Soil Indicators Research Articles

Induced and natural moss soil crusts accelerate the C, N, and P cycles of Pb[sbnd]Zn tailings

Nutrient deficiency is the primary obstacle in tailing ecological restoration besides high heavy metal content. Biological soil crusts (BSCs) are known for their C and N fixation capabilities and play a crucial role in soil P cycle. BSCs are widespread in tailings and provide a potential ecological restoration approach. In 2022, we carried out an on-site restoration on a PbZn tailing pond in Yunnan Province, China. BSCs were propagated by natural moss crust fragment inoculation. The induced moss crusts (IMCs) were monitored at 0, 45, 90, and 135 days and compared with natural moss crusts (NMCs). The chlorophyll-a content and abundance of biotic organisms increased over time, reaching a peak at 135 days and surpassing that of NMCs. Moss crusts increased the content of C, N, and P nutrients and enzyme activities in the 0.5 cm surface soil. They also reduced the DTPA-extractable Pb content. Moss crusts significantly increased the content of fulvic/humic and protein-like/polyphenol substances, thereby raising the humic index of soil dissolved organic matter (especially NMCs). Furthermore, moss crusts also raised the abundance of nitrification (AOB and Nsr), denitrification (narG, napA, qnorB, and nosZ), and P-cycling (gcd, appA, phoC, phoA, and phoD) genes, especially IMCs after a 135-day inoculation. NMCs exhibited higher moss abundance measured via eukaryotic photoautotrophs. Moss crusts increased photosynthetic bacteria abundance (e.g., Leptolyngbya and Nostocales) and reduced the dominance of chemoautotrophic bacteria, especially the dark sulfide oxidation bacteria (Betaproteobacteriales). This trend was more pronounced in NMCs. Overall, IMCs can recover the functions of NMCs, and in some cases (e.g., abundance and diversity of biotic community, soil nutrient and N & P cycle genes), even surpass them. Our research provides new insights into the tailing ecological restoration.

Soil health assessment under different soil and irrigation types in the agro-pastoral ecotone of northern China

Soil health indexes (SHIs) are widely used methods on a regional or national scale to evaluate the status of soils. However, how to unbiasedly select appropriate indicators and score these indicators to obtain comprehensive SHI is still a big issue, due to the multiformity and complexity of soil. The goal of the study was to assess various cropland soil and irrigation types soil health using two scoring methods (linear and nonlinear scoring) and three indexes choosing methods (minimum data set MDS, revised minimum data set RMDS and total data set TDS) in loess plateau. Soil and irrigation types were Kastanozems soil and irrigation farming (KA-IL), Kastanozems soil and rainfed farming (KA-RL), Regosols soil and irrigation farming (RE-IL), Regosols soil and rainfed farming (RE-RL). Twenty-four soil indexes of soil chemical, physical and biological properties were determined from the top soil layer (0–20 cm). One-way ANOVA and PCA were used to select the MDS, RMDS and TDS. The finding results demonstrated that fifteen soil indicators were incorporated into TDS. In addition, the six soil indicators were selected to establish the RMDS (BD, EC, TN, MBC, LAP and ALP) and MDS (BD, SOC, TN, NO3--N, MBN and BG), respectively. The consistency in SHIs of the nonlinear scoring was better than that of the linear scoring method. The nonlinear scoring method with MDS was considered the optimal method to calculate the SHI due to its maximum values of CV, F and highest correlation coefficient with TDS. The SHI values of the different soil and irrigation types showed the following order: RE-IL > RE-RL > KA-IL > KA-RL. This study demonstrated that different soil and irrigation types noticeably affected the soil health in cropland of an agro-pastoral ecotone of northern China, and the SHI-NLM method was a sensitive and accurate model for quantitative assessment of soil health in this region.

