Variations and the relative importance of parameters affecting THM formation in the studied river sections
The average values of six parameters affecting THM formation in the studied water samples (DOC, pH, Water Temperature, UV254, Bromide, and Chlorine Demand) are provided in Table 1.
Table 1
Annual means of parameters affecting the concentration of THMs in the studied sites
Location
|
DOC (mg/L)
|
pH
|
Water Temp. (°C)
|
UV254 (1/cm)
|
Bromide (µg/L)
|
Chlorine Demand (mg/L)
|
Ahvaz2
|
0.84±0.15
|
7.92±0.07
|
24.98±5.75
|
0.016±0.004
|
195±18
|
1.14±0.43
|
Ahvaz3
|
0.97±0.14
|
7.64±0.13
|
23.58±5.75
|
0.015±0.003
|
252±37
|
1.14±0.26
|
Shoushtar
|
0.09±0
|
7.43±0.12
|
17.84±2.28
|
0.003±0.001
|
162±17
|
1.39±0.38
|
Mahshahr
|
2.11±0.19
|
7.98±0.12
|
24.29±5.24
|
0.033±0.003
|
283±30
|
0.55±0.23
|
Khorramshahr
|
1.44±0.32
|
7.87±0.14
|
25.04±5.57
|
0.026±0.009
|
292±100
|
0.64±0.27
|
Karoun River
|
1.09±0.69
|
7.77±0.24
|
23.15±5.57
|
0.02±0.01
|
236.8±71.39
|
1.87±0.88
|
Table 2 shows the relative importance of the input parameters (DOC, pH, Water Temperature, UV254, Bromide, and Chlorine Demand) for predicting the concentration of THMs based on Eq. 5. As can be seen, among the considered inputs, the factors most greatly affecting the concentration of THMs in Ahvaz Water Treatment Plant No.2, Ahvaz Water Treatment Plant No.3, the Shoushtar section, the Mahshahr section, the Khorramshahr section, and Karoun River as a whole are DOC, DOC, water temperature, Chlorine Demand, Bromide, and DOC, respectively.
Table 2
Relative importance (100%) of the inputs of the artificial neural network model for the concentration of THMs in the studied sites
Location
|
DOC
|
pH
|
Water Temp.
|
UV254
|
Bromide
|
Chlorine Demand
|
Relative importance %
|
Ahvaz2
Ahvaz3
Shoushtar
Mahshahr
Khorramshahr
Karoun River
|
18.81
30.09
4.54
23.63
19.33
32.76
|
18.75
16.03
14.86
5.85
14.46
12.87
|
14.34
9.05
38.12
23.08
10.25
12.64
|
17.87
8.62
21.15
11.55
10.63
13.67
|
11.52
13.46
6.2
5.5
23.84
10.08
|
18.69
22.72
15.1
30.36
21.46
17.98
|
The DOC level of Karoun River gradually increases from Shoushtar to Khorramshahr. The DOC level of Ahvaz is almost twice that of Shoushtar. Because of the discharge of municipal and industrial wastewater of Ahvaz into the Karoun River, the DOC level of the river almost doubles before it reaches the Mahshahr section. In Khorramshahr, the discharge of industrial wastewaters, particularly from soap-making plants and fish and shrimp farms, further increases the DOC water level. According to the results of Table 2 and the artificial neural network model, DOC is the factor with the most significant impact on THM concentration in Ahvaz water treatment plants No.2 and 3 and Karoun River as a whole and is among the most important determinants of this parameter in other sections particularly Mahshahr and Khorramshahr. Although the DOC level of Karoun is not high enough to justify control and removal strategies, the seasonal changes of the DOC level suggests that the river tends to exhibit higher average DOC levels during summer, when river discharge is markedly lower. Examining the changes in the UV254 absorbance of Karoun, it was found that this parameter rises and falls with the river’s DOC level. The results indicate that the only place where the amount of UV254 has a significant effect on THM concentration is the Shushtar water distribution network, where the THM level is so low that no control measures are required.
The only place where pH significantly affects the concentration of THMs is Ahvaz water treatment plant No.2. In general, THM concentration tends to increase with increasing pH. In a study by Kim et al., it was reported that the potential for THM formation increased with increasing pH, resulting in THM concentrations of 9.7, 20.7, and 41.6µg/l at pH levels of 5.5, 7, and 7.9, respectively [21]. A study by Liang and Singer has also shown that more THM tends to form at pH=8 than at pH=6 [3]. Some studies have reported a linear relationship between pH and the formation of THMs [2]. However, in our study, the effect of pH on the THMs concentration in the studied areas and Karun River was not significant in general, which could be due to reasonably low variations in water pH over the length of Karoun River and during each year.
