Airborne particulate pollution from cement industries represents a significant environmental stressor, adversely affecting plant physiology and altering leaf epidermal traits that serve as reliable biomarkers of pollution impact. This study investigated the effects of cement dust pollution on the leaf epidermal indices of Calotropis procera and Azadirachta indica collected from the vicinity of the Dangote Cement Company in Obajana, Kogi State. Epidermal peels were obtained using both chemical and mechanical methods, prepared on slides, and examined under a light microscope. The epidermal cells were analyzed for stomatal index, length, and breadth. Results revealed that the abaxial leaf surface of A. indica at the polluted site exhibited reductions in epidermal cell number (95.50), stomatal number (27.20), stomatal length (1.52), and stomatal breadth (1.42), compared with 103.30, 54.00, 1.80, and 1.58, respectively, at the control site, with differences being statistically significant (p < 0.05). Similarly, C. procera leaves showed marked modifications, including reduced epidermal cells (78.70), stomatal number (12.30), and stomatal breadth (1.26) on the abaxial surface, compared with 112.10, 40.60, and 1.44, respectively, at the control site, which were significantly different (p < 0.05). These alterations in epidermal indices of A. indica and C. procera highlight their potential as sensitive biomarkers for monitoring the impacts of cement dust and other airborne pollutants on living organisms
The cement industry is a primary driver of the environmental footprint of the built environment, representing the most carbon-intensive component of construction material supply chains. Their energy and emissions trajectories directly shape the sustainability of buildings and urban infrastructure. However, forecasting these trajectories remains challenging due to limited data, technological transitions, and policy uncertainties. This study develops an integrated framework that combines a systematic review of forecasting methods with a comparative evaluation of the Grey Model (GM(1,1)) and the Markov-Chain Grey Model (MCGM). Using the cement sector of a rapidly developing economy as a case study, we embed forecasts within alternative scenarios, business-as-usual, efficiency improvement, and decline to assess future pathways of energy use and CO2 emissions. Results show that MCGM significantly improves forecasting accuracy relative to GM in this data-constrained and volatile industrial context and enables robust scenario analysis. Scenario outcomes highlight the risk of rising energy demand and emissions that could undermine sustainability targets in the construction sector, while efficiency pathways demonstrate alignment with international climate and development benchmarks. Beyond this case, the framework underscores the value of Grey–Markov forecasting as a transferable decision-support tool for evaluating the long-term environmental impacts of construction-related industries, supporting policymakers and sector stakeholders in achieving low-carbon development.
Environmental sciences, Environmental effects of industries and plants
Bhavya Surendran V. S., Lohit K. S. Gujjala, Avanthi Althuri
Abstract Superabsorbent polymers (SAP) are polymer networks that are gaining momentum due to applications in various industries due to their higher liquid absorption capacity and highly porous structure. Apart from their hydrophilic nature which is being exploited in hygiene sector, the structural, thermal, and rheological peculiarities are explored in numerous sectors such as biomedical, hygienic, agriculture, slurry dewatering, and cement and concrete. The current review focuses on the application and the environmental and economic profiling of SAP products. The wide spectrum application of SAP products interferes with the normalization of the associated data. The current review is a maiden attempt to explore the mechanism of absorption and discusses in detail the possible reuse -recycle options. This review further ventures to plot a general trend of SAP on the impact factors drawn from the Technoeconomic analysis (TEA) and life cycle assessment (LCA) studies of existing literature. The review highlights the differences in synthetic and bio-SAP usage in terms of economic and environmental footprint with respect to their area of application. It can be comprehended that both TEA and LCA depend on the life span of the product which in turn is a function of the field of application. The cost of production and environmental impact are discussed as a function of raw materials and unit operations involved in the production process. Hence, it was observed that for applications that have shorter life span synthetic SAP is suitable and for longer span applications bio-SAP can be used. Graphical Abstract
Bharath Balji Govindaraj, Jagan Sivamani, Govindaraj Palsamy
et al.
