Hasil untuk "Environmental engineering"

S. Bender, Cameron Wagg, Marcel G. A. van der Heijden et al.

Leonardo Morsut, Kole T. Roybal, X. Xiong et al.

Jiahe Peng, Xingzhu Chen, Wee‐Jun Ong et al.

Summary In 2011, a new and growing family of two-dimensional (2D) transition-metal carbides, nitrides, and carbonitrides (MXenes) was discovered. Benefitting from intriguing electronic and structural properties, MXenes have received increasing attention and emerged as next-generation nanomaterials for the exploration of environmentally friendly energy resources for catalysis in energy and environmental technologies. In this review, we systematically highlight the expeditious advances and achievements in design strategies, physico-chemical properties, and catalytic applications of 2D layered MXenes and their nanocomposites in environmental science and renewable energy. In addition, we unravel the structural, optical, and electronic properties of MXenes to elucidate their key roles of ameliorating the niche areas of photo(electro)catalytic hydrogen evolution reaction, oxygen evolution reaction, oxygen reduction reaction, carabon dioxide reduction reaction, nitrogen reduction reaction, and pollutant degradation. This review concludes with invigorating perspectives, outlooks, and formidable challenges in the future development of MXene-based materials for sustainable applications.

H. Panahi, M. Dehhaghi, Y. Ok et al.

Abstract A sustainable management of environment and agriculture is crucial to protect soil, water, and air during intensified agriculture practices as well as huge industrial and transportation activities. A promising tool to address these challenges could be the application of biochar, a carbonaceous product of biomass pyrolysis. The efficiency of biochar could be improved through physical, chemical and microbial procedures. Engineered biochar could then be applied for various applications ranging from sustainable agriculture to pollution remediation and catalytic reactions. Biochar engineering allows achieving biochar properties which are optimum for specific applications and/or under specific conditions. This would lead to harnessing the favorable features of biochar and to enhance its efficiency while simultaneously minimizing the existing tradeoffs. This review covers the production and applications of engineered biochar by summarizing great deals of research and knowledge on the field. Unlike previous reviews, herein biochar physical and chemical properties and the factors affecting them (i.e., biomass nature and pyrolysis conditions) have been discussed in detail. Moreover, the contributions of each physical and chemical activation/modification method to improving biochar characteristics with respect to environmental applications have been specifically scrutinized. By providing the state-of-the-art knowledge about engineered biochar production, properties, and applications, this review aims to help research in this field for identification of the culprits that must be addressed in future experiments.

Sundararaman Gopalakrishnan, Krishna K. Osuri, Dev Niyogi et al.

ABSTRACT Over the last decade, tropical cyclone (TC) track and intensity predictions have improved by nearly 50% in the Atlantic and Northern Indian Ocean, driven by advancements in ocean‐coupled numerical models, data assimilation techniques, and an expanding network of observations. However, the prediction of severe weather events driven by convection, particularly those associated with heavy precipitation over land, has not kept pace with these improvements in TC forecasting. While 1–2 km horizontal resolutions are crucial for capturing convection over land and ocean, seamless prediction across scales demands an accurate representation of the coupled evolution of ocean, land, and atmospheric states. To address the complex problem of severe weather across a spectrum of atmospheric motions—including TCs over the ocean and severe convective systems over coastal and inland regions—we have developed the Indian Ocean–Land–Atmosphere (IOLA) Coupled Mesoscale Prediction Framework. This Framework integrates the well‐tested nonhydrostatic model (NMM) dynamical core with advanced nesting techniques from the hurricane weather research and forecast (HWRF) system. It further incorporates ocean coupling from HWRF and physics packages adopted from the WRF community model. This represents the first‐ever coupled modeling system explicitly designed to tackle extreme weather events across multiple domains and scales. Extensive testing of this novel modeling framework demonstrates that a high‐resolution (1–2 km) “all‐purpose” severe weather prediction system can effectively address the challenges of forecasting extreme weather over the Indian region. One of the key focuses of this work is the application of 1‐km horizontal resolution moving nests over the monsoon region, where synoptic‐scale interactions play a critical role in modulating severe weather and heavy precipitation events. With this configuration, the model provides a high equitable threat score (ETS) > 0.18 for heavy to extreme rainfall events for 48 h and above lead times. This framework enables a unified approach to simulating severe weather phenomena accurately and flexibly. Also, it sets a new benchmark for seamless prediction of extreme weather, paving the way for improved resilience against coastal hazards and inland severe weather events.

