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18 pages, 14911 KiB  
Article
Understanding Zinc Transport in Estuarine Environments: Insights from Sediment Composition
by Hao-Qin Xiong, Yan-Yun Du, Yi-Chuan Fang, Hong Xiang, Jia-Zhuo Qu and Xiao-Long Sun
Sustainability 2024, 16(14), 6113; https://doi.org/10.3390/su16146113 - 17 Jul 2024
Viewed by 581
Abstract
Sediments are sources and sinks of heavy metals in water, and estuaries are heavily influenced by human production and life. Therefore, it is of great significance to study the composition of estuarine sediments and the relationship between their components to understand the transport [...] Read more.
Sediments are sources and sinks of heavy metals in water, and estuaries are heavily influenced by human production and life. Therefore, it is of great significance to study the composition of estuarine sediments and the relationship between their components to understand the transport and transformation pathways of heavy metals in the environment. In this research, we investigated the characteristics and patterns of Zn adsorption by organic–inorganic composites, organic–clay mineral composites, and iron oxide–clay mineral composites in eight estuarine sediment samples from Dianchi Lake. The results show that both Langmuir and Freundlich isothermal models can describe the adsorption behaviour of the adsorbent better. The order of the adsorption capacity of the three groups of samples for zinc was organic–inorganic composites > organic–clay mineral composites > iron oxide–clay mineral composites. Through FTIR and XRD analyses, the adsorption of Zn2+ on the three groups of samples was dominated by electrostatic attraction and coordination adsorption, accompanied by the occurrence of ion exchange and co-precipitation. After FTIR semi-quantitative analysis, it was found that the source of the differences in the high and low Zn adsorption of the three types of samples may be mainly due to the content of phenolic functional groups in the organic matter. This may be related to the low redox site of the phenolic hydroxyl group, which, as an electron donor, is susceptible to electrostatic attraction and complexation with heavy metal cations. The organic–inorganic composite has a higher adsorption capacity for Zn when the ratio of the active fraction of organic matter to the free iron oxide content is 0.65–0.70. In this range, the organic matter can provide enough negative charge without making the sample surface too dense. Iron oxides can also activate the sample by providing sufficient contact between the clay minerals and the organic matter. When this ratio is too high or too low, it will be unfavourable for Zn adsorption. Full article
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<p>Map of sampling points.</p>
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<p>Scanning electron microscopy of natural sediment complexes.</p>
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<p>Fourier infrared spectra of sediment complex before and after Zn adsorption.</p>
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<p>X-ray diffractograms before and after Zn adsorption on sediment complexes.</p>
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<p>X-ray diffractograms before and after Zn adsorption on sediment complexes.</p>
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<p>Variation of FT-IR characteristic peak areas before and after adsorption of Zn in three groups of sediment complexes.</p>
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<p>Schematic representation of Zn adsorption by natural sediment complexes.</p>
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13 pages, 8213 KiB  
Article
The Recycling Characteristics of Different Silicon Forms and Biogenic Silicon in the Surface Sediments of Dianchi Lake, Southwest China
by Yong Liu, Jv Liu, Guoli Xu, Jingfu Wang, Kai Xu, Zuxue Jin and Guojia Huang
Water 2024, 16(13), 1824; https://doi.org/10.3390/w16131824 - 26 Jun 2024
Viewed by 912
Abstract
Silicon (Si) is one of the main biogenic elements in the aquatic ecosystem of lakes, significantly affecting the primary productivity of lakes. Lake sediment is an important sink of Si, which exists in different Si forms and will be released and participate in [...] Read more.
Silicon (Si) is one of the main biogenic elements in the aquatic ecosystem of lakes, significantly affecting the primary productivity of lakes. Lake sediment is an important sink of Si, which exists in different Si forms and will be released and participate in the recycling of Si when the sediment environment changes. Compared to carbon (C), nitrogen (N) and phosphorus (P), the understanding of different Si forms in sediments and their biogeochemical cycling is currently insufficient. Dianchi Lake, a typical eutrophic lake in southwest China, was selected as an example, and the contents of different Si forms and biogenic silicon (BSi), as well as their correlations with total organic carbon (TOC), total nitrogen (TN), and chlorophyll a in the surface sediments, were systematically investigated to explore Si’s recycling characteristics. The results showed that the coupling relationship of the four different Si forms in the surface sediments of Dianchi Lake was poor (p > 0.05), indicating that their sources were relatively independent. Moreover, their formation may be greatly influenced by the adsorption, fixation and redistribution of dissolved silicon by different lake substances. The contents of different Si forms in the surface sediments of Dianchi Lake were ranked as iron-manganese-oxide-bonded silicon (IMOF-Si) > organic sulfide-bonded silicon (OSF-Si) > ion-exchangeable silicon (IEF-Si) > carbonate-bound silicon (CF-Si). In particular, the contents of IMOF-Si and OSF-Si reached 2983.7~3434.7 mg/kg and 1067.6~1324.3 mg/kg, respectively, suggesting that the release and recycling of Si in surface sediments may be more sensitive to changes in redox conditions at the sediment–water interface, which become the main pathway for Si recycling, and the slow degradation of organic matter rich in OSF-Si may lead to long-term and continuous endogenous Si recycling. The low proportion (0.3~0.6%) and spatial differences of biogenic silicon (BSi) in the surface sediments of Dianchi Lake, as well as the poor correlation between BSi and TOC, TN, and chlorophyll a, indicated that the primary productivity of Dianchi Lake was still dominated by cyanobacteria and other algal blooms, while the relative abundance of siliceous organisms such as diatoms was low and closer to the central area of Dianchi Lake. Additionally, BSi may have a faster release capability relative to TOC and may participate in Si recycling. Full article
(This article belongs to the Special Issue Soil Erosion and Contaminant Management in Watersheds)
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<p>Sediment sample collection site in Dianchi Lake.</p>
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<p>Sequential extraction steps of different silicon (Si) forms in sediments.</p>
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<p>Extraction steps of biogenic silicon (BSi) in sediments.</p>
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<p>Contents of different Si forms in surface sediments of Dianchi Lake. (Note: The different letter indicated that there was significant difference in the contents of same Si form at different surface sediment site (<span class="html-italic">p</span> &lt; 0.05)).</p>
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<p>Proportion of different Si forms in surface sediments of Dianchi Lake.</p>
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<p>Spatial distribution of different Si forms in surface sediments of Dianchi Lake.</p>
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<p>Contents of Valid-Si and Bioactive-Si in surface sediments of Dianchi Lake. (Note: The different letter indicated that there was significant difference in the contents of Valid-Si and Bioactive-Si at different surface sediment site, respectively. (<span class="html-italic">p</span> &lt; 0.05)).</p>
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<p>Spatial distribution of Valid-Si, Bioactive-Si, BSi and chlorophyll a in surface sediments of Dianchi Lake.</p>
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<p>BSi content and its ratio to total organic carbon (TOC (C/Si)) and total nitrogen (TN (N/Si)) in surface sediments of Dianchi Lake. (Note: The different letter indicated that there was significant difference in the contents of BSi at different surface sediment site. (<span class="html-italic">p</span> &lt; 0.05)).</p>
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<p>Chlorophyll a content in surface sediments of Dianchi Lake. (Note: The different letter indicated that there was significant difference in the contents of chlorophyll a at different surface sediment site. (<span class="html-italic">p</span> &lt; 0.05)).</p>
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<p>Correlation of BSi, OSF-Si and BSi-(OSF-Si) in surface sediments of Dianchi Lake.</p>
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15 pages, 2842 KiB  
Article
Denitrification Mechanism of Heterotrophic Aerobic Denitrifying Pseudomonas hunanensis Strain DC-2 and Its Application in Aquaculture Wastewater
by Xinya Sui, Xingqiang Wu, Bangding Xiao, Chunbo Wang and Cuicui Tian
Water 2024, 16(11), 1625; https://doi.org/10.3390/w16111625 - 6 Jun 2024
Viewed by 909
Abstract
A novel heterotrophic aerobic denitrifying Pseudomonas hunanensis strain DC-2 was screened from the sediments of Lake Dianchi and identified with high nitrification/denitrification ability. Within 30 h, the removal efficiency of ammonium-N and nitrate-N could reach 98.8% and 88.4%, respectively. The results of the [...] Read more.