Influence of Eggshell Ash and Stone Powder on Geotechnical Properties of Expansive Clay in Dukem Town

Expansive clays exhibit a distinctive swelling–shrinkage behavior, leading to structural distress and damage when subjected to varying moisture levels during different seasons. So, to mitigate these issues, stabilization techniques are commonly employed, aiming to diminish swelling tendencies and enhance the strength characteristics of expansive clays. This study delves into the transformative impact of utilizing eggshell ash (ESA) and stone powder (SP) as stabilizing agents on both the index properties and strength parameters of expansive clays. The selection of these stabilizing agents is guided by criteria encompassing cost-effectiveness, availability, and environmental compatibility. Through comprehensive analysis utilizing scanning electron microscopy (SEM) and X-ray diffraction (XRD), the mineralogical compositions of both soil and additives are meticulously examined. The XRD analysis of the expansive clay unveils the presence of minerals such as quartz, sanidine (potassium or sodium feldspar), and magnetite. Incorporating ESA into the expansive clay matrix yields substantial improvements across various soil index values, encompassing the free swell index, free swell ratio, linear shrinkage, and unconfined compressive strength value. To further amplify these benefits, the study extends its focus to the influence of SP while maintaining a consistent 6% ESA content. Notably, the addition of SP exhibits a significant enhancement in compressive strength, alongside other crucial properties. Synthesizing these findings, the laboratory results point to an optimal combination ratio of 6% ESA and 15% SP, as a recommended approach to achieve superior stabilization effects. This research contributes to a deeper understanding of the interplay between stabilizing agents and expansive clays, paving the way for improved engineering practices and more resilient structural designs.

A Learning Strategy for Amazon Deforestation Estimations Using Multi-Modal Satellite Imagery

Estimations of deforestation are crucial as increased levels of deforestation induce serious environmental problems. However, it is challenging to perform investigations over extensive areas, such as the Amazon rainforest, due to the vast size of the region and the difficulty of direct human access. Satellite imagery can be used as an effective solution to this problem; combining optical images with synthetic aperture radar (SAR) images enables deforestation monitoring over large areas irrespective of weather conditions. In this study, we propose a learning strategy for multi-modal deforestation estimations on this basis. Images from three different satellites, Sentinel-1, Sentinel-2, and Landsat 8, were utilized to this end. The proposed algorithm overcomes visibility limitations due to a long rainy season of the Amazon by creating a multi-modal dataset using supplementary SAR images, achieving high estimation accuracy. The dataset is composed of satellite data taken on a daily basis with relatively less monthly generated, ground truth masking data, which is called the many-to-one-mask condition. The Normalized Difference Vegetation Index and Normalized Difference Soil Index bands are selected to comprise the datasets. This yields better detection performance and a shorter training time than datasets consisting of RGB or all bands. Multiple deep neural networks are independently trained for each modality and an appropriate fusion method is developed to detect deforestation. The proposed method utilizes the distance similarity of the predicted deforestation rate to filter prediction results. The elements with high degrees of similarity are merged into the final result with average and denoising operations. The performances of five network variants of the U-Net family are compared, with Attention U-Net observed to exhibit the best prediction results. Finally, the proposed method is utilized to estimate the deforestation status of novel queries with high accuracy.

Relationship between consistency and compaction of clay soils (Saint-Petersburg)

Clay soils are often found under building foundations, construction properties of which depend on many factors. One of them is the natural soil compaction, which is relevant in the construction classification of soils. This paper analyzes the relationship between the consistency and compaction of clay soils of different genesis and consistency using the Priklonskii soil compaction index. Water saturation of clay soil affects the relative discrepancy between the consistency coefficient and compaction index for soils of different genesis and consistency. It is noted that for soils with the water saturation coefficient lower than one, the consistency coefficient differs from the compaction index. This difference increases with decreasing water saturation coefficient. It is essential that significant differences can also appear when the water saturation coefficient is equal to 0.95 or more, approaching to unity. It is shown that the assessment of the natural compaction of clay soils should be based on their formation conditions. A study of various genetic types of clay soils affecting natural densification and consistency provides a reasonable approach to use of soil densification in the construction classification of soils.

