Simulation of organic acid migration and transformation in mangrove soils based on soil column experiments

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The organic matter content at the discharge outlet of shrimp ponds is usually high. Therefore, it is necessary to collect soil from the discharge outlet of shrimp ponds and simulate the migration and transformation pathways of organic acids and related metabolic microorganisms in soil of mangrove wetlands through laboratory soil columns and the HYDRUS-1D model. Results showed that the content of oxalic acid remained relatively stable in the soil column at different depths, citric acid settled downward along the vertical direction, the concentration of acetic acid in the depth range of 30–50 cm increased. The organic acids formed insoluble or slightly soluble precipitates in the form of organic acid calcium, the organic acids in 40–50 cm were completely neutralized on the 18th day. The abundance of acid-producing Acinetobacter increased during the later stages of anaerobic acidification and disappeared after the addition of Ca(OH) 2 . The results of HYDRUS-1D simulation showed that the adsorption, deposition and transport of organic acids in the mangrove wetland were poor, the results of vertical infiltration modelling were in agreement with the soil column experiments. Earth and environmental sciences/Ocean sciences Earth and environmental sciences/Ocean sciences/Marine chemistry Earth and environmental sciences/Environmental sciences/Environmental impact mangrove wetland RT-HPLC method numerical modelling microbial sequencing Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Mangrove wetlands are one of the most diverse and productive marine ecosystems, with Hainan Province is the richest province in China in terms of mangrove species. Although Dongzhai Harbour Protected Area has taken a series of ecological restoration measures, such as returning ponds to forests, artificial restoration, environmental remediation, etc., the mangrove forests in the protected area have been damaged to varying degrees until 2016, with the mangrove forests in decline covering an area of about 100 hm 2 (accounting for 19% of the total area of mangrove forests in Dongzhai Harbour). More than 80% of the existing mangrove forests being inefficient degraded secondary forests, with the mangrove forest ecosystems health index continuously decreasing, and the mangrove ecosystem in Dongzhai Harbour is still deteriorating 1 . In Dongzhai Harbour, Hainan, the discharge of high-elevation shrimp culture ponds is the main source of pollution in mangroves, and a large amount of residual bait, excreta, chemical disinfectants and antibiotics discharged from shrimp ponds culture has caused serious pollution to the natural environment. Five year monitoring results of soil and pollution sources in Dongzhai Habour showed that soils in difficult mangrove restoration areas were usually acidic. the pH of soils in the mangrove restoration area of Tashi Town was < 4, and the pH of soils in the mangrove restoration area of Shanwei Village was < 6, so it can be preliminarily determined that acidic discharged from shrimp aquaculture in the high-elevation ponds are the main factors affecting the mangrove forests' restoration 2 . The results of literature research indicated that research on the toxicity of organic acids has focused on the impact of organic acids on crops 3 , 4 . Therefore, the study of the impact mechanism of organic acid substances in the mangrove ecosystem of the estuarine region is a pioneering research direction in the field of mangrove ecosystem research. Organic acids are one of the characteristic pollutants emitted or converted from aquaculture in high-elevation shrimp ponds. The expansion of aquaculture around mangroves greatly increased the emission of organic acids which can increase the mortality rate of mangrove plants. The emissions from high-elevation shrimp ponds include acidic chlorine dioxide disinfectants and iodine solutions such as povidone iodine solution, organic acid substances such as oxalic acid, citric acid, acetic acid, and other detoxifying agents are also presented in emissions 5 . In addition, a large amount of residual bait, excreta fish and shrimp carcasses discharged from shrimp pond culture into the mangrove forests usually underwent a large amount of enriched sedimentation in the mangrove forest inlet, which was decomposed by anaerobic microorganisms to generate organic acids 6 . This practice of directly applying unfermented and decomposed organic matter to plants is rare in the growth process of terrestrial plants, while it is a common phenomenon in China's mangrove wetland ecosystem. Previous studies showed that when the concentration of organic acids reaches 30 mg/L, mangrove plants will be poisoned and soil conditions in local areas within the protected area will be deteriorated 7 . In addition, shrimp pond aquaculture uses a large amount of quicklime disinfectant during pond cleaning, which is discharged into mangroves in the form of Ca(OH) 2 . The biological and chemical transformation of the soil during the discharge of such alkaline disinfectants, the temporal and spatial changes in the production of organic acids during the transformation process, as well as potential methane emissions from organic acids, all of these required the establishment of research methods to confirm. Therefore, it is necessary to conduct soil column simulation experiments in the laboratory to explore the chemical and biological changes of soil organic acids in mangrove ecosystems under the influence of some simple factors. This study aims to clarify the migration and transformation patterns of organic acids emitted and transformed from high-elevation shrimp ponds in mangrove wetland soil. Soil samples were collected from the discharge outlet of high-elevation shrimp ponds in Dongzhai Harbor mangrove wetland for column loading (Fig. 1 , 2 ). Based on the establishment of RT-HPLC method for simultaneous detection of multiple organic acids, the physical, chemical, and biological changes related to organic acids in the sediment at the discharge outlet of shrimp ponds in the Yanfeng area was studied. The effect of Ca(OH) 2 on the anaerobic digestion process using alkaline disinfectant in high-elevation shrimp ponds was examined after the end of anaerobic acidification, and the microbial changes were detected by high throughput sequencing at the beginning and end of anaerobic acidification and after the addition of alkaline disinfectant. The research results in this paper can provide a reference for the restoration and management of rapidly degrading mangrove wetlands. Results and discussion Establishment of organic acid standard curve The six organic acid standards were effectively separated by RT-HPLC when 10 µL of organic acid control samples of different mass concentrations were aspirated into the sample. A linear regression was performed on the peak area Y with the mass concentration X of organic acid standards, and the correlation coefficient R 2 of the regression equation ranged from 0.9975–0.9999, which indicated that a variety of organic acids could be detected at the same time by adopting the chromatographic conditions of RT-HPLC. Results of soil column experiments The water content, temperature and conductivity of the soil column device were monitored during the experiment using the ECH 2 O soil monitoring system, and a total of 76,758 records were recorded with Port1, Port2 and Port3 representing the monitoring of the upper, middle and lower monitoring positions. The monitoring results were shown in Fig. 3 , which showed that the water content of the soil column decreases gradually from the top to the bottom, while the water content at the same position remains relatively stable. The results of the conductivity showed that the deeper the subsoil, the lower the total dissolved solids content, which coincided with the results of the water content. the results of the temperature monitoring showed that the maximum temperature difference at the same depth in the course of the experiment of the soil column is 3.7 ℃, which is related to the weather condition. The maximum temperature difference at the same time was 2.5 ℃, which was caused by the fact that the upper layer of the subsoil is more likely to receive heat from the environment. The deeper the layer, the lower the efficiency of heat transfer in the subsoil. By sampling the effluent from the side outlet of the soil column device and using high performance liquid chromatography to determine the content of organic acids therein, the types and concentrations of organic acids in the substrate at different depths were obtained and varied with time as shown in Fig. 4 . The results of the soil column experiments showed that oxalic acid, with concentrations ranging from 6.3 mg/L to 7.5 mg/L, was the most widely distributed and stable organic acid in the mangrove wetland reserve, with its concentration remained relatively stable under different spatial and temporal changes. The change of citric acid with depth was more obvious, the concentration of citric acid in the shallow substrate was higher than that in the deep substrate at the beginning of the experiment. As the experiment proceeded, the concentration of citric acid in the shallow substrate gradually