Capacity Factor Research Articles

Pedological factors affecting pesticide retention in a series of tropical volcanic ash soils in the French West Indies

Specific pedoclimatic conditions in tropical environments have a considerable impact on pesticide fate. Tropical volcanic soils result from specific pedogenesis processes linked to successive volcanic projections; they are characterised by acidic conditions, high organic matter content, a specific mineralogical composition and the presence of organo-mineral complexes. The retention of pesticides in these soils is therefore often high but variable depending on the associated molecules and soil properties. Nevertheless, knowledge of pesticide sorption in these soils remains limited, and knowledge of the desorption process is scarce. The objectives of this work were to study the variability of the sorption and desorption coefficient of four pesticides currently used in banana and sugarcane crops on a set of representative tropical soils developed on volcanic ash (Guadeloupe, French West Indies). Adsorption and desorption isotherms were analysed for three ionizable pesticides (2,4-D, mesotrione, glyphosate) and a hydrophobic pesticide (difenoconazole) on ten soil samples from five soil profiles: one silandic andosol, one vitric andosol, two nitisols and one ferralsol. For ionizable pesticides, soil pH appears to be a first-order discriminating factor of the sorption capacity, and the organic carbon content appears to have a lesser impact on 2,4-D. For a strongly hydrophobic pesticides such as difenoconazole, the organic carbon content plays a major role in sorption. Desorption hysteresis has been observed regardless of the soils and the molecules considered, and tropical volcanic soils seem to be conducive to adsorption but show low to moderate release. The silandic andosol, which has the greatest sorption, buffers the dissemination of pesticides towards surface and groundwaters but also increases the risk of long-term contamination in the case of molecules that degrade slowly. Moreover, our results highlight that agronomic practices, such as liming, have a major impact on the sorption coefficient of pesticides and must be considered for when predicting sorption in a tropical volcanic context.

Grid-neutral hydrogen mobility: Dynamic modelling and techno-economic assessment of a renewable-powered hydrogen plant

The seasonally varying potential to produce electricity from renewable sources such as wind, PV and hydropower is a challenge for the continuous supply of hydrogen for transport and mobility. Seasonal storage of energy allows to avoid the use of grid electricity when it is scarce; storage systems can thus increase the resilience of the energy system. For grid-neutral and renewable hydrogen production, an electrolyser is considered together with a Power-to-Gas seasonal storage system, which consists of a methanation, the gas grid as intermediate storage and a steam reformer. As feed stream, electricity from an own photovoltaic (PV) system is considered, and for some cases additional electricity from the grid or from a wind turbine. The dynamic operation of the plant during a year is simulated. It is possible to safely supply fuel cell vehicles with hydrogen from the grid-neutral plant without using electricity when it is scarce and expensive. To supply 135 kgH2/day, unit sizes of 1 MW–2.9 MW for the PV system and 0.9 MW–2.6 MW for the electrolysis are required depending on the amount of available grid-electricity. The usage of grid-electricity increases the capacity factor of the electrolysis, which results in decreased unit sizes and in a better economic performance. Seasonal storage of energy is required, which results in an increased hydrogen production in summer of approximately 50% more than directly needed by the fuel cell vehicles. The overall efficiency from electricity to hydrogen is decreased due to the storage path by 10%-points to 56% based on the higher heating value. Assuming a cost-equivalent hydrogen price per driven kilometre in comparison to the actual diesel price and electricity costs of 10 Ct/kWhel from the grid, the revenues of the system are higher than the operating costs.

The role of green growth and institutional quality on environmental sustainability: A comparison of CO2 emissions, ecological footprint and inverted load capacity factor for OECD countries

Green growth is of great importance in terms of solving environmental problems and achieving sustainable development goals. However, the existing literature has not investigated how green growth affects environmental degradation and environmental sustainability variables. In light of this gap, this study aims to analyse the impact of green growth and institutional quality on CO2 emissions, ecological footprint and inverse load capacity factor in OECD countries by constructing three different models. The results of the analysis indicate that (i) green growth exerts a significant mitigating and differentiating effect on CO2, ecological footprint and inverted load capacity factor in the long run. This is evidenced by a 1% increase in green growth reducing CO2, ecological footprint and inverted load capacity factor by 0.563%, 0.373% and 0.198%, respectively. (i) The impact of green growth on CO2 and inverted load capacity factor in the long run is negative and statistically significant; (ii) the impact of green growth on CO2 and inverted load capacity factor in the short run is negative and statistically significant; (iii) the impact of institutional quality on deterioration is positive and significant in the long run; (iv) the impact of population on deterioration and sustainability is significant and mixed. The findings indicate that decision-makers in OECD countries should review green energy policies when setting the sustainable development goals, as environmental sustainability is more challenging than reducing pollution.

