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DRASTIC is a model used to reliably assess the pollution potential of aquifers at global location. Despite various criticisms of this model or its optimization, sometimes the territorial or geological conditions still must rely on this model approach based on its simplicity. The existence of sinkholes in the alluvial aquifer with an underlying karst aquifer is a clear example of the need to change or optimize the DRASTIC model to improve accuracy of prediction. In this study, the DRASTIC model was changed by adding two factors, the distance to a sinkhole and the sinkhole catchment factors. The modified DRASTIC model was then used to evaluate the pollution potential of Abarkouh aquifer in Yazd province, Central Iran. The intrinsic vulnerability index of Abarkouh aquifer was calculated by the weighted sum method in GIS. The resulting numerical values ranged between 62 and 176 for full study area. Without adding the two layers related to Sin-DRASTIC, the generic DRASTIC Index map showed summed values ranging between 54 and 130. The final Sin-DRASTIC map was divided it into 4 orders very low (<106; 722 km2, 78%), low (107-133; 186 km2, 20%), low to moderate (133-160; 15.3 km2, 1.7%), and moderate vulnerability (161-176; 1.7 km2, 0.3%) vulnerability. Both of the created maps showed high Area Under the Curve values at 76% and 78% for the generic DRASTIC and Sin-DRASTIC respectively. However, the area of the aquifer containing the sinkholes showed low and very low zones of contamination vulnerability on the generic DRASTIC map. The sensitivity analysis showed that the aquifer vulnerability to pollution is mainly controlled by the vadose zone impacts and the sinkhole catchment factor. Therefore, it is concluded that for geologic settings containing sinkholes that are open or contain higher vertical hydraulic conductivity sediment compared to the general matrix of the unconfined aquifer, the Sin-DRASTIC model method yields a superior prediction. Sin-DRASTIC is a modified version of the generic DRASTIC method that used in regions wherein sinkholes occur The Sin-DRASTIC models was applied to intrinsic vulnerability of Abarkouh aquifer, Central Iran. The high AUC values at 76% and 78% for the generic DRASTIC and Sin-DRASTIC respectively, show the accuracy of the results. In the Sin-DRASTIC map the sinkhole areas showed a high potential for groundwater contamination [ABSTRACT FROM AUTHOR]

Water, as the primary human needs, play a vital role in all aspects of our life, such as social environment, production, the economy, food security, and politics. In the present investigation, Heavy Metals (HM) concentrations including Cr, Pb, and Cd, have been ascertained in drinking water resources. Besides, point estimation using Hazard Quotient (HQ) and Hazard Index (HI) formula and sensitivity analysis (SA) using the Monte Carlo Simulation technique, by 10,000 repetitions in Oracle Crystal Ball®, were applied to demonstrate the non-carcinogenic effects of HMs on different exposed populations (Infant, children, teenagers, and adults groups). According to the results, the concentrations of Cr, Pb, and Cd in water resources ranged from 2.2 to 173.7 µg/L, 0.5 to 10 µg/L, and 0.2 to 0.9 µg/L, respectively. The results also showed that HI values were in the order of Pb>Cd >Cr. The results of SA, to determine the most effective parameter in increasing non-carcinogenic risk, showed that the concentration parameter of studied HMs had the most effect on increasing sensitivity in the four studied exposed groups. [ABSTRACT FROM AUTHOR]

During farming practices in rural areas, various fertilizers are used, which can lead to the contamination of groundwater resources with nitrate and, consequently, can affect the health of consumers. This study aimed to investigate the drinking suitability of the groundwater resources in rural areas of Divandarreh County, Kurdistan province, Iran using drinking water quality index (DWQI) and also estimation of the non-carcinogenic health risk induced by nitrate due to the drinking route. Sixty groundwater samples collected (2018) from active dug-wells and twelve parameters (TDS, pH, TH, EC, HCO3−, K+, Na+, Mg2+, Ca2+, Cl−, SO42-, and NO3−) were measured to calculate DWQI. Also, non-carcinogenic risk assessment was carried out for four exposed groupsusing two different approaches: deterministic andprobabilistic by Monte Carlo simulation. The results of WQI showed that 61.66, 31.66, and 6.66% of samples fall within the class of excellent, good, and poor quality, respectively. The nitrate concentration in drinking water ranged from 36.06 ± 14.32 mg/L. The HQ mean for infants, children, teenagers, and adults were 0.90158, 1.17205, 0.90158, and 0.70436, respectively.Probability estimation showed the HQ values for the 5th, and 95th percentile in infants, children, teenagers, and adult groups were (0.52–2.53), (0.27–1.54), (0.25–1.40), and (0.15–0.71), respectively. Sensitivity analysis results showed that the most effective parameter in the non-carcinogenic risk in all exposed groups was NO3− concentration. Generally, nitrate concentration was relatively high and required more attention especially in agriculture and management of the use of fertilisers. [ABSTRACT FROM AUTHOR]

