Spatiotemporal Variations and Future Trends of Sucralose Contamination in Major Rivers of Zhejiang, China: An Emerging Concern and Sustainability Challenge

Abstract

The environmental persistence of sucralose (SUC), a recalcitrant artificial sweetener, has raised significant ecological concerns owing to its notable resistance to both thermal and biological degradation. This study focused on the eight major river basins in Zhejiang Province and used the LC-MS/MS external standard method to systematically detect the environmental occurrence of SUC. Significant spatial variations were observed. The highest concentration recorded in the river was 6.60 μg/L in the Beijing-Hangzhou Grand Canal. In contrast, the Ou River showed almost no detectable concentration. Higher contamination levels were consistently found in urban-adjacent areas, particularly near Hangzhou metropolitan region. Distinct seasonal patterns were also identified, with peak concentrations occurring during summer months. Through the application of the seasonal Kendall trend analysis, an increasing trend was projected for seven of the eight river systems studied, with the Ou River being the sole exception. Furthermore, the accuracy of the model’s prediction results was verified by comparing the data from the 2024 experimental tests with the model’s predicted results. By comparing the data from the experimental tests in 2024 with the model’s predictions, the results showed that, except for the Beijing-Hangzhou Grand Canal, the relative deviations of the other rivers were all less than 5.00%. This indicates that the model had a high accuracy in predicting the changing trend of concentrations. This study provided fundamental data for understanding sucralose’s environmental behavior in the Yangtze River Delta watersheds, serving as a critical baseline data for ecological risk assessments and contributing to water resource sustainability. And the ecological or toxicological implications of SUC pollution require further study. Furthermore, this study developed a transferable methodological framework for monitoring artificial sweetener contamination across diverse aquatic ecosystems.

1. Introduction

Sucralose (SUC) is a non-caloric artificial sweetener that is widely utilized across various sectors, including food, beverages, candies, pharmaceuticals, personal care products, and animal feed [1]. Since its approval in China in 1999, it has become the most commonly used sweetener, with its consumption steadily increasing each year [2]. As shown in Table 1, the SUC molecule is a chlorinated disaccharide formed by selectively replacing three hydroxyl groups on the sucrose molecule with hydrogen atoms. Its chemical stability results in a half-life exceeding one year in natural waters, with conventional wastewater treatment removing less than 20%, and this leads to its widespread detection in surface water, groundwater, and even drinking water [2]. Research by Fu et al. revealed that the concentration of sucralose in artificial sweeteners in Wuhan’s surface water was notably high, ranging from 0.33 to 18.0 μg/L [3]. Recognized as an emerging contaminant by the U.S. EPA in 2011 [4], SUC’s spatiotemporal distribution patterns, particularly in rapidly urbanizing regions, remain poorly understood, necessitating further investigation into its seasonal dynamics and source–sink relationships.

Table 1. Structure and basic features of SUC.

Chemical Compound	Abbreviation	Structural Formula	Molecular Formula	Molecular Weight	CAS	Relative Sweetness
Sucralose	SUC		C12H19Cl3O8	397.63 g/mol	56038-13-2	600

However, long-term consumption of water containing high levels of sucralose may potentially have adverse effects on human health [5]. For example, after conducting a study on the influence of sucralose on metabolism and its potential role in obesity trends, Srishti et al. found that sucralose intake could be linked to weight management challenges, and their study also highlighted the need for further investigation into its potential relationship with cellular health [6]. Kim et al. observed that under certain conditions, high doses of sucralose might influence neural development in animals through complex interactions involving sweetness perception and energy metabolism [7]. Sucralose can also have an impact on aquatic animals. Sucralose may have neurotoxic effects on Daphnia magna and affect its cardiotoxicity and neurobehavior [8]. Sucralose may persist in water and soil after being discharged into the environment through wastewater. From an environmental perspective, the persistent presence of sucralose in the ecosystem has raised concerns about its potential bioaccumulation in the food chain [9]. In addition, sucralose degrades slowly in the environment, and long-term accumulation may disrupt the balance of the ecosystem. Given these concerns, it is evident that SUC presents certain ecological risks, underscoring the importance of researching its environmental behavior.

At present, some literature has analyzed and detected the SUC concentration in some natural water bodies in China, the United States, the United Kingdom, Italy, Canada, and other places, confirming that sucralose accumulates to a certain extent in different types of water bodies. The concentration of sucralose in surface water can even reach 20 μg/L [10]. SUC has been detected even in the Atlantic Gulf Stream [11]. Yu examined the mid-to-lower reaches of the Yellow River and discovered that the highest concentration of artificial sweeteners in surface water reached up to 10.498 μg/L, with sucralose accounting for the majority [12]. The research results of the Danube and Sava rivers indicated that the content of sucralose trichloride in the river water reached 68–206 ng/L [13]. But such research is still insufficient. Many places around the world lack the accumulation of raw data. Taking China as an example, a large number of rivers and water sources have not yet established a systematic monitoring system for sucralose. As a major producer of food additives in China, Zhejiang Province has particularly weak research in the field of sucralose (SUC) water pollution. Firstly, regional rivers generally lack regular monitoring of SUC, resulting in insufficient pollution background data. Secondly, although studies have confirmed seasonal differences in SUC concentrations in river basins, a systematic analysis of their spatiotemporal evolution patterns is still not comprehensive.

