Hasil untuk "Environmental effects of industries and plants"

Sheikh Mansoor, Asif Ali, Navneet Kour et al.

Although trace elements are essential for life, environmental contamination due to metal accumulation and overuse in various sectors, such as healthcare, agriculture, industry, and cosmetics, poses significant health concerns. Exposure of plants to heavy metals leads to the overproduction of reactive oxygen species (ROS) due to their ability to change mitochondrial membrane permeability and restrict the action of ROS clearance enzymes in the cellular antioxidant system. The interaction of ROS with cellular membranes, heavy-metal-induced interactions directly or indirectly with different macromolecules, and signaling pathways leads to the accumulation of environmental pollutants and oxidative stress in exposed organisms. The heavy metal–ROS–cell signaling axis affects various pathological processes such as ATP depletion, excess ROS production, mitochondrial respiratory chain damage, decoupling of oxidative phosphorylation, and mitochondrial death. This review focuses on discussing the toxic effects of different heavy metals on plants, with particular emphasis on oxidative stress, its consequences, and mitigation strategies.

Abdul Wahab, Murad Muhammad, Shahid Ullah et al.

Through the advancement of nanotechnology, agricultural and food systems are undergoing strategic enhancements, offering innovative solutions to complex problems. This scholarly essay thoroughly examines nanotechnological innovations and their implications within these critical industries. Traditional practices are undergoing radical transformation as nanomaterials emerge as novel agents in roles traditionally filled by fertilizers, pesticides, and biosensors. Micronutrient management and preservation techniques are further enhanced, indicating a shift towards more nutrient-dense and longevity-oriented food production. Nanoparticles (NPs), with their unique physicochemical properties, such as an extraordinary surface-to-volume ratio, find applications in healthcare, diagnostics, agriculture, and other fields. However, concerns about their potential overuse and bioaccumulation raise unanswered questions about their health effects. Molecule-to-molecule interactions and physicochemical dynamics create pathways through which nanoparticles cause toxicity. The combination of nanotechnology and environmental sustainability principles leads to the examination of green nanoparticle synthesis. The discourse extends to how nanomaterials penetrate biological systems, their applications, toxicological effects, and dissemination routes. Additionally, this examination delves into the ecological consequences of nanomaterial contamination in natural ecosystems. Employing robust risk assessment methodologies, including the risk allocation framework, is recommended to address potential dangers associated with nanotechnology integration. Establishing standardized, universally accepted guidelines for evaluating nanomaterial toxicity and protocols for nano-waste disposal is urged to ensure responsible stewardship of this transformative technology. In conclusion, the article summarizes global trends, persistent challenges, and emerging regulatory strategies shaping nanotechnology in agriculture and food science. Sustained, in-depth research is crucial to fully benefit from nanotechnology prospects for sustainable agriculture and food systems.

F. Coperchini, L. Croce, G. Ricci et al.

Per- and polyfluoroalkyl substances (PFAS) represent a group of synthetic compounds widely used in industry plants due to their low grade of degradation, surfactant properties, thermic and flame resistance. These characteristics are useful for the industrial production, however they are also potentially dangerous for human health and for the environment. PFAS are persistent pollutants accumulating in waters and soil and recoverable in foods due to their release by food packaging. Humans are daily exposed to PFAS because these compounds are ubiquitous and, when assimilated, they are difficult to be eliminated, persisting for years both in humans and animals. Due to their persistence and potential danger to health, some old generation PFAS have been replaced by newly synthesized PFAS with the aim to use alternative compounds presumably safer for humans and the environment. Yet, the environmental pollution with PFAS remains a matter of concern worldwide and led to large-scale epidemiological studies both on plants’ workers and on exposed people in the general population. In this context, strong concern emerged concerning the potential adverse effects of PFAS on the thyroid gland. Thyroid hormones play a critical role in the regulation of metabolism, and thyroid function is related to cardiovascular disease, fertility, and fetal neurodevelopment. In vitro, ex vivo data, and epidemiological studies suggested that PFASs may disrupt the thyroid hormone system in humans, with possible negative repercussions on the outcome of pregnancy and fetal-child development. However, data on the thyroid disrupting effect of PFAS remain controversial, as well as their impact on human health in different ages of life. Aim of the present paper is to review recent data on the effects of old and new generation PFAS on thyroid homeostasis. To this purpose we collected information from in vitro studies, animal models, and in vivo data on exposed workers, general population, and pregnant women.

