Chiang Mai Journal of Science

Effects of Sediment and Algal Growth on Water Quality of Water Samples from Water Conveyance Channel

1. INTRODUCTION

     Permanganate index (I_mn) is a commonly used index to determine pollution of water with organic and inorganic oxidizable substances, and an important indicator used to determine water quality categories [1]. A high I_mn indicates high levels of organic pollution in water, which reflects poor water quality. Water sources with high organic content are not suitable as drinking water sources because the water reduces the retention rate of nanofiltration membranes, leading to increased treatment costs [2]. More importantly, persistent organic pollutants (POPs) pose a serious threat to human health, such as the accumulation of polychlorobiphenyls (PCBs) in the food chain, which may cause cancer or other diseases [3]. Several studies have been conducted in the recent past to explore the causes of high I_mn in water and proposed measures to improve water quality. Therefore, controlling the I_mn is of paramount importance for ensuring the safety of drinking water and reducing treatment costs.
     The management of long-distance artificial water conveyance channels, which serve as critical infrastructure for drinking water sources, faces unique challenges in this regard. Compared with water from natural lakes and rivers, these channels are characterized by low nutrient concentration, stable flow rate and no heavy metal pollution. However, siltation is a common issue in these channels, leading to the accumulation of sediment. The nutrient source for algal growth mainly comes from this sediment, which contains high amounts of organic matter and nutrient elements such as available nitrogen and phosphorus. This creates a potential risk chain: sediment resuspension can directly release organic matter and nutrients into the water, which may subsequently trigger algal blooms. Both processes are suspected to elevate I_mn, thereby jeopardizing the water quality and increasing the operational burden of water treatment plants.
     Previous results showed that sediment pollution of overlying water causes increase in the amounts of suspended solids (SS) in water, and I_mn exhibits a significant association with the SS concentration, so the increase in I_mn can be attributed to increased SS levels [4]. Obviously, the resuspension of sediment is a factor contributing to the high I_mn in water, as there is a large amount of dissolved organic matter (DOM) in the pore water of sediment [5]. Analysis of water samples from the Three Gorges in recent years showed that algal density was significantly positively correlated with I_mn [6]. Recent studies on the Jiuqu River basin of the Tuojiang River had shown that the abundance of algae was closely related to the dynamic changes of I_mn in the water, and the newly formed humic substances in algal source materials contribute significantly to the I_mn in the overlying water [7]. Meanwhile, humic acid-like substances in sediment were also the main DOM components that affected algal growth [8]. Another report asserted that the increase in volatile organic compounds (VOC) in water was correlated with algal growth [9], which may be another cause of the increase in I_mn.
     Further supporting this viewpoint, studies of Yinghu Lake in Shaanxi identified a significant positive correlation between algal density and the I_mn, with total phosphorus as a growth-limiting factor [10]. Concurrently, research in Poyang Lake, Jiangxi, revealed a nonlinear relationship between the I_mn and chlorophyll-a [11]. Internationally, research on the algal contribution to I_mn in natural waters is also evolving, such as South Africa's River Health Programme (RHP) and Australia's National River Health Programme (NRHP). While these studies in natural water bodies have established links, a critical knowledge gap remains regarding the synergistic effect of sediment and algae on I_mn specifically within the context of managed artificial water conveyance channels. Although on-site monitoring in channels such as the South to North Water Diversion Project has shown a clear correlation between algal density and I_mn [12], the potential driving role of sediment in this process is not fully understood. Current research lacks quantitative evidence on how sediment resuspension and the subsequent algal growth jointly increase I_mn in these engineered ecosystems. Addressing this gap is essential for developing targeted and cost-effective strategies for water quality protection in these vital infrastructures.
