Subsurface Soil Research Articles

Empowering urban development: geospatial modeling and zonation mapping in New Kabul City, Afghanistan

The main difficulties in urban development, choosing a location, and creating preventative safety precautions are accurately characterizing and valuing subsurface soil information from a geotechnical and geological standpoint. This paper discusses how to define and build geotechnical subsoil soil zonation maps (SZMs) for the new Kabul city, Afghanistan, using traditional ArcGIS software assessing Kriging interpolation approaches. With the city’s expansion plans, including New Kabul City’s development, our research supports informed urban development strategies. Subsoil data from 2,13 locations across the city were collected from geotechnical studies, focusing on soil classification, Standard Penetration Test (SPT-N values), undrained shear strength, and consolidation characteristics up to 15 m depth. SPT-N and soil type were used to create SZMs, and other parameters were used to evaluate bearing capacity and settlement. The results revealed that SPT-N values divided the research region into three main sections: A (8–>50), B (13–>50), and C (14–>50). The subsurface strata consist of low-plasticity clay (CL) and clayey sand (SC) underlain by highly plastic clay (CH) and silt (MH). Linear regression predicted SPT-N values with depth, showing a strong R2 of 0.95. This speeds up sub-soil stiffness and strength assessments during building project planning and feasibility studies. The shallow Kabul foundation has an allowable bearing capacity of over 100 kPa, making it suitable for lightly loaded buildings. Predicting SPT-N levels has an 85% correlation coefficient, while soil type has 94%. Accurate geotechnical data on the soil’s underlying layers will help characterize the site and identify future project risks.

Radionuclides' Dispersion from Coal-Fired Brick Kilns: Geo-Environmental Processes, Potential Risks and Management.

In order to investigate the distributions and possible dispersion mechanism(s) of naturally occurring radioactive materials (NORMs: 226Ra, 232Th, and 40K) from coal-based brick kilns, a systematic set (n = 60) of coal, ash, surface-soil, and subsurface soil samples were analyzed. High-quality analytical data of U, Th and K obtained from HPGe detector and TRIGA Mark-II research reactor-based neutron activation analysis were converted to the corresponding radioactivities. Average (n = 10) radioactivities of 226Ra, 232Th, and 40K in coal samples were 15.6, 16.7, and 145.5Bq.kg-1, respectively, where only 40K surpassed the corresponding global mean value. Average (n = 10) radioactivities of 226Ra, 232Th, and 40K in ash samples were 62.7, 88.5, and 521Bq.kg-1, respectively, where only 226Ra was within the established limit. In soil samples, average (n = 40) activities of 226Ra, 232Th, and 40K were 62.7, 95.1, and 641Bq.kg-1, respectively, which have surpassed the corresponding worldwide mean values. The observed differences in activity levels between soil samples collected near and far from the kilns, as well as between topsoil and subsoil samples, suggest the presence of distinct transport mechanisms for NORMs within the pedosphere. Dispersions of NORMs from the brick kilns to the ambient pedosphere are largely governed by aerodynamic convection and hydrodynamic leaching. These mechanisms are also influenced by geochemical mobility and relative solubility of NORMs, as well as factors such as rainfall patterns and wind-flow direction. Radiological indices invoke long-term carcinogenic-risks, whereas aerodynamic convection of finer particles (coal fly ash) from chimneys can cause significant health hazards to the nearby dwellers. Scientific processes as well as public awareness are essential to mitigate the risks.

Performance analysis of solar-assisted ground source heat pump in severe cold regions

ABSTRACT Long-term operation of a ground source heat pump (GSHP) in severe cold regions leads to a gradual decrease in subsurface soil temperature, affecting system performance. This paper proposes a solar assisted ground source heat pump (SAGSHP) system consisting of solar photovoltaic thermal (PV/T) and GSHP. Through TRNSYS software, the system was analyzed for 10 years of dynamic simulation in a severe cold region. The results are listed below: (1) The SAGSHP system is capable of effectively lessening the maximal temperature of photovoltaic (PV) modules in Changchun, Shenyang, and Harbin, China, by 21.73%, 13.67%, and 19.10%, respectively. The power generation efficiency of PV modules in these three locations can be maintained at about 22% year-round. (2) After ten years of operation, the coefficients of performance (COP) of the heat pumps have increased by 5.70%, 6.80%, and 5.01%, respectively, relative to the first year, showing good stability. (3) The soil temperatures at all three locations were stabilized during the ten years of operation. This study provides a theoretical basis for devising and installing SAGSHP in severe cold regions.

