Study on the water eutrophication evolution characteristics of Junshan Lake Open Access

Freshwater is one of the indispensable substances required for human production and life (Castagna et al. 2015). At the same time, large-scale water eutrophication has brought serious challenges to water resources management (Zheng et al. 2018). At present, there are many studies on the eutrophication of lakes, reservoirs and rivers. Xu et al. (2014) evaluated the water eutrophication status of Xikeng Reservoir (Shenzhen City, China) based on a bioaccumulation model. Some scholars evaluated the eutrophication level of Dashahe Reservoir using the BP Neural Network and Remote Sensing Imagery (Zhang et al. 2021b). For instance, Zhang et al. (2019) assessed the eutrophication status of Fuyang River by the nutritional status index method. Lai et al. 2021 established the water eutrophication assessment standards of Xiajiang reservoir based on the chlorophyll-a level. Moreover, (He et al. 2021) combined application remote sensing technology and the comprehensive nutritional index method to water eutrophication evaluation.

Thus far, no unified method or standard has been developed for water eutrophication evaluation (Cabrita et al. 2015). The existing approaches are mainly the cluster analysis method, principal component analysis, neural network, fuzzy evaluation, and comprehensive nutritional status index method (TLI) (Al-Shayji et al. 2008; Chen et al. 2012; Wong & Hu 2013, 2014; Jekatierynczuk-Rudczyk et al. 2014; Ju & Yoo 2014; Ke et al. 2014); however, each of these methods have some disadvantages. For example, the cluster analysis method is more subjective in the determination of weights (Cai et al. 2002); data processing requires a large workload in principal component analysis (Alves et al. 2013); the comprehensive nutritional status index method cannot reflect the contribution rate of individual factors with heavier pollution (Cabrita et al. 2015); the fuzzy evaluation method is less accurate in the determination of the membership function (Chen et al. 2021); and the Neural Network Method cannot unify the dimensions of evaluation indexes (Zhang et al. 2021b). The comprehensive nutritional index method (TLI) (Zhi et al. 2013; Li et al. 2015; Lu et al. 2018) and the nutritional status index method (TSI) (Carlson 1977; Bekteshi & Cupi 2014) are two currently recognized eutrophication evaluation methods. In addition, their calculation is more convenient, and they have many applications (Yang et al. 2008). The comprehensive nutritional status index method is a direct reflection of the risk of eutrophication based on Chla (Gui & Yu 2008; Ouyang et al. 2021; Zhang et al. 2021a). At present, the application of the comprehensive nutritional index method comprises depth analysis on the final evaluation results and subdivision on the evaluation criteria (Zhang et al. 2017).

The Junshan Lake originally belonged to Poyang Lake (the largest freshwater lake in China). In 1959, the construction of a sluice embankment cut off its connection with Poyang Lake in 1959. The flow rate slowed down, the water ecosystem changed greatly, and the self-purification capacity of water decreased after the separation. In recent years, the intensity of aquaculture in Junshan lake has increased, the non-point source pollution has intensified, and the nutritional level of water has significantly increased. The current research on Junshan Lake mainly focuses on the analysis of water quality factors, zooplankton, etc. (Liu et al. 2018). Meanwhile, there are few reports about the nutritional level of Junshan Lake. With the increase in the intensity of aquaculture, it is necessary to analyze and evaluate the nutritional status of Junshan Lake. At present, the overall water quality of Junshan Lake is good, and there are no serious pollution factors. No significant difference was found in the concentration of pollutants between the wet season and the dry season (Lu et al. 2021). Therefore, it may be of little value to evaluate the nutritional status of Junshan Lake between seasons. Instead, this paper intends to use the comprehensive nutrient state index method to analyze and evaluate the eutrophication level of Junshan Lake on a longer timescale from 2018 to 2020. The research conclusions will hopefully provide a reference for the water resource management and water environment governance of Junshan Lake.

The Junshan Lake (116°15′–116°21′E, 28°23′–28°39′N) has an area of 185–210 km², a length of 25 km, a width of 5 km, and a maximum width of 16 km. The area of lake basin is 616 km², the maximum water depth is about 6.5 m, and the average water depth is 4.3 m. The Junshan Lake is the largest inner lake in Jinxian County, Nanchang City, Jiangxi Province, China, which is connected to Jinxi Lake and Qinglan Lake. It is rich in valuable natural resources such as turtle (Amyda sinensis), mandarin fish (Siniperca chuatsi) and silver fish (Hemisalanx prognathus Regan), and is a ‘breeding base for non-environmental pollution aquatic products’. The study area is surrounded by mountains and rivers. The terrain is high-altitude in the southeast and low in the northwest. It belongs to the tropical monsoon humid climate with abundant rainfall and distinct seasons. The average annual temperature is 17.1 °C, the average winter temperature is 5.4 °C, the average annual precipitation is 1075 mm, the average annual sunshine time is 2063 h, and the average annual wind speed is 3 m/s.

The SPSS 19.0 software was used to obtain the maximum, minimum, average, and standard deviation of each nutritional factor for each monitoring point, and t-test was used to eliminate any outliers (the nutrient factors at each point were counted in years).

