Abstract The offshore wind energy sector is experiencing rapid and large-scale expansion in Europe, driven by increasingly ambitious renewable energy targets that position it as a central component of global climate mitigation efforts. The increasing number of offshore wind projects in the North Sea requires comprehensive regulations to monitor and minimize the impacts on the marine environment during construction, operation and decommissioning. This policy brief aims to summarize current regulations for chemical emissions in the North Sea area by reviewing available national documents and websites for guidance in Belgium, Denmark, France, Germany, the Netherlands, and Norway in combination with information received from respective authorities. Based on the collected information, the policy brief will give recommendations for potential harmonization to increase the protection of the environment and facilitate procedures. The comparative analysis of national and transnational regulations for chemical emissions from offshore wind farms in North Sea bordering countries revealed that these are incomplete and differ between countries in terms of their specifications and level of detail. For example, specific rules for the application of galvanic anodes including the ban of zinc-based anodes are only available in Germany while several but not all countries prohibit the use of antifouling or other toxic paints. Incompleteness and differences may also be related to a lack of information on substances and their environmental effects. To achieve harmonization and more efficient protection of the marine environment, more data and minimum requirements on a regional level will be necessary, while at the same time, innovation may not be hampered and design and techniques should be further optimized and adapted based on latest available information.

The issue of waste generation is becoming increasingly pressing in numerous nations worldwide, especially in urban regions experiencing rapid population increase and industrialization. Diseases can spread, land can become degraded, and air and water pollution can all be caused by improper garbage disposal. Additionally, the production of waste uses a lot of energy and natural resources, which depletes vital resources and increases emissions of greenhouse gases. To mitigate these concerns, sustainable waste management strategies that reduce trash generation and promote source reuse and recycling are needed. Among these is the development of novel technologies for environmental bioremediation and protection, building materials, and the application is Microbial Induced Calcite Precipitation (MICP). This is an efficient way to turn waste into useful and long-lasting applications. Utilizing bacteria and chemical reagents in conjunction with biotic processes to generate mineral bicarbonate, MICP is an eco-friendly method. The material has the potential to be a sustainable, economical, and energy-efficient solution to technical and environmental problems. Recent research has shown that waste may be used in place of several MICP chemical modules that are present in the cementation reagents (urea and calcium source) and the medium used to cultivate microorganisms. Additionally, it has been established that the MICP is a sustainable and economically viable technology that works with a variety of waste media. With a focus on the role of recyclable waste, this in-depth review study attempts to give a full grasp of the engineering applications and environmental benefits of MICP technology. It also provides academic indications on how to recognize and address possible complexities when using recyclable waste sources for the use of the MICP technique.

Oceans are increasingly shaped by climate change, biodiversity loss, geopolitical tensions and maritime crime and insecurity. Climate-smart marine spatial planning (CSMSP) has emerged as a governance framework to integrate climate action, conservation, and equity into ocean planning. However, defense institutions–key actors in maritime security and major greenhouse gas emitters–remain absent from CSMSP discourse. This paper argues that integrating defense into CSMSP offers strategic and climate benefits: minimizing defense-driven offshore wind cancellation and thus accelerating approval, safeguarding environmental protection and undersea critical energy infrastructure, accelerating decarbonization through the military's green transition, and addressing the defense emissions gap. Yet, integration carries significant risks: power asymmetries, spatial exclusion, ecological harm, and militarization of green energy. To reconcile security imperatives with sustainability, transparency and equity, the paper proposes governance pathways: transparent data sharing, conflict-resolution and co-existence protocols, and defense marine zoning. “However, the paper warns that while green defense initiatives have climate benefits, there are reasons to curb our enthusiasm”. Specifically, rising global military spending and the resultant mineral-intensive extractivism to support war-readiness threaten to overshadow these benefits by locking in carbon-heavy supply chains and amplifying upstream emissions, environmental degradation, and social disposability. Aligning national security with climate security thus requires more than technological greening: it requires transparent cradle-to-grave emissions, and strategic restraint in defense spending, war-readiness, and material efficiency. Ultimately, integrating defense into CSMSP is not merely a technical exercise but a normative challenge that will determine whether ocean governance advances sustainability and transparency or succumbs to securitized emissions and green extractivism.

