Mosses have been neglected as a study subject for a long time. Recent research shows that mosses contain remarkable and unique substances with high biological activity. The aim of this study, accordingly, was to analyze the composition of mosses and to screen their antimicrobial and anticancer activity. The total concentration of polyphenols and carbohydrates, the amount of dry residue and the radical scavenging activity were determined for a preliminary evaluation of the chemical composition of moss extracts. In order to analyze and identify the substances present in mosses, two types of extrahents (chloroform, ethanol) and the GC/MS and LC-TOF-MS methods were used. The antimicrobial activity was tested on four bacteria strains, and the anticancer activity on six cancer cell lines. The obtained results show the presence of a high number of primary (fatty acids and amino acids), but mainly secondary metabolites in moss extracts—including, sterols, terpenoids, polyphenols and others—and a high activity with respect to the studied test organisms.

The study of the relationship between human economic activity and the state of the environment in recent decades is reflected, among others, in the study of the environmental Kuznets curve (EKC) hypothesis. Numerous attempts have been made to examine the existence of the EKC by correlating various measures of environmental devastation with GDP per capita indicators. In this study, the aim and research gap were to compare and confirm/exclude the obtained results with the studies previously conducted by the authors, which referred to the analysis of the EKC in relation to general GHG emissions. In this analysis, GHG emissions per capita were assumed. In recent years, in the EU countries, more and more attention has been paid to research on the EKC focused on GHG emissions, and a certain research gap has been noticed in the direction of EKC analyses for PM_2.5 and PM₁₀. In the context of PM emissions, the very negative impact on human health should be emphasized. The authors decided to analyze the EKC hypothesis based on the current available data also in relation to PM emissions. In this study, a group of socio-economic variables in the form of GDP, gross electricity production, passenger cars, and population were used in relation to GHG, PM_2.5, and PM₁₀ emissions in the EU-27 countries. The study used multiple regression analysis to test the direction of the relationship between pollutant emissions and GDP. In the case of Germany, a negative correlation was obtained for GHG, PM_2.5, PM₁₀ emissions, and GDP. The EKC approach can be successfully used both in system awareness (qualitative) and quantitative studies to prepare scenarios of changes in greenhouse gas and PM emissions and to create strategic planning, manage resources, promote innovation, and in climate policy.

Concentrations of air pollutants mainly depend on meteorological conditions and pollutant emission levels. Airborne particulate matter (PM_2.5 and PM₁₀) concentrations were measured in Riga (Latvia) from the end of April to the end of December 2007. The collected particulate matter was analyzed for the anion, cation and heavy metal content. The seasonal variations of particulate matter concentrations were investigated, together with their relationships with meteorological parameters and other pollutant gases. The maximum daily concentration of PM_2.5 and PM₁₀ reached 52.5 μg/m³ in June and 83.4 μg/m³ in December, respectively.

Corporate sustainability reporting has become a central component of corporate governance under the European Sustainability Reporting Standards (ESRS); yet organizations continue to face challenges in integrating sustainability data into decision-making processes. This study addresses this gap by developing a system dynamics (SD) model for analyzing corporate sustainability performance based on ESG indicators. The proposed framework integrates environmental, social, and governance variables into a unified dynamic structure, capturing feedback loops, time delays, and interdependencies among sustainability dimensions. The model is empirically tested using a ten-year dataset from a fuel retail company, enabling both historical validation and scenario-based analysis. The results demonstrate that the model successfully reproduces observed trends in resource efficiency, safety performance, and social indicators while highlighting trade-offs associated with business growth, particularly in energy consumption and CO₂ emissions. Sensitivity analysis demonstrates that the sustainability index remains stable under uncertainty in normalized ESG indicator values. Scenario simulations indicate that several sustainability targets for 2030 are achievable under current development trajectories; however, maintaining progress requires continuous investment and organizational commitment. The study contributes to sustainability research by integrating ESRS-based indicators into a dynamic modeling framework and provides a practical decision-support tool for corporate sustainability management, enabling more informed planning, performance evaluation, and strategic alignment with long-term sustainability goals.

The correlation between estimated national GHG emissions and uncertainty is generally known. The causes and sources of the uncertainties are diverse and relate to source activity (field studies and research, census data), methodologies, variations in emission factors, and scientific studies/publications. Uncertainty has occurred due to a lack of knowledge of true values, in which uncertainty is assessed by the probability density function. Uncertainty analysis helps identify and prioritize activities (monitoring, inventory, evaluation methods, etc.) to improve the evaluation and reduce uncertainty. A quantitative uncertainty analysis is often performed for a 95% confidence interval. Different calculation methodologies are used in each sector (waste, energy, LULUCF, industry, transport, and agriculture); the mechanisms for producing emissions of emitted substances are complex and variable and require scientifically based research to update them. Regional differences are also essential, as climate, access to technologies, the possibility of introducing them, and other physio-geographic conditions have a significant impact. One of the challenging issues in the GHG emissions assessment relates to future emission projections related to future unpredictability due to climate change; changes in economic growth plans also create a lot of uncertainty. For example, in the forest management and land use sectors, the intensity of CO2 sequestration in the ecosystem must be assessed. Recent studies, including those informing Latvia's LULUCF emission factors, reveal significant uncertainties in estimating GHG emissions from organic-rich soils due to short-term measurements, limited sampling, and neglect of long-term soil carbon dynamics. Another relatively more straightforward source of data uncertainty is identified in the waste management sector. In this sector, the analysis of methane emissions from landfills from disposed solid municipal waste requires a precise morphological composition of the waste, as the result of the calculation depends not only on the amount of waste but also on the content of organic matter and the intensity of aerobic or anaerobic degradation. It is self-evident that the composition of waste can be variable and monitored effectively today. Still, different waste fractions are characterized by different degradation intensities, and according to the assessment method, degradation should be assessed over a period of 100 years, which means that the historical morphological composition of waste is required. The lack of such data often leads to up to 150 % uncertainty. When analyzing the national reports of the Northeastern Region of Europe (Latvia, Finland, Estonia, and Lithuania), the most considerable uncertainties can be found in the LULUCF sector, which, in view of these countries' economic activities, is very substantial in the overall assessment. Reducing uncertainty in this area is of the utmost importance as it is closely linked to national climate plans and the measures taken to achieve climate neutrality.

Photometric measurement parameters such as Single Scattering Albedo (SSA), Aerosol Optical Depth (AOD), and Angstrom Exponent (AE) can be used for aerosol classification and source identification using artificial intelligence mechanisms or simplified classification trees. Sometimes these methods are difficult to apply in incomplete datasets or when using vaguely defined classification algorithms that provide ambiguous responses about their origin. In such cases, the origin of atmospheric aerosols may be identified using air-mass trajectory models of atmospheric pollution. This study uses a NOAA HYSPLIT model, which, in addition to atmospheric photometric properties, significantly improves the analysis of results in discutable situations and may, by its nature, even replace the measurement of aerosol chemical composition when profiling sources is performed. The experimental part was developed using the AERONET dataset for Lampedusa (Italy) for the period 2020-2024.