decreased, while the concentration of citric acid in the deep substrate gradually increased. Specifically, the concentration of citric acid in the 0–20 cm soil gradually decreased to 0 mg/L, the concentration of citric acid in the 20–30 cm soil first increased from 0 mg/L to 0.32mg/L, and then decreased to 0 mg/L. when it comes to the 30–40 cm soil, The concentration of citric acid increased from 0 mg/L to 0.39 mg/L, and the concentration of citric acid in the 40–50 cm soil increased from 0 mg/L to 0.50 mg/L, indicating that citric acid adsorption and sedimentation occurred in the substrate of the mangrove wetland. Acetic acid was not detected in the initial experiment, as the experiment progressed, the content of acetic acid in the deeper substrate of 30–50 cm gradually increased until it remained stable, indicating that in the anaerobic environment, the organic matter present in the substrate, such as fish and shrimp carcasses, bait from shrimp ponds, and excreta, etc., would undergo an anaerobic reaction to produce acetic acid. The changes of organic acids after adding Ca(OH) 2 are shown in Fig. 5 . The concentration of Ca(OH) 2 used in the simulation was 1.84 g/L, which was the discharge concentration during the dredging of shrimp ponds. This concentration was much higher than the content of organic acids in the substrate, so the organic acids were immediately neutralised when Ca(OH) 2 diffused to different depths, and the rate-limiting factor for the reduction of organic acids at this time mainly depended on the Ca(OH) 2 diffusion rate in the substrate. The organic acids in the 0–10 cm soil were completely neutralized on the 4th day, while the organic acids in the 40–50 cm soil were completely neutralized on the 18th day. Therefore, the calculated permeation rate of Ca(OH) 2 molecules was approximately 50/18 = 2.8 cm/d, indicating that the pores in the mangrove sediment were small and the permeability was poor, resulting in a slower permeation rate of Ca(OH) 2 molecules. After the neutralisation of organic acids in the 40–50 cm sediment, excess hydrochloric acid was added to the effluent, and the organic acids were detected by RT-HPLC. The total amount of organic acids in the effluent was 6.7 mg/L, while the total amount of each organic acid was 7.2 mg/L after the end of the anaerobic acidification, which indicated that the organic acids mainly existed as insoluble or slightly soluble precipitates of calcium organic acids after the addition of Ca(OH) 2 . Model fitting results The soils catalog item in the HYDRUS-1D water flow module contains 12 typical soil media such as sandy soil, silt, clay and other soil moisture characteristic curve related parameters, according to the column filled with soil media and the neural network prediction functionin HYDRUS-1D 8 , adjusted to speculate the moisture Characteristic parameters in the subsoil, θ r , θ s , α, n, K s , and l were 0.074 m 3 /m 3 , 0.355 m 3 /m 3 , 0.5 m − 1 , 1.09, 0.01 m/d, and 0.5, respectively, where the smaller K s indicated the worse ability of water transport in the soil 9 . In the parameter setting for solute transport, c was entered as the concentration of organic acids in the 10 cm subsoil at each sampling point 10 , where the partition coefficient of organic acids in the solid phase was 0.04; the integrated dispersion coefficient D was calculated according to the formula:D = 2.71×10 − 4 /M 0.71 , with M being the molar mass of the solute, and the smaller the value of D indicated the poorer ability of the soil to transport the substance. the reaction rate constant was 1.8×10 − 4 . The upper boundary of solute transport was selected as the concentration flux boundary according to the actual situation, and the lower boundary was selected as the zero-concentration gradient boundary without considering the background value of organic acid in soil, and at the same time, the concentration of the liquid phase was selected as the initial condition of the model, and five observation points were uniformly distributed from the top to the bottom of the term. The initial time step was set to be 0.1 d, and the minimum and maximum time steps were 0.01 d and 10 d, respectively; the permissible deviations of soil water content and pressure head were 0.0005 cm and 1 cm, respectively, and the pressure head was set to be 1 m. The simulation duration of this experiment was 1 a. The set parameters were used to predict the concentrations of oxalic acid and acetic acid at the depths of 0–10 cm, 10–20 cm, 20–30 cm, 30–40 cm, and 40–50 cm, and the actual measured concentrations were compared with those of oxalic acid and acetic acid, as shown in Fig. 6 . Table 1 Evaluation criteria for simulation results of oxalic acid content in 10–50 cm sediment 10 cm 20 30 40 cm 50 cm R 2 0.901 0.874 0.881 0.891 0.807 RMSE 0.014 0.019 0.024 0.016 0.034 Table 2 Evaluation criteria for simulation results of acetic acid content in 30–50 cm sediment 30 cm 40 cm 50 cm R 2 0.898 0.846 0.884 RMSE 0.017 0.013 0.019 The simulation accuracy of the model was evaluated using the coefficient of determination (R 2 ) and standard error (RMSE) based on the validation of soil measured soil oxalic acid concentration against the simulation results 11 . The R 2 and RMSE thresholds ranged from 0 to 1, where the larger the value of R 2 and the smaller the value of RMSE, the higher the simulation accuracy. The simulation accuracies are shown in Table 1 – 2 , the R 2 between the measured and simulated values of oxalic acid content in the substrate is between 0.8–0.9, and the RMSE is between 0.014–0.034. R 2 between the measured and simulated values of acetic acid content in the substrate is between 0.7–0.9, and the RMSE is between 0.013–0.019.The simulated values of the simulation and the measured values are in a higher degree of agreement, and the results of the model simulation can be better compared to the measured values. The model simulation results can better reflect the dynamic change characteristics of soil organic acids in each treatment. According to the neural network prediction function, K s was 1.01 cm/d, which was lower than the permeation rate of Ca(OH) 2 . The diffusion coefficient of oxalic acid was 0.111 cm 2 /d, and the diffusion coefficient of acetic acid was 0.148 cm 2 /d, which indicated that the vertical mobility of water and organic acid in the subsoil during the experiment of the soil column was relatively poor, and the subsoil had a strong adsorption capacity of organic acid, so that the concentration of organic acid in the subsoil in each level remained relatively stable, the overall potential contamination distribution pattern of organic acids was shown to be consistent. Microbial sequencing results The heatmap of species distribution of the top 30 species with the highest relative abundance at different stages during the soil column experiment is shown in Fig. 7 , and the most widely distributed species in the subsoil sampled this time are sva0081 , Woeseia , Sulfurovum , and some species that have not yet been identified, among which Woeseia is a common soil bacterium, which not only participates in the decomposition and cycling of soil organic matter, but also was able to synthesize some compounds that are beneficial to plant growth 12 . Moreover, Woeseia also help to maintain the structure and water balance of the soil 13 . Sulfurovum is a microorganism belonging to the sulphur-oxidising bacteria, which played a key role in the Sulphur cycle that can interact with iron and sulphides, leading to the precipitation and formation of minerals 14 . The most obvious change in the whole process was Acinetobacter , whose number was sparse and difficult to be detected at the beginning of the anaerobic reaction. Its number increased after stabilization of the anaerobic phase, and finally disappeared after the addition of Ca(OH) 2 solution, which coincided with the process of acetic acid change detected by HPLC. Among them, Acinetobacter colony appeared in large quantities in Acidification stage, while the content was low in Origin stage, and disappeared in Alkalization stage. This colony of microorganisms has a good acid tolerance, and it can secrete a large amount of organic acids, such as gluconic acid, acetic acid, amino carboxylic acid, and other small molecular organic acids 13 , 15 , which coincided with the detection results of HPLC, can prove that the microorganisms in the substrate consume the organic matter in the substrate to produce organic acids through the process of anaerobic fermentation in the process of soil column experiments. As shown in the Alkalization phase, the living environment of Acinetobacter was destroyed, leading to the disappearance of its community. Conclusion The practice of directly applying unfermented and decomposed organic matter to plants is rare in the growth process of terrestrial plants, but it is a common phenomenon in mangrove wetland ecosystems in China. The results of the soil column experiment showed that oxalic acid widely found in Dongzhai Harbour Mangrove Wetland Reserve was mainly derived from the residues of organic acid detoxifier and the decomposition of mangrove plant litter. Citric acid was mainly derived from organic acid detoxifier and migrated from top to bottom in the substrate; and acetic acid was mainly derived from the anaerobic acidification of the organic matter in the