In addition, the detection methods for sucralose are also constantly being developed and optimized. The traditional detection method is liquid chromatography. For example, high-performance liquid chromatography could be used to test the concentration of sucralose, but the detection limit of this method is relatively high [14]. Surface waters in industrialized regions of China, such as Zhejiang Province, often contain elevated levels of organic compounds and salts, necessitating effective impurity reduction during analysis. Studies [14,15] have demonstrated that evaporative light scattering detection (ELSD) can enhance the sensitivity of high-performance liquid chromatography (HPLC), but this method still suffers from notable limitations, including susceptibility to matrix interference and constrained detection ranges. liquid chromatography-tandem mass spectrometry (LC-MS/MS) combines the superior separation capability of liquid chromatography with the exceptional sensitivity of tandem mass spectrometry, outperforming conventional methods such as HPLC-ELSD [16]. In addition, LC-MS/MS can eliminate interference from complex matrices such as high salt and high organic matter water bodies, accurately identify characteristic ion pairs of sucralose such as m/z 397), and significantly reduce the risk of false positives [17]. As a practice, this study also adopted this efficient method LC-MS/MS for the detection of sucralose. This study could develop a transferable methodological framework for monitoring artificial sweetener contamination across diverse aquatic ecosystems.

This study focused on the eight major river basins in Zhejiang Province and used the LC-MS/MS external standard method to systematically detect the environmental presence of sucralose (SUC). The research first established an accurate detection method for watershed-scale SUC, obtaining comprehensive pollution background data across different hydrological units. Subsequently, it analyzed the spatiotemporal variability of SUC concentrations across seasons, revealing the significant impact of seasonal variations on pollutant migration. Finally, based on the seasonal Kendall trend test, the study quantified the evolution rate of pollution load in different watersheds of Zhejiang Province and conducted predictive modeling to assess future contamination trends.

2. Materials and Methods

2.1. The Research Area and Sample Collection

Zhejiang Province is located on the southeast coast of China and has numerous rivers, mainly including the Qiantang River, Ou River, Yong River, Beijing-Hangzhou Grand Canal, and other water systems [18]. Among them, Qiantang River is the most important river in Zhejiang Province and one of the four major tidal rivers in China. It flows through Hangzhou City and eventually empties into the East China Sea [19]. Zhejiang Province has diverse climates, mainly including a subtropical monsoon climate and an oceanic climate. The river characteristics of Zhejiang Province include dense rivers, numerous mountains, coastal landforms, and diverse climates. These characteristics collectively shape the unique hydrological system and water resource distribution in Zhejiang Province. The sampling area of this study was representative of eight major river basins and other important rivers in Zhejiang Province, such as the Beijing-Hangzhou Grand Canal, West Lake, Qiantang River, Yong River, Tiaoxi River, Ou River, etc., as shown in Table S1. Trace detection of sucralose was conducted at different times and in different river sections. The sample was collected from surface water at a depth of 0.5 m underwater from the sampling point. It should be noted that, due to practical limitations, this study only employed sampling at a depth of 0.5 m. Selecting a 0.5 m sampling depth held clear practical significance. This depth aligns with the conventional surface water intake depth for drinking water, allowing the study to directly focus on the water layer most relevant to human health. The obtained data thus more accurately reflected the potential health risks posed by water pollution, offering key insights into the core characteristics of sucralose contamination in the study area.

Sampling was conducted during a relatively warm period (12:00 noon). Sampling was conducted at noon primarily because the lighting and temperature conditions were relatively stable during this period, which helped minimize the interference of diurnal variations on the sampling results. The collection and storage of water samples were carried out in accordance with standard procedures described in the Standard for Surface Water Environmental Quality of China (GB3838-2002) [20] and Water and Wastewater Monitoring and Analysis Method (fourth edition) [21] published by the State Environmental Protection Agency. The collection of on-site blanks was synchronized for each batch of samples using ultrapure water from the same batch to assess potential contamination from sampling containers, filter membranes, and other sources, with blank values required to remain below the method’s detection limit. At least three parallel samples were collected per sampling event to evaluate sampling variability, and resampling was conducted if concentration differences exceeded 10%. Samples were stored in brown glass bottles at 4 °C protected from light, with measurement occurring within 24 h. During transportation, samples were maintained in refrigerated containers at 4 °C while avoiding excessive shaking to prevent container adsorption losses. For storage exceeding 24 h, samples were frozen at −20 °C, thawed, and thoroughly re-mixed prior to analysis.

