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24 pages, 4125 KiB  
Article
Responses of Soil Profile Hydrology, Structure and Microbial Respiration to Organic Amendments Under Different Tillage Systems on the Loess Plateau
by Lili Zhao, Lusheng Li, Xiangjie Chen, Yanbin Li, Jiankun Ge and Xiaowen Wang
Agronomy 2025, 15(1), 250; https://doi.org/10.3390/agronomy15010250 - 20 Jan 2025
Abstract
The combined effects of tillage and organic amendments on microbial respiration and its contribution to soil hydraulic conductivity are still uncertain in the 0–40 cm layer of a loess soil. We conducted a two-year field experiment to explore the effects of organic amendments, [...] Read more.
The combined effects of tillage and organic amendments on microbial respiration and its contribution to soil hydraulic conductivity are still uncertain in the 0–40 cm layer of a loess soil. We conducted a two-year field experiment to explore the effects of organic amendments, tillage and their interaction on soil microbial respiration, aggregate stability, pore parameters, and hydraulic conductivity on the Loess Plateau. Three tillage methods (conventional tillage (CT), deep tillage (DT) and no tillage (NT)) plus five fertilizer treatments (mineral fertilizer (control) alone and along with 20 t ha−1 wheat straw (MWS), wheat husk (MWH), farmyard soil (MFS) and bioorganic fertilizer (MBF)) were set up as experimental treatments. The findings demonstrated that the organic amendments significantly increased the soil microbial respiration and saturated hydraulic conductivity compared to the control in the 0–10 cm and 10–20 cm layers. Soil microbial respiration had indirect effects on hydraulic conductivity by improving the water aggregate stability and macroporosity. Additionally, the interaction effects of tillage and organic amendments on the pore and hydrological parameters were significant in the 20–40 cm layer. NT-MBF resulted in the greatest saturated hydraulic conductivity, which was directly correlated with the soil’s strong pore organization. Given the issue of subsurface soil compaction in our study area, it is recommended that local farmers adopt NT-MBF to enhance the soil’s microbial, structural and hydrological properties. Full article
(This article belongs to the Special Issue Advances in Tillage Methods to Improve the Yield and Quality of Crops)
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Figure 1

Figure 1
<p>Daily precipitation and air temperatures during the 2014–2015 and 2015–2016 summer maize–winter wheat rotation. The dashed line is used to distinguish the summer maize season from the winter wheat season.</p>
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<p>Responses of soil microbial respiration (<b>a</b>,<b>b</b>) and mineralization quotient of organic carbon (<b>c</b>,<b>d</b>) to tillage and organic amendments at 0–40 cm. Different letters indicate significant differences at <span class="html-italic">p</span> &lt; 0.05. CT, conventional tillage; DT, deep tillage; NT, no tillage. Control, mineral fertilizer; MWS, mineral fertilizer with wheat straw; MWH, mineral fertilizer with wheat husk; MFS, mineral fertilizer with farmyard soil; MBF, mineral fertilizer with bioorganic fertilizer.</p>
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<p>Responses of macroaggregate size distribution, determined by dry (<b>a</b>–<b>e</b>) and wet (<b>f</b>–<b>j</b>) sieving analysis, to tillage and organic amendments at 0–40 cm. CT, conventional tillage; DT, deep tillage; NT, no tillage. Control, mineral fertilizer; MWS, mineral fertilizer with wheat straw; MWH, mineral fertilizer with wheat husk; MFS, mineral fertilizer with farmyard soil; MBF, mineral fertilizer with bioorganic fertilizer.</p>
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<p>Responses of the mean weight diameters of aggregates, obtained by dry and wet sieving analysis, to tillage and organic amendments at 0–40 cm. Different letters indicate significant differences at <span class="html-italic">p</span> &lt; 0.05. CT, conventional tillage; DT, deep tillage; NT, no tillage. Control, mineral fertilizer; MWS, mineral fertilizer with wheat straw; MWH, mineral fertilizer with wheat husk; MFS, mineral fertilizer with farmyard soil; MBF, mineral fertilizer with bioorganic fertilizer.</p>
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<p>The dry (<b>a</b>–<b>o</b>) and wet (<b>p</b>–<b>D</b>) sieving aggregate size distribution fitted by power-exponential distribution models.</p>
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<p>Responses of soil total porosity (<b>a</b>,<b>b</b>), macroporosity (<b>c</b>,<b>d</b>), air permeability (<b>e</b>,<b>f</b>) and pore organization (<b>g</b>,<b>h</b>) to tillage and organic amendments at 0–40 cm. Different letters indicate significant differences at <span class="html-italic">p</span> &lt; 0.05. No letter annotation indicates no significant difference. CT, conventional tillage; DT, deep tillage; NT, no tillage. Control, mineral fertilizer; MWS, mineral fertilizer with wheat straw; MWH, mineral fertilizer with wheat husk; MFS, mineral fertilizer with farmyard soil; MBF, mineral fertilizer with bioorganic fertilizer.</p>
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<p>Heatmap indicating Pearson’s correlations between soil microbial respiration, soil aggregates, soil pores and soil saturated hydraulic conductivity across all depths. PAD, percentage of aggregate destruction; Dry-MWD, mean weight diameter of aggregates obtained by dry sieving analysis; Wet-MWD, mean weight diameter of aggregates obtained by wet sieving analysis; <span class="html-italic">Ks</span>, soil saturated hydraulic conductivity. ***, significant at <span class="html-italic">p</span> &lt; 0.001; **, significant at <span class="html-italic">p</span> &lt; 0.01; *, significant at <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>The direct and indirect effects of soil microbial respiration, aggregates and pores on the saturated hydraulic conductivity at 0–10 cm (<b>a</b>), 10–20 cm (<b>b</b>), 20–30 cm (<b>c</b>) and 30–40 cm (<b>d</b>). The blue and orange arrows indicate positive and negative relationships, respectively. The solid and dashed arrows indicate significant and non-significant relationships, respectively. The numbers adjacent to the arrows represent the path coefficients, while the thickness of the arrows denotes the strength of the significant standardized path coefficient. R<sup>2</sup> indicates the proportion of variance explained by all predictors. RMSEA, root mean square error of approximation; CFI, comparative fit index; Wet-&gt;0.25 mm and Wet-MWD, &gt;0.25 mm soil aggregates and the mean weight diameter obtained by wet sieving analysis; PO, pore organization; <span class="html-italic">Ks</span>, soil saturated hydraulic conductivity.</p>
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<p>Principal component analysis of soil properties for different treatment combinations of tillage and organic amendments in all soil layers. SOC, soil organic carbon; MR, microbial respiration; <span class="html-italic">q</span>mC, mineralization quotient; Dry-&gt;0.25 mm and Wet-&gt;0.25 mm, &gt;0.25 mm soil aggregates obtained by dry and wet sieving analysis; PAD, percentage of aggregate destruction; Dry-MWD and Wet-MWD, mean weight diameter obtained by dry and wet sieving analysis; <span class="html-italic">k<sub>a</sub></span>, air permeability; PO, pore organization; <span class="html-italic">Ks</span>, soil saturated hydraulic conductivity. CT, conventional tillage; DT, deep tillage; NT, no tillage. MF (control), mineral fertilizer; MWS, mineral fertilizer with wheat straw; MWH, mineral fertilizer with wheat husk; MFS, mineral fertilizer with farmyard soil; MBF, mineral fertilizer with bioorganic fertilizer.</p>
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17 pages, 5120 KiB  
Article
Topographic and Edaphic Influences on the Spatiotemporal Soil Water Content Patterns in Underground Mining Regions
by Yaodong Jing, Yu Chen, Jason Yang, Haoxi Ding and Hongfen Zhu
Appl. Sci. 2025, 15(2), 984; https://doi.org/10.3390/app15020984 (registering DOI) - 20 Jan 2025
Abstract
Understanding the dynamics of soil water content (SWC) is essential for effective land management, particularly in regions affected by underground mining. This study investigates the spatial and temporal patterns of SWC and its interaction with topographic and edaphic factors in coal mining and [...] Read more.
