Fabricating pentaazatetraethylene modified sulfonated polyacrylamide for dye adsorption from aqueous media: isotherms and kinetics models.

In this study, pentaazatetraethylene-modified sulfonated polyacrylamide (PAm-SO3-N5) was synthesized and used as a novel efficient adsorbent to remove calmagite from aqueous media. To this end, a central composite design (CCD) was applied to reduce the number of reaction variables (i.e., adsorbent concentration, temperature, initial concentration, and pH) on calmagite removal. The results showed that calmagite was entirely adsorbed by the PAm-SO3-N5 within 30 min. In addition, a pseudo-second-order (PSO) model was prepared as the optimum formula to fit the kinetics information. The modeling results revealed that film diffusion and adsorption are rate-limiting stages to remove the dyes. Using a Langmuir isotherm to fit the equilibrium data, the highest equilibrium adsorption was calculated to be 1732.5 mg/g. In the present study, the ΔH value indicates that the adsorption is of chemical type. Also, the negative sign of ΔS° shows that PAm-SO3-N5 removes calmagite during a relatively stable process with randomness in the system. The increase in ΔG° values with increasing temperature indicates a descending trend in the feasibility degree of calmagite adsorption. Eventually, recycling the adsorbent for 7 cycles to adsorb calmagite dye showed no remarkable activity loss.

Optimizing cropping pattern to improve the performance of irrigation network using system dynamics-Powell algorithm.

One of the strategies for agricultural development is the optimal use of irrigation and drainage networks, which leads to higher productivity and economic benefits. In this regard, quantitative and qualitative studies of drainage water from networks are essential for efficient water management. In the present study, we develop a model using a system dynamics approach to simulate the cropping pattern of an irrigation and drainage network as well as the discharge and salinity of drainage water from network farms. We apply the Powell algorithm to optimize the economic profitability of cultivated crops by considering the salinity and discharge of drainage water from the fields. With three aims, i.e., (1) maximizing benefit-cost ratio, (2) minimizing drainage water salinity and discharge of network, and (3) economic and environmental considerations simultaneously, the optimization of cropping pattern within the Kosar irrigation and drainage network is performed. Results based on five consecutive years under different scenarios showed that some crops, such as watermelon, are not economically recommened for production due to high costs, water consumption, and low selling price causes environmental pollution. On the other hand, wheat, grain maize, silage maize, sorghum, and alfalfa have different conditions, and their production is suitable by considering all scenarios. By comparing with experimental data, we find that the proposed model is accurate to simulate and optimize the irrigation network and to detect its cropping pattern.

Seasonal variations of polycyclic aromatic hydrocarbons in coastal sediments of a marine resource hot spot: the case of pars special economic energy zone, Iran.

Polycyclic aromatic hydrocarbons (PAHs) are an important group of compounds of major environmental concern, which are in the class of persistent organic pollutants. Therefore, the key purpose of this research was to analyze seasonal fluctuations and to determine the probability of polycyclic aromatic hydrocarbons in coastal sediments of the Iranian Marine Resource Center based on the evaluation of 16 US-EPA important PAH compounds. These compounds have been collected from intertidal sediments located in the marine resources of southern Iran in different seasons. These samples of the surficial sediment were collected at the PSEEZ area using a stainless steel grab sampler in four seasons, from depths between 0.5 and 30 m. Surface sediment samples were removed by spoons and carefully placed in an aluminum foil; they were taken to the laboratory on ice and held at 20° C until their study. After extraction, by using a rotary evaporator apparatus, samples were condensed. The assay was added to roughly 2 g of activated copper flasks in the refrigerator for 36 h for desulfurization. Among different seasons, the highest concentration was observed in winter, with a mean of 281.3 ng g-1. According to ecological risk assessment (concentrations of possible effects, low effect range, degree of threshold effects, and median effect range), PAH risks in surface sediments of PSEEZ were lower than the threshold results levels (TEL), possible effects levels (PEL), low range of effects (ERL), and median range of effects (ERM), indicating that a biological effect would rarely occur. The dry weight scale of the concentration of ∑PAHs ranges from 145.7 to 348.42 ng g-1 with a mean quantity of 260.52 ng g-1. Therefore, according to the amount of ∑PAH concentration, the sediments in the PSEEZ area indicated moderate to heavy pollutions. In this way, the sedimentary surface ecosystems of the Persian Gulf were considered as moderately polluted compared with other ecosystems worldwide. Our study highlighted some of the research gaps in PAH contamination studies and the level of PAH contamination. Therefore, this study will provide a scientific background, planning, and policies for PAH pollution control and environmental protection in Iran and similar regions around the world.

Potential use of grapevine cv Askari for heavy metal phytoremediation purposes at greenhouse scale.

