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In this study, the Jueju River, a representative seaward river in Nantong, Jiangsu Province, was analyzed for total nitrogen (TN) pollution sources. A calibrated MIKE model, using measured hydrological and water quality data, was developed to quantify the TN pollution at cross-sections, identify sensitive contributing areas, and prioritize pollution control strategies. The results revealed that TN concentrations in the Jueju River followed an overall ‘W-shaped’ trend annually, with a distinct ‘U-shaped’ pattern in 2022. Winter TN concentrations were consistently higher than those in other seasons. The seaward flux of TN was primarily sourced from upstream watershed areas, influenced by both the quantity of TN input from catchment units and transport distance. At the Huandong Gate section, catchment units 1, 2, and 3 were identified as the main contributors to TN pollution. Key pollution sources included centralized wastewater treatment plants, agricultural activities, and livestock farming, while unregulated rural domestic pollution also significantly impacted TN levels at the cross-section. These findings provide valuable insights for policymaking, emphasizing the need for strengthened management and control of TN pollution in seaward rivers, ultimately improving water quality in nearshore areas.

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  • Calibrated MIKE model accurately performed source analysis of TN for the Jueju River.

  • Seasonal and annual TN concentrations varied, with winter levels 130% of the annual average.

  • The contribution ratio of centralized wastewater treatment plants exceeded 43% in certain catchment units.

Jueju River, MIKE model, seaward river, source analysis, total nitrogen
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In recent decades, the rapid development of agriculture and industry has led to escalating nitrogen and phosphorus fluxes into rivers, resulting in water quality deterioration and non-compliance with environmental standards in certain sections (Bennett et al. 2001; Chen et al. 2019; Yang et al. 2021). Simultaneously, the deteriorating water quality has heightened the risks of ecological damage to the aquatic environment. As critical conduits between terrestrial and marine ecosystems, seaward rivers directly influence coastal water quality through pollutant transportation (Li & Cui 2012; Li et al. 2024). Excessive pollutants from land-based sources were discharged into nearshore waters, leading to the degradation of seawater quality. This was not only a threat to the human living environment but also a constraint on the high-quality development of the marine economy (Paerl et al. 2014; Stackpoole et al. 2021). In 2022, China's Ministry of Ecology and Environment launched the Action Plan for the Comprehensive Treatment of Key Sea Areas, emphasizing the importance of controlling total nitrogen (TN) pollution in seaward rivers to safeguard marine ecosystems. Subsequent provincial policies, such as Jiangsu's Implementation Opinions on TN Governance in Seaward Rivers (2023), further underscore the urgency of TN management. In addition, as early as the 1970s, the United States, Japan, and other foreign developed countries had formulated a series of policies to control the total amount of pollutants from land-based sources, including the Clean Water Act and the Seto Inland Sea Environmental Protection Provisional Measures Act, and they emphasized the need to carry out comprehensive management by watersheds and to strictly control the entry of pollutants from land-based sources into the sea (Jiang et al. 2001; Karr & Yoder 2004; Tomita et al. 2015). It can be seen that the management and control of TN in inlet rivers has become the key factor in promoting sustainable improvement of water quality in coastal areas.

Previous research on seaward rivers has mainly focused on pollutant fluxes, the spatial and temporal distribution, and risk assessment of heavy metals and microplastics (Zhang et al. 2020, Long et al. 2021). Among them, the study of pollutant fluxes into the sea mainly focused on utilizing the total control method to allocate regional pollutant capacity and to achieve the purpose of improving water quality. Commonly used methods included the model trial method, the concentration share ratio method, and the optimal discharge flux method (Zhao et al. 2011; Keisman & Shenk 2013; Dai et al. 2015). Miller used the spatially referenced regression on the watershed attributes model to estimate the response of TN loads delivered to the Chesapeake Bay Area under nine scenarios of seed source reduction and land use change (Miller et al. 2020). For instance, multivariate statistics were utilized to identify the sources of pollutants in the seaward rivers of Liao Dong Bay, taking into account the effects of runoff and concentration values in different hydrological periods (Li et al. 2022). With an emphasis on eutrophication in the coastal areas, some researchers also carried out a study on the control of TN in the inlet rivers. Based on the frame of total control, the pollutant transportation model was used to evaluate different TN load allocation scenarios for the four major rivers around the Bohai Sea, and thereby the optimum scheme was concluded (Wang et al. 2024). Leng et al. evaluated the total amount of TN fluxes on the Shenzhen side of the Pearl River Estuary, applying the zonal attainment control method to calculate the environmental capacity of TN (Leng & Jiang 2004). Source analysis of TN was also conducted in seaward rivers in Dongguan, revealing that wastewater treatment plants accounted for the largest proportion (Xie et al. 2023).

