Environmental Analysis Research Articles

Environmental fate and safety analysis of methoxyfenozide application to control litchi and longan pests.

Litchi and longan pests significantly affect crop yield and quality. Chemical prevention and control are very effective for production; therefore, it is crucial to study fate assessment and appropriate field efficacy before pesticide application on crops to appropriately assess the health and ecological risks linked with these agents. This study conducted Good Agricultural Practice (GAP) field trials and laboratory experiments to elucidate the dissipation, terminal residues, and efficacy of methoxyfenozide on litchi and longan in six locations throughout China. To detect methoxyfenozide residues on litchi and longan, a QuEChERS/UPLC-MS/MS-based method was designed. The initial methoxyfenozide levels in litchi and longan ranged from 2.21-2.86 to 0.83-0.95mgkg-1 and indicated half-lives of 5.1-5.3 and 5.3-5.7days, respectively. After 7days of foliage treatment, the concentrations of terminal methoxyfenozide residue were 0.78-2.61 and 0.02-1.01mgkg-1, which were less than the established maximum residue limit for methoxyfenozide in litchi and longan. The chronic (acceptable daily intake = 0.0055-0.0331%) dietary intake risk analysis for methoxyfenozide in longan and litchi indicated acceptable concentrations of terminal residue for the general population. Methoxyfenozide in litchi and longan was readily degraded in first-order kinetics models, the degradation rate on longan was higher than that on litchi, and their dietary risks were negligible to consumers. Two hundred forty grams per liter of methoxyfenozide suspension concentrate (SC) represents a highly efficacious insecticidal dose to control litchi and longan pests and indicates a significant application potential as it is rapidly degraded and linked with reduced post-treatment residue levels.

Beyond built environment: Unveiling the interplay of streetscape perceptions and cycling behavior

As an important means of shared micro-mobility, shared bicycles have become a crucial component of urban transportation in China. The impact of the built environment on bicycling has been widely acknowledged. However, can streetscape perceptions influence bicycle-sharing volume (BSV) and supplement the built environment? We first obtained millions of pieces of shared-cycling data from the Shenzhen Open Data Platform and carried out geographical quantification of BSV. As for streetscape, we improved the classification of subjective streetscape perception based on street view images using the k-means clustering algorithm and conducted predictions using XGBoost. Through the application of different regression models, we unveiled the nonlinear spatial interdependencies between BSV and streetscape perceptions as a complement to the built environment. Our findings indicate that greenery, vivid street-front facades, and diverse street facilities can promote BSV. Targeted strategies are proposed for different districts. For instance, urban planners can provide incentives for high-income groups in central urban areas to adopt active travel, and increase the supply of shared bicycles in suburban areas with high building density, particularly in industrial urban villages. As a supplement to the long-term planning recommendations derived from the macro-built environment analysis, an in-depth spatial perception quantitative assessment proffers a human-centric, flexible blueprint for urban street design.

Analysis of the indoor thermal environment of traditional residents in the South Sichuan China region in summer and plum rainy seasons—taking Baiyang village in Yibin city as an example

ABSTRACT In hot summer and cold winter areas, the humid and sultry summer and rainy season problems have long affected the health of residents. Through a questionnaire survey and site measurements, the indoor thermal environment of typical southern Sichuan village houses was investigated. The average indoor humidity of the old traditional dwelling (85–95%) in the rainy season was much greater than that of the new rural house (70–80%), both of which are outside the standard thermal comfort range of the human body. In summer, the situation is slightly better but still uncomfortable. The APMV evaluation results reveal that in the rainy season, the new residential house is more comfortable than the traditional residential house, while in summer, the opposite situation occurs. The measurements show that the thermal inertia index of the old stone masonry house is large, which indicates that the house has a strong ability to resist temperature fluctuations. Therefore, we discussed different performance improvement strategies for these two types of rural residences. This study is conducive to improving the thermal environment of local residential buildings and provides a reference for the future design of rural ecological residential buildings.

