Underground coal gasification (UCG) is essentially the multi-field coupled thermochemical conversion process. One of the most significant characteristics during the UCG process is the formation of the pronounced oxygen concentration gradient along the gasification channel, which leads to significant zoning characteristics in the underground reaction zone. In this work, the coal reaction behavior driven by oxygen concentration gradient from 0% to 100% (10 levels) is focused. Meanwhile, the numerical model combining reaction kinetics and empirical component distribution is used to compare the gas production performance under different oxygen concentrations. Under the low oxygen concentrations (<21% O-2), the maximum generation rate and content of CO are relatively high, reaching 0.23 mol/(kg & centerdot;K) and 0.75, respectively. In contrast, under the high oxygen concentration (21% similar to 100% O-2), the maximum generation contents of CO2, CH4, and H-2 are higher. As the oxygen concentration increases from 0% to 100%, the apparent activation energy of coal firstly increases from 31.56 to 90.69 kJ/mol, and then decreases exponentially to 20.62 kJ/mol. Meanwhile, compared to the oxygen-lean conditions, the enthalpy change, entropy change, and Gibbs free energy change are lower under the oxygen-enriched conditions. The critical oxygen concentration for the kinetic-thermodynamic parameters of coal is 8%. The gradient distribution of oxygen along the gasification channel is the core driver behind the zonal evolution in UCG. Therefore, in the actual UCG application, the precise regulation of oxygen concentration is essential to optimize syngas composition, improve gasification reaction intensity, and achieve stable evolution of the reaction three zones.
This article introduces the concept of improvised pragmatism of care to analyze how Indigenous communities, living in and around protected areas navigate animal health amidst ecological disruption and limited institutional capacities. Drawing on focus group discussions conducted in 2024 in seven communities of the Great Limpopo Transfrontier Conservation Area in Mozambique, we show how livestock care emerges not as static tradition nor simple adoption of biomedical protocols, but as dynamic practices that combine embodied diagnosis, herbal repertoires, and selective engagement with veterinary interventions. These plural strategies constitute resilient responses to epistemic injustice and ontological displacement in a conservation landscape. Improvised pragmatism of care captures this mode of governance from below: an active reconstitution of knowledge and practice under conditions of uncertainty. Our analysis cautions against treating animal health as an abstract, detachable component independent of human–animal relations or as separable from historical trajectories and contemporary territorial and environmental management policies. By foregrounding community improvisation as both epistemic and political practice, this study contributes to veterinary anthropology and health social sciences, highlighting how Indigenous actors can sustain multispecies health in contested landscapes marked by displacement and inequality.
Africa is experiencing a renewed natural resource boom. Heightened demand for the continent’s vast reserves of critical minerals and metals for green energy and technological applications, as well as its ability to serve oil and gas markets in a time of geopolitical turmoil, have triggered increased international interest and investment in African resource sectors. As in previous commodity booms, resource-rich African governments are articulating a desire to leverage natural resource wealth for economic growth and human development. Yet this promise also brings peril, as natural resource endowments have in the past generally failed to translate into improved living standards and economic growth for most resource-rich African states. Whether “this time will be different” for developmental outcomes will be shaped not just by resource endowments, but critically, by the interplay between global geopolitical dynamics, domestic political institutions, and local governance processes.
Surface water resources in arid regions are increasingly vulnerable to climate variability and growing demand, which calls for accurate, timely, and spatially explicitly monitoring approaches. This study developed a machine learning-based framework for quantifying seasonal and interannual surface water dynamics through multi-sensor fusion approach by combining radar and optical data. Sentinel-1 synthetic aperture radar (SAR), Sentinel-2 multispectral imagery, and Landsat-8 surface reflectance data were processed within the Google Earth Engine (GEE) platform for the period 2015-2021. The study used Random Forest (RF) and Gradient Tree Boosting (GTB) classifier, achieving a remarkable overall classification accuracy from Sentinel-1/2 (OA = 99.34-99.92%; Kappa = 0.8862-0.9983) and Landsat-8 (OA = 99.33-99.76%; Kappa = 0.9859-0.9949). Moreover, positive correlations were observed among water indices, particularly Modified Normalized Difference Water Index (MNDWI) and Normalized Difference Water Index (NDWI), whereas SAR backscatter variables (VV_median and VH_median) indicated a positive correlation with vegetation indices such as Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). Meanwhile, Shapley Additive Explanations (SHAP) revealed that the MNDWI, NDWI, and SAR backscatter metrics were the most influential predictors of surface water occurrence, followed by vegetation indices and lastly single bands (B2-B8). Seasonal analyses presented greater surface water variability in October and November compared to drier months of May to September. Interannual variability analysis was conducted, indicating maximum surface water extent in 2016 (28,869 km2), and minimum in 2019 (9743 km2). Meanwhile, descriptive analysis with hydroclimatic variables showed that variations in precipitation and evapotranspiration (ET) corresponded with observed spatiotemporal patterns of surface water extent except in 2017. Overall, the proposed framework offers a scalable approach for monitoring surface water variability in data-scarce arid regions, supporting water resource management, drought monitoring, and climate adaptation.
Underground coal gasification (UCG) is an innovative extraction method that converts in-situ coal seams into chemical energy. In the UCG process, the gasifying agent interacts with the surrounding high-temperature coal along the gasification channel, leading to the gradual formation of three reaction zones. Meanwhile, the pressure within the gasification chamber is typically maintained slightly above atmospheric pressure with minimal fluctuations to ensure directional transport of the syngas. In this study, the thermal release characteristics of coal under different gas atmospheres (O2, CO2, N2) and pressures (0-8 bar) are investigated based on standard Gaussian distribution functions. Compared with O2 and N2 atmospheres, the CO2 atmosphere exhibits the highest overall reaction activation energy and enthalpy change. Under the O2 atmosphere, the inflection pressure for the apparent activation energy is 2 bar. Under the N2 atmosphere, this inflection pressure is 4 bar. Under the CO2 atmosphere, the apparent activation energy increases logarithmically with pressure from 0 to 8 bar, rising from 554.35 kJ/mol to 1101.98 kJ/mol. With increasing pressure, the cumulative heat release of coal under all three atmospheres exhibits a trend of initially increasing and then decreasing. The dominant reaction pathway and energy requirement of coal vary significantly under different gas atmospheres, while pressure primarily governs reaction intensity and energy demand by modulating gas diffusion behavior and reaction equilibrium. This study provides a theoretical foundation for controlling the structure of reaction zones and optimizing gasifier operating parameters in the UCG process.
