An index and method for evaluating the degree of pollution of seaweed bed sediment
By using CCA and RDA analysis techniques, Kazachstania bacteria were used to assess seagrass bed sediment pollution. This approach addresses the insufficient sensitivity of existing assessment methods, enabling a more detailed and accurate assessment of pollution levels and promoting the monitoring and protection of seagrass bed ecosystems.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- HAINAN UNIV
- Filing Date
- 2024-05-23
- Publication Date
- 2026-04-17
AI Technical Summary
Existing methods for assessing the pollution level of seagrass bed sediments mainly rely on physicochemical parameters, which lack sensitivity and detail, making it difficult to comprehensively reflect the pollution status.
Using CCA and RDA analysis techniques, Kazachstania was used as an indicator of the pollution level of seagrass bed sediments. The pollution level was assessed by analyzing its relationship with the total nitrogen, total phosphorus, organic carbon content and seagrass biomass of the sediments.
This provides a more detailed and sensitive method for pollution assessment, improving the effectiveness of monitoring and protecting seagrass bed ecosystems and enabling a more accurate reflection of the degree of pollution.
Smart Images
Figure CN118604282B_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of environmental monitoring technology and relates to an index and assessment method for the degree of pollution of seagrass bed sediments. Background Technology
[0002] Seagrass bed ecosystems, located at the boundary between land and sea, are typical, sensitive, and fragile coastal ecosystems within the marine ecosystem. Along with coral reef and mangrove ecosystems, seagrass bed ecosystems are considered the "three major typical tropical marine ecosystems" and are among the most important coastal ecosystems in the world. Within seagrass beds, microorganisms drive numerous biogeochemical processes, and they play a crucial role in preventing ecosystem pollution. Summary of the Invention
[0003] The purpose of this invention is to provide an indicator and assessment method for the degree of pollution of seagrass bed sediments. By using CCA (canonical correspondence analysis) and RDA (restricted multiple regression analysis) techniques, Kazachstania bacteria are used as an indicator of the degree of pollution of seagrass bed sediments. This is a more detailed and sensitive assessment method, providing a new perspective and approach for the monitoring and protection of seagrass bed ecosystems.
[0004] To achieve the above objectives, the technical solution adopted by the present invention is as follows:
[0005] An indicator of the degree of pollution in seagrass bed sediments, wherein the indicator is Kazachstania bacteria.
[0006] Furthermore, Kazachstania showed the highest concentration of fungal species in the seagrass bed sediments, making it the most dominant species. The fungal species in the seagrass bed sediments included Alternaria, Penicillium, Trichosporon, Setophoma, Russula, Mortierella, Fusarium, Cladosporium, and Aspergillus.
[0007] Furthermore, Kazachstania bacteria showed the most significant response to changes in environmental indicators such as sediment total nitrogen (TN), sediment total phosphorus (TP), and sediment organic carbon (TC).
[0008] Furthermore, Kazachstania bacteria showed a significant negative correlation with seagrass physiological indicators, including seagrass root weight and seagrass leaf weight.
[0009] A method for assessing the degree of pollution in seagrass bed sediments includes: analyzing the relationship between physicochemical parameters of seagrass bed sediments and the abundance of Kazachstania bacteria using CCA and RDA to reveal the potential impact and degree of pollution from pollutants.
[0010] Furthermore, Kazachstania showed the highest concentration of fungal species in the seagrass bed sediments, making it the most dominant species. The fungal species in the seagrass bed sediments included Alternaria, Penicillium, Trichosporon, Setophoma, Russula, Mortierella, Fusarium, Cladosporium, and Aspergillus.
[0011] Furthermore, Kazachstania bacteria showed the most significant response to changes in environmental indicators such as sediment total nitrogen (TN), sediment total phosphorus (TP), and sediment organic carbon (TC).
[0012] Furthermore, Kazachstania bacteria showed a significant negative correlation with seagrass physiological indicators, including seagrass root weight and seagrass leaf weight.
[0013] Furthermore, the assessment method includes collecting seagrass bed sediment samples to ensure that the samples are representative and cover different locations within the area to be assessed; and performing necessary processing on the collected samples for subsequent experimental analysis.
[0014] Furthermore, the evaluation method includes using molecular biology techniques or culture methods to isolate and identify Kazachstania bacteria in seagrass bed sediments.
[0015] The beneficial effects of this invention are as follows:
[0016] Traditional assessments of seagrass bed sediment pollution often rely on physicochemical parameters, such as heavy metal content. This invention, however, utilizes microbial indicators, a more detailed and sensitive assessment method. Furthermore, the selection of *Kazachstania* as the indicator organism may be related to its sensitivity to specific pollutants, providing a new perspective and approach for monitoring and protecting seagrass bed ecosystems. This invention has broad application prospects, including marine ecological environment monitoring, pollutant emission control, and seagrass bed ecosystem protection, contributing to improved marine ecosystem health and sustainable development. Attached Figure Description
[0017] Figure 1 This shows the sampling sites for seagrass bed sediment samples in an embodiment of the present invention.