In the formula, denotes comprehensive trophic state index; denotes nutritional state index of the jth substance; denotes the proportion of the jth substance in the evaluation system.

In the formula, r_ij denotes the correlation coefficient between the jth substance and Chla, and m denotes the number of participating nutrients.

According to the recommendations of the State Environmental Protection Administration of China (China National Environmental Monitoring Center 2001), the correlation coefficients between nutrients and Chla are shown in Table 1 (Zhang & You 2017). The calculated weight values are shown in Table 1 (Zhang & You 2017), and the evaluation standard is shown in Table 2 (Wang et al. 2002; Gui & Yu 2008) (The relevant parameter values in Tables 1 and 2 apply to most lakes in China).

Table 2

Grades of trophic state for lakes (reservoirs)

Nutrition level .	Oligotrophic .	Medium trophic .	Light eutrophication .	Medium eutrophication .	Severe eutrophication .
	30 ≥ TLI	50 ≥ TLI > 30	60 ≥ TLI > 50	70 ≥ TLI > 60	TLI > 70
Grades	Grade I	Grade II	Grade III	Grade IV	Grade V

Nutrition level .	Oligotrophic .	Medium trophic .	Light eutrophication .	Medium eutrophication .	Severe eutrophication .
	30 ≥ TLI	50 ≥ TLI > 30	60 ≥ TLI > 50	70 ≥ TLI > 60	TLI > 70
Grades	Grade I	Grade II	Grade III	Grade IV	Grade V

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The analysis of Nutritional Status Index (Table 3) of monitoring points from 2018 to 2020 was carried out (the TLI of each parameter in Table 3 were obtained from an average of parameters measured in six months). In this three-year period, the nutritional status index (TLI) of the monitoring sites in the northern part of Junshan Lake was lower than those in the southern part, indicating that the water quality at the entrance of the lake (S1) during this time was good, and there was no new pollution source discharge nearby. In 2018, only the four points of S7, S10, S11, and S12 were in Grade II (medium trophic), while the other points were in Grade I (oligotrophic), and the comprehensive state index of these three points was close to 30 (the boundary value of Grade I and Grade II). This is mainly because aquaculture in Junshan Lake was mainly concentrated near the two monitoring points of S7 and S10, while S11 and S12 were in middle nutrition probably because these two points are at the end of the lake, the water body exchange cycle is long, and the water quality is affected by the aquaculture at S10. TLI (TP) was the highest among the five nutritional factor indexes at S7, and TLI (COD_Mn) was the highest at S10, S11 and S12. This is because different aquatic species are kept in captivity on both sides of the lake intersection (grass carp and loach are mainly farmed around Point S7, Monopterus albus and river crab are mainly farmed around Point S10, S11 and S12). Among the 12 monitoring points in 2019, only five points, S1, S2, S3, S4, and S5, were in trophic levels of Grade I (oligotrophic), and the other sites were in Grade II (medium trophic). This is because the local government encouraged the development of aquaculture in 2019, significantly increasing the area of aquaculture water, and also boosting the variety of aquatic products. In 2020, the four sites of S1, S2, S3 and S4 were in Grade I (oligotrophic), and the other sites were in Grade II (medium trophic). The nutritional index of breeding areas and nearby sites showed a more significant increase compared to 2019, which may be due to the fact that Junshan Lake is a relatively closed lake with a long water exchange cycle, coupled with the accumulation of pollutants produced by aquaculture all year round. The degree of eutrophication of lakes and reservoirs is related to the number of water exchange cycles, generally speaking, the longer the water exchange period, the lower the dissolved oxygen, the higher the risk level of eutrophication (Schindler 2012; Andersen et al. 2017; Lin et al. 2021). In 2020, the composite index values of S10, S11 and S12 were all above 40, especially S11 at 46.2. It indicates that the lake has a tendency to transform into Grade III (light eutrophication), and the control of breeding scale should be strengthened.

The evaluation results of water nutritional status in Junshan Lake from 2018 to 2020 are shown in Table 4. In the three years concerned, the water body of Junshan Lake was in a state of Grade I (oligotrophic) to Grade II (medium trophic). The value in 2018 was 24.34–31.75, the value in 2019 was 25.17–35.62, and the value in 2020 was 26.43–46.2. These data show that the degree of eutrophication in Junshan Lake increased year by year. Meanwhile, the maximum TLI value of each nutrient increased every year from 2018 to 2020. The variation range of TLI of each nutrient element in 2020 was much larger than that in 2018 and 2019, and the maximum value of TLI of each nutrient appeared in 2020. Thus, the water quality of Junshan Lake severely deteriorated in 2020. TLI (TP) had the maximum value of the nutritional status index in 2020, indicating that the most seriously polluted index of Junshan Lake in 2020 was TP.