The development of low-cost, stable, and effective fluorescent materials for detecting carcinogenic water contaminants and antibiotic drugs is a significant step toward protecting the environment and public health. In this work, we have prepared CDs using simple precursors, tartaric acid and di-aminopropane, via a facile, fast, one-step microwave-assisted method in 4 minutes. The as-prepared CDs were thoroughly investigated using sophisticated analytical techniques, including UV-Vis, PL lifetime, HR-TEM, and XRD. The exciting fluorescent excitation-dependent and independent character was revealed by photoluminescence, XPS, and FT-IR measurements, and it was found that CDs were made with a uniform core and an electron-rich surface functional group. Also, the prepared CDs exhibit greater stability in various environmental conditions. Furthermore, the core fluorescent character of CDs was effectively employed to detect Cr6+ and doxycycline, with lower detection limits of 0.14 and 0.09 µM, among the various metal cation and antibiotic groups. Additionally, it retains its sensitivity in the presence of multiple co-existing metal cations and antibiotics individually. In addition to environmental protection, we have utilized CDs for the secure transport of information via fluorescent ink and anti-counterfeiting security features. This present work displays the multifunctional ability of CDs, that can serve as a potential sensor for toxic metal ions and antibiotics in water based environments and also an excellent information encryptor for secure information transportation.

With the depletion of natural sand and gravel resources and increasing emphasis on environmental protection, natural aggregates suitable for concrete production are becoming increasingly scarce. Steel slag, a by-product of steelmaking, is produced in substantial quantities yet remains underutilized due to its low recycling rate. Owing to the high strength and excellent compatibility of steel slag particles with cementitious materials, they demonstrate significant potential as a replacement for natural river sand in fine aggregate applications. However, the volumetric instability of steel slag has long been a major impediment to its widespread adoption in cement-based composites. This study examines the stability performance of cement mortar containing steel slag aggregate, with the objective of clarifying the mechanisms responsible for dimensional instability resulting from steel slag incorporation. When the replacement level exceeds 40%, the dimensional stability of the mortar deteriorates markedly. The initial contents of free CaO (f-CaO) and free MgO (f-MgO) in the steel slag were determined to be 1.58% and 1.14%, respectively. Following 50 h of hydrothermal treatment, 69.6% of f-CaO and 44.3% of f-MgO had hydrated, causing internal volumetric expansion and subsequent particle fracturing. Under elevated temperature conditions, over-burned lime demonstrated 220% volumetric expansion and completed its reaction within 40 min, consequently impairing early-age stability. In contrast, periclase (dead-burned MgO) exhibited 34% expansion and attained a reaction degree of merely 13.3%, suggesting a more substantial impact on long-term stability. For each mixture, linear expansion measurements were performed on n = 5 independent specimens, and results are reported as mean ± standard deviation.

Driven by demands for energy efficiency, environmental protection and vehicle performance, aerodynamic optimization of automotive exterior shapes has become a core focus in the automotive industry. This review comprehensively summarizes its progress, methods and trends. It expounds basic aerodynamic characteristics, including fluid mechanics principles (Bernoulli's equation, boundary layer). main aerodynamic elements such as lift force, drag force, drag coefficient and their impacts on fuel economy. In aiming of handling stability and comfort. The paper outlines traditional methods such as streamline design evolution. Modern techno involves information technology such as CFD and Wind Tunnel simulation. Then development of algorithms makes new breakthrough. The new trends, by introducing advanced technologies like parametric modeling, machine learning and AI with neutral network(PINN, etc). Reviewing on evolution in fluid optimization tells future trends,challenges(EV design freedom, potential demands) and autonomous driving (sensor integration), as well as the need for multi-objective optimization. Continuous innovation in this field is essential to meet the industrys future requirements.

