Assessment of climate change on river streamflow under different representative concentration pathways.

Climate change and excessive greenhouse gas emissions profoundly impact hydrological cycles, particularly in arid and semi-arid regions, necessitating assessments of their effects on water resource management, agriculture, soil fertility, nutrient transport, hydropower generation, and flood risk. This study investigates climate change repercussions on streamflow in the Zarrineh River Basin, Iran, across three decadal intervals (2020-2029, 2055-2064, and 2090-2099) aiming to develop effective adaptation and mitigation strategies. Four General Circulation Models (GCMs), chosen based on distinct Representative Concentration Pathways (RCPs) determined by the annual mean temperature gradient, are employed. These models generate daily maximum (Tmax) and minimum (Tmin) temperatures along with precipitation data. Subsequently, these variables are integrated into the Soil and Water Assessment Tool (SWAT) model to analyze river flow alterations for each decadal timeframe. Comparison between future projections and observed climate data reveals a gradual decline in precipitation and Tmax, coupled with a substantial increase in Tmin. The average precipitation diminishes from 0.77 mm in the period 1985-1994 to a range of 0.42-0.28 mm in 2090-2099. The simulated flow at the basin outlet highlights that the GCM with the highest annual mean temperature gradient yields the lowest streamflow, while conversely, the model with the lowest gradient generates the highest. Consequently, streamflow experiences a decline from 52 m3/s in 1985-1994 to a range of 41-20 m3/s in 2090-2099.

A novel AI-based approach for modelling the fate, transportation and prediction of chromium in rivers and agricultural crops: A case study in Iran.

Chromium (Cr) pollution caused by the discharge of industrial wastewater into rivers poses a significant threat to the environment, aquatic and human life, as well as agricultural crops irrigated by these rivers. This paper employs artificial intelligence (AI) to introduce a new framework for modeling the fate, transport, and estimation of Cr from its point of discharge into the river until it is absorbed by agricultural products. The framework is demonstrated through its application to the case study River, which serves as the primary water resource for tomato production irrigation in Mashhad city, Iran. Measurements of Cr concentration are taken at three different river depths and in tomato leaves from agricultural lands irrigated by the river, allowing for the identification of bioaccumulation effects. By employing boundary conditions and smart algorithms, various aspects of control systems are evaluated. The concentration of Cr in crops exhibits an accumulative trend, reaching up to 1.29 µg/g by the time of harvest. Using data collected from the case study and exploring different scenarios, AI models are developed to estimate the Cr concentration in tomato leaves. The tested AI models include linear regression (LR), neural network (NN) classifier, and NN regressor, yielding goodness-of-fit values (R2) of 0.931, 0.874, and 0.946, respectively. These results indicate that the NN regressor is the most accurate model, followed by the LR, for estimating Cr levels in tomato leaves.

Marine plastics, circular economy, and artificial intelligence: A comprehensive review of challenges, solutions, and policies.

Global plastic production is rapidly increasing, resulting in significant amounts of plastic entering the marine environment. This makes marine litter one of the most critical environmental concerns. Determining the effects of this waste on marine animals, particularly endangered organisms, and the health of the oceans is now one of the top environmental priorities. This article reviews the sources of plastic production, its entry into the oceans and the food chain, the potential threat to aquatic animals and humans, the challenges of plastic waste in the oceans, the existing laws and regulations in this field, and strategies. Using conceptual models, this study looks at a circular economy framework for energy recovery from ocean plastic wastes. It does this by drawing on debates about AI-based systems for smart management. In the last sections of the present research, a novel soft sensor is designed for the prediction of accumulated ocean plastic waste based on social development features and the application of machine learning computations. Plus, the best scenario of ocean plastic waste management with a concentration on both energy consumption and greenhouse gas emissions is discussed using USEPA-WARM modeling. Finally, a circular economy concept and ocean plastic waste management policies are modeled based on the strategies of different countries. We deal with green chemistry and the replacement of plastics derived from fossil sources.