Sunusi Ibrahim Muhammad, Ismail Ismail Tijjani, Saadatu Yusuf Jumare et al.

This paper presents the Sesame Plant Segmentation Dataset, an open source annotated image dataset designed to support the development of artificial intelligence models for agricultural applications, with a specific focus on sesame plants. The dataset comprises 206 training images, 43 validation images, and 43 test images in YOLO compatible segmentation format, capturing sesame plants at early growth stages under varying environmental conditions. Data were collected using a high resolution mobile camera from farms in Jirdede, Daura Local Government Area, Katsina State, Nigeria, and annotated using the Segment Anything Model version 2 with farmer supervision. Unlike conventional bounding box datasets, this dataset employs pixel level segmentation to enable more precise detection and analysis of sesame plants in real world farm settings. Model evaluation using the Ultralytics YOLOv8 framework demonstrated strong performance for both detection and segmentation tasks. For bounding box detection, the model achieved a recall of 79 percent, precision of 79 percent, mean average precision at IoU 0.50 of 84 percent, and mean average precision from 0.50 to 0.95 of 58 percent. For segmentation, it achieved a recall of 82 percent, precision of 77 percent, mean average precision at IoU 0.50 of 84 percent, and mean average precision from 0.50 to 0.95 of 52 percent. The dataset represents a novel contribution to sesame focused agricultural vision datasets in Nigeria and supports applications such as plant monitoring, yield estimation, and agricultural research.

Daniel Mejer Christensen, Katja Stougård Jørgensen, Josefine Palsgaard Wyrtz et al.

Research in Human-Computer Interaction (HCI) has shown that caring for others, including both humans (e.g., close friends) and computers (e.g., Tamagotchi), can have a positive effect on people's wellbeing. However, we know less about the potential role of conversational AI in such settings. In this work, we explore how AI chatbots can support plant care and, in turn, positively influence people's well-being. We developed a mobile application that allows users to `talk' to their plants via chatbots. We evaluated the application with ten participants and conducted semi-structured interviews based on Seligman's PERMA model, which identifies pillars of psychological well-being. Our findings suggest positive effects, with participants reflecting on a sense of connection to their plants and corresponding feelings of accomplishment. While our findings suggest that participants were generally positive about the app, they also raised concerns about the diverse preferences and expectations of users regarding interactions with chatbots representing plants.

Haider Mahmood, Maham Furqan, G. Meraj et al.

COVID-19 has a deep impact on the economic, environmental, and social life of the global population. Particularly, it disturbed the entire agriculture supply chain due to a shortage of labor, travel restrictions, and changes in demand during lockdowns. Consequently, the world population faced food insecurity due to a reduction in food production and booming food prices. Low-income households face food security challenges because of limited income generation during the pandemic. Thus, there is a need to understand comprehensive strategies to meet the complex challenges faced by the food industry and marginalized people in developing countries. This research is intended to review the agricultural supply chain, global food security, and environmental dynamics of COVID-19 by exploring the most significant literature in this domain. Due to lockdowns and reduced industrial production, positive environmental effects are achieved through improved air and water quality and reduced noise pollution globally. However, negative environmental effects emerged due to increasing medical waste, packaging waste, and plastic pollution due to disruptions in recycling operations. There is extensive literature on the effects of COVID-19 on the environment and food security. This study is an effort to review the existing literature to understand the net effects of the pandemic on the environment and food security. The literature suggested adopting innovative policies and strategies to protect the global food supply chain and achieve economic recovery with environmental sustainability. For instance, food productivity should be increased by using modern agriculture technologies to ensure food security. The government should provide food to vulnerable populations during the pandemic. Trade restrictions should be removed for food trade to improve international collaboration for food security. On the environmental side, the government should increase recycling plants during the pandemic to control waste and plastic pollution.

Li Zheng, Qingwen Zhang, Ji Chen et al.