Estimation of Vs30 and site classification of Bhaktapur district, Nepal using microtremor array measurement

The severe damage observed in the Kathmandu Basin, Nepal, during past earthquakes necessitates a thorough study of the seismic behavior of the basin sediments. As the shear-wave velocity is directly related to the elastic shear modulus of the material, it is essential to determine it to incorporate the behavior of the soil in the design of the structure. Hence, we determined average shear-wave velocity in upper 30 m (Vs30) of soil in Bhaktapur district in the eastern part of the Kathmandu Basin at 73 observation points, employing two methods involving the use of non-invasive microtremor array measurements (MAMs). These MAMs are widely used for determining subsurface soil characteristics by analyzing the ambient vibrations of the ground. The first method involves inversion using a genetic algorithm, and the second is a method for obtaining Vs30 directly from the dispersion curve. We found that Vs30 in the southeastern part of the study area was higher than that in other parts. Conversely, Vs30 in the western region was lower. The calculated Vs30 values were used to classify the sites. The elevated eastern and southeastern areas with high Vs30 were categorized as dense soil or soft rock, whereas the areas with low Vs30 that had suffered significant damage during the 2015 Gorkha earthquake were classified as soft soil sites.Graphical

Study on field and numerical analysis of karst collapse as railway hazard

The vulnerability of karst ecosystems and the weak mechanical performance of caves pose hidden risks in railways, leading to increased karst collapses. To analyse karst collapse as a railway hazard, the Beijing–Guangzhou railway line in China was selected to identify karst features, using the multi-source frequency domain method because of its operational advantages. Abaqus was also used to numerically analyse the factors (dynamic loading, groundwater fluctuations, over-burden thickness) affecting the collapse mechanism. The study reveals diverse stratigraphy, transitioning from a Quaternary layer to stable rock formations. A notable finding is that the presence of a karst cavity in the railway embankment significantly increases the vertical dynamic displacements, particularly within the soil layer, by 72%, highlighting the significant impact of karst sections on soil dynamics. The study also reveals that due to the groundwater table fluctuations from the subsoil surface to the subgrade surface, the maximum displacement increases by 73% in the subgrade section of the railway embankment. The authors consider soil overburden thickness to validate confirmation of reliability through field data and theoretical study. The findings offer crucial insights for managing karst hazards, enhancing railway safety and ensuring durable infrastructure in challenging geological conditions.

Evaluating soil quality and carbon storage in Western Ghats Forests, Karnataka, India, for sustainable forest management.

Monitoring soil quality index (SQI) and soil organic carbon (SOC) stock status of the Western Ghats (WG) forests in India is crucial for providing vital ecosystem services alongside sustainable forest management practices. However, comprehensive profile data on SQI and SOC stock across different forest types under WG forests are limited. The study evaluated SQI and SOC stock under three forest types, i.e. tropical wet evergreen (TWE), tropical semi-evergreen (TSE), and tropical moist deciduous (TMD) across WG in Karnataka. SQI was assessed using principal component analysis with two indexing approaches and scoring methodologies, with weightage indexing through nonlinear scoring functions (NLSF) showing superiority over other methodologies. TMD forests exhibited the highest SQI, followed by TWE and TSE, while the lowest was observed in Rippon Pet RF (0.36 surface, 0.28 control section), primarily due to limitations in organic carbon and clay content. SOC stock mirrored SQI trends (TMD > TWE > TSE), with the highest values in Kollegal RF (339.3 MG ha-1) and lowest in Rippon Pet RF (102.5 MG ha-1). Although SOC and SQI were established to be ideal indicators for dynamic ecosystem services (ESs), high OC content in surface soils of Poomale NF induces pedogenic acidification and Al toxicities, indicating potential forest soil degradation. Significant correlation with control section SQI and SOC (p < 0.05) emphasises monitoring subsurface soil status to identify soil degradation, sustainable forestry practices, and complex ESs in forest systems.