substrate. There were significant differences in the diversity and structure of the bacterial communities at different stages of the soil column experiment. Acetic acid was not detected and Acinetobacter abundance was the lowest at the Origin stage, while Acetic acid appeared at the beginning of the anaerobic stage and the abundance of Acinetobacter increased, and Acetic acid and Acinetobacter disappeared after alkalinization, thus, there were differences in the structure of the bacterial communities. Therefore, differences in bacterial community structure were significantly correlated with differences in organic acids at different levels of the substrate, and Acinetobacter was the main factor influencing microorganisms in the concentration of acetic acid in the substrate. The results of HYDRUS-1D simulation showed that the adsorption, deposition and transport of organic acids in Hainan Dongzhai Harbour Mangrove Wetland Reserve were poor, and the results of the vertical infiltration model were in agreement with the results of the samples taken from the Dongzhai Harbour Mangrove Wetland Reserve, so the model can be applied to the study of the vertical transport of organic acid pollutants in the soil of the Dongzhai Harbour Mangrove Wetland Reserve. The results of this study can provide data support for the discharge of wastewater after shrimp farming in Dongzhai Harbour high-elevation ponds, early warning of organic acid pollution, and the formulation and implementation of management policies. Materials and Methods Experimental materials In this study, the sampling points were set up according to the growth of mangrove forests and the distribution of shrimp ponds, and the latitude and longitude of the specific sampling points were 110°584′E 19°957′N (Fig. 1 ). Mud samples were manually drilled at low tide using a sampler with sampling depths of 0–10 cm, 10–20 cm, 20–30 cm, 30–40 cm and 40–50 cm, and the amount of mud taken from each section was about 3000–5000 g. The samples were put into sampling bags, numbered for refrigeration and transported back to the laboratory by air. Simultaneous Detection of Multiple Organic Acids by RT-HPLC Chromatographic conditions for the simultaneous determination of multiple organic acids by RT-HPLC The chromatographic conditions were as follows: Xtimate XB-C18 column (4.6 mm×250 mm, 5 µm); mobile phase: 0.01 mol/L potassium dihydrogen phosphate solution (adjusted by phosphoric acid at pH = 2.5); G4212-60008 diode array detector at a detection wavelength of 215 nm; flow rate: 0.8 mL/min; column temperature: 40 ℃; injection volume: 10 µL 16 . Under the above chromatographic conditions, the standard curves for the simultaneous determination of various organic acids were obtained. Solution preparation The standards of oxalic acid, formic acid, malic acid, lactic acid, acetic acid and citric acid were weighed precisely, and then diluted with ultrapure water into 1, 5, 10, 15 and 20 mg/L standard solutions. The reagents used were Macklin purity of AR and GR grade. Experimental method of soil column Firstly, petroleum jelly was evenly applied to the inner wall of the drenching column to prevent the formation of preferential flow of raw water along the inner pipe wall. The soil for the experiment was collected from Dongzhai Harbour Mangrove Wetland Ecological Reserve and filled in layers with a height of 50 cm. The soil column experimental setup is shown in Fig. 2 , with a column height of 63 cm and a diameter of 15 cm; 1 represents the outlets, with a spacing of 10 cm between the outlets; 2 represents the monitoring plugs, which were used to monitor the changes of the indexes of the upper, middle, and lower subsoils in the operation of the column setup, respectively; and 3 represents the ECH 2 O workstation for timely data storage. The experiment was conducted using the upper water inlet and the left side outlet, and the monitoring plug of the ECH 2 O soil monitoring system was inserted into the right-side hole to record the data, using the ECH 2 O software, which recorded one piece of data per minute, with each piece of data containing 9 readings. Seawater was used to simulate local tides by giving 10–20 cm of water pressure for 12 hours of the day, and the remaining 12 hours were spent sucking water out using the siphon principle to maintain water flow in the in-situ treatment model. Due to the slow rate of natural infiltration, samples were taken instantly at the side outlets and the organic acid content was determined using HPLC. The experiment was carried out for a total of 31 days after the organic acid content in the soil column remained stable, and then the experiment on the effect of alkaline disinfectant was carried out by adding Ca(OH) 2 solution at a concentration of 1.84 g/L to the top every 12 hours, keeping the same experimental operation as before. The experiment was ended when the organic acid content in the soil column device was 0. After the neutralisation of organic acids in the soil column device, excess hydrochloric acid was added to the effluent, and the reduced organic acid content was measured by RT-HPLC. The experiment was carried out for a total of 18 days. Modelling The Hydrus model was used to simulate the diffusion of organic acids in the mangrove area of Dongzhai Harbour, and the results obtained were combined with the measured results in order to obtain the migration and transformation law of organic acids in the mangrove wetland of Dongzhai Harbour. The flow of water can be described using the Ricards equation: $$\:\frac{\partial\:\theta\:}{\partial\:t}=\frac{\partial\:\left[K\right(\frac{\partial\:ℎ}{\partial\:z}-cos\alpha\:\left)\right]}{\partial\:z}-S$$ where θ is the soil volumetric moisture content; H is the soil pressure head; T is the simulation time; α represents the angle between the flow direction and the vertical direction. According to the above physical experimental model, the water flow is one-dimensional vertical seepage, i.e. α = 0; K is the unsaturated permeability coefficient; S is the source and sink term 17 . Soil moisture transport models can be used to describe the process of water transport in the soil, and the HYDRUS-1D software water flow model includes various soil moisture transport models such as the single pore medium model and the double pore/double permeable medium model. In this paper, the soil hydraulic model proposed by VanGenuchten-Mualem is used for simulation prediction, and the phenomenon of water flow hysteresis is not considered in the simulation 10 . The equation is: $$\:\left(ℎ\right)={\theta\:}_{r}+\frac{{\theta\:}_{s}-{\theta\:}_{r}}{{\left[1+{\left|\alpha\:ℎ\right|}^{n}\right]}^{m}},ℎ<0$$ $$\:\left(ℎ\right)={\theta\:}_{s},ℎ\ge\:0$$ $$\:K\left(ℎ\right)={K}_{s}{S}_{e}^{l}{[1-{(1-{S}_{e}^{1/m})}^{n}]}^{2}$$ $$\:{S}_{e}=\frac{\theta\:-{\theta\:}_{r}}{{\theta\:}_{s}-{\theta\:}_{r}}$$ $$\:m=1-\frac{1}{n},n>1$$ where θ r is the residual moisture content of the soil; θ s is the saturated soil moisture content; S e is the effective saturation; α is the bubbling pressure; n is the distribution index of soil pore size; Ks is the saturated hydraulic conductivity coefficient; l is the soil pore connectivity parameter, usually taken as 0.5. There are three main processes of soil solute transport, namely convection, molecular diffusion and mechanical dispersion. The classical convection-dispersion equation was used in the simulation to describe the one-dimensional solute transport in saturated-unsaturated pore media 18 . $$\:\frac{\partial\:\theta\:c}{\partial\:t}+\rho\:\frac{\partial\:s}{\partial\:t}=\frac{\partial\:}{\partial\:x}\left(\theta\:D\frac{\partial\:c}{\partial\:x}\right)-\frac{\partial\:qc}{\partial\:x}-S$$ where c is the liquid phase concentration of the solution; ρ is the soil bulk density; s is the solid phase concentration of the solute; D is the comprehensive dispersion coefficient; q is the volumetric flow flux density; S is the source and sink term. The effectiveness of the model simulation was evaluated by the following statistical parameters, mainly the coefficient of determination ( R 2 ) and root mean square error (RMSE). $$\:RSME=\sqrt{\frac{\sum\:_{i=1}^{n}{({y}_{i}-\stackrel{-}{{y}_{i}})}^{2}}{n}}$$ $$\:{R}^{2}=\frac{{\sum\:}_{i=1}^{n}{(\widehat{{y}_{i}}-\stackrel{-}{{y}_{i}})}^{2}}{{\sum\:}_{i=1}^{n}{({y}_{i}-\stackrel{-}{{y}_{i}})}^{2}}$$ Where n is the number of samples; y i is the true value of the target variable; \(\:\widehat{{y}_{i}}\) is the predicted value of the model; and \(\:\stackrel{-}{{y}_{i}}\) is the mean value of the target variable. Determination of microorganisms in the substrate at different depths At the early and late stages of anaerobic acidification and after the addition of alkaline disinfectant Ca(OH) 2 solution in the soil column experiment, respectively, an appropriate amount of soil was taken at 0–10 cm, 20–30 cm and 30–40 cm in self-sealing bags, and the samples at the early stage of the experiment were recorded as Origin 1–3, the samples after the anaerobic acidification were recorded as Acidification 1–3, and the samples collected after adding Ca(OH) 2 solution were recorded as Alkalisation1-3, and were stored in a refrigerator for 16S rDNA whole-pass sequencing by Suzhou Jinwei Zhi Biotechnology Co. The samples collected after the addition of Ca(OH) 2 solution were recorded as Alkalisation1-3, and stored in the refrigerator for 16S rDNA full-throughput sequencing by Suzhou Jinwei Zhi Biotechnology Co. Declarations Author Contributions Statement Xinyu Liu: Formal Analysis, Methodology, Investigation, Writing-Original Draft. Yunan Yang: Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Project Administration, Resources, Supervision, Writing-Review & Editing. Yangang Lin: Investigation, Writing-Review & Editing. Author Contribution Xinyu Liu: Formal Analysis, Methodology, Investigation, Writing-Original Draft.Yunan Yang: Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Project Administration, Resources, Supervision, Writing-Review & Editing. Yangang Lin: Investigation, Writing-Review & Editing. Acknowledgements This work was supported by the National Natural Science Foundation of China [grant numbers 31971549], the National Key Project of International Science and Technology Cooperation Program of China [grant number 2016YFE0106800] and the Guangxi Innovation-driven Development Special Fund Project [grant numbers AA17202032]. 