A probabilistic water quality index for river water quality assessment: a case study

Available water quality indices have some limitations such as incorporating a limited number of water quality variables and providing deterministic outputs. This paper presents a hybrid probabilistic water quality index by utilizing fuzzy inference systems (FIS), Bayesian networks (BNs), and probabilistic neural networks (PNNs). The outputs of two traditional water quality indices, namely the indices proposed by the National Sanitation Foundation and the Canadian Council of Ministers of the Environment, are selected as inputs of the FIS. The FIS is trained based on the opinions of several water quality experts. Then the trained FIS is used in a Monte Carlo analysis to provide the required input-output data for training both the BN and PNN. The trained BN and PNN can be used for probabilistic water quality assessment using water quality monitoring data. The efficiency and applicability of the proposed methodology is evaluated using water quality data obtained from water quality monitoring system of the Jajrood River in Iran.     

Potential of Calendula alata for phytoremediation of stable cesium and lead from solutions

Calendula alata plants were tested for their potential to remove stable cesium and lead from solutions in a 15-day period. The plants were grown hydroponically and placed in solutions containing CsCl and Pb(C?H?O?)? at different concentrations (0.6, 2 and 5 mg l?1). When plants were incubated in CsCl solutions 46.84 ± 2.12%, 41.35 ± 1.59%, and 52.06 ± 1.02% cesium was found to be remediated after 15 days. Moreover, more than 99% lead was removed from the Pb(C?H?O?)? solution in all three concentrations after 15 days during the same period. When both CsCl and Pb(C?H?O?)? were supplemented together in the solution, 9.92 ± 1.22%, 45.56 ± 3.52%, and 46.16 ± 1.48% cesium and 95.30 ± 0.72%, 96.64 ± 0.30%, and 99.02 ± 0.04% lead were removed after 15 days. The present study suggests that hydroponically grown C. alata could be used as a potential candidate plant for phytoremediation of cesium and lead from solutions; however, plants were found to be more efficient for the remediation of lead than cesium.     

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.     

The effects of low frequency noise on mental performance and annoyance

Low frequency noise (LFN) as background noise in urban and work environments is emitted from many artificial sources such as road vehicles, aircraft, and air movement machinery including wind turbines, compressors, and ventilation or air conditioning units. In addition to objective effects, LFN could also cause noise annoyance and influence mental performance; however, there are no homogenous findings regarding this issue. The purpose of this research was to study the effects of LFN on mental performance and annoyance, as well as to consider the role of extraversion and neuroticism on the issue. This study was conducted on 90 students of Iran University of Medical Sciences (54 males and 36 females). The mean age of the students was 23.46 years (SD?=?1.97). Personality traits and noise annoyance were measured by using Eysenck Personality Inventory and a 12-scale self-reported questionnaire, respectively. Stroop and Cognitrone computerized tests measured mental performance of participants each exposed to 50 and 70 dBA of LFN and silence. LFNs were produced by Cool Edit Pro 2.1 software. There was no significant difference between mental performance parameters under 50 and 70 dBA of LFN, whereas there were significant differences between most mental performance parameters in quiet and under LFN (50 and 70 dBA). This research showed that LFN, compared to silence, increased the accuracy and the test performance speed (p?<?0.01). There was no association between LFN and noise annoyance (p?>?0.01). Introverts conducted the tests faster than extraverts (p?<?0.05). This research showed that neuroticism does not influence mental performance. It seems that LFN has increased arousal level of participants, and extraversion has a considerable impact on mental performance.     

Biopolymer-based membranes from polysaccharides for CO2 separation: a review

Environmental Chemistry Letters - The application of natural biopolymers such as polysaccharides for the fabrication of bio-based membranes has recently&nbsp;attracted attention for CO2...     

Multi-response optimization of Fenton process for applicability assessment in landfill leachate treatment

Fenton process, as a pretreatment method, was found to be effective in the primary treatment of mature/medium landfill leachate. However, the main problem of the process is the large amount of produced sludge that requires an accurate feasibility evaluation for operational applications. In this study, the response surface methodology was applied for the modeling and optimization of Fenton process in three target responses, (1) overall COD removal, (2) sludge to iron ratio (SIR) and (3) organics removal to sludge ratio (ORSR), where the latter two were new self-defined responses for prediction of sludge generation and applicability assessment of the process, respectively. The effective variables included the initial pH, [H₂O₂]/[Fe²⁺] ratio and Fe²⁺ dosage. According to the statistical analysis, all the proposed models were adequate (with adjusted R² of 0.9116–0.9512) and had considerable predictive capability (with prediction R² up to 0.9092 and appropriate adequate precision). It was found that all the variables had significant effects on the responses, specifically by their observed role in dominant oxidation mechanism. The optimum operational conditions obtained by overlay plot, were found to be initial pH of 5.7, [H₂O₂]/[Fe²⁺] ratio of 17.72 and [Fe²⁺] of 195 mM, which led to 69% COD removal, 2.4 (l sludge/consumed mole Fe²⁺) of SIR and 16.5 (gCOD removed/l produced sludge) for ORSR in verification test, in accordance with models-predicted values. Finally, it was observed that [H₂O₂]/[Fe²⁺] ratio and Fe²⁺ dosage had significant influence on COD removal, while Fe²⁺ dosage and [H₂O₂]/[Fe²⁺] ratio had remarkable effects on SIR and ORSR responses, respectively.     