Construction industries contribute significantly to CO₂ emissions through cement use, while invasive species such as Prosopis juliflora also cause ecological imbalance. This study investigates the use of Prosopis juliflora ash (PJFA) as a partial cement substitute in concrete. The fresh property evaluation revealed a reduction in workability with increasing PJFA content due to its finer particle size and water-holding capacity. Mechanical performance tests showed improvements in compressive, tensile, flexural, elastic modulus, and impact strengths up to an optimal 20 % replacement, beyond which reductions were observed. Microstructural analyses including XRD, SEM, FTIR, DTA, TGA, and EDAX confirmed the pozzolanic reactivity of PJFA, with SEM and overlay mapping indicating a densified matrix and enhanced interfacial transition zone at 20 % replacement. Life cycle assessment using OpenLCA demonstrated reductions in embodied energy, CO₂ emissions, and water use, highlighting PJFA’s dual role in mitigating the environmental burden of cement production and addressing the ecological challenges of invasive biomass. Overall, the findings establish PJFA as a promising sustainable material for concrete production, with optimal technical and environmental benefits at 20 % replacement. In comparison to control concrete, life cycle assessment showed reductions of up to 30 % in CO₂ emissions, 28 % in embodied energy, and 22 % in water use. At the ideal 20 % replacement level, compressive strength increased by 6.7 %, tensile strength by 5.7 %, flexural strength by 7.1 %, elastic modulus by 7.1 %, and impact resistance by 7.7 %.
Materials of engineering and construction. Mechanics of materials
Energy-intensive industries are expected to play a significant role in this deployment of carbon capture. However, the distribution of CO2 capture deployment across industry sectors remains uncertain as it will depend on various factors, and sectoral projections available in the literature have a wide spread and are often not comparable. In response to the identified research gap, this work examines projections for CO2 capture deployment within the EU industrial sectors, focusing on the cement, iron and steel, and chemical industries, for 2030 and 2050. We harmonize and discuss sectoral projections from seventeen scenarios, and in order to draw cross-sectoral conclusions, we compare them with 820 aggregated, EU-wide industry scenarios. The sectoral projections mapped project carbon capture to significantly reduce emissions in the cement sector by an average of 70 % by 2050. In the near term, projections for 2030 show the highest emission reductions in the chemical sector (10 %), followed by cement (7 %) and iron and steel (5 %). Sectoral projections align well with EU-wide scenarios, particularly with those complying with global 2 °C targets. Notably, many scenarios exceed the Net-Zero Industry Act target for 2030 and project capture levels beyond historical uptake trends of clean energy technologies. The findings highlight that while long-term projections consistently foresee large-scale deployment of CO₂ capture across EU industry, near-term expectations remain modest and risk falling short of the 2030 needs.
Atolo A. Tuinukuafe, Anuj Parashar, Xiaoqiang Hou
et al.
Electrical resistivity tests can be used to evaluate the transport properties of concrete and provide a durability assessment. However, the electrical resistivity is largely dependent on the pore solution composition and recent work suggests that some aggregates have the capacity for cation uptake. This study first aims to provide further evidence for adsorption of cations on aggregate surfaces, without formation of reaction products (e.g. alkali-silica reaction). Secondly, hardened mortar samples were prepared using a fine aggregate with a high alkali affinity and a non-reactive fine aggregate as a control. The electrical resistivity of mortars was measured, and the pore solution of these mortars was obtained through high-pressure extraction. The effect of aggregate moisture dilution on the pore solution was decoupled by using a pore partitioning model. The results indicate that aggregate minerology can influence the pore solution composition through cation uptake. Specific minerals of minor quantity, like biotite, may be responsible for cation exchange. While adsorbed cations strongly affected pore solution and formation factor measurement, the bulk resistivity measurements on hardened mortar were only marginally influenced. Research on other implications of similar aggregate interactions with pore solutions are an intriguing area for future research.
Vitor Affonso Lopes Silveira, Domingos Sávio de Resende, Augusto Cesar da Silva Bezerra
The sanitary ware industry led to significant waste generation with a long biodegradation period. To produce eco-friendly Portland blended cement, partial Portland cement (PC) substitution is proposed, reducing clinker consumption and mitigating adverse environmental impacts. This paper assessed the pozzolanic activity and the filler effect of clay-based sanitary ware waste (CSW) to study its feasibility of reutilization as a supplementary cementitious material (SCM). After being collected, the samples underwent a preparation process consisting of drying and sieving. The waste replaced 0 to 25 wt% PC. The CSW powder was characterized by laser diffraction granulometry, X-ray diffraction (XRD), X-ray fluorescence, and scanning electron microscopy (SEM). The pozzolanic activity was assessed by compressive strength test, isothermal calorimetry, and electrical conductivity. Durability was considered by acid attack, and the hardened mortar proprieties were shown. The utilization of CSW blended with PC is feasible for producing eco-friendly binders.
