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Article

Jinluo Low-Density Lotus Pond Wetland Water Purification Practice Experiment—A Case of Limited Efficacy

by
Bo Liu
1,
Yuan Gao
2,3,*,
Jing Zhou
4,
Yun Wang
2 and
Junxia He
1
1
Shandong Linyi Station of Positioning Observation and Research for Forest Ecosystem, College of Resources and Environment, Linyi University, Linyi 276005, China
2
Shandong Provincial Key Laboratory of Water and Soil Conservation and Environmental Protection, College of Resources and Environment, Linyi University, Linyi 276005, China
3
Linyi Scientific Exploration Laboratory, Linyi 276037, China
4
School of Life Sciences, Qufu Normal University, Jining 273165, China
*
Author to whom correspondence should be addressed.
Water 2025, 17(13), 1985; https://doi.org/10.3390/w17131985
Submission received: 1 April 2025 / Revised: 24 June 2025 / Accepted: 30 June 2025 / Published: 1 July 2025
(This article belongs to the Special Issue Constructed Wetlands as a Sustainable Technology for Wastewater Treatment: Current Trends and Future Potential)
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Versions Notes

Abstract

(1) Although lotus ponds exhibit ecological benefits in wetland restoration, their efficacy in water purification and eutrophication mitigation remains unclear. (2) This study utilized Jinluo lotus pond as the experimental group and the adjacent river as the control. Five sampling points were established in each area, with water samples collected in June 2022, April 2025, and May 2025. (3) The pH, BOD, COD, TN, and NH3-N concentrations in Jinluo lotus pond water are higher than those in rivers, while the TP, NO3-N, Chl-a, and algal cell density in rivers are higher. However, there was no significant difference in the nine parameters (p > 0.05) in June 2022. The pH, DO, algal cell density, and algal biomass of the Jinluo lotus pond were significantly higher (p < 0.05 for DO); the concentrations of BOD, COD, TN, TP, NH3-N, NO3-N, PI, and Chl-a in rivers are higher, with significant differences in Chl-a (p < 0.05) in April 2025. The BOD, COD, TP, NO3-N, and PI of the Jinluo lotus pond were relatively high (p < 0.05 for PI); the pH, TN, NH3-N, DO, Chl-a, algal cell density, and algal biomass of rivers are higher, with significant differences in Chl-a (p < 0.05) in May 2025. The results showed that there was no significant difference in the four diversity indicators in June 2022, April 2025, and May 2025. There was no significant difference in the algal diversity indices, including species richness (S), Shannon–Wiener diversity index (H), Simpson diversity index (P), and Pielou evenness index (E) between Jinluo lotus pond and rivers. (4) Conclusions and Recommendations: The Jinluo lotus pond and adjacent rivers suffer from severe nutrient overload, especially with BOD, COD, and TN all being classified as Class 5 water. Expanding natural and constructed reed communities is recommended to enhance nutrient removal. However, given the limited purification capacity of lotus ponds, maintaining or increasing their area may not be justified.