Artificial intelligence for predicting arctic permafrost and active layer temperatures along the Alaskan North Slope

AbstractClimate change in Arctic regions poses a threat to permafrost stability, potentially leading to issues such as infrastructure damage, altered ecosystems, and increased greenhouse gas emissions. The challenge of monitoring permafrost temperatures and identifying critical environmental factors is accentuated by the scarcity of subsurface thermal data in remote Arctic locales. To alleviate this lack of soil thermal data we set out to combine in situ observed soil temperatures with MERRA-2 reanalysis data and Machine Learning (ML) to develop local and season-specific subsurface soil temperature predictions in Alaska. First, reanalysis features are selected based on surface energy budget physics. Coupled with Julian calendar dates, reanalysis features are preprocessed and then fed to the ML prediction model. We conducted a series of computational experiments and tested the results against performance benchmarks to determine the optimal prediction models, length of look-back period of model inputs, and the training set size. Six conventional ML models (e.g., GBDT, RF, SVR) and five statistical baselines (e.g. ARIMA) using in situ time series of soil temperature data ranging from 0 - 1 m depth are considered for the prediction task. The models are trained using in situ soil temperatures at depths between 0 and 1 m, spanning 16+ years, from field sites at Deadhorse and Toolik Lake, Alaska. All prediction performances are assessed using root mean squared error (RMSE) and mean absolute error (MAE). Results show that locally trained ML models can estimate shallow soil temperatures with an average error of $$RMSE=1.308^{\circ }$$ R M S E = 1 . 308 ∘ C.

Soil moisture estimation in the unsaturated zone using surface wave measurements and hybrid modeling framework

Abstract Soil moisture plays a critical role in influencing various facets of ecosystem dynamics. The preference for measuring soil moisture without physical intrusion has been desirable for precise assessments while minimizing disruptions to soil structural, hydraulic, and biological characteristics. In this study, we explored the potential of surface elastic waves as a proxy to estimate soil moisture profiles to a depth of 1.05 m at intervals of 0.1 m. We conducted a multichannel analysis of surface waves (MASW) survey and measured soil moisture at depths of 0.15 m and 0.35 m. To address the limited availability of soil moisture measurements, we developed a mechanistic soil moisture model as a substitute for measured soil moisture profiles. Our results showed that as soil moisture increased, the propagation of surface waves became more pronounced due to reduced frictional resistance. However, it was not straightforward to link measured surface wave responses and subsurface soil moisture profile. To address these challenges, we developed a convolutional neural network (CNN) with the inputs of the frequency-velocity and frequency-wavenumber images obtained from the measured surface waves. We found that the integration of MASW and CNN proved effective in estimating soil moisture profiles to a depth of 1.05 m at intervals of 0.1 m without causing disturbances to the soil (MAE = 0.0035 m3 m−3). This study suggested that the combined use of surface waves and CNN hold promise in measuring soil moisture profiles without physical disruptions. As such, the proposed approach could serve as a viable alternative to noninvasive soil moisture sensors.

Vapor flux induced by temperature gradient is responsible for providing liquid water to hypoliths

Commonly comprised of cyanobacteria, algae, bacteria and fungi, hypolithic communities inhabit the underside of cobblestones and pebbles in diverse desert biomes. Notwithstanding their abundance and widespread geographic distribution and their growth in the driest regions on Earth, the source of water supporting these communities remains puzzling. Adding to the puzzle is the presence of cyanobacteria that require liquid water for net photosynthesis. Here we report results from six-year monitoring in the Negev Desert (with average annual precipitation of ~ 90 mm) during which periodical measurements of the water content of cobblestone undersides were carried out. We show that while no effective wetting took place following direct rain, dew or fog, high vapor flux, induced by a sharp temperature gradient, took place from the wet subsurface soil after rain, resulting in wet-dry cycles and wetting of the cobblestone undersides. Up to 12 wet-dry cycles were recorded following a single rain event, which resulted in vapor condensation on the undersides of the cobblestones, with the daily wet phase lasting for several hours during daylight. This ‘concealed mechanism’ expands the distribution of photoautotrophic organisms into hostile regions where the abiotic conditions limit their growth, and provides the driving force for important evolutionary processes not yet fully explored.

