Coarse Fragments Research Articles

Bulk density and coarse fragment content of the French soil monitoring network for better assessment of changes of soil organic carbon stocks

Bulk density (BD) and coarse fragments (CF) are often missing in soil monitoring networks. In the French Soil Quality Monitoring Network BD and CF have been measured in two campaigns in sites distributed in a grid over mainland France. The objective of this work is to evaluate i) how BD and CF have changed, ii) how volumetric methods and land use affect the observed trends, and iii) to simulate the impact of BD and CF changes on the estimations of changes of soil organic carbon stocks (SOCS). The results showed no significant change of (Δ) CF between campaigns in either the topsoil or subsoil, while there was a significant decrease of BD only in the topsoil (−3.1 ± 0.9 %). When methods were constant in both campaigns, BD decreased in croplands and grasslands topsoil and it did not change in forests. We could explain a rather low part of the ΔBD variance (R2 = 0.21), mostly linked to the within-site variability of CF and to changes in some methodological aspects between campaigns. We simulated changes in topsoil SOCS, assuming SOC content constant over time; we found negative estimates that were the largest in soils with low CF content (−0.2 ± 0.06 % yr−1 an-1) as well as differences between using site-specific average CF values over both campaigns and using site-specific values of each campaign [-0.17 ± 0.04 % yr−1 vs −0.05 ± 0.06 % yr−1, respectively]. These differences were caused by rocky soils, and were not significant when using only sites with low CF content. Our work highlights some limitations in assessing BD changes using broad-scale soil monitoring networks. These findings have important consequences on the methods used to assess changes in SOCS and their uncertainties at broad-scale. They are timely and relevant given the current proposals to implement soil health monitoring at national, continental and global scale.

Cold Consolidation of Pharmaceutical Waste Glass Powders Through Alkali Activation and Binder Jet 3D Printing.

The recent COVID-19 emergency has led to an impressive increase in the production of pharmaceutical vials. This has led to a parallel increase in the amounts of waste glass; manufacturers typically recover material from faulty containers by crushing, giving origin to an unrecyclable fraction. Coarse fragments are effectively reused as feedstock for glass melting; on the contrary, fine powders (<100 microns), contaminated by metal and ceramic particles due to the same crushing operations, are landfilled. Landfilling is also suggested for pharmaceutical containers after medical use. This study aims at proposing new opportunities for the recycling of fine glass particles, according to recent findings concerning alkali activation of pharmaceutical glass, combined with novel processing, i.e., binder jetting printing. It has already been shown that pharmaceutical glass, immersed in low-molarity alkaline solution (not exceeding 2.5 M NaOH), undergoes surface dissolution and hydration; cold consolidation is later achieved, upon drying at 40-60 °C, by a condensation reaction occurring at hydrated layers of adjacent particles. Binder jetting printing does not realize a full liquid immersion of the glass powders, as the attacking solution is selectively sprayed on a powder bed. Here, we discuss the tuning of key parameters, such as the molarity of the attacking solution (from 2.5 to 10 M) and the granulometry of the waste glass, to obtain stable printed blocks. In particular, the stability depends on the formation of bridges between adjacent particles consisting of strong T-O bonds (Si-O-Si, Al-O-Si, B-O-Si), while degradation products (concentrating Na ions) remain as a secondary phase, solubilized by immersion in boiling water. Such stability is achieved by operating at 5 M NaOH.

Providing quality-assessed and standardised soil data to support global mapping and modelling (WoSIS snapshot 2023)