     Algae has high CO₂ uptake rates and CO₂ consumption efficiency compared with other organisms [13]. Therefore, eukaryotic algae can grow on very low CO₂ levels because they have a CO₂ concentrating mechanism (CCM) and can accumulate organic matter in chloroplasts [14]. However, the content of organic matter in chloroplasts cannot accurately reflect algal biomass, just as chlorophyll is not an accurate measurement of algal biomass [15], as algae can release it into water. The release rate of organic matter is greatly affected by the characteristics of algal growth, and studies report that was algal growth is seasonal and positively correlated with temperature and light availability [16]. Analysis of the products of algal photosynthesis showed that algal blooms led to increased release of organic matter synthesized by algae, such as Chlorella releasing soluble organic matter [17]. Similarly, Microcystis aeruginosa and Anabaena also produced extracellular organic matter (EOM), and the release rate was greatly affected by temperature [18]. In addition, the microalgal strains isolated locally in Thailand could accumulate reducing substances such as high-fat acids in cells, and using them to produce bio-oil was of great significance for exploring new energy sources [19]. These conclusions indicated that the organic matter released during algal growth promoted the increase of I_mn.
     The sediment in water sources has high amounts of phosphorus, which results in increased algal growth. Previous findings showed that the release of phosphorus in sediment markedly accelerated eutrophication even after reduction of the external phosphorus input [20]. The proportion and release mechanism of available phosphorus in sediment and the interaction between phosphorus and algae have been reported in the recent past. For instance, a previous study reported that the bioavailable phosphorus in sediment accounted for 21-66% of the total phosphorus (TP), and a small amount of phosphorus could cause mild algal blooms [21]. In addition, research shows that benthic algae absorbs and affects the release of soluble reactive phosphorus in sediment, and the rate of absorption of phosphorus was correlated with the growth stage of benthic algae [22]. One-dimensional numerical models have been developed to simulate the flux of phosphorus diffusion, adsorption/desorption and mineralization [23]. Some scholars had applied the methods of flow injection amperometric detection and off-line sequential extraction column to the measurement of phosphorus in sediment, effectively improved the measurement accuracy of phosphorus and reduced interference from silicates, colloids, and other substances [24].
     Before the biological study of sediment addition, the physical and chemical indexes of the water sample should be evaluated. For example, scholars evaluated water and established that the water sample was a typical system limited by phosphorus rather than nitrogen before conducting experiments [25]. In a previous study, we confirmed that the nitrogen in the water sample used the present was sufficient, and the limiting factor of algal growth was phosphorus. The water quality of the water sample used in the present study was better than Class II surface water in China's surface water environmental quality standards, with a background value of I_mn of less than 2.00 mg/L. The primary objective of this study was to simulate the eutrophic conditions induced by sediment resuspension in artificial channels and to quantitatively elucidate the effects of sediment and algal growth on I_mn, and the trends in nitrogen and phosphorus changes. We hypothesized that (1) sediment resuspension directly increases Imn through the release of inherent organic matter, and (2) bioavailable phosphorus in sediment stimulates algal growth, which in turn further elevates I_mn through the release of algal metabolites. The findings of this study are expected to provide a scientific basis for formulating precise sediment dredging strategies and algal bloom prevention protocols, ultimately ensuring the long-term stability and safety of water quality in water conveyance projects