Soil carbon and nitrogen cycling at the atmosphere-soil interface: Quantifying the responses of biocrust-soil interactions to global change.

In drylands, where water scarcity limits vascular plant growth, much of the primary production occurs at the soil surface. This is where complex macro- and microbial communities, in an intricate bond with soil particles, form biological soil crusts (biocrusts). Despite their critical role in regulating C and N cycling in dryland ecosystems, there is limited understanding of the fate of biologically fixed C and N from biocrusts into the mineral soil, or how climate change will affect C and N fluxes between the atmosphere, biocrusts, and subsurface soils. To address these gaps, we subjected biocrust-soil systems to experimental warming and drought under controlled laboratory conditions, monitored CO2 fluxes, and applied dual isotopic labeling pulses (13CO2 and 15N2). This allowed detailed quantification of elemental pathways into specific organic matter (OM) pools and microbial biomass via density fractionation and phospholipid fatty acid analyses. While biocrusts modulated CO2 fluxes regardless of the temperature regime, drought severely limited their photosynthetic C uptake to the extent that the systems no longer sustained net C uptake. Furthermore, the effect of biocrusts extended into the underlying 1 cm of mineral soil, where C and N accumulated as mineral-associated OM (MAOM<63μm). This was strongly associated with increased relative dominance of fungi, suggesting that fungal hyphae facilitate the downward C and N translocation and subsequent MAOM formation. Most strikingly, however, these pathways were disrupted in systems exposed to warming, where no effects of biocrusts on the elemental composition of the underlying soil nor on MAOM were determined. This was further associated with reduced net biological N fixation under combined warming and drought, highlighting how changing climatic conditions diminish some of the most fundamental ecosystem functions of biocrusts, with detrimental repercussions for C and N cycling and the persistence of soil organic matter pools in dryland ecosystems.

Forest catchment structure mediates shallow subsurface flow and soil base cation fluxes

Hydrologic behavior and soil properties across forested landscapes with complex topography exhibit high variability. The interaction of groundwater with spatially distinct soils produces and transports solutes across catchments, however, the spatiotemporal relationships between groundwater dynamics and soil solute fluxes are difficult to directly evaluate. While whole-catchment export of solutes by shallow subsurface flow represents an integration of soil environments and conditions but many studies compartmentalize soil solute fluxes as hillslope vs. riparian, deep vs. shallow, or as individual soil horizon contributions. This potentially obscures and underestimates the hillslope variation and magnitude of solute fluxes and soil development across the landscape. This study determined the spatial variation and of shallow soil base cation fluxes associated with weathering reactions (Ca, Mg, and Na), soil elemental depletion, and soil saturation dynamics in upland soils within a small, forested watershed at the Hubbard Brook Experimental Forest, NH. Base cation fluxes were calculated using a combination of ion-exchange resins placed in shallow groundwater wells (0.3 – 1 m depth) located across hillslope transects (ridges to lower backslopes) and measurements of groundwater levels. Groundwater levels were also used to create metrics of annual soil saturation. Base cation fluxes were positively correlated with soil saturation frequency and were greatest in soil profiles where primary minerals were most depleted of base cations (i.e., highly weathered). Spatial differences in soil saturation across the catchment were strongly related to topographic properties of the upslope drainage area and are interpreted to result from spatial variations in transient groundwater dynamics. Results from this work suggest that the structure of a catchment defines the spatial architecture of base cation fluxes, likely reflecting the mediation of subsurface stormflow dynamics on soil development. Furthermore, this work highlights the importance of further compartmentalizing solute fluxes along hillslopes, where certain areas may disproportionately contribute solutes to the whole catchment. Refining catchment controls on base cation generation and transport could be an important tool for opening the black box of catchment elemental cycling.