Abstract. Snapshots derived from the World Soil Information Service (WoSIS) are served freely to the international community. These static datasets provide quality-assessed and standardised soil profile data that can be used to support digital soil mapping and environmental applications at broad scale levels. Since the release of the preceding snapshot in 2019, refactored ETL (extract, transform and load) procedures for screening, ingesting and standardising disparate source data have been developed. In conjunction with this, the WoSIS data model was overhauled, making it compatible with the ISO 28258 and Observations and Measurements (O&amp;amp;M) domain models. Additional procedures for querying, serving and downloading the publicly available standardised data have been implemented using open software (e.g. GraphQL API). Following up on a short discussion of these methodological developments we discuss the structure and content of the “WoSIS 2023 snapshot”. A range of new soil datasets was shared with us, registered in the ISRIC World Data Centre for Soils (WDC-Soils) data repository and subsequently processed in accordance with the licences specified by the data providers. An important effort has been the processing of forest soil data collated in the framework of the EU-HoliSoils project. We paid special attention to the standardisation of soil property definitions, description of the soil analytical procedures and standardisation of the units of measurement. The 2023 snapshot considers soil chemical properties (total carbon, organic carbon, inorganic carbon (total carbonate equivalent), total nitrogen, phosphorus (extractable P, total P and P retention), soil pH, cation exchange capacity and electrical conductivity) and physical properties (soil texture (sand, silt and clay), bulk density, coarse fragments and water retention), grouped according to analytical procedures that are operationally comparable. Method options are defined for each analytical procedure (e.g. pH measured in water, KCl or CaCl2 solution, molarity of the solution, and soil / solution ratio). For each profile we also provide the original soil classification (i.e. FAO, WRB and USDA system with their version) and pedological horizon designations as far as these have been specified in the source databases. Three measures for “fitness for intended use” are provided to facilitate informed data use: (a) positional uncertainty of the profile's site location, (b) possible uncertainty associated with the operationally defined analytical procedures and (c) date of sampling. The most recent (i.e. dynamic) dataset, called wosis_latest, is freely accessible via various web services. To permit consistent referencing and citation, we also provide a static snapshot (in this case, December 2023). This snapshot comprises quality-assessed and standardised data for 228 000 geo-referenced profiles. The data come from 174 countries and represent more than 900 000 soil layers (or horizons) and over 6 million records. The number of measurements for each soil property varies (greatly) between profiles and with depth, this generally depending on the objectives of the initial soil sampling programmes. In the coming years, we aim to gradually fill gaps in the geographic distribution of the profiles, as well as in the soil observations themselves, this subject to the sharing of a wider selection of “public” soil data by prospective data contributors; possible solutions for this are discussed. The WoSIS 2023 snapshot is archived and freely available at https://doi.org/10.17027/isric-wdcsoils-20231130 (Calisto et al., 2023).

Change in Land Use Affects Soil Organic Carbon Dynamics and Distribution in Tropical Systems

Anthropogenic land cover change is directly responsible for the deforestation and degradation of tropical forests. In this context, assessing soil organic carbon (SOC) stocks is key to understanding the impact of anthropogenic activities on SOC so that we can implement management practices that effectively reduce emissions or promote carbon sequestration. Our objective was to assess the effect of land-use change on the dynamics and distribution of SOC in three systems (agriculture, pasture and agroforestry) after 40 years of deforestation in a tropical dry forest in the central–eastern region of Honduras. For this purpose, the bulk density, percentage of coarse fragments (&gt;2 mm) and soil organic carbon content were determined at three depths (0.00–0.10 m, 0.10–0.20 m and 0.20–0.30 m). The results showed an increase in bulk density for all new uses, although soil compaction had not yet occurred. In terms of total soil organic carbon (TOC) stocks, deforestation caused a decrease from 17% to 48% in agricultural and agroforestry soils, respectively; on the other hand, grasslands did not show significant differences compared to tropical dry forest, suggesting that they have a high potential as carbon sinks in deforested tropical areas. However, this did not imply a better state of the system, as the greatest increases in bulk density were found in pastures.

Soil Carbon and Nitrogen Stocks Across Hillslopes Underlain by Continuous Permafrost in the Northern Arctic Foothills, Alaska, United States