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K., Ashigh, J. & Šimůnek, J. Numerical Evaluation of Nitrate Distributions in the Onion Root Zone under Conventional Furrow Fertigation. Journal of Hydrologic Engineering 21(2): 05015026.1-05015026.12. (1943). https://doi.org/10.1061/(ASCE)HE . -5584.0001304 (2016). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 11 Mar, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 27 Nov, 2024 Reviews received at journal 24 Nov, 2024 Reviewers agreed at journal 22 Nov, 2024 Reviews received at journal 22 Nov, 2024 Reviewers agreed at journal 22 Nov, 2024 Reviewers invited by journal 22 Nov, 2024 Editor assigned by journal 05 Nov, 2024 Editor invited by journal 04 Nov, 2024 Submission checks completed at journal 02 Nov, 2024 First submitted to journal 16 Oct, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5279180","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":376794684,"identity":"1a66900c-99ec-42ac-9a9a-964e1b7e3423","order_by":0,"name":"Xinyu Liu","email":"","orcid":"","institution":"School of Space and Environment, Beihang University","correspondingAuthor":false,"prefix":"","firstName":"Xinyu","middleName":"","lastName":"Liu","suffix":""},{"id":376794685,"identity":"918b51c4-4b8b-46fc-8aab-87f7237b4c35","order_by":1,"name":"Yunan Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYPACmwQIzUa8ljTStRwmQYvB8bOHX/PuOJ9ncPzwA4YPZYcZ+Gc3ENByJi/NmvfM7WKDM2kGjDPOHWaQuHOAgJYDOWbGvG23EzccSDBg5m07zGAgkUBAy/k3IC3nEjecf/6B+S9RWm7kGD/mbTuQuOFGjgEzIzFaJG+8MWOc25ZcDGQUHOw5l84jcYOAFr7zOcYf3rbZ5fGdT9/44EeZtRz/DAJaFA4wsEnxQDkHgJgHj2IIkG9gYP74g6CyUTAKRsEoGNEAANfNSq2qs1o1AAAAAElFTkSuQmCC","orcid":"","institution":"School of Space and Environment, Beihang University","correspondingAuthor":true,"prefix":"","firstName":"Yunan","middleName":"","lastName":"Yang","suffix":""},{"id":376794686,"identity":"76e943d5-f7a4-4c4a-aa2c-e7d6c975d52a","order_by":2,"name":"Yangang Lin","email":"","orcid":"","institution":"School of Space and Environment, Beihang University","correspondingAuthor":false,"prefix":"","firstName":"Yangang","middleName":"","lastName":"Lin","suffix":""}],"badges":[],"createdAt":"2024-10-17 02:53:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5279180/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5279180/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-91771-w","type":"published","date":"2025-03-11T15:58:25+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":68941372,"identity":"3eae1f82-858b-4320-a221-06daa34101cc","added_by":"auto","created_at":"2024-11-13 18:02:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":882737,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSampling point at shrimp pondsoutlet in Yanfeng Town\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5279180/v1/3f2bca236e0fc0e891680b4d.png"},{"id":68941369,"identity":"1c4755d3-3183-4fa5-a86f-cc65e0410d61","added_by":"auto","created_at":"2024-11-13 18:02:13","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":62623,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExperimental setup of soil column\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5279180/v1/889a5f3fccd93fc6eda7f632.jpeg"},{"id":68942290,"identity":"35faa356-13f0-4b21-951a-eaae7eb2abc8","added_by":"auto","created_at":"2024-11-13 18:18:13","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":106006,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePlot of conductivity and water content versus temperature for soil column experiments\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5279180/v1/acefa4c72a895cb83f9c57e6.jpeg"},{"id":68941368,"identity":"bdeaf061-55d7-4e15-b85b-28ed56092d9e","added_by":"auto","created_at":"2024-11-13 18:02:13","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":125924,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePlot of organic acids with time at different depths\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5279180/v1/c7b7d356a1f41c7e4f4ddb59.png"},{"id":68941795,"identity":"e3a38b1e-ebae-4785-a6e2-579ea1cdf2f9","added_by":"auto","created_at":"2024-11-13 18:10:13","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":139786,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePlot of organic acids with time after adding Ca(OH)\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sub\u003e\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-5279180/v1/b11ff8a33925ff59510f5672.png"},{"id":68941374,"identity":"59561d35-1167-4b64-84b9-29f90d3c1107","added_by":"auto","created_at":"2024-11-13 18:02:13","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":249512,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of simulated and measured values of oxalic acid and acetic acid in the bottom mud\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5279180/v1/b627d83715598d3059eca1ee.jpeg"},{"id":68941793,"identity":"6f8389d7-82f6-4fe3-9e52-570d30d0bad0","added_by":"auto","created_at":"2024-11-13 18:10:13","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":115083,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHeatmap of distribution at species level\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-5279180/v1/7b3a9313cc37ae26e586c612.png"},{"id":78689274,"identity":"34586433-f8c9-4385-a14a-c5fa66fb4956","added_by":"auto","created_at":"2025-03-17 16:12:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2799156,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5279180/v1/099ae29c-180a-41da-a6fc-7f9b066a2e3b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Simulation of organic acid migration and transformation in mangrove soils based on soil column experiments","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMangrove wetlands are one of the most diverse and productive marine ecosystems, with Hainan Province is the richest province in China in terms of mangrove species. Although Dongzhai Harbour Protected Area has taken a series of ecological restoration measures, such as returning ponds to forests, artificial restoration, environmental remediation, etc., the mangrove forests in the protected area have been damaged to varying degrees until 2016, with the mangrove forests in decline covering an area of about 100 hm\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e (accounting for 19% of the total area of mangrove forests in Dongzhai Harbour). More than 80% of the existing mangrove forests being inefficient degraded secondary forests, with the mangrove forest ecosystems health index continuously decreasing, and the mangrove ecosystem in Dongzhai Harbour is still deteriorating\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn Dongzhai Harbour, Hainan, the discharge of high-elevation shrimp culture ponds is the main source of pollution in mangroves, and a large amount of residual bait, excreta, chemical disinfectants and antibiotics discharged from shrimp ponds culture has caused serious pollution to the natural environment. Five year monitoring results of soil and pollution sources in Dongzhai Habour showed that soils in difficult mangrove restoration areas were usually acidic. the pH of soils in the mangrove restoration area of Tashi Town was \u0026lt;\u0026thinsp;4, and the pH of soils in the mangrove restoration area of Shanwei Village was \u0026lt;\u0026thinsp;6, so it can be preliminarily determined that acidic discharged from shrimp aquaculture in the high-elevation ponds are the main factors affecting the mangrove forests' restoration\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. The results of literature research indicated that research on the toxicity of organic acids has focused on the impact of organic acids on crops\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Therefore, the study of the impact mechanism of organic acid substances in the mangrove ecosystem of the estuarine region is a pioneering research direction in the field of mangrove ecosystem research. Organic acids are one of the characteristic pollutants emitted or converted from aquaculture in high-elevation shrimp ponds. The expansion of aquaculture around mangroves greatly increased the emission of organic acids which can increase the mortality rate of mangrove plants.