Microscopic Image Dataset with Segmentation and Detection Labels for Microplastic Analysis in Sewage: Enhancing Research and Environmental Monitoring

We introduce a novel microscopic image dataset augmented with segmentation and detection labels specifically designed for microplastic analysis in sewage environments. Recognizing the increasing concern over microplastics—particles of synthetic polymers smaller than 5 mm—and their detrimental effects on marine ecosystems and human health, our research focuses on enhancing detection and analytical methodologies through advanced computer vision and deep learning techniques. The dataset comprises high-resolution microscopic images of microplastics collected from sewage, meticulously labeled for both segmentation and detection tasks, aiming to facilitate accurate and efficient identification and quantification of microplastic pollution. In addition to dataset development, we present example deep learning models optimized for segmentation and detection of microplastics within complex sewage samples. The models demonstrate significant potential in automating the analysis of microplastic contamination, offering a scalable solution to environmental monitoring challenges. Furthermore, we ensure the accessibility and reproducibility 12 of our research by making the dataset and model codes publicly available, accompanied by detailed 13 documentation on GitHub and LabelBox.

Driving towards sustainability: A review of natural fiber reinforced polymer composites for eco-friendly automotive light-weighting

The automotive industry stands at a critical juncture, compelled by the imperative of sustainability to seek innovative materials for eco-friendly light-weighting. Natural fiber reinforced polymer composites (NFRPCs) have gained popularity due to their environmentally friendly nature and excellent engineering capabilities. This review paper comprehensively examines the landscape of NFRPCs in the context of automotive applications. Beginning with an overview of the ecological urgency and regulatory framework driving sustainable automotive materials, the review navigates through key advancements in NFRPC technology. The paper delineates the diverse array of natural fibers employed as reinforcements, elucidating their intrinsic properties, sources, and processing considerations. Concurrently, an in-depth analysis of various polymer matrices showcases their compatibility with different fiber types, emphasizing the critical interplay between fiber and matrix for optimal composite performance. A pivotal facet of this review manuscript lies in the rigorous evaluation of NFRPC performance across an array of metrics, including mechanical, thermal, and environmental considerations. Studies examining the interfacial interactions between natural fibers and polymers, as well as enhancements through additives and treatments, are critically assessed. Environmental and economic considerations are paramount in the quest for sustainable automotive materials. While economic evaluations delve into the viability and cost-effectiveness of widespread adoption, life cycle assessments and environmental impact analyses are evaluated to estimate the ecological footprint of NFRPCs. The paper also surveys current trends and prospects, offering insights into forthcoming innovations and directions for research. Therefore, this review article consolidates a comprehensive body of knowledge on NFRPCs for eco-friendly automotive light-weighting. By synthesizing findings from diverse studies, it provides a holistic perspective on the potential, challenges, and future trajectory of NFRPCs in the automotive sector.

Environmental Sustainability Analysis of Land Use/Land Cover Change Using the WEI Index: Application to the Municipalities around the Doñana Area in Spain

This paper presents a novel definition of the Weighted Environmental Index (WEI) specifically adapted to integrate with the official land use and land cover (LULC) cartographic historical database employed in Andalusia. This adapted WEI framework was applied to investigate land use changes within municipalities surrounding the Doñana area (South of Spain). The analysis focused on the period 1991–2016, with a detailed examination of land use modifications in thirteen individual municipalities. The results demonstrate the WEI’s adaptability in incorporating information from diverse databases. Furthermore, they highlight the importance of integrating the analysis of WEI evolution over time. This combined approach facilitates a deeper understanding of the socio-economic and environmental drivers influencing land use changes in municipalities around the Doñana area. The findings reveal a sustained decrease in WEI values over the analyzed period. This decline is attributed to the expansion of irrigated cropland at the expense of rain-fed agriculture. Additionally, a significant increase in greenhouse surface area was observed. This intensification of agriculture has contributed to aquifer overexploitation, with potential environmental consequences for the Doñana area.

Management of textile waste in Europe: An environmental and a socio-economic assessment of current and future scenarios

Textile production and consumption continues to grow, and yet textile waste management in Europe remains suboptimal. This study assesses and compares the life cycle impacts of managing selected textile waste fractions via selected technologies, under current and future framework conditions. The study combines environmental and socio-economic analyses with data input from stakeholder consultation within the European Union. Preparing for reuse is the most beneficial management pathway from both an environmental and a socio-economic perspective. Overall, mechanical recycling is the second-most preferred pathway for most environmental impact categories. However, for recycling pathways to be economically competitive against incineration, either CO2 prices will have to increase, or technology and processing costs will have to decrease. The uncertainty analysis identified critical parameters, such as substitution factors and yields of most desirable products from the recycling processes. The results offer a detailed basis for prioritising technology pathways for future textile waste management in Europe.