ABSTRACTConstraining the variability of soil organic carbon (SOC) and total nitrogen (TN) stocks across hillslopes in Low Arctic permafrost‐affected landscapes remains a significant challenge for improving global estimates of permafrost SOC stocks. We investigated SOC and TN stocks across hillslopes at two sites in the Arctic Foothills of Alaska, United States (Happy Valley and Sagwon Hills). Average SOC and TN stocks for the 0–1‐m depth interval were high (52.0 ± 15.1 kg C m−2 and 2.74 ± 0.82 kg N m−2) and linearly related (R2 = 0.74, p &lt; 0.0001). Unlike soils of other permafrost and nonpermafrost landscapes, variability was greatest within rather than between hillslope positions. Furthermore, SOC and TN stocks in the surface 1 m did not exhibit strong patterns by hillslope position and were only weakly associated with major geomorphic parameters that typically predict SOC and TN stocks well in other landscapes. Although sampling at upper hillslope positions was largely limited to depths of less than 1.5 m due to the presence of coarse fragments in reworked glacial till, deeper observations at lower hillslope positions (footslopes, toeslopes, and basins) revealed significantly larger SOC stocks (92.0 ± 18.0 kg C m−2 at 2 m; 117.1 ± 10.4 kg C m−2 at 3 m). The unique small‐scale variability in ice content, cryoturbation, patterned ground, and organic layer thickness on these broad, Low Arctic sites contributes to the relatively homogeneous distribution of SOC and TN stocks across hillslope positions in the top 1 m, but a future focus on deeper sampling may reveal greater differences in SOC and TN stocks.

Spodosol development and soil organic carbon distribution along a lithosequence in perhumid coastal temperate rainforest

AbstractA dense concentration of old‐growth forest and a wet, cold climate promote mineral weathering and leaching in coastal temperate rainforest soils. Our objective was to assess soil development and soil organic carbon (SOC) distribution across 18 soil profiles in remote, upland terrain of southeast Alaska where pedon data are sparse. We made soil morphological observations, collected samples, and completed laboratory analyses to measure SOC content, pH, and particle size distribution. The survey of upland backslope soils included north‐ and south‐facing hillslopes derived from three lithologies (slate, metavolcanic, and phyllite). The soils across all sites were very gravelly (51.8 ± 20.4% coarse fragments), acidic (mineral soil pH 4.85 ± 0.45), and moderately deep (96.56 ± 37.80 cm); thin, broken E horizons were underlain by thick, carbon‐rich spodic horizons. Soil development was relatively consistent as demonstrated by the Profile Development Index with values from 15 to 26 and Podzolization Index values spanning 8 to 14. A mean pedon SOC stock of 198.02 ± 81.42 Mg C ha−1 (n = 18) was calculated using data collected for all upland organic and mineral soils from our work. The accumulation of SOC was similar among soils formed from contrasting lithologies with averages of 182 ± 15.70 Mg C ha−1 for slate, 188 ± 53.80 Mg C ha−1 for metavolcanic, and 218 ± 124 Mg C ha−1 for phyllite. Our work contributes to soil morphological observations, laboratory data, and SOC stock estimates required to better constrain and model pedogenic processes and SOC stock in remote forests where data sets are limited.

Proximal sensor characterization of Haitian agricultural soils: A case study in the Central Plateau

Optimized agronomic production is vital to life and livelihood in Haiti. As such, rapid, low-cost soil characterization may inform specific decision making for improved crop production. Ahead of a larger soil characterization campaign (120,000 points) in Eastern Haiti, this study evaluated the use of two proximal sensors (portable X-ray fluorescence – pXRF; NixPro color sensor) at farms of Association Zanmi Agrikol (AZA) for soil characterization and evaluation of spatial variability of soil properties. Analysis of 166 surface soil samples (0–5 cm) collected in three different areas revealed that similar inter-elemental associations reported in other countries remain applicable in Haiti; weak, yet significant relationships between Al, K, and Fe contents and color parameters (L*, A*, B*) were observed. Considerable variability in soil morphology and chemical properties exist even across short distances, underscoring the need for high density sampling schemes. Soil pH (observed up to 8.4) likely imposes restrictions on the bioavailability of certain micronutrients; soil salinity was low (not agronomically limiting) across the study areas, and reflective of excessive drainage. Soil profile characterization identified one Eutrudept and one Quartzipsamment; both used for agronomic production. The proliferation of skeletal coarse fragments (e.g., cobbles, stones) and sandy matrix with depth emphasize the need for periodic supplemental irrigation and fertilization via either soil amendments (e.g., compost) or commercial fertilizers as water holding capacity and nutrient storage (e.g., clays, organic matter) are likely to be limited. Use of the NixPro color sensor showed differences in topsoil color likely reflective of differential soil organic carbon levels. The proximal sensors applied herein represent their first such use for agronomic management in Haiti and have shown considerable promise for additional study and application.