As shown in Fig. 2, the concentration of THMs in the drinking water of the studied networks usually exceeds the recommended level in summer and at the same time as the water temperature rises. Water temperature is an uncontrollable factor dictated by environmental conditions. Since rising temperature greatly accelerates the decrease of residual chlorine in water, it is challenging to maintain a specific chlorine concentration in water distribution networks during the hot months of the year. High doses of chlorine should be used to ensure sufficient residual chlorine in the water [22]. According to Villanova et al. and Rodriguez et al., water temperature is one of the factors that significantly affect the formation of THMs in water [22, 23]. One study reported that the total THM concentration in three water distribution systems was 34.2, 35.5, and 35.7µg/l when water was more relaxed than 15°C and increased to 64.2, 40.6, and 60.8µg/l when the water had a temperature above 15°C [22]. Our results showed that water temperature had a notable impact on the formation of THMs in the Shoushtar and Mahshahr water distribution networks.
Examining the bromide ion concentration along Karoun, it was observed that this parameter also gradually increases from Shoushtar to Khorramshahr. The results showed an increase in bromide ion levels due to a gradual increase in water EC throughout the river. This increase is much more pronounced in the Khorramshahr section, where the bromide ion concentration increases averagely of three times as much as in the Ahvaz section. According to the results, the only place where the bromide ion concentration significantly affects THM concentration is the Khorramshahr water distribution network. One of the reasons for the high bromide concentration ion in the Khorramshahr segment of Karoun is its proximity to the Persian Gulf and the effects of the tides. Bromide ion is an inorganic precursor for the formation of disinfectant by-products. This ion is naturally present in the groundwater of coastal areas (because of the seawater seepage). In chlorinated water, bromide ions are oxidized by hypochlorous acid (HOCl), forming hypobromous acid (HOBr), which reacts with natural organic matter to form disinfectant by-products. Many studies have shown that the simultaneous presence of bromide and chlorine in a drinking water source during the chlorination process can lead to bromine and bromochlorine by-products [24–26].
In a study by Kampioti et al. on the Greek coastal city of Heraclion, they observed high concentrations of bromide ions in raw water (4.0-4.2mg/L). They reported that the bromine components of THMs were dominant over the chlorine components of disinfectant by-products in drinking water [27]. In the present study, the amount of residual chlorine was found to be the factor with the most significant effect on THM concentration in the Mahshahr water distribution network and also an essential determinant of this parameter in other places, including Ahvaz water treatment plants No.2 and 3, Khorramshahr, and Karoun River as a whole. The significance of the effect of free residual chlorine concentration on THM concentration in the studied water distribution networks is directly associated with the dose of chlorine used.
Modeling and prediction of THM concentration
The optimal numbers of hidden neurons in the artificial neural network model were determined by examining 5 to 15 neurons, and in each case, they were trained several times, and the results were compared in terms of MSE, RMSE and R2.
Figure 3 shows the error of the models with different numbers of neurons for all points. The best neuron has the lowest MSE and RMSE while having an R2 of greater than 0.9. As can be seen, the network tries to find the best weights for the connections coming from every input and going into every neuron. At some point, the model has obtained the best possible weights, while producing worse results with more significant errors with any further change in the weight matrix.
In this study, for all sampling points, the model inputs were six parameters affecting the concentration of THMs (including DOC, water temperature, pH, bromide ion concentration, UV254 absorbance, and residual chlorine content of water), and the model output was the concentration of THMs. Accordingly, the model was built with six neurons (six water parameters) in the input layer and one neuron in the output layer (simulate THM concentrations). The hidden neurons for the Shoushtar, Ahvaz 2, Ahvaz 3, Mahshahr, and Khorramshahr water distribution networks were 7, 13, 8, 7, and 7 neurons, respectively (fig .3). Neural network training was performed with 70% of the database to determine the best weights and biases, then 15% of the database was used to validate the model and the last 15% of the database was used to test the ability of the model to predict and simulate THM concentrations. The results of the testing of the developed artificial neural network for all sites are presented in Fig. 4.
Part A of Fig. 4 shows the relationship between predicted and measured THMs concentrations at all sites. The THM concentration values predict by the network for each sites are incredibly close to and highly consistent with the measurements made at those points. Accordingly, no difference was observed between the predicted and measured THMs concentrations.
This consistency is shown more clearly in Part B of the Fig. 4, where the values simulated by the network are plotted against the measured values. In this diagram, most points are close to the bisector line representing R2 = 1, indicating that R2 is more significant than 0.95 for all sites. This is a pretty desirable level of consistency for environmental data.
Part C of Fig. 4 shows the error of the simulated values relative to the measured concentrations. As can be seen, over 90% of the data have almost zero error, indicating a high level of accuracy. The high accuracy of the model is also reflected in Part D of Fig. 4, which shows the histogram of error values. As this diagram demonstrates, the error histogram has a normal-like distribution, with the data points being more frequently located around the zero error. This indicates the excellent performance of the neural network in modeling and predicting the concentration of THMs [8, 9, 11, 12].