1. Introduction

With the rapid increase in global water demand, a series of water ecological issues caused by eutrophication, such as water quality deterioration and biodiversity loss, have become a global focus of concern [1]. Water eutrophication stands as one of the greatest challenges facing the global water environment, as it undermines the stability and functionality of aquatic ecosystems [2,3]. Water scarcity has emerged as a critical issue threatening sustainability in many regions worldwide [4,5]. Water environment governance constitutes an essential component of the United Nations Sustainable Development Goals (SDGs), with the United Nations Environment Programme (UNEP) emphasizing the pivotal role of water governance in addressing sustainable development challenges [6,7].
Selecting appropriate restoration methods is essential for the recovery of aquatic ecosystems [1]. Among these, ecological approaches—known for their cost-effectiveness and efficiency—have emerged as a preferred solution for water quality improvement in diverse aquatic systems [8]. Specifically, aquatic macrophyte-based remediation is widely regarded as one of the most economical, efficient, and environmentally sustainable methods, offering distinct advantages [8]. For instance, constructed wetlands are increasingly utilized for sustainable wastewater treatment, effectively removing organic matter and nutrients while delivering ecosystem services and recreational benefits. Notably, hybrid constructed wetlands represent the most efficient approach for enhancing water quality and mitigating greenhouse gas emissions. Their performance depends on multiple factors, including plant species, substrate selection, and environmental/hydraulic conditions, with pollutant removal efficiency largely influenced by temperature, hydraulic retention time, and pollutant loading rates [9].
Constructed wetlands composed of diverse aquatic macrophyte species, acknowledged as a convenient, environmentally friendly, low-cost, and efficient phytoremediation technology, have been widely used globally for polluted water treatment [10,11,12]. Artificial water bodies are increasingly becoming prominent features in urban landscapes [13], serving as temporary sanctuaries and phased wetland reserves [14], though most landscape water bodies face risks of pollution and eutrophication.
As a rapidly developing economic powerhouse, China has confronted escalating water pollution challenges resulting from decades of intensive industrialization and urbanization. In response to these pressing environmental concerns, the Chinese government initiated the groundbreaking “Sponge City” program in 2015 as a comprehensive national infrastructure strategy. This innovative approach systematically addresses multiple urban water management challenges, including the following: (1) urban surface water flooding mitigation, (2) stormwater runoff purification, (3) peak flow regulation, and (4) sustainable water resource utilization [15]. Through coordinated efforts led by the State Council and implemented by various governmental departments, sponge city development has been vigorously promoted nationwide. By 2024, the cumulative investment in this initiative had surpassed 60 billion yuan (approximately 8.3 billion USD), facilitating the creation of urban ecosystems with enhanced natural hydrological functions including water retention, infiltration, and purification capacity. However, the effectiveness of natural wetlands in pollution control remains constrained by two fundamental limitations: (1) restricted spatial expansion potential within urban environments and (2) overburdened biogeochemical processing capacities [16]. In this context, constructed wetlands have emerged as strategically important engineered ecosystems, demonstrating a proven efficacy in water quality remediation through controlled biological and physical–chemical processes [17]. Nevertheless, their operational sustainability faces challenges from two primary environmental impacts: (1) greenhouse gas emissions associated with microbial metabolic processes and (2) potential secondary pollution from accumulated contaminants [18].
Constructed wetland systems consistently demonstrate high removal efficiencies (typically 70–90%) for organic pollutants including biochemical oxygen demand (BOD), chemical oxygen demand (COD), and suspended solids, although nitrogen removal performance shows a greater variability (30–80%) depending on system configuration [19]. Typical designs incorporate extensive reed beds utilizing either single or mixed plant species to achieve multiple treatment objectives: the physical filtration of suspended solids, biochemical transformation of nutrients, and enhanced sedimentation processes through rhizosphere interactions [20,21]. Selected macrophyte species such as reeds (Phragmites australis), sedges, and other emergent vegetation effectively assimilate nutrients and pollutants through their root systems before translocating them to aerial biomass [22,23]. Particularly high-performing species commonly employed in these systems include Phragmites australis, Nelumbo nucifera, Typha domingensis, T. latifolia, Eichhornia crassipes, Pistia stratiotes, and Vallisneria natans, which are specifically valued for their exceptional nutrient uptake capacities demonstrated in numerous studies [22,23,24,25,26].
Numerous case studies have demonstrated the remarkable treatment efficiency of constructed wetlands across various applications: in Xiantao, a constructed wetland system achieved removal rates of 94% for total nitrogen, 90% for total phosphorus, 68% for COD, and 95% for ammonia nitrogen in municipal wastewater treatment [27]; at Universiti Sains Malaysia, wetlands dominated by Typha angustifolia and Eleocharis variegata effectively reduced nitrites, nitrates, ammonia nitrogen, and phosphates [28]; reed floating beds showed removal efficiencies of 55–60% for total solids, 45–55% for NH3-N, 33–45% for NO3−-N, 45–50% for TKN, and 40–50% for BOD, proving particularly suitable for the in-situ treatment of shallow, slow-flowing water bodies [29]; the Lotus Lake National Wetland Park in Tieling City, featuring Phragmites and Nelumbo nucifera, significantly reduced total phosphorus and ammonia nitrogen concentrations [30]; while lotus pond wetlands demonstrated effectiveness in treating garlic processing wastewater through the substantial removal of organic pollutants and the reduction of COD60 and BOD5 levels [31]. Comparative research has revealed distinct species-specific treatment efficiencies, with Eichhornia crassipes and Phragmites australis exhibiting superior nitrogen removal capabilities, whereas Pistia stratiotes and Nelumbo nucifera show an enhanced phosphorus removal performance [24,32]. Further studies conducted at Wuliangsuhai Lake and Baiyangdian Lake have elucidated Nelumbo nucifera’s dual effects on algal dynamics, demonstrating that low-density plantings can promote algal growth while high-density configurations effectively suppress it, underscoring the critical importance of optimal density management in wetland design [33,34,35].
The Yi River, a major watercourse in the Huai River Basin [36], is located in southern Shandong and northern Jiangsu, with geographical coordinates 34°23′–36°20′ N and 117°25′–118°42′ E. Spanning approximately 574 km, it originates from Yiyuan County in Shandong and flows into the Yellow Sea at Yanwei Port through the Xin-Yi River (Yi River Diversion Channel) from Wu Lou Village in Pi County, Jiangsu [37,38,39]. The Yi River has been listed as a key control and monitoring river in the Huai River Basin Water Pollution Prevention Plan. The Liuqing River, a tributary of the Yi River, suffers from severe excessive nutrient loads in its upper reaches.
Extensive research confirms that aquatic plants play a beneficial role in mitigating water eutrophication [4,10,40]. Common species employed in urban wetlands include reeds and reed ponds [4,41], as well as lotus and lotus ponds [10,32,33,42], all demonstrating ecological benefits for wetland restoration. The study site, Jinluo lotus pond, is situated on the north bank of the Liuqing River in Linyi City, Shandong Province, covering a total area of 7.109 km2 with lotus ponds accounting for 5.5667 km2. This project was designed to utilize lotus roots for regulating nutrient concentrations in wetland waters, alleviating eutrophication, and restoring polluted water bodies. Nevertheless, the actual efficacy of such systems in water quality purification remains questionable, particularly regarding their ability to achieve sustainable eutrophication control without causing secondary ecological impacts.