Grapevine varieties possess desirable characteristics for phytoremediation purposes. We investigated the potential of grapevine cv Askari in phytoremediation of heavy metal (HM) pollutions. In total 80 grapevine seedlings were exposed to four levels of HM stress (mild, low, moderate, and severe) in greenhouse condition during two growing years (2018 and 2019). The HM concentrations (Zn, Cu, Cd, Cr, and Pb) were subsequently determined in the soils, roots, and grapevine aboveground parts (AGPs), and then phytoextraction and phytostabilization potential assessment indices, i.e., biological absorption coefficient (BAC), bioconcentration factor (BCF), and translocation factor (TF), were calculated. Results led to ranking of the cumulative concentration order of the HMs, i.e.: soils (3476.6) > AGPs (1418.8) > roots (562.2) mg/kg-DW. The mean concentrations ranking order of studied HMs were in soil: Cu (1184.8) > Pb (865.5) > Cd (803.2) > Cr (623.0) > Zn (277.9) mg/kg-DW; roots, Cu (242.0) > Cd (239.5) > Zn (188.8) > Pb (63.5) > Cr (17.2) mg/kg-DW; and AGP environments, Cu (910.2) > Cd (322.9) > Zn (160.3) > Pb (152.9) > Cr (25.3) mg/kg-DW. Principal component analysis results demonstrated the same distribution pattern for the studied HMs between soil, root, and AGP environments, and the highest correlation coefficients were found for Cu, Zn, and Cd. Based on the obtained results (Cu-BAC (> 1), Zn-BCF (> 1), Zn-TF (< 1), Cu-AGP mean concentrations (> 1000 mg/kg-DW), and Cd-AGP mean concentrations (> 100 mg/kg-DW)), it can be concluded that grapevine cv Askari possesses potentials for phytoremediation purposes of Cu, Zn, and Cd. These results were acquired in a greenhouse environment and under controlled conditions; we suggest that the phytoremediation potential of this grapevine variety be assessed in a contaminated vineyard environment as well.

Ecological and health risks of soil and grape heavy metals in long-term fertilized vineyards (Chaharmahal and Bakhtiari province of Iran).

Heavy metal pollution in food-producing ecosystems resulting from long-term fertilizing and other agricultural practices is causing major concern due to its health risk potential for human and other organisms. This study was designed to investigate heavy metal concentrations (Cu, Zn, Pb, Cr and Cd) and assess their ecological risk using contamination factor (Cf), pollution load index (PLI) and ecological risk index. Health risk assessment (HRA) was also estimated by investigating non-carcinogenic and carcinogenic risks of soil via oral ingestion, dermal contact, inhalation, and health risk of grape consumption. To this aim, 38 fertilized vineyards of the region were investigated during harvesting seasons of 2016 and 2017. The results showed a same ranking order of the metals in the soil and grape that was Zn > Cu > Pb > Cr > Cd. The average concentrations of Zn and Pb in the grape samples were significantly higher than the national permissible limits. According to Cf and PLI indices, Cd had the most ecological risk compared to the other studied metals. The HRA results showed that adults and children are not exposed to non-carcinogenic risk from the studied heavy metals in the vineyard soils. But, it was a tolerable level of carcinogenic health risk only by Cr in some of the vineyards. The PLI had most significant correlation (p < 0.01) with the other studied indices. So, it can be considered as a rapid and applicable index to assess ecological and health risk of heavy metals in the region. To food-chain security and community health, some comprehensive managing plans are necessary to increase farmer's awareness about the standard amount of chemical fertilizers and pesticides. Furthermore, continuous monitoring of heavy metals in soil and grape of the vineyards should be performed.

Transboundary Basins Need More Attention: Anthropogenic Impacts on Land Cover Changes in Aras River Basin, Monitoring and Prediction.

Changes in land cover (LC) can alter the basin hydrology by affecting the evaporation, infiltration, and surface and subsurface flow processes, and ultimately affect river water quantity and quality. This study aimed to monitor and predict the LC composition of a major, transboundary basin contributing to the Caspian Sea, the Aras River Basin (ARB). To this end, four LC maps of ARB corresponding to the years 1984, 2000, 2010, and 2017 were generated using Landsat satellite imagery from Armenia and the Nakhchivan Autonomous Republic. The LC gains and losses, net changes, exchanges, and the spatial trend of changes over 33 years (1984-2017) were investigated. The most important drivers of these changes and the most accurate LC transformation scenarios were identified, and a land change modeler (LCM) was applied to predict the LC change for the years 2027 and 2037. Validation results showed that LCM, with a Kappa index higher than 81%, is appropriate for predicting LC changes in the study area. The LC changes observed in the past indicate significant anthropogenic impacts on the basin, mainly by constructing new reservoir dams and expanding agriculture and urban areas, which are the major water-consuming sectors. Results show that over the past 33 years, agricultural areas have grown by more than 57% from 1984 to 2017 in the study area. Results also indicate that the given similar anthropogenic activities will keep on continuing in the ARB, and agricultural areas will increase by 2% from 2017 to 2027, and by another 1% from 2027 to 2037. Results of this study can support transboundary decision-making processes to analyze potential adverse impacts following past policies with neighboring countries that share the same water resources.