A hybrid novel framework for flood disaster risk control in developing countries based on smart prediction systems and prioritized scenarios.

A Decision Support System (DSS) is a highly efficient concept for managing complex objects in nature or human-made phenomena. The main purpose of the present study is related to designing and implementation of real-time monitoring, prediction, and control system for flood disaster management as a DSS. Likewise, the problem of statement in the research is correlated to implementation of a system for different climates of Iran as a unique flood control system. For the first time, this study coupled hydrological data mining, Machine Learning (ML), and Multi-Criteria Decision Making (MCDM) as smart alarm and prevention systems. Likewise, it created the platform for conditional management of floods in Iran's different clusters of climates. According to the KMeans clustering system, which determines homogeneity of the hydrology of a specific region, Iran's rainfall is heterogeneous with 0.61 score, which is approved high efficiency of clustering in a vast country such as Iran with four seasons and different climates. In contrast, the relation of rainfall and flood disaster is evaluated by Nearest Neighbors Classification (NNC), Stochastic Gradient Descent (SGD), Gaussian Process Classifier (GPC), and Neural Network (NN) algorithms which have an acceptable correlation coefficient with a mean of 0.7. The machine learning outputs demonstrated that based on valid data existence problems in developing countries, just with verified precipitation records, the flood disaster can be estimated with high efficiency. In the following, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method as a Game Theory (GT) technique ranked the preventive flood damages strategies through three social (Se 1), environmental (Se 2), and economic (Se 3) crises scenarios. The solutions of flood disaster management are collected from literature review, and the opinion approves them of 9 senior experts who are retired from a high level of water resource management positions of Iran. The outcomes of the TOPSIS method proved that National announcement for public-institutional participation for rapid response and funding (G1-2), Establishment of delay structures to increase flood focus time to give the animals in the ecosystem the opportunity to escape to the upstream points and to preserve the habitat (G 2-8), and Granting free national financial resources by government agencies in order to rebuild sensitive infrastructure such as railways, hospitals, schools, etc. to the provincial treasury (G3-10) are selected as the best solution of flood management in Social, Environmental, and Economic crises, respectively. Finally, the collected data are categorized in Social, Environmental, and Economic aspects as three dimensions of Sustainable Development Goals (SDGs) and ranked based on the opinion of 32 experts in the five provinces of present case studies.

A hybrid computational intelligence approach for bioremediation of amoxicillin based on fungus activities from soil resources and aflatoxin B1 controls.

Nowadays, releasing the Emerging Pollutants (EPs) in the nature is one of the main reasons for many health and environmental disasters. Amoxicillin as an antibiotic is one of the EPs and categorized as the Endocrine Disrupting Compounds (EDCs) in hazardous materials. Accumulation of amoxicillin in the soil bulk increases the cancer risk, drug resistances and other epidemiological diseases. Hence, the soil bioremediation of antibiotics can be a solution for this problem which is more environmental-friendly system. This study technically creates a bio-engine setup in soil bulk for remediation of amoxicillin based on Aspergillus Flavus (AF) activities and Removal Percentage (RP) of amoxicillin with Aflatoxin B1 Generation (AG) controls. The main novelty is to propose a hybrid computational intelligence approach to do optimization for mechanical and biological aspects and to predict the behavior of bio-engine's effective mechanical and biological features in an intelligent way. The optimization model is formulated by the Central Composite Design (CCD) which is set by the Response Surface Methodology (RSM). The prediction model is formulated by the Random Forest (RF), Adaptive Neuro Fuzzy Inference System (ANFIS) and Random Tree (RT) algorithms. According to the experimental practices from real soil samples in different times and places, concentration of amoxicillin and Aflatoxin B1 are set equal to 25 mg/L (ppm) and 15 µg/L (ppb). Likewise, the outcomes of experiments in CCD-RSM computations are evaluated by curve fitting comparisons between linear, 2FI, quadratic and cubic polynomial equations with considering to regression coefficient and predicted regression coefficient values, ANOVA and optimization by sequential differentiation. Based on the results of CCD-RSM, the RP performance in the optimum conditions is measured around 86% and in 25 days after runtime, the RP and AG are balanced in the safe mode. The proposed hybrid model achieves the 0.99 accuracy. The applicability of the research is done using real field evaluations from drug industrial park in Mashhad city in Iran. Finally, a broad analysis is done and managerial insights are concluded. The main findings of the present research are: (I) with application of bioremediation from fungus activities, amoxicillin amounts can be control in soil resources with minimum AG, (II) ANFIS model has the best accuracy for smart monitoring of amoxicillin bioremediation in soil environments and (III) based on the statistical assessments Aeration Intensity and AF/Biological Waste ratio are most effective on the amoxicillin removal percentage.