This study aimed to assess the groundwater quality and human health risks of nitrate, nitrite, and fluoride contamination in rural areas of Khaf County, Iran. For this aim, 28 groundwater samples were collected from open dug wells during August 2018. Twelve parameters (pH, EC, TDS TH, Cl-, SO42-, HCO3−, NO3−, F−, Ca2+, Mg2+, and Na+) have been used to evaluate the water quality index. Also, point estimation using HQ formula and sensitivity analysis using the Monte Carlo Simulation (MCS) method, by 10,000 repetitions in Oracle Crystal Ball®, were applied to determine the additional risk of non-carcinogenicity in Nitrate, Nitrite, and Fluoride in four groups: infant, children, teenager, and adult. WQI showed that 7%, 60%, 10% and 21% of groundwater samples fall within the class of excellent, good, poor, and very poor quality, respectively. The HQ values of nitrate for infant, children, teenagers, and adults in 32%, 14%, 10% and 7% of the samples from rural areas were above the safe limit of 1. The HQ values of nitrite were mostly acceptable for infants, children, teenagers, and adults in all the rural areas because the mean HQ values of fluoride were lower than 1. Also, the calculated HQ values of fluoride for infants, children, and teenagers in 60.7%, 21.4% and 10% of the rural areas were above the safe limit. The results of HQ point estimation showed that nitrate and fluoride had non-carcinogenic risk in most exposed population groups, but exposure to nitrite did not have non-carcinogenic risk. The results of sensitivity analysis, to determine the most effective parameter in increasing non-carcinogenic risk, showed that the concentration parameter of Nitrate, Nitrite, and Fluoride had the most effect on increasing sensitivity in the four studied exposed groups. [ABSTRACT FROM AUTHOR]

The concentrations of 18 organophosphorus, carbamate, pyrethroid, and nicotinoid pesticides were measured, by use of gas chromatography coupled to mass spectrophotometry, in six cultivars of walnut from five geographical regions of Iran, including, Azarshahr, Damavand, Farouj, Shahmirzad, and Tuyserkan. Assessments of risks posed to humans were conducted by calculating the hazard indices (HIs), by use of the Monte Carlo Simulations. The 95th centile of HIs for humans based on exposure via ingestion of walnuts was estimated to be 1.68, which represented di minimis to moderate concern for human consumers. The most influential parameters, determined by sensitivity analysis conducted during the MCS, was concentration, which ranged from 0.71to 0.97. The results indicate that while the walnuts are, in general safe to eat, uses of organophosphorus, pesticides on walnut cultivation in Iran is not completely without risks so that guidelines should be established and a monitoring program should be established. [ABSTRACT FROM AUTHOR]

A supervised feedforward artificial neural network (ANN) trained with backpropagation algorithms, along with multiple linear regression (MLR) model, was applied to predict the biological yield (BY) of grass pea. For this purpose, a four-year study (2008–2012) was carried out under rainfed conditions in three dryland stations in Iran, and a panel of 12 grass pea genotypes were evaluated for agronomic and phonologic characters. Stepwise regression (SWR) and principal component analysis (PCA) were employed to evaluate 15 input parameters. To discover the optimum ANN structure, different training algorithms, transfer functions, number of hidden layers and neuron in each layer were studied and optimized using Taguchi's method. The multilayer perceptron (MLP) model with tangent sigmoid (tagsig) transfer function, Levenberg–Marquardt learning algorithm, and two hidden layers was identified as the best model to predict the BY of the grass pea. The sensitivity analysis of inputs revealed that seed yield (SY) followed by thousand seed weight (TSW) and number of days to maturity (NDM), respectively, were the most influential factors in predicting BY in both models. According to the adjusted ANN model, early flowering genotypes with long maturity and high TSW should be considered as the best model for enhancing BY in breeding programs. Abbreviations: ANFIS: Adaptive neuro-fuzzy inference system; ANN: Artificial neural network; BC: Bayesian classification; BY: Biological yield; CL: Capsule length; CRBD: Randomized complete block design; CW: Capsule weight; GE: Genetic expression, FY: Forage yield; HI: Harvest index; MAE: Mean absolute error; MLR: Multi linear regression; MLP: Multilayer perceptron; NCP: Number of capsule per plant; NDF: Number of days to flowering; NDM: Number of days to maturity; NSC: Number of seed per capsule; NSC: Number of seed per capsule; NSP: Number of seed per plant; ODAP: β-N-oxalyl-L-α,β-diaminopropionic acid; OA: Orthogonal array; PA: Path analysis; PCA: Principal component analysis; PH: Plant height; RMSE: Root mean square error; SAS: Statistical analysis system; SFP: Seed filling period; SWR: Stepwise regression; SY: Seed yield; TOL: Tolerance; TSW: Thousand seed weight; VIF: Variance inflation factor; VIG: Vigority; WY: Wet yield. [ABSTRACT FROM AUTHOR]