Assessment of Reclaimed Mined Area of Sahastradhara, Dehradun by Soil and Vegetation Analysis

This study investigates the impact of forest area mining on reclaimed limestone mined areas in Sahastradhara, Uttarakhand, India. Initiated in 1989-90, eco-restoration efforts aimed at rehabilitating the mined areas resulted in restoration ages of 25, 24, and 23 years across different sites. The study comprehensively examined vegetation and soil physicochemical properties at various depths, revealing the dominance of species like Murrayakoenigii, Acacia catechu, Vitex negundo, Eupatorium adenophorum, Grevillea robusta, and Lantana camara. Soil analyses unveiled variations in moisture content, bulk density, and coarse fragment percentage across the sites, a rise in organic carbon, phosphorous, and nitrogen levels, showcasing soil improvement post-restoration. Correlation analyses unveiled interrelationships between soil properties, emphasizing the importance of organic matter in influencing soil acidity. Vegetation analyses at different sites revealed evolving plant communities, with prominent species contributing to the ecological richness. The Importance Value Index (IVI) and Shannon Weiner Index (H') provided insights into species dominance and biodiversity. The results showed that the soil pH was slightly basic, and the available phosphorus and nitrogen levels were within the optimal range for plant growth. However, the bulk density and electrical conductivity values were higher than the recommended range. This study contributes valuable insights into the intricate interplay between mining activities, vegetation dynamics, and soil health, guiding future ecological restoration endeavors in similar contexts. The study concluded that the eco-restoration work carried out in the area has been successful in restoring the vegetation, but further assessment is required to ensure the long-term success of the restoration efforts.

A framework for optimizing environmental covariates to support model interpretability in digital soil mapping

A common practice in digital soil mapping (DSM) is to incorporate many environmental covariates into a machine-learning algorithm to predict the spatial patterns of soil attributes. Variance inflation factor (VIF), principal component analysis (PCA), and recursive feature elimination (RFE) are three statistical methods that can be used to reduce the number of covariates. This study aims 1) to compare VIF and PCA approaches; 2) to identify an approach to determine the minimum number of covariates in DSM to ensure model parsimony using RFE after using VIF; and 3) to examine methods to interpret the impact of covariates on the variability of the predicted soil properties. The study area was the province of British Columbia (BC), Canada. This study used legacy data for four soil properties to make digital soil maps: soil organic carbon (SOC%), pH, clay%, and coarse fragment (CF%). Seven models were made for each soil property to determine the influence on validation results by using a different number of covariates produced by various methods on validation results. The results showed that the number of covariates could be reduced from 70 to 4 to 12 with only a little or no difference in concordance correlation coefficient (CCC) validation results. The CCC results of pH models using 70 and 7 covariates were both 0.74, and for other soil properties, this difference was negligible. The validation results obtained from PCA models showed that the performance of PCA in reducing the number of covariates was not as effective as when using VIF. Moreover, this study showed that covariates related to precipitation were the most important for modeling SOC%, soil pH, and clay%. Topographic covariates were the most influential covariates for modeling soil CF%. This study emphasizes the potential benefits of combining various data reduction methods to achieve optimal outcomes and generate the most parsimonious and interpretable models.

Digital Mapping of Key Static Soil Attributes of Tamil Nadu, India using Legacy Soil Information

Abstract Acquiring spatial soil information is pivotal for land resource management, environmental and soil modelling. Digital soil mapping approach helps in prediction of spatial soil properties by establishing the relationship between soil and environmental covariates. In the current study, we attempted to predict and map spatial distribution of soil depth, coarse fragments (CF), and soil textural classes over 0.13 million sq km area of Tamil Nadu, India. About 2105 samples were used for the prediction of soil attributes viz., soil depth and coarse fragments using random forest (RF) regression model, multiple linear regression (MLR), and support vector machine (SVM), while the same set of soil data was used to predict the soil textural classes as categorical variables using Random Forest classifier. Different environmental covariates such as derivatives of digital elevation models, IRS LISS-III data and bioclimatic variables were related for predicting the soil properties. The predicted soil depth and CF ranged from 46-200 cm and 1-42 per cent, respectively. The RF model outperformed by explaining the variability (R2) of 43% for soil depth and 21% for coarse fragments with root mean square error (RMSE) of 38 cm and 13%, respectively, whereas, MLR and SVM could achieve the maximum variability of R2 of 0.20 and 0.25 for soil depth and R2 of 0.07 and 0.09 for CF. With respect to soil textural classes, RF classifier performed well with overall accuracy of 63.8% and kappa index of 0.43. Variable importance ranking of Random Forest model showed that elevation, multiresolution valley bottom flatness (MrVBF), multiresolution ridge top flatness (MrRTF) and remote sensing variables (NDVI &amp; EVI) displayed decisive role in prediction of the soil depth, coarse fragments and soil textural classes. In this study, 250 m resolution detailed soil depth, CF and textural class maps were prepared which will be useful for different environmental modelling and proper agricultural management purposes.