Understanding the dynamics of soil water content (SWC) is essential for effective land management, particularly in regions affected by underground mining. This study investigates the spatial and temporal patterns of SWC and its interaction with topographic and edaphic factors in coal mining and non-coal mining areas of the Chenghe watershed, located in the southeast of the Chinese Loess Plateau, which is divided by a river. Our findings revealed that the capacity to retain moisture in the top layer of coal mining areas is significantly higher (25.21%) compared to non-coal mining areas, although deeper layers exhibit lower SWC, indicating altered moisture dynamics due to underground mining disturbances. Coal mining areas show greater spatial and temporal variability in SWC, suggesting increased sensitivity to moisture fluctuations, which complicates water management practices. Additionally, underground mining activities introduce more intense effects on the relationship between SWC and topographic factors (i.e., GCVR across soil profile of 0–60 cm; slope at depth of 50 cm) or edaphic factors (i.e., soil organic matter and available potassium at depth of 30 cm; pH at depth of 50 cm) compared to non-coal mining areas. This variability is evident in the temporal shifts from positive to negative correlations, particularly in coal mining areas, reflecting modifications in both soil physical and chemical properties resulting from mining activities. In contrast, non-coal mining areas maintain a more stable moisture regime, likely due to preserved natural soil structures and processes. These contrasting findings emphasize the necessity for tailored management strategies in coal mining regions to address the unique challenges posed by altered soil characteristics and water dynamics. Full article
(This article belongs to the Special Issue Advances in Green Coal Mining Technologies)
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Figure 1

Figure 1
<p>(<b>a</b>) Location of Changhe watershed in the Chinese Loess Plateau; (<b>b</b>) distribution of soil sampling locations and digital elevation model (DEM) with 30 m resolution downloaded from the a public platform of <a href="http://www.gscloud.cn/sources" target="_blank">http://www.gscloud.cn/sources</a> (accessed on 9 September 2024); (<b>c</b>) the land use map was created by field investigation and survey in 2018; and (<b>d</b>) geological map from Shanxi Provincial Geological Prospecting Bureau.</p>
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<p>(<b>a</b>) Boundary of each coal mining region, (<b>b</b>) boundary of different-level damaged areas, (<b>c</b>) design of longwall mine, and (<b>d</b>) distributions of coal seams.</p>
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<p>Wavelet coherency between rainfall and correlation coefficients of SWC at 10 cm with (<b>a</b>) altitude, (<b>c</b>) GCVR, (<b>e</b>) VAD, (<b>g</b>) sand, (<b>i</b>) SOM, (<b>k</b>) SAN, (<b>m</b>) SAP, and (<b>o</b>) SAK in the coal mining area and (<b>b</b>) altitude, (<b>d</b>) GCVR, (<b>f</b>) VAD, (<b>h</b>) sand, (<b>j</b>) SOM, (<b>l</b>) SAN, (<b>n</b>) SAP, and (<b>p</b>) SAK in the non-coal mining area. The X axis indicates the number of days since the measurement started and Y axis indicates temporal scale (months), the color legend indicates the strength of correlation, the black solid line indicates 95% significant level, and the direction of the arrow indicates the type of correlation.</p>
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<p>Wavelet coherency between rainfall and correlation coefficients of SWC at 30 cm with (<b>a</b>) altitude, (<b>c</b>) aspect, (<b>e</b>) CGI, (<b>g</b>) GCVR, (<b>i</b>) silt, (<b>k</b>) SOM, (<b>m</b>) SAN, and (<b>o</b>) SAK in the coal mining area and (<b>b</b>) altitude, (<b>d</b>) aspect, (<b>f</b>) CGI, (<b>h</b>) GCVR, (<b>j</b>) silt, (<b>l</b>) SOM, (<b>n</b>) SAN, and (<b>p</b>) SAK in the non-coal mining area. The X axis indicates the number of days since the measurement started and the Y axis indicates temporal scale (months); the color legend indicates the strength of correlation, the black solid line indicates 95% significant level, and the direction of the arrow indicates the type of correlation.</p>
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<p>Wavelet coherency between rainfall and correlation coefficients of SWC at 50 cm with (<b>a</b>) altitude, (<b>c</b>) slope, (<b>e</b>) GCVR, (<b>g</b>) CGI, (<b>i</b>) clay, (<b>k</b>) SBD, (<b>m</b>) SOM, and (<b>o</b>) pH in the coal mining area and (<b>b</b>) altitude, (<b>d</b>) slope, (<b>f</b>) GCVR, (<b>h</b>) CGI, (<b>j</b>) clay, (<b>l</b>) SBD, (<b>n</b>) SOM, and (<b>p</b>) pH in the non-coal mining area. The X axis indicates the number of days since the measurement started and the Y axis indicates temporal scale (months); the color legend indicates the strength of correlation, the black solid line indicates 95% significant level, and the direction of the arrow indicates the type of correlation.</p>
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14 pages, 4897 KiB  
Article
Electrochemical Corrosion and Wear Behavior of Hot-Dip Galvanized Steel in Soils of Northern China
by Xiaoyu Jiao, Junhong Jia, Wei Chen and Wenlong Yang
Coatings 2025, 15(1), 112; https://doi.org/10.3390/coatings15010112 - 20 Jan 2025
Abstract
The study examined the corrosion and wear characteristics of hot-dip galvanized steel in complex soil environments. The results showed that hot-dip galvanized steel exhibited improved corrosion resistance characteristics. Additionally, the sliding speed was observed to influence both the coefficient of friction (COF) and [...] Read more.
The study examined the corrosion and wear characteristics of hot-dip galvanized steel in complex soil environments. The results showed that hot-dip galvanized steel exhibited improved corrosion resistance characteristics. Additionally, the sliding speed was observed to influence both the coefficient of friction (COF) and the state of the worn surface. Moreover, the corrosion resistance of hot-dip galvanized steel declined as the immersion period increased. Following the incorporation of friction behavior, the galvanized layer is prone to accelerated degradation. The wear of the galvanized layer resulted in the failure of its electrochemical protection, creating a pathway for corrosion to occur on the substrate as a result of the coupling effect of corrosion and wear. The use of hot-dip galvanized steel presents challenges when exposed to a tribocorrosion environment for a prolonged period. This study lays the groundwork for future research on the maintenance cycle of industrial structures constructed primarily with hot-dip galvanized steel. Full article
(This article belongs to the Special Issue Trends and Advances in Anti-Wear Materials and Coatings)
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Figure 1
<p>The thickness of hot-dip galvanized steel.</p>
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<p>Schematic diagram of electrochemical test sample.</p>
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<p>Flow graphs of (<b>a</b>) sample preparation, (<b>b</b>) electrochemical test and tribocorrosion test, (<b>c</b>) mechanism analysis.</p>
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<p>EIS plots of the samples with Q235 steel and hot-dip galvanized steel immersed in soil extract.</p>
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<p>Equivalent circuit for Q235 steel and hot-dip galvanized steel immersed in soil extract.</p>
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<p>Potential polarization curves of Q235 steel and hot-dip galvanized steel in soil extract.</p>
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<p>Variation of friction coefficient (<b>a</b>) average friction coefficient, (<b>b</b>) wear rate, (<b>c</b>) of Q235 steel and hot-dip galvanized steel at different speeds.</p>
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<p>SEM images of wear surfaces of Q235 steel and hot-dip galvanized steel in soil extract. (<b>a</b>) Q235 steel with a sliding speed of 15 mm/s, (<b>b</b>) Q235 steel with a sliding speed of 20 mm/s, (<b>c</b>) Q235 steel with a sliding speed of 25 mm/s, (<b>d</b>) hot-dip galvanized steel with a sliding speed of 15 mm/s, (<b>e</b>) hot-dip galvanized steel with a sliding speed of 20 mm/s, (<b>f</b>) hot-dip galvanized steel with a sliding speed of 25 mm/s.</p>
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<p>3D topography of wear area and unwear areas of Q235 steel (<b>a</b>–<b>c</b>) and hot-dip galvanized steel (<b>d</b>–<b>f</b>).</p>
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<p>Raman spectra of wear track of Q235 steel and hot-dip galvanized steel at a sliding speed of 25 mm/s.</p>
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<p>XRD patterns of wear tracks of hot-dip galvanized steel in soil extract at different friction rates.</p>
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<p>Schematic diagram of furrow formation and abrasive grains generated by surface crack propagation of hot-dip galvanized steel.</p>
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<p>Corrosion and tribocorrosion of hot-dip galvanized steel in soil extract.</p>
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29 pages, 9097 KiB  
Article
An Integrated Strategy to Treat and Control Acid Mine Drainage from Waste Rock and Underground Workings at the Former Franklin Mine in Nova Scotia, Canada: Field Performance Monitoring
by Christopher Power
Pollutants 2025, 5(1), 1; https://doi.org/10.3390/pollutants5010001 - 20 Jan 2025
Viewed by 64
Abstract
Acid mine drainage (AMD), which is primarily caused by the exposure of sulfidic minerals to oxygen and water during mining operations, remains a significant contributor to environmental pollution. Numerous technologies have been developed to prevent/control and treat AMD, including the isolation of waste [...] Read more.