Nutrient Distribution Patterns in Soil Across Various Locations in Muzaffarnagar and Ghaziabad Districts of Uttar Pradesh under a Rice-Sugarcane Cropping Sequence

A study was conducted to evaluate the macro and micronutrient levels in the soil under the sugarcane-wheat cropping sequence and its relationship with soil characteristics. Understanding the nutrient status is vital for the efficient application of fertilizers, as widespread deficiencies in both macro and micronutrients are being reported in the soils of Uttar Pradesh. The rice-sugarcane cropping sequence is a prominent practice in the northwestern plains of Uttar Pradesh and Uttarakhand, covering 10-11% of the net cultivated area in these regions. Over recent years, sugarcane and wheat yields have plateaued in these areas due to declining factor productivity. Soil samples were collected from depths of 0-15 cm, 15-30 cm, and 30-45 cm at four locations in the Muzaffarnagar and Ghaziabad districts under the rice-sugarcane sequence. The available zinc content in the surface soil (0-15 cm) ranged from 0.605 to 0.883 mg/kg, and in the sub-surface soil (30-45 cm), it varied between 0.111 to 0.629 mg/kg, with an average of 0.083 mg/kg in surface soil and 0.435 mg/kg in sub-surface soil. A correlation analysis between various soil properties across different locations at three soil depths (0-15 cm, 15-30 cm, and 30-45 cm) was also conducted. The findings indicated that the soils are generally low in nitrogen, low to medium in phosphorus and potassium, and sufficiently supplied with copper, iron, manganese, and zinc in the surface layer. Future research on nutrient distribution in these areas will help advance precision agriculture, improve fertilizer management, and boost crop productivity while minimizing environmental harm. This research will contribute to sustainable soil management, economic benefits for farmers, and provide valuable insights for regional agricultural policies, further supporting food security.

Evaluation of subsurface soil water content estimate methods: Maximum entropy vs. exponential filter

Profile soil water content (SWC) is a vital variable in the atmosphere-vegetation-soil system. Although remote sensing currently can provide reliable surface SWC data (∼5 cm depth), acquiring accurate subsurface SWC data from existing reanalysis products remain challenging. In this study, we evaluated two widely used methods in estimating subsurface SWC, namely the exponential filter (ExpF) and the principle of maximum entropy (POME). The evaluation is carried out in two distinct areas on the Tibetan Plateau using ground observations collected from two monitoring networks: the Maqu network area characterized by cold humid climate and grassland and the Shiquanhe network area characterized by cold arid climate and bare ground. The results indicate that POME generally performs better than ExpF in both areas, particularly in deeper soil layers. Specifically, the accuracy of estimated SWC using the ExpF method decreases with depth, while it increases with depth using the POME method. Additionally, both methods achieve commendable performance at a depth of 10 cm in both areas. The deficiency of ExpF is mainly reflected in underestimations for dry cases, which is amplified with increasing depth. Dry cases account for 51 % in the humid area and 68 % in the arid area throughout the study period. Consequently, the ExpF method yields higher root mean square differences (RMSD) by 30 % and 113 % in the humid area at depths of 20 and 40 cm, respectively, compared to the POME method. Similarly, it results in higher RMSD values by 220 % and 200 % in the arid area. As expected, the superior performance of POME in deeper soil layers is primarily attributed to the incorporation of additional bottom and profile mean SWC observations. However, it also potentially introduces uncertainties when integrated with satellite-based data, which inherently contains errors compared to ground observations. To assess the potential of these two methods in large-scale applications combined with satellite-based datasets, this study conducted further evaluation of both methods with required input data derived from the soil moisture active and passive mission (SMAP). The results demonstrate that the performance of both methods in estimating subsurface SWC is acceptable in both humid and arid areas, although some bias is transferred from the input data. They achieve average RMSD values of 0.034 and 0.055 m3/m−3(−|−) in the humid area for the ExpF and POME methods, respectively, and 0.021 and 0.014 m3/m−3(−|−) in the arid area.

