Environmental Impact Research Articles

NONLINEAR DYNAMICS METHODS APPLICATIONS TO PRODUCT LIFECYCLE MANAGEMENT

The research being carried out is aimed at applying nonlinear dynamics methods in product life cycle management to improve communication between processes and achieve environmental sustainability. The purpose of the article is to create a nonlinear mathematical model for analysing product lifecycle management. Chaotic nonlinear dynamical processes of product lifecycle are deterministic but have the property that even small uncertainties in initial conditions lead to exponentially growing errors in the management that limit long-term forecasts. The emergence of chaotic behaviour in processes of product lifecycle was explored, focusing on fluctuations. The study employed methods such as systems analysis, correlation analysis, nonlinear dynamics, and the Surrogate model of chaotic Lorenz waterwheel. It was identified that sharp technological innovations as the primary drivers of short-term fluctuations impacting fixed efficiency and effectiveness of product life cycle process development. The resulting nonlinear dynamic model allowed for flexible operation, transitioning between equilibrium, periodic, and chaotic states based on coefficient values for asset growth rates and time constants reflecting product life cycle process dynamics. The research results are valuable for companies seeking to reduce their environmental impact and implement circular economy principles.

DEVELOPMENT OF A ROBOTIC FABRICATION SYSTEM FOR CEMENTITIOUS MATERIALS

Our team at Klokner Institute under Czech Technical University in Prague is working in a joint effort with Technical University of Liberec and Klokner on a comprehensive 3D printing system for cementitious materials, including specially optimized robotic arm. The goal of this system is to decrease the environmental impact of construction industry by reducing the volume of materials and time required, as well as reducing need for human labor. 3D printing also makes possible to build complex functional shapes, that would be too complicated or prohibitively expensive to manufacture using standard industry methods. Combining expertise of these two institutions lead to development of a robust system, that seamlessly integrates in-house developed mechanics and print material. One of key areas of our research is also testing mechanical properties of printed parts which is essential to safely integrate this construction method in real-world use in the future.

ENHANCING TRANSPORTATION THROUGH ROUTE STRATEGY PROPOSAL USING LOCALIZATION MEASUREMENT TECHNOLOGY: A CASE STUDY

This case study investigates the revitalization of the Korytnica spa settlement in the Low Tatras, known for its rich natural and cultural heritage, including mineral springs. Despite its historical and therapeutic advantages, Korytnica currently experiencing challenges in renovation and development as a thriving spa and tourist resort. The article explores the strategic use of geolocation measurement technology and the potential of mineral springs to enhance the tourist infrastructure and promote the site. The article presents a multidisciplinary approaches, including sustainable development, and the integration of heritage into tourism products. It concludes with a proposal for optimizing transport routes and improving infrastructure in the area, thereby supporting accessibility and minimizing environmental impact. The main output of the article is a proposal for improving the route to the relevant area, improving accessibility by analyzing and improving infrastructure and transport processes.

Environmental impact assessment for a meta-model-based food-energy-water-nexus system

A growing global population uses essential resources such as food, energy, and water to drive economic growth. However, population growth, urbanization, and human activities strain these limited resources, resulting in serious harm, particularly from global warming and climate change. Sustainable resource allocation in the Nexus system is required for improved resource security. This issue is essential since it impacts resource management, sustainability, and the environment. This research proposes developing an integrated assessment model (IAM) for the FEW-N system. The system undergoes a comprehensive evaluation that considers sustainability, economics, social aspects, and the environment. This evaluation combines an environmental impact assessment with a meta-model-based approach to the FEW-N system. A multi-objective optimization model is utilized to optimize the system with three key targets: maximum economic benefit, positive environmental impact, and least negative environmental impact. The findings show significant improvements in resource security within a community-level FEW-N system, with the Nexus system sectors contributing to both food and energy security. Greenhouse farming, rainfed farming, and irrigated farming supply 55.6%, 11.9%, and 32.5% of food security, respectively. Similarly, bioenergy, solar/wind hybrid renewable energy, and hydropower contribute 52.7%, 40.1%, and 7.2% of total energy security, respectively. The FEW-N system optimizes resource allocation for sustainability, reliability, and efficiency while emphasizing the specific contributions of diverse sectors to food and energy security. These findings provide critical insights for making educated resource allocation policy decisions in South Africa, emphasizing balancing societal, economic, and environmental factors. The study has increased the evidence base for the FEW-N environmental impact assessment framework to address resource allocation issues.

