Odor and volatile organic compound (VOC) pollution were characterized through a 15-month mobile monitoring campaign (January 2024–March 2025) in the Tuoketuo Industrial Park, Hohhot, Inner Mongolia, an area experiencing frequent odor complaints. A portable multi-sensor odor analyzer was deployed across ambient routes, Priority Monitoring Zones (PMZs), and 52 Priority Regulated Enterprises (PREs), covering a total distance of 4072 km. The monitoring results indicated that mean odor unit (OU, dimensionless) values were higher in PMZs at night (10.2) than during the day (8.3) and on ambient routes (7.08). Seasonal peaks occurred in summer (July–August) and winter (December), corresponding to periods of low wind speeds and temperature inversions. Spatial analysis identified recurrent odor hotspots along J Avenue (night-time anomaly probability 65 %) and at the H Street–F Road intersection (48 %). Ammonia (NH<inf>3</inf>; peak 10 mg m−3), hydrogen sulfide (H<inf>2</inf>S; peak 7.85 mg m−3), and trimethylamine (TMA) were the dominant odorants, associated mainly with biopharmaceutical, coal-chemical, corn-processing and wastewater treatment activities. Although total VOC (TVOC) concentrations were low (0.11–0.15 mg m−3), odor activity values (OAVs) highlighted TMA and methanethiol (MM) as key contributors to perceived odor impact. These findings support targeted night-time enforcement in high-risk corridors and optimization of waste-gas treatment, and motivate the adoption of OAV-based odor management beyond mass-based TVOC metrics

This study investigates how artificial intelligence (AI), when deployed under sustainability-oriented policy regimes (e.g., the EU AI Act, CSRD, and the U.S. SEC climate disclosure rule), is catalyzing a shift in corporate governance toward stakeholder accountability. Using a curated corpus of seven open-access regulatory and policy texts, we apply a triangulated approach, corpus linguistics (AntConc) and semantic network analysis (InfraNodus), to map how disclosure, risk, assurance, and stakeholder terms structure the discourse. Robustness checks across three stopword specifications (Spec A/B/C) and phrase-level evidence (N-grams/KWIC) corroborate the centrality of disclosure/report/assurance and the conditional peripherality of transparency/accountability. We propose the AI-Policy-Governance Nexus, a conceptual model explaining how regulatory pressure and AI integration reconfigure governance practices beyond compliance. The findings inform strategy, policy design, and future empirical work on AI-enabled ESG systems

Near-infrared spectroscopy (NIRS) is a powerful tool for the rapid identification and quantitative analysis of dairy farm slurry components. Temperature variations significantly influence on the spectra measurements while their underlying spectral response mechanisms remain insufficiently understood. This study investigates the impact of temperature variations on the spectral characteristics of slurry to ascertain the availability of improving the NIRS-based analysis. Using a portable detection device of slurry nutrients, near-infrared diffuse reflectance spectra were acquired from slurry samples across temperatures ranging from 0 to 40 °C. Principal component analysis (PCA) and two-dimensional correlation spectroscopy (2D-COS) were applied to assess spectral response intensity, temporal sequence and correlations among functional groups. The results indicate that increasing temperature weakens hydrogen bonding strength, leading to intensity changes. In particular, hydrogen-bonded water partially dissociates, enhancing features associated with free O[sbnd]H groups and indicating temperature-induced changes in intermolecular interactions. Additionally, elevated temperatures increase the susceptibility of specific absorption bands ((N[sbnd]H), and (O[sbnd]H)) to shift, affecting the stability of slurry composition predictions. 2D-COS analysis reveals the sequential evolution of absorption band changes of specific functional groups under thermal perturbation, offering insight into dynamic transformations. While 2D-COS has been previously applied to various agricultural materials, this study extends its application to temperature-induced spectral analysis of dairy farm slurry, addressing a specific challenge in NIRS-based nutrient analysis where conventional temperature correction approaches have shown limitations. By identifying temperature-sensitive spectral regions and molecular transformations, this study provides a scientific basis for developing temperature-resilient NIRS models, which are crucial for facilitating real-time nutrient monitoring and precision slurry management on dairy farms

CiNURGi integrates perspectives from business owners, wastewatertreatment operators, policymakers, research institutes, and privateindustries across the Baltic Sea Region. By collaborating with such a diversestakeholder base, the project ensures its efforts are scientifically robust,practical, inclusive, and adaptable. This multifaceted approach enablesCiNURGi to address critical environmental challenges while fosteringregional cooperation. By focusing on circular nutrient systems, the projectfacilitates the recovery of valuable resources such as phosphorus andnitrogen from wastewater sludge and promotes practices like biocharproduction to reduce dependency on synthetic fertilizers. Moreover,cross-sector collaboration bridges agriculture, wastewater management,and policy, creating synergies that amplify the project’s regional and long-term impact.