Non-carbon dioxide greenhouse gas (non-CO2 GHG) emissions from global crop-livestock systems, primarily driven by enteric fermentation and manure-related emissions, pose significant challenges to climate mitigation. Identifying emission hotspots and simulating future mitigation potential are crucial for achieving agricultural carbon neutrality. Constructed a comprehensive dataset spanning 138 countries (1961–2020), integrating FAO statistics, literature-derived emission factors, and socioeconomic data, this study quantified spatiotemporal emission trends and identified decoupling patterns between emissions and economic development. The Logarithmic Mean Divisia Index (LMDI) was used to decompose drivers and six scenarios (BAU (Baseline), RSA (Rearing Structure Adjustment), LMO (Livestock Management Optimization), BMPs-30% and BMPs-50% (best management practices with a manure substitution ratio of 30% or 50%), and NMA (No Manure Application)) were developed to assess mitigation potential. Results revealed that global non-CO2 GHG emissions from crop-livestock systems increased by 61.9% from 2696.5 to 4365.0 Mt CO2-eq. yr−1 from 1961 to 2020, with India, Brazil, China, and the United States accounting for about 40% of total emissions in 2020. Significant decoupling occurred in developed countries, such as the United States, around in 1985 at approximately $10,000 per-capita GDP. In developing countries like China, significant decoupling was evident around in 1990 at approximately $3000 per-capita GDP. Ambitious scenarios (BMPs-50%) could reduce global emissions by 3.2–6.7 Gt CO2-eq. yr−1 by 2060, with major contributions from India (671.3 Mt CO2-eq. yr−1), Brazil (494.2 Mt CO2-eq. yr−1), China (486.1 Mt CO2-eq. yr−1), and the USA (238.8 Mt CO2-eq. yr−1). The findings confirmed the decoupling of non-CO2GHG emissions within crop-livestock systems from economic growth in developed countries but highlight persistent challenges in developing regions. Strategic shifts toward monogastric livestock, manure recycling, and dietary transitions emerge as critical pathways for aligning food security with carbon neutrality goals.

Yumna Yumna, Sukriming Sapareng, Burhanuddin Nur et al.

Land degradation in the Baliase River Basin continues to worsen due to increasingly complex hydrological phenomena. This study aimed to evaluate peak discharge as a flood reference for a return period of 2–200 years, estimate channel capacity, determine flood risk status, and simulate hydraulic responses to variations in Manning's n value. Peak discharge calculations use the Nakayasu synthetic unit hydrograph method, with field geometric data modeled using HEC-RAS 6.0 software in a steady-state manner. Four technical scenarios were tested: (1) river normalization, (2) clearing of wild vegetation, (3) bank hardening, and (4) channel deepening/straightening. The analysis results indicate that the Baliase River Basin is generally in critical flood conditions, especially in the downstream and central areas (Stations 6-14). Most river segments are unable to accommodate peak discharges for a repeat period of 2 years. The variation in the n value in each scenario suggests that technical intervention can significantly alter the hydraulic characteristics. Adjusting n values can reduce flood risk, primarily through changes in hydraulic form. Scenario 4 is most effective because it increases the cross-sectional area and hydraulic radius, while Scenarios 1 and 3 contribute by reducing channel roughness. Scenario 2 serves as a support strategy and is ideally combined with other strategies. Priority is given to Scenario 4 in areas where discharge exceeds channel capacity.

Mansoure Dormohamadi, Kai Kanafani, Harpa Birgisdottir et al.

Integrating fire-safety measures in buildings are widely recognized as essential for minimizing fire risks and the associated environmental impacts. Comprehensive environmental impact assessments of buildings must extend system boundaries to include technical installations, encompassing fire-safety protection systems. However, designers and stakeholders currently lack consistent, comparable data for these systems, making it difficult to assess their environmental performance during the early design stages. With an increasing number of countries mandating life-cycle assessments (LCAs) for buildings, there will be a growing need for accessible data on fire-safety measures derived from Environmental Product Declarations (EPDs). This study investigates data availability and the quality of environmental data for Active Fire Protection (AFP) and Egress (ME) systems using EPDs, which are often the most accessible and standardized sources of such data. Significant variation exists in the reporting and outcomes of EPDs. To ensure comparability across EPDs, this research employs data harmonization, aligning classification criteria and normalizing environmental data based on the functional performance of AFP and ME products. The analysis covers 157 third-party verified EN 15804 EPDs, representing 393 products, primarily from European manufacturers. Significant data gaps exist across 24 out of 32 categories of AFP and ME. Emergency lighting systems (52 %) and fire dampers (31 %) are the most frequently represented systems in the EPDs, while these two systems together with fire curtains exhibit the highest data dispersion across impact categories, complicating comparisons. Life-cycle stages A1–A3 were found to contribute most to the overall environmental impact across all systems. This underscores the value of integrating fire-safety EPDs into early-stage design decisions, where material and system choices have the greatest influence. Further research is required to improve the consistency and comparability of EPDs and to refine the impact calculations based on product specificity and functional performance.