Acid mine drainage (AMD), which is primarily caused by the exposure of sulfidic minerals to oxygen and water during mining operations, remains a significant contributor to environmental pollution. Numerous technologies have been developed to prevent/control and treat AMD, including the isolation of waste from the atmosphere and treatment systems for AMD-impacted water. Many field studies on mine site reclamation have involved an individual AMD source and/or technology, with a limited number of studies looking at reclamation programs integrating multiple approaches to manage AMD stemming from both surface and underground sources. The former Franklin mine site in Nova Scotia, Canada, was impacted by the deposition of waste rock across the site and the discharge of mine water from underground workings, with the adjacent Sullivan’s Pond serving as the main environmental receptor. Site reclamation was completed in 2010 and involved the following: (1) excavation of the dispersed waste rock (117,000 m2) and backfilling with clean soil; (2) consolidation of the excavated waste rock into a covered, compact waste rock pile (WRP) (25,000 m2); and (3) construction of a passive treatment system for the discharging underground mine water. An extensive field sampling program was conducted between 2011 and 2018 to monitor a range of meteorological, cover material, waste rock, groundwater, and surface water quality parameters. The results confirm that the multi-layer, geomembrane-lined WRP cover system is an extremely effective barrier to air and water influx, thereby minimizing the rate of AMD generation and seepage into groundwater and eliminating all contaminated surface water runoff. A small AMD groundwater plume emanates from the base of the WRP, with 50% captured by the underground mine workings over the long term and 50% slowly migrating towards Sullivan’s Pond. Excavation of the former waste disposal area eliminated the AMD source from the previously dispersed waste, with only clean surface water runoff and a diminishing legacy groundwater plume remaining. Finally, the passive treatment system, which contains a series of treatment technologies such as a limestone leach bed and settling pond, successfully treats all mine water loading (~50 kg/day) discharging from the underground workings and surface runoff. Its additional treatment capacity (up to ~150 kg/day) ensures it will be able to manage any potential drop in treatment efficiency and/or increased AMD loading from long-term WRP seepage. This comprehensive study of mine site reclamation and AMD management at an abandoned mining site can be of great reference value for environmental management and policymakers in the mining sector. Full article
(This article belongs to the Section Pollution Prevention and Control)
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Graphical abstract

Graphical abstract
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<p>(<b>a</b>) Site map of the reclaimed Franklin mine site located in Bras D’or, Nova Scotia, Canada (UTM coordinates: 709811E, 5126335N, 20T), and oblique aerial photographs showing (<b>b</b>) Franklin waste rock pile (WRP), and (<b>c</b>) passive treatment system.</p>
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<p>Conceptual model of the Franklin mine site along the cross-section A-A’ indicated in <a href="#pollutants-05-00001-f001" class="html-fig">Figure 1</a>a. The WRP and No. 5 Prospect drain tunnel are the main AMD sources, and Sullivan’s Pond is the main environmental receptor. The main AMD treatment/prevention features (WRP cover system, surface water treatment systems) are shown, along with the key groundwater monitoring wells and surface water sampling points along the cross-section.</p>
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<p>(<b>a</b>) Annual cumulative flux of each water balance component between 2012 and 2018, and (<b>b</b>) <span class="html-italic">O</span><sub>2</sub> and <span class="html-italic">CO</span><sub>2</sub> concentrations within the cover system and waste rock (also shown are <span class="html-italic">O</span><sub>2</sub> concentrations (black +) and <span class="html-italic">CO</span><sub>2</sub> concentrations (red x) within the deeper waste rock).</p>
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<p>(<b>a</b>) Aerial site map showing the groundwater elevation contours (light blue dashed lines) and groundwater flow direction (blue arrows) in January 2012, (<b>b</b>) groundwater level evolution at all MWs in the vicinity of the WRP (circles), and (<b>c</b>) groundwater level evolution at all MWs further downgradient of the WRP (triangles). Note that the monthly PPT is shown by the white bars in both plots.</p>
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<p>(<b>a</b>) Groundwater level measured at 10CM-2G to indicate drain-down within the waste rock, and (<b>b</b>) evolution of groundwater quality directly below the waste rock, indicated by acidity, sulfate (SO<sub>4</sub><sup>2−</sup>), alkalinity (Alk), iron (Fe<sup>2+</sup>), manganese (Mn<sup>2+</sup>), and aluminum (Al<sup>3+</sup>).</p>
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<p>Evolution of groundwater quality between 2011 and 2014 in terms of (<b>a</b>,<b>d</b>) acidity, (<b>b</b>,<b>e</b>) SO<sub>4</sub><sup>2−</sup>, and (<b>c</b>,<b>f</b>) pH. Note that the plots are organized relative to the location of the MWs, with the left-hand side plots (<b>a</b>–<b>c</b>) representing groundwater quality within the WRP area and the right-hand side plots (<b>d</b>–<b>f</b>) representing groundwater quality within the FSWA.</p>
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<p>Aerial map showing the AMD groundwater plume contour map (represented by acidity) in (<b>a</b>) October 2008 and (<b>b</b>) April 2014.</p>
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<p>Evolution of surface water quality between 2011 and 2014 in terms of (<b>a</b>,<b>d</b>) acidity, (<b>b</b>,<b>e</b>) SO<sub>4</sub><sup>2−</sup>, and (<b>c</b>,<b>f</b>) pH. Note that the plots are organized relative to the location of the sampling locations, with the left-hand side plots (<b>a</b>–<b>c</b>) representing the surface water quality upstream of the passive treatment system (runoff from WRP and FSWA) and the right-hand side plots (<b>d</b>–<b>f</b>) representing surface water quality entering/exiting the passive treatment system and Sullivan’s Pond.</p>
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<p>Acidity load as a function of water flow for (<b>a</b>) No. 5 Prospect water drain tunnel at 08SW-13 and (<b>b</b>) outfall of open limestone channel at 11SW-03.</p>
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<p>Aerial site map showing the AMD contamination of surface water (represented by acidity) in October 2008 (red) and April 2014 (green). The concentrations are represented as isolated bubbles rather than an interpolated contour map since the surface water sampling locations are not interconnected like groundwater. The circles’ dimensions represent the acidity level, with a larger diameter denoting a higher degree of acidity.</p>
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<p>Aerial photographs taken at the Franklin mine site during HDPE liner and drainage net installation (September 2010) and placement of the overlying growth medium (October 2010).</p>
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<p>Aerial photographs taken at the Franklin mine site before reclamation activities (2008) and after reclamation was complete (2011).</p>
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<p>Evolution of additional groundwater quality parameters between 2011 and 2014: (<b>a</b>,<b>e</b>) iron (Fe<sup>2+</sup>), (<b>b</b>,<b>f</b>) manganese (Mn<sup>2+</sup>), (<b>c</b>,<b>g</b>) aluminum (Al<sup>3+</sup>), and (<b>d</b>,<b>h</b>) alkalinity. Note that the plots are organized relative to the location of the MWs, with the left-hand side plots (<b>a</b>–<b>c</b>) representing groundwater quality within the WRP area and the right-hand side plots (<b>d</b>–<b>f</b>) representing groundwater quality within the FSWA.</p>
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<p>Evolution of additional surface water quality parameters between 2011 and 2014: (<b>a</b>,<b>e</b>) iron (Fe<sup>2+</sup>), (<b>b</b>,<b>f</b>) manganese (Mn<sup>2+</sup>), (<b>c</b>,<b>g</b>) aluminum (Al<sup>3+</sup>), and (<b>d</b>,<b>h</b>) alkalinity.</p>
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<p>Mine water flow from No. 5 Prospect water drain tunnel plotted with the corresponding (<b>a</b>) rainfall and (<b>b</b>) acidity concentration.</p>
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<p>Treated water flow from the open limestone channel at the end of the passive treatment system plotted with the corresponding (<b>a</b>) rainfall and (<b>b</b>) acidity concentration.</p>
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<p>Evolution of surface water quality (in terms of acidity) for (08SW-13) discharge from the No. 5 Prospect tunnel, and (11SW-03) outflow water from the open limestone channel at the end of the passive treatment system.</p>
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32 pages, 8121 KiB  
Article
Study on Robust Path-Tracking Control for an Unmanned Articulated Road Roller Under Low-Adhesion Conditions
by Wei Qiang, Wei Yu, Quanzhi Xu and Hui Xie
Electronics 2025, 14(2), 383; https://doi.org/10.3390/electronics14020383 - 19 Jan 2025
Viewed by 294
Abstract
To enhance the path-tracking accuracy of unmanned articulated road roller (UARR) operating on low-adhesion, slippery surfaces, this paper proposes a hierarchical cascaded control (HCC) architecture integrated with real-time ground adhesion coefficient estimation. Addressing the complex nonlinear dynamics between the two rigid bodies of [...] Read more.