An event-scene cooperative analysis network with dual-stream attention convolution module and soft parameter-sharing

Sound event detection (SED) and acoustic scene classification (ASC) are critical tasks in computational environmental sound scene analysis. Both exhibit significant potential across diverse applications, including urban environment detection, smart home integration, and anomaly monitoring. Considering the intricate relationship between acoustic scenes and sound events, researchers have proposed a joint analysis method for SED and ASC tasks using multi-task learning (MTL). However, conventional MTL-based methods rely mainly on a hard parameter-sharing mechanism, which restricts information flow and collaborative learning among tasks during training. To address this issue, this paper proposes a novel MTL method for SED that employs a soft parameter-sharing mechanism to explore the intrinsic correlation between acoustic scenes and sound events. A cross-stitch mechanism is introduced in this study to facilitate soft parameter-sharing between the SED and ASC tasks by employing scene information to improve the performance of the SED tasks and ensure training efficiency. Furthermore, a dual-stream attention convolution module (DACM) is designed to capture global and local contextual information through parallel branches of attention and convolution. To evaluate the performance of the proposed method in SED tasks, we tested two datasets: the TUT Acoustic Scenes 2016/2017 and TUT Sound Events 2016/2017 datasets and the synthetic sound scene datasets. Experimental results indicate that the proposed MTL method outperforms state-of-the-art approaches, demonstrating that acoustic scene information can improve the performance of SED tasks.

A Comprehensive Review on Advantages of Ultra Performance Liquid Chromatography (UPLC) for Analytical Method Development, Validation, and Stability Studies

Ultra Performance Liquid Chromatography (UPLC) represents a paradigm shift in analytical separation science, offering enhanced speed, sensitivity, and resolution while retaining the foundational principles of traditional liquid chromatography. This comprehensive review explores the principles, instrumentation, advantages, and applications of UPLC in analytical method development, validation, and stability studies, with a focus on pharmaceutical analysis. UPLC leverages finer particles and intensified flow rates to achieve unprecedented performance gains, facilitating rapid and precise separations without compromising analytical rigor. The discussion delves into the intricate instrumentation involved, including sample injection, column selection, and detector operation, highlighting the critical role of each component in ensuring optimal analytical performance. Advantages of UPLC include drastically reduced run times, streamlined method optimization, and increased sample throughput, while limitations such as higher back pressures and limited column regenerability are also discussed. Furthermore, the review outlines the diverse applications of UPLC across pharmaceutical, environmental, food and beverage, clinical, forensic, bioanalytical, and polymer analysis domains, underscoring its versatility and significance in advancing research, development, and quality control in various industries. In summary, UPLC represents a transformative tool in analytical chemistry, offering unparalleled capabilities for expedited and accurate analysis of complex sample matrices, ultimately driving innovation and scientific progress. Key Words: Ultra Performance Liquid Chromatography (UPLC), analytical method development, validation, stability studies, pharmaceutical analysis, instrumentation, advantages, applications, sample injection, column chemistry, detector technology, back pressures, method optimization, sample throughput, environmental analysis, food and beverage analysis, clinical diagnostics, forensic analysis, bioanalytical research, polymer analysis, analytical chemistry.

Accommodating detection limits of multiple exposures in environmental mixture analyses: an overview of statistical approaches