2. Materials and Methods

2.1. Site Description and Sampling Procedure

In this study, Jinluo lotus pond served as the experimental group, while the adjacent river outside the pond was designated as the control group, with five sampling areas established in each location. Sampling was performed in June 2022, April 2025, and May 2025 using sterilized 4 L sampling buckets to collect water samples for laboratory analysis in Figure 1.

2.2. Analytical Methods

pH, Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Total Nitrogen (TN), Total Phosphorus (TP), Ammonia Nitrogen (NH3-N), Nitrate Nitrogen (NO3−-N), Dissolved Oxygen (DO), Planktonic Index (PI), Chlorophyll-a (Chl-a), and algal cell density were studied. These selected parameters served dual purposes: (1) assessing the water purification efficacy of the lotus pond system, and (2) providing scientific basis for developing effective environmental management strategies for the Liuqing River watershed.
pH: Quantify using electrode method (HJ 1147-2020) and ST2100 pH meter (HLJC-243-2) [43]. BOD: Quantified using the standard Dilution and Inoculation Method (HJ 505-2009) with a 25 mL acid burette (Model B193) [44]. COD: Analyzed by the Potassium Dichromate Method (HJ 828-2017) employing a 50 mL acid burette (Model B192) [45]. TN: Determined through Ultraviolet Spectrophotometry (HJ 636-2012) using a UV-1750 UV-Vis Spectrophotometer (Model A11605031003CS) [46]. TP and NH3-N: Measured, respectively, by Ammonium Molybdate Spectrophotometry (GB 11893-1989) and Nessler’s Reagent Spectrophotometry (HJ 535-2009), with measurements conducted on a DR2008 Visible Spectrophotometer (Serial No. 1429121) [47,48]. NO3−-N: Determined via ion chromatography (HJ 84-2016) using a DIONEX AQUION ion chromatograph (HLJC-231) [49]. DO: Detection using electrochemical probe method (HJ 506-2009) and JPB-607A portable dissolved oxygen analyzer (HLJC-285) [50]. PI: Determination of permanganate index (GB/T 11892-1989) and detection using a 25mL acid burette (B-S-25-2) [51]. Phytoplankton analysis: Species identification and quantification were performed under an optical microscope using standardized counting chambers, with results expressed as cell density (cells/L) and species diversity indices [52,53]. Chl-a: Quantified following Acetone Spectrophotometry (HJ 897-2017) [54].