Eco-sustainable design of seawater sea-sand slag-based geopolymer mortars incorporating ternary solid waste

This study aimed to explore the feasibility of utilizing geopolymer as alternatives to Ordinary Portland Cement (OPC) in marine engineering. Eco-friendly seawater sea-sand slag-based geopolymer mortars (SS-SGMs) were prepared by blending seawater, sea-sand, Basalt Fibres (BF), Polypropylene Fibres (PPF), and ternary solid waste. The effects of activator modulus, alkaline content, water-to-binder ratio, and the volume fractions of BF and PPF on the mechanical performance, environmental impact, and cost of SS-SGMs were researched using the L16(4)5 Taguchi orthogonal method. Three evaluation methods were used to assess the impact of the different factors. First, the influence of a single factor was analyzed using methods of range and variance analysis. Subsequently, three new evaluation metrics were proposed to consider the synergistic effects of mechanical performance, environment, and cost. Finally, a Gray-Technique of Ordering Preferences by Similarity to Ideal Solution (Gray-TOPSIS) model was established, and the influence weights of each factor were proposed, thereby determining the optimal solution in a multi-index evaluation system. The recommended optimal mix proportion for SS-SGMs was determined as follows: activator modulus of 1.0, alkaline content of 4%, water-to-binder ratio of 0.42, PPF volume fraction of 0.4%, and BF volume fraction of 0.3%. Compared to OPC with the same strength grade, SS-SGMs demonstrated similar costs, a reduction in carbon emissions by approximately 75–83%, a decrease in energy consumption by around 50–67%, and lower drying shrinkage than traditional geopolymer. Under the influence of seawater, the products of SS-SGMs were capable of forming chloride-ion adsorbing products like hydrotalcite and Brucite. Additionally, the formation of C4AH13 and ion clusters can enhance the matrix density, thereby improving the mechanical performance of SS-SGMs.

Environmental footprints of the data center service sector in Sweden

The global data center (DC) sector has expanded rapidly during the last decades, due to the rising demand for digital services. In the Nordic region, Sweden has emerged as a global hub, attracting leading technology companies like Amazon, Facebook, Microsoft, and Google. Server halls of DCs are energy intensive buildings, which puts pressure on local water resources and contributes to global greenhouse gas emissions. This study aims to, firstly, quantify the environmental impact of DCs, based on energy usage, water consumption, and greenhouse gas (GHG) emissions. Secondly, it develops a planning tool by employing a multi-criteria approach to optimally locate new DCs and to assess the site suitability of existing ones in Sweden. Data of various performance indicators (geographical data on renewable energy accessibility, free cooling conditions, excess heat receivers, and resilience to water shortages) of DCs was collected through different means, e.g., questionnaire surveys, permit applications, company websites, and other open online data repositories. ArcGIS Pro was employed for spatial analysis, and 68 DCs with a site suitability index (SSI) ≤ 45% were identified as less ideally located. The principal findings are centered on Sweden, and thereby primarily benefit stakeholders engaged in decision-making for evaluating existing or strategic planning of new DCs by incorporating a comprehensive environmental perspective. Given the rapidly changing climate, strategically siting DCs will become crucial for minimizing the sector's environmental impact.