Pollution index of soils along and against the wind direction within a 40 km distance of Morupule Fly Ash Dumpsite, Botswana

AbstractArsenic, chromium, copper, lead, and zinc concentrations were measured in the leeward and windward directions within 40 km of the Morupule power station to assess the long‐term effects of fly ash deposition on the hazardous metal contamination of soils. Based on the quantities of these metals in the soil, the contamination factor, pollution load index, and potential ecological risk index were calculated to determine the potential of these metals will harm the ecosystem and human health. The results showed that areas (0.1–2 km) nearer to the dumpsite, especially in the leeward direction had higher contamination factors, 3.08–3.51 for Cr, 4.28–5.18 for Cu, and 3.10–3.17 for Pb. On the same distance, along the windward direction, the average contamination factor was 1.17× for Cr, 1.31× for Cu, and 1.7×x for Pb, less than that of the leeward direction. On the other hand, the potential ecological risk index was low for all the toxic metals being less than 40 index value independent of the wind direction. Similarly, the comprehensive ecological risk of various toxic metals indicated that the study area had a low ecological exposure as the index value was below 150. Overall, the toxic metal contamination in the soil due to the contamination factor is moderately polluted to unpolluted, especially on the leeward side, and the ecological risk of contamination is low across the study area, but continuous deposition of fly ash without proper management will increase the ecological risk.

Study on Mechanical Properties of Deep Expansive Soil and Coupling Damage Model of Freeze–Thaw Action and Loading

This study is primarily intended to present a damage constitutive equation under the combined action of confining pressures and freeze–thaw cycles by subjecting deep expansive clay to the consolidated undrained triaxial tests. We study the influence of the numbers of freeze–thaw cycles on various mechanical indexes of soil by using the TSZ-2 instrument (fully automatic triaxial instrument). As the number of freeze–thaw cycles increases, the ultimate peak stress of the soil decreases, and then, the effect of the freeze–thaw effect on the shear strength gradually weakened. By combining the expression method of the damage variable under the action of loading alone with the expression method under the action of freeze–thaw cycles alone, we brought in the damage evolution equation to obtain the damage constitutive equation under the combined action of confining pressures and freeze–thaw cycles. The stress values under three confining pressures (100 kPa, 200 kPa, and 300 kPa) can be determined by using the final damage constitutive model. The measured data with a water content of 17% and six freeze–thaw cycles were compared with the theoretical data. The actual strength values were 118.4 kPa, 152.3 kPa, and 184.1 kPa, and the theoretical strength values were 120 kPa, 150 kPa, and 186 kPa. The fitting degree of the strength value was as high as 99%, which verifies the feasibility of this model. This study can serve as an available reference for well wall construction and disaster prediction in deep coal mining.

Ecological stoichiometry and homeostasis characteristics of plant-litter-soil system with vegetation restoration of the karst desertification control.

It is of great significance to clarify the ecologically chemical stoichiometric characteristics of plant-litter-soil in vegetation restoration process for elucidating the nutrient cycling law and soil nutrient management of karst ecosystem. The carbon (C), nitrogen (N) and phosphorus (P) contents of leaves, litter and soil and their stoichiometry were determined in loquat (Eribotrya japonica) plantations in a karst plateau canyon after 3, 6, 10 and 15 years of restoration. The homeostasis characteristics of leaf N, P, and N:P with the change in soil nutrients during restoration were revealed. The results showed that leaf C, N, and P contents initially increased and then decreased with increasing years of restoration at the same sampling time. The contents of nutrients in soil and litter varied with increasing restoration years, with the highest values mostly appearing in May and July. This could be due to greater moisture in May and July, which helps with nutrient absorption and transformation. The leaf N:P ratio of loquat with different restoration years was 35.76-47.39, with an average of 40.06. Therefore, loquat leaves may experience P limitation in the growth process. The relationships between N, P and N:P in leaves and soil indexes could be simulated by a homeostasis model. Except for the weak sensitivity of loquat leaf N in 10 years, the other indexes and treatments had a certain homeostasis. Plants maintain homeostasis by regulating physiological responses in vivo in response to soil nutrient changes, indicating that loquat has good adaptability in karst desertification environments, but attention should focus on the management of soil P in the field as part of the vegetation restoration process. Therefore, in future research, we should combine the soil water and fertilizer conditions of different growing seasons in karst rocky desertification areas and provide scientific field management to ensure that the results of rocky desertification management can play a role in rural revitalization.