To enhance the path-tracking accuracy of unmanned articulated road roller (UARR) operating on low-adhesion, slippery surfaces, this paper proposes a hierarchical cascaded control (HCC) architecture integrated with real-time ground adhesion coefficient estimation. Addressing the complex nonlinear dynamics between the two rigid bodies of the vehicle and its interaction with the ground, an upper-layer nonlinear model predictive controller (NMPC) is designed. This layer, based on a 4-degree-of-freedom (4-DOF) dynamic model, calculates the required steering torque using position and heading errors. The lower layer employs a second-order sliding mode controller (SOSMC) to precisely track the steering torque and output the corresponding steering wheel angle. To accommodate the anisotropic and time-varying nature of slippery surfaces, a strong-tracking unscented Kalman filter (ST-UKF) observer is introduced for ground adhesion coefficient estimation. By dynamically adjusting the covariance matrix, the observer reduces reliance on historical data while increasing the weight of new data, significantly improving real-time estimation accuracy. The estimated adhesion coefficient is fed back to the upper-layer NMPC, enhancing the control system’s adaptability and robustness under slippery conditions. The HCC is validated through simulation and real-vehicle experiments and compared with LQR and PID controllers. The results demonstrate that HCC achieves the fastest response time and smallest steady-state error on both dry and slippery gravel soil surfaces. Under slippery conditions, while control performance decreases compared to dry surfaces, incorporating ground adhesion coefficient observation reduces steady-state error by 20.62%. Full article
(This article belongs to the Section Electrical and Autonomous Vehicles)
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Figure 1
<p>UARR hardware layout.</p>
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<p>Causality-based modeling simulation platform for road roller.</p>
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<p>Force analysis of UARR dual bodies: (<b>a</b>) 3D force analysis; (<b>b</b>) planar force analysis and structural parameters.</p>
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<p>Fitting the Dugoff model to shearing stress–shearing displacement data [<a href="#B39-electronics-14-00383" class="html-bibr">39</a>].</p>
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<p>Feasibility verification of the Dugoff model for the drum: (<b>a</b>) circular test; (<b>b</b>) X coordinate.</p>
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<p>Equivalent schematic of the UARR hydraulic steering system.</p>
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<p>Relative displacement between the valve spool and valve sleeve.</p>
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<p>Principle diagram of piston rod movement.</p>
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<p>Validation of the dynamics model on wet dirt road: (<b>a</b>) yaw angle; (<b>b</b>) yaw rate.</p>
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<p>Validation of the dynamics model on wet gravel road: (<b>a</b>) yaw angle; (<b>b</b>) yaw rate.</p>
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<p>Validation of the dynamics model on wet dirt road: (<b>a</b>) drum centroid latitude; (<b>b</b>) drum centroid longitude.</p>
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<p>Validation of the dynamics model on wet gravel road: (<b>a</b>) drum centroid latitude; (<b>b</b>) drum centroid longitude.</p>
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<p>Hierarchical cascaded framework integrating NMPC and SOSMC with adhesion coefficient estimation.</p>
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<p>SOSMC tracking performance verification: (<b>a</b>) tracking target steering torque <span class="html-italic">M<sub>j</sub></span>; (<b>b</b>) corresponding steering wheel angle output.</p>
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<p>(<b>a</b>) Experimental scenario; (<b>b</b>) dry surface; (<b>c</b>) slippery surface.</p>
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<p>Step-tracking experiment under dry conditions: (<b>a</b>) lateral error; (<b>b</b>) steady-state error distribution.</p>
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<p>Straight line experiment under dry conditions: (<b>a</b>) lateral error; (<b>b</b>) lateral error distribution.</p>
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<p>Step-tracking experiment under dry wet and slippery conditions: (<b>a</b>) lateral error; (<b>b</b>) steady-state error distribution.</p>
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<p>Straight line experiment under wet and slippery conditions: (<b>a</b>) lateral error; (<b>b</b>) lateral error distribution.</p>
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<p>Ground surface adhesion coefficient estimation based on ST-UKF.</p>
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<p>Comparison of lateral errors across controllers on roads with varying adhesion coefficients.</p>
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<p>Lateral error distribution of different controllers under varying adhesion coefficients.</p>
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30 pages, 1598 KiB  
Article
Comparative Evaluation of Crithmum maritimum and Origanum dictamnus Cultivation on an Extensive Urban Green Roof
by Aikaterini N. Martini and Maria Papafotiou
Land 2025, 14(1), 195; https://doi.org/10.3390/land14010195 - 19 Jan 2025
Viewed by 154
Abstract
Considering that urban horticulture benefits from green roof technology, the effects of substrate type (compost-perlite-pumice 3:3:4, v/v and compost-perlite-pumice-soil 3:3:2:2, v/v) and depth (7.5 cm and 15 cm) were comparatively evaluated in the cultivation of Crithmum maritimum and Origanum dictamnus [...] Read more.
Considering that urban horticulture benefits from green roof technology, the effects of substrate type (compost-perlite-pumice 3:3:4, v/v and compost-perlite-pumice-soil 3:3:2:2, v/v) and depth (7.5 cm and 15 cm) were comparatively evaluated in the cultivation of Crithmum maritimum and Origanum dictamnus on an urban green roof in modules that included a green roof infrastructure layering. During the first cultivation period (December 2015–August 2016), plants of C. maritimum were taller and had greater diameter than those of O. dictamnus. Greater fresh and dry weights of all plant parts were observed in C. maritimum, as well as in the deep substrates compared to the shallow ones. During the second cultivation period (September 2016–August 2017), the growth of O. dictamnus surpassed that of C. maritimum, while plant height and foliage diameter, as well as the fresh and dry weight of all plant parts were greater in the deep substrates for both species. Conclusively, both species grew satisfactorily on an extensive urban Mediterranean green roof, while the deep substrate favored all their growth parameters. O. dictamnus responded better in the soil-containing substrate regarding survival, growth, and flowering, as opposed to C. maritimum that showed equal response in both substrate types. Full article
(This article belongs to the Special Issue Green Roofs in Arid and Semi-arid Climates)
25 pages, 5271 KiB  
Article
Design and Experimental Research on a Chisel-Type Variable Hierarchical Deep Fertilization Device Suitable for Saline–Alkali Soil
by Nan Xu, Zhenbo Xin, Jin Yuan, Zenghui Gao, Yu Tian, Chao Xia, Xuemei Liu and Dongwei Wang
Agriculture 2025, 15(2), 209; https://doi.org/10.3390/agriculture15020209 - 18 Jan 2025
Viewed by 294
Abstract
In China, there are around 36.7 million hectares of saline–alkali lands that hold utilization potential. Precision fertilization stands as a vital measure for enhancing the quality of saline–alkali soil and promoting a significant increase in crop yields. The performance of the fertilization device [...] Read more.