Atmospheric mesoscale modeling to simulate annual and seasonal wind speeds for wind energy production in Mexico

Numerical models have been used widely to reproduce wind resources around the globe. Mexico’s vast territory has a wide range of geographical characteristics with abundant wind potential. This work explores WRF simulations applied to reproduce the wind speed and capacity factor (CF) of 22 wind masts, organized into seven regions delimited by geographic conditions and consisting of 33 years of data. Biases, correlations, dispersion indexes and terrain gradient are selected to study the model and experimental data annually and seasonally. Results indicate that WRF simulations show a persistent positive bias in all regions, leading to overestimating CF. In a seasonal analysis, 86% of the CF data falls between the -0.1 and 0.1 bias range. Bias is not related to a physical seasonal phenomenon; instead, it appears to be related to geographic conditions. The findings indicate that different combinations of settings should be chosen to better reflect the geographical conditions and physical phenomena that affect the intricate Mexican landscape for wind energy production. This research identify regions with best reproducibility and suggests potential areas for future research on wind energy forecasting.

The effect of nuclear and fossil fuel energy R&D expenditures on environmental qualities in Canada

Minimizing environmental problems and a clean ecosystem are at the top of countries’ political agendas. Although Canada is an eco-friendly country, the rapid increase in its ecological footprint (EF) and the decreasing load capacity factor (LCF) in recent years make the analysis of Canada’s environmental determinants important. In this context, the study analyzes the effects of fossil fuel and nuclear-related research and development (R&D) expenditures on the EF and LCF under the load capacity curve (LCC) and environmental Kuznets curve (EKC) conditions for the period 1981–2022, employing the Bayer-Hanck cointegration test and the Fourier-ADL approach. The findings indicate that the EKC and LCC hypotheses are valid for Canada, that fossil fuel R&D reduces the LCF, and that nuclear energy R&D contributes to environmental development. Therefore, Canadian policymakers should promote nuclear energy technologies and use the financial resources provided by economic expansion for clean energy purposes.

Determining the effectiveness of the forest load capacity factor in assisting decarbonization in India

The atmospheric concentration of carbon dioxide (CO2) has increased dramatically due to various human activities, with deforestation playing an important role. Forests act as carbon sinks and have the capacity to absorb CO2 and other harmful emissions from the atmosphere. In particular, the Sustainable Development Goal (SDG)-15 (Life on Land) addresses the importance of reforestation and forest management. Accordingly, this study analyzes the impact of the forest load capacity factor and renewable energy on CO2 emissions in the context of the Environmental Kuznets Curve (EKC) hypothesis. For this purpose, the study focuses on India from 1990 to 2021. The empirical results indicate that (i) the EKC hypothesis is valid; (ii) the forest load capacity factor has an inhibitory effect on CO2 emissions; and (iii) renewable energy consumption has no significant effect on ecological degradation. This study points to the crucial role of forest load capacity for ecological sustainability. Moreover, renewable energy sources do not seem to be a viable option to achieve India's environmental goals. Therefore, the Indian government should focus on improving afforestation and forest conservation policies to improve ecological conditions.

Integration between constrained optimization and deep networks: a survey.

Integration between constrained optimization and deep networks has garnered significant interest from both research and industrial laboratories. Optimization techniques can be employed to optimize the choice of network structure based not only on loss and accuracy but also on physical constraints. Additionally, constraints can be imposed during training to enhance the performance of networks in specific contexts. This study surveys the literature on the integration of constrained optimization with deep networks. Specifically, we examine the integration of hyper-parameter tuning with physical constraints, such as the number of FLOPS (FLoating point Operations Per Second), a measure of computational capacity, latency, and other factors. This study also considers the use of context-specific knowledge constraints to improve network performance. We discuss the integration of constraints in neural architecture search (NAS), considering the problem as both a multi-objective optimization (MOO) challenge and through the imposition of penalties in the loss function. Furthermore, we explore various approaches that integrate logic with deep neural networks (DNNs). In particular, we examine logic-neural integration through constrained optimization applied during the training of NNs and the use of semantic loss, which employs the probabilistic output of the networks to enforce constraints on the output.