To our knowledge, there has been limited research on the source analysis of TN in seaward rivers in Jiangsu province. The Jueju River is an important flooding and drainage channel in Rudong County, Nantong City, and also one of the three key rivers for TN control in Nantong City. Regional pollutant sources are concentrated in the central and downstream regions, including centralized domestic pollution, rural domestic, agricultural surface, livestock and aquaculture, and so on. The sources of pollution are multiple and complex. This study combined field measurements with a calibrated MIKE model to analyze the spatial and temporal distribution patterns of TN concentrations in the Jueju River – a representative seaward river in Nantong, Jiangsu province, aiming to provide actionable insights for targeted TN pollution control. A calibrated MIKE model, based on measured hydrological and water quality data, was developed to quantify TN pollution weights at cross-sections, identify sensitive contributing areas, and prioritize pollution control sources.

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Study area

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The Jueju River is situated in the northeastern part of Nantong, spanning between longitudes 121°10′–121°12′ and latitudes 32°18′–32°29′. Originating from the Rutai Canal in the south and terminating at the Liubu Sluice Gate in the north (Figure 1), the river has a total length of 22.1 km. As the most crucial navigation channel in Rudong County, it traverses the Chengzhong Sub-district, the Economic Development Zone, and the Juzhen Sub-district before flowing into the Yellow Sea. The width of the riverbed bottom of the Jueju River ranges from 12 to 25 m, with a bottom elevation of approximately −1.0 m and a normal water level of around 2.3 m. The watershed of the Jueju River lies within the subtropical oceanic monsoon climate zone, featuring an average annual rainfall of 1,074 mm. The seasonal distribution of rainfall is uneven, with summer rainfall accounting for 42% of the total annual precipitation. Notably, the rainfall in July alone accounts for up to 77% of the summer rainfall. During dry and low-flow periods, the Jueju River mainly depends on the Rutai Canal for water replenishment. Agricultural cultivation dominates the Jueju River watershed. The cultivated area amounts to as high as 92.89 km2, among which 81.60 km2 is paddy fields and 9.41 km2 is dry land. The main crops are rice and wheat.
Figure 1
Map of the geographical location and watershed of the Jueju River.
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Map of the geographical location and watershed of the Jueju River.

Figure 1
Map of the geographical location and watershed of the Jueju River.
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Map of the geographical location and watershed of the Jueju River.

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Data acquisition

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The Nantong Rudong Ecological Environment Bureau furnished monthly TN concentration data from 2019 to 2022 at the national examination section of Huandong Gate, along with monthly average TN concentration data in 2022 at the municipal examination sections of Beihuanlu Bridge, Dingpeng Bridge, and No. 8 Bridge. These data were employed for the temporal analysis of TN concentrations in the Jueju River, as well as for the calibration and validation of the MIKE model. The runoff volume data at the new gate of the Jueju River were sourced from the Bureau of Hydrographic and Water Resources Survey of Jiangsu Province. The data on rainfall and maximum and minimum temperature in the study area during 2022 were retrieved from the China Meteorological Data Network. Hydrological data such as runoff and evaporation, which were necessary for the construction of the MIKE model, were primarily obtained from the Nantong Hydrological Yearbook and Nantong Water Resources Bulletin. Meanwhile, the topographical data and water system data were acquired from the Resources and Environmental Science and Data Centre and Geo-spatial Data Centre of the Chinese Academy of Sciences. Agricultural management data, encompassing fertilizer application, livestock and poultry farming, and aquaculture were obtained from the Rudong County Statistical Yearbook. Data regarding domestic sources, centralized wastewater treatment plants, and point source pollution from industrial discharges were derived from the statistical and monitoring data of the Rudong Ecological and Environmental Bureau.

