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In this recent study, we analyse the religious diversity of Central and Eastern Europe, from the Balkans up to the Baltic region. This region has many religious confessions, without claiming completeness, Roman Catholic, Reformed, Lutheran, Orthodox, Islam, Hussite and many people without any religion. The recent spatial distribution of the religious confessions has been shaped by different drivers across Central and Eastern Europe. We chose a quantitative method to visually interpret the pluralism of the religious confessions and we selected diversity indices. We calculated the diversity of the religious confessions and ethnicities in a very detailed resolution, at municipality level of each country, based on population census data of 2011. We found statistically significant relationship between the diversity of religious confessions and the diversity of ethnicities. We have also shown that near the national borders, the religious pluralism is higher than in another areas. There is statistically significant connection between the former national borders (1900s and 1930s) and the religiously plural areas. The results of this study provide the evidence of the spatial distribution of borderline syndrome and serves as a good basis for further research (theoretical and statistical) of the religion pluralism in Central-Eastern Europe.

Despite significant progress in recent decades, air pollution remains the leading environmental cause of premature death in Europe. Urban populations are particularly exposed to high concentrations of air pollutants, such as particulate matter smaller than 10 µm (PM10). Understanding the spatiotemporal variations of PM10 is essential for developing effective control strategies. This study aimed to enhance PM10 prediction models by integrating landscape metrics as ecological indicators into our previous models, assessing their significance in monthly average PM10 concentrations, and analyzing their correlations with PM10 air pollution across European urban landscapes during heating (cold) and non-heating (warm) seasons. In our previous research, we only calculated the proportion of land uses (PLANDs), but according to our current research hypothesis, landscape metrics have a significant impact on PM10 air quality. Therefore, we expanded our independent variables by incorporating landscape metrics that capture compositional heterogeneity, including the Shannon diversity index (SHDI), as well as metrics that reflect configurational heterogeneity in urban landscapes, such as the Mean Patch Area (MPA) and Shape Index (SHI). Considering data from 1216 European air quality (AQ) stations, we applied the Random Forest model using cross-validation to discover patterns and complex relationships. Climatological factors, such as monthly average temperature, wind speed, precipitation, and mean sea level air pressure, emerged as key predictors, particularly during the heating season when the impact of temperature on PM10 prediction increased from 5.80% to 22.46% at 3 km. Landscape metrics, including the SHDI, MPA, and SHI, were significantly related to the monthly average PM10 concentration. The SHDI was negatively correlated with PM10 levels, suggesting that heterogeneous landscapes could help mitigate pollution. Our enhanced model achieved an R² of 0.58 in the 1000 m buffer zone and 0.66 in the 3000 m buffer zone, underscoring the utility of these variables in improving PM10 predictions. Our findings suggest that increased urban landscape complexity, smaller patch sizes, and more fragmented land uses associated with PM10 sources such as built-up areas, along with larger and more evenly distributed green spaces, can contribute to the control and reduction of PM10 pollution. [ABSTRACT FROM AUTHOR]

Understanding the comprehensive impacts of environmental factors on soil organic carbon (SOC) and soil inorganic carbon (SIC) in different land use types is of great significance for sustainable soil management. Least absolute shrinkage and selection operator (LASSO) and structural equation modelling were applied to reveal the driving mechanism of SOC, SIC and the ratio between SOC to SIC (SOC/SIC) in three major land use types (forest, grassland and farmland) in a forest-grassland ecotone (FGE) of Inner Mongolia, Northeast China. Mean annual temperature (MAT), mean annual temperature (MAP) and Enhanced Vegetation Index (EVI) were selected by LASSO as the three most important environmental factors affecting SOC, SIC and SOC/SIC in all land use types. MAT had the strongest direct effects on SOC in forest and grassland among all the environmental factors, suggesting temperature was the most important control factor of SOC in forest and grassland. EVI had relatively strong indirect effects on SOC by influencing total nitrogen and total phosphorus in grassland and farmland, respectively. MAT and MAP had significantly direct effects on SIC in all land use types, demonstrating the balance between precipitation and evapotranspiration affected by temperature were the major control factors of SIC. In addition, MAT had significantly positive direct effects (>42%) on SOC/SIC in all land use types, suggesting climate warming can have positive feedbacks on the proportion of SOC in the soil carbon pool in FGE. This study provides a comprehensive understanding of the driving mechanism of SOC, SIC and SOC/SIC in FGE of Northeast China., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)