Seasonal Controls on Microbial Depolymerization and Oxidation of Organic Matter in Floodplain Soils.

Floodplain soils are vast reservoirs of organic carbon often attributed to anaerobic conditions that impose metabolic constraints on organic matter degradation. What remains elusive is how such metabolic constraints respond to dynamic flooding and drainage cycles characteristic of floodplain soils. Here we show that microbial depolymerization and respiration of organic compounds, two rate-limiting steps in decomposition, vary spatially and temporally with seasonal flooding of mountainous floodplain soils (Gothic, Colorado, USA). Combining metabolomics and -proteomics, we found a lower abundance of oxidative enzymes during flooding coincided with the accumulation of aromatic, high-molecular weight compounds, particularly in surface soils. In subsurface soils, we found that a lower oxidation state of carbon coincided with a greater abundance of chemically reduced, energetically less favorable low-molecular weight metabolites, irrespective of flooding condition. Our results suggest that seasonal flooding temporarily constrains oxidative depolymerization of larger, potentially plant-derived compounds in surface soils; in contrast, energetic constraints on microbial respiration persist in more reducing subsurface soils regardless of flooding. Our work underscores that the potential vulnerability of these distinct anaerobic carbon storage mechanisms to changing flooding dynamics should be considered, particularly as climate change shifts both the frequency and extent of flooding in floodplains globally.

Assessment of Soil Physico-chemical Properties in Sugarcane Cultivation Areas of Navsari District, Gujarat, India

Sentinel 2 satellite data from the year 2021 were acquired from the Copernicus site to identify the sugarcane producing area in the Navsari district. Hybrid classification approach i.e., supervised and unsupervised with ground truth data were applied using ERDAS IMAGINE software. After image classification, 2.5 km x 2.5 km grid was prepared in Q-GIS software which along with classified sugarcane area were overlapped for site identification. Then, random soil surface and sub-surface samples were collected with reference from grid of intensive sugarcane growing area. The particle density (2.10 to 2.76 g cm-3 with the mean value of 2.58 g cm-3) and bulk density (1.10 to 1.68 g cm-3 with the mean value of 1.33 g cm-3) of surface soil were found to be lower than sub-surface soil (2.18 to 2.79 g cm-3 and 1.15 to 1.68 g cm-3 with the mean value of 2.62 g cm-3 and 1.42 g cm-3 respectively) while porosity (31.60 to 59.26% with the mean value of 48.39%) and maximum water holding capacity (22.77 to 51.92% with the mean value of 39.60%) of surface soil were found to be higher than sub-surface soil (29.54 to 57.71% and 20.98 to 48.91% with the mean value of 45.60% and 36.81% respectively). The pH of soil surface showed range from 6.08 to 8.37 while pH of soil sub-surface noted range of 6.00 to 8.50. The electrical conductivity of surface and sub-surface soil showed the range of 0.011 to 1.580 dS m-1 and 0.010 to 1.586 dS m-1. The soil organic carbon of surface soil ranged from 0.06 to 0.89% while that of sub-surface soil varied from 0.02 to 0.85%.

Investigating the atmospheric conditions associated with impactful shallow landslides in California (USA)

Abstract Shallow landslides are often triggered during rainfall events, which can increase subsurface soil water pressure and destabilize hillslopes. The likelihood of regional shallow landslide initiation is often assessed through a comparison of rainfall intensity and duration to pre-established thresholds. While informative for landslide warning, this exclusive focus on rainfall exceeding thresholds does not consider the meteorological conditions producing the rainfall. Here, we ask the question, are there common meteorological characteristics that lead to landslide triggering precipitation? We develop a catalog of 18 post-1995 widespread, impactful shallow landslide events occurring within 13 storms across California, USA, where initiation time could be constrained to a &lt;=6-hour window. We examine storm characteristics during the landslide initiation window using atmospheric reanalysis products, radar observations, and quantitative precipitation estimates. We find that, while there are some common atmospheric characteristics across landslide events, they can occur under a range of atmospheric conditions. For example, all northern California landslide events assessed are associated with moderate to strong atmospheric rivers (ARs) while southern California landslides feature non-AR to strong AR conditions. The storm events evaluated herein share many characteristics of hydrologically important storms in California that did not necessarily result in landslides, thus atmospheric characteristics alone may not be sufficient to determine whether landslides will occur. However, documenting the characteristics of landslide-triggering storms defines the conditions under which landslides tend to occur, provides analog events that can be useful in forecast applications, helps to define future research directions relating atmospheric conditions and landslides, and supports interdisciplinary research efforts.