\u003c/p\u003e \u003cp\u003eThe emissions from high-elevation shrimp ponds include acidic chlorine dioxide disinfectants and iodine solutions such as povidone iodine solution, organic acid substances such as oxalic acid, citric acid, acetic acid, and other detoxifying agents are also presented in emissions\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. In addition, a large amount of residual bait, excreta fish and shrimp carcasses discharged from shrimp pond culture into the mangrove forests usually underwent a large amount of enriched sedimentation in the mangrove forest inlet, which was decomposed by anaerobic microorganisms to generate organic acids\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. This practice of directly applying unfermented and decomposed organic matter to plants is rare in the growth process of terrestrial plants, while it is a common phenomenon in China's mangrove wetland ecosystem. Previous studies showed that when the concentration of organic acids reaches 30 mg/L, mangrove plants will be poisoned and soil conditions in local areas within the protected area will be deteriorated\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. In addition, shrimp pond aquaculture uses a large amount of quicklime disinfectant during pond cleaning, which is discharged into mangroves in the form of Ca(OH)\u003csub\u003e2\u003c/sub\u003e. The biological and chemical transformation of the soil during the discharge of such alkaline disinfectants, the temporal and spatial changes in the production of organic acids during the transformation process, as well as potential methane emissions from organic acids, all of these required the establishment of research methods to confirm. Therefore, it is necessary to conduct soil column simulation experiments in the laboratory to explore the chemical and biological changes of soil organic acids in mangrove ecosystems under the influence of some simple factors.\u003c/p\u003e \u003cp\u003eThis study aims to clarify the migration and transformation patterns of organic acids emitted and transformed from high-elevation shrimp ponds in mangrove wetland soil. Soil samples were collected from the discharge outlet of high-elevation shrimp ponds in Dongzhai Harbor mangrove wetland for column loading (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Based on the establishment of RT-HPLC method for simultaneous detection of multiple organic acids, the physical, chemical, and biological changes related to organic acids in the sediment at the discharge outlet of shrimp ponds in the Yanfeng area was studied. The effect of Ca(OH)\u003csub\u003e2\u003c/sub\u003e on the anaerobic digestion process using alkaline disinfectant in high-elevation shrimp ponds was examined after the end of anaerobic acidification, and the microbial changes were detected by high throughput sequencing at the beginning and end of anaerobic acidification and after the addition of alkaline disinfectant. The research results in this paper can provide a reference for the restoration and management of rapidly degrading mangrove wetlands.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Results and discussion","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eEstablishment of organic acid standard curve\u003c/h2\u003e \u003cp\u003eThe six organic acid standards were effectively separated by RT-HPLC when 10 \u0026micro;L of organic acid control samples of different mass concentrations were aspirated into the sample. A linear regression was performed on the peak area Y with the mass concentration X of organic acid standards, and the correlation coefficient R\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e of the regression equation ranged from 0.9975\u0026ndash;0.9999, which indicated that a variety of organic acids could be detected at the same time by adopting the chromatographic conditions of RT-HPLC.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eResults of soil column experiments\u003c/h3\u003e\n\u003cp\u003e \u003c/p\u003e \u003cp\u003eThe water content, temperature and conductivity of the soil column device were monitored during the experiment using the ECH\u003csub\u003e2\u003c/sub\u003eO soil monitoring system, and a total of 76,758 records were recorded with Port1, Port2 and Port3 representing the monitoring of the upper, middle and lower monitoring positions. The monitoring results were shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, which showed that the water content of the soil column decreases gradually from the top to the bottom, while the water content at the same position remains relatively stable. The results of the conductivity showed that the deeper the subsoil, the lower the total dissolved solids content, which coincided with the results of the water content. the results of the temperature monitoring showed that the maximum temperature difference at the same depth in the course of the experiment of the soil column is 3.7 ℃, which is related to the weather condition. The maximum temperature difference at the same time was 2.5 ℃, which was caused by the fact that the upper layer of the subsoil is more likely to receive heat from the environment. The deeper the layer, the lower the efficiency of heat transfer in the subsoil.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBy sampling the effluent from the side outlet of the soil column device and using high performance liquid chromatography to determine the content of organic acids therein, the types and concentrations of organic acids in the substrate at different depths were obtained and varied with time as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The results of the soil column experiments showed that oxalic acid, with concentrations ranging from 6.3 mg/L to 7.5 mg/L, was the most widely distributed and stable organic acid in the mangrove wetland reserve, with its concentration remained relatively stable under different spatial and temporal changes. The change of citric acid with depth was more obvious, the concentration of citric acid in the shallow substrate was higher than that in the deep substrate at the beginning of the experiment. As the experiment proceeded, the concentration of citric acid in the shallow substrate gradually decreased, while the concentration of citric acid in the deep substrate gradually increased. Specifically, the concentration of citric acid in the 0\u0026ndash;20 cm soil gradually decreased to 0 mg/L, the concentration of citric acid in the 20\u0026ndash;30 cm soil first increased from 0 mg/L to 0.32mg/L, and then decreased to 0 mg/L. when it comes to the 30\u0026ndash;40 cm soil, The concentration of citric acid increased from 0 mg/L to 0.39 mg/L, and the concentration of citric acid in the 40\u0026ndash;50 cm soil increased from 0 mg/L to 0.50 mg/L, indicating that citric acid adsorption and sedimentation occurred in the substrate of the mangrove wetland. Acetic acid was not detected in the initial experiment, as the experiment progressed, the content of acetic acid in the deeper substrate of 30\u0026ndash;50 cm gradually increased until it remained stable, indicating that in the anaerobic environment, the organic matter present in the substrate, such as fish and shrimp carcasses, bait from shrimp ponds, and excreta, etc., would undergo an anaerobic reaction to produce acetic acid.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe changes of organic acids after adding Ca(OH)\u003csub\u003e2\u003c/sub\u003e are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The concentration of Ca(OH)\u003csub\u003e2\u003c/sub\u003e used in the simulation was 1.84 g/L, which was the discharge concentration during the dredging of shrimp ponds. This concentration was much higher than the content of organic acids in the substrate, so the organic acids were immediately neutralised when Ca(OH)\u003csub\u003e2\u003c/sub\u003e diffused to different depths, and the rate-limiting factor for the reduction of organic acids at this time mainly depended on the Ca(OH)\u003csub\u003e2\u003c/sub\u003e diffusion rate in the substrate. The organic acids in the 0\u0026ndash;10 cm soil were completely neutralized on the 4th day, while the organic acids in the 40\u0026ndash;50 cm soil were completely neutralized on the 18th day. Therefore, the calculated permeation rate of Ca(OH)\u003csub\u003e2\u003c/sub\u003e molecules was approximately 50/18\u0026thinsp;=\u0026thinsp;2.8 cm/d, indicating that the pores in the mangrove sediment were small and the permeability was poor, resulting in a slower permeation rate of Ca(OH)\u003csub\u003e2\u003c/sub\u003e molecules. After the neutralisation of organic acids in the 40\u0026ndash;50 cm sediment, excess hydrochloric acid was added to the effluent, and the organic acids were detected by RT-HPLC. The total amount of organic acids in the effluent was 6.7 mg/L, while the total amount of each organic acid was 7.2 mg/L after the end of the anaerobic acidification, which indicated that the organic acids mainly existed as insoluble or slightly soluble precipitates of calcium organic acids after the addition of Ca(OH)\u003csub\u003e2\u003c/sub\u003e.