2. MATERIALS AND METHODS

     Surface water was collected from a single sampling site at the end of a channel (the confidentiality of which is maintained) in Xiqing District, Tianjin. The water depth of the water channel was about 4 m, and the flow rate was about 0.7 m/s. The water sample was obtained from 50 cm below the water surface. Using a vertical water sampling device, three replicate samples were obtained from the site to ensure sufficient volume. The sample was transferred into 50 L plastic buckets using a vertical water sampling device, mixed and left to stand for 2 days. The water quality parameters, including pH, I_mn, TN, TP, and algal density, were 8.1, 2.3 mg/L, 1.03 mg/L, <0.01 mg/L and 1×10⁶ cells/L, respectively, according to our pre-experimental measurements. No additional algae were added to the water sample. Sediment was obtained from the surface mud at the end of the channel. The sediment sample was collected using a grab-type mud collector. Refer to the method of air-drying, crushing and sieving sandy loam [26], the sediment was air-dried and crushed to 60-100 mesh, and mixed evenly. The sediment parameters, including pH, organic matter, TN, and available phosphorus, were 8.06, 7.91%, 4697 mg/kg and 537.3 mg/kg, respectively, according to our pre-experimental measurements. Our pre-tests demonstrated the absence of significant effects on key sediment parameters from the air-drying and crushing process. This experiment was conducted in March 2020.
     Five liters of surface water were obtained from the 50 L plastic buckets and transferred to 6L glass chambers with a size of 22 cm×15 cm×18 cm. This experiment comprised three experimental groups: a control, 0.8% sediment treatment group and 1.6% sediment treatment group. Each treatment was conducted in triplicate, with a total of 9 chambers (as shown in Figure 1). Samples of the dry sediment were obtained using the quartering method and transferred separately into two treatment chambers at a dry sediment/water mass ratio of 0.8% and 1.6%. The sediment concentrations (0.8% and 1.6%) were chosen to simulate the realistic sediment concentrations observed in the channel, reflecting its current siltation level. The sediment samples were mixed evenly with the water samples, and allowed to settle naturally at the bottom of the microcosms without further disturbance. No sediment was added to the control group. A controlled light environment was ensured using full-spectrum LED lights. The emission spectrum spanned the photosynthetically active radiation range (400-700 nm), optimized for algal growth with dominant peaks at 450 nm (blue) and 650 nm (red). The photon flux density was consistently maintained at 100 μmoL/(m²·s), with a 16/8-hour light/dark regimen. The light sources were positioned to ensure uniform illumination across all experimental microcosms. The temperature was controlled at 20-30 °C and the humidity was controlled at 30-60%.
     The measurements of Imn and algal density were taken six times during the experiment: on days 1, 7, 15, 21, 28, and 41 after the beginning of the experiment. The measurements of TN and TP were taken four times during the experiment: on days 1, 15, 28, and 41 after the beginning of the experiment. The 41-day duration was chosen to ensure coverage of the complete algal growth cycle, based on pre-experimental observations that biomass typically peaked between days 25-35, and this approach was consistent with the 42-day culture cycle used by Zimmo [24]. The analysis method of Imn was the oxidation-reduction method [1]. The reducing substance in the sample was oxidized by KMnO₄ under acidic conditions and heating in a boiling water bath for 30 minutes. We added a fixed amount of Na₂C₂O₄ to the sample and then titrated the remaining Na₂C₂O₄ with KMnO₄. Algal density was determined by transferring 100 μL of sample into a counting plate, then the algal genera were identified under an optical microscope and the number of algal cells was counted. The genus was identified through the morphological characteristics of algae, and unicellular and filamentous algae were counted based on the number of cells. The analysis of TN and TP followed the Chinese national standard, which involves digesting the sample with K₂S₂O₈ and subsequent measurement by spectrophotometry. The specific detection methods for algal density, TN and TP refer to the "Water and Wastewater Monitoring and Analysis Methods" [27].
     Data were logarithmically transformed to meet the assumptions of normal distribution and homogeneity of variance. Two-way analysis of variance (ANOVA) was performed to determine the effects of sediment and time on Imn and algal density. LSD method was used for post hoc testing. Pearson correlation analysis was performed to evaluate the relationship between Imn and algal density. A Generalized Linear Model (GLM) was used to quantify the proportional contributions of sediment and algal growth to I_mn. Statistical analyses were performed using SPSS 17.0 software.

3. RESULTS

3.1 Effects of Sediment on Algal Density and I_mn
     The results showed that both sediment and time significantly affected algal density (**p < 0.01), and significant interaction was observed between sediment and time (**P < 0.01). Post-hoc test results showed a significant difference between 0.8% sediment treatment and 1.6% sediment treatment (**P < 0.01). The algal density in the control group reached a peak of 7.63×10⁶ cells/L on the 28^th day after culture, but it was about 2.00×10⁶ cells/L at other time points (Figure 2a). The algal density of the groups with sediment treatments was significantly different on the 15^th day of cultivation compared with the control (Figure 2a), accounting for the interaction between sediment and time. The algal density in the groups with sediment treatments exhibited significant growth on day 7 and reached the peak on day 41 of culture. The algal density of the groups with sediment treatments changed significantly with time (Figure 2a). At the initial stage of culture (1-7 days), there was no significant difference in algal density among all treatments (Figure 2a). In the middle period of culture (7-28 days), the algal density of the groups with sediment treatments was significantly higher than the control, and the algal density of the group with 1.6% sediment treatment was significantly higher than the group with 0.8% sediment treatment. Algal density in the control group was still low at the later stage of culture (28-41 days), but the algal density in the two groups with sediment treatments exhibited the highest levels at 82.73×10⁶ cells/L and 76.45×10⁶ cells/L, respectively.
     Sediment and time were significantly correlated with I_mn (**P < 0.01), and the results showed significant interaction between sediment and time (**P < 0.01). Post-hoc test results showed a significant difference between 0.8% sediment treatment and 1.6% sediment treatment (**P < 0.01). The I_mn in the control was very low at all culture periods, about 2.3 mg/L (Figure 2b). However, the I_mn under the two sediment treatments increased significantly after adding the sediment on the first day compared with the control. I_mn in the 1.6% sediment treatment group was higher (14.7 mg/L) compared with 0.8% sediment treatment group (10.7 mg/L) (Figure 2b). The results showed that the addition of different amounts of sediment had a significant impact on Imn. The Imn in the groups with sediment treatments increased with time. The I_mn in the 1.6% sediment treatment group and 0.8% sediment treatment group were 30.1 mg/L and 19.1 mg/L (Figure 2b), respectively on day 41 of culture in still water, which were significantly higher than the I_mn on the first day.