Evaluation of the Greenness, Whiteness, and blueness profiles of stability indicating HPLC method for determination of Denaverine hydrochloride and benzyl alcohol in pharmaceuticals

Recently, there has been important emphasis in pharmaceutical analysis on the stability of drug formulations and the development of appropriate stability-indicating studies. In this regard, a comprehensive stability-illustrating RP-HPLC approach has been established and validated for the assay of denaverine hydrochloride (DEN) and benzyl alcohol (BNZ) in Sensiblex® Injectable Solution.Different stress conditions were applied to both components, following the guidelines of the International Conference on Harmonization (ICH). Using the isocratic elution technique, DEN, BNZ, and their degradation products were effectively isolated and quantified on an Inertsil C8 column at room temperature. The mobile phase consisted of acetonitrile: water: orthophosphoric acid (80:20:0.1, by volume), and the flow speed was maintained at 0.8 mL/min. Detection was performed at 220 nm using a diode array detector, with concentration ranges of 10.00–30.00 and 5.00–15.00 μg/mL for DEN and BNZ, respectively. The total analysis time was less than five minutes, establishing this novel chromatographic method as a priority for routine quality control analysis of both drugs. The inclusion of acetonitrile was necessary for achieving adequate resolution. The environmental impact was evaluated using three approaches: Green Analytical Procedure Index (GAPI), Analytical Eco-Scale, and Analytical GREEnness metric (AGREE). Additionally, White Analytical Chemistry (WAC) was employed to evaluate the quality (R), ecological impact (G), and economic viability (B) of the developed approach.Additionally, the newly presented Blue Applicability Grade Index (BAGI) metric was also assessed. The final Eco-scale score of 85 and the AGREE score of 0.65 indicate the environmentally friendly nature of the new chromatographic method. The BAGI final score was 82.5, while the overall score of the WAC tool was 91.8, highlighting the method's practicality and utility. DEN exhibits the highest degradation (24%) in acidic conditions, whereas BNZ is particularly susceptible to oxidative degradation, with a degradation percentage of 35%. New four degradation products were formed, and the two investigated compounds were efficiently separated and analyzed in their presence. Further identification of degradation products for DEN and BNZ is strongly recommended, employing a mass spectrophotometric detector. Notably, the novel method is accurate, reliable, economical, and time-efficient. In conclusion, it can be effectively employed for routine quality control and stability assessment of DEN and BNZ in both their pure form and pharmaceutical formulations.

Energy and environmental assessment of a novel multigeneration plant powered by infectious medical waste

This study highlights the energy and environmental impacts of a multigeneration system for infectious medical waste -to-energy (IMWtE). Thermal performance and life cycle assessment (LCA) are investigated via the energy conservation, ReCiPe method, SimaPro database, and situation of environmental issues in Thailand and worldwide. A lifespan of 20y is considered in this study. The energy results show that the multigeneration system can produce a net power of 15.80 kWe from an organic Rankine cycle (ORC), 4.62kW of cooling power from an absorption chiller, and 13.21kW of heating power from a drying room. An overall system efficiency of 13.40% is directly driven from an infectious medical waste (IMW) of 81.98kg/s. According to the LCA results, the construction phase has the highest emissions (81%), followed by the operation phase (6%) and the landfill (1%). The recycling process reduces the environmental impact by 12%. The total lifetime midpoint impact is 3.88E-01 kg emission-eq/kWh. The endpoint includes 4.33E-07 DALY/kWh of human health, 1.50E-06 Species·y/kWh of ecosystem, and 3.81E-03 USD/kWh of resources. The LCA single score is approximately 6.08E+02 Pt/lifespan or 5.63E-04 Pt/kWh.