Designing a smart risk analysis method for gas chlorination units of water treatment plants with combination of Failure Mode Effects Analysis, Shannon Entropy, and Petri Net Modeling.

Today, all modern industrial units acknowledge the necessity of efficient and effective safety, health, and environment (HSE) systems. To become practical, these systems must be localized and customized to serve the exact needs of the industry. Nevertheless, most HSE plans are developed upon a set of common presumptions. In the water industry, gas chlorination units require strong HSE plans to mitigate the possibility of chlorine explosion and leak. This study aimed to provide an efficient HSE system for gas chlorination process within water treatment plants. This goal was achieved through a case study performed on a water treatment plant in Razavi-Khorasan province, Iran. In the first stage of this study, the researchers made combined use brainstorming sessions and modified Delphi technique to identify the risk factors of gas chlorination units and classify them into six groups in terms of association with chlorination unit building, gas cylinder storage, technical details of gas cylinders, gas cylinder transport, chlorinator connections, and chlorination unit management. In the second stage, the extracted factors were analyzed by Failure Mode Effects Analysis (FMEA) and Shannon Entropy approaches using two different panels of experts, and the results were compared for validation. Finally, the analysis results were structured by Petri Net modeling. The results showed that, according to FMEA, the risk factors with risk priority number (RPN) of over 46 are of highest importance for the studied unit. Once observed, these factors necessitate shutting down the operation until a risk mitigation solution is reached. Among the analyzed factors, (i) the presence of compounds such as NH3, O2, gas and liquid hydrocarbons and oil in gas chlorine cylinders and (ii) non-vertical and non-mechanized handling of full and empty cylinders during loading and unloading, with RPNs of respectively 160 and 120, were found to be significantly more important than others. In the SE analysis, in addition to the above factors, poor implementation of airflow control mechanism inside the chlorination chamber (W = 0.359), storage of chlorine cylinders near electrical and mechanical installations such as elevators or power panels (W = 0.327), poor pipe placement for connecting the injector to the water inlet and the possibility of air suction (W = 0.433), and failure to provide scientific and practical training to the chlorination staff (W = 0.342) were found to be of highest importance.

Statistical analysis of effective variables on the performance of waste storage service using geographical information system and response surface methodology.

Waste storage service (WSS) of Municipal Solid Waste Management (MSWM) systems is a functional element that residents encounter system directly, so related authorities should consider a trade-off between social consideration and system performance at this stage. Not only should they pay attention to the efficiency and effectiveness of the MSWM system, but also take account of social welfare resulting in public engagement. This study introduces three important factors including number of waste stations, maximum allowed walking distance and container capacity devoted to each station having effect on the performance of waste storage service. To investigate how these variables affect the performance of WSS, geographical information system (GIS) and response surface methodology (RSM) were applied. First, according to these three variables, fifteen experiments were designed by Box Behnken Design (BBD), then, all the experiments were modeled by maximized capacitated coverage (MCC) in GIS environment, and the parameters evaluating the performance of WSS were measured. The final response was achieved through integration of effective parameters by two different MADM methods, TOPSIS and OWA. The results showed negative effects of the number of stations and container capacity of each station on the final response, whereas increase in the maximum allowed walking distance improved the performance of WSS.