Yusuff Ridwan, Shunde Yin

The accelerating development of offshore wind energy in the Great Lakes region necessitates cost-effective solutions for auxiliary infrastructure, such as meteorological masts. While monopile foundations are well-established for turbine generators, their high flexural rigidity and capital cost are often disproportionate for non-generating platforms. This study presents a parametric optimization of a guyed tower foundation situated in the nearshore limestone shelf of Lake Huron (Point Clark), specifically designed to balance strict signal serviceability with foundation economy. Using a non-linear static solver with Ernst equivalent cable moduli, a full factorial sweep of 48 design configurations was conducted under site-specific hydrodynamic loads (1300 kN Average/3500 kN Storm). The results demonstrate that while all configurations satisfied the 0.004 rad rotation limit mandated by TIA-222-H, significant non-linear trade-offs exist between structural stiffness and foundation demand. Specifically, a “cost of rigidity” was identified, where increasing cable pretension to 800 kN resulted in foundation overturning moments exceeding 9.6 × 10⁴ kN·m—a threefold increase compared to lower-pretension alternatives. To resolve this trade-off, a formal multi-objective scoring function was applied to rank designs based on rotation, moment, and displacement. The analysis identifies a “balanced” configuration comprising three guys with high-stiffness anchors (5 × 10⁷ N/m) and moderate pretension (300–500 kN) as the optimal design. This configuration leverages the competent bedrock to minimize cable tension requirements, offering a resilient and economically efficient solution for Great Lakes offshore monitoring.

Cláudio Lúcio do Val Lopes, João Marcus Pitta, Fabiano Belém et al.

The integration of Artificial Intelligence (AI) into clinical settings presents a software engineering challenge, demanding a shift from isolated models to robust, governable, and reliable systems. However, brittle, prototype-derived architectures often plague industrial applications and a lack of systemic oversight, creating a ``responsibility vacuum'' where safety and accountability are compromised. This paper presents an industry case study of the ``Maria'' platform, a production-grade AI system in primary healthcare that addresses this gap. Our central hypothesis is that trustworthy clinical AI is achieved through the holistic integration of four foundational engineering pillars. We present a synergistic architecture that combines Clean Architecture for maintainability with an Event-driven architecture for resilience and auditability. We introduce the Agent as the primary unit of modularity, each possessing its own autonomous MLOps lifecycle. Finally, we show how a Human-in-the-Loop governance model is technically integrated not merely as a safety check, but as a critical, event-driven data source for continuous improvement. We present the platform as a reference architecture, offering practical lessons for engineers building maintainable, scalable, and accountable AI-enabled systems in high-stakes domains.

Nusrat Ehsan, Agha Dawood, Fajar Waheed et al.

Abstract Landfills are the most commonly used Municipal Solid Waste (MSW) disposal method in the world. However, poorly engineered open landfill sites pose significant environmental threats, particularly groundwater contamination from leachate infiltration. In alignment with Sustainable Development Goals (SDGs), SDG 3- Good Health and well-being, Goal 6- Clean water and sanitation (SDG#5), this study conducts a comprehensive hydro-chemical assessment of twenty-four (24) groundwater samples extracted across four georeferenced zones surrounding Sialkot landfill sites, Punjab, Pakistan. A total of 22 physicochemical parameters were analyzed. Findings reveal non-compliance with Punjab Environmental Quality Standards (PEQs) and WHO drinking water guidelines. Elevated heavy metal concentrations- Chromium (Cr), Zinc (Zn), Lead (Pb), and Nickel (Ni) exceeded safe thresholds in all zones, while Cyanide (CN), Manganese (Mn), and Copper (Cu) remained within limits. Metal concentration order was observed as Mg > Zn > Cu > Fe > Pb > Cr > Mn > Ni > CN. Geo-accumulation factor and ecological risk index flagged Zone 1 as critically impacted, particularly by Cr and Ni. Fe levels remained constantly lower than 1 mgL− 1. The health risk assessment using the US EPA probabilistic model showed that the risk of Cr and Ni-related chronic diseases is higher in both adults and children through ingestion and dermal absorption.

