2.1. Study Area
Changxing County (30°43′—31°11′ N, 119°33′—120°06′ E), Huzhou City, Zhejiang Province, is located in the Hangjiahu Plain within the Changjiang River Delta. It is situated adjacent to the southwestern shore of Taihu Lake. Changxing County covers an area of 1431.2 square kilometers (km
2) and had a permanent population of approximately 674,000 in 2020. Its regional gross domestic product (GDP) reaches CNY 70.239 billion. The terrain gradually descends from west to east, and the climate is characterized by a subtropical marine monsoon climate. The annual average precipitation is 1347.7 mm, with the majority occurring from April to October. The average number of rainy days per year is 144, accounting for 39.45% of the total annual days. The three major water systems in Changxing County are the He Xi River System, the Si An Tang River System, and the Wu Xi River System [
25]. The distribution of these important water systems in Changxing County is shown in
Figure 1.
Changxinggang River is located in the northern part of Changxing County, flowing through the central plain area and the urban district before entering Taihu Lake. It has a total length of approximately 31.58 km (kilometers). Due to variations in the timing of precipitation, the period from May to October is designated as the flood period, the period until 15 July is the Meiyu period, and the period after that is the Typhoon period. The months of January to April and November to December are designated as the non-flood period. The flat terrain and low slopes of the plain river network area that Changxinggang River is located in result in inadequate hydraulic conditions [
24]. Pollutants tend to accumulate in the water, and disruptions caused by shipping activities release pollutants from riverbed sediments, leading to internal pollution and the occasional backflow of cyanobacteria from Taihu Lake. These factors collectively contribute to water quality issues, such as the exceedance of standards and the occurrence of algal blooms in the Changxinggang River.
Based on data collection and field surveys, this study utilized the primary hydrological station on the Changxinggang River, specifically the Changxing (II) hydrological station. Discharge and water level monitoring data from 2016 to 2020 were employed to analyze the daily variations in water level and discharge, as well as the monthly average discharge and water level in the Changxinggang River. The details are presented in
Figure 2,
Table 1 and
Table 2.
Currently, the Changxinggang River has a nationally controlled Xintang section, a provincially controlled Xiashenqiao section, and three municipal and county-level controlled sections [
24]. According to the “Changxing County Watershed Protection Planning Revision Report” obtained from the Changxing County Water Resources Bureau, the Xintang section is classified as an area for agricultural and industrial water use, with water environmental control standards adhering to Grade III standards, as per the “Surface Water Environmental Quality Standards” (Standard) [
26]. Furthermore, the Xintang section is located at the end of the Changxinggang River and serves as the only nationally controlled section. It is the most representative section for evaluating the environmental quality of the water and analyzing the aquatic ecological environment in the Changxinggang River. An analysis of the current water ecological environment in the Changxinggang River reveals that the common water quality parameters that exceed the standards include the chemical oxygen demand (manganese) (COD
Mn), biochemical oxygen demand (BOD), NH
3-N, chemical oxygen demand (chromium reduction) (COD
Cr), and TP.
2.3. Data and Materials
Due to the varying water pollution levels in the Changxinggang River during the non-flood period (November to April) and the flood period (May to October), this study determines the primary water environmental control indicators for these two periods. The selection of the primary water environmental control indicators is based on principal component analysis (PCA), using monthly monitoring data from the nationally controlled Xintang section of the Changxinggang River from 2016 to 2020.
The monthly data for the Changxinggang River Xintang section from 2016 to 2020 were primarily obtained via on-site monitoring conducted by our research team and via the provision of monitoring data by the hydrological station in Changxing County, Zhejiang Province. The monitoring of initial parameters is typically conducted by collecting water samples from rivers and performing laboratory analyses. In this study, the monitored initial parameters primarily include dissolved oxygen (
Table 3), water temperature (
Table 4), chemical oxygen demand (manganese) (COD
Mn), biochemical oxygen demand (BOD), ammonium nitrogen (NH
3-N), chemical oxygen demand (chromium reduction) (COD
Cr), and total phosphorus (TP). The following are the methods for monitoring each parameter: Dissolved oxygen: Iodometric method, Electrochemical Probe method; Temperature: Thermometer method; COD
Mn: Potassium Permanganate method; BOD: Dilution and Seeding method; NH
3-N: Nessler’s Reagent Colorimetric method, Salicylic Acid Spectrophotometric method; COD
Cr: Dichromate method; TP: Ammonium Molybdate Spectrophotometric method. The monitoring of initial parameter data follows the standard [
26] and relevant national and local water quality monitoring standards in China.
PCA is a statistical analysis method used to reduce the dimensionality of data and to extract features. It is primarily employed to transform the original data into a set of linearly independent variables, thereby compressing the original variables and simplifying their complexity. The variables generated through this transformation reflect the main information represented by the original variables [
27]. Its advantages include its ability to reduce dimensionality, remove secondary features, perform decorrelation to reduce the redundancy between data, increase interpretability to facilitate the understanding of the structure of the original data, and enhance the ease of data visualization by mapping high-dimensional data to two or three dimensions. The drawbacks of PCA include the potential loss of some data information during the dimensionality reduction process, its suboptimal performance for nonlinear relationships in the data, its sensitivity to outliers that may affect the calculation of principal components if significant errors are present, and its increased computational complexity and time consumption for large-scale datasets. In this study, considering the abundance of data information, the need to remove secondary features, and the relatively low prevalence of outliers in the data, PCA was considered a suitable choice. The computational steps for PCA are as follows:
It is generally thought that PCA is applicable to a dataset when the KMO verification coefficient is greater than 0.500 and when the significance probability (Sig.) from Bartlett’s sphericity test is less than 0.050.