Simultaneous Removal of Nitrate and Natural Organic Matter from Drinking Water Using a Hybrid Heterotrophic/Autotrophic/Biological Activated Carbon Bioreactor

Simultaneous removal of nitrate ([Formula: see text]) and natural organic matter (NOM) from drinking water using a hybrid heterotrophic/autotrophic/BAC bioreactor (HHABB) was studied in continuous mode. The HHABB consisted of three compartments: ethanol heterotrophic part, sulfur autotrophic part, and biological activated carbon (BAC)-part (including anoxic and aerobic sections). Experiments were performed with [Formula: see text] concentration 30?mg N/L, [Formula: see text] loading rate 0.72?kg N/m(3)/d, C?:?N ratio 0.53, and three concentrations of NOM (0.6, 2.6, and 5.7?mg C/L). Overall denitrification rate and efficiency of the HHABB were not affected by NOM concentration and were in the suitable ranges of 0.69-0.70?kg N/m(3)/d and 96.0%-97.7%, respectively. NOM removal at concentration 0.6?mg C/L was not efficient because of organic carbon replacement as soluble microbial products. At higher NOM concentrations, total NOM removal efficiencies were 55%-65%, 55%-70%, and 55%-65% for dissolved organic carbon, trihalomethane formation potential, and UV absorbance at 254?nm (UV(254)), respectively. The more efficient compartments of the HHABB for the removal of NOM were the ethanol heterotrophic phase and aerobic BAC-phase. The efficiency of the HHABB in the removal of NOM was considerable, and the effluent dissolved organic carbon and trihalomethane formation potential concentrations were relatively low. This study indicated that the HHABB without the anoxic BAC-phase could be a feasible alternative for simultaneous removal of [Formula: see text] and NOM from drinking water at full scale.     

Aminobenzenesulfonamide functionalized SBA-15 nanoporous molecular sieve: A new and promising adsorbent for preconcentration of lead and copper ions

A rapid method for the extraction and monitoring of nanogram level of Pb²⁺ and Cu²⁺ ions using uniform silanized mesopor (SBA-15) functionalized with aminobenzenesulfonamide groups and flame atomic absorption spectrometry (FAAS) is presented. Aminobenzenesulfonamide functionalized SBA-15 was synthesized according to procedure in the literature and the presence of organic groups in the silica framework was demonstrated by FT-IR spectra. The functionalized product showed the BET surface area 110 m²/g and pore diameter 5.1 nm, based on adsorption-desorption of N₂ at 77 K. The effect of several variables such as (amount of adsorbent, stirring time, pH and presence of other ions in the medium) has been studied. Lead and copper were completely extracted at pH greater than 3 after stirring for 10 min. The maximum capacity of the adsorbent was found to be 191.3 ± 1.4 and 155.0 ± 1.0 μg of lead and copper ions/mg functionalized SBA-15, respectively. The preconcentration factor of the method was found to be 200. The detection limit of the technique was 3.4 and 0.4 ng/mL for Pb²⁺ and Cu²⁺, respectively. The applications of this methodology for real samples were examined by various water type, black tea and pepper samples.     

High-risk behaviors while driving: A population-based study from Iran

Objective: Traffic injuries are becoming one of the most important challenges of public health systems. Because these injuries are mostly preventable, the aim of this study is to evaluate the four main high-risk behaviors while driving.

Methods: This cross-sectional study was conducted on a random sample from the population of Mashhad, Iran, in 2014. A checklist and a previously validated questionnaire for the transtheoretical stages of change model (TTM) were used for data collection. Statistical analyses were performed using SPSS 11.5 software with P <.05 statistically significant.

Results: Totally 431 individuals were included with a mean age of 30 ± 11.3 years. Forty-three percent (183) were male. The TTM model revealed that participants were mostly in pre-actional phases regarding not using a cell phone while driving (80%), fastening the driver's seat belt (66%), front seat belt (68%), and rear seat belt (85%) The penalty was a protective factor only for using cellphone (odd ratio [OR] = 0.82, 95% confidence interval [CI], 0.68–0.98). Lower education (OR = 0.12, 95% CI, 0.01–0.94) and male gender (OR = 0.35, 95% CI, 0.14–0.83) were indicative of lower rates of fastening the front and rear seat belts.

Conclusion: The stages of change model among study participants is a proper reflection of the effectiveness of the current policies. More serious actions regarding these high-risk behaviors should be considered in legislation.     

Randomly selected order statistics in ranked set sampling: A less expensive comparable alternative to simple random sampling

Rank-based sampling designs are powerful alternatives to simple random sampling (SRS) and often provide large improvements in the precision of estimators. In many environmental, ecological, agricultural, industrial and/or medical applications the interest lies in sampling designs that are cheaper than SRS and provide comparable estimates. In this paper, we propose a new variation of ranked set sampling (RSS) for estimating the population mean based on the random selection technique to measure a smaller number of observations than RSS design. We study the properties of the population mean estimator using the proposed design and provide conditions under which the mean estimator performs better than SRS and some existing rank-based sampling designs. Theoretical results are augmented with some numerical studies and a real-life example, where we also study the performance of our proposed design under perfect and imperfect ranking situations.