McLuret, S. Joe Patrick Gnanaraj, Vanthana Jeyasingh

This study focuses on optimizing IoT-enabled stepped basin solar stills by integrating the Taguchi method, Particle Swarm Optimization (PSO), and Artificial Bee Colony (ABC) algorithms. The objective was to enhance distillate yield, thermal efficiency, and system performance by optimizing key parameters—water depth, basin material, phase change material (PCM) type, and reflector angle. The Taguchi orthogonal array minimized experimental runs, while PSO and ABC algorithms refined parameter selection. Experimental results showed that a combination of 5 mm water depth, black copper basin, salt hydrate PCM, and a 45° internal reflector angle achieved a distillate yield of 3200 ml/day with 78.05 % efficiency, nearing the theoretical maximum of 4100 ml/day. Real-time IoT monitoring enabled dynamic adjustments, further improving efficiency. The findings highlight the effectiveness of combining smart monitoring and advanced optimization techniques to create scalable and sustainable solar desalination solutions for water-scarce regions.

A.Y.A. Mohamed, Laurence Gill, Alejandro Monleon et al.

Microbial aggregates of different sizes in aerobic granular sludge (AGS) systems have been shown to exhibit distinct microbial community compositions. However, studies comparing the microbial activities of different-sized aggregates in AGS systems remain limited. In this study, genome-resolved metatranscriptomics was used to investigate microbial activity patterns within differently sized aggregates in a full-scale AGS plant. Our analysis revealed a weak correlation between the relative abundance of metagenome-assembled genomes (MAGs) and their transcriptomic activity, indicating that microbial abundance does not directly correspond to metabolic activity within the system. Flocculent sludge (FL; <0.2 mm) predominantly featured active nitrifiers and fermentative polyphosphate-accumulating organisms (PAOs) from Candidatus Phosphoribacter, while small granules (SG; 0.2–1.0 mm) and large granules (LG; >1.0 mm) hosted more metabolically active PAOs affiliated with Ca. Accumulibacter. Differential gene expression analysis further supported these findings, demonstrating significantly higher expression levels of key phosphorus uptake genes associated with Ca. Accumulibacter in granular sludge (SG and LG) compared to flocculent sludge. Conversely, Ca. Phosphoribacter showed higher expression of these genes in the FL fraction. This study highlights distinct functional roles and metabolic activities of crucial microbial communities depending on aggregate size within AGS systems, offering new insights into optimizing wastewater treatment processes.

Lanbo Lai, Xiaolin Wang, Gholamreza Kefayati et al.

The indirect evaporative cooling system, which exploits the water evaporation process to generate cooling loads without introducing additional moisture, has been recognised as a viable alternative to conventional air-conditioning systems. This acknowledgment is due to its attributes of energy efficiency and environmental friendliness. The meticulous selection of wetting materials for an indirect evaporative cooler is of paramount importance as it significantly influences the heat and mass transfer performance of the system. Therefore, this paper experimentally examined a novel material produced by laser-resurfaced technology, and this material was compared with four other distinct materials (kraft paper, cotton fibre, polyester fibre, and polypropylene + nylon fibre) while considering the wicking ability, water-holding capacity, and thermal response performance. The results revealed that the fabric materials, specifically cotton fibre and polyester fibre, exhibited outstanding water-wicking ability, with a vertical wicking distance exceeding 16 cm. Cotton fibre also demonstrated an exceptional water-holding ability, registering a value of 0.0754 g/cm². In terms of thermal response performance, polypropylene + nylon fibre and the laser-resurfaced polymer achieved stable conditions within one minute, which could be attributed to the absence of a mechanical support plate and adhesive layer. All five materials attained stability after 4.2 min. Cotton and polyester fibres exhibited advantages in the duration of the evaporation process, maintaining stable conditions for 24 and 90 min, respectively. Based on the experimental results, appropriate water-spray strategies are proposed for each material.