- 2.
Standardization of raw data:
The raw data were standardized by applying the z-score normalization method, ensuring that the mean of each variable was 0 and that the standard deviation was 1. The mathematical principle of the z-score normalization method is represented by Equation (1):
where
xij represents the
ith sample and
jth indicator,
is the mean of the
jth indicator, and
Si denotes the standard deviation of the
jth indicator.
Zij represents the standardized result for the
ith sample and
jth indicator.
- 3.
The establishment of the correlation matrix R:
Compute the correlation matrix for the standardized data using the formula given in Equation (2):
where
R is the correlation matrix,
n is the number of samples, and
Z is the matrix of the standardized data.
- 4.
Calculation of eigenvalues, contribution ratios of eigenvalues, and cumulative contribution ratios for the correlation matrix:
The determination of the number of principal components depends on the calculation results of the eigenvalues, the contribution ratio of eigenvalues, and the cumulative contribution ratio of the correlation matrix. The criterion is as follows: if the cumulative contribution ratio of the first n eigenvalues reaches 85%, the first
n eigenvalues corresponding to the principal components cover most of the information in the dataset. If there are
m eigenvalues greater than 1.000 among the first
n eigenvalues, these m eigenvalues, along with their corresponding principal components, can explain the dataset. The eigenvalue decomposition of the correlation matrix
R is performed to obtain the eigenvalues and their corresponding eigenvectors. The calculation is as follows:
- 5.
Calculation of principal component loadings.
Principal component loadings are used to reflect the correlation between principal components and the original variables. Generally, a higher correlation coefficient indicates that the variable is more representative of the corresponding principal component. Principal component loadings can be calculated using the following method:
where P
ij represents the loading of the
ith principal component on the
jth variable,
λi is the
ith eigenvalue, and
vij is the corresponding eigenvector.
By comparing the water environmental data obtained from Changxinggang River for the years 2016 to 2020 with the “Standard” [
26] Class III water standard, it was found that the water environmental indicators exceeding the standard in the Xintang section were COD
Mn, BOD, NH
3-N, COD
Cr, and TP. The “Standard” [
26] is a standard that is used in China to assess and monitor surface water quality. This standard categorizes surface water quality into different grades, with the “Class III standard” representing the minimum water quality standard required for centralized drinking water sources. Different grades of standards define the allowed pollutant types and concentration limits. Specifically, for the Class III standards, the permissible values are as follows: the COD
Mn standard value is 6 mg/L, the BOD standard value is 4 mg/L, the NH
3-N standard value is 1 mg/L, the COD
Cr standard value is 20 mg/L, and the TP standard value is 0.2 mg/L. The monthly water quality indicators for the Xintang section are illustrated in
Figure 4. Based on
Figure 4, it is observed that from 2016 to 2020, COD
Mn exceeded the standard value three times, with relatively heavier pollution present during the non-flood period. BOD exceeded the standard value three times, with relatively heavier pollution present during the flood period. NH
3-N and COD
Cr exceeded the standard value five times, with relatively heavier pollution present during the non-flood period. TP exceeded the standard value once, indicating relatively lighter pollution among the five water quality indicators. The monthly compliance status of the water environmental indicators can be seen in
Figure 4.
As shown in
Figure 5, the calculation of the average concentrations of pollutants during the flood and non-flood periods from 2016 to 2020 indicated a significant difference between the concentrations of pollutants during these two periods. Moreover, the concentrations of pollutants during the non-flood period were consistently and significantly higher than those during the flood period. This suggests that there was a statistically significant difference in the pollutant concentrations between different hydrological periods.
To establish the control objectives, this study employed PCA to analyze the five indicators mentioned above, thus obtaining the principal component loadings for the Changxinggang River during non-flood and flood periods, as detailed in
Table 5 and
Table 6.
Table 5 reveals that the indicators most correlated with principal component 1 were COD
Mn, COD
Cr, and BOD, while those most correlated with principal component 2 were NH
3-N and TP.
Table 6 indicates that COD
Mn, COD
Cr, and TP contributed significantly to principal component 1, whereas NH
3-N had a higher contribution to principal component 2. Considering the high correlation between COD
Mn and COD
Cr and the absence of TP exceedances during non-flood periods, this study determined that NH
3-N, COD
Mn, and BOD were the water quality control indicators for non-flood periods and that COD
Mn, TP, and NH
3-N were the indicators for flood periods. Furthermore, due to the issue of algal blooms in the Changxinggang River, it was determined that NH
3-N is an indicator for algal bloom nutrient conditions, accounting for the conditions that occur during algal blooms.