2.3. Diversity Indices Calculation

Species Richness (S): Total number of identified phytoplankton species per sample.
Shannon–Wiener Index (H): H = −∑(Pi × lnPi)
Simpson’s Diversity Index (P): D = 1 − ∑(Pᵢ2)
Pielou’s Evenness Index (E): E = H/lnS
where Pi denotes the proportion of individuals of the i-th species relative to the total phytoplankton count [55,56,57].

2.4. Statistical and Spatial Analysis

All data were processed using SPSS 19.0 for statistical analysis.

3. Results

3.1. Differences in Water Quality Factors

This study conducted a systematic comparison of water quality parameters between the Jinluo lotus pond and its adjacent river system in Figure 2 and Figure 3. Results indicated that while the lotus pond exhibited elevated levels of water pH, BOD, COD, TN, and NH3-N compared to the river, the adjacent river conversely showed higher concentrations of TP, NO3−-N, Chl-a, and algal cell density. Statistical analysis revealed that none of the nine measured parameters demonstrated statistically significant differences (p > 0.05) in June 2022.
Relative to adjacent rivers, the lotus pond exhibited a significantly higher pH, dissolved oxygen (DO), algal cell density, and algal biomass (p < 0.05 for DO). In contrast, river water showed elevated levels of BOD, COD, TN, TP, NH3-N, NO3−-N, planktonic index (PI), and Chl-a, with a statistically significant difference in Chl-a (p < 0.05) in April 2025.
Compared to rivers, the lotus pond had a higher BOD, COD, TP, NO3−-N, and PI, with a significant difference in PI (p < 0.05). Conversely, river samples displayed a greater pH, TN, NH3-N, DO, Chl-a, algal cell density, and algal biomass, with Chl-a showing a significant difference (p < 0.05) in May 2025.

3.2. Water Quality Assessment

In accordance with China’s Surface Water Environmental Quality Standards (GB 3838-2002) [52], we evaluated the water quality of Jinluo lotus pond and its adjacent river using seven key parameters: BOD, COD, TN, TP, NH3-N, PI, and DO (Table 1). The results demonstrated severe exceedances of national standards for BOD, COD, and TN in both systems, reflecting significant organic pollution and nutrient loading. All sampling sites consistently exceeded Class V water quality thresholds—the most polluted classification under GB 3838-2002—indicating critically degraded water conditions throughout the study area.

3.3. The Correlation of Water Quality Factors

As shown in Table 2, BOD and COD exhibited a strong positive correlation (p < 0.01), demonstrating a close relationship between these key organic pollution indicators. A significant positive correlation was also observed between COD and chlorophyll-a (p < 0.05). Among nitrogen components, TN and NH3-N showed a particularly strong positive correlation (p < 0.01), implying shared sources or transformation processes.
The analysis revealed significant linkages between algal dynamics and multiple water quality parameters. Notably, algal cell density showed positive correlations with both NH3-N and Chl-a (all p < 0.05), suggesting a potential coupled biogeochemical cycling of these nutrients within the lotus pond ecosystem.
In Table 3, pH is significantly negatively correlated with COD (p < 0.05). BOD is significantly positively correlated with COD, TN, TP, NH3-N, Permanganate index, and dissolved oxygen (p < 0.01). COD is significantly positively correlated with TP, NH3-N, NO3-N, Permanganate index, dissolved oxygen, Algae cell density, and Algal biomass (p < 0.05). TP is significantly positively correlated with TN and NH3-N (p < 0.01), Permanganate index, and dissolved oxygen (p < 0.05). TN is significantly positively correlated with NH3-N, Permanganate index, and dissolved oxygen (p < 0.01), NO3-N (p < 0.05). NH3-N is significantly positively correlated with dissolved oxygen (p < 0.01). Permanganate index is significantly positively correlated with dissolved oxygen (p < 0.01). Dissolved oxygen is significantly positively correlated with Chl-a (p < 0.05). Algae cell density is significantly positively correlated with Algal biomass (p < 0.01).