The impact of greenhouse gas (GHG) emission reduction on firm financial performance is increasingly contested in prior research and there remains a lack of agreement regarding this relationship. This highlights the unanswered question of whether environmental protection investment pays off. This study investigates the association between greenhouse gas (GHG) emission and firm financial performance of 58 high-polluting companies listed on the Johannesburg Stock Exchange (JSE). The study employed a two-step system generalized method of moment (SGMM) to analyse the relationship between GHG emissions and firm financial performance. Our study reports no statistical association between greenhouse gas (GHG) emission reduction initiatives and firm financial performance of high-polluting companies listed on the Johannesburg Stock Exchange. This paper recommends that firms in high polluting intensify carbon emission reduction initiatives as a long-term investment that can improve competitive advantage and resilience to greenhouse gas emission related risks. We suggest that tax incentives and supportive regulatory mechanisms to offset the short-term financial costs associated with adoption of carbon emission reduction strategies to align firm pollution abatement practices with sustainable development goals.

Abstract Precast building structures have been extensively used in civil engineering due to their advantages of energy conservation and environmental protection, controllable quality, and rapid construction. Precast reinforced concrete (PRC) slabs, as the main load‐bearing components in precast structures, are vulnerable to gas explosions and terrorist attacks. Consequently, it is essential to study the dynamic response of PRC under explosions with the aim of improving the blast resistance. In this study, firstly, an explosion database for PRC slabs was established. Eight parameters related to size, material, and explosion load were selected as input features to predict the maximum displacement of PRC slabs subjected to blast loads. Then, a displacement prediction model was established using two single models and two ensemble machine learning (ML) algorithms and verified by performance evaluation indexes and experimental results in literature. Finally, the causes of errors are analyzed, followed by a discussion on the improvement and application of the model. The results show that the ML model can predict the maximum displacement of PRC slabs under blast load. It is indicated that the extreme gradient boosting algorithm has higher accuracy and computational efficiency with an R 2 of 0.975 compared with existing methods. Furthermore, the ML algorithms have potential applications in predicting damage to the PRC slabs for blast engineering.

Association of Lead in Drinking Water With Head and Neck Cancer in the United States.

The idea of rewarding individuals and institutions for environmentally responsible behavior has evolved gradually alongside global concerns over environmental degradation. Green Credit Programs represent a policy innovation that links ecological conservation with economic incentives. This paper traces the historical development of green credit concepts, examining their roots in early environmental movements, economic theories of incentives, and evolving models of sustainable governance. It highlights how green credit programs emerged as tools to balance development with environmental protection and discusses their growing relevance in contemporary climate policy frameworks.

This study examines the evolving competencies required for quality engineers in contemporary industry. A non-anonymous questionnaire was distributed among members of the Israeli Society for Quality. Since respondents provided personal and professional details, the verified group of professionals was treated as an expert panel representing recognized knowledge and experience in the field. The questionnaire included items on routine competencies (e.g. statistics, auditing, communication) and emerging ones (e.g. risk management, big data analytics, sustainability). Data were analyzed using statistical tests, reporting the relative importance and significance of different competencies. Findings show that quality professionals assign the highest importance to interpersonal and quality management skills, while traditional technical competencies are ranked lower. Among emerging competencies, risk management was identified as the most critical, surpassing digitalization and environmental protection. Overall, the results highlight a gap between academic preparation, which still emphasizes technical expertise, and practical industry requirements for hybrid profiles that combine managerial, interpersonal, and resilience-oriented competencies. The study suggests that curricula should rebalance technical training with greater focus on quality management and risk management competencies to better align graduates with market needs.

ABSTRACT This study aims to examine the role of artificial intelligence (AI) adoption in fostering green innovation and green entrepreneurship in Algeria during the period 2018–2024, within the broader framework of the country’s transition toward a sustainable green economy. The research follows an analytical statistical methodology, combining a theoretical review of the green economy and AI with an empirical analysis of Algerian environmental and economic indicators. The dataset includes greenhouse gas emissions, emissions intensity, renewable energy deployment (solar, wind, and off-grid systems), public spending on environmental protection, and green investment flows. These indicators were assessed in relation to the integration of AI technologies as enablers of innovation and entrepreneurship in Algeria’s green transition. The findings reveal that Algeria has made significant progress in renewable energy projects, particularly solar and wind power, while achieving a relative decline in emissions intensity and improvements in environmental performance. Moreover, the study highlights that the adoption of AI has contributed to enhanced resource management, accelerated innovation processes, and the promotion of green entrepreneurial models. This indicates that AI is becoming a key driver in Algeria’s shift toward a low-carbon economy and in strengthening the role of the private sector in sustainable development.