On a global basis, walnuts rank second behind almonds in nut production and China, the United States and Iran are, respectively top producers of this nut. Generally, the nuts are exposed to relatively high temperatures and high humidity which predispose fungal growth. Hence, it is of crucial importance to regularly check the levels of toxins produced by fungi in these products, and subsequently, assess possible risk posed to human health via exposure to such toxins. Aflatoxin B1 as a secondary fungal metabolite widely found in many foodstuffs is considered a public health concern worldwide due to its genotoxic and carcinogenic properties. In this study, walnut samples collected from five different orchards located in five cultivation zones in Iran, were evaluated for aflatoxin B1 concentrations using high performance liquid chromatography with fluorescence detection. Aflatoxin B1 were found in more than half of the samples and its level ranged from 0.8–14.5 µg/kg. The present analysis showed a low lifetime carcinogenic risk for walnut consumption in Iranian consumers. The annual cancer risks (individual lifetime hepatic cancer risk) posed by consumption of walnuts were 1.0 × 10−4 and 3.4 × 10−6 in 105 persons (for hepatitis B positive and hepatitis B negative, respectively). [ABSTRACT FROM AUTHOR]

Energy management and reduction of CO2 emission lead to many investigates about energy input–output analyses especially in agricultural sector. The main objectives of this study are to assess the energy use pattern and to select the best method among Cobb–Douglas (CD), multiple linear regressions (MLR), multilayer perceptron (MLP), radial basis function (RBF) and support vector machine (SVM) models to estimate potato output energy in Jiroft city, located in the south of Kerman province, Iran. Data were collected with questioner method from expert farmers. Results indicated that the average of total input energy is about 84309.43 MJ ha−1 and the average of total output energy is 130217.14 MJ ha−1. Irrigation water (36%) and fertilizers (26%) were found to be the most important energy inputs in potato production. Unlike most literature reviews, in this study for better and more accurate model evolutions in energy forecasting, five different sizes of training selection (TS) were used: 50%, 60%, 70%, 80% and 90%. Some statistical indexes (RMSE, MAPE, and R2) of the different data selection calculated from k-fold in two training sets. The results showed that RBF model has a great prediction performance at all different values of training data selection. The average value of R2 was found to be more than 0.98. Between SVM and MLP models, the test performance will be improved by size reduction of training selection. Thus, the RBF model is chosen as the best model for fitting and modeling the output energy of potato production. [ABSTRACT FROM AUTHOR]

This research was conducted in order to determine the importance of the effect of some soil properties on some soil physical quality indicators (SPQIs) in southeast Iran. To this end, 169 points from different locations in Kerman province were selected which had different agricultural land uses, and disturbed and undisturbed soil samples were taken from a depth of 0–20 cm. Soil properties such as soil pH, soil texture, field capacity (FC), permanent wilting point (PWP), soil bulk density (BD), electrical conductivity (ECe), calcium carbonate equivalent (CCE), and soil organic matter (SOM), and soil physical quality indicators (SPQIs) such as field capacity (FC), mean weight diameter (MWD), air capacity (AC), and relative field capacity (RFC) were determined. Subsequently, soil properties affecting SPQIs were ascertained using Particle Swarm Optimization-Imperialist Competitive Algorithm-Support Vector Regression (PSO-ICA-SVR) hybrid algorithm, and after the sensitivity analysis, the importance of each selected property in terms of its impact on SPQIs was recognized. The results indicated OM, BD, clay content, and CaCO3 in general, affect soil physical quality and this selection was carefully made by the hybrid algorithm. Furthermore, after modeling using the features selected by the SVR method and performing the sensitivity analysis, clay content and BD, among the selected properties, had the most significant effects on SPQIs. The highest value of the coefficient of determination was associated with MWD index (R2 = 83.33) and the lowest error was related to AC and RFC indicators (RMSE = 0.031). [ABSTRACT FROM AUTHOR]