Ziyou Li, Agnia Sergeyuk, Maliheh Izadi

Large Language Models are transforming software engineering, yet prompt management in practice remains ad hoc, hindering reliability, reuse, and integration into industrial workflows. We present Prompt-with-Me, a practical solution for structured prompt management embedded directly in the development environment. The system automatically classifies prompts using a four-dimensional taxonomy encompassing intent, author role, software development lifecycle stage, and prompt type. To enhance prompt reuse and quality, Prompt-with-Me suggests language refinements, masks sensitive information, and extracts reusable templates from a developer's prompt library. Our taxonomy study of 1108 real-world prompts demonstrates that modern LLMs can accurately classify software engineering prompts. Furthermore, our user study with 11 participants shows strong developer acceptance, with high usability (Mean SUS=73), low cognitive load (Mean NASA-TLX=21), and reported gains in prompt quality and efficiency through reduced repetitive effort. Lastly, we offer actionable insights for building the next generation of prompt management and maintenance tools for software engineering workflows.

Marina Araújo, Júlia Araújo, Romeu Oliveira et al.

[Context] Domain knowledge is recognized as a key component for the success of Requirements Engineering (RE), as it provides the conceptual support needed to understand the system context, ensure alignment with stakeholder needs, and reduce ambiguity in requirements specification. Despite its relevance, the scientific literature still lacks a systematic consolidation of how domain knowledge can be effectively used and operationalized in RE. [Goal] This paper addresses this gap by offering a comprehensive overview of existing contributions, including methods, techniques, and tools to incorporate domain knowledge into RE practices. [Method] We conducted a systematic mapping study using a hybrid search strategy that combines database searches with iterative backward and forward snowballing. [Results] In total, we found 75 papers that met our inclusion criteria. The analysis highlights the main types of requirements addressed, the most frequently considered quality attributes, and recurring challenges in the formalization, acquisition, and long-term maintenance of domain knowledge. The results provide support for researchers and practitioners in identifying established approaches and unresolved issues. The study also outlines promising directions for future research, emphasizing the development of scalable, automated, and sustainable solutions to integrate domain knowledge into RE processes. [Conclusion] The study contributes by providing a comprehensive overview that helps to build a conceptual and methodological foundation for knowledge-driven requirements engineering.

Sonja M. Hyrynsalmi, Grischa Liebel, Ronnie de Souza Santos et al.

The discipline of software engineering (SE) combines social and technological dimensions. It is an interdisciplinary research field. However, interdisciplinary research submitted to software engineering venues may not receive the same level of recognition as more traditional or technical topics such as software testing. For this paper, we conducted an online survey of 73 SE researchers and used a mixed-method data analysis approach to investigate their challenges and recommendations when publishing interdisciplinary research in SE. We found that the challenges of publishing interdisciplinary research in SE can be divided into topic-related and reviewing-related challenges. Furthermore, while our initial focus was on publishing interdisciplinary research, the impact of current reviewing practices on marginalized groups emerged from our data, as we found that marginalized groups are more likely to receive negative feedback. In addition, we found that experienced researchers are less likely to change their research direction due to feedback they receive. To address the identified challenges, our participants emphasize the importance of highlighting the impact and value of interdisciplinary work for SE, collaborating with experienced researchers, and establishing clearer submission guidelines and new interdisciplinary SE publication venues. Our findings contribute to the understanding of the current state of the SE research community and how we could better support interdisciplinary research in our field.