Assessment of landfill leachate in semi-arid climate and its impact on the groundwater quality case study: Hamedan, Iran.

To evaluate environmental impacts of solid waste landfilling, groundwater quality near the MSW landfill in a semi-arid climate of Iran (Hamedan) and its leachates were analyzed. To this aim, heavy metal concentrations, COD, BOD5, TOC, EC, NO3-, Cl-, TDS, and pH of two leachate ponds (active and closed sites) as the sources of contamination as well as the shallow groundwater of the area were measured. Monthly and seasonal monitoring program of 13 sampling points in the area were designed during the period of 2014-2016. Principal components analysis has been carried out using chemical data to deduce relationship between the samples. A special statistical approach including a main factor (age of leachate) and a subfactor (distance from the source of pollutant) was designed in order to identify the landfill role on the groundwater contamination. The physicochemical analysis of the leachate characteristics confirmed a high variation in the contaminants (i.e., organic compounds, salts, and heavy metals) related to leachate age. The BOD5/COD ratio of the active (0.73) and closed (0.77) sites ponds indicated that the leachates were in a biodegradable and unstabilized condition. The seasonal physicochemical analysis of the leachates showed that rainfall events increase the decomposition rate of the waste and affect pollutant concentration of the leachate. The proposed statistical analysis illustrated a direct relationship between the groundwater quality parameters and the leachates physicochemical characteristics.

Scenario-based discrimination of common grapevine varieties using in-field hyperspectral data in the western of Iran.

Field spectroscopy is an accurate, rapid and nondestructive technique for monitoring of agricultural plant characteristics. Among these, identification of grapevine varieties is one of the most important factors in viticulture and wine industry. This study evaluated the discriminatory ability of field hyperspectral data and statistical techniques in case of five common grapevine varieties in the western of Iran. A total of 3000 spectral samples were acquired at leaf and canopy levels. Then, in order to identify the best approach, two types of hyperspectral data (wavelengths from 350 to 2500 nm and 32 spectral indices), two data reduction methods (PLSR and ANOVA-PCA) and two classification algorithms (LDA and SVM) were applied in a total of 16 scenarios. Results showed that the grapevine varieties were discriminated with overall accuracy of 89.88%-100% in test sets. Among the data reduction methods, the combination of ANOVA and PCA yielded higher performance as opposed to PLSR. Accordingly, optimal wavelengths in discrimination of studied grapevine varieties were located in vicinity of 695, 752, 1148, 1606 nm and 582, 687, 1154, 1927 nm at leaf and canopy levels, respectively. Optimal spectral indices were R680, WI, SGB and RATIO975_2, DattA, Greenness at leaf and canopy levels, respectively. Also, the importance of spectral regions in discriminating studied grapevine varieties was ranked as near-infrared > mid-infrared and red edge region > visible. As a general conclusion, the canopyspectral indices-ANOVA-PCA-SVM scenario discriminated the studied species most accurately.

Optimal Spectral Wavelengths for Discriminating Orchard Species Using Multivariate Statistical Techniques.

Sustainable management of orchard fields requires detailed information about the tree types, which is a main component of precision agriculture programs. To this end, hyperspectral imagery can play a major role in orchard tree species mapping. Efficient use of hyperspectral data in combination with field measurements requires the development of optimized band selection strategies to separate tree species. In this study, field spectroscopy (350 to 2500 nm) was performed through scanning 165 spectral leaf samples of dominant orchard tree species (almond, walnut, and grape) in Chaharmahal va Bakhtiyari province, Iran. Two multivariable methods were employed to identify the optimum wavelengths: the first includes three-step approach ANOVA, random forest classifier (RFC) and principal component analysis (PCA), and the second employs partial least squares (PLS). For both methods we determined whether tree species can be spectrally separated using discriminant analysis (DA) and then the optimal wavelengths were identified for this purpose. Results indicate that all species express distinct spectral behaviors at the beginning of the visible range (from 350 to 439 nm), the red edge and the near infrared wavelengths (from 701 to 1405 nm). The ANOVA test was able to reduce primary wavelengths (2151) to 792, which had a significant difference (99% confidence level), then the RFC further reduced the wavelengths to 118. By removing the overlapping wavelengths, the PCA represented five components (99.87% of variance) which extracted optimal wavelengths were: 363, 423, 721, 1064, and 1388 nm. The optimal wavelengths for the species discrimination using the best PLS-DA model (100% accuracy) were at 397, 515, 647, 1386, and 1919 nm.