E. Fikri, Y. W. Firmansyah, S. Suhardono, W. Mikana and L. Y. J. Noya

The port is a place for ships as sea transportation to dock. The port, as a place of entry and exit for goods or passengers from various regions, places, and environments, encourages the potential for disease transmission to a new environment. Pathogens present in the environment can directly contact the human body through air, touch, and transmission through food around areas with high mobilization. Therefore, this study aims to look at the results of hygiene observations and laboratory testing related to food, drinking water, and air samples at Galala Port, Ambon City. This study used descriptive research with a cross-sectional research design. From all parameter examination results, several examination results do not meet the standards such as food microbiology examination results (E. coli bacteria > 3.6MPN/gr), sanitation (walls and floors are not watertight), the presence of mosquito larvae (seven Aedes albopictus mosquito larvae), drinking water microbiology (total Coliforms 64 CFU.100 mL-1), and clean water microbiology (E. coli > 250 CFU. 100 mL-1 and total Coliforms 8 CFU.100 mL-1). Therefore, it can be concluded that the inspection of restaurants carried out at Galala port, Ambon City, is not appropriate and does not meet the standards according to the Minister of Health Decree number 942 of 2003.

Justin London

Plants need regular and the appropriate amount of watering to thrive and survive. While agricultural robots exist that can spray water on plants and crops such as the , they are expensive and have limited mobility and/or functionality. We introduce a novel autonomous mobile plant watering robot that uses a 6 degree of freedom (DOF) manipulator, connected to a 4 wheel drive alloy chassis, to be able to hold a garden hose, recognize and detect plants, and to water them with the appropriate amount of water by being able to insert a soil humidity/moisture sensor into the soil. The robot uses Jetson Nano and Arduino microcontroller and real sense camera to perform computer vision to detect plants using real-time YOLOv5 with the Pl@ntNet-300K dataset. The robot uses LIDAR for object and collision avoideance and does not need to move on a pre-defined path and can keep track of which plants it has watered. We provide the Denavit-Hartenberg (DH) Table, forward kinematics, differential driving kinematics, and inverse kinematics along with simulation and experiment results

Ziqi Mu

Galaxies evolve within a web-like cosmic structure, and their properties are strongly shaped by their surrounding environments. We apply a nonparametric Bayesian two-sample test based on Pólya tree priors to galaxy data from SDSS DR7 and DESI DR1 BGS to quantify the differences between galaxies in dense and sparse cosmic environments. Compared to the Kolmogorov-Smirnov test and parametric Bayesian test, our approach does not require strong assumptions about the underlying distributional form and provides a more sensitive and robust comparison of galaxy evolution across different environments. In particular, galaxies in VoidFinder voids tend to be fainter, less massive, and more star-forming compared to wall galaxies, while such contrasts are diminished under the V^2 REVOLVER pruning. These findings underscore the importance of both the statistical framework and the void classification algorithm in interpreting environmental effects on galaxy evolution.

Shubham Kejriwal, Enrico Barausse, Alvin J. K. Chua

The upcoming Laser Interferometer Space Antenna (LISA) will detect up to thousands of extreme-mass-ratio inspirals (EMRIs). These sources will spend $\sim 10^5$ cycles in band, and are therefore sensitive to tiny changes in the general-relativistic dynamics, potentially induced by astrophysical environments or modifications of general relativity (GR). Previous studies have shown that these effects can be highly degenerate for a single source. However, it may be possible to distinguish between them at the population level, because environmental effects should impact only a fraction of the sources, while modifications of GR would affect all. We therefore introduce a population-based hierarchical framework to disentangle the two hypotheses. Using simulated EMRI populations, we perform tests of the null vacuum-GR hypothesis and two alternative beyond-vacuum-GR hypotheses, namely migration torques (environmental effects) and time-varying $G$ (modified gravity). We find that with as few as $\approx 20$ detected sources, our framework can statistically distinguish between these three hypotheses, and even indicate if both environmental and modified gravity effects are simultaneously present in the population. Our framework can be applied to other models of beyond-vacuum-GR effects available in the literature.