Reshaping sustainability through climate attention, financial regulations, and energy policy

The United States is classified as “insufficient” by the climate action tracker (CAT), indicating a lack of progress in addressing crucial environmental concerns. This highlights the inadequacy of current policy measures, especially in achieving the sustainable development goal (SDG) 13. In view of this, the present study examines the impact of public climate attention, financial regulations, and energy policy uncertainty on the load capacity factor in the United States. The quarterly data spanning from 2004 to 2021 has been analyzed by employing a unique bootstrap rolling window Granger causality test. The findings confirm that an increase in public climate attention and financial regulations positively affects the load capacity factor, indicating an improvement in environmental quality. Further, it was found that an increase in energy policy uncertainty negatively affects the load capacity factor, indicating environmental degradation. The study highlights policy implications to support the United States in achieving SDG 13.

The concept of autonomous hydrogen energy complex: Adaptation of large nuclear power units to uneven energy consumption schedules

Evaluation of flexible operation of nuclear power plants (NPP) during load reduction hours shows inefficiency of the process with account for high capital investments in NPPs at relatively low fuel costs. The concept of producing hydrogen fuel at NPPs during the hours of low power demand in the energy grid will be an effective solution to the problem. Generating electricity through utilization of electrolysis hydrogen and oxygen production provides a possibility to significantly extend the flexibility range for power released by the NPP in the energy grid under a constant reactor capacity factor. Utilization of a low-power steam turbine can be a good solution to address the challenges of electricity production from hydrogen and oxygen. This will ensure high reliability of the main NPP steam turbine. In addition, in their previous works based on the experimental data relating the operation of NPPs, the authors showed that a low-power steam turbine and residual energy release from the reactor can be used for NPP auxiliary needs backup in case of a complete blackout. Thus, increased safety of NPPs will be ensured by installing an autonomous hydrogen energy complex. Moreover, this can reduce the risk of an accident when using both the newly developed and existing safety systems. This work presents an analysis of the current state of hydrogen production at NPPs, including the research results and rationale for efficiency of the developed energy complex. The calculations showed that development of NPPs in combination with autonomous hydrogen energy complexes will increase economic attractiveness of newly designed NPPs and will increase the competitiveness of existing nuclear plants on electricity market, will also provide their survival in the energy sector in the long term.

An exergoeconomic exploration of solar-geothermal integration

This study investigates the integration of a solar field using parabolic trough collectors into a binary geothermal plant with isopentane as the secondary fluid, employing the Engineering Equation Solver (EES) and System Advisor Model (SAM) for analysis. The investigation evaluates the performance of two Heat Transfer Fluids (HTFs)—Therminol VP-1 and molten salts—in hybrid configurations, emphasizing variations in solar field size and thermal storage capabilities. First, a direct correlation has been identified between second law efficiency and Levelized Cost of Energy (LCOE), demonstrating that increases in thermal storage from 0 to 12 h result in LCOE increments up to 33%. The study highlights that molten salts, achieving the lowest LCOE of 0.075 USD/kWh, enhance net energy output by up to 15.55% compared to Therminol VP-1, particularly under extended storage conditions. Additionally, the integration of larger solar fields and increased storage capacities significantly impacts the costs of high-pressure steam production, which rise from 33.02 to 50.55%. By expanding plant capacity factors up to 97% for Therminol and 93% for molten salts with optimal thermal storage and solar multiple configurations, the study demonstrates that molten salts enable greater annual power output despite lower capacity factors. This comprehensive analysis underscores the pivotal role of second law analysis in optimizing the economic and operational efficiencies of hybrid geothermal-solar power systems, offering significant insights for enhancing the design and economic viability of renewable energy integrations.

Comparative techno-economic and environmental analysis of a relocatable solar power tower for low to medium temperature industrial process heat supply