Numerical experiment

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Model construction

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The MIKE model, developed by the Danish Hydraulic Institute, has been extensively applied to simulate water environments in rivers, lakes, and coastal areas (Li et al. 2016). In this study, a two-dimensional hydrodynamic water quality model of the Jueju River was established based on the MIKE 21 model, which was utilized to simulate the transport process of TN in the Jueju River and its tributaries within a complex water environment. The model incorporated 21 boundaries, including an inlet boundary (upstream of the control area of the Jueju River) and an outlet boundary (downstream of the Jueju River flowing into the sea). Given the significance of the Huandong Gate as a key water quality monitoring section, local grid refinement was carried out in the areas near the tributaries of the Changjiao River and the Liubuchekou River. The mesh delineation and the river network topography are shown in Figure 2. The model employed a low-order, high-speed algorithm. The running time step was set at 3,600 s, and the total simulation duration was 1 month. The parameter rate was determined through continuous adjustment of the riverbed Manning's coefficient. Eventually, a Manning's coefficient of 32 m1/3/s was obtained for the Jueju River. The formulas involved in the model were shown in references (Justesen et al. 1996; Patro et al. 2009; Alam 2015).
Figure 2
Mesh delineation and bathymetry of the Jueju River.
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Mesh delineation and bathymetry of the Jueju River.

Figure 2
Mesh delineation and bathymetry of the Jueju River.
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Mesh delineation and bathymetry of the Jueju River.

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Source analysis for TN

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Based on the pollution source data, this study integrated the watershed water environment model to trace the TN in the cross-sections of the Huandong gate downstream of the Jueju River, identifying the sensitive areas and the main sources of TN contribution. Specifically, the elemental key model was utilized to partition the Jueju River watershed into several catchment units. Subsequently, the TN emission loads from various pollution sources within each catchment unit were computed. Finally, source analysis of TN at the cross-sections was conducted using the MIKE 21 model.

By applying the elemental key model, the Jueju River watershed was divided into multiple catchment units. Based on the upstream-downstream relationships of the Jueju River and its tributaries, a hierarchical river relationship network was established within the target watershed. Taking into account the spatial locations of the confluences of the mainstream and tributaries, as well as the positions of point and non-point sources, the Jueju River watershed was ultimately divided into 14 catchment units (Figure 3).
Figure 3
Catchment unit delineation of the Jueju River watershed.
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Catchment unit delineation of the Jueju River watershed.

Figure 3
Catchment unit delineation of the Jueju River watershed.
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Catchment unit delineation of the Jueju River watershed.

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Moreover, with the 14 sub-basins serving as the basic calculation units, the TN discharge load of each unit was calculated using the discharge coefficient method, considering the determined pollution source structure (Xie et al. 2019; Chen et al. 2022).

Accounting for pollution sources from different catchment units, the TN transport process was simulated using the established model of the Jueju River. The annual contribution of the ith catchment unit to the TN flux at the cross-sections of the Huandong Gate was calculated as follows:
(1)
where represents the annual contribution of the ith catchment unit to the TN flux at the control section (t/a); is the concentration of TN at the section when only the discharges from sources in catchment unit i are considered (mg/L); is the concentration of TN at the section when discharges from sources in all catchment units are excluded (mg/L); and is the flow rate at the section (m3/s).
The results of these calculations were used as the basis for the cross-section pollution weighting analysis. Eventually, the impact weights of TN discharge from different catchment units were obtained through the following formula:
(2)
where is the weight contribution of catchment unit i to the TN flux at the cross-section and n denotes the total number of catchment units in the watershed.
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Calibration and validation of the MIKE model

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A comparative analysis of the measured and simulated data of the Jueju River in 2022 revealed that the relative error in water level was approximately 3.94% and the relative error in flow rate was around 4.23%. Both of these errors fell within the permissible range. The measured data of TN concentrations at three typical sections, the Huandong Gate of the Jueju River, the No. 8 Bridge, and the Dingpeng Bridge, were chosen to calibrate and validate the model. As shown in Table 1, the absolute values of the calibrated average relative errors between the measured and modeled values at the cross-sections of the Huandong Gate, the No. 8 Bridge, and the Dingpeng Bridge were 12.67, 12.77, and 15.67%, respectively. All of these values were within the acceptable range. Consequently, the calibrated model could serve as the foundation for the subsequent source analysis of TN at the cross-sections.

Table 1

Comparison of simulated and measured values

Cross-sections
TN
Simulated valueMeasured valueRelative error
Huandong Gate 2.43 2.58 12.67% 
No. 8 Bridge 1.85 2.12 12.77% 
Dingpeng Bridge 2.69 2.31 15.67% 
Cross-sections
TN
Simulated valueMeasured valueRelative error
Huandong Gate 2.43 2.58 12.67% 
No. 8 Bridge 1.85 2.12 12.77% 
Dingpeng Bridge 2.69 2.31 15.67% 
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Temporal analysis of TN concentrations in the Jueju River