Eco-Friendly Estimation of Heavy Metal Contents in Grapevine Foliage Using In-Field Hyperspectral Data and Multivariate Analysis.

Heavy metal monitoring in food-producing ecosystems can play an important role in human health safety. Since they are able to interfere with plants' physiochemical characteristics, which influence the optical properties of leaves, they can be measured by in-field spectroscopy. In this study, the predictive power of spectroscopic data is examined. Five treatments of heavy metal stress (Cu, Zn, Pb, Cr, and Cd) were applied to grapevine seedlings and hyperspectral data (350-2500 nm), and heavy metal contents were collected based on in-field and laboratory experiments. The partial least squares (PLS) method was used as a feature selection technique, and multiple linear regressions (MLR) and support vector machine (SVM) regression methods were applied for modelling purposes. Based on the PLS results, the wavelengths in the vicinity of 2431, 809, 489, and 616 nm; 2032, 883, 665, 564, 688, and 437 nm; 1865, 728, 692, 683, and 356 nm; 863, 2044, 415, 652, 713, and 1036 nm; and 1373, 631, 744, and 438 nm were found most sensitive for the estimation of Cu, Zn, Pb, Cr, and Cd contents in the grapevine leaves, respectively. Therefore, visible and red-edge regions were found most suitable for estimating heavy metal contents in the present study. Heavy metals played a significant role in reforming the spectral pattern of stressed grapevine compared to healthy samples, meaning that in the best structures of the SVM regression models, the concentrations of Cu, Zn, Pb, Cr, and Cd were estimated with R2 rates of 0.56, 0.85, 0.71, 0.80, and 0.86 in the testing set, respectively. The results confirm the efficiency of in-field spectroscopy in estimating heavy metals content in grapevine foliage.

A robust multi-objective bargaining methodology for inter-basin water resource allocation: a case study.

In this study, a new methodology is proposed to balance environmental and economic issues in water allocation under uncertainty. Two objective functions, including maximizing economic income (EI) and minimizing environmental pollution (EP), were considered as two groups of players to construct a deterministic multi-objective bargaining methodology (DMOBM). In the next step, it is enhanced to a robust multi-objective bargaining methodology (RMOBM), which is capable of incorporating the main uncertainties exist in the problem. A large-scale inter-basin water transfer case study was utilized to investigate the applicability of the developed model. The outputs of the models showed that Nash equilibrium provide a rather narrow range of solutions. According to the results, the required rounds to reach Nash equilibrium raised as the uncertainty level increased. In addition, higher levels of uncertainty lead to higher reduction in water allocating of receiving basin. Sensitivity analysis showed that economic income values are less sensitive to changes of uncertain parameters than the environmental objective function. The developed methodology could provide a framework to incorporate the behavior of different stakeholders. Furthermore, the proposed method can be reliable under the condition of facing water allocation uncertainties.

Evaluation of statistical distributions to analyze the pollution of Cd and Pb in urban runoff.

Heavy metal pollution in urban runoff causes severe environmental damage. Identification of these pollutants and their statistical analysis is necessary to provide management guidelines. In this study, 45 continuous probability distribution functions were selected to fit the Cd and Pb data in the runoff events of an urban area during October 2014-May 2015. The sampling was conducted from the outlet of the city basin during seven precipitation events. For evaluation and ranking of the functions, we used the goodness of fit Kolmogorov-Smirnov and Anderson-Darling tests. The results of Cd analysis showed that Hyperbolic Secant, Wakeby and Log-Pearson 3 are suitable for frequency analysis of the event mean concentration (EMC), the instantaneous concentration series (ICS) and instantaneous concentration of each event (ICEE), respectively. In addition, the LP3, Wakeby and Generalized Extreme Value functions were chosen for the EMC, ICS and ICEE related to Pb contamination.

Long-term variations of water quality parameters in the Maroon River, Iran.

Sixteen water quality parameters have been monitored at four stations located along the Maroon River during 1989-2008. The trend analysis was performed on seasonal and annual time-scales using the Mann-Kendall test, the Sen's slope estimator and the linear regression. The relationships of the water quality parameters to river discharge were also investigated. The statistical methods showed both positive and negative trends in annual water quality data. However, significant trends were detected by the statistical methods only in calcium, magnesium, sodium absorption ratio, pH, and turbidity series. The results indicated that the concentrations of the water quality parameters increased in spring and winter seasons, while the concentrations were diluted in summer and autumn seasons in the last two decades. Moreover, the highest numbers of significant trends were found in the spring and summer series, respectively. According to the regression analysis, most of the water quality parameters were negatively correlated with river discharge.