The primary objective of this study is to promote the adoption of solar power tower (SPT) technology in low to medium-temperature industrial process heat (IPH) applications and compare it with other heat generation alternatives. Through comprehensive techno-economic and environmental analyses, a proposed relocatable SPT-based IPH plant (PR-SPT-IPH), with a capacity of 1 MWth, is thoroughly evaluated against traditional concentrating solar-thermal power (CSP) technologies like parabolic trough collectors (PTC) and linear Fresnel reflectors (LFR), as well as natural gas (NG) and photovoltaic (PV)-based IPH systems. The techno-economic assessment of the PR-SPT-IPH is performed using an in-house developed MATLAB code, while the system advisor model (SAM) is used to simulate equivalent PTC-, LFR-, and PV-based IPH plants. The primary techno-economic assessment metric is the levelized cost of heat (LCOH), while the environmental analysis quantifies the avoided greenhouse gas (GHG) emissions and other pollutants resulting from NG combustion. The results show that the PR-SPT-IPH plant achieves the lowest LCOH of 2.42 cents/kWhth, generating annual thermal energy of 5.62 GWhth with a capacity factor of 48.27 %. With certain assumptions, this cost can be further reduced to 1.5 cents/kWhth, meeting the U.S. Department of Energy targets. The PR-SPT-IPH plant could annually avoid emitting 1112 kg of CO2, 54 kg of particulate matter, 1201.76 kg of NOx, and 5.08 kg of SO2. It could also save annual external costs between $43,100.28 and $153,194.67, while the annually saved fuel cost could reach $151,632. The study indicates that the PR-SPT-IPH plant surpasses PTC-, LFR-, NG-, and PV-based IPH plants both technically and financially. With reduced capital costs and appropriate incentives, the PR-SPT-IPH plant emerges as an economically and environmentally viable choice for low to medium-temperature IPH applications.

Performance assessment of large-scale rooftop solar PV system: a case study in a Malaysian Public University

Adopting rooftop solar PV systems in various domestic and non-domestic sectors (including commercial, industrial, and agricultural) exhibits their commitment to green energy ventures. This study intends to evaluate the effectiveness of a grid-connected solar system that has been installed so far: a 6.9 MWp photovoltaic (PV) system implemented at University Tun Hussein Onn (UTHM) in Batu Pahat, Johor. This green energy system was installed as a part of the Self-Consumption (SELCO) program utilizing a Supply Agreement for Renewable Energy (SARE) contract. To assess the mounted energy system's efficiency, performance monitoring was carried out between December 2021 and November 2022. By evaluating the enactment of the 6.9 MWp solar system at UTHM, this present work has attempted to compile the achievement of implementation of self-consumption in terms of performance analysis and financial benefits to consumer. Based on International Electrotechnical Commission (IEC) 61724 standard, this study also investigates several performance characteristics of the PV system, such as final yield, clearness index (CI), array yield, PV module efficiency, inverter efficiency, reference yield, and capacity factor (CF) performance ratio (PR). Moreover, an assessment of Carbon Dioxide (CO2)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${CO}_{2})$$\\end{document} avoidance was also conducted to quantify the amount of CO2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${CO}_{2}$$\\end{document} reduction achieved. In addition, the self-consumption ratio and self-sufficiency ratio pattern for solar power plant in UTHM has been assessed. The monthly clearness index at the project location from December 2021 to November 2022 ranges from 0.63 to 0.66. The PV system was monitored throughout this time and generated 8529 MWh of energy. The installed PV system has shown an efficiency of 11.86%, a final yield of 108.28%, and a PR of 0.78, respectively. Further, the installed 6.9 MWp PV system at UTHM will cut CO2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${CO}_{2}$$\\end{document} emissions by 5450 tons annually. The 6.9 MWp solar PV system is also estimated to reduce 25.1 RM mil in 25 years. Making the system larger to 6.9 MWp would reduce the consumption from the grid during the week, makes it possible to achieve self-consumption levels of up to 100% and self-sufficiency of this solar plant is relatively low ranges from 22 to 35%. The research finding bridges the gap between policy makers, investors, and the public regarding acceptance of the large-scale Self-Consumption (SELCO) and strengthen the strategic collaborations between solar service provider and educational bodies to support of the government’s agenda to achieve carbon emission intensity reduction in Malaysia.Graphical

Renewable exergy return on investment (RExROI) in energy systems. The case of silicon photovoltaic panels

The ongoing energy transition towards renewable sources faces reliance on fossil fuels throughout their life cycle, primarily due to mining and material production for infrastructure. This study proposes the Renewable Exergy Return on Investment (RExROI) metric, which quantifies the renewable exergy obtained from each unit of non-renewable exergy invested in energy systems. It can be seen as a Renewation Index, applicable to any energy production system, indicating the degree of renewability of such technologies. Focused on silicon photovoltaic panels, the study explores five material intensities, nine scenarios based on the capacity factor and lifespan, and two alternatives for electricity used in the manufacture. Results show that material intensity increases RExROI from −0.6 MJ/MJ (non-renewable exergy is higher than electricity produced) to 5.7 MJ/MJ, increasing until 19 MJ/MJ with the best location and lifespan, and reaching 34 MJ/MJ if renewable electricity is used in manufacturing. Thus, carbon intensity can range from 734 to 7 gCO2eq/kWh. Furthermore, some strategies to enhance RExROI are discussed based on the (i) energy sources, (ii) materials, and (iii) production stages of photovoltaic panels. Thus, this study demonstrates the usefulness of RExROI in evaluating the energy-material-emission nexus of energy systems through exergy in the context of energy transition.