Anomaly segmentation is essential for industrial quality, maintenance, and stability. Existing text-guided zero-shot anomaly segmentation models are effective but rely on fixed prompts, limiting adaptability in diverse industrial scenarios. This highlights the need for flexible, context-aware prompting strategies. We propose Image-Aware Prompt Anomaly Segmentation (IAP-AS), which enhances anomaly segmentation by generating dynamic, context-aware prompts using an image tagging model and a large language model (LLM). IAP-AS extracts object attributes from images to generate context-aware prompts, improving adaptability and generalization in dynamic and unstructured industrial environments. In our experiments, IAP-AS improves the F1-max metric by up to 10%, demonstrating superior adaptability and generalization. It provides a scalable solution for anomaly segmentation across industries
P. Vijaya Bharati, J. S. V. Siva Kumar, Sathish K Anumula
et al.
Fourth Industrial Revolution has brought in a new era of smart manufacturing, wherein, application of Internet of Things , and data-driven methodologies is revolutionizing the conventional maintenance. With the help of real-time data from the IoT and machine learning algorithms, predictive maintenance allows industrial systems to predict failures and optimize machines life. This paper presents the synergy between the Internet of Things and predictive maintenance in industrial engineering with an emphasis on the technologies, methodologies, as well as data analytics techniques, that constitute the integration. A systematic collection, processing, and predictive modeling of data is discussed. The outcomes emphasize greater operational efficiency, decreased downtime, and cost-saving, which makes a good argument as to why predictive maintenance should be implemented in contemporary industries.
Katharina Ledebur. Ladislav Bartuska, Klaus Friesenbichler, Peter Klimek
The automotive industry is undergoing transformation, driven by the electrification of powertrains, the rise of software-defined vehicles, and the adoption of circular economy concepts. These trends blur the boundaries between the automotive sector and other industries. Unlike internal combustion engine (ICE) production, where mechanical capabilities dominated, competitiveness in electric vehicle (EV) production increasingly depends on expertise in electronics, batteries, and software. This study investigates whether and how firms' ability to leverage cross-industry diversification contributes to competitive advantage. We develop a country-level product space covering all industries and an industry-specific product space covering over 900 automotive components. This allows us to identify clusters of parts that are exported together, revealing shared manufacturing capabilities. Closeness centrality in the country-level product space, rather than simple proximity, is a strong predictor of where new comparative advantages are likely to emerge. We examine this relationship across industrial sectors to establish patterns of path dependency, diversification and capability formation, and then focus on the EV transition. New strengths in vehicles and aluminium products in the EU are expected to generate 5 and 4.6 times more EV-specific strengths, respectively, than other EV-relevant sectors over the next decade, compared to only 1.6 and 4.5 new strengths in already diversified China. Countries such as South Korea, China, the US and Canada show strong potential for diversification into EV-related products, while established producers in the EU are likely to come under pressure. These findings suggest that the success of the automotive transformation depends on regions' ability to mobilize existing industrial capabilities, particularly in sectors such as machinery and electronic equipment.
Domain-adaptive continual pretraining (DAPT) is a state-of-the-art technique that further trains a language model (LM) on its pretraining task, e.g., masked language modeling (MLM), when common domain adaptation via LM fine-tuning is not possible due to a lack of labeled task data. Although popular, MLM requires a significant corpus of domain-related data, which is difficult to obtain for specific domains in languages other than English, such as the process industry in the German language. This paper introduces an efficient approach called ICL-augmented pretraining or ICL-APT that leverages in-context learning (ICL) and k-nearest neighbors (kNN) to augment target data with domain-related and in-domain texts, significantly reducing GPU time while maintaining strong model performance. Our results show that the best configuration of ICL-APT performed better than the state-of-the-art DAPT by 28.7% (7.87 points) and requires almost 4 times less GPU-computing time, providing a cost-effective solution for industries with limited computational capacity. The findings highlight the broader applicability of this framework to other low-resource industries, making NLP-based solutions more accessible and feasible in production environments.