3.2 Effects of Algae Dominant Genera on I_mn
     The algae in all treatments were composed of Bacillariophyta and Chlorophyta (the micrographs of the main algal genera were shown in Figure 3). The dominant genera of Bacillariophyta included Cyclotella, Synedra, Navicula, Cymbella. The dominant genera of Chlorophyta included Chlorella, Scenedesmus, Cladophora, Cosmarium. The density of different genera of algae varied during the cultivation process (Table 1), and dominant genera in all sediment treatments gradually evolved from Bacillariophyta to Chlorophyta.
     The dominant algae significantly affected the I_mn (**P < 0.01). The succession of the algal community, as detailed in Table 1, revealed a notable shift in dominant species from Bacillariophyta to Chlorophyta over the 41-day cultivation period. In both sediment-treated groups, Chlorophyta genera such as Chlorella and Cladophora began to appear on the 15^th day and became dominant on the 41^th day, coinciding with a further increase in I_mn. This temporal alignment was statistically supported by multiple comparison results of I_mn (Figure 2b), which showed significant elevations around on the 15^th day of cultivation. These findings suggest that the emergence and proliferation of Chlorophyta are closely associated with the secondary rise in I_mn.

3.3 Contributions of Sediment and Algal Growth to I_mn
     Strong positive correlations were observed between Imn and algal density in the two groups with sediment treatments (**P < 0.01, Figure 4). However, there was no significant relationship between I_mn and algal density in the control group (P > 0.05), due to almost no variation in algal density and I_mn at all time points.
     To quantify the relative contributions of sediment and algal growth to the I_mn, a Generalized Linear Model (GLM) with Type III analysis was performed. The results demonstrated that both factors had extremely significant effects (**P < 0.01). The chi-square value shows that sediment resuspension is the main driving factor, accounting for approximately 82% of the variance explained by Imn, while algal growth accounts for the remaining 18%. It is important to note that the GLM analysis specifically quantifies the relative contribution of sediment and algae. The model does not, however, capture the full complexity of the system. For example, microbial decomposition of organic matter in sediments and the release of reducing inorganic species during sediment resuspension processes may also lead to an increase in I_mn.

3.4 Effects of Sediment on Total Phosphorus (TP) and Total Nitrogen (TN)
     The TP of water increased significantly after adding the sediment compared with the control, indicating presence of a large amount of soluble phosphate in the sediment (Figure 5a). The TP of the 0.8% sediment treatment group and 1.6% sediment treatment group immediately increased to 0.19 mg/L and 0.41 mg/L, respectively, after adding the sediment. However, TP significantly decreased with increase in incubation time in the two groups with sediment treatments (**P < 0.01). The TP of the two groups with sediment treatments decreased significantly during the algal growth period compared with the control, with the TP of the 0.8% sediment treatment group and 1.6% sediment treatment group decreasing to 0.05 mg/L and 0.12 mg/L, respectively.
     The TN of water increased significantly after adding the sediment compared with the control, indicating that there was a large amount of soluble nitrogen in the sediment (Figure 5b). The TN of the 0.8% sediment treatment group and 1.6% sediment treatment group increased to 23.6 mg/L and 26.0 mg/L after adding the sediment. The variations in TN with incubation time in the three groups were not significant during the culture period (P > 0.05).