Laboratory modeling and analysis of slopes of different geometry under the effect of precipitation

Back stability analysis, in-lab testing, and field tests may all be used to assess the behavior of stability of slopes. Each of these approaches has benefits and drawbacks compared to one another. Amongst these approaches, laboratory modeling stands out with its ability to prepare identical samples, keep external conditions under control, and measure deformations precisely. In this study, laboratory-based slope models at 1(Horizontal)/1(Vertical), 2/3, and 1/3 angles including the effects of precipitation and external loading were created. The results of these models were compared with those of the Plaxis 2D software. First, models were built using highly permeable cohesionless coarse-grained soils, and mixtures containing high plasticity clay (bentonite) at different rates were then prepared to investigate the effect of fine-grained soils on stability. Laboratory tests such as sieve analysis, specific gravity, consistency limits, Standard Proctor, and direct shear were used to assess the geotechnical index and mechanical properties of soils. Incremental surcharge loads were placed on the slope models and surface deformations, and local and general collapses under the effect of precipitation were observed. Laboratory model results highlighted that the fines content had a non-negligible effect on stability. When the slope behaviors were examined, it was observed that the models with a 1/3 slope had more severe local fractures and collapses. The stability of the slope is negatively affected when bentonite content in soil mixtures rises. The results of Plaxis 2D analysis are compatible with those of laboratory model tests and the factor of safety values obtained from Plaxis 2D range from 0.98 to 11.4.

A Study of Dominant Vegetation Phenology in a Sphagnum Mountain Peatland Using In Situ and Sentinel‐2 Observations

AbstractPeatlands store more than a third of the global soil organic carbon stock. Bryophytes, more specifically Sphagnum mosses, play a major role in the carbon and water cycles of these ecosystems. There is a need to include Sphagnum mosses into Earth system models to better simulate the dynamics of peatlands in a changing environment. Leaf area index (LAI) is a key plant trait that characterizes the plant photosynthesizing capacity. Moreover, LAI is a variable calculated by land surface models used in climate models, allowing control of the exchange of matter and energy between vegetation and the environment. There is extremely little data on Sphagnum LAI and none on its seasonal change. We monitored Sphagnum mosses LAI phenology in a mountainous peatland site (altitude of 1,343 m) from June to December 2021 using two methods: 2D scans of monthly Sphagnum moss samples and analysis of Sentinel‐2 images. LAI derived from field campaigns and the remote sensing approach show a strong seasonality, with high peak values reaching 10 and 7 m2·m−2, respectively. The Sentinel‐2 images were also used to derive common vegetation indices. The moisture soil index effectively discriminates Sphagnum‐dominated areas in the peatland. Satellite‐derived LAI of Sphagnum mosses is directly correlated to gross primary production monitored by gas exchange measurements (R2 = 0.83) but also to physical drivers of the environment such as air temperature (R2 = 0.74) or water table depth (R2 = 0.61) over the 2017–2021 period. It is therefore highly suitable to investigate ecosystemic functions.

Investigation on the Compaction Characteristic of Sabah Peat Soil Stabilised with Eco-Processed Pozzolan (EPP)

Peat soil was defined as the highly organic surface layer derived primarily from plant remains. Peat, on the other hand, was the subsurface of wetland systems, consisting of unconsolidated superficial layers with high non-crystalline colloid (humus) content. It has a dark brown to black colour, an organic odour, and a spongy consistency in general. Peat soil was mainly found in swamp areas. It was a partly decomposed organic layer of soil generated primarily from plant matter that has collected under waterlogging, high acidity, oxygen scarcity, and nutritional insufficiency. Peat soils have a low shear strength of 5 to 20 kPa, high compressibility of 0.9 to 1.5, and a high moisture content of >100%. Peat also has a lot of deformation, a lot of magnitudes, and many screens, and it contains a lot of organic stuff (>75%). The purpose of the study was to prognosticate the potential of Eco-Processed Pozzolan (EPP) as peat soil stabilisation material with improved technique and its consequence of the methods, which was the peat soils index properties and analyse the characteristics of the peat soil stabilisation before and after treatment using Eco-Processed Pozzolan (EPP). The undistributed soil sample was taken 0.5m underground from the surface in cylindrical shape 150 mm high and 90 mm inner diameter. The soil was mixed with 10%, 20%, and 30% Eco-Processed Pozzolan (EPP) then compacted (compaction test) in a metal mould of internal diameter 105 mm using a 2.5 kg rammer, of 50 mm diameter, free-falling from 300 mm above the top of the soil Three layers compaction of approximately equal depth and 27 blows spread evenly over the soil surface for each layer. The expected result to accomplish the main purpose was to prognosticate the potential Eco-Processed Pozzolan (EPP) as peat soil stabilization material with improvement technique and its consequence of the methods. According to the findings, peat soil treated with EPP will transform its qualities from peat to usable soil. However, the presence of moisture will reduce the mixture's ability. According to the findings of this study, the optimum EPP for stabilising peat soils was 30-40%.

