Nicolaus Copernicus University

Drought is an increasing concern in Morocco’s semi-arid regions, where water resources are under growing pressure from climate variability and land degradation. Most previous studies on drought monitoring in Morocco have examined meteorological or hydrological dimensions separately, overlooking vegetation dynamics. This fragmented approach limits the ability to capture the full spectrum of drought impacts across natural and managed ecosystems. This study assesses drought dynamics in the Ahmed El Hansali Dam basin, located in the Upper Oum Er-Rbia watershed, during 1975–2023. The novelty of this research lies in the long-term, combined assessment of meteorological, hydrological, and remote sensing indices at the basin scale, providing one of the most comprehensive drought evaluations for Morocco’s inland watersheds. A multi-index approach combining Standardized Precipitation Index (SPI), Streamflow Drought Index (SDI), and Normalized Difference Vegetation Index (NDVI) was applied to assess meteorological, hydrological, and vegetation responses to drought. Meteorological data from five stations, hydrological inflows at the dam, and Landsat-derived NDVI composites were utilized. The SPI and SDI revealed a marked increase in drought frequency and severity beginning in the early 1990s, with the most critical period between 2020 and 2023. SPI values below − 1.5 and SDI near − 1.6 confirmed intensifying precipitation deficits and declining inflows. The NDVI showed moderate interannual variability with lower values during drought years, though weak correlation with SPI and SDI suggests that vegetation response is influenced by altitude, land cover, and soil water retention, and agricultural practices including irrigation. Spatial analyses highlighted the greater resilience of forested highlands compared to lower-elevation agricultural areas. Overall, the combined use of these three indices offers a robust picture of drought evolution in the basin. By bridging the gap between single-dimension studies and proposing a comprehensive multi-source monitoring framework, this work provides a more holistic understanding of drought impacts and delivers actionable insights for water resource managers and policymakers.

Air quality improvement represents a critical challenge for the European Union, with particulate matter (PM) being the most harmful pollutant in urban areas. Urban Green Infrastructures (UGIs) provide essential ecosystem services that mitigate air pollution, notably through PM10 removal via deposition on leaf surfaces, reducing health risks associated with poor air quality. This study quantifies the PM10 removal supplied by urban forests in the Bydgoszcz-Toru & nacute; area (Poland) using a spatially explicit modeling framework. Remotely sensed Leaf Area Index, vegetation cover, and PM10 concentration data were integrated within a GIS environment, with all analyses conducted on a seasonal basis to capture temporal variability in vegetation phenology and pollutant levels. Resulting maps of mean seasonal PM10 removal efficiency (kg/ha) reveal distinct functional group patterns: deciduous broadleaves reach peak efficiency in summer, whereas conifers provide a more consistent year-round contribution, resulting in the highest annual removal. Monetary valuation was performed using externality costs from the European Environmental Agency. Overall, urban forests remove 3360.40 Mg of PM10 annually, corresponding to an estimated value of 255.69 M & euro;. Integrating biophysical and economic perspectives supports urban planning and highlights UGIs as nature-based solutions to enhance air quality, protect public health and promote ecosystem biodiversity and resilience.

Municipal sewage sludge is an environmental liability but also an energy-rich biomass that can support circular economy resource recovery. Here, we benchmark thin-layer drying of dewatered municipal sewage sludge (sludge cake) (40 g; layer thickness <= 5 mm) under open-air, convective hot air (40-150 degrees C), and microwave (70-1200 W) conditions to quantify drying kinetics, hygienisation indicators, and a screening-level energy-use proxy. High-power microwave drying reduced the time to constant mass from 32 h (open air) and 25 h 05 min (40 degrees C convection) to 20 min (900 W) and 14 min 05 s (1200 W). Faecal indicators (total/thermotolerant coliforms and presumptive Escherichia coli) were below detection after >= 100 degrees C convection or >= 300 W microwave treatment, while mesophilic aerobes and sulfite-reducing Clostridium spp. decreased by similar to 3-4 log10 with increasing exposure. A dragonfly-optimised epsilon-support vector regression model (DA-SVR) predicted drying trajectories across modes (overall RMSE approximate to 0.79 percentage points; held-out RMSE approximate to 1.47; R-2 >= 0.99). Overall, microwave thin-layer drying coupled with DA-SVR decision support enables constraint-based screening of sewage-sludge conditioning windows for logistics and thermal valorisation pathways; the framework can be extended to incorporate additional analytical endpoints where available.

The article aims to examine how the ruling elite of Russia has solved the fundamental dilemma of the policy towards war emigration: between the desire to have as many resources as possible for waging war, including a large number of young and educated people, and the fear of the survival in Russia of the social base for the anti-systemic opposition. An analysis of statements by members of the Security Council about emigration was conducted to determine whether pragmatic thinking, characteristic of autocratic political myths, prevailed, and fundamentalist or typical of totalitarian political gnosis. A substantial part of the statements can be classified as the myth of conspiracy. Only a few statements by Putin can be treated as pragmatic. The entire Russian ruling elite treats the vast majority of emigrants with varying levels of refutation and pejoratization as a component of the widely understood hostile West.