2.2. Monitoring Point Layout

Zhejiang Province is located on the southeast coast of China, bordering the East China Sea to the east, Fujian Province to the south, Jiangxi Province to the west, Shanghai and Jiangsu Province to the north, and Anhui Province to the northeast. In this study, a total of 24 monitoring points were set up in the eight major river basins in Zhejiang Province, with 3 points being established in each section of the river. The specific river and point layout information is shown in Figure 1 and Table S2. The 24 monitoring points were selected in river sections that were located close to cities and industrial areas. This sampling method could better reflect the mutual influence between sucralose and human life and was more representative. The monitoring frequency for each point was 12 times a year, spanning from January 2019 to December 2024, with monitoring occurring once in the first ten days of each month (usually from the 5th to the 10th of each month). During sampling, extreme weather events were avoided, such as no sampling after rainstorm. The monitoring of rivers entering and leaving the lake was in line with routine monitoring, and the time period for this also ranged from January 2019 to December 2024.

Figure 1. Layout of monitoring points in each river.

2.3. Main Reagents and Instruments for Detection

The equipment used in this research includes a liquid phase instrument (Waters I-Class, Waters Company, Milford, MA, USA), a mass spectrometer (Waters XEVO-TQS micro, Waters Company, Milford, MA, USA), a chromatographic column (ACQUITY UPLCr BEH C18, Waters Company, Milford, MA, USA), an electronic analytical balance (CP225D, Sartorius GMBH, Göttingen, Germany), and an ultra-pure water meter (Milli-Q system, 0.22 μm filtration, Millipore Corporation, Burlington, MA, USA). The reagents used include sucralose (Standard goods, purity 98%, Aladdin Reagent Co., Ltd., Shanghai, China), methanol, and acetonitrile (HPLC grade, CNW Germany, Teltow, Germany).

2.4. Detection of Sucralose

The trace detection method for sucralose was the LC-MS/MS external standard method, with a minimum detection limit of 0.01 μg/L. The liquid chromatography instrument model used was the Waters I-Class. The mass spectrometry instrument model was the Waters XEVO-TQS micro, and the chromatographic column employed was ACQUITY UPLCr BEH C18. The specific detection parameters were as follows: ion source: ESI; flow rate: 0.3 mL/min; injection volume: 10 μL; column temperature: 35 °C; mobile phase: A: 0.05% ammonia solution, B: methanol. Since the water sample was clean, it could be directly tested on the machine after passing through the membrane. If the water sample contained suspended solids, the sample was centrifuged (3000 rpm, 10 min) and then filtered. The 0.22 μm nylon membrane was used for filtration, and the recovery was verified by adding standard. The average recovery rate was 97.8% ± 2.1% (n = 6), which met the requirements of the method. The chromatogram and mass spectrum (primary mass spectrum) of sucralose were presented in Figure 2. In the mass spectrum, the quasi-ion peak at m/z 395 and the chloride isotope peaks at m/z 397 and m/z 399 of sucralose were clearly visible, which perfectly matched the standard mass spectrum in negative ion mode.

The standard curve was as follows: y = 49.2911x − 5.06063 (R² = 0.9998)

where x was the concentration of sucralose (μg/L) and y was the response value (mAU). There was a strong positive correlation between the two, and the linear fitting effect was excellent.

Figure 2. Chromatogram and mass spectrometry of sucralose detection for the sample.

2.5. Quality Control and Assurance

In the experiment, the samples were measured several times through repeated analysis, and the quality control was carried out to ensure the accuracy of the analysis results. And in order to improve its accuracy, the standard material with high purity was selected experimentally to ensure the accuracy of the analysis results. Secondly, suitable chromatographic columns were selected to improve the sensitivity and resolution of the analysis. Third, the instrument was calibrated regularly to ensure stable performance. Finally, statistical methods were used to analyze data, the results of multiple measurements were statistically analyzed, and indicators such as relative standard deviation were calculated to evaluate the repeatability and accuracy of the analysis method.

The accuracy and precision of the instrument were tested by 1 μg/L and 10 μg/L sucralose standard storage solution (Guangzhou Kehong Food Additives Co., Ltd., Guangzhou, China). Data display that the standard concentration was 1.0 μg/L, the result was 0.98 μg/L, RSD = 1.25%, δ = −2%; the standard concentration was 10 μg/L, the result was 9.9 μg/L, RSD = 0.51%, δ = −1%. In this study, the detection of sucralose was only controlled for quality through spiked recovery rate (accuracy) and repeated measurements (precision), which had limitations.

