HCA assembles the objects into different clusters based on their similarity, and is used for recognizing the temporal and spatial distribution recognition. Then, standard and stepwise modes of DA are used to confirm the cluster found by HCA and to determine the most significant parameters determining spatial and temporal variations. In combination with the DA and Spearman’s correlation analysis, the temporal and spatial distribution characteristics of water environmental element can be obtained.
3.2.1. Temporal Variations
Both of seasons and points are non-numeric parameters, so an assignment is necessary before the statistical analysis. The dendrogram of temporal HCA is shown in
Figure 2a. It is observed that all the monitoring months can be obviously classified into two statistically meaningful clusters at (D
link/D
max)*100 < 15 (the quotient between the linkage distance for a particular case divided by the maximal distance). Cluster 1 contains May–October, and Cluster 2 contains March–April. As indicated before, 85% of the precipitation is concentrated in May–September; therefore, Cluster 1 and 2 basically correspond to the rapid-flow period and gentle-flow period, respectively.
Integrated evaluation of water quality can be derived through FCA, which demonstrates in
Table 2 that water quality are both beyond level V in the selected two periods. The results are largely affected by TN and TP’s heavy weighting, which means severe N and P pollution in TSBR. By comparison between the pollution levels between two periods, it is observed that the pollution level in the rapid-flow period (0.976) is higher than gentle-flow period (0.942), which reveals the scouring movement by rainfall in the rapid-flow period. In addition, runoff carries large amounts of land and atmosphere pollutants into the river-course, so non-point source pollution becomes more serious [
36]. On the other hand, due to the runoff erosion, endogenous load within the river course (which is mainly from point source pollutants) can be released, which in turn increases pollutant concentration in rapid-flow period [
37,
38,
39].
Table 3 and
Table 4 show classification functions and verification obtained from the standard, stepwise modes of DA. For temporal DA, standard mode assigning 97.8% of cases means the selected 10 parameters can explain temporal variation of water quality successfully. While for stepwise mode, correct assignations reach 93.3% using only three parameters, which means Chl-a, TN, PO
4-P should be enough to illustrate temporal variation. This suggests that the monitoring intensity of these three parameters should be appropriately increased in future works.
The results of SCA are shown in
Table 5. NH
4-N, TN, TP, PO
4-P, BOD
5, Chl-a are found significantly (sig. < 0.01) correlated with the periods, suggesting that these parameters can be easily affected by temporal factors like water stage or flow rate. Through the results from SCA and DA, three parameters selected by SCA cannot be found through DA. NH
4-N, TP, BOD
5 which correlates with the periods. The temporal variation is explained by. NH
4-N, TP, whose loss of discriminant ability is mainly caused by flexible water discharge from factories along the main river course in Tongzhou section. In addition, the degradation ability of microbial factors varies along with the scale and frequency of the water discharge, which explains BOD
5 fails to show seasonal variations. The results also suggest that NH
4-N, TP, BOD
5 are highly vulnerable to human activities.
As identified by DA, box and whisker plots of the selected parameters showing temporal trends are demonstrated in
Figure 3. Significant change occurs in all the 3 parameters. NH
4-N varies from 3.14 to 19.20 mg/L in the rapid-flow period and 1.87–16.30 mg/L in the gentle-flow period. Average value in the gentle-flow period (11.31 mg/L) is higher than that in the rapid-flow period (7.56 mg/L), which can be explained as in the gentle-flow period. The microbial degradation rate of nitrogen is limited by the lower temperature. TP varies from 0.71 to 2.87 mg/L in the rapid-flow period and 0.57–1.63 mg/L in the gentle-flow period. Average value in the rapid-flow period (1.69 mg/L) is higher than that in the gentle-flow period (1.15 mg/L). Different kinds of non-point source pollution (including phosphorous pollutant) flow into the river course along with rainfall runoff in the rapid-flow period, and a higher temperature in the rapid-flow period may enhance the sediment phosphorus release [
40]. For BOD
5, the average value in the gentle-flow period (12.14 mg/L) is higher than that in the rapid-flow period (7.60 mg/L). Studies have shown a significant correlation between BOD
5 and Chl-a. While BOD
5 peaks, algal blooms at the greatest level at the same time. Scarce water volume, insufficient water power, and higher nutrient concentrations can all enhance the respiration of phytoplankton, finally resulting in higher BOD
5 in the gentle-flow period.