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Based on the cross-section monitoring data, the inter-annual variation and seasonal differences in TN concentrations at the Huandong Gate were analyzed from 2019 to 2022 (Figure 4). Results showed that the TN concentration at the cross-section of the Huandong Gate decreased from 2.75 mg/L in 2019 to 2.58 mg/L in 2022, representing a 7.3% reduction. From 2019 to 2021, the TN concentration exhibited a ‘W-shape’ curve, with a significant increase in July. This trend was consistent with the pollutant concentration patterns in other regions of Jiangsu Province, being influenced by the flood season and winter temperatures. However, in 2022, the high TN concentration values were predominantly concentrated in the winter months from December to February, with the overall trend presenting a ‘U-shaped’ curve. Previous studies have demonstrated that the intensity and frequency of rainfall could affect the nutrient flux carried by surface runoff (Botter et al. 2006; Yang et al. 2012). In 2022, the rainfall during the flood season in the Jueju River watershed was unusually low, approximately 90% less than that of a normal year. This significantly reduced the non-point pollution generated by the flood season rainfall, resulting in a decrease in the TN concentration at the Huandong Gate during the same period. Regarding different seasons, the TN concentration was generally higher in winter compared to the other three seasons, accounting for approximately 130% of the annual average concentration. This could be ascribed to the low rainfall in winter and the consequently slower denitrification rate at lower temperatures (Li et al. 2023). Additionally, due to the influence of non-point pollution during the flood season, there was an increase in the TN concentration at the Huandong Gate section in the summer.
Figure 4
Temporal patterns of TN concentrations at the Huandong Gate section from 2019 to 2022. (a) Inter-annual variation in total nitrogen (b) Quarterly change in total nitrogen.
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Temporal patterns of TN concentrations at the Huandong Gate section from 2019 to 2022. (a) Inter-annual variation in total nitrogen (b) Quarterly change in total nitrogen.

Figure 4
Temporal patterns of TN concentrations at the Huandong Gate section from 2019 to 2022. (a) Inter-annual variation in total nitrogen (b) Quarterly change in total nitrogen.
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Temporal patterns of TN concentrations at the Huandong Gate section from 2019 to 2022. (a) Inter-annual variation in total nitrogen (b) Quarterly change in total nitrogen.

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The Dingpeng Bridge and the Beihuanluqiao Bridge sections were situated upstream of the Jueju River, while the No. 8 Bridge section was located in the midstream, and the Huandong Gate section was positioned downstream. As depicted in Figure 5, there was evident spatial heterogeneity in the TN concentrations along the Jueju River. In 2022, the annual mean TN concentration in the upstream was 2.25 mg/L, which was significantly lower than that in the downstream section (2.58 mg/L). The mean TN concentrations from January to March and from November to December at the Huandong Gate section were higher than those at the Beihuanluqiao Bridge, Dingpeng Bridge, and No. 8 Bridge sections. Based on the regional land use type, the midstream and downstream areas were predominantly agricultural. These areas were more vulnerable to agricultural non-point source pollution triggered by rainfall (Huang et al. 2019). During different seasons, the temporal trends of TN concentrations at the four cross-sections remained largely consistent, with higher concentrations in winter. Figure 5 indicated that the TN concentration upstream of the Jueju River had a certain impact on the downstream Huandong Gate section. However, no positive correlations were detected.
Figure 5
Distributions of TN concentrations along the Jueju River in 2022.
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Distributions of TN concentrations along the Jueju River in 2022.

Figure 5
Distributions of TN concentrations along the Jueju River in 2022.
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Distributions of TN concentrations along the Jueju River in 2022.

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Source analysis of TN emissions at the sea-entry section

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Based on the average annual water inflow from the Rutai Canal, the average annual discharge from the Jueju New Sluice, and the TN concentration at the upstream cross-section, the TN flux from the upstream was calculated to account for 70.2%. Because the quality of upstream water cannot be easily controlled, the study focused on traceability analysis of TN for regional catchment units and identified priority control areas.