A novel heterotrophic aerobic denitrifying Pseudomonas hunanensis strain DC-2 was screened from the sediments of Lake Dianchi and identified with high nitrification/denitrification ability. Within 30 h, the removal efficiency of ammonium-N and nitrate-N could reach 98.8% and 88.4%, respectively. The results of the single-factor experiments indicated that strain DC-2 exhibited excellent denitrification ability under the conditions of using sodium citrate as the nitrogen source, with an initial pH of 7, a C/N ratio of 10, and a temperature of 30 °C. Nitrogen balance experiments suggested that this strain removed N mainly via assimilation. Moreover, the N removal pathway was explored by genome and enzymatic assays, and a complex nitrogen metabolism pathway was established, including heterotrophic nitrification-aerobic denitrification (HN-AD), assimilatory reduction of nitrate (ANRA), and ammonia assimilation. Additionally, strain DC-2 was immobilized into particles for denitrification, demonstrating excellent efficacy in continuous total nitrogen removal (84.8% for TN). Hence, strain DC-2 demonstrated significant potential in treating real aquaculture wastewater. Full article
(This article belongs to the Section Wastewater Treatment and Reuse)
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<p>The characteristics of strain DC-2: (<b>a</b>) scanning electron microscopy image, (<b>b</b>) the phylogenetic tree based on 16S rRNA gene sequences.</p>
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<p>HN-AD characteristics of strain DC-2 in different N sources: (<b>a</b>) NH<sub>4</sub><sup>+</sup>-N (100 mg/L); (<b>b</b>) NO<sub>2</sub><sup>−</sup>-N (100 mg/L); (<b>c</b>) NO<sub>3</sub><sup>−</sup>-N (100 mg/L). (<b>d</b>) Mixed nitrogen sources: NH<sub>4</sub><sup>+</sup>-N (50 mg/L), NO<sub>3</sub><sup>−</sup>-N (30 mg/L), NO<sub>2</sub><sup>−</sup>-N (20 mg/L).</p>
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<p>Effects of various environmental conditions on the growth and N removal of strain DC-2: (<b>a</b>) C source; (<b>b</b>) C/N ratio; (<b>c</b>) initial pH; (<b>d</b>) temperature.</p>
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<p>Circular map of strain DC-2 genome. From outside to inside, there are Reverse CDS (marigold), Positive CDS (steel blue), tRNA (orange) and rRNA (purple), prphage (cadet blue), Genomic island (sea green), Crispr (light sea green), GC ratio (carmine means GC% &lt; 50%), GC-SKEW (olive drab means G% ≥ C%, grayish purple means G% &lt; C%) and coverage (cadet blue).</p>
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<p>Sequential arrangements of putative genes for nitrogen metabolic pathway on the genome of strain DC-2.</p>
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<p>The integrated N metabolic pathways of strain DC-2.</p>
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<p>Study on the continuous treatment of ammonium nitrogen and nitrate nitrogen in real aquaculture wastewater by bioaugmented immobilized strain DC-2: (<b>a</b>) NH<sub>4</sub><sup>+</sup>-N; (<b>b</b>) NO<sub>3</sub><sup>−</sup>-N.</p>
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19 pages, 2825 KiB  
Article
Seeing and Thinking about Urban Blue–Green Space: Monitoring Public Landscape Preferences Using Bimodal Data
by Chenglong Dao and Jun Qi
Buildings 2024, 14(5), 1426; https://doi.org/10.3390/buildings14051426 - 15 May 2024
Viewed by 629
Abstract
Urban blue–green spaces (UBGSs) are a significant avenue for addressing the worldwide mental health crisis. To effectively optimise landscape design and management for the promotion of health benefits from UBGS, it is crucial to objectively understand public preferences. This paper proposes a method [...] Read more.
Urban blue–green spaces (UBGSs) are a significant avenue for addressing the worldwide mental health crisis. To effectively optimise landscape design and management for the promotion of health benefits from UBGS, it is crucial to objectively understand public preferences. This paper proposes a method to evaluate public landscape preference from the perspective of seeing and thinking, takes the examples of seven parks around the Dianchi Lake in Kunming, China, and analyses the social media data by using natural language processing technology and image semantic segmentation technology. The conclusions are as follows: (1) The public exhibits significantly high positive sentiments towards various UBGSs, with over 93% of comments expressed positive sentiments. (2) Differences exist in the frequency and perception of landscape features between image and text modalities. Landscape elements related to stability are perceived more in images than in text, while dynamic and experiential elements are perceived more in text than in images. (3) In both modalities, the distinctive landscape features of parks are more frequently perceived and preferred by the public. In the end, the intrinsic links between landscape elements and public sentiment and preferences are discussed, and suggestions for design and management improvements are made to consolidate their health benefits to the public. Full article
(This article belongs to the Special Issue Text Mining and Natural Language Processing in the Built Environment)
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<p>Framework of landscape preference assessment based on bimodal data.</p>
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<p>The case study encompasses 7 parks surrounding Dianchi Lake in southwest Kunming, China.</p>
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<p>Text and image data are integrated to extract and classify landscape elements.</p>
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<p>The number of park comments and the number of three sentiments.</p>
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<p>Similarity scores of element components in different parks and different sentiments. (p) and (n) indicate positive and negative comments, respectively.</p>
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<p>Comparison of landscape element proportions in image and text modes for two sentiment tendencies. (a), (b), (c) and (d) represent urban park, historic park, forest park and wetland park, respectively.</p>
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<p>Comparison of landscape element preferences and dislikes.</p>
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17 pages, 1834 KiB  
Article
Pollution Characteristics and Risk Assessment of Heavy Metals in the Sediments of the Inflow Rivers of Dianchi Lake, China
by Liwei He, Guangye Chen, Xinze Wang, Jian Shen, Hongjiao Zhang, Yuanyuan Lin, Yang Shen, Feiyan Lang and Chenglei Gong
Toxics 2024, 12(5), 322; https://doi.org/10.3390/toxics12050322 - 29 Apr 2024
Viewed by 1422
Abstract
To explore the contamination status and identify the source of the heavy metals in the sediments in the major inflow rivers of Dianchi Lake in China, sediment samples were collected and analyzed. Specifically, the distribution, source, water quality, and health risk assessment of [...] Read more.