To date, many researches have been conducted on the application of ArcGIS in water environments, but most of them focused on extracting some basic information (such as boundary information, bottom elevation coordinates, land use types, etc.), while the application of spatial analysis tools has been scarce. In our study, the distance between monitoring points was relatively large, and it would be one-sided to evaluate the eutrophication status of the whole lake with several existing monitoring points. We tried to build a model in ArcGIS software to solve this problem. The model building steps were as follows: ① Extract the boundary information of Junshan Lake by an online map and import the geographic coordinates of each monitoring point; ② Enter the TLI value of the corresponding year and month in the monitoring point attribute table; ③ Calculate the TLI value for the corresponding year using the ‘Field Calculator’ tool; ④ The spatial distribution map was generated by interpolation using ‘Kriging interpolation’ and ‘inverse distance weight interpolation’ tools (taking 2018 data as an example, the information of S3 and S5 points was deducted during interpolation); ⑤ Extract the TLI scores of the S3 and S5 points according to the generated map, compare the extracted value with the actual value, and select a method with better accuracy to re-interpolate all the monitoring points (a total of 12 points); ⑥ A total of 83 points and their TLI values were extracted in the study area with a distance of 500 × 500 m using the tool ‘Create Fishnet’; ⑦ Re-interpolate the TLI values of these 83 points to generate a spatial distribution map.

Our analysis shows that the water body of Junshan Lake in 2019 was between the Grade I (oligotrophic) to Grade II (medium trophic) nutrition levels, which is basically consistent with the existing research conclusions (Zhao et al. 2018). However, previous works only explained the comprehensive level of Junshan Lake for 2019, without elaborating the nutritional level of specific monitoring points. Herein, we not only obtained the water nutrition level of specific monitoring points, but also considered the proportion of the affected areas.

A surplus of nitrogen, phosphorus and other nutrients in eutrophic waters leads to the growth of phytoplankton. The imbalance in nutrient input and output leads to algal blooms and further mass death of phytoplankton. The nitrogen:phosphorus ratio is one of the limiting factors of water eutrophication; excessive or low nitrogen_phosphorus ratio will inhibit the occurrence of water eutrophication. Although the range of nitrogen and phosphorus ratio suitable for algal growth was roughly the same for the whole study area, there were some local differences.

There was little variation in the pollutant concentration between the wet, dry and normal seasons in the Junshan Lake. This contrasts with the previous conclusion that ‘the content of each nutrient in the water body varies greatly with seasons’. This is because Junshan Lake receives less pollutants discharged from the environment, which is basically consistent with the existing research conclusions (Liu et al. 2018). From the above analysis, it is found that Chla was positively correlated with TN and TP in Junshan Lake, indicating that the supply of TN and TP here was beneficial to the rapid growth of phytoplankton. We speculated that although the types of aquaculture as well as the concentrations of pollutants vary between different regions of Junshan Lake, TN/TP in the whole lake is always in a proportion that is suitable for algal growth and reproduction.

At present, although many evaluation methods exist for water eutrophication, the most commonly used ones are the comprehensive nutrition state index method and the nutrition state index method (Carlson 1977; Zhi et al. 2013; Zhang & You 2017; Lu et al. 2018; Stamou et al. 2019). Although the evaluation factors selected by these two methods are the same, the latter needs to solve each calculation formula according to the standard value, which is relatively complex. When there are few monitoring points and the collected information is not too representative, using the ‘Create Fishnet’ tool in ArcGIS can effectively solve this problem. Thus far, most studies on the eutrophication of water bodies have not analyzed the water body area proportion of each trophic level, the shortcoming of which is addressed by this paper. ArcGIS software is a powerful spatial analysis tool and is widely used in various fields (Guo et al. 2018); the combination of ArcGIS and water eutrophication evaluation in a deeper level may be a topic worthy of further exploration.

In order to generate the spatial distribution map, this paper only used the ‘kriging interpolation’ and ‘inverse distance weight interpolation’ methods, and finally, the ‘inverse distance weight interpolation’ method was selected to interpolate the spatial distribution map. There are many interpolation methods in ArcGIS software, but the optimal choice of interpolation method to make the results more accurate remains to be further investigated. In addition, some scholars have proposed that the ratio of nitrogen to phosphorus is one of the important factors limiting algal reproduction (Zhang et al. 2017), which item was not considered in the eutrophication evaluation in this paper, thus needs further attention.

This paper combines ArcGIS and the comprehensive nutritional status index method to reveal the spatio-temporal characteristics of the nutritional status of Junshan Lake from 2018 to 2020. From the results, the following conclusions can be drawn:

• (1)

  The nutrient status of Junshan Lake changed from Grade I (oligotrophic) in 2018 to Grade II (medium trophic) in 2020, with the water nutrition level increasing year by year. The proportion of water area with Grade II (medium trophic) in the comprehensive nutritional index rose from 24.2% in 2018 to 78.6% in 2020.

• (2)

  The water quality in the northern part of Junshan Lake is obviously better than that in the southern part, and the water at the entrance and near the dam of Poyang Lake has been in a oligotrophic nutritional state from 2018 to 2020.

• (3)

  The water body in the southern part of Junshan Lake is at a greater risk of changing to Grade III (light eutrophication) state.

This study does not involve any animal and human sample.

Not applicable.

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

The authors declare there is no conflict.