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

A new empirical approach for modelling fate and transport of Chromium bioaccumulation in irrigated crops: A water-food-pollution nexus

Discharge of chromium (Cr) into receiving water bodies is a serious problem in water resources worldwide that inevitably gets taken up by agricultural crops and hence threatens both the environment and human health. This study investigates the fate and transport modelling of Cr discharged into the Kashaf River by leather industries in Mashhad city, Iran and the bio magnification effects on agricultural crops irrigated by the river. The accumulative concentration of Cr in tomato in the present case study from the time of planting until harvest day shows an increasing trend of up to 126 μg/L. The sensitivity analysis illustrates that the accumulated chromium ions in tomato are affected by time in growth cycle, chromium dosage in water, and total hardness of water more than any other factors. This study adopts an empirical approach by developing statistical modelling for bio-accumulated Cr in tomato during the growth period and evaluates different 3D mathematical distribution such as Polynomial, Interpolant, and Lowest models. The results demonstrate Polynomial with x and y more than four-degree model has the best efficiency for the measurement of accumulated chromium ion in tomato as per qualitative factors. The outputs in this study can be viewed in the context of water-food-pollution nexus and how the pollution discharged from the industry into the water resources can have a major impact on the safety of food that is dependent on irrigation from freshwater resources

Developing a smart and clean technology for bioremediation of antibiotic contamination in arable lands

This study presents a smart technological framework to efficiently remove azithromycin from natural soil resources using bioremediation techniques. The framework consists of several modules, each with different models such as Penicillium Simplicissimum (PS) bioactivity, soft computing models, statistical optimisation, Machine Learning (ML) algorithms, and Decision Tree (DT) control system based on Removal Percentage (RP). The first module involves designing experiments using a literature review and the Taguchi Orthogonal design method for cultural conditions. The RP is predicted as a function of cultural parameters using Response Surface Methodology (RSM) and three ML algorithms: Instance-Based K (IBK), KStar, and Locally Weighted Learning (LWL). The sensitivity analysis shows that pH is the most important factor among all parameters, including pH, Aeration Intensity (AI), Temperature, Microbial/Food (M/F) ratio, and Retention Time (RT), with a p-value of < 0.0001. AI is the next most significant parameter, also with a p-value of < 0.0001. The optimal biological conditions for removing azithromycin from soil resources are a temperature of 32°C, pH of 5.5, M/F ratio of 1.59 mg/g, and AI of 8.59 m3/h. During the 100-day bioremediation process, RP was found to be an insignificant factor for more than 25 days, which simplifies the conditions. Among the ML algorithms, the IBK model provided the most accurate prediction of RT, with a correlation coefficient of over 95%

A new integrated agent-based framework for designing building emergency evacuation: a BIM approach

Today, safety control is considered one of the most important pillars of building construction processes due to maintaining security in major incidents such as fire, earthquake, and flood, and placing a basis of mutual trust between builders and residents for building design and construction. The evacuation process is a key aspect of safety control in case of an emergency such as a fire. This study develops a new integrated agent-based framework for designing building emergency evacuation by using Building Information Model (BIM). Three main steps of the framework include data collection, building model development, and evacuation simulation with a combination of Revit-MassMotion. The methodology is demonstrated through its application to a real case of a multi-story commercial building located in Iran. The building model is simulated through three scenarios with a different number of floors (i.e., one, two, and three floors). In each scenario, the safety of evacuation is evaluated for three designs of stairs in the building. The results show the best performance of the building evacuation in all scenarios can be achieved when two individual stairs are designed for each floor. Other influential factors including the maximum density, vision time, and agent count are more acceptable compared to other design factors. These parameters can also be used to design a control system by using smart conceptual models based on both decision tree and auto-work break structure methods

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

Ranking of cadmium low amount measurement systems according to economic, environmental, and functional indicators using ELECTRE analytical method