The complex dynamic equilibrium state of rivers, in which the amount of deposition and erosion are in balance, has been a fundamental research topic in river engineering. In this research, three advanced machine learning approaches, including M5 Model Tree (M5Tree), Multivariate Adaptive Regression Splines (MARS) and Least Square Support Vector Regression (LSSVR), are employed to gain new insights and develop more accurate methods for assessment of the longitudinal slope (S), water-surface width (W) and mean water depth (D) of rivers in regime state. Geometric and hydraulic characteristic of 85 cross-sections of Gamasiab River (located in western of Iran), Kaaj River (located in southwestern of Iran) and Behesht-Abad River (located in southwestern of Iran) are used to train and evaluate the employed methods (M5Tree, MARS, and LSSVR). Seven different models comprising various combinations of effective parameters influencing regime river geometry (the flow discharge (Q), median bed grain size (d50) and Shields parameter (τ)), are developed to evaluate the effect of each of these variables on the prediction of the geometry of regime rivers (S, W and D). The M5Tree method outperformed the other approaches with respect to correlation coefficient (R) values of 0.872, 0.951, and 0.770 and Mean Absolute Relative Error (MARE) values of 0.484, 0.102, and 0.126 for slope, width, and depth prediction, respectively. Furthermore, the flow discharge Q was the key variable governing regime channel width and depth while the regime channel slope was found to be mainly controlled by the Shields parameter. [ABSTRACT FROM AUTHOR]

This study aims to modify the SINTACS and DRASTIC models with a land-use (LU) layer and compares the modified-DRASTIC, modified-SINTACS and SI methods for groundwater vulnerability assessment (GVA) in the southern Tehran aquifer, Iran. Single parameter sensitivity analysis (SPSA) served to determine the most significant parameters for the modified-DRASTIC, modified-SINTACS and SI approaches, and to revise model weights from "theoretical" to "effective." The inherent implementation of LU in the SI model may explain its better performance compared to unenhanced versions of DRASTIC and SINTACS models. Validation of all models, using nitrate concentrations from 20 wells within the study area, showed the modified-SINTACS model to outperform other models. The SPSA showed that the vadose zone and LU strongly influenced the modified-DRASTIC and modified-SINTACS models, while SI was strongly influenced by aquifer media and LU. To improve performance, models were implemented using "effective" instead of "theoretical" weights. Model robustness was assessed using nitrate concentrations in the aquifer and the outcomes confirmed the positive impact of using "effective" versus "theoretical" weights in the models. Modified-SINTACS showed the strongest correlation between nitrate and the vulnerability index (coefficient of determination = 0.75). Application of the modified-SINTACS while using "effective" weights, led to the conclusion that 19.6%, 55.2%, 23.4%, and 1.6% of the study area housed very high, high, moderate and low vulnerability zones, respectively. [ABSTRACT FROM AUTHOR]

In this study, a simulation-optimization framework is built to present two approaches of long-term and real-time optimal operation for the regulation of gates during flood in urban drainage systems (UDSs). The modeling approaches are applied to a prototype network in a portion of the UDS of Tehran, Iran. A sensitivity analysis is conducted to determine the number of decision variables of the optimization models. Results are then compared with those of an uncontrolled (UC) approach as a baseline business-as-usual scenario. It has been inferred that the two optimization approaches outperform the UC method. Additionally, the real-time optimal (RTOP) operation approach, benefiting from both the current system's state and the latest storm data, is superior to the long-term optimal (LTOP) operation approach with respect to flood mitigation efficiency. The RTOP approach has the flexibility to adapt to changes in operational conditions when part of the system's regulation capacity is lost. [ABSTRACT FROM AUTHOR]

Geographic information system (GIS)-based multi-criteria decision analysis (MCDA) is commonly used to solve a range of complex spatial problems. We have incorporated an analytical network process (ANP) as a central part of MCDA and used this approach for land subsidence susceptibility mapping (LSSM). We used an ANP approach in combination with the concepts of global sensitivity and uncertainty analyses, in order to minimize any associated errors. This approach was tested for a LSSM application in north-western Iran, using a methodology that consisted of three distinct phases. An ANP approach was used in the first phase to derive criteria weightings by analysing relevant criteria, which included topographic, hydrologic, climatologic, geological, and anthropological indicators. The second phase involved evaluating the uncertainty and sensitivity of areas susceptible to subsidence as a function of the derived weightings, using Monte Carlo simulations. The third phase then used a land subsidence inventory database to validate the results and to measure any improvement in accuracy following sensitivity analysis. Results indicated a 6% improvement in the accuracy of the ANP method as a result of using an integrated global sensitivity analysis. [ABSTRACT FROM AUTHOR]