Estimating groundwater geogenic arsenic contamination and the affected population of river basins underlain mostly with crystalline rocks in Ghana

Arsenic is a human carcinogen and contributes to the incidences of cancer, diabetes, and skin diseases in the exposed populations. Therefore, its occurrence in drinking water is of public health concern worldwide, especially in the Tano, Pra, and Ankobra River Basins in Ghana. The metasediments and metavolcanic formations which underlie these basins (42,092.5 km2) host arsenic sulphide minerals that are a source of arsenic contamination of the weathered and fractured aquifers that serve as drinking water supply to most of the population (∼9.8 million), especially in the rural areas. A random forest machine learning method and partial dependence plots were applied to maps of spatially continuous environmental factors (fault density, surface bulk density, coarse fragment and soil pH; and sub-surface silt and soil pH) and hydrochemical variables, respectively, and arsenic measurements from wells (n = 229) which included 57 new datasets of which 7.86 % exceeded the 10 µg/L to develop arsenic prediction models for the basins at thresholds of 1 µg/L, 5 µg/L, and 10 µg/L. Fault density and coarse fragments were more reliable contributors to the models. A partial dependence plot and Spearman's rank correlation analysis showed a significant positive correlation (p < 0.01) between arsenic enrichment and HCO3−, pH, Fe, and Mn, except NO3− in groundwater. Arsenic hotspots occurred in urban districts in the Ashanti Region and known gold-mining areas in Nzema, Obuasi, Prestea, and Tarkwa. Spatially, arsenic contamination in groundwater covered 49.44 % of the land area of the basins. By cross-referencing population density estimates and the arsenic hazard map,15.8 % of the population is exposed to arsenic-contaminated groundwater in the basins. The hazard model will facilitate efforts to reduce rural poverty and protect public health by delivering potable water in Ghana. The hazard models apply to crystalline terrain in other sub-Saharan African countries.

Dynamics of soil quality in a conserved landscape in the highland sub humid ecosystem, Northwestern Ethiopia

Several studies have assessed the dynamics of soil quality induced by soil and water conservation (SWC), but many showed disagreement over the efficacy of SWC interventions in the Ethiopian highlands. This study used a before and after soil and water conservation practices (SWCP) comparison approach to evaluate the effect of SWCP on soil quality dynamics. Fifty-four composite and 10 undisturbed soil samples were collected in 2012 (before SWCP) and 2022 (after SWCP). Statistical mean, analysis of variance, and principal component analysis were applied to test the significant differences among treatments. The findings demonstrated that SWCP has significantly improved most of the soil quality indicators such as soil organic matter, total nitrogen, available phosphorous, pH, total porosity, field capacity, and available water, and reduced the value of bulk density and coarse fragments. The interaction effect of landscape position and types of structures provided statistically significant results for soil organic matter, total nitrogen, magnesium, calcium, and base saturation. Soil and stone-faced soil bunds treated at lower landscapes were superior in improving soil quality attributes. The soil quality indexing showed, the overall soil quality improvement as a result of SWCP was about 32.15%. The level of improvement for different SWCPs was 32% for stone faced soil bunds and 33% for soil bunds. The findings revealed that SWCP implementation can improve soil quality. Soil organic matter is a key biological quality component that contributed 25% to the soil quality index and highly impacted soil physicochemical properties. We suggest additional assessment of best and integrated land management practices to ensure further improvement in soil quality, crop productivity, and ecosystem services in the subhumid ecosystems.

An interpretable deep learning model to map land subsidence hazard.

