The physical-chemical and biological analyses of the collected samples, which are considered in the WQI calculation, are represented in Figure 5. Comparing the data with the Maximum Values Allowed by CONAMA nº 357 (2005), it was possible to observe that for the phosphate, total solids, temperature and pH parameters, the data are by the values established by CONAMA.
The Nitrate concentration values indicate that the water body is classified as Class 1. However, the data for Thermotolerant Coliforms showed a wide range of values, with some samples exceeding the maximum limit for Class 3, which is 2500 UFC.100mL-1. On the other hand, some samples showed no detectable presence of bacteria.
The Biochemical Oxygen Demand (BOD) showed varying ranges of values, placing the river's source in Class 2. However, downstream, the BOD value exceeded the Class 3 threshold. The Turbidity values obtained were classified as Class 1, with only one sample having a turbidity level classified as Class 2.
With the data and parameters selected, the diagnosis of the water body would be incomplete, only suggesting that the minimum Class that the river would be classified as Class 4 due to the variation in some data related to Thermotolerant Coliforms. This diagnosis would be considered in Effluent Discharge Licenses, allowing for a more significant load of pollutants. For this reason, using WQI makes it possible to represent the water body's quality better.
The results for each parameter were used to calculate the WQI and were organized according to sampling points and collection date (Figure 6). In some cases, the measured value was below the detection limit of the methodology used. In these cases, the minimum detection values were defined as the parameter's value in that sample to enable the calculation of the quality index. For the variable BOD5.20, the minimum detection limit was 2.0 mg.L-1. In some samples, the concentration was undetectable, as it was below the cited value, and the minimum value of 2.0 mg.L-1 was considered. as sample data.
The results of the physicochemical and biological analyses were statistically evaluated. They did not present a Gaussian distribution, as expected, as they are accurate environmental data influenced by climate, rainfall, temperature, consumption, and disposal of domestic sewage.
When classifying the WQI, according to the ranges used by the NSF, it was possible to observe that most of the samples from the source of the river have good quality, while downstream, the quality is poor (Table 3).
Based on the average of the samples collected from that year, the water body can be classified as having average quality in the East and poor in the Downstream. Additionally, it was obtained that the WQI (Water Quality Index) values at the Source were approximately 42.5% higher than the values obtained at the Downstream. This numerical difference reveals the impact of human activity on reducing water quality.
Figure 7 illustrates a noticeable variation between the effluent and influent collected. It shows that the samples collected from the months had the lowest WQI downstream and the highest WQI upstream. In Figure 7(a) from October 2022 and Figure 7(b) from June 2022, a marked difference in color can be observed in the sample from the East and Downstream. This visible variation indicates contamination of the water body.
Table 3 WQI values and averages classified according to NSF.
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)
In Figure 8, we can see the amount of rainfall accumulated in the month before collection. The K-means Clustering method was used to group the months based on the amount of rainfall during dry and rainy periods, which relied on the Euclidean distance between them. November and December 2021 and January and February 2022 were identified as the rainy period, while the months between March and October 2022 were classified as the dry period. Statistical analysis using the Mann-Whitney test showed that the p-value between the two groups was 0.0084, indicating statistical significance.
The WQI value and the parameters that compose it were divided into rainy and dry groups in the months previously grouped and organized according to the upstream and downstream collection points.
Analyzing all associated variables in four groups: dry-upstream, dry-downstream, rain-upstream and rain-downstream, Kruskal-Wallis tests were recorded, and the p-values between the four groups are presented in Table 4.
After evaluating the results statistically, it was found that there is a significant difference between the WQI groups and some parameters like BOD, Dissolved Oxygen, and Turbidity. This difference can be attributed to the impact of rainfall on physical-chemical and biological parameters. As the number of rainy periods increases, the river's water volume also increases, which leads to the dilution of compounds and changes in their flow (Silva et al., 2008). The collection point also seems significant, indicating that human activities have caused a deterioration in water quality and reduced the support capacity of aquatic life. This justifies the values of Dissolved Oxygen concentration and Biological Demand for Oxygen consumption (Costa et al., 2020). Turbidity is also significant, which can be attributed to the discharge of domestic wastewater, leading to an increase in suspended solids and color content in the water body.
Table 4 Result of the p-value between the dry-upstream, dry-downstream, rain-upstream and rain-downstream groups of the Parameters and WQI of the Kruskal-Wallis test.
|
WQI
|
BOD5,20
|
DO
|
Nitrate
|
Total Phosphate
|
p-valor
|
0.0125
|
0.0432
|
0.0006
|
0.3507*
|
1.0000*
|
|
Temperature
|
Turbidity
|
pH
|
Total solids
|
Thermotolerant Coliforms
|
p-valor
|
0.1405*
|
0.0032
|
0.2119*
|
0.4714*
|
0.0881*
|
* p-value greater than 0.05, indicating statistical insignificance between the groups.
The parameters that did not reach statistical significance between the WQI groups were Nitrate, Total Phosphate, Temperature, pH, Total Solids and Thermotolerant Coliforms. Total Nitrate and Phosphate were statistically insignificant as the measured values were below the detection limit of the methodology used. Water temperature is related to natural phenomena, such as climate and local humidity, so it did not present statistical significance. The probable statistical insignificance of pH may be associated with temperature, influencing water autoprotolysis. Thermotolerant Coliforms were higher than downstream in most of the months evaluated, except in July, August and September 2022, indicating contamination of the source caused by the dry period. This dry period caused a loss of riparian vegetation, favoring the proximity of animals to the collection point where they carried out their physiological needs.
Throughout the sampling period, the water quality index (WQI) values ranged from a low of 33, recorded during a dry period at the downstream location, to a high of 81, recorded at the source in June. The month with the highest accumulated precipitation was January 2022, and during this period, a WQI value of 62 was recorded at the source. These details are further illustrated in Figure 7.
During the dry season, the water quality index (WQI) was generally higher in most of the samples taken upstream compared to downstream, except for the months of July, August, and September 2022, when there was low rainfall. This dry period caused an increase in contaminants in the water source. Additionally, animals were more likely to gather near the collection point to drink water during this period, further contributing to the contamination. Moreover, the water evaporation rate from the water body increased during this time, leading to a decrease in the water volume in the river. As a result, the concentration of pollutants in the remaining water increased.
During the studied period, it was observed that the region experienced uneven rainfall, with periods of both rain and drought. The water quality at the source of the São Gonçalo River was significantly better than downstream, highlighting the negative impact of effluent release from various sources along the river's course.