Experimental investigation and mathematical modeling for microbial removal using potassium permanganate as an oxidant-case study: water treatment plant No. 1, Mashhad, Iran.

Preoxidation is an important unit process which can partially remove organic and microbial contaminations. Due to the high concentrations of organic matter entering the water treatment plant, originating from surface water resources, preoxidation by using chlorinated compounds may increase the possibility of trihalomethane (THM) formation. Therefore, in order to reduce the concentration of THMs, different alternatives such as injection of potassium permanganate are utilized. The present study attempts to investigate the efficiency of the microbial removal from raw water entering the water treatment plant No. 1 in Mashhad, Iran, through various doses of potassium permanganate. Then, an examination of the predictive models is done in order to indicate the residual Escherichia coli and total coliform resulted from injecting the potassium permanganate. Finally, the coefficients of the proposed models were optimized using the genetic algorithm. The results of the study show that 0.5 mg L-1 of potassium permanganate would remove 50% of total coliform as well as 80% of Escherichia coli in the studied water treatment plant. Also, assessing the performance of different models in predicting the residual microbial concentration after injection of potassium permanganate suggests the Gaussian model as the one resulting the highest conformity. Moreover, it can be concluded that employing smart models leads to an optimization of the injected potassium permanganate at the levels of 27% and 73.5%, for minimum and maximum states during different seasons of a year, respectively.

Enhancing flood risk mitigation by advanced data-driven approach.

Flood events in the Sefidrud River basin have historically caused significant damage to infrastructure, agriculture, and human settlements, highlighting the urgent need for improved flood prediction capabilities. Traditional hydrological models have shown limitations in capturing the complex, non-linear relationships inherent in flood dynamics. This study addresses these challenges by leveraging advanced machine learning techniques to develop more accurate and reliable flood estimation models for the region. The study applied Random Forest (RF), Bagging, SMOreg, Multilayer Perceptron (MLP), and Adaptive Neuro-Fuzzy Inference System (ANFIS) models using historical hydrological data spanning 50 years. The methods involved splitting the data into training (50-70 %) and validation sets, processed using WEKA 3.9 software. The evaluation revealed that the nonlinear ensemble RF model achieved the highest accuracy with a correlation of 0.868 and an root mean squared error (RMSE) of 0.104. Both RF and MLP significantly outperformed the linear SMOreg approach, demonstrating the suitability of modern machine learning techniques. Additionally, the ANFIS model achieved an exceptional R-squared accuracy of 0.99. The findings underscore the potential of data-driven models for accurate flood estimating, providing a valuable benchmark for algorithm selection in flood risk management.

Modelling and optimising the performance of graphene oxide-Cu2SnS3-polyaniline nanocomposite as an adsorbent for mercury ion removal.