3.4. Phytoplankton Diversity

Phytoplankton diversity indices—including species richness (S), Shannon–Wiener diversity index (H), Simpson diversity index (P), and Pielou evenness index (E)—were analyzed for the lotus pond. The results showed that there was no significant difference in the four diversity indicators in June 2022, April 2025, and May 2025 in Figure 4.
There is no significant difference between the Phytoplankton cell density of Jinluo lotus pond and the river outside the pond. In order of cell density, the phytoplankton in the Jinluo lotus pond water are Cyclotella meneghiniana, Pseudanabaena sp., and Scenedesmus quadricauda, but in the river outside the pond, the water has P. sp., C. meneghiniana, and S. quadricauda in 2022 in Table 4. The phytoplankton in the Jinluo lotus pond water is Tetrastrum staurogeniaforme, but in the river outside the pond, the water has Tetrastrum staurogeniaforme, Coelastrum microporum, Scenedesmus quadricauda, and Scenedesmus bicaudatus in 2025 in Table 5.

4. Discussion

Ecological restoration strategies frequently incorporate three principal phytoremediation approaches: riparian vegetation buffer zones, ecological floating beds, and constructed wetlands, each offering distinct advantages for water quality improvement [58,59,60]. The effectiveness of these phytoremediation systems is primarily determined by two critical design parameters: appropriate plant species selection and optimal planting density, which collectively govern nutrient uptake capacity and treatment performance [61,62,63]. As fundamental elements of ecological engineering solutions, aquatic plants demonstrate marked variations in removal efficiencies depending on pollutant composition, with species-specific responses to different contaminant mixtures [64,65]. Research indicates that under eutrophic conditions, superior nutrient removal performance correlates strongly with three key plant traits: (1) high biomass production capacity, (2) elevated leaf dry matter content (LDMC), and (3) reduced specific leaf area (SLA), suggesting that wetland species exhibiting this combination of characteristics—particularly those with high biomass and LDMC coupled with low SLA values—may represent optimal candidates for nutrient-rich wastewater treatment applications [66]. Notably, engineered sequential wetland systems have proven particularly effective for purifying polluted urban waterways even in challenging cold climate conditions, demonstrating robust year-round treatment capabilities [67].
This study investigates the wastewater treatment capacity of constructed wetlands in the context of livestock effluent, particularly given the upstream location of a globally significant swine slaughtering center that generates substantial volumes of nutrient-rich wastewater containing elevated concentrations of carbon, nitrogen, and phosphorus compounds. Aquaculture effluents, similarly characterized by high nutrient loads, present significant environmental challenges with nitrogen and phosphorus being primary contributors to ecological degradation [68]. Research demonstrates that constructed wetland systems can reliably achieve total nutrient removal efficiencies exceeding 60% when treating bullfrog aquaculture wastewater [69], while Euryale ferox Salisb-based ecological ponds exhibit exceptional performance in both the in-situ and ex-situ treatment of shrimp aquaculture effluent, showing particularly high removal rates for total nitrogen (TN) and total phosphorus (TP) [70]. Advanced multi-stage treatment systems combining lotus ponds with surface flow wetlands have proven particularly effective for swine wastewater remediation, capable of transforming heavily contaminated influent into lightly polluted effluent through efficient nitrogen and phosphorus removal mechanisms [71], though these systems require careful operational management including seasonal lotus root harvesting during winter–spring periods and periodic sediment dredging to maintain treatment efficacy.
Constructed wetlands have emerged as a highly efficient, cost-effective, and environmentally sustainable solution for wastewater treatment, offering distinct advantages over conventional treatment systems in terms of operational simplicity, minimal maintenance requirements, and ecological compatibility [72]. The field has witnessed an evolutionary shift from basic treatment wetlands to sophisticated, multi-functional integrated systems. This transition is exemplified by the Integrated Constructed Wetland (ICW) concept, which adopts a comprehensive design philosophy that systematically incorporates four key dimensions—economic viability, social acceptance, environmental performance, and landscape integration—throughout all project phases from initial planning to long-term operation. Beyond their core treatment functions, these integrated wetland systems provide substantial secondary benefits, particularly in terms of biodiversity enhancement and habitat creation [73,74]. In this context, while performance assessments reveal that the Jinluo lotus pond demonstrates relatively modest water purification capabilities as a standalone treatment system, it nonetheless makes valuable contributions as a constructed wetland through its significant ecological services and exceptional aesthetic qualities that enrich the surrounding landscape.
Derived from Liebig’s Law of the Minimum, this paradigm suggests algal growth in aquatic ecosystems becomes N-limited when the aqueous N:P ratio falls below 16 (molar basis), while P-limitation occurs when ratios exceed 16 [75,76]. Alternative formulations using the TN:TP mass ratio propose N-limitation thresholds at TN:TP < 9 and P-limitation thresholds at TN:TP > 23 [42], with some studies establishing a nitrogen limitation threshold at TN:TP < 25 [77]. Our research shows that from 2022 to 2025, the nitrogen phosphorus ratio in this water area will decrease in Jinluo lotus pond and the river outside the pond in Table 6.