Optimization of energy use efficiency and environmental assessment in soybean and peanut farming using the imperialist competitive algorithm

This study proposes using input-output analysis to improve energy use efficiency and assess environmental indicators in soybean and peanut farming. The research findings show that soybean production consumes 43170.20 MJ ha−1 of total energy and yields 40417.28 MJ ha−1 of output energy, while peanut production requires 28677.36 MJ ha−1 of total energy and produces 28677.36 MJ ha−1 of output energy. The LCA results indicate that on-farm emissions are associated with production inputs for both soybean and peanut systems, with diesel fuel and chemical fertilizers being the main contributors. Soybean farming shows higher levels of diesel fuel pollutants due to reduced usage, while peanuts have a higher prevalence of contaminants associated with diesel fuel. Using ICA to calculate optimized energy consumption results in 36672.77 MJ ha−1 for soybean production and 23485.45 MJ ha−1 for peanut production, leading to energy savings of 6075.33 MJ ha−1 and 5191.91 MJ ha−1, respectively. Additionally, the environmental impact category quantities for ICA analysis of soybean and peanut production reveal the resources per ton for soybean production to be 111.25 USD2013 and for peanut production to be 45.43 USD2013 in terms of energy use. The use of ICA demonstrates potential in maximizing energy efficiency and evaluating environmental indicators in soybean and peanut farming. By optimizing resource allocation and decision-making processes, ICA can help farmers reduce energy consumption and minimize environmental impact, ultimately leading to more sustainable and eco-friendly farming practices that benefit both the environment and the agricultural industry. Further research and implementation of ICA in farming practices have the potential to significantly improve energy efficiency and environmental sustainability in the soybean and peanut farming industry.

The effectiveness of a hybrid MPPT controller based on an artificial neural network and fuzzy logic in low-light conditions

Technological advancement and economic progress have made power consumption a big issue. Concern is growing as traditional energy sources dwindle. In the future, numerous fossil fuels will be insufficient to satisfy human requirements. This motivates research into the feasibility of using renewable energy sources. Renewable energy sources offer a multitude of advantages, including their cost-effectiveness, lack of environmental impact, and sustainable nature. Sunlight is currently the most prevalent source of energy because it is both free and readily accessible. Consequently, photovoltaic (PV) energy is gaining importance in the field of electricity generation. Tracking the maximum power point (MPP) in a solar PV system is challenging due to varying meteorological conditions (irradiance and temperature). To maximise the efficiency of a solar power installation, it is essential to monitor the PV array's optimum power point. This analysis compares the perturb and observe (PO), fuzzy logic (FL), and suggested artificial neural network (ANN)-fuzzy strategy for determining the MPP of a PV system with minimal radiation exposure. Simulation results show that at low irradiation levels, the proposed ANN-fuzzy maximum power point tracking (MPPT) unit controller is superior to the FL and PO MPPT controllers in terms of tracking maximum power.

Plastic Waste Identification Using Deep Learning for Adequate Waste Management

Plastic waste management has emerged as a critical global challenge, prompting concerted efforts from conservation authorities and international organizations like the United Nations to enhance detection and classification strategies. This research distinguishes itself by harnessing advanced deep learning techniques to identify plastic materials at the micron level, surpassing traditional macro-level methods. The primary goal is to classify plastics into four major distinctions, addressing a key challenge in plastic segregation by accurately measuring thickness. Leveraging the YOLOv8 architecture, this approach enables precise classification of plastics into Polyethylene terephthalate (PETE), High Density Polyethylene (HDPE), low density polyethylene (LDPE0, and Polyvinyl Chloride (PVC) categories based on thickness. To achieve this, incorporation of hardware components such as ultrasonic sensors and NodeMCU for detecting thickness variations is used. By facilitating effective segregation according to environmental impact, this innovation revolutionizes waste reduction efforts, offering real-time identification and enhancing overall sustainability in plastic waste management.