Achieving ecological sustainability in OECD countries: The role of fiscal decentralization and green energy

The global push for sustainable development has increased interest in understanding the complex interplay between various factors that affect the load capacity factor. The load capacity factor is a metric used to assess the utilization or capacity of a system or resource. It indicates how effectively the system or resource is used at any given time. This study aims to provide an understanding of the potential environmental effects of human activities by examining the effects of fiscal decentralization, renewable energy share, and natural resources on the load capacity factor in OECD countries from 1995 to 2020. The study's findings emphasize the need to promote renewable energy sources, optimize the utilization of natural resources, and empower local authorities in determining energy policy. Furthermore, findings reveal that fiscal decentralization can significantly affect the load capacity factor, particularly at higher levels of decentralization, and that as the level of fiscal decentralization increases, the load capacity factor continues to exert a significant influence. Moreover, economic growth negatively impacts the load capacity factor, indicating that higher levels of economic growth have a greater impact. A comprehensive and effective policy promoting sustainable development, enhancing load capacity, and mitigating environmental risks should be the focus of policymakers in the OECD countries. A greener and more sustainable future can be achieved by aligning fiscal decentralization, renewable energy promotion, and sustainable resource management.

Accessing the efficacy of green growth, energy efficiency, and green innovation for environmental performance in top manufacturing nations in the framework of sustainable development

AbstractIn recent years, the quality of the environment has declined dramatically as a result of human activities, which threaten the sustainability of our ecosystem. In this context, a number of earlier studies have investigated the environmental problems through the lens of different environmental indicators such as ecological footprint and carbon dioxide emissions. However, it is possible that by taking these factors into account, the supply side of environmental quality will be neglected. This research seeks to address this deficiency by investigating the impact of green growth, energy efficiency, green technology innovation, economic growth, trade openness, and human capital on the level of load capacity factor. This indicator places equal emphasis on the supply and demand sides of the ecosystem by taking into account both ecological footprint and bio capacity. In this setting, the present study makes use of CS-ARDL model for the top 10 manufacturing countries from the years 1990 to 2019. The short- and long-run findings of the CS-ARDL model unveil that green growth, energy efficiency, green technology innovation, and human capital all positively influence the load capacity factor, suggesting that these factors help the top 10 manufacturing countries to improve their sustainability limits. In contrast, trade openness and economic expansion in underlying countries increase environmental degradation by lowering the level of LCF. These outcomes are also validated by the AMG and CCEMG approaches and the granger causality test. On the basis of the findings, important policy recommendations are presented with the aim of assisting the top 10 manufacturing economies in enhancing the quality of their environment and fulfilling the United Nations Sustainable Development Goals.

Do circular economy, renewable energy, industrialization, and globalization influence environmental indicators in belt and road initiative countries?

This paper is the first comprehensive research to examine the effect of circular economy on environment employing two environmental degradation indicators (CO2 emissions, ecological footprint) and one environmental quality indicator (load capacity factor) for 57 Belt and Road Initiative (BRI) countries during 2000-2019. The effect of other variables such as renewable energy, industrialization, and globalization was also controlled. The study applied the cross-sectional autoregressive distributed lag method (CS-ARDL), the augmented mean group (AMG), and common correlated effects mean group (CCEMG) methods as a robustness checks. The empirical findings reveal that circular economy and renewable energy have pro-environmental effects by decreasing carbon emissions and ecological footprint and increasing the load capacity factor in BRI countries. However, industrialization and globalization have detrimental effects on the environment. The result of causality shows a bidirectional causality between renewable energy, circular economy, industrialization, and three environmental indicators, but the relationship of globalization with CO2 emissions and the load capacity factor is unidirectional and with the ecological footprint is bidirectional. All the results are confirmed by the robustness tests. The study suggests policy implications for the BRI government.