5. Conclusions

The main findings indicate that Jinluo lotus pond, as a low-density lotus pond artificial wetland system, has not shown significant water quality improvement effects. Based on these findings, we propose the following management recommendations: (1) a strategic expansion of both natural and constructed reed (Phragmites australis) communities along riparian zones, as these demonstrate superior nitrogen removal capabilities, a and (2) discontinuation of current lotus pond maintenance practices and an avoidance of further lotus cultivation area expansion, given their demonstrated tendency to exacerbate nitrogen accumulation in aquatic systems. This integrated approach would optimize nutrient removal efficiency while enhancing the overall ecological functionality of the watershed.

Author Contributions

Conceptualization, B.L., Y.G., J.Z., Y.W. and J.H.; Methodology, B.L., Y.G., J.Z. and Y.W.; Software, Y.G., J.Z. and J.H.; Validation, Y.G.; Formal analysis, Y.G. and Y.W.; Investigation, Y.G.; Resources, B.L. and Y.G.; Data curation, Y.G. and J.H.; Writing—original draft, B.L., Y.G. and J.Z.; Writing—review & editing, Y.G., J.Z., Y.W. and J.H.; Visualization, Y.G.; Supervision, Y.G.; Project administration, B.L. and Y.G.; Funding acquisition, B.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Shandong Provincial Key Laboratory of Water and Soil Conservation and Environmental Protection (No. STKF202307), and Shandong Province Youth Innovation and Technology Support Program for Higher Education Institutions (2020KJE009).