In China, there are around 36.7 million hectares of saline–alkali lands that hold utilization potential. Precision fertilization stands as a vital measure for enhancing the quality of saline–alkali soil and promoting a significant increase in crop yields. The performance of the fertilization device is a decisive factor in determining the effectiveness of fertilization. To optimize the fertilizer utilization rate in coastal saline–alkali soils and substantially reduce fertilizer waste, it is imperative to transport fertilizers to the deep soil layers and execute layered variable-rate fertilization. In light of this, a chisel-type variable-rate layered electronically controlled deep-fertilization device specifically designed for saline–alkali soils has been developed. Extensive experimental research on its fertilization performance has also been carried out. Drawing on the principles of soil dynamics, this paper meticulously investigates the structures of key components and the operating parameters of the fertilization device. Key parameters such as the penetration angle of the fertilizer shovel, the penetration clearance angle, the curvature of the shovel handle, the angle between the fertilizer baffle and the fertilizer pipe wall, the angle between the fertilizer pipe and the horizontal plane, and the forward speed are precisely determined. Moreover, this study explores the quantitative relationship between the fertilizer discharge amount of the fertilizer applicator and the effective working width. Simultaneously, this research mainly focuses on analyzing the impact of the forward speed on the operational effect of layered and variable-rate fertilization. Through a series of field experiments, it was conclusively determined that the optimal fertilization effect was attained when the forward speed was set at 6 km/h. Under this condition, the average deviation in the fertilization amount was merely 2.76%, and the average coefficients of variation in the fertilizer amount uniformity in each soil layer were 7.62, 6.32, 6.06, and 5.65%, respectively. Evidently, the experimental results not only successfully met the pre-set objectives, but also fully satisfied the design requirements. Undoubtedly, this article can offer valuable methodological references for the research and development of fertilization devices tailored for diverse crops cultivated on saline–alkali lands. Full article
(This article belongs to the Section Agricultural Technology)
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<p>Chisel-type deep-loosening fertilization device with adjustable depth.</p>
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<p>Force analysis of the shovel tip.</p>
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<p>Force analysis of the soil.</p>
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<p>Exponential function shovel shank curve.</p>
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<p>Structural parameters of the fertilizer tube and the force diagram of the fertilizer.</p>
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<p>Principle of the control system.</p>
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<p>Key dimensional parameters of the fertilization device.</p>
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<p>Discrete element simulation model for layered fertilization operation. From top to bottom, different colors represent six different soil layers.</p>
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<p>Discrete element simulation process of layered fertilization operation. (<b>a</b>) Simulation process of layered fertilization operation—rear view, (<b>b</b>) simulation process of layered fertilization operation—top view.</p>
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<p>Field test equipment.</p>
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<p>The operational effects of the layered fertilization device at different forward speeds using the DEM.</p>
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<p>Relationship between the rotational speed of the fertilizer discharging device and the fertilizer discharge amount per single rotation.</p>
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<p>Relationship between the effective working width of the fertilizer discharging device and the fertilizer discharge amount per single rotation.</p>
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<p>Field Test Equipment and Results (<b>a</b>) Field test equipment; (<b>b</b>) Results of field tests.</p>
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<p>Measurement results of the coefficient of variation in fertilizer quantity uniformity in each soil layer.</p>
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19 pages, 3415 KiB  
Article
Recycling Waste Soils for Stability Enhancement in Bored Pile Construction
by Feng Li, Lei Zhang, Zhengzhen Wang, Qiqi Liu, Tiantao Su and Jinke Wang
Buildings 2025, 15(2), 272; https://doi.org/10.3390/buildings15020272 - 18 Jan 2025
Viewed by 242
Abstract
Instability in the hole wall of bored pile may cause serious environmental problems. Therefore, using the small hole expansion theory and elastic–plastic theory, we studied the instability mechanism of the hole wall of bored pile, determined the stress expansion solution of the soil [...] Read more.
Instability in the hole wall of bored pile may cause serious environmental problems. Therefore, using the small hole expansion theory and elastic–plastic theory, we studied the instability mechanism of the hole wall of bored pile, determined the stress expansion solution of the soil layer after the excavation of pile holes in the semi-infinite elastic soil layer, and established a mechanical model. Then, the stability of the hole wall of bored pile in the cohesive soil layer and sandy soil layer was analyzed, and a formula for calculating pile hole wall stability was obtained. Finally, the stability of the hole wall of bored pile under the action of mud slurry was calculated, and the stress on the pile hole wall was analyzed when local instability and overall instability occurred, respectively. The results show that in a sandy soil layer, the safety factor of the hole wall of bored pile has no connection with the depth of the pile hole but is related to the density of mud slurry in the pile hole. In unstable soil layers, the pile hole wall was prone to instability, and the stability of the hole wall could be improved by appropriately increasing the gravity of mud slurry. With the increase in pile diameter, the lateral displacement and deformation of the hole wall increase, and the displacement of the soil layer increases when the hole wall is unstable, increasing the possibility of forming variable cross-section piles correspondingly. Full article
(This article belongs to the Special Issue Recycling of Waste in Material Science and Building Engineering)
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<p>Calculation model of the hole wall of the bored pile.</p>
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<p>Calculation diagram of the hole wall stress of a cast-in-place pile.</p>
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<p>Microelements in the cross-section of the hole wall of a cast-in-place pile.</p>
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<p>Mohr–Coulomb failure criterion.</p>
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<p>Geometric analysis model.</p>
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<p>Force analysis diagram of a pile hole wall.</p>
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<p>Finite element model of pile hole wall stability analysis.</p>
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<p>Lateral displacement of a pile hole wall with different specific gravities of mud slurry.</p>
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<p>Lateral displacement of a hole wall under different pile hole diameters.</p>
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15 pages, 2384 KiB  
Article
Application of PS2M Aptamer as Receptor Layer for Electrochemical Detection of Lead Ions
by Izabela Zaras, Olga Kujawa, Marcin Olszewski and Marta Jarczewska
Biosensors 2025, 15(1), 59; https://doi.org/10.3390/bios15010059 - 17 Jan 2025
Viewed by 312
Abstract
Since lead can cause severe effects on living organisms’ health and life, the regular monitoring of Pb levels in water and soil is of particular significance. Recently, it was shown that lead ions can also be detected using affinity-based biosensors, namely, using aptamers [...] Read more.
Since lead can cause severe effects on living organisms’ health and life, the regular monitoring of Pb levels in water and soil is of particular significance. Recently, it was shown that lead ions can also be detected using affinity-based biosensors, namely, using aptamers as recognition elements. In most cases, thrombin binding aptamer (TBA) was utilized; however, there are more examples of DNA aptamers which could also serve that purpose. Herein, we present studies on the electrochemical detection of lead ions using PS2M aptamer, which contains several guanine nucleotides, as the receptor element. Firstly, the method of aptamer-based layer fabrication was optimized along with the choice of a redox active indicator, which was a source of current signal. The experiments revealed the possibility of lead ion detection from 50 to 600 nM, which covers the range below and above the maximum accepted limit stated by US EPA (72 nM). Moreover, the sensing layer exhibited high selectivity towards lead ions and was successfully applied both for the analysis of tap water spiked with Pb2+ ions and as a miniaturized sensor. Finally, stability and regeneration studies on the aptamer-based receptor layer were executed to confirm the utility of the elaborated tool. Full article
(This article belongs to the Special Issue Electrochemical DNA Biosensors)
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<p>An exemplary square-wave voltammogram (cathodic scan) evidencing current increase originated from reduction in methylene blue molecules after incubation of aptamer-modified gold electrode in solution containing lead ions.</p>
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<p>Comparison of aptasensor response after PS2M aptamer/MCH-modified electrode incubation in blank sample and solution of 1 μM lead ions in the presence of redox indicators: 5 mM ferri/ferrocyanide, 10 μM RuHex, 100 μM AQMS, and 1 μM methylene blue. All experiments were recorded using square-wave voltammetry (cathodic scan).</p>
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<p>Calibration curve for PS2M-aptamer-based biosensor for the detection of lead ions. All the experiments were recorded using square-wave voltammetry (cathodic scan) in the presence of 1 μM methylene blue. The experiments were conducted in 3–4 repetitions. The black dots represent tested lead ions concentration, and a dotted line is a linear regression.</p>
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<p>Selectivity studies for PS2M-aptamer-based biosensor. All the experiments were recorded using square-wave voltammetry (cathodic scan) in the presence of 1 μM methylene blue. The experiments were conducted in 3–4 repetitions.</p>
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<p>Comparison of aptamer-based biosensor response towards lead ions in a laboratory sample (Tris-HCl containing methylene blue) and a tap water sample diluted with Tris-HCl (containing methylene blue) without and spiked with 250 nM lead nitrate. All the experiments were recorded using square-wave voltammetry (cathodic scan) in the presence of 1 μM methylene blue. The experiments were conducted in 3–4 repetitions.</p>
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<p>Comparison of aptamer-based biosensor response towards lead ions at 250 nM for gold disk electrodes and the miniaturized three-electrode system used as transducer elements. All the experiments were recorded using square-wave voltammetry (cathodic scan) in the presence of 1 μM methylene blue. The experiments were conducted in 3–4 repetitions.</p>
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<p>Comparison of aptamer-based biosensor responses towards lead ions at 250 nM for gold disk electrodes immediately after preparation and after storage in defined conditions. All the experiments were recorded using square-wave voltammetry (cathodic scan) in the presence of 1 μM methylene blue. The experiments were conducted in 3–4 repetitions.</p>
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<p>(<b>A</b>) Comparison of aptamer-based biosensor responses towards lead ions at 250 nM for gold disk electrodes immediately after preparation and after storage in a climate chamber. (<b>B</b>) Calibration curve for aptamer-based electrodes stored for 30 min at 50% humidity and 37 °C, preceded by 20 s incubation in 1.5% glucose and BSA solutions. All the experiments were recorded using square-wave voltammetry (cathodic scan) in the presence of 1 μM methylene blue. The experiments were conducted in 3–4 repetitions. The black dots in (<b>B</b>) represent defined concentrations of lead ions.</p>
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<p>Comparison of aptamer-based biosensor responses towards lead ions at 250 nM for gold disk electrodes subjected to regeneration at defined conditions. All the experiments were recorded using square-wave voltammetry (cathodic scan) in the presence of 1 μM methylene blue. The experiments were conducted in 3–4 repetitions. The black columns represent aptasensor responses before regeneration whereas the grey ones after electrodes’ regeneration at defined conditions.</p>
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<p>Schematic representation of aptamer-based layer behavior and formation of G-quadruplex structure after incubation with lead ions along with two most favorable PS2M aptamer conformations (<b>A</b>,<b>B</b>) in the absence of Pb<sup>2+</sup> ions.</p>
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15 pages, 2901 KiB  
Brief Report
Innovative Reforestation Strategies to Combat Desertification in Algeria: Insights from the Djelfa Region
by Jüri Liiv, Mohamed Mefti, Morten Poolakese, Ergo Rikmann and Merrit Shanskiy
Sustainability 2025, 17(2), 715; https://doi.org/10.3390/su17020715 - 17 Jan 2025
Viewed by 427
Abstract
North Africa, including the Sahara Desert, was historically forested, but over the past 10,000 years, the region has undergone significant desertification due to climate change and human activity. The use of wood for heating and grazing destroyed grass cover, which was replaced by [...] Read more.