2.6. Seasonal Kendall Test Method

Hirsch first proposed the seasonal Kendall test based on the Mann–Kendall test principle in 1982 [22], which is a monotonic trend analysis method [23]. Its advantage is that it can avoid a situation with “undetected values” and “missed values” in river water quality monitoring data. It can weaken the influence of seasons on trends and obtain more accurate water quality change trends. The principle is to subtract the water quality data of the same month in the previous year from the water quality data of each month each year. Based on the principle of chronological order, if the subsequent monitoring data is less than the previous monitoring data, it is marked with a “−” sign; if the subsequent monitoring data is greater than the previous monitoring data, it is marked with a “+” sign. Finally, count the number of “+” and “−” signs. If “+” is more than “−”, it indicates an upward trend. If “+” is less than “−”, it indicates a downward trend. If “+” is equal to “−”, it indicates there is no significant change trend.

The null hypothesis H0 is a random variable that is independent in time, assuming that the water quality monitoring data for the 12 months in a year have the same probability distribution.

Let X be the water quality monitoring data sequence for n years and p months:

$$X=\left[\begin{array}{cccc}{x}_{11}& x_{12}& \dots & x_{1p}\\ x_{21}& x_{22}& \dots & x_{2p}\\ ⋮& ⋮& ⋮& ⋮\\ x_{n1}& x_{n2}& ⋮& x_{np}\end{array}\right]$$

(1)

In the formula, X_np represents the monitoring values of various water quality indicators in the nth year and p month, measured in μ g/L.

Let the number of non-missing values in the water quality sequence of the i-th month be n_i. For the difference between monthly monitoring data values X_ij − X_ik, let the number of comparison difference data sets for the i-th month be Q_i for positive differences and P_i for negative differences. Let S_i = P_i − Q_i. Under the null hypothesis, S_i approximately follows a normal distribution, and its mean and variance are:

$$E(S_i)=0$$

(2)

$${\sigma }^2=Var(S_i)={\displaystyle \frac{n_i(n_i-1)(2n_i+5)}{18}}$$

(3)

For the overall situation of P month:

$$S={\displaystyle \sum _{i=1}^r{S}_i}$$

(4)

$$m={\displaystyle \sum _{i=1}^p{m}_i}$$

(5)

$$VarS={\displaystyle \sum _{i=1}^p(Var{S}_i)}$$

(6)

If S also follows a normal distribution, then the standard deviation Z is:

$$Z=\left\{\begin{array}{c}{\displaystyle \frac{S-1}{\sqrt{Var(S)}}}\\ 0\\ {\displaystyle \frac{S+1}{\sqrt{Var(S)}}}\end{array}\right.\begin{array}{c}when\\ when\\ when\end{array}\begin{array}{c}S>0\\ S=0\\ S<0\end{array}$$

(7)

Kendall’s test measures t = S/m. In the two-tailed trend test, if the standard deviation |Z| ≤ Za/2, the null hypothesis H0 is accepted. Here, FN (Za/2) = a/2, where FN is the standard normal distribution function, then:

$$F_N={\displaystyle \frac{1}{\sqrt{2\Pi }}}{\displaystyle {\int }_{\left|Z\right|}^{\infty }{e}^{-\frac{1}{2}{t}^2}dt}$$

(8)

The significance level a of the trend test is:

$$a={\displaystyle \frac{2}{\sqrt[2]{2\Pi }}}{\displaystyle {\int }_{\left|Z\right|}^{\infty }{e}^{-\frac{1}{2}{t}^2}}dt$$

(9)

The significance levels, a, are 0.1 and 0.01. When a ≤ 0.01, the Kendall test exhibits a high level of significance. When 0.01 ≤ a ≤ 0.1, the Kendall test is significant. Under the conditions where the value of a satisfies the aforementioned criteria, if t > 0, there is a significant (or highly significant) upward trend; if t < 0, there is a significant (or highly significant) downward trend; and if t = 0, there is no discernible trend.

When using the seasonal Kendall test to determine water quality trends, it is generally advisable to choose a sequence length of 5–8 years. The evaluation sections selected during the evaluation period should be the same or similar. The analysis of water quality change trends should include water quality change trend analysis and river and regional water quality change trend analysis [24]. This article mainly involved the analysis of water quality change trends, using data from January 2019 to December 2024. This study constructed a model using data from 2019 to 2023, then predicted data for 2024, and compared it with actual data for 2024.

2.7. Statistical Analysis

All assays were conducted in triplicate, and the average value was calculated. An analysis of variance (one-way ANOVA) was used to test the significance of results, and p < 0.05 was considered to be statistically significant. In the analysis of variance, homogeneity of variance and normality tests were conducted to ensure the accuracy of the analysis of variance results. The Bonferroni post hoc test was used for inter-group comparison. All the statistical analysis was conducted in SPSS software 27.0.1.