Jan Pišl, Marc Rußwurm, Lloyd Haydn Hughes et al.

The rates of tropical deforestation remain high, resulting in carbon emissions, biodiversity loss, and impacts on local communities. To design effective policies to tackle this, it is necessary to know what the drivers behind deforestation are. Since drivers vary in space and time, producing accurate spatially explicit maps with regular temporal updates is essential. Drivers can be recognized from satellite imagery but the scale of tropical deforestation makes it unfeasible to do so manually. Machine learning opens up possibilities for automating and scaling up this process. In this study, we developed and trained a deep learning model to classify the drivers of any forest loss—including deforestation—from satellite image time series. Our model architecture allows understanding of how the input time series is used to make a prediction, showing the model learns different patterns for recognizing each driver and highlighting the need for temporal data. We used our model to classify over $588^{^{\prime}}000$ sites to produce a map detailing the drivers behind tropical forest loss. The results confirm that the majority of it is driven by agriculture, but also show significant regional differences. Such data is a crucial source of information to enable targeting specific drivers locally and can be updated in the future using free satellite data.

Alisa Luangrath, Joorim Na, Pandi Kalimuthu et al.

Biodegradable plastics (BPs) are widely used as alternatives to non-BPs due to their inherent ability to undergo facile degradation. However, the ecotoxicological impact of biodegradable microplastics (MPs) rarely remains scientific documented especially to aquatic ecosystem and organisms compared to conventional microplastics. Therefore, this study aimed to investigate the ecotoxicity of biodegradable polylactic acid (PLA) MPs to Daphnia magna with that of conventional polyethylene (PE) MPs with and without ultraviolet (UV) treatment (4 weeks). The acute toxicity (48 h) of PLA MPs was significantly higher than that of PE MPs, potentially attributable to their elevated bioconcentration resulting from their higher density. UV treatment notably reduced the particle size of PLA MPs and induced new hydrophilic functional groups containing oxygen. Thus, the acute lethal toxicity of PLA MPs exhibited noteworthy increase, compared to before UV treatment after UV treatment, which was greater than that of UV-PE MPs. In addition, UV-PLA MPs showed markedly elevated reactive oxygen species concentration in D. magna compared to positive control. However, there was no significant increase in the level of lipid peroxidation, possibly due to successful defense by antioxidant enzymes (superoxide dismutase and catalase). These findings highlight the ecotoxicological risks of biodegradable MPs to aquatic organisms, which require comprehensive long-term studies.

Pavlos Tafidis, Mehdi Gholamnia, Payam Sajadi et al.

Abstract Air pollution is a significant and pressing environmental and public health concern in urban areas, primarily driven by road transport. By gaining a deeper understanding of how traffic dynamics influence air pollution, policymakers and experts can design targeted interventions to tackle these critical issues. In order to analyse this relationship, a series of regression algorithms were developed utilizing the Google Project Air View (GPAV) and Dublin City’s SCATS data, taking into account various spatiotemporal characteristics such as distance and weather. The analysis showed that Gaussian Process Regression (GPR) mostly outperformed Support Vector Regression (SVR) for air quality prediction, emphasizing its suitability and the importance of considering spatial variability in modelling. The model describes the data best for particulate matter (PM2.5) emissions, with R-squared (R2) values ranging from 0.40 to 0.55 at specific distances from the centre of the study area based on the GPR model. The visualization of pollutant concentrations in the study area also revealed an association with the distance between intersections. While the anticipated direct correlation between vehicular traffic and air pollution was not as pronounced, it underscores the complexity of urban emissions and the multitude of factors influencing air quality. This revelation highlights the need for a multifaceted approach to policymaking, ensuring that interventions address a broader spectrum of emission sources beyond just traffic. This study advances the current knowledge on the dynamic relationship between urban traffic and air pollution, and its findings could provide theoretical support for traffic planning and traffic control applicable to urban centres globally.