To explore the contamination status and identify the source of the heavy metals in the sediments in the major inflow rivers of Dianchi Lake in China, sediment samples were collected and analyzed. Specifically, the distribution, source, water quality, and health risk assessment of the heavy metals were analyzed using correlation analysis (CA), principal component analysis (PCA), the heavy metal contamination factor (Cf), the pollution load index (PLI), and the potential ecological risk index (PERI). Additionally, the chemical fractions were analyzed for mobility characteristics. The results indicate that the average concentration of the heavy metals in the sediment ranked in the descending order of Zn > Cr > Cu > Pb > As > Ni > Cd > Hg, and most of the elements existed in less-mobile forms. The Cfwas in the order of Hg > Zn > Cd > As > Pb > Cr > Ni; the accumulation of Hg, Zn, Cd, and As was obvious. Although the spatial variability of the heavy metal contents was pronounced, the synthetical evaluation index of the PLI and PERI both reached a high pollution level. The PCA and CA results indicate that industrial, transportation, and agricultural emissions were the dominant factors causing heavy metal pollution. These results provide important data for improving water resource management efficiency and heavy metal pollution prevention in Dianchi Lake. Full article
(This article belongs to the Section Metals and Radioactive Substances)
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<p>Sampling site distribution.</p>
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<p>Chemical form distribution for different heavy metals.</p>
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<p>Chemical form distribution for different heavy metals.</p>
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<p>Correlation analysis for heavy metals in sediment (** <span class="html-italic">p</span> ≤ 0.01, * <span class="html-italic">p</span> ≤ 0.05).</p>
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17 pages, 3307 KiB  
Article
Occurrence and Risk Assessment of Antibiotics in Urban River–Wetland–Lake Systems in Southwest China
by Yanbo Zeng, Lizeng Duan, Tianbao Xu, Pengfei Hou, Jing Xu, Huayu Li and Hucai Zhang
Water 2024, 16(8), 1124; https://doi.org/10.3390/w16081124 - 15 Apr 2024
Viewed by 968
Abstract
Antibiotics in the aquatic environment are of great concern as novel contaminants. In this study, we investigated the occurrence, distribution, potential sources, and risk assessment of antibiotics in an interconnected river–wetland–lake system. Thirty-three target antibiotics, including sulfonamides (SAs), macrolides (MLs), fluoroquinolones (FQs), tetracyclines [...] Read more.
Antibiotics in the aquatic environment are of great concern as novel contaminants. In this study, we investigated the occurrence, distribution, potential sources, and risk assessment of antibiotics in an interconnected river–wetland–lake system. Thirty-three target antibiotics, including sulfonamides (SAs), macrolides (MLs), fluoroquinolones (FQs), tetracyclines (TCs), and chloramphenicol (CLs) belong to five common groups of antibiotics, were tested from water samples collected in the Panlong River, Xinghai Wetland, and Lake Dian (or Dianchi). Mass spectrophotometry was used to detect the target antibiotics, and the water quality parameters were measured in situ. We found four antibiotics, lincomycin (LIN), trimethoprim (TMP), sulfamethoxazole (SMX), and ofloxacin (OFL), with relatively low concentrations at the ng/L level, and detection rates among sample sites ranged from 42.3% to 76.9%, with maximum concentrations of 0.71 ng/L~5.53 ng/L. TMP was not detected in the Panlong River but appeared in the wetlands and Lake Dian. Midstream urban areas of the Panlong River showed the highest pollution among sites. Antibiotic concentrations were positively correlated with total nitrogen (TN) (p < 0.05) and showed some negative correlation with pH, salinity, and DO. According to the risk assessment, antibiotics in water do not pose a threat to human health and aquatic ecosystems, but a potentially harmful combined effect cannot be excluded. Our research offers a geographical summary of the distribution of antibiotics in urban river, wetland, and lake ecosystems in the plateau (PWL), which is important for predicting the distribution characteristics of antibiotics in the plateau water environment and establishing a standardized antibiotic monitoring and management system for the government. Full article
(This article belongs to the Section Water Quality and Contamination)
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<p>Locations of the sampling sites around the Panlong River, Xinghai Wetland and Lake Dianchi.</p>
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<p>Box-and-whisker plots of the levels and occurrence rates of antibiotics in the water, (<b>a</b>) Panlong river; (<b>b</b>) Xinghai Wetland; (<b>c</b>) Lake Dianchi.</p>
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<p>Spatial distribution of antibiotic concentrations in water, (<b>a</b>) Panlong River; (<b>b</b>) Xinghai Wetland and Lake Dianchi.</p>
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<p>Composition of antibiotics at different sampling sites in water.</p>
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<p>The correlation between antibiotics and water quality parameters.</p>
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<p>Noncarcinogenic risk of the Panlong River.</p>
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<p>The ecological risk posed by antibiotics in Panlong River to three typical organisms (fish, daphnia, and algae), (<b>a</b>) Acute toxicity; (<b>b</b>) chronic toxicity.</p>
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<p>Source compositions (<b>a</b>) and mean contributions (<b>b</b>) of water quality variables in the PWD area according to the PMF analysis.</p>
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15 pages, 3036 KiB  
Article
Effects of Dredging on Nitrogen and Phosphorus Storage Patterns and Retention Mechanisms in Column Core Sediments in the Caohai Region of Dianchi Lake
by Mingyan Liu, Yan Yang, Zhi Shao, Yaping Liu, Ziqi Wang, Zhengqing Chen, Mingang Chen, Lixin Jiao, Di Song, Jingyu Li and Jing Wang
Water 2024, 16(3), 449; https://doi.org/10.3390/w16030449 - 30 Jan 2024
Cited by 2 | Viewed by 1912
Abstract
Dredging is a common technique for managing eutrophication problems in waters, reducing the accumulation of pollutants by removing sediments from the bottom of water bodies. However, dredging can have complex impacts on lake ecosystems, and it is crucial to understand its benefits and [...] Read more.
Dredging is a common technique for managing eutrophication problems in waters, reducing the accumulation of pollutants by removing sediments from the bottom of water bodies. However, dredging can have complex impacts on lake ecosystems, and it is crucial to understand its benefits and mechanisms for the environment. In this paper, the dredged and undredged areas in the Caohai portion of Dianchi Lake were studied to analyze the effects of dredging on nitrogen–phosphorus transport and conversion and changes in nitrogen–phosphorus morphology content and its mechanisms by comparing the nitrogen–phosphorus morphology content and percentage, the nitrogen–phosphorus ratio, and the release contribution of the two areas. It was found that the ratio of stabilized nitrogen (SN) to stabilized phosphorus (SP) in the dredged area was lower than that in the undredged area and the BD-P and TOC content had a large turnaround at the 16–20 cm position of the sediment in the dredged area. The main conclusions were that the dredging would disrupt the internal equilibrium of the lake system for many years, with the greatest effect on the balance of the BD-P in the phosphorus forms of the sediment, and that the column cores of the dredged area at 0 to 16 cm might be newly accumulated sediments after the dredging project. However, with time, the distribution of nitrogen and phosphorus forms in the newly accumulated sediments will gradually reach a new equilibrium. In addition, dredging will also cause significant changes in the retention efficiency of nitrogen and phosphorus in the sediment, and the stable nitrogen and phosphorus forms will be released and transformed into unstable nitrogen and phosphorus forms. Full article
(This article belongs to the Section Hydraulics and Hydrodynamics)
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<p>Location of the study area.</p>
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<p>Variation of TN, TP, and TOC content with depth in dredged and undredged areas.</p>
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<p>Sediment nitrogen form percentage variation with depth in dredged and undredged areas.</p>
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<p>Sediment phosphorus form percentage as a function of depth in dredged and undredged areas.</p>
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<p>(<b>A</b>) Mobile nitrogen:phosphorus ratio in sediment column cores; (<b>B</b>) stabilized nitrogen:phosphorus ratio in sediment column cores; and (<b>C</b>) total nitrogen:phosphorus ratio in sediment column cores.</p>
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<p>Mechanisms of N and P retention in dredged versus undredged areas. <a href="#sec3dot2-water-16-00449" class="html-sec">Section 3.2</a> and <a href="#sec3dot3-water-16-00449" class="html-sec">Section 3.3</a> describe the relevant data in the figure.</p>
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15 pages, 9374 KiB  
Article
Development and Evaluation of the Plankton Biological Integrity Index (P-IBI) in Dry and Wet Seasons for Dianchi Lake
by Jia-Le Cao, Hong-Yi Liang, Ya-Hui Zhang, Shi-Lin Du, Jin Zhang and Yong Tao
Ecologies 2024, 5(1), 68-82; https://doi.org/10.3390/ecologies5010005 - 26 Jan 2024
Cited by 1 | Viewed by 1432
Abstract
As an important component of lake ecosystems, plankton are often used as indicators to evaluate the health of aquatic ecosystems, such as lakes and reservoirs. The plankton integrity index (P-IBI) is a highly utilized method for evaluating the ecological health of the lakes. [...] Read more.