\u003c/p\u003e\n\u003ch3\u003eModel fitting results\u003c/h3\u003e\n\u003cp\u003eThe soils catalog item in the HYDRUS-1D water flow module contains 12 typical soil media such as sandy soil, silt, clay and other soil moisture characteristic curve related parameters, according to the column filled with soil media and the neural network prediction functionin HYDRUS-1D\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, adjusted to speculate the moisture Characteristic parameters in the subsoil, θ\u003csub\u003er\u003c/sub\u003e, θ\u003csub\u003es\u003c/sub\u003e, α, n, K\u003csub\u003es\u003c/sub\u003e, and l were 0.074 m\u003csup\u003e3\u003c/sup\u003e/m\u003csup\u003e3\u003c/sup\u003e, 0.355 m\u003csup\u003e3\u003c/sup\u003e/m\u003csup\u003e3\u003c/sup\u003e, 0.5 m\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, 1.09, 0.01 m/d, and 0.5, respectively, where the smaller K\u003csub\u003es\u003c/sub\u003e indicated the worse ability of water transport in the soil\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e .\u003c/p\u003e \u003cp\u003eIn the parameter setting for solute transport, c was entered as the concentration of organic acids in the 10 cm subsoil at each sampling point\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, where the partition coefficient of organic acids in the solid phase was 0.04; the integrated dispersion coefficient D was calculated according to the formula:D\u0026thinsp;=\u0026thinsp;2.71\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e/M\u003csup\u003e0.71\u003c/sup\u003e, with M being the molar mass of the solute, and the smaller the value of D indicated the poorer ability of the soil to transport the substance. the reaction rate constant was 1.8\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe upper boundary of solute transport was selected as the concentration flux boundary according to the actual situation, and the lower boundary was selected as the zero-concentration gradient boundary without considering the background value of organic acid in soil, and at the same time, the concentration of the liquid phase was selected as the initial condition of the model, and five observation points were uniformly distributed from the top to the bottom of the term.\u003c/p\u003e \u003cp\u003eThe initial time step was set to be 0.1 d, and the minimum and maximum time steps were 0.01 d and 10 d, respectively; the permissible deviations of soil water content and pressure head were 0.0005 cm and 1 cm, respectively, and the pressure head was set to be 1 m. The simulation duration of this experiment was 1 a.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe set parameters were used to predict the concentrations of oxalic acid and acetic acid at the depths of 0\u0026ndash;10 cm, 10\u0026ndash;20 cm, 20\u0026ndash;30 cm, 30\u0026ndash;40 cm, and 40\u0026ndash;50 cm, and the actual measured concentrations were compared with those of oxalic acid and acetic acid, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEvaluation criteria for simulation results of oxalic acid content in 10\u0026ndash;50 cm sediment\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 cm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40 cm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e50 cm\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.874\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.881\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.807\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRMSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEvaluation criteria for simulation results of acetic acid content in 30\u0026ndash;50 cm sediment\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 cm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40 cm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50 cm\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.884\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRMSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe simulation accuracy of the model was evaluated using the coefficient of determination (R\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e) and standard error (RMSE) based on the validation of soil measured soil oxalic acid concentration against the simulation results\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. The R\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e and RMSE thresholds ranged from 0 to 1, where the larger the value of R\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e and the smaller the value of RMSE, the higher the simulation accuracy. The simulation accuracies are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the R\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e between the measured and simulated values of oxalic acid content in the substrate is between 0.8\u0026ndash;0.9, and the RMSE is between 0.014\u0026ndash;0.034. R\u003csub\u003e2\u003c/sub\u003e between the measured and simulated values of acetic acid content in the substrate is between 0.7\u0026ndash;0.9, and the RMSE is between 0.013\u0026ndash;0.019.The simulated values of the simulation and the measured values are in a higher degree of agreement, and the results of the model simulation can be better compared to the measured values. The model simulation results can better reflect the dynamic change characteristics of soil organic acids in each treatment.\u003c/p\u003e \u003cp\u003eAccording to the neural network prediction function, K\u003csub\u003es\u003c/sub\u003e was 1.01 cm/d, which was lower than the permeation rate of Ca(OH)\u003csub\u003e2\u003c/sub\u003e. The diffusion coefficient of oxalic acid was 0.111 cm\u003csup\u003e2\u003c/sup\u003e/d, and the diffusion coefficient of acetic acid was 0.148 cm\u003csup\u003e2\u003c/sup\u003e/d, which indicated that the vertical mobility of water and organic acid in the subsoil during the experiment of the soil column was relatively poor, and the subsoil had a strong adsorption capacity of organic acid, so that the concentration of organic acid in the subsoil in each level remained relatively stable, the overall potential contamination distribution pattern of organic acids was shown to be consistent.\u003c/p\u003e\n\u003ch3\u003eMicrobial sequencing results\u003c/h3\u003e\n\u003cp\u003e \u003c/p\u003e \u003cp\u003eThe heatmap of species distribution of the top 30 species with the highest relative abundance at different stages during the soil column experiment is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, and the most widely distributed species in the subsoil sampled this time are \u003cem\u003esva0081\u003c/em\u003e, \u003cem\u003eWoeseia\u003c/em\u003e, \u003cem\u003eSulfurovum\u003c/em\u003e, and some species that have not yet been identified, among which \u003cem\u003eWoeseia\u003c/em\u003e is a common soil bacterium, which not only participates in the decomposition and cycling of soil organic matter, but also was able to synthesize some compounds that are beneficial to plant growth\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Moreover, \u003cem\u003eWoeseia\u003c/em\u003e also help to maintain the structure and water balance of the soil\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eSulfurovum\u003c/em\u003e is a microorganism belonging to the sulphur-oxidising bacteria, which played a key role in the Sulphur cycle that can interact with iron and sulphides, leading to the precipitation and formation of minerals\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. The most obvious change in the whole process was \u003cem\u003eAcinetobacter\u003c/em\u003e, whose number was sparse and difficult to be detected at the beginning of the anaerobic reaction. Its number increased after stabilization of the anaerobic phase, and finally disappeared after the addition of Ca(OH)\u003csub\u003e2\u003c/sub\u003e solution, which coincided with the process of acetic acid change detected by HPLC.\u003c/p\u003e \u003cp\u003eAmong them, \u003cem\u003eAcinetobacter\u003c/em\u003e colony appeared in large quantities in Acidification stage, while the content was low in Origin stage, and disappeared in Alkalization stage. This colony of microorganisms has a good acid tolerance, and it can secrete a large amount of organic acids, such as gluconic acid, acetic acid, amino carboxylic acid, and other small molecular organic acids\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, which coincided with the detection results of HPLC, can prove that the microorganisms in the substrate consume the organic matter in the substrate to produce organic acids through the process of anaerobic fermentation in the process of soil column experiments. As shown in the Alkalization phase, the living environment of \u003cem\u003eAcinetobacter\u003c/em\u003e was destroyed, leading to the disappearance of its community.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe practice of directly applying unfermented and decomposed organic matter to plants is rare in the growth process of terrestrial plants, but it is a common phenomenon in mangrove wetland ecosystems in China. The results of the soil column experiment showed that oxalic acid widely found in Dongzhai Harbour Mangrove Wetland Reserve was mainly derived from the residues of organic acid detoxifier and the decomposition of mangrove plant litter. Citric acid was mainly derived from organic acid detoxifier and migrated from top to bottom in the substrate; and acetic acid was mainly derived from the anaerobic acidification of the organic matter in the substrate.