The main goal of this research is the interpretability of deep learning (DL) model output (e.g., CNN and LSTM) used to map land susceptibility to subsidence hazard by means of different techniques. For this purpose, an inventory map of land subsidence (LS) is prepared based on fieldwork and a record of LS presence points, and 16 features controlling LS were mapped. Thereafter, 11 effective features controlling LS were identified by means of a particle swarm optimization (PSO) algorithm, which was then used as input in the CNN and LSTM predictive models. To address the inherent black box nature of DL models, six interpretation methods (feature interaction, permutation importance plot (PFIM), bar plot, SHapley Additive exPlanations (SHAP) main plot, heatmap plot, and waterfall plot) were used to interpret the predictive model outputs. Both models (CNN and LSTM) had AUC > 90 and therefore provided excellent accuracy for mapping LS hazard. According to the most accurate model-the CNN predictive model-the range from very low to very high hazard classes occupied 20%, 20%, 25%, 16.3%, and 18.7% of the study area, respectively. According to three plots (bar plot, SHAP main plot, and heatmap plot), which were constructed based on the SHAP value, distance from the well, GDR and DEM were identifiedas the three most important features with the highest impact on the DL model output. The results of the waterfall plot indicate two effective features consisting of distance from the well and coarse fragment, and two effective features comprising landuse and DEM, contributed negatively and positively to LS, respectively. Overall, these explanation techniques can address critical concerns with respect to the interpretability of sophisticated DL predictive models.

Hetero‐blast from a structural reactive material cylinder under explosive loading

AbstractA structural reactive material (SRM) cylinder is considered here as a limiting case of a dense metallic energetic system in which a mixture of metal particles is consolidated to the theoretical maximum density excluding porosity, to possess both high energy density and mechanical strength. Dynamic fragmentation and free‐field explosion of a 103 mm inner diameter SRM cylinder charge is experimentally studied, with a wall thickness varying in a range of metal‐to‐explosive mass ratio M/C=1.3 to 4.0. Under explosive loading, the SRM cylinder produces a designated fragment size distribution divided into two groups: fine fragments with sizes on the order of 102 μm and below, and coarse fragments with sizes on the order ranging between 100‐101 mm. Prompt detonation shock‐induced reaction (DSIR) of the expanding cloud of high‐concentration fine fragments supplements the energy to enhance the primary blast as it propagates, while the coarse fragments form a high‐speed, high‐concentration metal momentum flux crossing the fireball and blast front to contribute to the total impulse loading to a nearby structure. Rapid impact‐induced reaction (IIR) of the secondary fragments from high‐speed coarse SRM fragments further enhances the reflected blast loading or generates a high interior explosion pressure as fragments perforate into the structure. The above distinctive characteristics of a unique hetero‐blast are coupled effectively in the near‐field range.

Guatemala soil organic carbon database (GTMSOC)

AbstractStudies on soil organic carbon stocks (SOCS) are increasingly relevant to developing efficient mitigation and adaptation strategies for climate change. Reliable information on soil organic carbon (SOC) content, bulk density (BD), and coarse fragments (CF) are required for precise SOCS calculation. The data quality of SOCS‐related variables is important to represent SOCS realistically across different levels of disturbance in the terrestrial ecosystems of Guatemala. The main objective is to develop Guatemala's first national SOC database to support studies of SOCS magnitudes and spatial trends. We identified and collected national sources of variables related to SOCS (SOC, BD, CF) across the country, mainly distributed in the central zone dominated by disturbed ecosystems and agricultural territories. We integrated 910 observations (soil samples and soil profiles) of SOC content (range 1.45–162 g·kg−1), 704 of BD (range 0.42–1.69 g·cm−3), and 8 of CFs (0%–21% weight). This new database represents the edaphic, climatic, and land use variability of Guatemalan territory. The database contains soil observations collected from 1965 to 2010. The year 2010 has the majority of soil observations (41%). SOCS‐related variables are standardized at 0–30 centimetres of depth using mass conservative splines, which are used to represent SOCS and soil depth relationships. We provide new information on SOCS across various ecological and environmental conditions to enable SOC monitoring systems to report reliable and accurate estimates. The new database is appealing for scientific and commercial purposes, such as representing Guatemalan soils in Earth system models or using soil information in the ecosystem services market (e.g., carbon markets). The new database is accessible to all users through the platform of the Environmental Data Initiative https://doi.org/10.6073/pasta/8dd15238c604c3ac75daf985548bd05c.