As an important component of lake ecosystems, plankton are often used as indicators to evaluate the health of aquatic ecosystems, such as lakes and reservoirs. The plankton integrity index (P-IBI) is a highly utilized method for evaluating the ecological health of the lakes. This study took Dianchi Lake, located in the Yangtze River Basin, as the research object and analyzed the phytoplankton, zooplankton communities, and environmental factors at 11 sampling points in this lake during the wet season (July) in 2022 and the dry season (February) in 2023. The P-IBI was established to evaluate the health status of this lake ecosystem. The results showed that a total of 83 species of phytoplankton and 31 species of zooplankton were identified in Dianchi Lake, and the number of plankton species in the dry season was significantly higher than that in the wet season. The P-IBI evaluation results for the two hydrological periods were generally “good”. Linear regression analysis showed that there was a certain negative correlation between the P-IBI value and the comprehensive trophic level index (TLI), and the evaluation results were generally in line with the actual situation of the water body. Redundancy analysis (RDA) showed that there was a significant correlation between the P-IBI and its constituent parameters and individual water quality environmental factors, such as total nitrogen (TN) and electrical conductivity (EC). In summary, by reducing errors caused by spatial and temporal changes across various hydrological periods, P-IBI represents a more scientifically rigorous technique for lake water ecological health assessments within a certain time range. Full article
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<p>The geographical location of Dianchi Lake in China, surrounding rivers, and sampling sites (red circles).</p>
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<p>Community structure and biomass of plankton. (<b>a</b>) Phytoplankton. (<b>b</b>) Zooplankton.</p>
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<p>Community structure and density of plankton. (<b>a</b>) Phytoplankton. (<b>b</b>) Zooplankton.</p>
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<p>Correlation analysis of candidate parameters in Dianchi Lake. (<b>a</b>) Wet season. (<b>b</b>) Dry season.</p>
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<p>Spatial distribution of P-IBI scores in Lake Dianchi. (<b>a</b>) Wet season. (<b>b</b>) Dry season.</p>
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<p>Comparison of P-IBI scores between reference points and damaged points in different hydrological periods. Note: * indicates <span class="html-italic">p</span> &lt; 0.05, ** indicates <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>P-IBI scores and TLI linear regression analysis. (<b>a</b>) Wet season. (<b>b</b>) Dry season.</p>
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<p>Redundancy analysis of between-environmental parameters, the component metrics, and P-IBI. (<b>a</b>) Wet season. (<b>b</b>) Dry season.</p>
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16 pages, 4022 KiB  
Article
Water Quality Parameter Retrieval with GF5-AHSI Imagery for Dianchi Lake (China)
by Hang Zhang, Wenying Hu and Yuanmei Jiao
Water 2024, 16(2), 225; https://doi.org/10.3390/w16020225 - 9 Jan 2024
Viewed by 1092
Abstract
In response to the rapid changes in the chlorophyll-a concentration and eutrophication issues in lakes, with Dianchi Lake as an example, a remote sensing estimation model for chlorophyll-a, total phosphorus, and total nitrogen in Dianchi Lake was constructed using the three band method [...] Read more.
In response to the rapid changes in the chlorophyll-a concentration and eutrophication issues in lakes, with Dianchi Lake as an example, a remote sensing estimation model for chlorophyll-a, total phosphorus, and total nitrogen in Dianchi Lake was constructed using the three band method and ratio band method based on the visible-light shortwave infrared (AHSI) hyperspectral satellite data from Gaofen 5 (GF-5) and the water quality data collected at Dianchi Lake. The model results were compared with the multispectral data from the Gaofen 1 (GF-1) wide field-of-view (WFV) camera. The accuracy evaluation results indicate that the overall mean absolute percentage error of the remote sensing estimation models for chlorophyll a, total phosphorus, and total nitrogen are 7.658%, 4.511%, and 4.577%, respectively, which can meet the needs of lake water quality monitoring and evaluation. According to the remote sensing simulation results, chlorophyll a is mainly distributed in the northern part of Dianchi Lake, with phosphorus and nitrogen pollution throughout Dianchi Lake and relatively more abundant in the central and southern regions. The pollution is mainly concentrated in the northern and southern regions of Dianchi Lake, which is consistent with the actual situation. Further confirming the feasibility of using GF-5 satellite AHSI data for water quality parameter retrieval can provide new technical means for relevant departments to quickly and efficiently monitor the inland lake water environment. Full article
(This article belongs to the Section Water Quality and Contamination)
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<p>Schematic location of the study area.</p>
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<p>Comparison of GF-5 satellite images before and after atmospheric correction, where Figure (<b>a</b>) is the original image and Figure (<b>b</b>) is the atmospherically corrected image.</p>
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<p>Hyperspectral reflectance curves at sites on Dianchi Lake.</p>
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<p>Correlation coefficients between various water quality parameters and single-band reflectance in Dianchi Lake.</p>
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<p>Chlorophyll a model diagram based on the three-band method for GF-5 data.</p>
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<p>Chlorophyll a model diagram of GF-1 data based on the three-band method.</p>
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<p>Correlation coefficients between specific band reflectance and the total phosphorus and total nitrogen concentrations.</p>
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<p>Model diagram of total phosphorus and total nitrogen based on the ratio band method for the GF-5 data.</p>
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<p>Model diagram of total phosphorus and total nitrogen based on the ratio band method for GF-1 data.</p>
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<p>Spatial distribution of water quality parameter concentrations in the Dianchi Lake watershed.</p>
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13 pages, 4262 KiB  
Article
Spatial and Temporal Variations in Phytoplankton Community in Dianchi Lake Using eDNA Metabarcoding
by Yuanyuan Lin, Jingge Xu, Liang Shen, Xiaohua Zhou, Liwei He, Zheng Zhao and Shan Xu
Water 2024, 16(1), 32; https://doi.org/10.3390/w16010032 - 21 Dec 2023
Viewed by 1289
Abstract
The growth and reproduction of phytoplankton are closely associated with the changes of water environment; thus, phytoplankton have been taken as environmental indicator organisms and provided references for water environment protection. However, the phytoplankton community characteristics of Dianchi Lake (a seriously polluted lake [...] Read more.