Quantification of the environmental impact of radiotherapy and associated secondary human health effects: a multi-institutional retrospective analysis and simulation

Quantification of the environmental impact of radiotherapy and associated secondary human health effects: a multi-institutional retrospective analysis and simulation

A Multi-Stage Framework for Coordinated Scheduling of Networked Microgrids in Active Distribution Systems Considering Economic and Environmental Impact of Hydrogen Refueling and Charging Stations

Due to the increase in electric energy consumption and the significant growth in the number of electric vehicles (EV) at the level of the distribution network, new networks have started using new fuels such as hydrogen to improve environmental indicators and at the same time better efficiency from the excess capacity of renewable resources. In this article, the services that can be provided by hydrogen refueling stations and charging electric vehicles in the optimal performance of microgrids have been investigated. The model proposed in this paper includes a two-stage stochastic framework for scheduling resources in microgrids, especially hydrogen refueling stations and electric vehicle charging. In this model, two main goals of cost minimization and greenhouse gas emissions are considered. In the proposed framework and in the first stage, the service range of microgrids is determined precisely according to the electrical limitations of distribution systems in emergency situations. Then, in the second stage, the problem of energy management in each microgrid will be solved centrally. In this situation, various indicators including the output energy of renewable sources, smart charging of hydrogen and electric vehicle charging stations (EV/FCV) and flexible loads (FL) are evaluated. The final mathematical model is implemented as a multivariate integer multiple linear problem (MILP) using the GUROBI solver in GAMS software. The simulation results on the modified IEEE 118-Bus network show the positive effect of the presence of flexible loads and smart charging strategies by charging stations. Also, the numerical derivation shows that the operating costs of the entire system can be reduced by 4.77% and the use of smart charging strategies can reduce greenhouse gas emissions by 49.13%.

Reducing the environmental impact of immunotherapy treatment

Reducing the environmental impact of immunotherapy treatment

Environmental Impact Analysis in the Cement Industry with Life Cycle Assessment Method

Environmental Impact Analysis in the Cement Industry with Life Cycle Assessment Method

Selective Extraction of Lithium from Spent-NMC Battery Cathodes Using Sodium Hydroxide as a Leaching Agent at Elevated Temperatures

Introduction: The demand for lithium-ion batteries (LIBs) is rapidly increasing due to the growth of the electronics and electric vehicle industries. Even though the batteries are rechargeable, their storage capacity decreases, and they eventually end up being wasted. Recycling the spent LIBs is necessary to reduce the environmental impact and utilize the precious metals contained in the waste Methods: The present work focuses on the selective recovery of lithium from the cathodes of spent NMC batteries through the hydrometallurgical process using a sodium hydroxide solution. The leaching process was carried out in 2 M and 4 M NaOH concentrations for 120 minutes at high pressure and at temperatures of 398.15 K, 423.15 K, 448.15 K, and 473.15 K. Experimental results showed that 56.53% of lithium could be recovered with nearly 100% selectivity under the optimum leaching conditions of 473.15 K and 4 M NaOH. The release of lithium ions was due to a combination of sodium adsorption, ion exchange, and impregnation mechanisms. Result: Calculation results showed that the activation energy of the lithium leaching process was 2.1990×104 J/mol, the reaction was endothermic with enthalpy and entropy at standard conditions (298.15 K) of 4.8936×105 J/mol and 1.4421×103 J/mol/K, respectively. Conclusion: The present work also suggested that total lithium recovery can be increased through a series of leaching processes.

Eco-endocrinological dynamics: Unraveling dexamethasone's influence on the interrenal axis in juvenile carp Cyprinus carpio