BackgroundIdentifying the impact of environmental mixtures on human health is an important topic. However, such studies face challenges when exposure measurements lie below limit of detection (LOD). While various approaches for accommodating a single exposure subject to LOD have been used, their impact on mixture analysis has not been thoroughly investigated. Our study aims to understand the impact of five popular LOD accommodation approaches on mixture analysis results with multiple exposures subject to LOD, including omitting subjects with any exposures below LOD (complete case analysis); single imputations by LOD/2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sqrt{2}$$\\end{document}, and by estimates from a censored accelerated failure time (AFT) model; and multiple imputation (MI) with or without truncation based on LOD.MethodsIn extensive simulation studies with high-dimensional and highly correlated exposures and a continuous health outcome, we examined the performance of each LOD approach on three mixture analysis methods: elastic net regression, weighted quantile sum regression (WQS) and Bayesian kernel machine regression (BKMR). We further analyzed data from the National Health and Nutrition Examination Survey (NHANES) on how persistent organic pollutants (POPs) influenced leukocyte telomere length (LTL).ResultsComplete case analysis was inefficient and could result in severe bias for some mixture methods. Imputation by LOD/2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sqrt{2}$$\\end{document} showed unstable performance across mixture methods. Conventional MI was associated with consistent mild biases, which can be reduced by using a truncated distribution for imputation. Estimating censored values by AFT models had a minimal impact on the results. In the NHANES analysis, imputation by LOD/2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sqrt{2}$$\\end{document}, truncated MI and censored AFT models performed similarly, with a positive overall effect of POPs on LTL while PCB126, PCB169 and furan 2,3,4,7,8-pncdf being the most important exposures.ConclusionsOur study favored using truncated MI and censored AFT models to accommodate values below LOD for the stability of downstream mixture analysis.

Life Cycle Economic and Environmental Assessment of Producing Synthetic Jet Fuel Using CO2/Biomass Feedstocks.

The aviation industry is responsible for over 2% of global CO2 emissions. Synthetic jet fuels generated from biogenic feedstocks could help reduce life cycle greenhouse gas (GHG) emissions compared to petroleum-based fuels. This study assesses three processes for producing synthetic jet fuel via the synthesis of methanol using water and atmospheric CO2 or biomass. A life cycle assessment and cost analysis are conducted to determine GHG emissions, energy demand, land occupation, water depletion, and the cost of producing synthetic jet fuel in Switzerland. The results reveal that the pathway that directly hydrogenates CO2 to methanol exhibits the largest reductions in terms of GHG emission (almost 50%) compared to conventional jet fuel and the lowest production cost (7.86 EUR kgJF-1); however, its production cost is currently around 7 times higher than the petroleum-based counterpart. Electrical energy was found to be crucial in capturing CO2 and converting water into hydrogen, with the sourcing and processing of the feedstocks contributing to 79% of the electric energy demand. Furthermore, significant variations in synthetic jet fuel cost and GHG emissions were shown when the electricity source varies, such as utilizing grid electricity pertaining to different countries with distinct electricity mixes. Thus, upscaling synthetic jet fuels requires energy-efficient supply chains, sufficient feedstock, large amounts of additional (very) low-carbon energy capacity, suitable climate policy, and comprehensive environmental analyses.

Finding Priority Areas in the Evaluation of Strategies for the Prevention of Leishmaniasis in an Endemic Municipality of Brazil

Visceral leishmaniasis is a zoonotic disease that affects humans and dogs. The infection is endemic in the municipality of Araçatuba, São Paulo, Brazil. Given the role of dogs in the epidemiology of leishmaniasis, strategies to enhance surveillance and reduce transmission are focused on dogs. In this study, we retrospectively analyzed records of canine visceral leishmaniasis from 2013 to 2022. According to this database, the prevalence of dogs testing positive for leishmaniasis fluctuated, with an average of 65.04% (6590/10,133). Cases were clustered in 10 statistically significant areas. Environmental analyses identified a significant geographical association between animals testing positive and higher vegetation density rates compared with animals testing negative. The period from sample collection to diagnosis and euthanasia, as recommended by the Brazilian Ministry, correlated with disease prevalence and decreased over time. These findings serve to implement different action plans against leishmaniasis for each geographic region and to understand the impact and efforts of strategies in an endemic area.

Ten Quick and Easy Ways to Enhance Early Literacy Instruction: Tips from a School Occupational Therapist

Occupational therapists (OTs) can be valuable team members because of their expertise in activity and environmental analysis and, in particular, their skills in facilitating access to curricular and extracurricular activities for students with unique learning needs (AOTA, 2017). School OTs can assist with creating multi-sensory activities and providing suggestions for environmental modifications to increase children’s engagement in learning activities to support literacy learning. Research shows that when children use multiple senses to learn new skills, they are not only able to remember the new skills more quickly, but they are also able to attend to activities longer (Golden & McCraken, 2016). The more parts of the brain involved in tasks, the greater the likelihood of learning; thus, multisensory learning through participation in meaningful, fun literacy activities can increase learning!