The growth and reproduction of phytoplankton are closely associated with the changes of water environment; thus, phytoplankton have been taken as environmental indicator organisms and provided references for water environment protection. However, the phytoplankton community characteristics of Dianchi Lake (a seriously polluted lake in China) are unclear under the background of the cumulative effects of historical pollutants and current control measures, and environmental DNA (eDNA) metabarcoding monitoring has rarely been applied in phytoplankton research at Dianchi Lake. Therefore, this study investigated the temporal and spatial characteristics of phytoplankton community and the environmental stressors of Dianchi Lake via eDNA metabarcoding monitoring. A total of 10 phyla, 22 classes, 50 orders, 82 families, 108 genera and 108 species of phytoplankton were detected, and distinct temporal and spatial variations in the phytoplankton community (e.g., ASV number, dominant taxon, the relative abundance) were observed in Dianchi Lake. Microcystis dominated the prokaryotic phytoplankton community from the dry period to the wet period, but interestingly, the first dominant cyanobacteria genus was changed from Microcystis (dry period) to Planktothrix (wet period). Cryptophyta dominated in the eukaryotic phytoplankton community from the dry period to the wet period, and eukaryotic-phytoplankton-dominant genera included Cryptomonas, Aulacoseira, Plagioselmis and others. A temporal–spatial heterogeneity of the relationships between the phytoplankton community and environmental factors was shown in Dianchi Lake. Dissolved oxygen was the crucial environmental stressor influencing the phytoplankton community structure in Dianchi Lake during the dry period, while pH was the crucial one during the wet period. The impacts of total phosphorus and nitrogen also showed differences at different periods. This research provides an interesting perspective on phytoplankton diversity monitoring and the health assessment and restoration of Dianchi Lake. Full article
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<p>Map of the Dianchi Lake with monitoring sites indicated, and the monitoring sites (D1–D8) were shown in the map.</p>
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<p>Spatial and temporal distributions of the cyanobacteria community in Dianchi Lake during the dry period (<b>a</b>) and wet period (<b>b</b>) based on the relative abundances of each genus.</p>
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<p>Spatial and temporal distributions of the eukaryotic phytoplankton community in Dianchi Lake during the dry period (<b>a</b>) and wet period (<b>b</b>) based on the relative abundances of each phylum.</p>
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<p>Temporal variation in dominant genera of the phytoplankton community during the dry period and wet period in Dianchi Lake, (<b>a</b>) cyanobacteria community (<b>b</b>) eukaryotic phytoplankton community.</p>
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<p>Temporal variation in phytoplankton diversity during the dry period and wet period in Dianchi Lake. (<b>a</b>) α−diversity difference; (<b>b</b>) PCoA analysis. Significant terms ‘<span class="html-italic">p</span> &lt; 0.0001’ are marked as ****. D1–D8 means sampling sites, _1, _2, _3 means the replicates, F_ at the front of sampling sites represents Wet period.</p>
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<p>Correlation cluster analysis between environmental factors and the relative abundances of phytoplankton genera (top 20). Significant terms are marked as: *, 0.01 &lt; <span class="html-italic">p</span> &lt; 0.05; **, <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>The relations between phytoplankton community structures and environmental factors in Dianchi Lake during the dry period (<b>a</b>) and wet period (<b>b</b>) shown by the RDA ordination plot. D1–D8 means sampling sites, _1, _2, _3 means the replicates.</p>
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15 pages, 7674 KiB  
Article
A Comparative Study on the Removal of Microcystis and Cylindrospermopsis Blooms in Two Lakes by Flocculation–Filtration Treatment
by Cheng Zhou, Sisi Deng, Lei Xu, Xiang Liu, Chunbo Wang and Junjun Chang
Environments 2024, 11(1), 3; https://doi.org/10.3390/environments11010003 - 20 Dec 2023
Viewed by 1590
Abstract
Dianchi Lake and Yilong Lake, two prominent plateau lakes in Yunnan Province, China, have suffered from Microcystis and Cylindrospermopsis blooms for decades. While cyanobacteria harvest boats utilizing cationic polyacrylamide (CPAM) flocculation and screen filtration have been proven effective for harvesting Microcystis biomass in [...] Read more.
Dianchi Lake and Yilong Lake, two prominent plateau lakes in Yunnan Province, China, have suffered from Microcystis and Cylindrospermopsis blooms for decades. While cyanobacteria harvest boats utilizing cationic polyacrylamide (CPAM) flocculation and screen filtration have been proven effective for harvesting Microcystis biomass in Dianchi Lake, they struggle against Cylindrospermopsis blooms in Yilong Lake. This study systematically compared the removal of Microcystis and Cylindrospermopsis blooms using flocculation–filtration treatment, aiming to identify key factors influencing flocculation and propose enhancements to improve treatment efficiency for Cylindrospermopsis blooms. The reduction of turbidity, OD680, biovolume and phytoplankton density all revealed significantly better treatment efficiency for Microcystis blooms compared to Cylindrospermopsis blooms. In Dianchi Lake, 1 mg/L CPAM achieved a 95% turbidity reduction, while in Yilong Lake, even with 4.0 mg/L CPAM, the removal efficiency remained below 90%. Post-treatment, Dianchi Lake’s water quality showed substantial improvements, including over 50% reductions in total nitrogen, total phosphorus, permanganate index, and chemical oxygen demand. Conversely, nutrient level reductions were limited in Yilong Lake’s treated water. The average molecular weight of dissolved organic matters (DOM) in Yilong Lake was notably smaller than in Dianchi Lake. The treatment selectively removed high molecular weight, microbial-sourced, and protein-like DOM components, leading to a decrease in average molecular weight and an increase in humification index (HIX) in both lakes. Excessive humic matters in the water of Yilong Lake was found to hamper algae flocculation significantly. Introducing additional acidic polysaccharides or oxidants emerged as potential strategies to enhance Yilong Lake’s treatment efficiency. Full article
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<p>The reduction of turbidity, OD680, biovolume, and phytoplankton density by the flocculation–filtration treatment for water from Yilong Lake (<b>a</b>,<b>c</b>,<b>e</b>,<b>g</b>) and Dianchi Lake (<b>b</b>,<b>d</b>,<b>f</b>,<b>h</b>).</p>
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<p>The removal of (<b>a</b>) total nitrogen (TN), (<b>b</b>) total phosphorus (TP), (<b>c</b>) chemical oxygen demand (COD), (<b>d</b>) and permanganate index (PI) for Yilong Lake and Dianchi Lake. The utilization of distinct capital letters (A and B) signifies a significant disparity in the statistical analysis findings.</p>
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<p>The change of DOC concentration (<b>a</b>), a<sub>254</sub> (<b>b</b>), and SUVA<sub>254</sub> (<b>c</b>) before and after the flocculation–filtration treatment. The utilization of distinct capital letters (A and B) signifies a significant disparity in the statistical analysis findings.</p>
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<p>The change of M (<b>a</b>) and S<sub>275–295</sub> (<b>b</b>) after the flocculation–filtration treatment for water from Yilong Lake and Dianchi Lake. The utilization of distinct capital letters (A and B) signifies a significant disparity in the statistical analysis findings.</p>
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<p>The change in the fluorescence index (FI, (<b>a</b>)) and the humification index (HIX, (<b>b</b>)) after the flocculation–filtration treatment for water from Yilong Lake and Dianchi Lake. The utilization of distinct capital letters (A and B) signifies a significant disparity in the statistical analysis findings.</p>
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<p>Influence of DOM concentration on the treatment efficiency in lake water with the addition of kaolin particles ((<b>a</b>), Yilong Lake; (<b>b</b>), Dianchi Lake).</p>
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<p>The fluorescence intensity C1 (<b>a</b>); C2 (<b>b</b>); C3 (<b>c</b>), and C4 (<b>d</b>) of the fluorescence components in the raw and treated water from Yilong Lake and Dianchi Lake. The utilization of distinct capital letters (A and B) signifies a significant disparity in the statistical analysis findings.</p>
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<p>Influence of humic acid concentration on the treatment efficiency in lake water with the addition of kaolin particle ((<b>a</b>), Yilong Lake; (<b>b</b>), Dianchi Lake).</p>
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16 pages, 4731 KiB  
Article
Spatial and Temporal Variation in the Fish Diversity in Dianchi Lake and the Influencing Factors
by Kaisong Zhao, Xiaoqin Li, Han Meng, Yuanyuan Lin, Liang Shen, Zhen Ling, Xiaowei Zhang and Shan Xu
Water 2023, 15(24), 4244; https://doi.org/10.3390/w15244244 - 11 Dec 2023
Cited by 2 | Viewed by 1268
Abstract
The survey of fish diversity is an important part of the protection of the ecological health of rivers and lakes. Environmental DNA technology is a new tool to improve the accuracy of traditional morphological surveys of biodiversity and to monitor the amount of [...] Read more.