M. Umlauft, M. Schranz

Optimizing modern production plants using the job-shop principle is a known hard problem. For very large plants, like semiconductor fabs, the problem becomes unsolvable on a plant-wide scale in a reasonable amount of time using classical linear optimization. An alternative approach is the use of swarm intelligence algorithms. These have been applied to the job-shop problem before, but often in a centrally calculated way where they are applied to the solution space, but they can be implemented in a bottom-up fashion to avoid global result computation as well. One of the problems in semiconductor production is that the production process requires a lot of switching between machines that process lots one after the other and machines that process batches of lots at once, often with long processing times. In this paper, we address this switching problem with the ``boids'' flocking algorithm that was originally used in robotics and movie industry. The flocking behavior is a bio-inspired algorithm that uses only local information and interaction based on simple heuristics. We show that this algorithm addresses these valid considerations in production plant optimization, as it reacts to the switching of machine kinds similar to how a swarm of flocking animals would react to obstacles in its course.

Samara Ghrer, Christophe Godin, Stefanie Wuhrer

Plants frequently contain numerous organs, organized in 3D branching systems defining the plant's architecture. Reconstructing the architecture of plants from unstructured observations is challenging because of self-occlusion and spatial proximity between organs, which are often thin structures. To achieve the challenging task, we propose an approach that allows to infer a parameterized representation of the plant's architecture from a given 3D scan of a plant. In addition to the plant's branching structure, this representation contains parametric information for each plant organ, and can therefore be used directly in a variety of tasks. In this data-driven approach, we train a recursive neural network with virtual plants generated using a procedural model. After training, the network allows to infer a parametric tree-like representation based on an input 3D point cloud. Our method is applicable to any plant that can be represented as binary axial tree. We quantitatively evaluate our approach on Chenopodium Album plants on reconstruction, segmentation and skeletonization, which are important problems in plant phenotyping. In addition to carrying out several tasks at once, our method achieves results on-par with strong baselines for each task. We apply our method, trained exclusively on synthetic data, to 3D scans and show that it generalizes well.

Peter A. Gloor

We present a comprehensive investigation into plant bioelectric responses to human presence and emotional states, building on five years of systematic research. Using custom-built plant sensors and machine learning classification, we demonstrate that plants generate distinct bioelectric signals correlating with human proximity, emotional states, and physiological conditions. A deep learning model based on ResNet50 architecture achieved 97% accuracy in classifying human emotional states through plant voltage spectrograms, while control models with shuffled labels achieved only 30% accuracy. This study synthesizes findings from multiple experiments spanning 2020-2025, including individual recognition (66% accuracy), eurythmic gesture detection, stress prediction, and responses to human voice and movement. We propose that these phenomena represent evolved anti-herbivory early warning systems, where plants detect approaching animals through bioelectric field changes before physical contact. Our results challenge conventional understanding of plant sensory capabilities and suggest practical applications in agriculture, healthcare, and human-plant interaction research.

Teddy Aryanto Simbolon, Enny Mutryarny, Indra Purnama

The cultivation of cayenne pepper (Capsicum frutescens L.) is important due to its widespread use in the food and pharmaceutical industries, as well as in everyday cooking. This study aimed to evaluate the effectiveness of combining shallot skin compost and NPK fertilizer on the growth and yield of cayenne pepper plants grown in Ultisol soil. The experiment was conducted using a Completely Randomized Design (CRD) in a factorial arrangement, with two factors: shallot skin compost (0, 200, 400 g per polybag) and NPK fertilizer (0, 13, 25 g per polybag), each replicated three times. The observed parameters included plant height, stem diameter, number of branches, fruit count, and fruit weight. Data were analyzed using ANOVA followed by Duncan's Multiple Range Test (DMRT) at a 5% significance level. Results indicated that the application of 200 g of shallot skin compost and 25 g of NPK fertilizer per polybag significantly improved plant height, stem diameter, and overall fruit production. Furthermore, a significant interaction between shallot skin compost and NPK fertilizer was observed, particularly in terms of fruit yield and weight. This study suggests that combining organic and inorganic fertilizers can enhance the productivity of cayenne pepper plants in Ultisol soil, offering a sustainable approach to increasing crop yield. Further research is recommended to explore the long-term effects of these treatments on soil health and plant growth.

Xiang Guo, Dandan Chen, Peishan Huang et al.