Commonsense knowledge bases (KB) are a source of specialized knowledge that is widely used to improve machine learning applications. However, even for a large KB such as ConceptNet, capturing explicit knowledge from each industry domain is challenging. For example, only a few samples of general {\em tasks} performed by various industries are available in ConceptNet. Here, a task is a well-defined knowledge-based volitional action to achieve a particular goal. In this paper, we aim to fill this gap and present a weakly-supervised framework to augment commonsense KB with tasks carried out by various industry groups (IG). We attempt to {\em match} each task with one or more suitable IGs by training a neural model to learn task-IG affinity and apply clustering to select the top-k tasks per IG. We extract a total of 2339 triples of the form $\langle IG, is~capable~of, task \rangle$ from two publicly available news datasets for 24 IGs with the precision of 0.86. This validates the reliability of the extracted task-IG pairs that can be directly added to existing KBs.
Purpose: Inquire into the significant correlation allying corporate governance mechanisms (CGMs) with financial performance (FP) of the prominent quoted cement firms in Nigeria. Methodology/approach: The study use panel data statistical modelling to investigate the time-dependent effects across different firms. The data analysis is based on a purely numerical dataset obtained through desk research, which was then scrutinized using the STATA 14.0 software package along with suitable statistical and econometric tools. Results/findings: The findings indicate a positive correlation between board structures and the FP of the selected cement companies in Nigeria. While the size of the board does not significantly influence performance, the presence of independent directors on the board positively affects financial performance. Conversely, there is a negative correlation between directors' compensation and the financial performance of these firms, suggesting that an increase in directors' compensation may lead to a decline in financial FP. Conclusion: The study concludes that there is a positive relationship between board structures and the financial performance, though board size is not a critical factor but the presence of independent directors on the board positively impacts financial performance. Limitations: The analysis is confined to five years Annual report and financial statements of three major firms in the Nigerian cement industry, covering a period from 2019 to 2023 using Panel-Corrected Standard Errors Regression model. Contribution: This comprehensive study evaluates the current state of corporate governance practices (CGPs) in Nigeria, aiming to identify improvements for CG policies.
Ahmed S. Abdelzaher, Gaber Sallam Salem Abdalla, Atef F. Hashem
et al.
This study investigates the optimal risk retention strategy for cement companies in the Kingdom of Saudi Arabia, focusing on the period from 2012 to 2024. Using a quantitative approach, we model the frequency and severity of pure risks faced by the industry, applying Poisson and Gamma distributions alongside compound loss modeling to estimate the Maximum Probable Yearly Aggregate Loss (MPY). The results indicate that the optimal risk retention level for Saudi cement firms is 29.4%, suggesting that a hybrid risk management strategy—combining self-insurance and commercial insurance—offers the most cost-effective solution. By retaining a portion of the risk and transferring the remainder to insurers, firms can optimize their risk financing costs while maintaining protection against catastrophic losses. This approach is consistent with recent studies, including those by Nocco and Stulz [1] and Foster [2] which highlight the value of combining self-insurance and market insurance to balance cost control and risk mitigation. Ultimately, this research provides actionable insights for risk managers in the cement industry, enabling them to adopt strategies that enhance financial resilience and operational continuity in the face of an increasingly complex risk landscape.
Arash Motalebi, Mohammad Abu Hasan Khondoker, Golam Kabir
The construction industry plays a crucial role in shaping our built environment, and it is imperative to adopt more sustainable and innovative practices, technologies, and tools to minimize the environmental impact. Recently, 3D printing technology has emerged as the main element of the fourth industrial revolution, Industry 4.0 which offers numerous benefits in manufacturing, including complete design freedom, savings in materials and time, enhanced efficiency, and so on. This novel technology is positively impacting various industries, including automotive, aerospace, biomedical, and now the construction industry as well. The present study aims to investigate the ecological impacts of 3D concrete printing (3DCP) by conducting a comprehensive literature review of the published articles that focused on the life cycle assessment of 3DCP-processed units. The objective was to identify current trends, areas of study that require further attention, and opportunities to lower energy consumption and environmental impacts. The literature review found that 3DCP associates with a significant reduction in global warming potential when compared to traditional construction using ordinary Portland cement-based concrete. From the life cycle analysis for 3D printed concrete performed in some articles, this review has identified opportunities to enhance the durability of 3DCP by using non-traditional materials. Additionally, improving the energy efficiency of the printing system and optimizing the structural design of printed structures can further enhance their environmental performance.