3. Results and Discussion

3.1. Sucralose Concentration in Eight Major River Basins in Zhejiang Province

Sucralose has been detected in surface water around the world and cannot always be removed by conventional water treatment processes. Recipient concentrations of sucralose found in Europe range between 0.1 and 1.0 μg/L [11]. Experiments conducted by American researchers have shown that over a period of three years, the average influent surface water concentration of sucralose was 24 ppb, and the average outfall concentration was 20 ppb, which indicated the sucralose was not subjected to chemical or physical adsorption in the sediment within the wetland [25]. It can be seen that the concentration of sucralose in water may further accumulate. Therefore, research data on the concentration of sucralose is crucial.

The main research focused on the eight major river basins, which consisted of the Qiantang River, Yong River, Beijing-Hangzhou Grand Canal, Ou River, Jiao River, Tiaoxi River, Feiyun River, and Ao River. The data obtained from three monitoring points for each river was calculated and processed, with the mean serving as the recorded data for that river in the current month. A one-way ANOVA was conducted to study the changes in sucralose concentration in the same river across different years. Furthermore, the concentration of sucralose in the rivers was categorized into six levels based on the average concentration of the detected watershed, which was as follows: I: trace (x < 0.7 μg/L), II: low (0.7 ≤ x < 1.3 μg/L), III: medium low (1.3 ≤ x < 2.0 μg/L), IV: medium high (2.0 ≤ x < 2.7 μg/L), V: high (2.7 ≤ x < 3.4 μg/L), and VI: very high (≥3.4 μg/L). This classification intuitively reflected the changes in the sucralose concentration and content in various rivers. This classification method ensured that there were cases in each concentration range. It should be noted that this was not a strict classification method, but only to visually display the concentration levels of different rivers. Taking the Qiantang River and the Beijing-Hangzhou Grand Canal as examples, this study investigated the changes in sucralose concentrations in these two rivers, and the data for the other rivers are shown in Figure 3.

Figure 3. Concentration variation curves of sucralose in different years and seasons in various rivers.

3.1.1. The Qiantang River

As shown in Figure 3, it could be seen that the concentration of sucralose varied between 0 and 3.2 μg/L, with the maximum value occurring in June 2023 and the minimum value occurring in January and February 2021. The concentration of sucralose showed a trend of first increasing and then decreasing over the course of a year. In 2020, the average concentration of sucralose was 1.66 μg/L, ranking at level III. The next year, in 2021, the average concentration of sucralose was 1.05 μg/L, ranking at level II. However, in 2022, the average concentration rose to 2.23 μg/L, ranking at level IV. And in 2023, it increased slightly to 2.31 μg/L, also ranking at level IV. At the beginning of 2021, due to the impact of the epidemic, the concentration of sucralose in rivers sharply decreased and fluctuated greatly. In the subsequent three years, it shifted from level III to level IV, and the annual average concentration of sucralose exhibited an overall upward trend.

A one-way ANOVA was conducted to examine the differences in the impact of different years on sucralose concentration. The results revealed a statistically significant difference in concentration across the years, with p < 0.001 and effect size η² = 0.42. Taking the year 2020 as an example, the average 95% confidence interval was 1.22–2.01 μg/L. The p-values of homogeneity of variance test and normality test were both greater than 0.05, indicating that the results of one-way ANOVA were reliable. The result of one-way ANOVA was significant, and the Bonferroni post hoc test could be conducted. The Bonferroni post hoc test further indicated that the concentrations in 2022 (M = 2.23, SD = 0.14) and 2023 (M = 2.31, SD = 0.14) were significantly higher than those in 2021 (M = 1.05, SD = 0.21). However, no statistically significant differences were observed between the groups for 2020, 2022, and 2023 (p_2020–2022 = 0.117, p_2020–2023 = 0.078, p_2022–2023 = 1.000) (M is the mean, and SD is the standard deviation).

3.1.2. The Beijing-Hangzhou Grand Canal

As shown in Figure 3, it could be seen that the concentration of sucralose varied between 0 and 6.6 μg/L, with the maximum value occurring in March 2023 and the minimum value occurring in January and February 2021. The trend of the sucralose concentration in 2020, 2021, and 2022 was to first increase and then decrease. The average concentration of sucralose in 2020 was 3.10 μg/L, which was at level V. In 2021, the average concentration of sucralose was 1.02 μg/L, which was at level II. For 2022, the average sucralose concentration was 3.17 μg/L, which was at level V. In 2023, the average sucralose concentration was 5.35 μg/L, exceeding the range of level VI and indicating a high concentration content. At the beginning of 2021, due to the impact of the epidemic, the sucralose concentration in rivers sharply decreased and fluctuated greatly, while the average annual concentration of sucralose in the subsequent three years showed an overall upward trend.

