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The mechanism of biochar improving acidic soil. | SciDraw Gallery

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The mechanism of biochar improving acidic soil.

Environmental Science

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The mechanism of biochar improving acidic soil.

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Title Suggestion: Figure 2. Framework Diagram of Soil Particle Size Inversion Mechanism Based on Multi-Angle BRDF Features.

Content Conceptualization:

Top (Data Input): Showcase multi-angle satellite imagery acquired from satellites (e.g., Terra satellite, with MISR and MODIS sensors indicated).

Middle (Core Processing): Input the multi-angle observation data into a "BRDF Model" module (e.g., a semi-empirical kernel-driven model). The model outputs several key physical model parameters (e.g., fiso, fvol, fgeo, or represented as k1, k2, k3).

Bottom (Inversion Output): Represent the "Novel Photometric Parameter Index Construction" using a formula or chart box (e.g., calculate an index P = fvol / fgeo using model parameters). Finally, output the final spatial distribution map of soil particle size through a "Particle Size Inversion Model" (e.g., a statistical model or a machine learning model).

Arrow Flow: Clearly connect the above modules with arrows to form a complete technical flowchart.

Title Suggestion: Figure 2. Framework Diagram of Soil Partic...

Generate a "J"-shaped curve depicting the biological invasion of rabbits in Australia. The image should be black with a white background.

Generate a "J"-shaped curve depicting the biological invasio...

Illustration: A schematic diagram of the synergistic mechanism of "phosphorus pre-recovery - FO membrane VFA retention - ammonia nitrogen migration" is constructed by combining Zeta potential, FTIR characterization results, and full-process performance data. The core synergistic logic can be divided into three closely linked steps to achieve full-chain synergy of "water quality optimization - interface regulation - substance distribution":

The first step: Water quality optimization synergy of phosphorus pre-recovery. The vivianite method achieves efficient phosphorus recovery through the crystallization reaction of Fe²⁺ and PO₄³⁻. At the same time, it removes some organic pollutants and colloids through crystal adsorption, reduces wastewater turbidity, ionic strength, and pollutant load, avoids inorganic scale deposition and aggravated organic pollution during subsequent FO membrane separation, and provides a stable influent water quality basis for efficient FO membrane separation.

The second step: Regulation synergy of membrane surface characteristics. Phosphorus pre-recovery avoids the modification effect of inorganic scale on the membrane surface and maintains the original strong negative charge characteristics of the membrane surface. It strengthens the directional retention of VFA⁻ through Donnan repulsion. At the same time, the electrostatic attraction of the membrane surface negative charge to NH₄⁺, combined with the osmotic pressure drive of the draw solution, accelerates the permeation and migration of NH₄⁺ and water molecules, and realizes the synchronous recovery of ammonia nitrogen and water. In addition, phosphorus pre-recovery reduces the adsorption of organic pollutants on the membrane surface, maintains membrane flux stability, and further improves the synergistic efficiency of "retention-migration".

The third step: Full-process synergy of substance migration. The PO₄³⁻ removed by phosphorus pre-recovery does not chemically react with VFA and ammonia nitrogen, avoiding the loss of target resource components. VFA is directionally retained on the feed liquid side to achieve enrichment, and ammonia nitrogen permeates through the membrane into the draw solution side to achieve recovery. The migration paths of the two do not interfere with each other and complement each other, forming a precise substance distribution pattern of "retention-migration", and ultimately achieving efficient synergy in wastewater resource utilization.

Illustration: A schematic diagram of the synergistic mechani...

H1: Environmental Effect  Gall richness differs between environments due to differences in the composition and structure of the plant community. Savanna and dry swamp  greater gall richness. Forest and wet swamp  lower richness. H2: Functional Diversity X Gall Richness --> Environments with greater functional diversity (FD) will have greater gall richness, regardless of the type of environment. FD explains more variation than taxonomic richness. H3: Phylogenetic Diversity X Gall Richness  Phylogenetically more aggregated environments (closely related plants) will have greater gall richness. Savanna and dry swamp  Lower phylogenetic diversity (PD), greater gall richness. Forest  greater PD, lower richness. Linear regression plots as predictors for each hypothesis.

H1: Environmental Effect  Gall richness differs between env...