A Study on the Distribution Pattern of Banana Blood Disease (BBD) and Fusarium Wilt Using Multispectral Aerial Photos and a Handheld Spectrometer in Subang, Indonesia

Knowledge of the health of banana trees is critical for farmers in order to profit from banana cultivation. Fusarium wilt and banana blood disease (BBD), two significant diseases that infect banana trees, are caused by Fusarium oxysporum and Ralstonia syzygii, respectively. They have caused a decline in crop yield, as they destroy trees, starting sequentially from the pseudostem to the fruit. The entire distribution of BBD and fusarium on a plantation can be understood using advanced geospatial information obtained from multispectral aerial photographs taken using unmanned aerial vehicles (UAVs) and a reliable data field for infected trees. Vegetation and soil indices derived from multispectral aerial photographs, such as the normalized difference vegetation index, the modified chlorophyll absorption ratio index, the normalized difference water index (NDWI), and soil pH, may have to be relied upon to explain the precise location of these two diseases. This study used a random forest algorithm to handle a large dataset consisting of multispectral and spectral models. The results show that the soil indices, soil pH, and NDWI are the most important variables for predicting the spatial distribution of these two diseases. Simultaneously, the plantation area affected by BBD is more extensive than that affected by fusarium if variations in planted banana cultivars are not considered.

Digital Mapping of Soil Organic Carbon Using Machine Learning Algorithms in the Upper Brahmaputra Valley of Northeastern India

Soil Organic Carbon (SOC) is a crucial indicator of ecosystem health and soil quality. Machine learning (ML) models that predict soil quality based on environmental parameters are becoming more prevalent. However, studies have yet to examine how well each ML technique performs when predicting and mapping SOC, particularly at high spatial resolutions. Model predictors include topographic variables generated from SRTM DEM; vegetation and soil indices derived from Landsat satellite images predict SOC for the Lakhimpur district of the upper Brahmaputra Valley of Assam, India. Four ML models, Random Forest (RF), Cubist, Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM), were utilized to predict SOC for the top layer of soil (0–15 cm) at a 30 m resolution. The results showed that the descriptive statistics of the calibration and validation sets were close enough to the total set data and calibration dataset, representing the complete samples. The measured SOC content varied from 0.10 to 1.85%. The RF model’s performance was optimal in the calibration and validation sets (R2c = 0.966, RMSEc = 0.159%, R2v = 0.418, RMSEv = 0.377%). The SVM model, on the other hand, had the next-lowest accuracy, explaining 47% of the variation (R2c = 0.471, RMSEc = 0.293, R2v = 0.081, RMSEv = 0.452), while the Cubist model fared the poorest in both the calibration and validation sets. The most-critical variable in the RF model for predicting SOC was elevation, followed by MAT and MRVBF. The essential variables for the Cubist model were slope, TRI, MAT, and Band4. AP and LS were the most-essential factors in the XGBoost and SVM models. The predicted OC ranged from 0.44 to 1.35%, 0.031 to 1.61%, 0.035 to 1.71%, and 0.47 to 1.36% in the RF, Cubist, XGBoost, and SVM models, respectively. Compared with different ML models, RF was optimal (high accuracy and low uncertainty) for predicting SOC in the investigated region. According to the present modeling results, SOC may be determined simply and accurately. In general, the high-resolution maps might be helpful for decision-makers, stakeholders, and applicants in sericultural management practices towards precision sericulture.