The survey of fish diversity is an important part of the protection of the ecological health of rivers and lakes. Environmental DNA technology is a new tool to improve the accuracy of traditional morphological surveys of biodiversity and to monitor the amount of diversity. At present, there are few studies on monitoring fish diversity in lake inlets using eDNA technology. In this study, we used various types of estuaries in the Dianchi basin as the research object, used environmental DNA technology to monitor the fish diversity in typical estuaries, and analyzed the temporal and spatial changes and the relationship between environmental factors and fish diversity. In the Dianchi basin, we identified a total of 63 fish species belonging to 8 different orders, 21 families, and 51 genera across two seasons. The Carpidae family had the highest number of species, with Carassius auratus being the most prevalent species. The Shannon index analysis yielded a p-value of 0.0018 (<0.05), suggesting significant seasonal variations in the fish community structure within the typical estuaries of the Dianchi basin. Furthermore, the β-diversity accounted for 59.6% and 57% of the variations in fish communities among the various estuary types in March and July, respectively. Fish species varied considerably between estuaries, with Carassius auratus, Cyprinus carpio, Rhodeus sinensis, Acheilognathus chankaensis, and Coilia nasus all occurring at various points. The agricultural estuary differed substantially from the urban, suburban, and lake areas. Redundancy analysis revealed that the fish community structure during the dry period was primarily influenced by total phosphorus, pH, total nitrogen (TN), and chlorophyll. Conversely, during the rich period, the fish community structure was mainly influenced by dissolved oxygen and TN. This study demonstrated the utilization of environmental DNA technology in assessing the ecological health of rivers and lakes, specifically highlighting its effectiveness in exposing the ecological condition of a representative Dianchi estuary. Full article
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<p>Schematic diagram of the sampling locations at the estuaries in the Dianchi basin. D1: Panlong River; D2: Baoxiang River; D3: Cailian River; D4: Laoyu River; D5: Nanchong River; D6: Da River; D7: Chai River; D8: Dongda River; D9: Dachun River; D10: Dianchi Lake Center.</p>
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<p>Plot of PCR amplification results of DNA extracted from the decamer site using teleo primers. 1: Takara 50 bp DNA Marker; 2: Teleo primer negative control; 3–12: 10 spot Teleo primer amplification products.</p>
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<p>Percentage of fish species composition in dry (<b>a</b>) vs. abundant (<b>b</b>) water periods.</p>
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<p>Percentage of fish species composition in dry (<b>a</b>) vs. abundant (<b>b</b>) water periods.</p>
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<p>Boxplots of the differential analysis of fish abundance between the two periods of abundance and depletion using the calculation of α-diversity.</p>
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<p>β-diversity analysis of the fish communities (OTUs) in the Dianchi basin between the dry and rich water periods: Pco, principal coordinate.</p>
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<p>Difference analysis of Shannon index and Simpson index of fish community diversity between dry and abundant water periods. (<b>a</b>) Shannon index; (<b>b</b>) Simpson index analysis.</p>
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<p>tb-RDA analysis of the relationship between ten environmental factors and fish communities. (<b>a</b>) Dry period; (<b>b</b>) rich water period.</p>
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<p>Heat map of fish distribution based on the serial readings of the representative operational taxonomic units (genus taxonomic level) of the fish at each sampling site in the Dianchi basin during the (<b>a</b>) dry and (<b>b</b>) rich periods.</p>
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<p>Differences in fish community abundance in different types of estuaries during dry (<b>a</b>) and rich (<b>b</b>) water periods were analyzed using Alpha Diversity.</p>
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<p>Principal Coordinate Analysis (PCoA) of fish community composition between sites using beta diversity analysis for both periods. (<b>a</b>) Dry period; (<b>b</b>) rich water period.</p>
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19 pages, 3785 KiB  
Article
Improved Deep Learning Predictions for Chlorophyll Fluorescence Based on Decomposition Algorithms: The Importance of Data Preprocessing
by Lan Wang, Mingjiang Xie, Min Pan, Feng He, Bing Yang, Zhigang Gong, Xuke Wu, Mingsheng Shang and Kun Shan
Water 2023, 15(23), 4104; https://doi.org/10.3390/w15234104 - 27 Nov 2023
Cited by 2 | Viewed by 1157
Abstract
Harmful algal blooms (HABs) have been deteriorating global water bodies, and the accurate prediction of algal dynamics using the modelling method is a challenging research area. High-frequency monitoring and deep learning technology have opened up new horizons for HAB forecasting. However, the non-stationary [...] Read more.
Harmful algal blooms (HABs) have been deteriorating global water bodies, and the accurate prediction of algal dynamics using the modelling method is a challenging research area. High-frequency monitoring and deep learning technology have opened up new horizons for HAB forecasting. However, the non-stationary and stochastic process behind algal dynamics monitoring largely limits the prediction performance and the early warning of algal booms. Through an analysis of the published literature, we found that decomposition methods are widely used in time-series analysis for hydrological processes. Predictions of ecological indicators have received less attention due to their inherent fluctuations. This study explores and demonstrates the predictive enhancement for chlorophyll fluorescence data based on the coupling of three decomposition algorithms with conventional deep learning models: the convolutional neural network (CNN) and long short-term memory (LSTM). We found that the decomposition algorithms can successfully capture the time-series patterns of chlorophyll fluorescence concentrations. The results indicate that decomposition-based models can enhance the accuracy of single models in predicting chlorophyll concentrations in terms of the improvement percentages in RMSE (with increases ranging from 25.7% to 71.3%), MAE (ranging from 28.3% to 75.7%), and R2 values (increasing ranging from 14.8% to 34.8%). In addition, the comparison experiment for different decomposition methods might suggest the superiority of singular spectral analysis in hourly predictive tasks of chlorophyll fluorescence over the wavelet transform and empirical mode decomposition models. Overall, while decomposition methods come with their respective strengths and weaknesses, they are undeniably efficient in combination with deep learning models in dealing with the high-frequency monitoring of chlorophyll fluorescence data. We also suggest that model developers pay more attention to online data preprocessing and conduct comparative analyses to determine the best model combinations for forecasting algal blooms and water management. Full article
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<p>Overview of the study area in Caohai, Lake Dianchi, and the distribution of sampling sites. (<b>a</b>) Location of Lake Dianchi in Yunnan, Southwest China. (<b>b</b>) Distribution of Lake Dianchi and riverway networks in Lake Dianchi Basin. (<b>c</b>) The red pentagon marks the location of the two sampling sites inside Caohai of Lake Dianchi.</p>
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<p>Schematic flow chart of the decomposition-based hybrid deep learning models.</p>
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<p>One-dimensional CNN model flowchart.</p>
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<p>LSTM model flowchart.</p>
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<p>(<b>a</b>) Decomposition results of the Chl<span class="html-italic">a</span> concentrations for (<b>a</b>,<b>b</b>) WT; (<b>c</b>,<b>d</b>) EEMD; (<b>e</b>,<b>f</b>) SSA in Duanqiao and Caohai center sites, respectively.</p>
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<p>Predicted and observed time series of Chl<span class="html-italic">a</span> concentrations by the decomposition-based hybrid deep learning models and independent CNN and LSTM approaches in the Lake Dianchi at (<b>a</b>,<b>b</b>) Caohai Center and (<b>c</b>,<b>d</b>) Duanqiao.</p>
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<p>Predicted and observed time series of Chl<span class="html-italic">a</span> concentrations by the decomposition-based hybrid deep learning models and independent CNN and LSTM approaches in the Lake Dianchi at (<b>a</b>,<b>b</b>) Caohai Center and (<b>c</b>,<b>d</b>) Duanqiao.</p>
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<p>Scatter diagrams of the Chl<span class="html-italic">a</span> with one-step prediction lead times at the Caohai center and Duanqiao stations in Lake Dianchi. Red dots represent the single models, green dots represent the hybrid EEMD-based models, orange dots represent the hybrid WT-based models, and blue dots represent the hybrid SSA-based models. (<b>a</b>) Comparison of single model and hybrid CNN-based models at Caohai center station. (<b>b</b>) Comparison of single model and hybrid CNN-based models at Duanqiao station. (<b>c</b>) Comparison of single model and hybrid LSTM-based models at Caohai center station. (<b>d</b>) Comparison of single model and hybrid CNN-based models at Duanqiao station.</p>
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17 pages, 9513 KiB  
Article
Analysis of the Effect of Ultra-Fine Cement on the Microscopic Pore Structure of Cement Soil in a Peat Soil Environment
by Jing Cao, Chenhui Huang, Huafeng Sun, Yongfa Guo, Wenyun Ding and Guofeng Hua
Appl. Sci. 2023, 13(23), 12700; https://doi.org/10.3390/app132312700 - 27 Nov 2023
Viewed by 898
Abstract
Treating peat soil foundations around Dianchi Lake and Erhai Lake in Yunnan is a complex problem in practical engineering projects. Peat soil solely reinforced with ordinary cement (OPC) does not satisfy demand. This study aims to solidify soil to achieve better mechanical properties. [...] Read more.