Green total factor productivity (GTFP), as an important indicator considering both economic development and environmental protection, has prompted countries around the world to actively explore ways to improve it in the context of the global transition to a green economy. The Low-Carbon City Policy (LCCP) implemented by the Chinese government, along with the National Big Data Comprehensive Pilot Zone Policy (NBDCPZ), which serve as key carriers of green regulation and digital innovation, respectively, play an important role in improving green total factor productivity (GTFP) and achieving high-quality economic development. This study aims to deeply explore whether there is a collaborative enabling effect of the Low-Carbon City Policy (LCCP) and the National Big Data Comprehensive Pilot Zone Policy (NBDCPZ) on green total factor productivity (GTFP) and to reveal the internal mechanism by which they improve GTFP through green technological innovation and industrial agglomeration. Specifically, based on the panel data of 269 prefecture-level cities in China from 2006 to 2022, a “dual-pilot” policy is constructed through LCCP and NBDCPZ, and a multi-period difference-in-differences model (DID) is used to evaluate the collaborative effect of the “dual-pilot” policy on GTFP. The results show that the “dual-pilot” policy has a significant collaborative effect on green total factor productivity (GTFP), and its enabling effect is more obvious than that of the “single-pilot” policy. These conclusions still hold after a series of endogeneity and robustness tests. Mechanism analysis shows that the “dual-pilot” policy can also improve green total factor productivity (GTFP) through green technological innovation and industrial agglomeration. Heterogeneity analysis reveals that the collaborative enabling effect of the “dual-pilot” policy is influenced by geographical location and population density. Specifically, the “dual-pilot” policy significantly promotes green total factor productivity (GTFP) in coastal cities and those with high population density. These research results provide a scientific basis for formulating green development policies in China and other countries, as well as a direction for subsequent research on the collaborative enabling effect of multiple policies.

Reliable projections of future surface ozone are crucial for air quality management and health risk assessment. However, potential biases in spatial distribution, magnitude, and trends in ozone simulated by global chemistry-climate models limit their applicability in regional evaluations. In this study, LightGBM, a machine learning (ML) algorithm, is applied to correct biases in CESM2-simulated ozone over China, the United States, and Europe and to calibrate future projections under two Shared Socioeconomic Pathways (SSP1-2.6 and SSP5-8.5) from 2020 to 2060. The ML-based correction significantly improves spatial distribution and reduces bias by 40 to 60%, also reversing the potentially incorrect trend under SSP1-2.6 in eastern China. When the ML-based correction is applied to CESM2 projections, the warm-season mean ozone shows substantial changes from 2020 to 2060. Under SSP1-2.6, corrected ozone decreases by 13.5, 17.9, and 13.7 μg/m3 in China, the United States, and Europe, respectively. In contrast, under SSP5-8.5, ozone increases over the same period by 9.4, 2.0, and 5.2 μg/m3 in these regions. The decomposition analysis shows that anthropogenic emission changes dominate future ozone trends, while a strong climate penalty occurs in polluted eastern China and climate benefits are found in western China, the United States, and Europe under SSP5-8.5. These findings demonstrate the value of combining ML with chemistry-climate models to produce more accurate air quality projections, indicating more effective and region-specific environmental protection strategies.

This paper explores the potential of Artificial Intelligence (AI) as a transformative tool to support the achievement of the Sustainable Development Goals (SDGs). It presents a theoretical and analytical framework linking AI technologies to the three core dimensions of sustainable development: economic growth, social inclusion, and environmental protection. Drawing on recent literature (2020–2024) and global case studies, the paper analyzes practical applications of AI in key sectors such as health, environment, education, and governance. It identifies critical challenges—technical, ethical, institutional, and educational—that hinder AI adoption, particularly in developing countries. Finally, the paper proposes an integrated model to localize AI within national development strategies, emphasizing digital governance, capacity building, inclusive innovation, and strategic partnerships. The study concludes that responsible and inclusive deployment of AI can significantly accelerate progress towards SDGs by 2030, provided that ethical, infrastructural, and policy-related gaps are addressed. Keywords: Artificial Intelligence, Sustainable Development Goals, Digital Transformation, Ethical AI, Public Policy, Developing Countries.