North Africa, including the Sahara Desert, was historically forested, but over the past 10,000 years, the region has undergone significant desertification due to climate change and human activity. The use of wood for heating and grazing destroyed grass cover, which was replaced by shrubby vegetation. The slow-growing nature of the forest flora, often taking thousands of years to mature, has hindered natural regeneration, accelerating desert expansion. Today, desert encroachment is a critical issue, exacerbated by intensive farming and deforestation, which have caused severe soil erosion and the loss of the humus layer, diminishing the soil’s ability to retain water and nutrients. A project led by the Estonian University of Life Sciences and Ecole Nationale Supérieure Agronomique (Algiers) under the EU Climate Action program aims to develop effective methods for reforesting arid areas and restoring the soil’s humus layer. This approach is also suitable for establishing urban greenery in arid and semi-arid climates. The method involves planting tree seedlings equipped with individual water reservoirs in holes lined with water-impermeable biodegradable pipes. These holes are filled with a hardening composite that stores water, sustaining the plants until their roots reach deeper water sources. The composite is primarily made from locally sourced organic waste and ashes. The Djelfa region in central Algeria has been selected as the test site for this method, following comprehensive studies of the area’s soil, climate, and ecosystems. Due to COVID-19 pandemic, the work was transferred to Kenyatta University in Kenya where the preliminary tests show excellent results. The conditions in Nairobi during the dry season are comparable to Djelfa area. The results reveal a significant increase in plant biomass without watering during a dry period. This is extremely important for a desert region where watering is not feasible. Full article
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<p>Sahara desert. Djelfa region marked with arrow. Photo from: NASA’s MODIS instrument.</p>
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<p>Desertifying area in Djelfa region. Photo: M. Mefti.</p>
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<p>Preserved forest area in the Djelfa area. Photo: M. Mefti.</p>
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<p>Flow chart of the workflow in the current study.</p>
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<p>Tree planted in composite block.</p>
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<p>Calculated depth of dewpoints in the Djelfa area in January and June.</p>
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<p>Ironwood (casuarina) seedling in a bioplastic reservoir (Variant 6). Photo: C. Githuku.</p>
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<p>Filling and mixing device attached to a tractor (<b>above</b>) or attached to a truck (<b>below</b>).</p>
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15 pages, 7129 KiB  
Article
Exploration and Empirical Study on Spatial Distribution of SOC at the Core Area in Coastal Tamarix Forests’ Inland Side of Changyi National Marine Ecological Area
by Ruiting Liu, Ping Han, Jin Wang, Huiqian Zong, Xuewan Zhang, Qianxun Chen, Feiyong Chen, Yufeng Du, Zhao Li, Yaohui Liu, Pingjie Fu, Xiaoxiang Cheng and Jingtao Xu
Forests 2025, 16(1), 169; https://doi.org/10.3390/f16010169 - 17 Jan 2025
Viewed by 349
Abstract
The forest soil carbon pool plays a vital role in terrestrial ecosystems, being of great significance for maintaining global balance, regulating the global carbon cycle, and facilitating ecological restoration. Shandong Changyi Marine Ecological Special Protection Area is the only state-level marine special protection [...] Read more.
The forest soil carbon pool plays a vital role in terrestrial ecosystems, being of great significance for maintaining global balance, regulating the global carbon cycle, and facilitating ecological restoration. Shandong Changyi Marine Ecological Special Protection Area is the only state-level marine special protection area in China with tamarisk as the main object of protection, and it is the largest continuous and the best preserved natural tamarisk forest distribution area on the mainland coast of China. Compared to other forested areas, research on the spatial distribution of SOC at the core area in coastal Tamarix forests’ inland side appears to be relatively scarce. Based on this, this paper takes the core area of the Changyi National Marine Ecological Special Protection Zone, located on the southern coast of Laizhou Bay, as the research subject, based on the potassium dichromate oxidation-external heating, one-way ANOVA, and Bonferroni methods, analyzing the spatial distribution of the SOC content inland of coastal Tamarix forests. The research yielded the following conclusions: (1) The surface layer (0–10 cm) contributes significantly to the total SOC content within a 0–60 cm depth, accounting for at least 31% and shows notable surface accumulation. (2) The combined SOC content in the surface and subsurface layers (10–20 cm) accounts for at least 50% of the total SOC content within a 0–60 cm depth, indicating the dominance of these two soil layers in carbon storage. (3) The SOC content decreases with the soil depth at all six sampling points within the 0–60 cm range, with a marked drop from 0–10 cm to 10–20 cm. (4) One-way ANOVA and multiple comparisons reveal that the soil depth significantly affects the SOC distribution, particularly between the surface and 20–30 cm layers (p < 0.001), indicating high robustness and statistical significance. (5) Horizontally, the total SOC at 0 m is 45% lower than at 2 m in the 0–60 cm layer. The SOC in the 0–20 cm layer fluctuates significantly with distance from the shrub trunk, while the SOC in the 30–60 cm layers is low and stable, with minimal variations with depth. In addition, this study found that the SOC content in the core area of the protected area is lower than that in the common forest ecosystem. In the future, scientific ecological restoration projects and management protection methods should be used to improve soil’s carbon storage and carbon sink capacity. These findings not only validate the patterns of SOC’s spatial distribution in coastal Tamarix forest wetlands but also provide a scientific basis for carbon assessment and the formulation of ecological protection measures in coastal wetlands. Full article
(This article belongs to the Special Issue Remote Sensing Approach for Early Detection of Forest Disturbance)
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<p>The technical roadmap.</p>
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<p>Geographic location of the study area.</p>
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<p>The actual view of the Tamarix shrub in the vicinity of the sampling point.</p>
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<p>The schematic diagram of the <span class="html-italic">sampling points</span> layout.</p>
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<p>Real scene image after sampling at the sampling point.</p>
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<p>Proportion of <span class="html-italic">SOC content</span> in soil layers at each depth at different sampling points.</p>
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<p>The trend of vertical distribution of SOC at 6 sampling points.</p>
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<p>The proportion of the average <span class="html-italic">SOC content</span> of each soil layer in all sampling points to the total <span class="html-italic">SOC content</span> in the 0–60 cm soil layer.</p>
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<p>The spatial distribution diagram of variations in <span class="html-italic">SOC content</span> with <span class="html-italic">soil depth</span> and distance from the center of the Tamarix shrub.</p>
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<p>The actual photo of the structure of the aboveground and underground parts of Tamarix seedlings in the study area.</p>
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20 pages, 2034 KiB  
Article
The Effect of Mulching on the Root Growth of Greenhouse Tomatoes Under Different Drip Irrigation Volumes and Its Distribution Model
by Jiankun Ge, Yuhao Zhu, Xuewen Gong, Chuqi Yao, Xinyu Wu, Jiale Zhang and Yanbin Li
Horticulturae 2025, 11(1), 99; https://doi.org/10.3390/horticulturae11010099 - 16 Jan 2025
Viewed by 499
Abstract
Despite the continuous development of greenhouse cultivation technology, the influence mechanism of covering conditions on the root distribution of greenhouse crops remains unclear, which is emerging as a significant research topic at present. The interaction between mulching and irrigation plays a key role [...] Read more.