Effect of flamboyant flower (Delonix regia) powder on root knot nematodes (Meloidogyne incognita) infestation on tomato plant (Solanum lycopersicum) in Yola, Adamawa State

Plant Parasitic Nematodes (PPN) are regarded as one of the major challenges of sustainable tomato production in the world. Their control which involves the use of synthetic chemicals is being restricted because of their toxicity coupled with the environmental risk associated with their use. To this effect this study was carried out to evaluate the effect of flamboyant flower powder on M. incognita inoculated on tomato plants as an alternative control strategy of PPN. Screen house experiment (potted experiment) was conducted at the landscape garden of Modibbo Adama University Yola to evaluate the efficacy of plant powder for the control of root- knot nematode in tomato plant. The experimental design used was the Completely Randomized Design (CRD) with five treatments replicated three times. D. regia powders were incorporated at different levels into the bucket each containing 4kg of sterilized soil. The plant powder was incorporated at the rate of 40g, 30g, 20g and 10g (T1, T2, and T3) respectively and T4 with no level of powder 0g. D. regia powder and 40g gave the best effect on M. incognita in the potted experiments as higher plant height, number of leaves; fresh shoot weight, galling index and least final nematode of both soil and roots were recorded. Therefore, from these findings, D. regia at 40g exhibited nematicidal effect on M. incognita in tomato plant followed by 30g, 20g, and 10g respectively. The nematicidal characteristics exhibited by this plant material (flamboyant flower) might be due to some phytochemical constituents present in the plant material. The control plant has least plant height, number of leaves, fresh shoot weight, plant fruit and higher fresh root weight as well as galling index and highest final root and soil nematode population.

Tigris River water surface quality monitoring using remote sensing data and GIS techniques

Remote sensing and GIS technologies help in decision-making processes to reduce pollution and treatment time. In this study, we aim to investigate using remote sensing data in predicting water quality parameters of the Tigris River. Our approach involves the development of mathematical and statistical models that leverage satellite imagery to predict relevant water parameters. Over 2018 and 2019, fourteen different locations along the Tigris River were surveyed. Measurements for eight parameters were collected simultaneously with satellite images at each location. These parameters included temperature (Temp), electrical conductivity, total dissolved solids (TDS), pH, turbidity, chlorophyll A, blue-green algae, and dissolved oxygen. The spectral bands from Landsat 8 images and spectral indices of soil, vegetation, and water were adjusted as a preprocessing step. Spectral bands and indices were then implemented in the least absolute shrinkage and selection operator (LASSO) to predict the eight water parameters. The evaluation of the prediction model showed that the LASSO model has a determination coefficient (R2) of more than 0.8 for pH and Temp, and the minimum R2 of 0.52 was for TDS. It was found that incorporating spectral indices, as additional features in the prediction models, has significantly improved the models' performance, as demonstrated by an average R2 of 0.7 compared to 0.42 when using spectral bands only. The predictive model for each parameter provided cost-effective alternatives to frequent monitoring of Tigris water quality using field data. The predicted parameters were then utilized to calculate the water quality index (WQI) to indicate water quality along the river. The WQI showed that the river had poor water quality during the year except for April and June, which was very poor. This information will be beneficial in enforcing standards and controlling pollution activities in the study region.

The spatial distribution of interleukin-4 (IL-4) reference values in China based on a back propagation (BP) neural network.

This study aimed to investigate the geospatial distribution of normal reference values of Interleukin 4 (IL-4) in healthy Chinese adults and to provide a basis for the development of standard references. IL-4 values of 5,221 healthy adults from 64 cities in China were collected and analyzed for a potential correlation with 24 topographical, climatic and soil factors. Seven of these factors were extracted and used to build a back propagation (BP) neural network model that was used to predict IL-4 reference values in healthy individuals from 2,317 observation sites nationwide. The predicted values were tested for normality and geographic distribution by analytic Kriging interpolation to map the geographic distribution of IL-4 reference values in healthy Chinese subjects. The results showed that IL-4 values generally decreased and then increased from the South to the North. We concluded that the BP neural network model applies to this approach, where certain geographical factors determine levels of various biochemical and immunological standards in healthy adults in regions with different topography, climate and soil indices.

Configurating of Plastic Index and Liquid Limit for calcareous soil

Configurating of Plastic Index and Liquid Limit for calcareous soil

Straw, biochar, and nanocarbon altered the enzymatic reaction kinetics and thermodynamic process of catalase in the black soil under continuous warming