Geochemical Insights into Environmental Studies

Environmental pollutions are considered a significant challenge in all human societies. Heavy metals, recognized as fundamental environmental pollutants, enter the environment through human activities and can pose various environmental hazards. In this study, a comprehensive investigation of environmental pollutions in Aligudarz County is considered as a strategic point in Iran and serves as a model for similar studies worldwide. The research utilizes geochemical, mineralogical, and statistical approaches to examine pollutions, providing innovative and quantitative insights into understanding the geochemistry of the environment and the relationship between pollutions and environmental factors. The results indicate that the study area is subject to moderate to severe pollution from toxic elements, including molybdenum, lead, cadmium, and copper. These pollutions are correlated with the geochemical, mineralogical, and environmental analysis of the region, and the information derived from this study contributes to environmental management and protection. Considering the research findings, this research model can serve as a global template for environmental studies, playing a crucial role in enhancing understanding of pollutions and advancing knowledge in this field. Therefore, the current research has a broad impact on fostering innovations and advancing knowledge in pollution management and environmental conservation.

Understanding Work Ability in Employees with Pain and Stress-Related Ill-Health: An Explorative Network Analysis of Individual Characteristics and Psychosocial Work Environment.

There is a wide range of individual and work environment factors that influence work ability among workers with pain and stress-related ill-health. The multiple interactions and overlap between these factors are insufficiently understood, and a network approach could mitigate limitations of previous research. This pilot study aimed to explore interactions between individual characteristics and psychosocial work environment and potential links to long-term work ability. Prospective data from a prevention project was used. Individuals (N = 147) with pain and/or stress-related ill-health (95% women) at public sector workplaces filled out baseline questionnaires about a collection of individual and work environment factors, which were used for constructing undirected networks. The model was run in three subsamples of workplaces. Finally, a separate model was established with work ability at 6-month follow-up as outcome variable. A shortest pathway analysis was calculated to identify mediators of work ability. Symptom catastrophizing and perceived stress were the most influential factors in all network models. Symptom catastrophizing and pain-disability risk were found to mediate the relation between perceived stress and long-term work ability. Further, demand-control-support factors were interrelated, and patterns of interaction differed between different types of workplaces. The findings support the importance of individual factors, specifically symptom catastrophizing in an individual's coping with pain or stress-problems and its influence on long-term work ability. Catastrophizing might play a role in stress-related disorders which should be further investigated. Individual and work environment factors interact and vary across context, which needs to be taken into consideration to prevent pain and stress-related ill-health at work.

Hayao Miyazaki’s Pastoral Nostalgia:

In the post-war years, rapid industrialization in the name of economic recovery resulted in several Japanese environmental crises. A number of these crises were high profile and reported by new agencies and environmental activists on a national and international level. As a result, Japanese culture and media has been influenced by images of pollutions' effect on the body and developed into robust Japanese environmental movements and policies. This essay builds on the environmental analysis of Hayao Miyazaki's films, but offers a counter perspective arguing that the body horror of Princess Mononoke and Spirited Away is more representative of Japanese pastoral nostalgia (which suggests a feeling of cultural loss and a rejection of a possible return human-nature harmony) rather than pure environmentalism.

Mechanical and Environmental Analysis of Granular Subbase with Fine Crumb Rubber

AbstractThe increasing manufacturing of tires, driven by the automotive sector, has led to various sustainability and waste management issues. Consequently, it is necessary to explore alternatives for the reuse of these wastes. Rubber has demonstrated properties that could make it an attractive material for use as a granular aggregate. The aim of this study is to evaluate the influence of crumb rubber (CR) on the mechanical behavior of a granular subbase (GSB) by replacing fine CR at 10% and 15%. An environmental analysis of the proposal was conducted using the SimaPro tool, and a series of laboratory tests were performed to assess physical and mechanical characteristics. The strength results indicate a decreasing trend as the CR content in the GSB increases. Specifically, in abrasion tests, the natural material showed an average result of 30.86%, while the mixes with 10% and 15% CR exceeded the maximum limit (50%), achieving results of 59.24% and 53.98%, respectively. In terms of the California Bearing Ratio (CBR), only the natural samples compacted at maximum energy exceeded the minimum requirements for low and medium traffic levels (30%). The highest CBR value for samples containing CR was 5%, whereas the natural GSB reached a maximum CBR of 33%. A similar trend is observed in environmental outcomes, with increases of up to 17% in abiotic depletion for the sample with 15% CR. Overall, the environmental analysis suggests that incorporating CR into the GSB could lead to an increase in various environmental impacts.