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

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Water 17 01985 g001
Figure 1. The sampling point layout of Jinluo lotus pond and the river outside the pond. (A,B) show the exterior views of the lotus pond, (C) displays the distribution of sampling points in this study (The red line in the diagram represents the boundary of the lotus pond, yellow triangles indicate the lotus pond area, and pink triangles represent the river outside the lotus pond).
Figure 1. The sampling point layout of Jinluo lotus pond and the river outside the pond. (A,B) show the exterior views of the lotus pond, (C) displays the distribution of sampling points in this study (The red line in the diagram represents the boundary of the lotus pond, yellow triangles indicate the lotus pond area, and pink triangles represent the river outside the lotus pond).
Water 17 01985 g001
Water 17 01985 g002
Figure 2. Differences of (A) pH, (B) BOD, (C) COD, (D) TP, (E) TN, (F) NH3-N, (G) NO3−-N, (H) Chl-a, and (I) algal cell density in the water quality factors of Jinluo lotus pond and the river outside the pond.
Figure 2. Differences of (A) pH, (B) BOD, (C) COD, (D) TP, (E) TN, (F) NH3-N, (G) NO3−-N, (H) Chl-a, and (I) algal cell density in the water quality factors of Jinluo lotus pond and the river outside the pond.
Water 17 01985 g002
Water 17 01985 g003
Figure 3. Differences of (A) PI, (B) DO, and (C) algal biomass in the water quality factors of Jinluo lotus pond and the river outside the pond.
Figure 3. Differences of (A) PI, (B) DO, and (C) algal biomass in the water quality factors of Jinluo lotus pond and the river outside the pond.
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Water 17 01985 g004
Figure 4. (A) The Phytoplankton richness index (S), (B) Shannon–Wiener diversity index (H), (C) Simpson diversity index (P), and (D) Pielou evenness index (E) of Jinluo lotus pond and the river outside the pond.
Figure 4. (A) The Phytoplankton richness index (S), (B) Shannon–Wiener diversity index (H), (C) Simpson diversity index (P), and (D) Pielou evenness index (E) of Jinluo lotus pond and the river outside the pond.
Water 17 01985 g004
Table 1. The water quality assessment of Jinluo lotus pond and the river outside the pond.
Table 1. The water quality assessment of Jinluo lotus pond and the river outside the pond.
Jinluo Lotus PondRiver Outside the Pond
BODSuper Class V water (2022)
Super Class V water (2025)		Super Class V water (2022)
Super Class V water (2025)
CODSuper Class V water (2022)
Super Class V water (2025)		Super Class V water (2022)
Super Class V water (2025)
TNSuper Class V water (2022)
Super Class V water (2025)		Super Class V water (2022)
Super Class V water (2025)
TPClass III water (2022)
Super Class V water (2025)		Class III water (2022)
Super Class V water (2025)
NH3-NClass II water (2022)
Class III water (2025)		Class II water (2022)
Class IV water (2025)
PIClass V water (2025)Class V water (2025)
DOClass IV water (2025)Class IV water (2025)
Table 2. The correlation of water quality factors of Jinluo lotus pond and the river outside the pond (2022).
Table 2. The correlation of water quality factors of Jinluo lotus pond and the river outside the pond (2022).
pHBODCODTPTNNH3-NNO3−-NChl-aACD
pH1.000
BOD−0.2401.000
COD−0.1230.927 **1.000
TP−0.452−0.027−0.1041.000
TN−0.0300.3180.169−0.1051.000
NH3-N−0.1600.3810.331−0.2060.775 **1.000
NO3−-N0.230−0.431−0.4520.3310.103−0.2921.000
Chl-a0.0230.2520.547 *−0.066−0.1190.341−0.2931.000
ACD−0.0350.1760.2450.2310.2240.584 *−0.1060.536 *1.000
Note: ** p < 0.01; * p < 0.05; ACD, Algae cell density.
Table 3. The correlation of water quality factors of Jinluo lotus pond and the river outside the pond (2025).
Table 3. The correlation of water quality factors of Jinluo lotus pond and the river outside the pond (2025).
pHBODCODTPTNNH3-NNO3−-NPIDOChl-aACDAB
pH1.000
BOD−0.0891.000