A one-way ANOVA was used to assess whether there were significant differences in the impact of different years on sucralose concentration. The results indicated a statistically significant difference in concentration across different years, with p < 0.001 and effect size η² = 0.82. Taking the year 2020 as an example, the average 95% confidence interval was 2.72–3.47 μg/L. The p-values of homogeneity of variance test and normality test were both greater than 0.05, indicating that the results of one-way ANOVA were reliable. The result of one-way ANOVA was significant, and the Bonferroni post hoc test could be conducted. The Bonferroni post hoc test revealed that, apart from no significant difference between 2020 and 2022 (p_2020–2022 = 1.000), the lowest concentration was observed in 2021 (M = 1.02, SD = 0.21), while the highest concentration was noted in 2023 (M = 5.35, SD = 0.19).

In summary, sucralose concentrations exhibited certain variations across different years. This indicates that the temporal factor (year) was a key variable influencing sucralose concentrations. Concurrently, water pollution showed a deteriorating trend, necessitating attention to long-term ecological risks. This aligned with findings from numerous studies on emerging contaminants [26,27]. The COVID-19 pandemic exerted a short-term inhibitory effect on pollutant levels. Reduced human activities such as decreased consumption of food and beverages directly lowered sucralose emissions. The low standard deviations in annual pollutant concentration fluctuations suggested that emission sources might be relatively stable, potentially originating from fixed industrial discharges or municipal wastewater. Increased attention must be directed toward the emission and disposal challenges posed by emerging contaminants [28].

3.2. Analysis of Spatiotemporal Variation Characteristics of Sucralose in the Eight Major Rivers of Zhejiang Province

As shown in Figure 4 and Figure 5, based on the monitoring data from 24 water quality monitoring sections established in the eight major river basins, the average values of each river throughout that year were calculated to analyze the spatiotemporal distribution of sucralose in the eight major river basins in Zhejiang Province.

Figure 4. Changes in sucralose concentration levels in different seasons in various rivers.

Figure 5. Changes in sucralose concentration in different rivers and seasons (QT: the Qiangtang River, Y: the Yong River, G: the Beijing-Hangzhou Grand Canal, O: the Ou River, J: the Jiao River, TX: the Tiaoxi River, FY: the Feiyun River, A: the Ao River).

As shown in Figure 4 and Figure 5, the trace amounts of sucralose in different rivers exhibited similar trends in seasonal variations. Taking representative river basins as an example, with the exception of the Ou River, the SUC levels in different rivers demonstrated similar patterns across different seasons. The concentration of sucralose rose from spring to summer, and, in the majority of areas, the sucralose content in rivers peaked during summer and subsequently began to decline. The phenomenon may be caused by high temperatures, an increase in the consumption of sugar-substitute drinks or foods (such as ice cream, cakes, etc.), and an increase in the discharge of chlorinated sucrose in domestic sewage, resulting in higher concentrations of chlorinated sucrose in summer compared to other seasons. Additionally, summer is always the rainy season in most parts of our country, and the eight monitored rivers are all located in regions where summer is a rainy season. Rainfall-induced surface runoff can carry chlorinated sucrose wastewater from urban surfaces, farmland, or sewage treatment plant overflow into the rivers [29]. The increase in surface runoff caused by summer rainfall might be an important pathway for the input of chlorinated sucrose into water bodies. Furthermore, summer is usually when people engage in outdoor activities and leisure travel, which leads to an increase in pollutants associated with human activities [30]. In summer, as temperatures and light intensity might increase, it promotes increased biological activity in water, leading to the release of pollutants produced by a series of biological metabolic processes into the water. When sucralose forms a composite pollution with other pollutants, it may have a greater environmental impact. The risk assessment model for a single pollutant may significantly underestimate the actual ecological risks.

As shown in Figure 6, the overall spatial distribution of sucralose concentration in the eight major river basins in Zhejiang varied significantly. Based on regional distribution, trace amounts of sucralose were notably higher in areas proximate to or near Hangzhou, while lower concentrations were observed in regions farther from urban centers, in remote areas, and in national scenic tourist regions, water sources, and other river basins. In some watersheds, the concentration of sucralose was below the detection line and was marked as 0. In addition, it should be noted that the reason why no sucralose concentration was detected in all rivers in the spring of 2021 was that at the beginning of 2021, due to the impact of the epidemic, the concentration of sucralose in the rivers dropped sharply with large fluctuations, resulting in an extreme situation. According to the Zhejiang Statistical Yearbook, due to the impact of the epidemic, tourism activities had significantly slowed down in 2021. Compared with 2020 (domestic tourists), the number of tourists in Zhejiang Province decreased by 28.5%, which was a significant decrease. The tourism industry is one of the important usage scenarios for sucralose. Sugar-free foods containing sucralose, such as sugar-free soda and ice cream, are widely used in scenic spots, hotels, restaurants, and other places. The packaging cleaning and production wastewater may contain residual sucralose. The decrease in tourists directly lead to a decrease in the consumption of foods containing sucralose, resulting in a reduction in the discharge of related wastewater. During the off-season of tourism or when the industry shrinks, food and beverage companies may reduce their procurement of sugar substitutes (such as sucralose), indirectly reducing the concentration of pollutants in production wastewater.