4. DISCUSSION

     The results showed that I_mn increased immediately after adding the sediment, as the sediment contained a large amount of organic matter. This was consistent with previous results that I_mn exhibits a positive correlation with suspended solids [4], which was organic matter in sediment. A previous study reported that suspended solids have high amount of organic matter by examining natural water collected from seven sections of the Yangtze River. The I_mn and algal density synchronously increased after addition of sediments and culturing for a long period. As the metabolites of algae are mainly organic matter, this led to the sustained increase in I_mn. Algal density and I_mn in the groups with sediment treatments exhibited a strong positive correlation, indicating that algal growth played a crucial role in the increase in I_mn in the water sample. This result is consistent with findings reported after analysis of water samples collected from the Three Gorges [6]. They evaluated the indicators of 26 natural rivers and principal components analysis showed that Imn was positively correlated with algal density. Zheng et al. also confirmed this argument by studying the relationship between chlorophyll a and I_mn during the algal bloom period in the Daning River of the Three Gorges Reservoir, and observed a positive correlation (r=0.641) [28].

     The nutrients in the sediment may have stimulated algal growth, resulting in a significant increase in algal density. Analysis of TN and TP levels during the culture process showed that algal growth was potentially stimulated by phosphorus. Guo et al. (2008) observed a positive linear relationship between algal density and phosphorus concentration in natural water. Moreover, the scholars reported that phosphorus was a limiting factor for the growth of freshwater algae, with a more significant impact than nitrogen [29]. Freshwater exhibits high nitrogen content, but a low phosphorus content. Low phosphorus levels significantly inhibit the growth and reduce the photosynthetic efficiency of phytoplankton [30]. Their findings were not contradictory to the negative correlation observed between algal density and phosphorus content in our study. Due to the frequent input of exogenous phosphorus into natural water bodies, while our indoor cultivation experiment only added phosphorus once at the beginning of the experiment, the negative correlation occurred. Different sediment concentrations led to different degrees of algal growth and sustained increase in Imn for the water samples with limited phosphorus amounts. In the early stage of growth, a higher sediment concentration was correlated with higher algal density. This observation indicated that the degree of algal proliferation was correlated with the level of sediment concentration. This was consistent with the conclusion in previous research that significant differences in algae in multiple spaces were associated with sediment characteristics [31]. The different degrees of algal proliferation were due to different nitrogen/phosphorus ratios or nitrogen sources in the water, which could affect the cell proliferation process of phytoplankton [32].
     The results in the present study showed that phosphorus was the limiting factor for algal growth in the water samples. The sediment contained many nutrient elements such as nitrogen and phosphorus, which were resuspended, resulted in the release of nutrients. In this study, a large amount of phosphorus was consumed during algal growth, resulting in a sharp decline in the concentration of TP during the late stages of culture. The variation of TP over culture time was consistent with the results reported by Zimmo, who used algae to eliminate phosphorus from domestic sewage. Algae consumed a large amount of phosphorus during indoor cultivation, which also proved that phosphorus was a limiting factor for algal growth in water bodies. However, the variation of TN with time observed in this study was inconsistent with the previous findings, as the nitrogen concentration in the previous study significantly decreased over time [33]. The inconsistent change in nitrogen levels can be attributed to the differences in the chemical indicators of water and the differences in the dominant genera of algae in the two studies. This finding also shows the existence of different strategies of algae nutrient uptake when different algae genera are cultured under varying nutritional conditions [34]. Rapidly growing algae have a high demand for nitrogen, whereas the dominant genera of algae in our experiment are members of slowly growing algae.
     The observed succession of dominant algae from Bacillariophyta to Chlorophyta (Table 1) provides crucial insights into the dynamic relationships between algal community structure and the key water quality parameters. The proliferation of Chlorophyta genera, particularly Chlorella and Cladophora, which began around on the 15^th day and became dominant later in the cultivation, coincided with the sustained increase in I_mn. This temporal alignment suggests that these Chlorophyta genera significantly contribute to I_mn by releasing oxidizable organic metabolites. The greater contribution of Chlorophyta to I_mn compared to Bacillariophyta is likely due to higher production of extracellular organic matter (EOM), as significant differences in EOM yield and composition have been reported among different algae [18]. This algal community shift also aligned with the notable depletion of TP in sediment-treated groups, indicating the high phosphorus utilization efficiency of these Chlorophyta species. Meanwhile, the relatively stable TN concentrations throughout the experiment likely provided a stable nitrogen source that supported the continued growth of Chlorophyta, despite the phosphorus-limited conditions. These findings collectively highlight that the evolution of dominant algal species not only drives the increase in I_mn but also mediates the nutrient cycling processes in these aquatic systems. The shift from Bacillariophyta to Chlorophyta dominance aligns with nutrient-driven succession patterns observed in natural waters like the Three Gorges Reservoir [6]. This unique successional outcome, driven by sediment-phosphorus release under low-nutrient background conditions, underscores the need for ecosystem-specific water quality management strategies.
     Although several studies have reported on the relationship between sediment and algae in natural rivers and reservoirs, there is very little research on this relationship in artificial waterways. Algae are a primary producers, which plays a key role in the ecosystem. The long-distance water conveyance canal used as a research object in this study is an open ecosystem, and there is a risk of excessive algal growth in areas with sediment and low flow rate. Sediment resuspension can release organic matter into the water, resulting in a significant increase in I_mn. Moreover, the release of phosphate present in the sediment can stimulate algal growth. Algae can fix CO₂ for photosynthesis and generate oxygen and organic matter under the optimal water temperature, light, and flow rate conditions. The secondary metabolites of algae are reducing agents and can be oxidized by KMnO₄ [35], so they can further increase in I_mn. Beyond the direct release of organic matter and algal metabolites, sediment indirectly enhances oxidative demand by fostering microbial and geochemical processes. Organic-rich sediment promotes benthic bacterial activity, which decomposes complex organic matter into low-molecular dissolved compounds that are readily oxidized by permanganate [5]. Additionally, sediment resuspension disturbs redox conditions at the sediment-water interface, releasing reduced chemical species such as Fe²⁺, Mn²⁺ and sulfides, which also consume permanganate. Thus, the rise in Imn results from a combination of direct sediment inputs, algal metabolites, and sediment-mediated microbial activity and geochemical reactions.
     In the present study, we only preliminarily explored the relationship between sediment, algal density and Imn, but we did not conduct chromatographic analysis on the metabolites of algae. We will conduct further studies to determine the algal metabolites that cause an increase in I_mn, explore the forms of phosphorus present in sediment that stimulate algal growth, and propose measures to suppress extensive algal growth to preserve the water quality.