Antibiotic resistance genes (ARGs) in livestock wastewater resulting from excessive antibiotics used in animal farming pose significant environmental and public health risks. Conventional treatment methods are often costly, inefficient, and may inadvertently promote ARG transmission. Microalgae, with their long genetic distance from bacteria and strong ability to utilize wastewater nutrients, offer a sustainable solution for ARG mitigation. This review studied the abundance and characterization of ARGs in livestock wastewater, highlighted microalgal-based removal mechanisms of ARGs, including phagocytosis, competition, and absorption by extracellular polymeric substances (EPS), and explored factors influencing their efficacy. Notably, the microalgae-EPS system reduced ARGs by 0.62–3.00 log, demonstrating significant potential in wastewater treatment. Key challenges, such as optimizing algal species, understanding EPS-ARG interactions, targeted reduction of host bacteria, and scaling technologies, were discussed. This work provides critical insights for advancing microalgal-based strategies for ARG removal, promoting environmentally friendly and efficient wastewater management.

European Union (EU) water governance operates through structured regulatory discourse that constructs meanings around water quality, risk, and circularity. These semiotic framings shape how environmental law is implemented, how compliance is defined, and how sustainability transitions are managed. This study applies a triadic semiotic framework of Greimassian semiotics, Social Semiotics, and Ecosemiotics, to analyze 11 foundational EU water directives. Using legal text analysis supported by AntConc software, the study deconstructs how regulatory language encodes categories, assigns agency, and positions ecological processes. The analysis reveals that water quality is primarily framed through rigid binary classifications such as compliant versus non-compliant, while risk is spatialized through threshold-based mapping and delineations of responsibility. Circularity is positioned mainly as an industrial-efficiency paradigm rather than an ecologically embedded process. These framings provide legal clarity and facilitate enforcement, but they also limit flexibility and reduce alignment with ecosystem dynamics. Social semiotic patterns show a consistent privileging of state and industrial actors, often marginalizing local communities and multispecies perspectives. Ecosemiotic analysis suggests that governance models rarely reflect the adaptive and fluid nature of aquatic systems. As a result, regulatory language may hinder ecosystem-based and transboundary approaches to water management. This research demonstrates that semiotic structures play a central role in shaping how environmental governance is operationalized. It argues for increased semiotic flexibility in legal design to better accommodate ecological complexity, institutional diversity, and climate variability. By advancing an interdisciplinary method that links semiotic theory with regulatory studies, this work offers new insights into how legal discourse mediates environmental outcomes in the EU context.

The structure of microalgal extracellular polymeric substances (EPS) plays a key role in influencing the transfer and distribution of antibiotic resistance genes (ARGs) in livestock wastewater. This study investigated how lead stress and gibberellin stimulation affect microalgal growth, biomass composition, photosynthetic pigment content, pollutant removal efficiency, and ARGs dynamics within wastewater and different EPS layers. Low lead concentrations (1 mg/L) stimulated microalgal growth, whereas high concentrations (5 mg/L) inhibited it. Gibberellin alleviated lead-induced stress by increasing chlorophyll content (by 0.384 mg/L) and enhancing polysaccharide levels in both microalgal cells (by 0.895 mg/L) and EPS (by 3.382 mg/L), compared to lead stress alone. Lead exposure reduced the efficiency of nitrogen and COD removal and significantly altered the bacterial community, increasing the relative abundance of Pseudomonadota and redistributing bacteria from tightly bound EPS (TB-EPS) to soluble EPS (S-EPS). These changes were most pronounced in the TB-EPS layer. Additionally, lead stress promoted the proliferation and spread of ARGs, whereas gibberellin mitigated this trend by enhancing microalgal vitality and ARGs removal capacity. The findings offer new insights into optimizing microalgal systems for livestock wastewater treatment and controlling the environmental dissemination of ARGs.

This study examines the adaptation of the Uppsala Model to enhance the management of European Union (EU)-funded projects, particularly focusing on the macro- and micro-foundational elements of the model. The Uppsala Model, originally developed for firm-level internationalization, provides a valuable framework for addressing the complex challenges of EU project implementation, which include bureaucratic hurdles, diverse stakeholder management, and the intricacies of European integration. This paper highlights the persistent issues faced by project managers despite improved application processes and skilled beneficiaries. By applying the Uppsala Model, which emphasizes incremental knowledge development and resource commitment, this study aims to bridge the gap between fund acquisition and project delivery. The integration of both macro- (broad external factors) and micro- (individual and organizational behaviors) perspectives of the Uppsala Model offers a comprehensive approach to managing international, multi-stakeholder EU initiatives. This approach is exemplified through the SuMaNu project, which addresses nutrient recycling and sustainable manure management in the Baltic Sea Region. The findings suggest that the Uppsala Model’s principles can be effectively applied to enhance the execution of complex EU projects by fostering better stakeholder relationships, incremental learning, and adaptive strategies. This study underscores the relevance of experiential learning and network perspectives in achieving successful project outcomes in the EU context

The science of gastronomy is a context-sensitive and qualitative application of human knowledge. While qualitative content analysis is a widely recognized research method, it is time-consuming, and its results heavily depend on the analyst’s expertise. This study addresses these challenges by employing automated content analysis (ACA), including tools like VOSViewer and AntConc, alongside qualitative methods from systemic functional linguistics (SFL). By analyzing 25 interviews with various professionals in gastronomy, the research aims to reduce subjectivity in interpreting data and uncover the essence of being a gastronomic chef. The findings highlight the complexity of a chef’s role, emphasizing creativity, collaboration, and innovation as key factors in delivering exceptional service and crafting the ultimate dining experience. Understanding a chef’s knowledge is crucial for enhancing service quality and comprehending consumer behavior, which is vital for service development.