This article evaluates the health risk raised by exposure to naturally occurring radionuclides in soil around Khak-Sefid, Ramsar, Iran, which is an area of high natural background radiation. A high purity germanium detector was used to determine levels of radionuclides in soil samples and the cancer morbidity risk for a hypothetical resident farmer was evaluated using the RESidual RADioactivity (RESRAD) code. The average activity concentrations of226Ra,232Th, and40K were found to be 13,201 ± 391, 27.9 ± 2.4, and 415.5 ± 16 Bq/kg, respectively. The maximum assessed cancer morbidity risks were calculated from external and internal exposure pathways as 4.73 × 10−2and 3.40 × 10−2for226Ra, 1.41 × 10−4and 7.88 × 10−5for232Th, and 1.3 × 10−4and 4.233 × 10−4for40K. The RESRAD calculations also showed total cancer morbidity risks from external gamma and plant ingestion pathways were more important than from other exposure pathways. A sensitivity analysis was also performed to determine the input parameter values in the risk assessment process. In general, due to the high calculated risk of226Ra compared with232Th and40K it can be the major source of concern for human heath in the study area. [ABSTRACT FROM AUTHOR]

Cutting arms are the main productive units of coal shearer machine that contact directly with coal seam. More importantly, the cutting arms operate under a high-stressed environment. In this paper, in order to suggest an optimum maintenance plan for the subsystem of shearer machine, firstly, we conducted a failure and reliability analysis using the data acquired from the Parvade coal mine, Iran over the two years. The analysis shows that the Power Law Process model provides a good fit to the failure data. Also, we applied estimated distribution functions and calculated cost parameters to identify the cost-based optimum preventive maintenance interval. Furthermore, we conducted a sensitivity analysis of the studied machine and identified that if we consider the variation of the world price of the raw coal then the maintenance plan is sensitive to the price. The analysis showed that, big changes in world price of raw coal are not so effective on maintenance plan but have a considerable effect on maintenance costs of the cutting arms of the shearer machine. However, the effects of this change on reliability improvement are almost zero. [ABSTRACT FROM PUBLISHER]

This paper applies a novel adaptive approach consisting of Artificial Neural Network (ANN) and Fuzzy Linear Regression (FLR) to improve car ownership forecasting in complex, ambiguous, and uncertain environments. This integrated approach is applied to forecast car ownership in Iran from 1930 to 2007. In this study, the level of car ownership is viewed as the result of demographic, politico-social, and urban structure factors including average family size, total population density, urban population density, urbanization rate, gross national product per capita, gasoline price, and total road length. To capture the potential complexity, uncertainty, and linearity relation between the car ownership function and its determinants, ANN and FLR (including eight well-known FLR) approaches are applied to the collected data. Next, the preferred ANN is selected based on sensitivity analysis results for the test data while the preferred FLR is identified with regard to ANOVA and MAPE results. The results obtained from the performance comparison demonstrate the considerable superiority of the preferred ANN over the preferred FLR regarding the nonlinear and complex nature of the car ownership function in Iran. This is the first study that presents an ANN-FLR approach for car ownership forecasting capable of handling complexity and non-linearity, uncertainty, pre-processing, and post-processing. [ABSTRACT FROM PUBLISHER]

This study examines energy use patterns and the relationship between energy inputs and yield for soybean production in the Golestan province of Iran. Data used in this study were obtained from randomly selected soybean farms using a face-to-face survey. The results revealed that total energy of 35372.23 MJ ha-1 was required for soybean production and electricity, chemical fertilizers, and diesel fuel were the main energy consuming inputs. Energy use efficiency, energy productivity, and specific energy were 2.29, 0.09 kg MJ-1, and 10.94 MJ kg-1, respectively. Moreover, in specifying a functional relationship the Cobb-Douglas production function was selected as the best function. The results showed that fertilizer, water for irrigation, diesel fuel, machinery, human labor, and electricity energies significantly contributed to yield; while, the use of chemicals and seed energies was inconsistent with output. Also, the marginal physical productivity (MPP) technique was applied to analyze the effect of energy use on soybean production. It was found that, soybean production had more sensitivity on machinery, human labor, and water for irrigation energies; so that, an additional use of 1 MJ from each of the machinery, human labor, and water for irrigation energies would lead to increase in output by 0.57, 0.44, and 0.23 kg, respectively. [ABSTRACT FROM AUTHOR]