\u003c/p\u003e \u003cp\u003eThere were significant differences in the diversity and structure of the bacterial communities at different stages of the soil column experiment. Acetic acid was not detected and \u003cem\u003eAcinetobacter\u003c/em\u003e abundance was the lowest at the Origin stage, while Acetic acid appeared at the beginning of the anaerobic stage and the abundance of \u003cem\u003eAcinetobacter\u003c/em\u003e increased, and Acetic acid and \u003cem\u003eAcinetobacter\u003c/em\u003e disappeared after alkalinization, thus, there were differences in the structure of the bacterial communities. Therefore, differences in bacterial community structure were significantly correlated with differences in organic acids at different levels of the substrate, and \u003cem\u003eAcinetobacter\u003c/em\u003e was the main factor influencing microorganisms in the concentration of acetic acid in the substrate.\u003c/p\u003e \u003cp\u003eThe results of HYDRUS-1D simulation showed that the adsorption, deposition and transport of organic acids in Hainan Dongzhai Harbour Mangrove Wetland Reserve were poor, and the results of the vertical infiltration model were in agreement with the results of the samples taken from the Dongzhai Harbour Mangrove Wetland Reserve, so the model can be applied to the study of the vertical transport of organic acid pollutants in the soil of the Dongzhai Harbour Mangrove Wetland Reserve. The results of this study can provide data support for the discharge of wastewater after shrimp farming in Dongzhai Harbour high-elevation ponds, early warning of organic acid pollution, and the formulation and implementation of management policies.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eExperimental materials\u003c/h2\u003e \u003cp\u003eIn this study, the sampling points were set up according to the growth of mangrove forests and the distribution of shrimp ponds, and the latitude and longitude of the specific sampling points were 110\u0026deg;584\u0026prime;E 19\u0026deg;957\u0026prime;N (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Mud samples were manually drilled at low tide using a sampler with sampling depths of 0\u0026ndash;10 cm, 10\u0026ndash;20 cm, 20\u0026ndash;30 cm, 30\u0026ndash;40 cm and 40\u0026ndash;50 cm, and the amount of mud taken from each section was about 3000\u0026ndash;5000 g. The samples were put into sampling bags, numbered for refrigeration and transported back to the laboratory by air.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSimultaneous Detection of Multiple Organic Acids by RT-HPLC\u003c/h3\u003e\n\u003cp\u003eChromatographic conditions for the simultaneous determination of multiple organic acids by RT-HPLC\u003c/p\u003e \u003cp\u003eThe chromatographic conditions were as follows: Xtimate XB-C18 column (4.6 mm\u0026times;250 mm, 5 \u0026micro;m); mobile phase: 0.01 mol/L potassium dihydrogen phosphate solution (adjusted by phosphoric acid at pH\u0026thinsp;=\u0026thinsp;2.5); G4212-60008 diode array detector at a detection wavelength of 215 nm; flow rate: 0.8 mL/min; column temperature: 40 ℃; injection volume: 10 \u0026micro;L\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Under the above chromatographic conditions, the standard curves for the simultaneous determination of various organic acids were obtained.\u003c/p\u003e \u003cp\u003eSolution preparation\u003c/p\u003e \u003cp\u003eThe standards of oxalic acid, formic acid, malic acid, lactic acid, acetic acid and citric acid were weighed precisely, and then diluted with ultrapure water into 1, 5, 10, 15 and 20 mg/L standard solutions. The reagents used were Macklin purity of AR and GR grade.\u003c/p\u003e \u003cp\u003eExperimental method of soil column\u003c/p\u003e \u003cp\u003eFirstly, petroleum jelly was evenly applied to the inner wall of the drenching column to prevent the formation of preferential flow of raw water along the inner pipe wall. The soil for the experiment was collected from Dongzhai Harbour Mangrove Wetland Ecological Reserve and filled in layers with a height of 50 cm. The soil column experimental setup is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, with a column height of 63 cm and a diameter of 15 cm; 1 represents the outlets, with a spacing of 10 cm between the outlets; 2 represents the monitoring plugs, which were used to monitor the changes of the indexes of the upper, middle, and lower subsoils in the operation of the column setup, respectively; and 3 represents the ECH\u003csub\u003e2\u003c/sub\u003eO workstation for timely data storage. The experiment was conducted using the upper water inlet and the left side outlet, and the monitoring plug of the ECH\u003csub\u003e2\u003c/sub\u003eO soil monitoring system was inserted into the right-side hole to record the data, using the ECH\u003csub\u003e2\u003c/sub\u003eO software, which recorded one piece of data per minute, with each piece of data containing 9 readings. Seawater was used to simulate local tides by giving 10\u0026ndash;20 cm of water pressure for 12 hours of the day, and the remaining 12 hours were spent sucking water out using the siphon principle to maintain water flow in the in-situ treatment model. Due to the slow rate of natural infiltration, samples were taken instantly at the side outlets and the organic acid content was determined using HPLC. The experiment was carried out for a total of 31 days after the organic acid content in the soil column remained stable, and then the experiment on the effect of alkaline disinfectant was carried out by adding Ca(OH)\u003csub\u003e2\u003c/sub\u003e solution at a concentration of 1.84 g/L to the top every 12 hours, keeping the same experimental operation as before. The experiment was ended when the organic acid content in the soil column device was 0. After the neutralisation of organic acids in the soil column device, excess hydrochloric acid was added to the effluent, and the reduced organic acid content was measured by RT-HPLC. The experiment was carried out for a total of 18 days.\u003c/p\u003e \u003cp\u003eModelling\u003c/p\u003e \u003cp\u003eThe Hydrus model was used to simulate the diffusion of organic acids in the mangrove area of Dongzhai Harbour, and the results obtained were combined with the measured results in order to obtain the migration and transformation law of organic acids in the mangrove wetland of Dongzhai Harbour.\u003c/p\u003e \u003cp\u003eThe flow of water can be described using the Ricards equation:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\frac{\\partial\\:\\theta\\:}{\\partial\\:t}=\\frac{\\partial\\:\\left[K\\right(\\frac{\\partial\\:ℎ}{\\partial\\:z}-cos\\alpha\\:\\left)\\right]}{\\partial\\:z}-S$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eθ\u003c/em\u003e is the soil volumetric moisture content; \u003cem\u003eH\u003c/em\u003e is the soil pressure head; \u003cem\u003eT\u003c/em\u003e is the simulation time; \u003cem\u003eα\u003c/em\u003e represents the angle between the flow direction and the vertical direction. According to the above physical experimental model, the water flow is one-dimensional vertical seepage, i.e. \u003cem\u003eα\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0; K is the unsaturated permeability coefficient; S is the source and sink term\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSoil moisture transport models can be used to describe the process of water transport in the soil, and the HYDRUS-1D software water flow model includes various soil moisture transport models such as the single pore medium model and the double pore/double permeable medium model. In this paper, the soil hydraulic model proposed by VanGenuchten-Mualem is used for simulation prediction, and the phenomenon of water flow hysteresis is not considered in the simulation\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. The equation is:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:\\left(ℎ\\right)={\\theta\\:}_{r}+\\frac{{\\theta\\:}_{s}-{\\theta\\:}_{r}}{{\\left[1+{\\left|\\alpha\\:ℎ\\right|}^{n}\\right]}^{m}},ℎ\u0026lt;0$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:\\left(ℎ\\right)={\\theta\\:}_{s},ℎ\\ge\\:0$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:K\\left(ℎ\\right)={K}_{s}{S}_{e}^{l}{[1-{(1-{S}_{e}^{1/m})}^{n}]}^{2}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Eque\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e\n$$\\:{S}_{e}=\\frac{\\theta\\:-{\\theta\\:}_{r}}{{\\theta\\:}_{s}-{\\theta\\:}_{r}}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equf\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equf\" name=\"EquationSource\"\u003e\n$$\\:m=1-\\frac{1}{n},n\u0026gt;1$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eθ\u003c/em\u003e\u003csub\u003e\u003cem\u003er\u003c/em\u003e\u003c/sub\u003e is the residual moisture content of the soil; \u003cem\u003eθ\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e is the saturated soil moisture content; \u003cem\u003eS\u003c/em\u003e\u003csub\u003e\u003cem\u003ee\u003c/em\u003e\u003c/sub\u003e is the effective saturation; \u003cem\u003eα\u003c/em\u003e is the bubbling pressure; \u003cem\u003en\u003c/em\u003e is the distribution index of soil pore size; \u003cem\u003eKs\u003c/em\u003e is the saturated hydraulic conductivity coefficient; \u003cem\u003el\u003c/em\u003e is the soil pore connectivity parameter, usually taken as 0.5.