Finding a cost-effective, efficient, and environmentally friendly technique for the removal of mercury ion (Hg2+) in water and wastewater can be a challenging task. This paper presents a novel and efficient adsorbent known as the graphene oxide-Cu2SnS3-polyaniline (GO-CTS-PANI) nanocomposite, which was synthesised and utilised to eliminate Hg2+ from water samples. The soft-soft interaction between Hg2+ and sulphur atoms besides chelating interaction between -N and Hg2+ is the main mechanism for Hg2+ adsorption onto the GO-CTS-PANI adsorbent. Various characterisation techniques, including Fourier transform infrared spectrophotometry (FT-IR), field emission scanning electron microscopy (FESEM), energy-dispersive X-ray spectroscopy (EDX), transmission electron microscopy (TEM), elemental mapping analysis, and X-ray diffraction analysis (XRD), were employed to analyse the adsorbent. The Box-Behnken method, utilising Design Expert Version 7.0.0, was employed to optimise the crucial factors influencing the adsorption process, such as pH, adsorbent quantity, and contact time. The results indicated that the most efficient adsorption occurred at pH 6.5, with 12 mg of GO-CTS-PANI adsorbent, and 30-min contact time that results in a maximum removal rate of 95% for 50 mg/L Hg2+ ions. The study also investigated the isotherm and kinetics of the adsorption process that the adsorption of Hg2+ onto the adsorbent happened in sequential layers (Freundlich isotherm) and followed by the pseudo-second-order kinetic model. Furthermore, response surface methodology (RSM) analysis indicates that pH is the most influential parameter in enhancing adsorption efficiency. In addition to traditional models, this study employed some artificial intelligence (AI) methods including the Random Forest algorithm to enhance the prediction of adsorption process efficiency. The findings demonstrated that the Random Forest algorithm exhibited high accuracy with a correlation coefficient of 0.98 between actual and predicted adsorption rates. This study highlights the potential of the GO-CTS-PANI nanocomposite for effectively removing of Hg2+ ions from water resources.

An intelligent decision support system for groundwater supply management and electromechanical infrastructure controls.

This study presents an intelligent Decision Support System (DSS) aimed at bridging the theoretical-practical gap in groundwater management. The ongoing demand for sophisticated systems capable of interpreting extensive data to inform sustainable groundwater decision-making underscores the critical nature of this research. To meet this challenge, telemetry data from six randomly selected wells were used to establish a comprehensive database of groundwater pumping parameters, including flow rate, pressure, and current intensity. Statistical analysis of these parameters led to the determination of threshold values for critical factors such as water pressure and electrical current. Additionally, a soft sensor was developed using a Random Forest (RF) machine learning algorithm, enabling real-time forecasting of key variables. This was achieved by continuously comparing live telemetry data to pump design specifications and results from regular field testing. The proposed machine learning model ensures robust empirical monitoring of well and pump health. Furthermore, expert operational knowledge from water management professionals, gathered through a Classical Delphi (CD) technique, was seamlessly integrated. This collective expertise culminated in a data-driven framework for sustainable groundwater facilities monitoring. In conclusion, this innovative DSS not only addresses the theory-application gap but also leverages the power of data analytics and expert knowledge to provide high-precision online insights, thereby optimizing groundwater management practices.

A risk-based soft sensor for failure rate monitoring in water distribution network via adaptive neuro-fuzzy interference systems.

Water Distribution Networks (WDNs) are considered one of the most important water infrastructures, and their study is of great importance. In the meantime, it seems necessary to investigate the factors involved in the failure of the urban water distribution network to optimally manage water resources and the environment. This study investigated the impact of influential factors on the failure rate of the water distribution network in Birjand, Iran. The outcomes can be considered a case study, with the possibility of extending to any similar city worldwide. The soft sensor based on the Adaptive Neuro-Fuzzy Inference System (ANFIS) was implemented to predict the failure rate based on effective features. Finally, the WDN was assessed using the Failure Modes and Effects Analysis (FMEA) technique. The results showed that pipe diameter, pipe material, and water pressure are the most influential factors. Besides, polyethylene pipes have failure rates four times higher than asbestos-cement pipes. Moreover, the failure rate is directly proportional to water pressure but inversely related to the pipe diameter. Finally, the FMEA analysis based on the knowledge management technique demonstrated that pressure management in WDNs is the main policy for risk reduction of leakage and failure.

Evaluation of traffic noise pollution using geographic information system and descriptive statistical method: a case study in Mashhad, Iran.