Treating peat soil foundations around Dianchi Lake and Erhai Lake in Yunnan is a complex problem in practical engineering projects. Peat soil solely reinforced with ordinary cement (OPC) does not satisfy demand. This study aims to solidify soil to achieve better mechanical properties. The preparation of peat soil incorporates a humic acid (HA) reagent into cohesive soil, and cement and ultra-fine cement (UFC) are mixed by stirring to prepare cement soil samples. They are then immersed in fulvic acid (FA) solution to simulate cement soil in the actual environment. X-ray diffraction (XRD), mercury intrusion porosimetry (MIP), scanning electron microscopy (SEM), and pores and cracks analysis system (PCAS) tests are used to study the impact of the UFC on the microscopic pore structure of cement soil in a peat soil environment. The unconfined compressive strength (UCS) test is used for verification. The microscopic test results indicate that incorporating UFC enhances the specimen’s micropore structure. The XRD test results show the presence of C–S–H, C–A–S–H, and C–A–H. SEM and PCAS tests show that the UFC proportion increases by between 0% and 10%, and the percentage reduction in the macropore volume is the largest, at 38.84%. When the UFC admixture is 30%, the cumulative reduction in the percentage of macropore volume reaches 71.55%. The MIP test results show that the cumulative volume greater than 10 µm in pore size decreases from 7.68% to 0.17% with an increase in the UFC proportion. The UCS test results show that the maximum strength growth of cement soil is 12.99% when the UFC admixture is 0–10%. Incorporating UFC to form a compound curing agent solves the problem of the traditional reinforcement treatment of peat soil foundation being undesirable and decreases the amount of cement. This study provides practical guidance for reducing carbon emissions in actual projects. Full article
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<p>Test material diagram.</p>
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<p>(<b>a</b>) Microstructure image of the cohesive soil aggregate [<a href="#B30-applsci-13-12700" class="html-bibr">30</a>]. (<b>b</b>) Microstructure images of the HA aggregate [<a href="#B30-applsci-13-12700" class="html-bibr">30</a>].</p>
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<p>Microstructure image of the FA aggregate.</p>
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<p>(<b>a</b>) The cumulative distribution curve of the OPC and UFC grain size gradation. (<b>b</b>) OPC and UFC grain size distribution curve.</p>
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<p>Test procedure.</p>
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<p>XRD pattern of the test soil (cohesive soil).</p>
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<p>X-ray diffraction patterns of specimens with various UFC proportions (HA 15%, pH = 6.0, 90 d).</p>
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<p>Pore diameter distribution curves of cement soil with different UFC proportions. (<b>a</b>) The distribution curve of pore volume percentage. (<b>b</b>) Accumulation curve of pore volume percentage greater than a certain diameter (HA 15%, pH = 6.0, 90 d).</p>
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<p>Microstructure images of cement soil with various UFC proportions [<a href="#B19-applsci-13-12700" class="html-bibr">19</a>] and PCAS pore segmentation processing images. (<b>a</b>) UFC proportion 0%. (<b>b</b>) UFC proportion 10%. (<b>c</b>) UFC proportion 20%. (<b>d</b>) UFC proportion 30%. (<b>e</b>) UFC proportion 40%. (<b>f</b>) UFC proportion 50%. (1) 500 times SEM image. (2) 2000 times SEM image of yellow frame. (3) PCAS pore segmentation image.</p>
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<p>Microstructure images of cement soil with various UFC proportions [<a href="#B19-applsci-13-12700" class="html-bibr">19</a>] and PCAS pore segmentation processing images. (<b>a</b>) UFC proportion 0%. (<b>b</b>) UFC proportion 10%. (<b>c</b>) UFC proportion 20%. (<b>d</b>) UFC proportion 30%. (<b>e</b>) UFC proportion 40%. (<b>f</b>) UFC proportion 50%. (1) 500 times SEM image. (2) 2000 times SEM image of yellow frame. (3) PCAS pore segmentation image.</p>
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<p>PCAS test results (percentage of macropore volume).</p>
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<p>UCS curves of cement soil with various UFC proportions.</p>
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<p>The strength growth rate of specimens within various UFC proportion ranges.</p>
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26 pages, 31104 KiB  
Article
Modeling and Assessment of Landslide Susceptibility of Dianchi Lake Watershed in Yunnan Plateau
by Guangshun Bai, Xuemei Yang, Zhigang Kong, Jieyong Zhu, Shitao Zhang and Bin Sun
Sustainability 2023, 15(21), 15221; https://doi.org/10.3390/su152115221 - 24 Oct 2023
Cited by 2 | Viewed by 948
Abstract
The nine plateau lake watersheds in Yunnan are important ecological security barriers in the southwest of China. The prevention and control of landslides are important considerations in the management of these watersheds. Taking the Dianchi Lake watershed as a typical research area, a [...] Read more.