Affected by natural conditions and human activities, the variation of water quality in urban river courses may have a unique pattern of temporal variation; this paper shows that different study objects may lead to different seasonal variation ranges. Pejman [
11] indicated that the natural parameters (temperature and discharge), the inorganic parameter (total solid) and the organic nutrients (nitrate) were the most significant parameters contributing to water-quality variations for all seasons. Prakash Raj Kannel’s partition was based on precipitation duration (pre-monsoon season and post-monsoon seasons), high level of BOD
5, COD and low DO indicated that the level of organics was higher in the pre-monsoon season, while during post-monsoon season, the main pollutants were nutrients [
41].
3.2.2. Spatial Variations
The dendrogram of spatial HCA is shown in
Figure 2b; eight sampling stations can be grouped into three statistically meaningful clusters at (D
link/D
max)*100 < 18. From the site location and distribution of supplement sites in
Figure 1, these three clusters correspond to up-, middle and down stream of the reach which is filled with reclaimed water. Three sites from Cluster 1 (site 1, site 6 and site 7) comprise the closer sites to drainage channels. Cluster 2 (site 2 and site 3) corresponds to moderate distance sites. Cluster 3 (site 4, site 5 and site 8) makes up the farthest ones.
Spatial FCA is shown in
Table 2, upstream of the reach has the largest membership degree to level V (0.982), followed by middle-stream (0.969) and downstream (0.960). The results show that water pollution has a slightly decrease trend when it travels from the upper stream to downstream.
As shown in
Table 6, standard mode DA yields 64.4% accuracy, which means spatial variation exists in TSBR, but is not as obvious as the temporal variation. Only one parameter (NO
3-N) is selected by stepwise DA with the accuracy of less than 52.0%, which indicates the fact that only one parameter cannot be enough in describing spatial variation of water quality. Therefore, the “F” value is changed to improve the determination accuracy. With “F = 2” in the arithmetic [
42], three parameters are identified, and the determination accuracy rises to 64.2% (69.6%, 64.3% and 56.3% for up, middle and downstream, respectively).
NO3-N (Sig. < 0.01) and DO (Sig. < 0.05) are correlated to spatial variations through SCA. Relatively fewer parameters in spatial SCA indicate that there is lack of dissimilarity in spatial scale; all the three river-courses may have a similar pollution condition. According to the results from SCA and DA, TN and Chl-a can be determined by DA but not SCA, which means the variation of these two parameters only exist in specific parts of the river course. DO is significant in SCA but not in SCA, and loss of discriminant ability may contribute to the unset distribution of pollutants, which are caused by point source pollution.
Box and whisker plots of the selected parameters of spatial trends are illustrated in
Figure 4. From the point of variation amplitude, NO
3-N varies between 1.33 and 15.18 mg/L at upstream, 4.62–12.30 mg/L at middle-stream, and 1.20–9.65 mg/L at downstream, the great variation means an uneven distribution in different parts of the river course.TN has the greatest variation at up-stream. For Chl-
a, the greatest variation appears at down-stream. From comparison between average values in the three parts of the river course, NO
3-N does not show a dramatic variation, as well as TN and Chl-
a, which further explains the low accuracy in spatial DA. As the main sewage drainage river course, TSBR is largely affected by domestic wastewater. Automatically, the high NO
3-N, which is the degradative product of nitrogen organic matter by microbial action, is mainly from domestic wastewater [
43]. Besides, TN, Chl-
a are infected by the fact that factories along the river course do not have obvious geographical characteristics or discharge frequency.