An assessment of metal contamination risk in sediments of Hara Biosphere Reserve,southern Iran with a focus on application of pollution indicators

The aim of this study was to assess the pollution status of metals in sediments of Hara Biosphere Reserve using pollution indicators. For this purpose, sediment samples from nine locations were collected and characterized for metal content (Pb, Cr, Zn, Cu, and Fe) using the total digestion technique. Comparison of metal concentrations with that of sediment quality guidelines (SQGs) demonstrated no association with negative biological effects for Cu and Zn, while the values of Pb and Cr mainly illustrated to have association with negative biological effects. The results of the geo-accumulation index (I _geo) indicated no contamination for Cr, Cu, Zn, and Fe, while the values of Pb demonstrated to have moderate contamination based on I _geo values. The analysis of the enrichment factor (EF) showed no enrichment for Cu and Zn and minor enrichment for Pb and Cr. Similar results were also found for quantification of contamination (QoC) analysis, where the values of Cu and Zn demonstrated to have a geogenic source of contamination, while the values of Pb and Cr mainly illustrated to have an anthropogenic source of contamination. According to EF and QoC calculations, the values of Cu and Zn were derived mainly from natural processes and exposure of material from the earth’s crust, while the values for Pb and Cr were enriched by anthropogenic activities. The results of the contamination factor (C _f ⁱ ) demonstrated low contamination levels for Fe, Cr, Zn, and Cu and moderate contamination levels for Pb. The pollution load index (PLI), showing the overall contamination of metals, demonstrated moderate pollution status in the study area.     

Green synthesis of 1,3-dihydrobenzimidazol-2-ones from aromatic diamines by microwave in a tetrabutylammonium bromide–ethanol molten salt paste

Environmental Chemistry Letters - The replacement of hazardous solvents by greener alternatives is a major goal in green chemistry. Benzimidazol-2-ones are heterocycles exhibiting a wide range of...     

Higher efficiency and lower environmental impact of membrane separation for carbon dioxide capture in coal power plants

Environmental Chemistry Letters - Global warming may be slowed down by carbon capture and storage systems that allow to sequester carbon dioxide from large fixed point sources such as power plants...     

Predicting non-carcinogenic hazard quotients of heavy metals in pepper (Capsicum annum L.) utilizing electromagnetic waves

• There was significant absorption of heavy metals by the pepper in contaminated soils. • The target hazard quotient (THQ) indices followed the order of Pb>Zn>>Cd » Ni. • Relationships exist between contaminated plants and electromagnetic wave. • PCA and random search can select the main spectra and predict THQ for each element. Given the tendency of heavy metals to accumulate in soil and plants, the purpose of this study was to determine the contamination levels of Cd, Ni, Pb, and Zn on peppers (leaves and fruit) grown in contaminated soils in industrial centers. For this purpose, we measured the uptake of the four heavy metals by peppers grown in the heavy metal contaminated soils throughout the four growth stages: two-leaf, growth, flowering, and fruiting, and calculated various vegetation indices to evaluate the heavy metal contamination potentials. Electromagnetic waves were also applied for analyzing the responses of the target plants to various heavy metals. Based on the relevant spectral bands identified by principal component analysis (PCA) and random search methods, a regression method was then employed to determine the most optimal spectral bands for estimating the target hazard quotient (THQ). The THQ was found to be the highest in the plants contaminated by Pb (THQ= 62) and Zn (THQ= 5.07). The results of PCA and random search indicated that the spectra at the bands of b₅₇₀, b₆₅₀, and b₇₆₀ for Pb, b₄₀₀ and b₁₀₃₀ for Ni, b₄₀₀ and b₈₈₀ for Cd, and b₅₆₀, b₉₁₀, and b₁₀₅₀ for Zn were the most optimal spectra for assessing THQ. Therefore, in future studies, instead of examining the amount of heavy metals in plants by chemical analysis in the laboratory, the responses of the plants to the electromagnetic waves in the identified bands can be readily investigated in the field based on the established correlations.     