This study delves into the eco-endocrinological dynamics concerning the impact of dexamethasone (DXE) on the interrenal axis in juvenile carp, Cyprinus carpio. Through a comprehensive analysis, we investigated the effects of DXE exposure on oxidative stress, biochemical biomarkers, gene expression, and bioaccumulation within the interrenal axis. Results revealed a concentration-dependent escalation of cellular oxidation biomarkers, including 1) hydroperoxides content (HPC), 2) lipid peroxidation level (LPX), and 3) protein carbonyl content (PCC), indicative of heightened oxidative stress. Concurrently, the activity of critical antioxidant enzymes, superoxide dismutase (SOD), and catalase (CAT), significantly increased, underscoring the organism's response to oxidative insult. Notable alterations were observed in biochemical biomarkers, particularly Gamma-glutamyl-transpeptidase (GGT) and alkaline phosphatase (ALP) activity, with GGT displaying a significant decrease with increasing DXE concentrations. Gene expression analysis revealed a significant upregulation of stress and inflammation response genes, as well as those associated with sensitivity to superoxide ion presence and calcium signaling, in response to DXE exposure. Furthermore, DXE demonstrated a concentration-dependent presence in interrenal tissue, with consistent bioconcentration factors observed across all concentrations tested. These findings shed light on the physiological and molecular responses of juvenile carp to DXE exposure, emphasizing the potential ecological implications of DXE contamination in aquatic environments. Understanding these dynamics is crucial for assessing the environmental impact of glucocorticoid pollutants and developing effective management strategies to mitigate their adverse effects on aquatic ecosystems.

The Environmental Impact of Vascular Surgery: An Audit of Physical Waste and Fluoroscopy Energy Consumption Within Operating Rooms

The Environmental Impact of Vascular Surgery: An Audit of Physical Waste and Fluoroscopy Energy Consumption Within Operating Rooms

Towards a Reduction of Emissions and Cost-Savings in Homes: Techno-Economic and Environmental Impact of Two Different Solar Water Heaters

South Africa currently has the highest carbon emission intensity per kilowatt of electricity generation globally, and its government intends to reduce it. Some of the measures taken by the government include a reduction of emissions in the building sector using solar water heating (SWH) systems. However, there is currently no study in the country that comprehensively assesses the technical, economic, and environmental impact of SWH systems across the country. This study therefore used the System Advisor Model (SAM) to model two different technologies of SWH systems (i.e., flat plate (FPC) and evacuated tube (EPC) SWH) at five different locations (i.e., Pretoria, Upington, Kimberley, Durban, and Cape Town) strategically selected across the country. According to the study, the optimum azimuth for both the evacuated tube and flat plate SWH system in South Africa is 0o. Installing FPC and EPC at the different locations would yield payback periods of 3.2 to 4.4 years and 3.5 to 4.3 years, respectively. Comparably, levelized cost of energy for the FPC and EPC will range from 7.47 to 9.62 cents/kWh and 7.66 to 9.24 cents/kWh, respectively, based on where the SWH system is located. Depending on where the facility is located, the annual cost savings for the FPC system would be between $486 and $625, while the EPC system would save between $529 and $638. Using SWHs can reduce CO2 emissions by 75%–77% for the evacuated tube system and 69%–76% for the flat plate system annually, depending on the location.

Enhancing energy efficiency in tyre pressure and temperature monitoring systems

This study addresses the pivotal challenge of enhancing power efficiency in tyre pressure and temperature monitoring systems (TPMS) for heavy vehicles and trailers. Employing field-programmable gate arrays (FPGA) and adaptive channel frequency hopping in bluetooth low energy (BLE) communication, the research focuses on mitigating power consumption issues specific to heavy vehicles with multiple tyres. The proposed solution incorporates strategic BLE channel blocking and adaptive frequency hopping on the FPGA platform to alleviate channel congestion and interference, ultimately reducing TPMS power consumption. The FPGA's adaptability tailors frequency hopping strategies to automotive TPMS nuances, optimizing channel selection and minimizing energy-intensive processes. Empirical results showcase a significant reduction in power consumption, with the TPMS operating at 100 MHz during active mode consuming 66 mW, dropping to 11 mW in sleep mode, and reaching 0 mW in hibernate mode for the majority of operational time. This research establishes a practical FPGA-based approach for power optimization in commercial TPMS, promising heightened reliability, safety improvements, and environmental impact reduction in the automotive sector.