The nine plateau lake watersheds in Yunnan are important ecological security barriers in the southwest of China. The prevention and control of landslides are important considerations in the management of these watersheds. Taking the Dianchi Lake watershed as a typical research area, a comprehensive modeling and assessment process of landslide susceptibility was put forward. The comprehensive process was based on the weight of evidence (WoE) method, and many statistical techniques were integrated, such as cross-validation, multi-quantile cumulative Student’s comprehensive weight statistics, independence testing, step-by-step modeling, ROC analysis, and ROC-based susceptibility zoning. In this paper, fourteen models with high accuracy and validity were established, and the AUC reached 0.83–0.87 and 0.85–0.88, respectively. In addition, according to the susceptibility zoning map compiled via the optimal model, 80% of landslides can be predicted in the very-high- and high-susceptibility areas, which only account for 19.58% of the study area. Finally, this paper puts forward strategies for geological disaster prevention and ecological restoration deployment. Full article
(This article belongs to the Topic Environmental Geology and Engineering)
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<p>Study area. (<b>a</b>) The distribution of nine plateau lake watersheds in Yunnan, and the location of the Dianchi Lake watershed. The base map is the distribution map of land coverage types in Yunnan Province in 2020 [<a href="#B33-sustainability-15-15221" class="html-bibr">33</a>]. (<b>b</b>) The distribution map of landslide points in Dianchi Lake watershed. The black points are landslides under investigation, the blue blocks are the water surface, and the gray diagonal lines are the areas with the attribute of “flat” [<a href="#B34-sustainability-15-15221" class="html-bibr">34</a>,<a href="#B35-sustainability-15-15221" class="html-bibr">35</a>]. The bottom picture is rendered via elevation and hill shade.</p>
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<p>The geological map of lithology and faults.</p>
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<p>Process flowchart of cross-validation landslide dataset compilation based on random sampling.</p>
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<p>Flowchart of the improved WoE landslide susceptibility assessment.</p>
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<p>Process flowchart of single-factor WoE statistic. WoE_ALL, sC_ALL, and AUC_ALL are the weight, sC, and AUC calculated on ALL, respectively; WoE_trn, sC_trn, and AUC_trn are the mean weight, sC, and AUC calculated 100 times on trn, respectively; ROC_trn2trn and AUC_trn2trn are the single-factor accuracy assessment indexes modeled by single-factor weight WoE_trn and fit to trn; and ROC_trn2TST and AUC_trn2TST are the single-factor validity assessment indexes modeled by single-factor weight WoE_trn and fit to TST.</p>
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<p>Process flowchart of accuracy and validation assessment of models.</p>
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<p>Process flowchart of factor classification optimization strategy based on the cumulative <math display="inline"><semantics> <mrow> <mi>s</mi> <mi>C</mi> </mrow> </semantics></math> curve and WoE statistics.</p>
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<p>The cumulative <math display="inline"><semantics> <mrow> <mi>s</mi> <mi>C</mi> </mrow> </semantics></math> statistical curve of six factors according to six quantiles of 100, 80, 60, 40, 20, and 10.</p>
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<p>Graphical result of WoE for the factor dF. Class 1 is 0–121 m; class 3 is 262–460 m; class 5 is 657–864 m; class 7 is 1355–2317 m; and class 99 is other ranges. The first picture is the C histogram of factor classification based on statistics of ALL, and the black vertical line is the error bar of C. The second picture presents the ROC_ALL and AUC_ALL based on statistics of ALL. The third picture is a C violin-box diagram of factor classification based on statistics of trn with 100 subsets. The fourth picture presents the ROC and AUC, which have been counted 100 times based on trn with 100 subsets, where the red line is the mean ROC and the gray band is the ROC range.</p>
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<p>Graphical result of WoE for the factor Lth. Class 10 is loose gravel soil; class 23 is sandstone, mudstone, and shale; class 24 is mudstone, shale, and siltstone; class 51 is basalt; and class 199 is other lithologic strata, including limestone and metamorphic rocks.</p>
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<p>Graphical result of WoE for the factor NDVIlog. Class 1 is 2.79–3.64; class 2 is 3.64–3.71; class 3 is 3.71–3.76; class 4 is 3.76–3.81; class 5 is 3.81–3.84; class 6 is 3.84–3.85; class 7 is 3.85–3.88; and class 8 is 3.88–3.99.</p>
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<p>Graphical result of WoE for the factor CLCD. Class 2 is forest; class 4 is grassland; and class 99 is others (cropland, shrub, barren, impervious, wetland).</p>
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<p>Graphical result of WoE for the factor dRD. Class 1 is 0–22.81 m; class 2 is 22.81–44.56 m; class 3 is 44.56–71.39 m; class 4 is 71.39–99.68 m; class 5 is 99.68–157.42 m; class 6 is 157.42–306.85 m; class 7 is 306.85–458.95 m; class 8 is 458.95–602.39 m; and class 9 is 602.39–2936.07 m.</p>
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<p>Graphical result of WoE for the factor SL. Class 1 is 0–4.12°; class 3 is 6.44–7.65°; class 5 is 10.83–11.65°; class 6 is 11.65–16.13°; class 8 is 17.12–21.10°; class10 is 25.60–28.27°; class 11 is 28.27–39.98°; and class 99 is other slopes.</p>
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<p>Graphical result of WoE for the factor RSP. Class 1 is 0–0.01; class 2 is 0.01–0.02; class 3 is 0.02–0.05; class 4 is 0.05–0.06; class 5 is 0.06–0.08; class 6 is 0.08–0.14; class 7 is 0.14–0.29; class 8 is 0.29–0.45; and class 9 is 0.45–1.02.</p>
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<p>Graphical result of WoE for the factor TRI. Class 1 is 0.00–11.58 m; class 2 is 11.58–20.62 m; class 3 is 20.62–22.98 m; class 5 is 41.98–45.47 m; class 7 is 48.89–52.50 m; class 8 is 52.50–58.39 m; class 10 is 112.52–125.38 m; and class 99 is others in the range of 0–447.60 m.</p>
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<p>Graphical result of WoE for the factor Rou. Class 1 is 0.00–8.93; class 2 is 8.93–16.53; class 3 is 16.53–24.95; class 4 is 24.95–28.88; class 5 is 28.88–40.73; class 6 is 40.73–44.33; class 7 is 44.33–49.50; class 8 is 49.50–52.52; class 9 is 52.52–57.22; class 10 is 57.22–62.32; and class 11 is 62.32–398.73.</p>
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<p>Graphical result of WoE for the factor Cprof. Class 1 is −12,611.46~−4084.50 (×10<sup>−6</sup>); class 2 is −4084.50~−2981.60 (×10<sup>−6</sup>); class 3 is −2981.60~−1533.30 (×10<sup>−6</sup>); class 4 is −1533.30~−973.62 (×10<sup>−6</sup>); class 5 is −973.62~−686.55 (×10<sup>−6</sup>); class 6 is −686.55~37.07 (×10<sup>−6</sup>); and class 7 is 37.07~10596.92 (×10<sup>−6</sup>).</p>
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<p>Graphical result of WoE for the factor HANDV. Class 1~class 15 are divided by 0 m, 4.15 m, 6.93 m, 13.03 m, 15.61 m, 17.89 m, 24.11 m, 26.22 m, 34.53 m, 37.77 m, 41.57 m, 55.48 m, 66.60 m, 77.37 m, 101.59 m, and 570.01 m.</p>
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<p>Graphical result of WoE for the factor HANDH. Class 1~class 13 are divided by 0 m, 38.06 m, 49.60 m, 65.22 m, 100.45 m, 115.44 m, 184.98 m, 1255.91 m, 271.86 m, 302.28 m, 323.25 m, 439.08 m, 1176.82 m, and 2831.14 m.</p>
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<p>Graphical result of WoE for the factor dCN. Class 1~class 14 are divided by 0 m, 22.33 m, 24.98 m, 40.21 m, 49.85 m, 67.45 m, 94.96 m, 113.16 m, 134.62 m, 174.57 m, 240.09 m, 279.41 m, 320.53 m, 394.72 m, and more than 394.72 m.</p>
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<p>Thirteen factors with AUCs ≥ 0.6 and their AUC values.</p>
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<p>Factor classification with <math display="inline"><semantics> <mrow> <msup> <mi>W</mi> <mo>+</mo> </msup> <mo>≥</mo> <mn>0.6</mn> </mrow> </semantics></math>.</p>
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<p>Test results for conditional dependence. The upper right half represents the Pearson’s C results, and the factors with a strong correlation indicated by &gt;0.7 are designated by black circles, such as Rou and TRI (0.81), Rou and SL (0.71), and dCN and HANDH (0.82). The lower left presents the Cramer’s V results.</p>
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<p>Accuracy and validity assessment of the models. Accuracy assessment of the models of susceptibility to landslides with the ROC_trn2trn of models (the blue line and the grey range). The total weights for the models were based on trn, and the performance of the models was evaluated using trn. One hundred iterations were carried out. The blue line is the mean ROC_M of 100 iterations. The grey range marks the model uncertainty based on the ROCs’ MSE for 100 iterations. Test of validity of the models with the ROC_M_trn2TST (the orange line). The total weight maps were based on trn, and the validation was assessed using TST.</p>
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<p>Comparison of validity and accuracy (AUCs) of models.</p>
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<p>Map of susceptibility to landslides based on model M11 and trn. The model M11 has the highest rate of accuracy and validity; (<b>a</b>,<b>b</b>) are compiled using the same susceptibility partition data. The differences are as follows: (<b>b</b>) MS, LS, and VLS use the same general gray color to highlight VHS and HS; the bottom picture is rendered using elevation and hill shade; the red ellipse roughly delineates the areas of high susceptibility and contiguous distribution.</p>
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