Distribution Characteristics of Soil Aggregate Stability and Organic Carbon of Different Grassland Types in the Temperate Desert of Longzhong Loess Plateau

Soil aggregate stability and organic carbon （SOC） are important indicators of soil structure and quality and play a key role in the improvement of soil quality in temperate deserts. This study aimed to investigate the distribution patterns, stability of soil aggregates, and variation characteristics of the content of aggregate organic carbon in different grassland types in temperate deserts and their interrelationships. Four grassland types in a temperate desert （Kalidium foliatum type, Reaumuria songarica type, Salsola passerina type, and Sympegma regelii type） in the Longzhong Loess Plateau as research objects, and the soil aggregate particle size distribution characteristics were determined using the wet sieving method. The stability of soil aggregates was analyzed by calculating aggregate stability indicators and the contribution of aggregate particle size SOC to bulk soil SOC content. Correlation analysis, principal component analysis, and linear fitting equations were used to reveal the relationship between the soil aggregate content and aggregate particle size SOC and aggregate stability. The results showed that the content of &gt;0.25 mm aggregates （R0.25）, mean weight diameter （MWD）, geometric mean diameter （GMD）, and bulk soil SOC content in each soil layer （0-10, 10-20, and 20-30 cm） of the K. foliatum type grassland were significantly higher than that of the R. songarica type and S. regelii type （P&lt;0.05）. The SOC content of 0.053-0.25 mm and &lt;0.053 mm particle size in each soil layer of the K. foliatum type grassland were significantly higher than that of the S. regelii type （P&lt;0.05）. Surface and subsurface soils （0-10 cm and 10-20 cm） had the significantly highest contribution of 0.25-2 mm particle size SOC to the bulk soil SOC content （P&lt;0.05）. Additionally, as the soil layer deepened, the R0.25, MWD, GMD, bulk soil, and aggregate SOC contents of the K. foliatum type grassland showed a tendency to increase first and then decrease, with the highest contents from 10-20 cm. Kalidium foliatum type grassland aggregate content was dominated by 0.25-2 mm aggregates, whereas the other three grassland types were dominated by 0.053-0.25 mm aggregates. In addition, bulk soil SOC content was significantly correlated with R0.25, MWD, GMD, and ELT （P&lt;0.01）, and the 0.25 mm aggregate was the critical size of positive and negative correlation. R0.25, MWD, GMD, and ELT values were the key factors influencing bulk soil SOC in grassland. The equation fitted to bulk soil SOC content, and GMD was the most suitable to describe the relationship between SOC content and the stability of soil aggregates. Therefore, compared with other grassland types, K. foliatum type grassland had a promoting effect on the soil aggregate stability and the improvement of soil quality.

Understanding the impact of flow mechanisms in unsaturated layer on heat transfer near a partially submerged geothermal heat exchanger

Heat transfer near geothermal heat exchangers in the presence of groundwater flow has been extensively studied over the past years. However, previous studies often overlooked the presence of an unsaturated layer in the shallow subsurface soil, or disregarded phase change and fluid flow within this layer. In this study, a comprehensive hydro-thermal model is developed to evaluate heat transfer near a partially submerged geothermal borehole in groundwater flow while incorporating phase change and liquid and vapor flow in the unsaturated layer. The effect of unsaturated soil hydraulic analysis on heat transfer and energy efficiency of the geothermal piles is explored under varying soil permeabilities, air-entry suctions, solid thermal conductivities, and soil types. Consideration of water content variation and unsaturated fluid flow in the model enhances heat injection rate by up to 11.23 % in highly permeable soils. However, in scenarios with higher solid thermal conductivity, or lower air-entry suction values, the heat injection rate reduces by as much as 16.13 % due to unsaturated soil hydraulic analysis. Furthermore, it is found that consideration of unsaturated soil hydraulic analysis is of greater importance in Bonny silt than sandy soil, lowering the heat injection rate by 8.53 % and 2.53 %, respectively.