COD−0.428 *0.835 **1.000
TP0.1810.642 **0.452 *1.000
TN0.1790.536 **0.1790.526 **1.000
NH3-N0.2350.647 **0.384 *0.518 **0.685 **1.000
NO3−-N0.136−0.181−0.392 *0.0040.461 *−0.2431.000
PI−0.0930.745 **0.536 **0.414 *0.589 **0.2980.2451.000
DO0.098−0.603 **−0.463 *−0.492 *−0.656 **−0.622 **−0.028−0.529 **1.000
Chl-a−0.3660.1240.2660.0520.2900.1240.2270.040−0.392 *1.000
ACD−0.2150.378 *0.454 *0.1900.1860.355−0.2730.342−0.267−0.0941.000
AB−0.3100.3010.460 *0.037−0.0040.076−0.2130.340−0.033−0.1590.902 **1.000
Note: ** p < 0.01; * p < 0.05; PI, Permanganate index; DO, dissolved oxygen; ACD, Algae cell density; AB, Algal biomass.
Table 4. The Phytoplankton cell density of Jinluo lotus pond and the river outside the pond (2022).
Table 4. The Phytoplankton cell density of Jinluo lotus pond and the river outside the pond (2022).
Jinluo Lotus PondRiver Outside the Pond
1Cyclotella meneghiniana, 3,986,800, 36.30%Pseudanabaena sp., 6,576,000, 43.13%
2Pseudanabaena sp., 3,336,000, 30.37%Cyclotella meneghiniana, 3,718,000, 24.39%
3Scenedesmus quadricauda, 1,155,200, 10.52%Scenedesmus quadricauda, 1,476,800, 9.69%
4Aphanocapsa delicatissima, 560,000, 5.10%Trachelomonas superba, 592,000, 3.88%
5Crucigenia tetrapedia, 497,600, 4.53%Oscillatoria chlorine, 560,000, 3.67%
6Coelastrum microporum, 320,000, 2.91%Crucigenia tetrapedia, 544,000, 3.57%
7Oscillatoria chlorine, 280,000, 2.55%Coelastrum microporum, 358,400, 2.35%
8 Acanthosphaera sp., 204,000, 1.34%
9 Dictyosphaerium pulchellum, 192,000, 1.26%
total10,135,600/10,984,400, 92.27%14,221,200/15,246,400, 93.28%
Note: This table only lists phytoplankton that account for over 1% of the total.
Table 5. The Phytoplankton cell density of Jinluo lotus pond and the river outside the pond (2025).
Table 5. The Phytoplankton cell density of Jinluo lotus pond and the river outside the pond (2025).
Jinluo Lotus PondRiver Outside the Pond
1Tetrastrum staurogeniaforme, 1,492,800, 17.04%Tetrastrum staurogeniaforme, 2,964,000, 25.27%
2Anabaena sp., 567,300, 6.47%Coelastrum microporum 1,900,200, 16.20%
3Scenedesmus quadricauda, 499,500, 5.70%Scenedesmus quadricauda, 1,694,400, 14.45%
4Coelastrum microporum, 377,700, 4.30%Scenedesmus bicaudatus, 1,689,000, 14.40%
5Scenedesmus bicaudatus, 176,100, 2.01%Scenedesmus dimorphus, 641,100, 5.47%
6 Scenedesmus denticulatus, 175,800, 1.50%
7 Actinastrum hantzschii,168,600, 1.44%
8 Pediastrum tetras, 163,800, 1.40%
9 Pediastrum biradiatum, 157,300, 1.34%
10 Scenedesmus acuminatus, 150,300, 1.28%
11 Microcystis sp., 146,700, 1.25%
total3,113,400/8,762,100, 35.53%9,851,200/11,729,100, 83.99%
Note: This table only lists phytoplankton that account for over 1% of the total.
Table 6. The N:P of Jinluo lotus pond and the river outside the pond.
Table 6. The N:P of Jinluo lotus pond and the river outside the pond.
N:PRestrictiveSample Time
Jinluo lotus pond39.67P restrictive2022
12.00 2025
River outside the pond98.97P restrictive2022
14.71 2025
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Liu, B.; Gao, Y.; Zhou, J.; Wang, Y.; He, J. Jinluo Low-Density Lotus Pond Wetland Water Purification Practice Experiment—A Case of Limited Efficacy. Water 2025, 17, 1985. https://doi.org/10.3390/w17131985

AMA Style

Liu B, Gao Y, Zhou J, Wang Y, He J. Jinluo Low-Density Lotus Pond Wetland Water Purification Practice Experiment—A Case of Limited Efficacy. Water. 2025; 17(13):1985. https://doi.org/10.3390/w17131985

Chicago/Turabian Style

Liu, Bo, Yuan Gao, Jing Zhou, Yun Wang, and Junxia He. 2025. "Jinluo Low-Density Lotus Pond Wetland Water Purification Practice Experiment—A Case of Limited Efficacy" Water 17, no. 13: 1985. https://doi.org/10.3390/w17131985

APA Style

Liu, B., Gao, Y., Zhou, J., Wang, Y., & He, J. (2025). Jinluo Low-Density Lotus Pond Wetland Water Purification Practice Experiment—A Case of Limited Efficacy. Water, 17(13), 1985. https://doi.org/10.3390/w17131985

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