Раціональні технологічні засади отримання коксу із заданими показниками питомого електричного опору

DOI: 10.31081/1681-309X-2024-0-1-8-15 Specialty 161. U.D.C. 661.66.2:537.311/.312 RATIONAL TECHNOLOGICAL BASES OF PRODUCTION OF COKE WITH SPECIFIED ELECTRICAL RESISTIVITY © I.V. Shulga, Ph.D. in Technical Sciences, O.V. Sytnyk, Ph.D. in Technical Sciences (State Enterprise “Ukrainian State Research Institute for Carbochemistry (UKHIN)”, 7 Vesnina str., Kharkiv, 61023, Ukraine), O.I. Zelenskii, Ph.D. in Technical Sciences, V.V. Vladymirenko (National Technical University "Kharkiv Polytechnic Institute", 2, Kyrpychova str., Kharkiv, 61002, Ukraine) The article is devoted to the practically important issue of obtaining coke with a specific electrical resistance of no more than 0.1 Ohm∙cm for the needs of blast furnace production. Achievement of this indicator should occur simultaneously with the improvement of the entire range of properties of blast furnace coke. It is noted that the concept of production of blast furnace coke of improved quality consists of the formation of a rational raw material base, ensuring proper coking technology and post-furnace coke treatment. It has been shown that the production of high-quality blast furnace coke with low resistivity requires: high sinterability and coking capacity of the charge based on highly sinterable coals; coking pressure of the coal charge not exceeding 7 kPa; coking speed not exceeding 24 mm/hour; final coking temperature of 1050-1100 °C; maximum temperature level in the heating system not exceeding 1410 °C. It is confirmed that in order to obtain a low electrical resistivity, it is necessary to produce coke with the most ordered anisotropic structure. It is shown that the resistivity of coke naturally changes when used in blast furnace production: the destruction of lumps leads to the formation of fragments of different sizes. At the same time, coarser fragments have lower resistivity values, and smaller ones, characterized by an increased concentration of structural defects that are the centers of crack formation, have a higher resistivity. After gasification with carbon dioxide, the number of structural defects increases in grains of any size due to the weakening of the surface due to chemical interaction, so the resistivity of coke increases and becomes almost equal for both coarser and finer grains. Keywords: hard coal coke, blast furnace production, resistivity, vertical temperature, final coking temperature, coke gasification. Corresponding author I.V. Shulga, e-mail: ko@ukhin.org.ua

Effects of Environmental Factors on Plant Productivity in the Mountain Grassland of the Mountain Zebra National Park, Eastern Cape, South Africa

The relationship between plant productivity, measured according to biomass and species richness, is a fundamental focal point in community ecology, as it provides the basis for understanding plant responses or adaptive strategies. Although studies have been conducted on plant biomass and environmental factors, research concerning mountainous grassland areas is scarce. Therefore, the aim of the present study was to examine the influence of environmental factors on aboveground plant biomass in the mountainous grassland of the Mountain Zebra National Park, South Africa. Biomass distribution was uneven within the park, owing to certain species having relatively higher biomass values. These differences may be attributed to the chemical and physical properties of the soil, including carbon and nitrogen content, soil pH, and soil texture (sand, silt, and coarse fragments). A disc pasture meter was used to collect biomass data. Multiple regression analysis revealed that most environmental factors did not significantly influence plant biomass. The only environmental factor influencing plant biomass was soil pH; the influences of other factors were not statistically significant. The results of this study elucidate the interactions of environmental factors with plant biomass. Future research could investigate how environmental factors influence plant biomass, both below and above the ground in mountainous grassland.