A Study on a Health Impact Assessment and Healthcare Cost Calculation of Beijing–Tianjin–Hebei Residents under PM2.5 and O3 Pollution

Excessive fine particulate matter (PM2.5) and ozone (O3) are invisible killers affecting our wellbeing and safety, which cause great harm to people’s health, cause serious healthcare and economic losses, and affect the sustainable development of the social economy. The effective evaluation of the impact of pollutants on the human body, the associated costs, and the reduction of regional compound air pollution is an important research direction. Taking Beijing–Tianjin–Hebei (BTH) as the research area, this study constructs a comprehensive model for measuring the healthcare costs of PM2.5 and O3 using the Environmental Benefits Mapping and Analysis Program (BenMAP) as its basis. First, this study establishes a health impact assessment model and calculates the number of people affected by PM2.5 and O3 exposure using the health impact function in the BTH region. Then, the willingness to pay (WTP) and cost of illness (COI) methods are used to estimate the healthcare costs inflicted by the two pollutants upon residents from 2018 to 2021. The calculation results show that the total healthcare costs caused by PM2.5 and O3 pollution in BTH accounted for 1%, 0.7%, 0.5%, and 0.3% of the regional GDP in 2018, 2019, 2020, and 2021, respectively. Based on the research results, to further reduce these high healthcare costs, we propose policy suggestions for PM2.5 and O3 control in the BTH region.

Development of Multi-Strip Image Mosaicking for KOMPSAT-3A Images

Abstract. High-resolution satellite imagery has a limitation in terms of coverage area. This limitation presents challenges for extensive-scale analysis at regional or national levels. To maximize the utility of high-resolution satellite imagery, the implementation of image mosaicking techniques is essential. In this paper, we have developed seamline extraction techniques and relative geometric correction optimized for high-resolution satellite imagery. Ultimately, we proposed a multi-strip image mosaicking method for KOMPSAT-3A (Korea Multi-Purpose Satellite-3A) images. We applied the Dijkstra's shortest path algorithm to efficiently extract seamlines. we also performed image registration based on feature matching and homography transformation to correct the relative geometric errors between input images. We conducted experiments with our methods using 29 scenes from KOMPSAT-3A L1G data. The results indicated high relative geometric accuracy, with an average error of 1.63 pixels. Furthermore, we were able to obtain high-quality seamless mosaic images. Our proposed method is expected to enhance the utility of KOMPSAT-3A imagery for large-scale environmental and urban analysis and to provide more accurate and comprehensive data.

Lightweight Convolutional Network with Integrated Attention Mechanism for Missing Bolt Detection in Railways

Railway infrastructure safety is a paramount concern, with bolt integrity being a critical component. In the realm of railway maintenance, the detection of missing bolts is a vital task that ensures the stability and safety of tracks. Traditionally, this task has been approached through manual inspections or conventional automated methods, which are often time-consuming, costly, and prone to human error. Addressing these challenges, this paper presents a state-of-the-art solution with the development of a lightweight convolutional neural network (CNN) featuring an integrated attention mechanism. This novel model is engineered to be computationally efficient while maintaining high accuracy, making it particularly suitable for real-time analysis in resource-constrained environments commonly found in railway inspections. The proposed CNN utilises a distinctive architecture that synergises the speed of lightweight networks with the precision of attention-based mechanisms. By integrating an attention mechanism, the network selectively concentrates on regions of interest within the image, effectively enhancing the model’s capability to identify missing bolts with remarkable accuracy. Comprehensive testing showcases a remarkable 96.43% accuracy and an impressive 96 F1-score, substantially outperforming existing deep learning frameworks in the context of missing bolt detection. Key contributions of this research include the model’s innovative attention-integrated approach, which significantly reduces the model complexity without compromising detection performance. Additionally, the model offers scalability and adaptability to various railway settings, proving its efficacy not just in controlled environments but also in diverse real-world scenarios. Extensive experiments, rigorous evaluations, and real-time deployment results collectively underscore the transformative potential of the presented CNN model in advancing the domain of railway safety maintenance.