BACKGROUND Proteolysis during ensiling primarily occurs due to undesirable microbial and plant protease activities, which reduce the protein supply to ruminant livestock and cause a series of environmental problems. The objective of this study was to investigate the effects of the tannin-tolerant lactic acid bacterium strain Lactiplantibacillus plantarum 4 (LABLP4) in combination with tannic acid (TA) on protein preservation in stylo (Stylosanthes guianensis) silage. The stylos were either ensiled without additives (control) or treated with LABLP4 (106 colony-forming units per gram of fresh matter), 1% (fresh matter basis) TA, 2% TA, LABLP4 + 1% TA and LABLP4 + 2% TA. Fermentation quality, protein composition, protease activity and bacterial diversity were determined at 3, 7, 14 and 31 days of ensiling. RESULTS The combination of LABLP4 and TA decreased the pH, coliform bacteria count, non-protein nitrogen, ammonia-nitrogen (NH3-N) content and protease activities (P < 0.05) and increased the true protein content (P < 0.05) compared to the control. LABLP4 + TA led to a lower pH and NH3-N content than LABLP4 or TA alone (P < 0.05). On the last day (31 days) of ensiling, LABLP4 + TA increased the relative abundances of Firmicutes and Lactiplantibacillus (P < 0.05), except for the LABLP4 treatment, and decreased the relative abundance of Actinobacteria (P < 0.05). CONCLUSION The combination of tannin-tolerant LABLP4 and TA effectively improved the fermentation quality of stylo silage and reduced protein degradation by altering the bacterial community structure. © 2024 Society of Chemical Industry.

M. I. Al Biajawi, R. Embong, M. M. Al Bakri Abdullah

In the current era, concern about the responsible disposal of industrial waste and its reuse has increased in all societies from the industry. Therefore, the researchers’ institution is focusing its efforts on developing more environmentally friendly products from recycled waste, particularly in the area of sustainable construction. For instance, one of recycled waste is Coal Bottom Ash (CBA), a by-product of coal combustion that is produced in large quantities from thermal power plants. The aims of this study to investigate the physical, chemical and element characteristics of CBA obtained from thermal power plant in Malaysia. Also, CBA compared with cement characteristics to be used as cement replacement in the concrete mixture. Therefore, numerous tests have been performed to investigate CBA’s physical and chemical characteristics. For physical properties such as specific gravity, particle size analysis, fineness modulus, bulk density and loss on ignition. For chemical properties such as X-ray fluorescence (XRF), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR) and thermogravimetric analysis (TGA) in an effort to obtain sustainable materials from thermal power plant waste. Based on the findings in this study, it can be concluded that CBA can be utilized as cement substitute in the production of concrete mixtures.

Fernanda Ellen Francisco, Ana Carolina Ferreira Costa, Paulo Alexandre Costa Araújo Sampaio et al.

In the face of the high competitiveness in the marketplace, many companies have sought ways to differentiate themselves from others by improving the quality of their products and services, reducing the environmental impact, and ensuring employee safety. In this context, many companies implemented some management systems such as ISO 9001 (Quality Management), ISO 14001 (Environmental Management), and ISO 45001 (Occupational Health and Safety Management) to improve their performance. The International Organization for Standardization (ISO) created in 2012 a high-level structure (Annex SL) that serves as the basis for the development and integration of these Certifiable Management systems (CMSs), making them more compatible and effective. However, many companies do not have sufficient expertise about the latest versions of ISO 9001 and ISO 14001 and about the publication of ISO 45001 based on the structure proposed by Annex SL, bringing difficulties for their Integrated Management Systems (IMS). The objective of this work is to propose recommendations that direct companies to adapt their IMSs to the modifications arising from Annex SL. To develop this study, four case studies were made in Brazilian industrial companies with the following data collection instruments (semi-structured interviews, on-site observation, and document analysis). Our case studies were carried out in large companies because these types of organizations generally have more than two management systems implemented, and our article focuses on systems integration. In addition, these companies develop several interesting solutions that can serve as benchmarking for small and medium-sized companies. With this study it was possible to develop guidelines related to greater emphasis on the analysis of the organizational context; the importance of promoting a culture of integration; the need for leadership responsible for the integration of CMSs, etc. The main scientific contribution of this work was to deepen and update the theory about certifiable management systems and IMS according to the results obtained from empirical studies. The main applied contribution of this study is to generate a competitive advantage for the company over its competitors by providing leaders with management solutions that make it possible to implement or improve IMS based on the modifications resulting from Annex SL.