ABSTRACT Different C influences the kinetic and thermodynamic behavior of catalase (CAT) remains unknown. The study aimed to assess the impact of three different C [straw, biochar, nanocarbon] on kinetic and thermodynamic characteristic of CAT in 5 °C ~ 45 °C. These three C-sources increased Vmax, Vmax/Km, and decreased Km, ΔG, ΔH, ΔS at different temperatures. The addition of nanocarbon induced significant changes in the soil enzymatic characteristics, resulting in the highest soil CAT-activity. After continuous-heating, the addition of three C-sources to the soil increased and then decreased the CAT-activity and its kinetic parameters and their values peaked at 15 °C or 25 °C. On the other hand, the ΔG showed decreasing and then increasing trend and ΔS gradually increased. Q 10 decreased and correlations between CAT and kinetic thermodynamic parameters were observed with increasing-temperature in three C-treatments. There was a significant correlation between soil’s physicochemical properties and soil CAT-activity and their enzymatic reaction characteristics after straw and biochar-treatments. However, an insignificant correlation was observed between the soil indexes after the nanocarbon-treatment. Thus, different C-sources could increase the soil CAT-activity by promoting the enzymatic reaction process under continuous-warming. The continues-warming without any change in the temperature-adaptation of CAT, but its temperature-sensitivity decreased after C-source addition.

Converting optimum compaction properties of fine-grained soils between rational energy levels

This study introduces a practical energy conversion (EC)-type modeling framework capable of converting the optimum compaction properties of fine-grained soils between any two rational compaction energy levels (CELs). Model development/calibration was carried out using a database of 242 compaction test results — the largest and most diverse database of its kind, to date, entailing 76 fine-grained soils (covering liquid limits of 16–256%), with each soil tested for at least three different CELs. On establishing the framework, an independent database of 91 compaction test results (consisting of 34 fine-grained soils tested for varying CELs) was employed for its validation. The proposed EC-based models employ measured optimum water content (OWC) and maximum dry unit weight (MDUW) values obtained for a rational CEL (preferably standard Proctor) to predict the same for higher and/or lower compactive efforts (covering 214–5416 kJ/m3). The 95% lower and upper statistical agreement limits between the predicted/converted and measured OWCs were obtained as −2.16 wc % and +2.25 wc %, both of which are on par (in terms of magnitude) with the ASTM D1557 allowable limit of 2.1 wc %. For the MDUW predictions, these limits were calculated as −0.71 and +0.66 kN/m3, which can also be deemed acceptable when compared against ASTM’s allowable limit of ±0.7 kN/m3 (= ±4.4 lb/ft3). The proposed framework offers a reasonably practical procedure to accurately convert the optimum compaction parameters across different CELs (without the need for any soil index properties), and thus can be used with confidence for preliminary project design assessments.

Assessment of the environmental impacts of gold mining activities at Gankombol (Adamawa-Cameroon) using Leopold matrix, Fecteau grid and remote sensing approach

In the present study, the environmental impacts of gold mining activities at Gankombol (Adamawa Cameroon) are assessed over four-year period. The Leopold matrix and the Fecteau grid are used to identify the environmental impacts and to determine their absolute importance. The spatio-temporal variation of spectral indices and land degradation are assessed using remote sensing analysis. The approach adopted is based on the calculation of spectral indices and spatio-temporal analysis. For this purpose, from Sentinel-2 satellite images, spectral indices such as the normalized difference vegetation index (NDVI), the brightness index (BRI), the normalized difference water index (NDWI), the bare soil index (BSI) and the dry bare soil index (DBSI) are computed during four years (2016, 2019, 2020 and 2021). The environmental degradation maps are generated by using supervised Random Forests (RF) algorithm and field observations. Finally, the confusion matrix and the kappa coefficient are used to check the performance and the accuracy of the classification maps. The environmental impact assessment by the Leopold matrix and the Fecteau grid reveal that the environmental impacts of mining activities are mainly negative and manifested by deforestation, degradation of water and soil quality, and high risks to human health. These impacts have a major absolute importance. The analysis of the spectral indices suggest a clear difference between the values obtained for each year due to the evolution of the mining activity over time. The environmental degradation maps which made it possible to discriminate water bodies, exploited area, vegetation and barren soils show that the values of water bodies decrease from 16.33 to 11.69 km2 between 2016 and 2020, then increase to 13.46 km2 in 2021. The exploited area increases from 0.00 to 13.75 km2 between 2016 and 2021. Vegetation cover decreases from 24.49 to 15.25 km2 between 2016 and 2021, while barren soils increase from 19.65 to 21.53 km2 between 2019 and 2021. These results reveal significant environmental degradation in the study area due to mining activities.