Application of soft computing to predict water quality in wetland

Prediction of water quality is a critical issue because of its significant impact on human and ecosystem health. This research aims to predict water quality index (WQI) for the free surface wetland using three soft computing techniques namely, adaptive neuro-fuzzy system (ANFIS), artificial neural networks (ANNs), and group method of data handling (GMDH). Seventeen wetland points for a period of 14 months were considered for monitoring water quality parameters including conductivity, suspended solid (SS), biochemical oxygen demand (BOD), ammoniacal nitrogen (AN), chemical oxygen demand (COD), dissolved oxygen (DO), temperature, pH, phosphate nitrite, and nitrate. The sensitivity analysis performed by ANFIS indicates that the significant parameters to predict WQI are pH, COD, AN, and SS. The results indicated that ANFIS with Nash-Sutcliffe Efficiency (NSE = 0.9634) and mean absolute error (MAE = 0.0219) has better performance to predict the WQI comparing with ANNs (NSE = 0.9617 and MAE = 0.0222) and GMDH (NSE = 0.9594 and MAE = 0.0245) models. However, ANNs provided a comparable prediction and the GMDH can be considered as a technique with an acceptable prediction for practical purposes. The findings of this study could be used as an effective reference for policy makers in the field of water resource management. Decreasing variables, reduction of running time, and high speed of these approaches are the most important reasons to employ them in any aquatic environment worldwide.

     

Transformer oils as a potential source of environmental exposure to polychlorinated biphenyls (PCBs): an assessment in three central provinces of Iran

Environmental Science and Pollution Research - Polychlorinated biphenyl (PCB) contamination of oils from all transformers of the national electrical grid in Tehran, Qom, and Alborz, three central...     

The production of exclusive spaces in coastal pre-urban areas: Causes and motivations: Middle shoreline of Caspian Sea in north of Iran

The most common form of building in coastal peri-urban area, in middle shoreline of Caspian Sea in North of Iran, is gated community with the exclusive ownership and restricted access to the public shoreline. This research aims to clear the main causes, motivations and mechanism of exclusive space production in North of Iran. In contrast to the fact that many studies revealed as gated communities rose from the service-base motivations of the residents such as safety and high quality of the environment, this research shows the market-base motivations for living inside the gates. The factors of maintaining and increasing the property capital and protecting the family prestige are the main motivations for residence in gated communities in North of Iran. Due to the privileged life style and importance of agricultural lands for building gated communities, the main incentive of land owners for selling the agricultural lands is to derive benefit from selling lands and saving financial capital in Banks, instead of agricultural activities. The negligence of regulations in guidance and control of agricultural lands and in the processes of land use changes, causes the effectiveness of informal relations and illegal production of exclusive space in the context of environmental lands.     

Parametric study along with selection of optimal solutions in dry wire cut machining of cemented tungsten carbide (WC-Co)

This work deals with parametric study of dry wire EDM (WEDM) process of cemented tungsten carbide. Experiments have been conducted using air as dielectric medium to investigate effects of pulse on time, pulse off time, gap set voltage, discharge current and wire tension on cutting velocity (CV) surface roughness (SR) and oversize (OS). Firstly, a series of exploratory experiments were carried out to identify appropriate gas and its pressure. Afterward, preliminary experiments were conducted to investigate effects of process parameters on dry WEDM characteristics and find appropriate ranges for each factor. Then a central composite rotatable method was employed to design experiments based on response surface methodology (RSM). Empirical models were developed to create relationships between process factors and responses by considering to analysis of variances (ANOVA). To increase the predictability of the process, intelligent models have been developed based on back-propagation neural network (BPNN) and accuracy of these models was compared with mathematical models based on root mean square error (RMSE) and prediction error percent (PEP). In order to select optimal solutions in the cases of single-objective and multi-objectives optimization problems, optimization includes two main approaches. First approach was based on mathematical model and desirability function. Also second approach was designed based on neural network and particle swarm optimization. These approaches were applied in both cases of single-objective and multi-objectives optimization problems and their results were compared with together. Results indicated that selection of air at inlet pressure of 1.5 bar is really appropriate for conducting experiments of next stages. Also, the BPNN creates more accurate prediction rather than mathematical model. Moreover, the BPNN-PSO approach was more efficient in optimization of process rather than mathematical model-desirability function in respect with validation tests.     

Selecting a binary Markov model for a precipitation process

This paper uses rth-order categorical Markov chains to model the probability of precipitation. Several stationary and non-stationary high-order Markov models are proposed and compared using BIC. The number of parameters increases exponentially by adding the Markov order. Several classes of high-order Markov models are proposed which their increase of number of parameters are modest. For example models that use the number of precipitation days in a period prior to date, temperature of the previous day and sines/cosines periodic functions (to model the seasonality) are considered. The theory of partial likelihood is used to estimate the parameters. Parsimonious non-stationary first order Markov models with few seasonal terms are found optimal using BIC and temperature does not turn out to be a useful covariate. However BIC seems to underestimate the number of seasonal terms. We have also compared the results with AIC in some cases which tends to pick parsimonious models with more seasonal terms and higher order. We also show that ignoring seasonal terms result in picking higher order Markov chains. Finally we apply the methods to build confidence intervals for the probability of periods with no precipitation or low number of precipitation days in Calgary using historical data from 1980 to 2000.