Figure 6. The concentration of sucralose in various rivers changes with years and seasons.

However, food factories or food additive factories, especially manufacturers of sucralose, produce wastewater containing sucralose during the production process, and the conventional treatment method is usually a biological method [31]. Therefore, if the treated wastewater from the food factory is discharged, sucralose may be transferred to nearby water environments. So, this situation may be related to the distribution of sucralose food factories, population base, and living standards. Furthermore, based on other literature reports, such as the study conducted by Zhu et al., the concentration of sucralose in the Q River water source in Zhejiang Province had reached 0.4–1.0 μg/L, whereas the concentration of sucralose in Lake T had attained 0.7–2.0 μg/L. Different pollution sources lead to differences in sucralose concentration in various types of water bodies. In addition, the concentration of sucralose in water bodies may also be related to residents’ dietary habits. If the intake of sweet foods increases, the concentration of sucralose in domestic sewage will also increase in the water body [32].

Comparing the concentrations of sucralose in eight river basins throughout the four seasons, the concentration from high to low was as follows: the Beijing-Hangzhou Grand Canal > the Tiaoxi River > the Qiantang River > the Jiao River > the Yong River > the Feiyun River > the Ao River > the Ou River. The concentration of sucralose in the north was relatively high, while the concentration of sucralose in the south was relatively low. Sucralose is metabolized by the human body and excreted through urine. Its detection rate is very high in the influent of sewage treatment plants in densely populated and economically active areas [11]. The popularity of sugar-substitute foods (such as beverages and baked goods) has led to an increase in per capita intake, indirectly increasing the emission load. Therefore, the emission intensity is high in densely populated and economically active areas. Additionally, food processing enterprises (e.g., beverage factories) use SUC as an additive, and their production wastewater can diffuse through regional water systems. Taking the Beijing-Hangzhou Grand Canal and the Oujiang River as examples for analysis, the Beijing-Hangzhou Grand Canal runs through the core urban belt of the Hangjiahu Plain, with a high population density and clustered cities. There are food processing factories along its banks, leading to multiple pathways for sucralose to enter the water body. Moreover, as an artificial river, it has a slow flow rate, is prone to algal blooms in summer, and has poor fluidity, resulting in a higher concentration of sucralose in the water. In contrast, the upper reaches of the Ou River have a relatively sparse population and fewer industrial zones. Additionally, as the largest river in southern Zhejiang, it has a good environmental capacity, so the concentration of sucralose in its water is extremely low. At the same time, the Ou River flows mainly through Lishui City and Wenzhou City. The Grand Canal mainly flows through Hangzhou City and Huzhou City. According to the Zhejiang Provincial Statistical Yearbook, the permanent population of Lishui City in 2023 was 2.528 million, while the permanent population of Hangzhou was 12.522 million. From this, it can be inferred that the higher concentration of sucralose in the northern river basins is due to the high degree of urbanization, developed economy, high living standards, and dense population in the north. Furthermore, the flow rate and volume of the water bodies also have a certain impact.

In addition to the eight major rivers in Zhejiang Province, several bodies of water were also monitored, such as Shangtang River, West Lake, Lingjiang River, and Taihu Lake. As shown in Figure 7, these rivers also showed the spatiotemporal variation in sucralose concentration mentioned above. And the concentration was highest in summer. This was consistent with our previous analysis of the eight major rivers in Zhejiang Province. It is worth noting that in some rivers, such as Siming Lake, the concentration of sucralose was lower than that of other rivers, possibly due to its status as a scenic spot and the government’s strict water protection measures. Overall, our research provides valuable insights into the pollution status of sucralose in Zhejiang’s water bodies and highlights the need for effective measures to reduce its environmental impact.

Figure 7. Line graph of changes in sucralose concentration with years and seasons in other rivers.

3.3. Application of the Seasonal Kendall Test Mathematical Model in the Trend Analysis of Sucralose Concentration

Water quality monitoring data from the eight major river basins in Zhejiang Province, spanning from 2019 to 2023, were selected for trend testing. The test results, obtained using software, were then summarized in Table 2. Except for the Ou River, the concentration of sucralose in other rivers showed an increasing trend.

Table 2. Trend of sucralose concentration changes in various rivers.

