases of HARD attributed to sulfur dioxide level at central of relative risk (RR) were estimated 24, 25, and 30 persons for 2011, 2012, and 2013, respectively. The finding of this study showed that total mean of sulfur dioxide was higher than standard concentration. We also noticed that wintertime concentrations of sulfur dioxide during three successive years were higher than of those levels in summer.

Temporal profile of PM10 and associated health effects in one of the most polluted cities of the world (Ahvaz, Iran) between 2009 and 2014.

Ahvaz, Iran ranks as the most polluted city of the world in terms of PM10 concentrations that lead to deleterious effects on its inhabitants. This study examines diurnal, weekly, monthly and annual fluctuations of PM10 between 2009 and 2014 in Ahvaz. Health effects of PM10 levels are also assessed using the World Health Organization AirQ software. Over the study period, the mean PM10 level in Ahvaz was 249.5 µg m-3, with maximum and minimum values in July (420.5 µg m-3) and January (154.6 µg m-3), respectively. The cumulative diurnal PM10 profile exhibits a dominant peak between 08:00-11:00 (local time) with the lowest levels in the afternoon hours. While weekend PM10 levels are not significantly reduced as compared to weekdays, an anthropogenic signature is instead observed diurnally on weekdays, which exhibit higher PM10 levels between 07:00-17:00 by an average amount of 14.2 µg m-3 as compared to weekend days. PM10 has shown a steady mean-annual decline between 2009 (315.2 µg m-3) and 2014 (143.5 µg m-3). The AirQ model predicts that mortality was a health outcome for a total of 3777 individuals between 2009 and 2014 (i.e., 630 per year). The results of this study motivate more aggressive strategies in Ahvaz and similarly polluted desert cities to reduce the health effects of the enormous ambient aerosol concentrations.

Effect of bioaugmentation to enhance phytoremediation for removal of phenanthrene and pyrene from soil with Sorghum and Onobrychis sativa.

The use of plants to remove Poly-aromatic-hydrocarbons (PAHs) from soil (phytoremediation) is emerging as a cost-effective method. Phytoremediation of contaminated soils can be promoted by the use of adding microorganisms with the potential of pollution biodegradation (bioaugmentation). In the present work, the effect of bacterial consortium was studied on the capability of Sorghum and Onobrychis sativa for the phytoremediation of soils contaminated with phenanthrene and pyrene. 1.5 kg of the contaminated soil in the ratio of 100 and 300 mg phenanthrene and/or pyrene per kg of dry soil was then transferred into each pot (nine modes). The removal efficiency of natural, phytoremediation and bioaugmentation, separately and combined, were evaluated. The samples were kept under field conditions, and the remaining concentrations of pyrene and phenanthrene were determined after 120 days. The rhizosphere as well as the microbial population of the soil was also determined. Results indicated that both plants were able to significantly remove pyrene and phenanthrene from the contaminated soil samples. Phytoremediation alone had the removal efficiency of about 63% and 74.5% for pyrene and phenanthrene respectively. In the combined mode, the removal efficiency dramatically increased, leading to pyrene and phenanthrene removal efficiencies of 74.1% and 85.02% for Onobrychis sativa and 73.84% and 85.2% for sorghum, respectively. According to the results from the present work, it can be concluded that Onobrychis sativa and sorghum are both efficient in removing pyrene and phenanthrene from contamination and bioaugmentation can significantly enhance the phytoremediation of soils contaminated with pyrene and phenanthrene by 22% and 16% respectively.

Simultaneous derivatization and extraction of nitrophenols in soil and rain samples using modified hollow-fiber liquid-phase microextraction followed by gas chromatography-mass spectrometry.

A simple and sensitive method based on a modified hollow-fiber liquid-phase microextraction followed by gas chromatography-mass spectrometry has been successfully developed for the extraction and simultaneous derivatization of some nitrophenols (NPs) in soil and rain samples. Microwave-assisted solvent extraction was used for the extraction of NPs from the soil, while the rain sample was directly applied to the previously mentioned method. Briefly, in this method, the analytes were extracted from aqueous samples into a thin layer of organic solvent (dodecane + 10% tri-n-octylphosphine oxide) sustained in the pores of a porous hollow fiber. Then, they were back-extracted using a small volume of organic acceptor solution (25 µl; 10 mg/L N-methyl-N-(trimethylsilyl)trifluoroacetamide, as derivatization reagent, in acetonitrile) that was located inside the lumen of the hollow fiber. Under the optimized extraction conditions, enrichment factors of 255 to 280 and limits of detection of 0.1 to 0.2 µg/L (S/N = 3) with dynamic linear ranges of 1-100 µg/L were obtained for the analytes. The accuracy of the approach was tested by the relative recovery experiments on spiked samples, with results ranging from 93 to 113%. The method was shown to be rapid, cost-effective, and potentially interesting for screening purposes.