[0018] Figure 2 This shows the contamination index of 6 sites in this embodiment of the invention.
[0019] Figure 3 This shows the relative abundance of different fungi at different sites at the genus level in the embodiments of the present invention.
[0020] Figure 4 This shows the relative abundance of different fungi at the genus level in different samples in the embodiments of the present invention.
[0021] Figure 5 This invention demonstrates the environmental indicators of seagrass bed sediments and the RDA of microorganisms in different samples in embodiments of the present invention.
[0022] Figure 6 This invention demonstrates the physiological indicators of seaweed and the RDA of microorganisms in different samples in the embodiments of the present invention. Detailed Implementation
[0023] To more clearly illustrate the present invention, the invention will be further described in detail below with reference to embodiments and accompanying drawings. Those skilled in the art should understand that the specific description below is illustrative rather than restrictive and should not be construed as limiting the scope of protection of the present invention.
[0024] Example
[0025] The specific implementation steps are as follows:
[0026] 1. Sample collection and processing: First, it is necessary to collect seagrass bed sediment samples to ensure that the samples are representative and cover different locations within the area to be evaluated.
[0027] This experiment selected samples from six locations in Hainan Province with different levels of pollution: Gangdong Village in Wenchang City, Bianhai Village in Wenchang City, Tanmen Town in Qionghai City, Lingzai Village in Sanya City, Houhai Village in Sanya City, and Xincun Town in Lingshui County. Sediment samples were collected from the coast at depths of 0-20cm, 20-40cm, and 40-60cm at each location, with five replicates collected from each location. Figure 1 .
[0028] like Figure 2 As shown, based on the pollution coefficients of the six sites, the concentrations from highest to lowest are: Gd > Bh > Xc > Zi > Hh > Tm. According to the pollution concentration levels, the six sites are redefined as high concentration (Gd, Bh), medium concentration (Xc, Zi), and low concentration (Hh, Tm), named HH, HL, MH, ML, LH, and LL respectively. The suffix numbers represent the different sediment sample depths collected: 0 (0-20 cm), 20 (20-40 cm), and 40 (40-60 cm).
[0029] Samples for physicochemical analysis were air-dried at room temperature, while samples for microbial diversity analysis were stored in a -80°C freezer before testing. A total of 90 samples were sent for testing.
[0030] 2. Data collection: Collect physicochemical parameters of seagrass bed sediment samples, such as heavy metal content, organic matter content, and abundance data of Kazachstania bacteria.
[0031] i. Measurement of Organic Carbon in Sediments: Weigh 0.5 g of sediment sample, pass it through a 100-mesh sieve, and place it in a hard-shell test tube. Add 5 mL of potassium dichromate solution and concentrated sulfuric acid solution sequentially to the hard-shell test tube. After shaking well, place a small glass funnel at the spout of the hard-shell test tube to equalize the internal and external air pressure. Place the prepared test tube and the blank control group (silica) on an iron stand, place the zeolite at the bottom of the test tube, and finally place it in a digestion furnace. Digest for 6–8 hours using a procedure of heating at 180℃ for 1 hour, 280℃ for 1 hour, and 320℃ for 4–6 hours. After digestion, cool the test tube to room temperature. Add approximately 80 mL of distilled water and 3–5 drops of colorimetric reagent to the solution. FeSO4 solution can be used for titration. The solution color changes from yellow to grayish-green, with the endpoint being reddish-brown. Record the data.
[0032] ii. Measurement of total nitrogen and total phosphorus in sediments: 0.5 g of sediment sample was passed through a 100-mesh sieve, and 5 mL of concentrated sulfuric acid and 1 g of copper sulfate catalyst were added to a test tube and allowed to stand overnight. The sample was then placed in a digestion furnace, and the digestion time and temperature were controlled at 180℃ for 1 h, 280℃ for 1 h, and 320℃ for 4 h, respectively. The cooled liquid was filtered and stored in a 100 mL volumetric flask, and automated comparison and analysis were performed using a flow analyzer (Proxima 1022 / 1 / 1, Elians Scientific Instruments, France).