Environmental consequences and the epidemiologic results of noise pollution have chronic effects leading to widespread complications in the long run. As far as we know, there are a few studies for pollution monitoring and control systems in comparison with other environmental pollutants. One of the largest metropolitan cities located in Iran is Mashhad city as known as one of the biggest religious cities in the world. Different properties of this city including historical, industrial, and religious draw thousands of visitors to Mashhad, yearly. This fact motivates us to contribute to the concept of noise pollution in streets and sidewalks around the Holy Shrine, namely, Imam Reza. In this regard, different measurements using geographic information system (GIS) and descriptive statistical methods were conducted for our case study in Mashhad, Iran. All measurements and records were done during the peak of morning crowd (10-12 AM) and evening crowd (4-6 PM) on both sidewalks of each street around the Holy Shrine. This study showed that the pollution in the evening time span (4-6 PM) has the maximum level of noise. Among all streets in our case study in Mashhad, Iran, Tabarsi street has the most amount of noise pollution with a mean of 78 dB(A) for the mean intensity for each point, and Imam Reza street has the minimum amount of pollution with a mean of 72.75 dB(A). Our findings from the temporal perspective analysis confirm that the noise pollution peaks in the evening, when weather conditions are favorable. From the spatial perspective analysis, the most intensive noise pollution was observed around residential and accommodation land uses, which have the highest number of arterial routes towards the Holy Shrine.

A novel viewpoint to the green city concept based on vegetation area changes and contributions to healthy days: a case study of Mashhad, Iran.

One of the significant challenges in urbanization is the air pollution. This highlights the need of the green city concept with reconsideration of houses, factories, and traffic in a green viewpoint. The literature review confirms that this reconsideration for green space has a positive effect on the air quality of large cities and to reduce the air pollution. The purpose of this study is to evaluate the annual vegetation changes in the green space of Mashhad, Iran as a very populated city in the middle east to study the air pollution. To investigate the relationship between the air pollution and vegetation, the Landsat 8 satellite images for summer seasons of 2013-2019 were used to extract changes in vegetation by calculating the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and the optimized soil adjusted vegetation index (OSAVI). The main contribution in comparison with the relevant studies is to study the relationship between clean, healthy, and unhealthy days with the green space area for the first time in Mashhad, Iran. The results show that the implementation of green city concept in Mashhad, Iran, has been increased by 64, 81, and 53% by NDVI, EVI, and OSAVI, respectively, during the study period. The vegetation area of this city is positively correlated to clean and healthy days and has a negative correlation to unhealthy days, in which the greatest values for NDVI, EVI and OSAVI are 0.33, 0.52, and -0.53, respectively.

A sustainable Decision Support System for soil bioremediation of toluene incorporating UN sustainable development goals.

Decision Support System (DSS) is a novel approach for smart, sustainable controlling of environmental phenomena and purification processes. Toluene is one of the most widely used petroleum products, which adversely impacts on human health. In this study, Fusarium Solani fungi are utilized as the engine of the toluene bioremediation procedure for the monitoring part of DSS. Experiments are optimized by Central Composite Design (CCD) - Response Surface Methodology (RSM), and the behavior of the mentioned fungi is estimated by M5 Pruned model tree (M5P), Gaussian Processes (GP), and Sequential Minimal Optimization (SMOreg) algorithms as the prediction section of DSS. Finally, the control stage of DSS is provided by integrated Petri Net modeling and Failure Modes and Effects Analysis (FMEA). The findings showed that Aeration Intensity (AI) and Fungi load/Biological Waste (F/BW) are the most influential mechanical and biological factors, with P-value of 0.0001 and 0.0003, respectively. Likewise, the optimal values of main mechanical parameters include AI, and the space between pipes (S) are equal to 13.76 m3/h and 15.99 cm, respectively. Also, the optimum conditions of biological features containing F/BW and pH are 0.001 mg/g and 7.56. In accordance with the kinetic study, bioremediation of toluene by Fusarium Solani is done based on a first-order reaction with a 0.034 s-1 kinetic coefficient. Finally, the machine learning practices showed that the GP (R2 = 0.98) and M5P (R2 = 0.94) have the most precision for predicting Removal Percentage (RP) for mechanical and biological factors, respectively. At the end of the present research, it is found that by controlling seven possible risk factors in bioremediation operation through the FMEA- Petri Net technique, efficiency of the process can be adjusted to optimum value.

