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.     

Geochemical characterisation of pyrite oxidation and environmental problems related to release and transport of metals from a coal washing low-grade waste dump, Shahrood, northeast Iran

Pyrite oxidation and release of the oxidation products from a low-grade coal waste dump to stream, groundwater and soil was investigated by geochemical and hydrogeochemical techniques at Alborz Sharghi coal washing plant, Shahrood, northeast Iran. Hydrogeochemical analysis of water samples indicates that the metal concentrations in the stream waters were low. Moreover, the pH of the water showed no considerable change. The analysis of the stream water samples shows that except the physical changes, pyrite oxidation process within the coal washing waste dump has not affected the quality of the stream water. Water type was determined to be calcium sulphate. The results of the analysis of groundwater samples indicate that the pH varies from 7.41 to 7.51. The concentrations of the toxic metals were low. The concentration of SO4 is slightly above than its standard concentration in potable water. It seems that the groundwater less affected by the coal washing operation in the study area. Geochemical analysis of the sediment samples shows that Fe concentration decreases gradually downstream the waste dump with pH rising. SO(4) decreases rapidly downstream direction. Copper, Zn and Co concentrations decrease with distance from the waste dump due to a dilution effect by the mixing of uncontaminated sediments. These elements, in particular, Zn are considerably elevated in sediment sample collected at the nearest distance to the waste dump. There is no doubt that such investigations can help to develop an appropriate water remediation plan.     

Revising river water quality monitoring networks using discrete entropy theory: the Jajrood River experience

This paper aims at evaluating and revising the spatial and temporal sampling frequencies of the water quality monitoring system of the Jajrood River in the Northern part of Tehran, Iran. This important river system supplies 23% of domestic water demand of the Tehran metropolitan area with population of more than 10 million people. In the proposed methodology, by developing a model for calculating a discrete version of pair-wise spatial information transfer indices (SITIs) for each pair of potential monitoring stations, the pair-wise SITI matrices for all water quality variables are formed. Also, using a similar model, the discrete temporal information transfer indices (TITIs) using the data of the existing monitoring stations are calculated. Then, the curves of the pair-wise SITI versus distance between monitoring stations and TITI versus time lags for all water quality variables are derived. Then, using a group pair-wise comparison matrix, the relative weights of the water quality variables are calculated. In this paper, a micro-genetic-algorithm-based optimization model with the objective of minimizing a weighted average spatial and temporal ITI is developed and for a pre-defined total number of stations, the best combination of monitoring stations is selected. The results show that the existing monitoring system of the Jajrood River should be partially strengthened and in some cases the sampling frequencies should be increased. Based on the results, the proposed approach can be used as an effective tool for evaluating, revising, or redesigning the existing river water quality monitoring systems.     

Distribution of heavy metals in surface sediments from the South China Sea ecosystem, Malaysia

The concentrations of arsenic, cadmium, chromium, copper, mercury, nickel, lead and zinc in surface sediments collected from the east coast of peninsular Malaysia, along the South China Sea, were measured by two methods instrumental neutron activation analysis and inductively coupled plasma mass spectroscopy. The obtained results were use to determine the areal distribution of the metals of in the east coast of peninsular Malaysia and potential sources of these metals to this environment. The geochemical data propose that most of the metals found in the east coast of peninsular Malaysia constitute a redistribution of territorial materials within the ecosystem. Then, the metal concentrations can be considered to be present at natural background levels in surface sediments.