Despite the continuous development of greenhouse cultivation technology, the influence mechanism of covering conditions on the root distribution of greenhouse crops remains unclear, which is emerging as a significant research topic at present. The interaction between mulching and irrigation plays a key role in the root growth of greenhouse tomatoes, but its specific impact awaits in-depth exploration. To explore the response patterns of greenhouse crop root distribution to the drip irrigation water amount under mulching conditions, the tomato was chosen as the research object. Three experimental treatments were set up: mulched high water (Y0.9), non-mulched high water (N0.9), and mulched low water (Y0.5) (where 0.9 and 0.5 represent the cumulative evaporation from a 20 cm standard evaporation pan). We analyzed the water and thermal zone of tomato roots as well as the root distribution. Based on this, a root distribution model was constructed by introducing a mulching factor (fm) and a water stress factor (Ks). After carrying out two years of experimental research, the following results were drawn: (1) The average soil water content in the 0–60 cm soil layer was Y0.9 > N0.9 > Y0.5, and the average soil temperature in the 0–30 cm soil layer was Y0.5 > Y0.9 > N0.9. (2) The interaction between mulching and irrigation had a significant impact on the distribution of tomato roots. In the absence of mulch, the root surface area, average root diameter, root volume, and root length density initially increased and then decreased with depth, with the maximum root distribution concentrated around the 20 cm soil layer. Under mulched conditions, roots were predominantly located in the top layer (0–20 cm). Under the film mulching condition, the distribution range of root length density of low water (Y0.5) was wider than that of high water (Y0.9). (3) Root length density exhibited a significant cubic polynomial relationship with both the soil water content and soil temperature. In the N0.9 treatment, root length density had a bivariate cubic polynomial relationship with soil water and temperature, with a coefficient of determination (R2) of 0.97 and a normalized root mean square error (NRMSE) of 20%. (4) When introducing the film mulching factor (fm) and water stress factor (Ks) into the root distribution model to simulate the root length density distribution of Y0.9 and Y0.5, it was found that the NRMSE was 22% and R2 was 0.90 under the Y0.9 treatment, and the NRMSE was 24% and R2 was 0.98 under the Y0.5 treatment. This study provides theoretical support for the formulation of scientifically sound irrigation and mulching management plans for greenhouse tomatoes. Full article
(This article belongs to the Special Issue Optimized Irrigation and Water Management in Horticultural Production)
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<p>Schematic diagram of sampling plots for greenhouse tomato root system.</p>
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<p>Changes in greenhouse soil water content (SWC) in 2020 and 2021. Y0.9: mulched high water; N0.9: non-mulched high water; Y0.5: mulched low water. (<b>a</b>) The 0–20 cm soil layer in 2020; (<b>b</b>) 0–20 cm soil layer in 2021; (<b>c</b>) two-year average of 0–20 cm soil layer; (<b>d</b>) 20–40 cm soil layer in 2020; (<b>e</b>) 20–40 cm soil layer in 2021; (<b>f</b>) two-year average of 20–40 cm soil layer; (<b>g</b>) 40–60 cm soil layer in 2020; (<b>h</b>) 40–60 cm soil layer in 2021; (<b>i</b>) two-year average of 40–60 cm soil layer.</p>
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<p>Changes in greenhouse soil temperature in 2020 and 2021. Y0.9: mulched high water; N0.9: non-mulched high water; Y0.5: mulched low water. (<b>a</b>) The 5 cm soil layer in 2020; (<b>b</b>) 5 cm soil layer in 2021; (<b>c</b>) two-year average of 5 cm soil layer; (<b>d</b>) 10 cm soil layer in 2020; (<b>e</b>) 10 cm soil layer in 2021; (<b>f</b>) two-year average of 10 cm soil layer; (<b>g</b>) 20 cm soil layer in 2020; (<b>h</b>) 20 cm soil layer in 2021; (<b>i</b>) two-year average of 20 cm soil layer; (<b>j</b>) 30 cm soil layer in 2020; (<b>k</b>) 30 cm soil layer in 2021; (<b>l</b>) two-year average of 30 cm soil layer.</p>
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<p>Changes in greenhouse soil temperature in 2020 and 2021. Y0.9: mulched high water; N0.9: non-mulched high water; Y0.5: mulched low water. (<b>a</b>) The 5 cm soil layer in 2020; (<b>b</b>) 5 cm soil layer in 2021; (<b>c</b>) two-year average of 5 cm soil layer; (<b>d</b>) 10 cm soil layer in 2020; (<b>e</b>) 10 cm soil layer in 2021; (<b>f</b>) two-year average of 10 cm soil layer; (<b>g</b>) 20 cm soil layer in 2020; (<b>h</b>) 20 cm soil layer in 2021; (<b>i</b>) two-year average of 20 cm soil layer; (<b>j</b>) 30 cm soil layer in 2020; (<b>k</b>) 30 cm soil layer in 2021; (<b>l</b>) two-year average of 30 cm soil layer.</p>
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<p>Root length density distribution of greenhouse tomatoes during the late growth stages under different treatments in 2020 and 2021. Y0.9: mulched high water; N0.9: non-mulched high water; Y0.5: mulched low water. (<b>a</b>) Y0.9 treatment in 2020; (<b>b</b>) N0.9 treatment in 2020; (<b>c</b>) Y0.5 treatment in 2020; (<b>d</b>) Y0.9 treatment in 2021; (<b>e</b>) N0.9 treatment in 2021; (<b>f</b>) Y0.5 treatment in 2021. (The position at −15 is between the plants, and the position at 22.5 is between the rows.)</p>
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<p>Relationship between root length density of greenhouse tomatoes in N0.9 treatment and soil water content and soil temperature in 2020. (<b>a</b>) Soil water content (SWC) in 2020; (<b>b</b>) soil temperature in 2020.</p>
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<p>The relationship graph (1:1) between the simulated root length density and the measured root length density of greenhouse tomatoes under treatment N0.9 in 2021.</p>
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<p>Relationship between average root length density and depth of greenhouse tomatoes in 2020.</p>
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<p>Relationship plot (1:1) of simulated vs. measured root length density of greenhouse tomatoes under different treatments in 2021. (<b>a</b>) Y0.9 treatment in 2021; (<b>b</b>) Y0.5 treatment in 2021.</p>
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39 pages, 351130 KiB  
Article
Research on the Composition and Casting Technology of Bronze Arrowheads Unearthed from the Ruins of the Imperial City of the Minyue Kingdom
by Lei Zhang, Yile Chen, Liang Zheng and Ruyi Zheng
Materials 2025, 18(2), 402; https://doi.org/10.3390/ma18020402 - 16 Jan 2025
Viewed by 376
Abstract
The ruins of the Imperial City of the Minyue Kingdom were an important site of the Minyue Kingdom during the Han Dynasty. Characteristic bronze arrowheads unearthed from the East Gate, with their exquisite craftsmanship, provide important physical evidence for studying ancient bronze casting [...] Read more.
The ruins of the Imperial City of the Minyue Kingdom were an important site of the Minyue Kingdom during the Han Dynasty. Characteristic bronze arrowheads unearthed from the East Gate, with their exquisite craftsmanship, provide important physical evidence for studying ancient bronze casting technology and the military activities of that time. However, there is still a lack of systematic research on the alloy composition, casting process, and chemical stability of these arrowheads in long-term burial environments. The bronze arrowheads that were found in the East Gate warehouse are the subject of this study. Metallographic analysis, scanning electron microscopy (SEM), and energy-dispersive spectroscopy (EDS) were used to carefully examine their composition and microstructure, as well as the casting process characteristics. The findings reveal the following: (1) The East Gate bronze arrowheads primarily consist of copper–tin binary alloys, and certain samples exhibit a lead (Pb) content of up to 11.19%, potentially due to element addition during casting or element migration in the burial environment. (2) The metallographic structure shows that the sample matrix has a typical α-dendrite structure, indicating that a high-temperature casting process was used, and then a certain surface treatment was performed to enhance corrosion resistance. (3) Under a scanning electron microscope, it was observed that a three-layer structure was formed on the surface of the arrowhead, including a fully mineralized layer, an intermediate transition layer, and the original core tissue. (4) The detection of molybdenum (Mo) in some samples suggests a close relationship between the complexity of the buried soil environment and human activities. (5) By comparing the microstructure and corrosion degree of the longitudinal section and the cross-section, it was found that the longitudinal section has a stronger corrosion resistance due to its denser structure. Comprehensive analysis shows that the technical details of the bronze arrowheads unearthed from the Minyue Imperial City in terms of material selection, casting process, and later use reflect the outstanding achievements of the Minyue Kingdom in the field of bronze manufacturing in the Han Dynasty. Full article
(This article belongs to the Special Issue Corrosion Studies on Metallic Cultural Heritage)
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<p>Location analysis (image source: drawn by the authors).</p>
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<p>A panoramic photo of the ruins of the Imperial City of the Minyue Kingdom (image source: the 44th World Heritage Conference).</p>
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<p>Researchers originally collected photos of 10 samples. The label on the packaging bag is the corresponding sample number (image source: photographed by the authors from the School of Civil Engineering and Architecture, Wuyi University).</p>
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<p>Researchers originally collected photos of 10 samples. The label on the packaging bag is the corresponding sample number (image source: photographed by the authors from the School of Civil Engineering and Architecture, Wuyi University).</p>
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<p>The distribution of locations where the 8 samples were unearthed is presented (image source: drawn by the authors).</p>
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<p>Photos of 8 samples after metallographic grinding (image source: photographed by the authors from the School of Civil Engineering and Architecture, Wuyi University).</p>
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<p>Element distribution of 8 bronze arrowhead samples (image source: compiled by the author based on energy-dispersive spectrometer analysis results).</p>
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<p>Element distribution of 8 bronze arrowhead samples (image source: compiled by the author based on energy-dispersive spectrometer analysis results).</p>
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<p>Element distribution of 8 bronze arrowhead samples (image source: compiled by the author based on energy-dispersive spectrometer analysis results).</p>
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<p>The shape of the bronze arrowhead (image source: photographed by the authors from the School of Civil Engineering and Architecture, Wuyi University).</p>
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<p>Bronze arrowheads unearthed in batches (image source: photographed by the authors from the School of Civil Engineering and Architecture, Wuyi University).</p>
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26 pages, 12970 KiB  
Article
An Investigation of the Usability of Alkali-Activated Blast Furnace Slag-Additive Construction Demolition Waste as Filling Material
by Talha Sarici, Tacettin Geckil, Bahadir Ok and Huseyin Suha Aksoy
Materials 2025, 18(2), 398; https://doi.org/10.3390/ma18020398 - 16 Jan 2025
Viewed by 303
Abstract
In this study, the usability of construction and demolition waste (CDW) aggregates as filling when stabilized with alkaline activator solution (AAS) and blast furnace slag (BFS) was investigated. The initial stage of this study involved determining the engineering properties of CDW by laboratory [...] Read more.