To trace the main source areas and pollutant emission sources of TN in the Huandong Gate section of the Jueju River, the migration of pollutant inputs to the catchment units was simulated by the model. The contribution weights of each catchment to the TN entering the sea were calculated. Moreover, with respect to the TN emissions in each catchment, the types of pollution sources that should be prioritized for control were further clarified. As depicted in Figure 6(a), there were distinct spatial differences in the contribution weights of different catchment units to the TN at the Huandong Gate section. The areas with high contribution weights were primarily concentrated upstream of the Jueju River watershed. Catchment units 1, 2, and 3 were the main source areas of cross-section TN, with contribution weights of 49.21, 11.03, and 11.57%, respectively. Notably, catchment unit 1 alone contributed as much as 237.20 t/a. In contrast, catchment units 8 and 11 contributed to cross-section TN with fluxes of 1.2 and 2.6 t/a, and their contribution weights were merely 0.25 and 0.55%, respectively. Significantly, catchment unit 1, located upstream in the watershed, had the highest contribution weight to the TN, which was 196 times higher than that of the catchment unit with the lowest contribution weight. In the catchment units where a larger amount of TN entered the river, the upstream areas of the watershed could contribute more to the cross-section of TN. This was due to factors such as a denser population and a higher proportion of farmland coverage compared to the midstream and downstream areas of the watershed (Huang et al. 2019). Additionally, the contribution weight of catchment unit 14 was also relatively high, at 7.33%. This indicated that the contribution weights of each catchment unit to cross-section TN were not only related to the regional TN input to the river but also influenced by the distance indicator.
Figure 6
Contribution weight of each catchment unit to TN at Huandong Gate and TN emissions from different pollution sources. (a) Spatial distribution of TN contribution weights (b) Discharges of pollution by catchment unit.
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Contribution weight of each catchment unit to TN at Huandong Gate and TN emissions from different pollution sources. (a) Spatial distribution of TN contribution weights (b) Discharges of pollution by catchment unit.

Figure 6
Contribution weight of each catchment unit to TN at Huandong Gate and TN emissions from different pollution sources. (a) Spatial distribution of TN contribution weights (b) Discharges of pollution by catchment unit.
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Contribution weight of each catchment unit to TN at Huandong Gate and TN emissions from different pollution sources. (a) Spatial distribution of TN contribution weights (b) Discharges of pollution by catchment unit.

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In terms of the pollution source structure in the whole region, the Jueju River watershed consisted of seven pollution source types, with centralized wastewater treatment plants dominating, accounting for up to 40%. The loads from uncontrolled rural domestic sources and farmland cultivation sources were 82.17 and 70.25 t/a, respectively, with contribution ratios higher than 15%. A further source analysis of TN emissions from areas with high contribution weights indicated that centralized wastewater treatment plants contributed up to 88% of the TN inflow to catchment unit 1. The reason for this could be the concentration of urban construction land. In catchment unit 2, farmland cultivation sources and unconnected rural domestic sources accounted for 44% of the TN inflow, respectively. While in catchment unit 3, livestock and aquaculture sources made up 50% of the TN inflow. These results indicated that centralized wastewater treatment plants, farmland cultivation, and livestock and aquaculture sources were the main contributors to TN at the cross-section of the Huandong Gate. Surface pollution covered a high degree in the watershed. Previous studies also demonstrated that nitrogen from cultivation and urban living processes was a major source of non-point pollution in the watershed (Chen et al. 2013). As shown in Figure 6(b), in over 50% of the catchment units, the highest proportion of TN inflow originated from unconnected rural domestic sources, whose impact should not be overlooked.

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  • (1)

    The MIKE model, established based on measured hydrological and water quality data, demonstrated good applicability in the Jueju River watershed. The relative errors of water level and flow rate were within 5%, and the absolute value of the average relative error of TN was within 16%. Thus, the calibrated model holds great promise for the source analysis of TN in subsequent cross-sections.

  • (2)

    There were distinct annual variations in TN at the cross-section of the Huandong Gate. From 2019 to 2022, the TN concentration ranged from 2.39 to 2.75 mg/L. During this period, the TN concentration exhibited a ‘W-shaped’ trend, and in 2022, it presented a ‘U-shaped’ curve, which was influenced by the rainfall during the flood season. The TN concentration in winter accounted for approximately 130% of the annual average concentration, generally being higher than that in the other three seasons.

  • (3)

    The areas with high TN contribution weights to the cross-section at the Huandong Gate of the Jueju River were predominantly concentrated upstream of the watershed. These areas were significantly affected by regional TN inflows and distance-related factors. The main source areas of cross-section TN were catchment units 1, 2, and 3. In these units, centralized wastewater treatment plants, farmland cultivation, and livestock farming were the major contributors. Among them, the contribution ratio of centralized wastewater treatment plants exceeded 43%. Additionally, unconnected rural domestic sources played a crucial role in over 50% of the catchments, where they had the highest contribution ratio of TN flux.

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This work was supported by the Natural Science Foundation of Jiangsu Province (BK20230763) and the Young Elite Scientists Sponsorship Program by JSAST (JSTJ-2023-XH022).

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All relevant data are included in the paper or its Supplementary Information.

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The authors declare there is no conflict.

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