Improvement of wastewater sludge dewatering using ferric chloride, aluminum sulfate, and calcium oxide (experimental investigation and descriptive statistical analysis).

In this research, application of chemical conditioners for the conditioning of sludge and their effects on the improvement of sludge thickening of the wastewater treatment plant in the city of Bojnourd (Iran) is investigated. The concentration of chemical conditioners, pH and coagulation and flocculation time is from among the parameters studied in this research work. The results obtained indicate that sludge volume reduction for the chemical conditioners used, including Ferric Chloride (FeCl3 ), Aluminum Sulfate (Al2 (SO4 )3 ), and Calcium Oxide (CaO) are 41, 17, and 33 percent, respectively. The optimal concentration for FeCl3 , Al2 (SO4 )3, and CaO are 550, 1100, and 292 mg/L, respectively, and the optimal values of pH are 9, 7.5, and 10, respectively. The time to filtration (TTF) and reduction in sludge moisture content (SMC) for Ferric Chloride, Aluminum Sulfate, and Calcium Oxide are 45 s and 6.2%, 135 s and 3.3%, 190 s and 2.4% respectively. PRACTITIONER POINTS: Investigating the sludge conditioning by Ferric Chloride, Aluminum Sulfate, and Calcium Oxide. Determining the optimal concentration, pH, and coagulation/flocculation time. Calculating the time to filtration (TTF) and reduction in sludge moisture content (SMC). Predicting the settled sludge volume using descriptive statistical analysis.

Presenting a soft sensor for monitoring and controlling well health and pump performance using machine learning, statistical analysis, and Petri net modeling.

Groundwater resources play a key role in supplying urban water demands in numerous societies. In many parts of the world, wells provide a reliable and sufficient source of water for domestic, irrigation, and industrial purposes. In recent decades, artificial intelligence (AI) and machine learning (ML) methods have attracted a considerable attention to develop Smart Control Systems for water management facilities. In this study, an attempt has been made to create a smart framework to monitor, control, and manage groundwater wells and pumps using a combination of ML algorithms and statistical analysis. In this research, 8 different learning methods and regressions namely support vector regression (SVR), extreme learning machine (ELM), classification and regression tree (CART), random forest (RF), artificial neural networks (ANNs), generalized regression neural network (GRNN), linear regression (LR), and K-nearest neighbors (KNN) regression algorithms have been applied to create a forecast model to predict water flow rate in Mashhad City wells. Moreover, several descriptive statistical metrics including mean squared error (MSE), root mean square error (RMSE), mean absolute error (MAE), and cross predicted accuracy (CPA) are calculated for these models to evaluate their performance. According to the results of this investigation, CART, RF, and LR algorithms have indicated the highest levels of precision with the lowest error values while SVM and MLP are the worst algorithms. In addition, sensitivity analysis has demonstrated that the LR and RF algorithms have produced the most accurate models for deep and shallow wells  respectively. Finally, a Petri net model has been presented to illustrate the conceptual model of the smart framework and alarm management system.

Cadmium and copper heavy metal treatment from water resources by high-performance folic acid-graphene oxide nanocomposite adsorbent and evaluation of adsorptive mechanism using computational intelligence, isotherm, kinetic, and thermodynamic analyses.