In this study, the usability of construction and demolition waste (CDW) aggregates as filling when stabilized with alkaline activator solution (AAS) and blast furnace slag (BFS) was investigated. The initial stage of this study involved determining the engineering properties of CDW by laboratory experiments. In the next stage, modified Proctor tests were performed to investigate the compaction behavior of CDW, to which 5% to 30% BFS was added with water or AAS. In the following stage, California bearing ratio experiments were performed to determine the mixture specimen with the highest strength. In the final stage, a weak soil layer was created in a test tank, and fillings of different thicknesses were built on it using CDW with and without additives in the determined optimum mixing ratio. Then, plate-loading tests were conducted using a model foundation to evaluate the load–deformation behavior of the fillings. The study’s results indicated that adding BFS with water or AAS to CDW increased strength. Furthermore, the addition of 20% BFS yielded the highest strength value, and the CDW aggregates with the added BFS increased the ultimate bearing capacity by up to 4.72 times compared to those without the additive. Full article
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<p>CDW aggregates: (<b>a</b>) image; (<b>b</b>) particle-size distribution curve.</p>
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<p>XRD patterns of CDW.</p>
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<p>(<b>a</b>) Compaction curve; (<b>b</b>) stress–strain behavior in the CBR test; (<b>c</b>) gradation change before and after the modified Proctor test.</p>
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<p>Curves of fine-grained soil: (<b>a</b>) gradation; (<b>b</b>) compaction.</p>
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<p>The strength of fine-grained soil based on water content variation: (<b>a</b>) c<sub>u</sub> (<b>b</b>) CBR.</p>
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<p>Model plate-loading test system: (<b>a</b>) image; (<b>b</b>) drawing.</p>
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<p>Model plate-loading tests: (<b>a</b>) Series I; (<b>b</b>) Series II; (<b>c</b>) Series III and IV.</p>
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<p>The compaction curves of BFS-added CDW: (<b>a</b>) with water; (<b>b</b>) with AAS.</p>
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<p>CBR tests’ results of CDW samples with BFS: (<b>a</b>) cured for 7 days; (<b>b</b>) cured for 28 days.</p>
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<p>CBR tests’ results of CDW samples with BFS and AAS: (<b>a</b>) cured for 7 days; (<b>b</b>) cured for 28 days.</p>
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<p>Effect of BFS additive ratio on BCR<sub>CBR.</sub></p>
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<p>Series I: (<b>a</b>) water content and strength values along the soil depth; (<b>b</b>) q–s/D curve.</p>
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<p>Series II: (<b>a</b>) water content and strength values along the soil depth for test number 4; (<b>b</b>) q–s/D curves.</p>
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<p>Series III: (<b>a</b>) water content and strength values along the soil depth for test number 7; (<b>b</b>) q–s/D curves.</p>
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<p>Series IV: (<b>a</b>) water content and strength values along the soil depth for test number 10; (<b>b</b>) q–s/D curves.</p>
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<p>The BCR<sub>MPL</sub> values obtained from the results of the model plate-loading tests.</p>
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<p>The SEM images of the BFS sample.</p>
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<p>The SEM images of the CDW sample.</p>
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<p>The SEM images of the CDW+AAS sample.</p>
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<p>The SEM images of the CDW+20%BFS sample.</p>
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<p>The SEM images of the CDW+20%BFS+AAS sample.</p>
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<p>A comparison of the results of tests with those of literature [<a href="#B56-materials-18-00398" class="html-bibr">56</a>,<a href="#B57-materials-18-00398" class="html-bibr">57</a>,<a href="#B58-materials-18-00398" class="html-bibr">58</a>,<a href="#B59-materials-18-00398" class="html-bibr">59</a>,<a href="#B60-materials-18-00398" class="html-bibr">60</a>] for CBR tests.</p>
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<p>A comparison of the results of plate-loading tests with those of the literature [<a href="#B58-materials-18-00398" class="html-bibr">58</a>]: (<b>a</b>) Series III; (<b>b</b>) Series IV.</p>
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13 pages, 3154 KiB  
Article
Climate and Soil Properties Drive the Distribution of Minor and Trace Elements in Forest Soils of the Winter Olympic Core Area
by Xiaochang Wu, Huayong Zhang, Zhongyu Wang, Wang Tian and Zhao Liu
Biology 2025, 14(1), 82; https://doi.org/10.3390/biology14010082 - 16 Jan 2025
Viewed by 360
Abstract
Minor and trace elements in soil play a crucial role in regulating ecological processes that sustain the functionality of forest ecosystems. In this study, we have selected three conifer forests (Pinus sylvestris, Picea asperata, Larix principis-rupprechtii), one broadleaf forest [...] Read more.
Minor and trace elements in soil play a crucial role in regulating ecological processes that sustain the functionality of forest ecosystems. In this study, we have selected three conifer forests (Pinus sylvestris, Picea asperata, Larix principis-rupprechtii), one broadleaf forest (Betula Platyfilla) and one mixed forest of Betula Platyfilla and Larix principis-rupprechtii in the Winter Olympic core area and determined the pattern of 12 typical elements (B, Fe, V, Cr, Ni, Co, Mn, As, Cu, Zn, Sn and Se) in soils and their main drivers in the three different soil layers (A, B and C horizon) in each soil profile. Our results showed that the concentrations of B, Fe, Cr, Cu, Ni and Sn were mainly enriched in the broadleaf forest and mixed broadleaf–conifer forest zones, and the average concentrations of Co, Mn, V, Zn, As and Se were mainly enriched in coniferous forest zones in contrast. We have observed that the mean concentrations of Fe, Cr, Ni, Zn, As, Sn and Co increase with soil depth in the BP forest. The concentrations of Se and Cu were higher in the A layer than the C layer. The piecewise structural equation modeling (piecewiseSEM) results visualized a direct and negative effect on B, Fe, V, Cr and Ni concentrations due to soil temperature, while the concentrations of Se is mainly influenced by soil temperature and soil properties. Full article
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<p>Violin plot of soil minor and trace elements with boxes in five forests.</p>
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<p>Vertical distribution of soil minor and trace elements in different layers among five forests.</p>
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<p>Principal component analysis of minor and trace elements in five forests. (<b>a</b>) Loading plot, (<b>b</b>) variance explained. (<b>c</b>) Bar plots illustrating the loadings of each minor or trace element. The orange bars denote the loadings and contributions that are deemed significant.</p>
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<p>Pearson correlation between selected soil properties and soil minor and trace elements. Note: *, correlation is significant at the 0.05 level.</p>
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<p>The influence of environmental factors on minor and trace elements in soil through structural equation modeling (SEM). (<b>a</b>) PC1 SEM plot, (<b>b</b>) PC2 SEM plot, (<b>c</b>) PC3 SEM plot, (<b>d</b>) PC4 SEM plot. Red solid arrows indicate positive effects, blue solid arrows indicate negative effects and dashed lines indicate non-significant paths. Significance levels of each predictor are * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001.</p>
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