\u003c/p\u003e \u003cp\u003eThere are three main processes of soil solute transport, namely convection, molecular diffusion and mechanical dispersion. The classical convection-dispersion equation was used in the simulation to describe the one-dimensional solute transport in saturated-unsaturated pore media\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003cdiv id=\"Equg\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equg\" name=\"EquationSource\"\u003e\n$$\\:\\frac{\\partial\\:\\theta\\:c}{\\partial\\:t}+\\rho\\:\\frac{\\partial\\:s}{\\partial\\:t}=\\frac{\\partial\\:}{\\partial\\:x}\\left(\\theta\\:D\\frac{\\partial\\:c}{\\partial\\:x}\\right)-\\frac{\\partial\\:qc}{\\partial\\:x}-S$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003ec\u003c/em\u003e is the liquid phase concentration of the solution; \u003cem\u003eρ\u003c/em\u003e is the soil bulk density; \u003cem\u003es\u003c/em\u003e is the solid phase concentration of the solute; \u003cem\u003eD\u003c/em\u003e is the comprehensive dispersion coefficient; \u003cem\u003eq\u003c/em\u003e is the volumetric flow flux density; \u003cem\u003eS\u003c/em\u003e is the source and sink term.\u003c/p\u003e \u003cp\u003eThe effectiveness of the model simulation was evaluated by the following statistical parameters, mainly the coefficient of determination (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e) and root mean square error (RMSE).\u003cdiv id=\"Equh\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equh\" name=\"EquationSource\"\u003e\n$$\\:RSME=\\sqrt{\\frac{\\sum\\:_{i=1}^{n}{({y}_{i}-\\stackrel{-}{{y}_{i}})}^{2}}{n}}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equi\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equi\" name=\"EquationSource\"\u003e\n$$\\:{R}^{2}=\\frac{{\\sum\\:}_{i=1}^{n}{(\\widehat{{y}_{i}}-\\stackrel{-}{{y}_{i}})}^{2}}{{\\sum\\:}_{i=1}^{n}{({y}_{i}-\\stackrel{-}{{y}_{i}})}^{2}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere n is the number of samples; \u003cem\u003ey\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e is the true value of the target variable; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\widehat{{y}_{i}}\\)\u003c/span\u003e\u003c/span\u003e is the predicted value of the model; and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{{y}_{i}}\\)\u003c/span\u003e\u003c/span\u003e is the mean value of the target variable.\u003c/p\u003e \u003cp\u003eDetermination of microorganisms in the substrate at different depths\u003c/p\u003e \u003cp\u003eAt the early and late stages of anaerobic acidification and after the addition of alkaline disinfectant Ca(OH)\u003csub\u003e2\u003c/sub\u003e solution in the soil column experiment, respectively, an appropriate amount of soil was taken at 0\u0026ndash;10 cm, 20\u0026ndash;30 cm and 30\u0026ndash;40 cm in self-sealing bags, and the samples at the early stage of the experiment were recorded as Origin 1\u0026ndash;3, the samples after the anaerobic acidification were recorded as Acidification 1\u0026ndash;3, and the samples collected after adding Ca(OH)\u003csub\u003e2\u003c/sub\u003e solution were recorded as Alkalisation1-3, and were stored in a refrigerator for 16S rDNA whole-pass sequencing by Suzhou Jinwei Zhi Biotechnology Co. The samples collected after the addition of Ca(OH)\u003csub\u003e2\u003c/sub\u003e solution were recorded as Alkalisation1-3, and stored in the refrigerator for 16S rDNA full-throughput sequencing by Suzhou Jinwei Zhi Biotechnology Co.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contributions Statement\u003c/h2\u003e\n\u003cp\u003eXinyu Liu: Formal Analysis, Methodology, Investigation, Writing-Original Draft.\u003c/p\u003e\n\u003cp\u003eYunan Yang: Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Project Administration, Resources, Supervision, Writing-Review \u0026amp; Editing.\u003c/p\u003e\n\u003cp\u003eYangang Lin: Investigation, Writing-Review \u0026amp; Editing.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eXinyu Liu: Formal Analysis, Methodology, Investigation, Writing-Original Draft.Yunan Yang: Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Project Administration, Resources, Supervision, Writing-Review \u0026amp; Editing. Yangang Lin: Investigation, Writing-Review \u0026amp; Editing.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China [grant numbers 31971549], the National Key Project of International Science and Technology Cooperation Program of China [grant number 2016YFE0106800] and the Guangxi Innovation-driven Development Special Fund Project [grant numbers AA17202032].\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eAll data supporting the finding of this study are available within this article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLiu, J. \u0026amp; Myat, T. Contaminants and heavy metals along the mangrove area of Dongzhai Harbor, China: distribution and assessment. \u003cem\u003eSN Appl. 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J., Sahoo, B., Rabindra, K. \u0026amp; Panda Modeling the water and nitrogen transports in a soil\u0026ndash;paddy\u0026ndash;atmosphere system using HYDRUS-1D and lysimeter experiment. \u003cem\u003ePaddy Water Environ.\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e (4), 831\u0026ndash;846. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10333-017-0596-9\u003c/span\u003e\u003cspan address=\"10.1007/s10333-017-0596-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeb, S. K., Sharma, P., Shukla, M. K., Ashigh, J. \u0026amp; Šimůnek, J. Numerical Evaluation of Nitrate Distributions in the Onion Root Zone under Conventional Furrow Fertigation. \u003cem\u003eJournal of Hydrologic Engineering\u003c/em\u003e 21(2): 05015026.1-05015026.12. (1943). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1061/(ASCE)HE\u003c/span\u003e\u003cspan address=\"10.1061/(ASCE)HE\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. -5584.0001304 (2016).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"mangrove wetland, RT-HPLC method, numerical modelling, microbial sequencing","lastPublishedDoi":"10.21203/rs.3.rs-5279180/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5279180/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe practice of directly applying unfermented and decomposed organic matter to plants is rare in the growth process of terrestrial plants. The organic matter content at the discharge outlet of shrimp ponds is usually high. Therefore, it is necessary to collect soil from the discharge outlet of shrimp ponds and simulate the migration and transformation pathways of organic acids and related metabolic microorganisms in soil of mangrove wetlands through laboratory soil columns and the HYDRUS-1D model. Results showed that the content of oxalic acid remained relatively stable in the soil column at different depths, citric acid settled downward along the vertical direction, the concentration of acetic acid in the depth range of 30\u0026ndash;50 cm increased. The organic acids formed insoluble or slightly soluble precipitates in the form of organic acid calcium, the organic acids in 40\u0026ndash;50 cm were completely neutralized on the 18th day. The abundance of acid-producing \u003cem\u003eAcinetobacter\u003c/em\u003e increased during the later stages of anaerobic acidification and disappeared after the addition of Ca(OH)\u003csub\u003e2\u003c/sub\u003e. The results of HYDRUS-1D simulation showed that the adsorption, deposition and transport of organic acids in the mangrove wetland were poor, the results of vertical infiltration modelling were in agreement with the soil column experiments.\u003c/p\u003e","manuscriptTitle":"Simulation of organic acid migration and transformation in mangrove soils based on soil column experiments","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-13 18:02:08","doi":"10.21203/rs.3.rs-5279180/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-11-27T07:26:51+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-24T20:37:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"25675180854365751368311719964745064925","date":"2024-11-22T15:49:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-22T13:06:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"241321718289096049417357941943476982451","date":"2024-11-22T06:43:45+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-11-22T06:39:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-11-05T16:31:02+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-11-05T03:07:49+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-11-02T05:00:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-10-17T02:42:59+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6b8f3600-4614-439a-b1f0-18ec914c3d41","owner":[],"postedDate":"November 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":40110461,"name":"Earth and environmental sciences/Ocean sciences"},{"id":40110462,"name":"Earth and environmental sciences/Ocean sciences/Marine chemistry"},{"id":40110463,"name":"Earth and environmental sciences/Environmental sciences/Environmental impact"}],"tags":[],"updatedAt":"2025-03-17T16:08:00+00:00","versionOfRecord":{"articleIdentity":"rs-5279180","link":"https://doi.org/10.1038/s41598-025-91771-w","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-03-11 15:58:25","publishedOnDateReadable":"March 11th, 2025"},"versionCreatedAt":"2024-11-13 18:02:08","video":"","vorDoi":"10.1038/s41598-025-91771-w","vorDoiUrl":"https://doi.org/10.1038/s41598-025-91771-w","workflowStages":[]},"version":"v1","identity":"rs-5279180","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5279180","identity":"rs-5279180","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}