5. CONCLUSIONS

     Our results confirm that both sediment resuspension and algal growth drive the I_mn increase, with sediment processes (contributing 82% of the explained variance) being the dominant factor. The significant impact of algae (18%) is largely dependent on the phosphorus released from sediment, highlighting a nutrient-driven synergistic effect. This provides a clear operational guideline for water quality management: targeted sediment dredging is the most effective strategy to address both the direct and algal-mediated increases in I_mn. However, the long-term ecological impacts of sediment dredging strategies still need to be further evaluated, among which are the destruction of benthic communities and the release of pollutants during the dredging process. The significant phosphorus consumption by algae further highlights the importance of controlling internal nutrients from sediment to ensure long-term water quality safety. Furthermore, these findings can be applied to establish early-warning thresholds for algal blooms, optimize water diversion schedules to disrupt algal growth cycles, and develop predictive models for water quality management.

ACKNOWLEDGEMENTS

     This research was supported by the China South-to-North Water Diversion Corporation Limited. All authors had received support from the project on the Water Quality Change Law of Long-distance Water Conveyance Channel, with contract number ZXJ/TJ/KY/SZZX2018-002.

AUTHOR CONTRIBUTIONS

     Junpeng Liu: Methodology, Project administration, Writing- Reviewing. Pan Hu: Writing - Original draft preparation. Hongzhao Gao: Data curation. Tongtong Wang: Conceptualization. Haicheng Jiang: Visualization. Yiting Wang: Formal analysis. Xinyong Liu: Supervision. Liang Qu: Software. Mianda Huang: Validation. Di Fu: Investigation.

CONFLICT OF INTEREST STATEMENT

     No potential conflict of interest was reported by the authors.

FUNDING

     This project was funded by the research on Water Quality Change Law of Long-distance Water Conveyance Channel of China South-to-North Water Diversion Corporation Limited: Contract number ZXJ/TJ/KY/SZZX2018-002.

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