Cadmium is one of the transition metals, known by the scientific name Cd. One of its main characteristics is the high toxicity, even in very little amounts. Cadmium is often released through industrial effluents, pesticides, chemical fertilizers, and the burning of fossil fuels. Since the presence of cadmium ions in the living organisms&rsquo; body, especially humans, can cause serious damage to the liver and pancreas, and also because its role in causing cancer has been proven, measuring very low amounts of this metal is of high importance. In the first step, this study has reviewed and analyzed common laboratory methods for measuring small amounts of cadmium. Then, according to economic, environmental, feasibility, speed, and accuracy factors, all available methods were evaluated using the ELECTRE technique. The results showed that the extraction methods using Dowex Optipore V-493 resin and extraction system in Triton X-114 surfactant, placed in the first and second positions

Behavior evaluation of freundlich and langmuir isotherms in cadmium preconcentration using solid phase extraction method for linear and nonlinear numerical computational patterns

Cadmium is naturally present in the mineral cadmium sulfide which is a rare form of this element and the highest amount of cadmium is obtained from the extraction process of other minerals such as lead, copper and zinc. The release of this metal into the environment leads to widespread epidemiological effects. Therefore, measuring small amounts of this metal is also of particular importance. Small amount measuring methods of this metal are such as,preconcentration using solid phase extraction system using adsorbents. The main part of the preconcentration process is achieved by adsorption processes. In this study, the behavior of Freundlich and Langmuir adsorption isotherms for the capacity of TMON and IMNM adsorbents in cadmium adsorption has been evaluated by Power and Rational statistical distributions. At the end of the study, the constant coefficients of the Freundlich and Langmuir models were compared in both linear and non-linear modes. The results showed; the linearization method for the Kf coefficient of the Freundlich isotherm can cause errors equal to 41.6% in TMON adsorbent and 39.3% in IMNM adsorbent. Also, in parameter b, errors of 66.66% are obtained in TMON adsorbent and 32.45% in IMNM adsorbent

Evaluation of ceramic water filters’ performance and analysis of managerial insights by SWOT matrix

Filtration is a crucial step in the water treatment process, typically preceding disinfection. Filters trap microorganisms and suspended solids, reducing their amount in the environment. The latest technology in filtration is ceramic filters, and in this study, the performance of silicon carbide ceramic filters (SIC) is evaluated. These filters were installed at three different locations within a water treatment plant (entrance storage, raw water, and backwash water), and changes in physical and chemical water parameters were measured. Results indicate high efficiency in turbidity removal, effectively clarifying volatile suspended solids (VSS) and fixed suspended solids (FSS). The turbidity removal efficiency was 99% for entrance storage and 65% for raw water. The SWOT (Strengths, Weaknesses, Opportunities, and Threats) matrix was used to analyse the results of the SIC and highlight its strengths, weaknesses, opportunities, and threats

A Smart Sustainable Decision Support System For Water Management Of Power Plants In Water Stress Regions

Power Plants (PPs) is considered as critical facilities in each region because of essential role through energy generation processes. These facilities are also depended to water availability especially in water stress areas. Due to the critical water shortage in many areas around the world, it is necessary to make an optimal condition among water consumption and the increasing demand for electricity to prevent any further conflict of interests between industry, householders and the environmental goals. There are different techniques for controlling Water Consumption (WC) in these industries. This paper develops a smart Decision Support System (DSS) for monitoring, prediction and control sections based on Artificial Intelligent (AI) and integration of the PESTEL matrix and Multi Criteria Decision Making (MCDM) methods. Monitoring section comprises Fuel Consumption (FC), Atmospheric Temperature (AT), Power Plant Temperature (PPT) and Power Plant Efficiency (PPE), in which FC has the most influence on WC based on ANOVA evaluations in both cold and warm seasons. The prediction results have illustrated that Adaptive Neuro Fuzzy Inference System model is more efficient for the WC estimation with a correlation coefficient over 0.99. Ordered Weighted Averaging (OWA) also demonstrated that in the optimistic and pessimistic states, the most priority is linked to E3 (Establishment of evaporation control systems by contractor companies and concluding a guaranteed purchase contract with a power plant worth one and a half times the current amount of water price). In the last step of technical approaches, the smart controlling system is added for execution of water-energy nexus in the PP based on proportional–integral–derivative controller system. Finally, the performance of the DSS is approved with more than 80% agreement of experts and more than 90% precision in prediction procedure through this investigation. Application of this DSS can also be helpful for developing countries to achieve the UN Sustainable Development Goals

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