Detection Object	Median Concentration (μg/L)	Concentration Trend (μg/L/year)	Significant Level (%)	Evaluation Results
The Qiantang River	1.6	0.275	0.00	Rise
The Yong River	1	0.167	0.00	Rise
The Beijing-Hangzhou Grand Canal	3	0.41	0.00	Rise
The Ou River	0	0.00	100.00	No significant upward or downward trend (=0)
The Jiao River	1.1	0.20	0.00	Rise
The Tiaoxi River	2.7	0.20	0.00	Rise
The Feiyun River	1.4	0.27	0.00	Rise
The Ao River	1.5	0.20	0.00	Rise

In this study, each monitoring point was monitored 12 times a year, with the monitoring period from January 2024 to December 2024, and once in the first ten days of each month. The monitoring of rivers entering and exiting the lake was consistent with the routine monitoring during the time period from January 2024 to December 2024, so as to obtain the concentration of sucralose in each river in 2024. The data for 2024 was compared with the data predicted by the model to verify the consistency of the trend between the predicted data and the actual data.

As shown in Table 3, the data predicted by the model have basic accuracy, and the results are of reference value. The actual detection results in 2024 showed that, except for the unchanged concentration of sucralose in the Ou River, the concentrations of sucralose in the other rivers all showed an upward trend, which was consistent with the changing trend predicted by the model analysis. Moreover, except for the Beijing-Hangzhou Grand Canal, the relative deviations of the other rivers were all less than 5.00%. However, river regulation was carried out on the Beijing-Hangzhou Grand Canal in 2024, which enhanced the river’s fluidity, thus resulting in an excessively large relative deviation.

Table 3. Comparison between actual detection data and model prediction data in 2024.

Test Object	Actual Sucralose Concentration in 2024 (μg/L)	Predicted Concentration in 2024 (μg/L)	Absolute Deviation	Relative Deviation (%)
The Qiantang River	2.47	2.59	0.12	4.63
The Yong River	1.74	1.80	0.06	3.33
The Beijing-Hangzhou Grand Canal	5.30	5.76	0.46	8.00
The Ou River	0.00	0	0.00	/
The Jiao River	2.02	2.09	0.07	3.35
The Tiaoxi River	3.67	3.70	0.03	0.81
The Feiyun River	3.01	2.90	−0.11	−3.79
The Ao River	2.40	2.42	0.02	0.83

Based on the above analysis and discussion, the following conclusions and suggestions can be drawn. Firstly, except for the Ou River, all the other river basins show a highly significant upward trend. The Ou River basin is mountainous and hilly with high vegetation coverage, which can effectively intercept pollutants. Secondly, the seasonal Kendall test can determine the degree of trend rise and fall and provide specific numerical values of the changes. Thirdly, the rates of change in the three river basins, namely the Qiantang River, the Yong River, and the Jiao River significantly increased. It is speculated that the Qiantang River has a higher concentration growth rate due to its flow through economically developed and densely populated areas. The Jiao River, located in Taizhou, has vigorously developed its tourism industry in recent years and its population has a sweet diet, resulting in a rapid increase in concentration. According to Liu’s study, the abundance of emerging contaminants (such as microplastics) in surface water bodies was positively correlated with regional GDP [33]. However, new pollutants have not been fully included in the standard system, leading to regulatory gaps and a significant increase in disease risk for residents in economically developed areas [34]. This also highlights the importance of this research.

4. Conclusions

This research primarily involved conducting trace detection and trend analysis prediction of sucralose in selected water bodies within Zhejiang Province. Initially, tests were performed on water bodies adjacent to the eight major river basins, revealing that trace levels of sucralose were notably higher in regions proximal to or near Hangzhou, whereas lower concentrations were observed in areas farther from urban centers and remote regions. Additionally, it exhibited a pattern of higher concentrations in summer and lower in winter. Notable variations in sucralose trace amounts were identified between cities and rivers within Zhejiang Province, with concentrations ranging from above 6 μg/L at the highest to below the detection limit. Subsequently, the seasonal Kendall test method was employed to analyze trends across the eight major river basins, revealing a continual increase in sucralose concentrations, particularly in the Qiantang River, the Yong River, and the Jiao River, with concentration change rates that were relatively large. This study accumulated corresponding data on the pollution of sucralose and provided feasible methods for studying sucralose pollution in other regions. Subsequent research will focus on investigating the combined effects of sucralose with other pollutants, including studies on its combined toxicity with microplastics and antibiotics, to establish long-term exposure thresholds for aquatic organisms. The research should expand multi-medium and multi-source data by supplementing sediment monitoring and wastewater treatment plant effluent analysis, while clarifying migration pathways and source apportionment. Additionally, a pollution–social-economic driver correlation model will be developed to quantify contributions from population size, consumption patterns, and urbanization rates, thereby supporting targeted pollution control measures.

In addition, in the management of sucralose pollutants, it is recommended to improve the optimization process of drinking water treatment technology, enhance the removal efficiency of sucralose pollutants. The government should have stricter requirements for the maximum concentration of sucralose in food, list the excessive use of sucralose by enterprises as a key inspection item, and clearly publicize the daily allowable intake of sucralose to the public.