Jason Shun Fui Pei, Chung Siung Choo, Deni Shidqi Khaerudini
et al.
In this study, one-part alkali-activated mortars are formulated using circulating fluidised bed combustion (CFBC) fly ash, derived from lignite (brown coal) combustion, as the precursor, and sodium hydroxide (NaOH) and sodium metasilicate (Na2SiO3) as the solid activators. Experimental findings indicate that an increase in solid activator-to-precursor ratio correlates with improved workability and compressive strength of the mortars. The influence of Na2SiO3-to-NaOH ratio on the compressive strength of the mortars is apparent only in mixes with a high solid activator-to-precursor ratio of 0.4 and 0.5, indicating its relatively lesser significance compared to the solid activator-to-precursor ratio. The mechanism through which an increase in the solid activator-to-precursor ratio improves the compressive strength of the mortars is elucidated using attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy. The results indicate that increasing the solid activator-to-precursor ratio enhances the degree of alkali-activation for fly ash, thereby improving the compressive strength with increasing solid activator-to-precursor ratio.
Zengliang Yue, Yuvaraj Dhandapani, Samuel Adu-Amankwah
et al.
This study evaluates the reaction kinetics, phase assemblage, and microstructure evolution of Na2SO4-activated slag cements produced with three commercial slags. The main reaction products identified are ettringite and calcium aluminosilicate hydrates, alongside a poorly crystalline SO42- intercalated Mg-Al-layered double hydroxide (LDH) phase. Results revealed that the Al2O3 slag content alone does not correlate with the cement performance. While pastes made with a higher Al2O3 content slag exhibit faster reaction kinetics, those made with a slag with a higher Mg/Al ratio developed superior compressive strength and reduced porosity over extended curing periods. Thermodynamic modelling simulations indicate that sulfate consumption occurs via ettringite and LDH phase formation, influencing the slag reaction degree, pH value, and porosity reduction in these cements. This research highlights the critical role of slag composition in controlling microstructure and, consequently, performance of sodium sulfate activated slag cement pastes.
Getaneh Bezie, Endalu Tadele Chala, Nagessa Zerihun Jilo
et al.
Abstract Rock slope failures pose significant challenges in geotechnical engineering due to the intricate nature of rock masses, discontinuities, and various destabilizing factors during and after excavation. In mining industries, such as national cement factories, multi-benched excavation systems are commonly used for quarrying. However, cut slopes are often designed with steep angles to maximize economic benefits, inadvertently neglecting critical slope stability issues. This oversight can lead to slope instability, endangering human lives and property. This study focuses on analyzing the stability of existing quarry cut slopes, estimating their final depth, and conducting a parametric study of geometric profiles including bench height, width, face angle, and rump width. Kinematic analysis helps identify potential failure modes. The results reveal that the existing quarry cut slope is prone to toppling, wedge failure, and planar failure with probabilities of 42.68%, 19.53%, and 14.23%, respectively. Numerical modeling using the finite element method (Phase2 8.0 software) was performed under both static and dynamic loading conditions. The shear reduction factor (SRF) of the existing quarry cut slope was 1.01 under static loading and 0.86 under dynamic loading. Similarly, for the estimated depth, the SRF was 0.82 under static loading and 0.7 under dynamic loading. These values indicate that the slope stability falls significantly below the minimum acceptable SRF, rendering it unstable. The parametric study highlights the face angle of the bench as the most influential parameter in slope stability. By adjusting the bench face angle from 90° to 75°, 70°, and 65°, the SRF increased by 31.6%, 35.4%, and 37.9%, respectively. Among these, a 70° bench face angle is recommended for optimal stability with a SRF of 1.27 under static loading and 1.18 under dynamic loading.