M.I. Sayyed, Abdelmoneim Saleh, Anjan Kumar et al.

Investigations were conducted on the addition of barium's impact on the radiation shielding and physical attributes of five different glasses, designated S1–S5, with varying BaO contents. Using two point sources namely Co60 and Cs137 along with a scintillation detector [NaI(TL)], experimental measurements were made of the shielding parameters of γ-rays, namely the effective atomic number (Zeff), electron density (Nel), half-value layer (HVL), linear attenuation coefficient (μ), mass attenuation coefficient (μm), mean free path (λ), and radiation protection effectiveness at the energies of 0.664, 1.177, and 1.334 MeV, and comparisons made with recently considered glasses as well as frequently employed materials for γ-ray shielding. The results show that the examined glasses' physical and radiation shielding qualities are improved by the addition of BaO. The μ values increased from 0.245 to 0.275 cm−1 (0.662 MeV), from 0.174 to 0.198 cm−1 (1.173 MeV), and from 0.161 to 0.189 (1.332 MeV). The observed values of HVL decreased from 2.83, 3.98, and 4.3 cm to 2.5, 3.5, and 3.62 cm at 0.662, 1.173, and 1.332 MeV, respectively, for the samples S1 and S5. In addition, the S5 glass sample was determined to have the best protection against photon among all the samples that were evaluated, as well as against recently considered glasses and those materials often utilized for gamma-ray shielding purposes.

James S. Wheaton, Daniel R. Herber

Traditional requirements engineering tools do not readily access the SysML-defined system architecture model, often resulting in ad-hoc duplication of model elements that lacks the connectivity and expressive detail possible in a SysML-defined model. Without that model connectivity, requirement quality can suffer due to imprecision and inconsistent terminology, frustrating communication during system development. Further integration of requirements engineering activities with MBSE contributes to the Authoritative Source of Truth while facilitating deep access to system architecture model elements for V&V activities. The Model-Based Structured Requirement SysML Profile was extended to comply with the INCOSE Guide to Writing Requirements updated in 2023 while conforming to the ISO/IEC/IEEE 29148 standard requirement statement templates. Rules, Characteristics, and Attributes were defined in SysML according to the Guide to facilitate requirements definition and requirements V&V. The resulting SysML Profile was applied in two system architecture models at NASA Jet Propulsion Laboratory, allowing us to explore its applicability and value in real-world project environments. Initial results indicate that INCOSE-derived Model-Based Structured Requirements may rapidly improve requirement expression quality while complementing the NASA Systems Engineering Handbook checklist and guidance, but typical requirement management activities still have challenges related to automation and support with the system architecture modeling software.

Sayan Chatterjee, Ching Louis Liu, Gareth Rowland et al.

The increasing popularity of AI, particularly Large Language Models (LLMs), has significantly impacted various domains, including Software Engineering. This study explores the integration of AI tools in software engineering practices within a large organization. We focus on ANZ Bank, which employs over 5000 engineers covering all aspects of the software development life cycle. This paper details an experiment conducted using GitHub Copilot, a notable AI tool, within a controlled environment to evaluate its effectiveness in real-world engineering tasks. Additionally, this paper shares initial findings on the productivity improvements observed after GitHub Copilot was adopted on a large scale, with about 1000 engineers using it. ANZ Bank's six-week experiment with GitHub Copilot included two weeks of preparation and four weeks of active testing. The study evaluated participant sentiment and the tool's impact on productivity, code quality, and security. Initially, participants used GitHub Copilot for proposed use-cases, with their feedback gathered through regular surveys. In the second phase, they were divided into Control and Copilot groups, each tackling the same Python challenges, and their experiences were again surveyed. Results showed a notable boost in productivity and code quality with GitHub Copilot, though its impact on code security remained inconclusive. Participant responses were overall positive, confirming GitHub Copilot's effectiveness in large-scale software engineering environments. Early data from 1000 engineers also indicated a significant increase in productivity and job satisfaction.