The Vietnamese Mekong Delta (VMD) is an internationally significant region for agricultural production and biodiversity, yet it faces escalating challenges to its water resources. Total Suspended Solids (TSS) represent a critical water quality parameter within this complex deltaic system, with profound implications for its ecological integrity and socio-economic activities. TSS in the VMD originate from a combination of natural processes, such as riverbank erosion and sediment resuspension and increasingly dominant anthropogenic activities, including intensive agriculture, aquaculture, urban and industrial wastewater discharge, upstream hydropower dam development leading to sediment trapping and localized sand mining. Elevated TSS concentrations and alterations in sediment composition directly degrade water quality by increasing turbidity, reducing light penetration essential for aquatic photosynthesis and acting as a transport mechanism for nutrients and various contaminants, including heavy metals and pesticides. These TSS-induced changes in water quality have substantial adverse consequences for key water uses. Agricultural productivity is affected through impacts on irrigation infrastructure and alterations to soil fertility due to reduced beneficial sediment deposition. Aquaculture operations face challenges related to fish and shrimp health, pond management and overall productivity. The suitability of surface water for domestic supply is compromised, necessitating more complex and costly treatment processes and posing potential health risks. Furthermore, TSS contributes to the degradation of aquatic ecosystem health by altering habitats, affecting biodiversity and disrupting fundamental ecological processes. The cumulative effect of these impacts, particularly the long-term reduction in sediment delivery to the delta, also threatens the VMD's geomorphological stability in the face of land subsidence and sea-level rise. Understanding and effectively managing TSS loads and their sources are therefore paramount for the sustainable development and environmental protection of the VMD.

Melt-based porous material and PMMA as an eco-friendly tool for the visual detection of cationic dyes in environmental fish samples.

Environmental Protection: An Important Contemporary Challenge (Case of the European Parliament)

Background: Hylocereus Undatus Extract (HUE) It is obtained from the fruit of the white dragon fruit cactus and is rich in antioxidants and vitamin C, Among its benefits are providing environmental protection and reducing the appearance of wrinkles. Methods: Forty female albino mice were divided for fifteen days into four groups: G1: Control , G2: HUE group with a dose of 200 mg/kg/body weight, G3: EAC group, G4:.EAC + HUE Groub, respectively. Result: Our results from the third group showed that mice treated with cancer cells experienced a significant increase in the weight of mice, a increase in both ALT, AST, ALP and a decrease in both Albumin,total protein, as well as damage to liver tissue. When animals infected with cancer cells were treated with an extract of HUE, we observed an increase in the weight of mice, a decrease in both ALT, AST, ALP and an increase in both Albumin,total protein, as well as an improvement in liver tissue.

Background:The United States Environmental Protection Agency-designated Superfund sites are a source of potential exposure to harmful contaminants, including endocrine-disrupting chemicals and carcinogens, that may be relevant to breast cancer risk. However, there is limited research on the possible association between residential proximity to Superfund sites and the risk of breast cancer.Methods:Using data from the prospective US-wide Sister Study cohort (N = 49,594), geocoded enrollment addresses were linked to active United States Environmental Protection Agency Superfund sites to determine residential proximity to sites. Proximity metrics included residing within 3-, 5-, and 10-miles of Superfund sites. Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the relationship between residential proximity to Superfund sites and breast cancer incidence. We further evaluated whether associations for residential proximity and breast cancer incidence varied according to individual contaminants and latent classes that grouped Superfund sites based on the mixture of contaminants present at the site.Results:Over an average of 12 years of follow-up, 4,565 breast cancer cases were identified. Overall, there was little evidence to support that living within three miles of a single Superfund site was strongly related to breast cancer incidence. However, living within three miles of two or more sites was associated with a 20% higher breast cancer incidence (HR = 1.20; 95% CI = 1.00, 1.45). There was little evidence of an association by contaminant latent classes, although when considering individual contaminants, we observed a possible association with proximity to sites containing organophosphates (HR = 1.33, 95% CI = 0.90, 1.96).Conclusion:In this large, US-wide cohort, no overall pattern of association between residential proximity to Superfund sites and breast cancer incidence was found. However, elevated HRs were observed for living near two or more sites or sites that are contaminated with organophosphates.