Enhanced retention of hydrophobic pesticides in subsurface soils using organic amendments

The rapid global population growth since the early 2000s has significantly increased the demand for agricultural products, leading to widespread pesticide use, particularly organophosphorus pesticides (OPPs). This extensive application poses severe environmental risks by contaminating air, soil, and water resources. To protect groundwater quality, it is crucial to understand the transport and fate of these pesticides in soil and sediment. This study investigates the effects of hydrochars and biochars derived from sugar beet shreds (SBS) and Miscanthus×giganteus (MIS) on the retardation and biodegradation of OPPs in alluvial Danube sandy soil. The research is novel in its approach, isolating native OPP-degrading bacteria from natural alluvial sandy soil, inoculating them onto chars, and reapplying these bioaugmented chars to the same soil to enhance biodegradation and reduce pesticide leaching. The amendment of chars with immobilized Bacillus megaterium BD5 significantly increased bacterial abundance and activity. Metabarcoding of the 16S rRNA gene revealed a dominance of Proteobacteria (48.0–84.8 %) and Firmicutes (8.3–35.6 %). Transport modeling showed retardation coefficients (Rd) for OPPs ranging from 10 to 350, with biodegradation rates varying between 0.05 % and 75 %, indicating a positive correlation between retardation and biodegradation. The detection of biodegradation byproducts, including derivatives of phosphin, pyridine, and pyrazole, in the column leachate confirmed that biodegradation had occurred. Additionally, principal component analysis (PCA) revealed positive correlations among retardation, biodegradation, specific surface area (SSA), aldehyde/ketone groups, and bacterial count. These findings demonstrate the potential of biochar and hydrochar amendments to enhance OPP immobilization in contaminated soils, thereby reducing their leaching into groundwater. This study offers a comprehensive approach to the remediation of pesticide-contaminated soils, advancing both our fundamental understanding and the practical applications of environmental remediation techniques.

Effects of Different Straw Return Modes on Soil Carbon, Nitrogen, and Greenhouse Gas Emissions in the Semiarid Maize Field.

Returning straw to the field is a crucial practice for enhancing soil quality and increasing efficient use of secondary crop products. However, maize straw has a higher carbon-to-nitrogen ratio compared to other crops. This can result in crop nitrogen loss when the straw is returned to the field. Therefore, it is crucial to explore how different methods of straw return affect maize (Zea mays L.) farmland. In this study, a field experiment was performed with three treatments (I, no straw returned, CK; II, direct straw return, SR; and III, straw returned in deep furrows, ISR) to explore the effects of the different straw return modes on soil carbon and nitrogen content and greenhouse gas emissions. The results indicated that the SR and ISR treatments increased the dissolved organic carbon (DOC) content in the topsoil (0-15 cm). Additionally, the ISR treatment boosted the contents of total nitrogen (TN), nitrate nitrogen (NO3--N), ammonium nitrogen (NH4+-N), dissolved organic nitrogen (DON), and DOC in the subsurface soil (15-30 cm) compared with CK. When it comes to greenhouse gas emissions, the ISR treatment led to an increase in CO2 emissions. However, SR and ISR reduced N2O emissions, with ISR showing a more pronounced reduction. The ISR treatment significantly increased leaf and grain biomass compared to CK and SR. The correlation analyses showed that the yield was positively correlated with soil DOC, and soil greenhouse gas emission was correlated with soil NO3--N. The ISR technology has great potential in sequestering soil organic matter, improving soil fertility, and realizing sustainable agricultural development.