In this paper, folic acid-coated graphene oxide nanocomposite (FA-GO) is used as an adsorbent for the treatment of heavy metals including cadmium (Cd2+) and copper (Cu2+) ions. As such, graphene oxide (GO) is modified by folic acid (FA) to synthesize FA-GO nanocomposite and characterized by the atomic force microscopy (AFM), Fourier transform-infrared (FT-IR) spectrophotometry, scanning electron microscopy (SEM), and C/H/N elemental analyses. Also, computational intelligence tests are used to study the mechanism of the interaction of FA molecules with GO. Based on the results, FA molecules formed a strong π-π stacking, chemical, and hydrogen bond interactions with functional groups of GO. Main parameters including pH of the sample solution, amounts of adsorbent, and contact time are studied and optimized by the Response Surface Methodology Based on Central Composite Design (RSM-CCD). In this study, the equilibrium of adsorption is appraised by two (Langmuir and Freundlich and Temkin and D-R models) and three parameter (Sips, Toth, and Khan models) isotherms. Based on the two parameter evaluations, Langmuir and Freundlich models have high accuracy according to the R2 coefficient (more than 0.9) in experimental curve fittings of each pollutant adsorption. But, multilayer adsorption of each contaminant onto the FA-GO adsorbent (Freundlich equation) is demonstrated by three parameter isotherm analysis. Also, isotherm calculations express maximum computational adsorption capacities of 103.1 and 116.3 mg g-1 for Cd2+ and Cu2+ ions, correspondingly. Kinetic models are scrutinized and the outcomes depict the adsorption of both Cd2+ and Cu2+ followed by the pseudo-second-order equation. Meanwhile, the results of the geometric model illustrate that the variation of adsorption and desorption rates do not have any interfering during the adsorption process. Finally, thermodynamic studies show that the adsorption of Cu2+ and Cd2+ onto the FA-GO nanocomposite is an endothermic and spontaneous process.

Synthesize of silver-nanoparticles by plant extract and its application for preconcentration of cadmium followed by flame atomic absorption spectrometry.

In this paper, Mentha pulegium leaves extract was used as a green reducing agent for the synthesis of silver-nanoparticles. The synthesized silver-nanoparticles were characterized by UV-VIS spectrophotometry, transmission electron microscopy, X-ray spectroscopy and used as an adsorbent for preconcentration of trace levels of cadmium (ІІ). After the desorption of cadmium (ІІ) in 5 mol L-1 formic acid, the desorbent solution was aspirated into the flame atomic absorption spectrometry for the determination of cadmium. In order to optimize the experimental condition, a response surface methodology based on central composite design was used. The optimum conditions are: pH: 8.6, amounts of adsorbent: 30 mg, 10 min extraction time and desorption time of 2 min. Under the optimum condition, the calibration curve was linear in the range of 5-200 µg L-1 cadmium (ІІ) ion with a correlation coefficient of 0.9995. The limit of detection was 1.1 µg L-1 and the relative standard deviation for 25 µg L-1 cadmium (ІІ) ion was 3.0% (n = 5). In order to check the applicability of the proposed method, different real samples were analyzed. Also, the accuracy of this method was successfully checked by the analysis of certified reference material and spike tests.

Controlling air pollution in a city: A perspective from SOAR-PESTLE analysis.

Strengths, opportunities, aspirations, and results (SOAR) analysis is a strategic planning framework that helps organizations focus on their current strengths and opportunities to create a vision of future aspirations and the results they will bring. PESTLE is an analytical framework for understanding external influences on a business. This research paper describes a field study and interviews of city hall managers from the city of Mashhad, Iran, conducted to investigate the application of SOAR and PESTLE frameworks for managing Mashhad's air pollution. Strategies are prioritized by the technique for order of preference by similarity to ideal solution (TOPSIS), Shannon entropy (SE), and analytic network process (ANP) multicriteria decision-making (MCDM) methods, considering economic conditions, managers' opinions, consensus, city council approvals, and national documents. The results of this research study show that creating centralized databases, supporting local governments, and developing smart city infrastructure, with weights of 0.194, 0.182, and 0.161, respectively, are the highest ranked strategies for managing air pollution in Mashhad. It can also be concluded that citizen involvement is key to achieving success in the employment of any management strategy. Integr Environ Assess Manag 2018;14:480-488. © 2018 SETAC.