Influence of material properties of liquid absorption filters for concentrated photovoltaic/thermal hybrid systems

A concentrated photovoltaic/thermal system with a liquid filter is an effective technique to utilize the whole solar spectrum for power production. However, fundamental issues still need further investigation, including the structure of flow design and absorptive fluid selection principles. This paper presents a comparative study of optimized configurations of fluid flow channels in a concentrated photovoltaic/thermal system. MATLAB was used to study the influence of fluid thermal properties and transmittance on the system performance. The results showed that increasing the ideality factor from 0.5 to 1 can improve the overall exergy efficiencies of the coupled and decoupled concentrated photovoltaic/thermal system by 2.2 % and 2.4 %, respectively. Moreover, it was found that the coupled concentrated photovoltaic/thermal is more suitable for electrical production when the heat capacity exceeds 2500 J/kgK. The coupled system’s outlet temperature is boosted to 20˚C when the ideality factor is decreased by 20 % but requires significantly lower specific heat capacities. Overall energy efficiency tends to be higher at high heat capacity and low ideality factor values, while exergy efficiency tends to be higher at high heat capacity and high ideality factor values.

Why insurgents engage in kidnappings: A coercive strategy in quasi-state governance and control?

Despite the common perception viewing kidnappings as means to generate ransom income and to obtain political concessions, it remains unclear why kidnappings are disproportionately employed by some violent insurgent groups but not by the others. Combining data from the Global Terrorism Database and the Big Allied and Dangerous Insurgency Dataset, we empirically examined this question with a theoretical focus on the possible role of insurgents’ performance of state-like functions, which may necessitate the use of kidnappings as an illicit form of ‘policing’ and punishment for social control. Our analyses mainly focused on three aspects of quasi-state activities: extraction; provision of public services; and warring activities. A series of negative binomial regressions were conducted to examine the effects of insurgents’ quasi-state activities on their kidnapping activities over a base model with only group capacity and resource factors. We found that the initial effects of territory-control and membership size disappeared when variables measuring quasi-state activities were included into the model. This suggests that the influence of group capacity and resources on kidnappings may be an indirect one via insurgents’ strategic need for coercive control when contending for quasi-state status.

Effect of Photoaging on Adsorption of Cu(Ⅱ) by Polystyrene Microplastics with Different Particle Sizes

In order to evaluate the effect of aging and particle size on the adsorption of heavy metals by microplastics, the adsorption behavior of Cu(Ⅱ) by three different particle sizes of polystyrene (PS; 1, 50, and 100 μm) under UV irradiation was systematically studied. The results demonstrated that UV aging significantly changed the surface morphology and physicochemical properties of PS, and 1 μm PS had the strongest aging degree. The adsorption kinetics of PS on Cu(Ⅱ) conformed to the pseudo-second-order kinetic model, and the Freundlich model was more suitable for the experimental data of isothermal adsorption of Cu(Ⅱ) by PS. These results indicated that the adsorption of Cu(Ⅱ) by PS occurred on the non-uniform surface of PS, and the adsorption behavior was multilayer adsorption. Parameter "n" of the Freundlich model was less than 1, indicating that the adsorption behavior of PS on Cu(Ⅱ) was a higher intensity physical adsorption behavior. The order of theoretical maximum adsorption capacity of different particle sizes PS for Cu(Ⅱ) was as follows:1 μm > 50 μm > 100 μm, indicating that the size of PS was an important influence factor for the adsorption capacity of PS to pollutants. For the same particle size PS, aging enhanced its adsorption capacity for Cu(Ⅱ). The results on the adsorption of Cu(Ⅱ) by PS under different environmental conditions indicated that the adsorption capacity of PS for Cu (II) increased with the increase in pH, whereas an increase in salinity had the opposite effect. Surface complexation and electrical adsorption were the main mechanisms of adsorption of Cu(Ⅱ) by PS. This study provides an important scientific basis for understanding the adsorption behavior of microplastics to heavy metals in the environment.

Quantitative Analysis of Influencing Factors on Changzhou Ship Lock Capacity

The Changzhou ship lock is approaching its capacity limit. In order to quantitatively analyze the influencing factors that restrict the capacity of the Changzhou ship lock, this study proposes an influencing factor analysis method based on principal component analysis (PCA). This method estimates the confidence interval of ship passing time by fitting a lognormal distribution curve, eliminates redundancy in navigability data by combining the hydrological data and cargo load data, and quantitatively analyzes the influencing factors of ship lock capacity under saturated operating conditions. The results show that the influencing factors of Changzhou ship lock capacity are classified according to their influence contribution rate as minimum water depth above the lock sill, operation direction, ship dimensions, draft, loading capacity, and actual load. The research results can provide a theoretical basis for improving the ship lock capacity and have application value for lock scheduling management.