Gully erosion susceptibility mapping and prioritization of gully-dominant sub-watersheds using machine learning algorithms: Evidence from the Silabati River (tropical river, India)

Gully erosion is a severe environmental issue that poses threats to agriculture, human safety, habitats, infrastructure, and soil integrity. Selecting the right machine learning model is vital for accurate Gully Erosion Susceptibility Mapping (GESM) due to varying environmental hazard performance. This study employed a comparative analysis of two machine learning techniques, Random Forest (RF) and Extreme Gradient Boost (XGBoost), to develop a highly precise GESM for the Silabati watershed (India). The analysis incorporated 24 controlling factors and examined a dataset of 460 sample points, with equal representation of gullies and non-gullies. Variance Inflation Factors (VIF) and Information Gain Ratio (IGR) techniques were applied to assess multicollinearity test among the controlling factors. Lithology, elevation, distance from the road, LULC, geomorphology, rainfall, drainage density, and coarse fragments emerged as crucial factors in determining GESM. Statistical tests, including the Kappa index, Root Mean Square Error (RMSE), Accuracy (ACC), Mean Absolute Error (MAE), Coefficient of Determination (R2), and Receiver Operating Characteristic (ROC), were employed to evaluate the RF and XGB models on training and testing data. Both models demonstrated strong performance, with the XGBoost and RF models achieving ROC values of 84.1% and 83.2%, respectively. The applied quantile classification method resulted in the creation of five distinct GESMs, categorized as very high (VH), high (H), moderate (M), low (L), and very low (VL). We found that the very high GESM areas of the watershed were 4.10% in the XGBoost model and 4.61%. in the RF model. Out of the 26 sub-watersheds, the results have identified five sub-watersheds as highly prioritized for sustainable management. Therefore, the present study provides an accurate gully erosion identification using advanced models, offering valuable insights for policymakers to proper management implementation.

Influence of Environmental Factors on Species Richness and Diversity in a Semi-Arid Environment, South Africa

The Nama Karoo biome is one of the least well-studied biomes in the semi-arid region of South Africa, and essential baseline biodiversity data for this region are lacking. The aim of the present study was to examine the influence of environmental factors on the species diversity and richness of Mountain Zebra National Park, South Africa, which includes this vital biome. Vegetation data were obtained using the step-point method. Both species richness and diversity were unaffected by slope, aspect, coarse fragments, and soil texture. Multiple linear regression analyses indicated that a combination of four variables (nitrogen, clay, and sand contents, and longitude) should be included in the optimal model for species richness, and the optimal model for species diversity also revealed four influencing variables: soil organic carbon, clay and sand contents, and longitude. Overall, both species richness and diversity could be predicted by a combination of climatic, topographic, and soil properties. The findings of this study can be used as a reference for the effects of environmental factors on plant species richness and diversity in semi-arid environments.

Sintering of vesiculating pyroclasts

Hot volcanic pyroclasts can sinter, vesiculate, and outgas in concert – a combination of processes which remains poorly constrained. And yet this combination of processes can occur coincidently during deposition from pyroclastic density currents, in conduit-filling pyroclastic debris, and in tuffisites. In many of these settings, it is the sintering-driven evolution of permeability that is key to gas transport through the evolving deposit. Here, we experimentally and theoretically investigate the evolution of the permeable networks during sintering of hot fragmental volcanic systems, which are hydrous and oversaturated at the experimental conditions. Firstly, we find that vesiculation results in shutting of the inter-granular porous network as bubble growth drives expansion of the particles into one another, destroying interconnected pores. Secondly, we observe that degassing by diffusion out of the particle edge results in contraction of the vesicular particles, re-opening pore spaces between them. Therefore, we find that vesiculation, and diffusive outgassing compete to determine both the intra-fragment vesicularity and the permeability during sintering. The development of intra-fragment vesicularity directly impacts the inter-fragment pore space and its connectivity, which decreases during vesiculation and subsequently increases during diffusive outgassing, prompting complex, non-linear permeability evolution.The relative dominance of these processes is fragment size dependent; proportionally, fine fragments lose gas at a higher rate than coarser fragments during diffusive outgassing due to larger surface area to volume ratios. As the systems progress, larger fragments retain a higher proportion of gas and so attain greater vesicularities than finer ones – and therefore, the coarse fragmental pyroclasts experience a greater, yet transient, reduction in connected porosity and permeability. We suggest that where vesiculation is sufficient, it can lead to the complete loss of connected porosity and the sealing of permeable pathways much earlier than in a sintering-only system. Our results suggest that classical sintering models must be modified to account for these vesiculation and diffusive degassing processes, and that only a combined vesiculation, sintering, and diffusive outgassing model can resolve the evolution of permeability in hot clastic volcanic systems.