[0033] iii. Microbial analysis: DNA was extracted, amplified, and sequenced from the sediment samples, and then compared and identified by gene comparison at the National Center for Biotechnology Information. The specific steps were as follows: The reaction was carried out in a 50 μL reaction volume, including 2 μL (30 ng) template DNA; 2 μL ITS1-F forward primer (CTTGGTCATTTAGAGGAAGTAA) and 2 μL ITS2-R reverse primer (TGCGTTCTTCATCGATGC) (Li and Liu, 2019), both at a concentration of 10 μM; 4 μL dNTPs (2.5 mM); 5 μL 10× Pyrobest buffer; 0.3 μL Pyrobest DNA polymerase (2.5 U / 2 μL, TaKaRa code: DR005A); and 34.7 μL ddH2O. Amplicons were extracted from 2% agarose gels and purified using the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA) according to the manufacturer's instructions, and then purified using QuantiFluor. TM Quantification was performed using ST(Promega, USA). Purified amplicon samples were pooled in equimolar amounts and sequenced in pairs (2×300) on an Illumina MiSeq PE300 platform according to standard protocol.
[0034] Gene comparisons were performed using the National Center for Biotechnology Information (https: / / www.ncbi.nlm.nih.gov / ). Biodiversity analysis was performed using R (MathSoft, 4.0.4) software. Graph processing was performed using Origin Pro (2018) software. The significance level for differences in this study was set at p<0.05. Reference: Li, W.-H. and Liu, Q.-Z. 2019. Changes in fungal community and diversity in strawberry rhizospheresoil after 12 years in the greenhouse. Journal of Integrative Agriculture 18(3), 677-687.
[0035] A total of 526 fungi were identified, as shown in Table 1.
[0036] Table 1. Statistical analysis of fungal identification results in different samples
[0037]
[0038]
[0039] The abundance data of Kazachstania at six locations are as follows: Figure 3 The specific percentages are shown in Table 2.
[0040] Table 2. Statistics on the percentage of different fungi at six locations.
[0041]
[0042] 3. CCA and RDA analysis: The collected data were subjected to CCA and RDA analysis. By analyzing the relationship between the physicochemical parameters of seagrass bed sediments and the abundance of Kazachstania bacteria, the potential impact of pollutants and the degree of pollution were revealed.
[0043] 4. Interpretation of Results: Based on the results of CCA and RDA analysis, the degree of pollution of seagrass bed sediments is interpreted.
[0044] like Figure 4 As shown, Kazachstania can serve as a proxy for measuring sediment pollution levels and estimating seagrass biomass because it has the highest concentration of fungal species in sediments and is the most dominant species. The content of Kazachstania was significantly higher in each treatment than in the other groups, except for the LL treatment, which showed a lower content. This suggests that Kazachstania proliferates significantly after periods of low pollution and can serve as an indicator of the fungal category of pollutants.
[0045] CCA analysis results showed that the key fungus Kazachstania responded most significantly to changes in environmental indicators such as sediment total nitrogen (TN), sediment total phosphorus (TP), and sediment organic carbon (TC). Figure 5 As shown.
[0046] RDA results showed that Kazachstania was significantly negatively correlated with seagrass physiological indicators such as seagrass root weight and seagrass leaf weight, such as Figure 6 As shown.
[0047] Obviously, the above embodiments of the present invention are merely examples to illustrate the present invention more clearly, and are not intended to limit the implementation of the present invention. For those skilled in the art, other variations or modifications can be made based on the above description. It is impossible to exhaustively list all implementation methods here. Any obvious variations or modifications derived from the technical solutions of the present invention are still within the protection scope of the present invention.
Claims
1. A method for assessing the degree of pollution in seagrass bed sediments, comprising: The relationship between physicochemical parameters of seagrass bed sediments and the abundance of Kazachstania bacteria was analyzed by CCA and RDA to reveal the potential impact and degree of pollution from pollutants. Kazachstania bacteria showed the most significant response to changes in environmental indicators such as sediment total nitrogen (TN), sediment total phosphorus (TP), and sediment organic carbon (TC). Kazachstania bacteria also showed a significant negative correlation with seagrass physiological indicators such as seagrass root weight and seagrass leaf weight.
2. The evaluation method according to claim 1, characterized in that, Kazachstania was the most concentrated and dominant fungal species in the seagrass bed sediments. The fungal species in the seagrass bed sediments included Alternaria, Penicillium, Trichosporon, Setophoma, Russula, Mortierella, Fusarium, Cladosporium, and Aspergillus.
3. The evaluation method according to claim 1, characterized in that, The assessment method includes collecting seagrass bed sediment samples to ensure the samples are representative and cover different locations within the area to be assessed; and performing necessary processing on the collected samples for subsequent experimental analysis.
4. The evaluation method according to claim 1, characterized in that, The assessment method includes using molecular biology techniques or culture methods to isolate and identify Kazachstania bacteria in seagrass bed sediments.