This study addresses the limited understanding of factors affecting the efficiency of water treatment plants in reducing trihalomethane (THM) formation through total organic carbon (TOC) removal, highlighting significant challenges in improving treatment effectiveness. The aim of this study was to examine the influence of water quality on the efficiency of water treatment plants to remove TOC and reduce THM formation. Linear regression and correlation analyses were conducted to examine the relationship between water quality parameters and THM concentrations. The results showed that there was a negative relationship between turbidity, metals, and TOC concentration with TOC removal efficiency. Positive correlations were found between parameters and the formation of THMs in water. Of these parameters, water temperature was observed to have relatively less influence on THM formation. It was observed that seasonal variations in water quality affect the efficiency of TOC removal and THM content in treated water. THM levels in chlorinated water were found to be within the permissible range of the World Health Organization's drinking water quality guidelines. However, it is still important to maintain continuous monitoring and take measures to reduce THMs. The model demonstrated a strong correlation (R2 = 0.906) between predicted and measured THM values.

  • The variability of water sources’ qualities such as turbidity, water temperature, metallic concentration, and total organic carbon strongly affects the performance of the water treatment plant in removing total organic carbon, and this leads to an increase in trihalomethane formation and variation in chlorinated drinking water supply.

  • The model developed from the analysis of water quality parameters accurately predicts the variation and formation of THMs (trihalomethanes) in drinking water.

Ensuring safe and affordable drinking water is vital for public health and human wellbeing. Access to clean drinking water not only serves as a barrier against the spread of waterborne diseases caused by microorganisms found in water sources but also plays a critical role in ensuring the long-term availability of water resources for the wellbeing of future generations (Carvalho et al. 2019). Although surface water is the main source of drinking water after treatment processes, the problem of water pollution remains a major challenge with widespread impacts on human wellbeing and ecosystems (Zularisam et al. 2010; Yimer & Geberkidan 2020; Assegide et al. 2022a).

In developing countries such as Ethiopia, chlorine disinfectants are often used in water treatment to reduce the risk of pathogenic infections. However, the reaction between chlorine and natural organic substances can result in the creation of potentially harmful disinfection by-products such as trihalomethane (THM), haloacetic acids, and haloacetonitriles (Luo et al. 2020; Mazhar et al. 2020). THMs are the most common disinfection by-products in chlorinated drinking water, and long-term exposure to high levels of THMs is associated with various health risks, including bladder cancer, and damage to vital organs and the immune system (Chen et al. 2019; Chaukura et al. 2020).

Most surface water sources such as rivers, lakes, and streams are highly susceptible to contamination from various sources, including industrial wastewater, agricultural runoff, and urban runoff. These different types of wastewater introduce a variety of pollutants into water and pose significant risks to human health and the environment. The aforementioned pollutants, which include organic carbon, have the potential to participate in the production of THM when present in chlorinated water sources (Sharma et al. 2021; Ghanadi et al. 2023). In addition, the reaction between natural organic carbon and metals in water sources leads to the formation of organometallic complexes that can significantly affect the water treatment processes and efficiency of the plant (Reddy et al. 2012; Iloms et al. 2020).

The Awash River, a vital water source for urban and rural communities in Adama City, Ethiopia, is facing pollution problems due to the discharge of industrial, urban, and agricultural wastes (Assegide et al. 2022a, 2022b; Namara et al. 2022). The uncontrolled discharge of metals and natural organic matter (NOM) into the river poses a serious threat as it leads to the formation of complex compounds that significantly reduce the effectiveness of water treatment plants in removing NOM (Wang et al. 2021; Xu et al. 2022).

Despite these concerns, there is lack of a comprehensive research that examines the relationship between Awash River water quality, the effectiveness of the Koka water treatment plant in organic carbon removal, and the formation of THMs in Adama City's drinking water supply. Previous studies have mainly focused on the effects of common factors such as NOM, temperature, chlorine dosage, and residence time on THM formation, without considering the variability of contaminants in water sources (Sikder et al. 2023). Laboratory-scale experiments have been the primary basis for studying THM formation after treatment, but they may not fully capture real-world conditions in water treatment plant processes (Sathasivan et al. 2020; Han et al. 2023). In addition, improving coagulation processes to improve organic carbon removal poses potential risks related to increased levels of aluminum sulfate, which may increase the risk of developing Alzheimer's disease (Krupińska et al. 2019; Krupińska 2020).

The limited understanding of the factors affecting the performance of water treatment plants in reducing THM formation by removing TOC poses significant challenges in improving treatment efficiency. These gaps pose significant challenges in developing appropriate and effective water treatment strategies to improve drinking water quality. It is critical to address these limitations to develop targeted water treatment strategies aimed at reducing THMs in chlorinated drinking water. Given these concerns, there is an urgent need for further research on the effects of water quality parameters on THM formation and variation in conventionally treated drinking water. Thus, the objective of this study was to investigate the effects of water quality parameters and fluctuations on the TOC removal efficiency of the Koka water treatment plant and the reduction of THM formation in the chlorinated drinking water supply. The results of this study will provide reliable information for evidence-based decision-making and facilitate the development of water treatment strategies to mitigate THM formation in water supplies.

Water sources and sampling procedures

The Awash River serves as an important source of water for domestic and irrigation purposes. However, the river's water quality has been significantly affected by pollution resulting from industrialization, urbanization, and agricultural activities (Eliku & Leta 2018; Yimer & Geberkidan 2020; Assegide et al. 2022a). It serves as an important source of drinking water for the town of Adama and surrounding rural communities. The Koka water treatment plant is located approximately 100 km southeast of Addis Ababa in the Oromia region of Ethiopia (Figure 1) and plays a critical role in providing chlorinated drinking water to urban and rural areas. The treatment process includes coagulation, sedimentation, filtration, and disinfection. During coagulation, 30 mg/L aluminum sulfate is added to promote the settling of suspended solids. The treated water is then disinfected with 20–30 mg/L chlorine before being distributed via an extensive pipe network to the town of Adama and the surrounding rural communities.
Figure 1

Water sources and the water sampling site.

Figure 1

Water sources and the water sampling site.

Close modal

Extensive water sampling was conducted for a year, from early September 2022 to late August 2023, to investigate water quality parameters. The water sampling sites were selected based on geographic coverage and proximity to pollution sources. The water samples were collected from points such as Modjo and Awash Rivers before they entered Koka Lake, at the inlet and outlet of the Koka water treatment plant and Adama City drinking water, as shown in Figure 1. A total of 36 water samples were collected from various designated sample sites, ensuring a comprehensive, all-inclusive, and yet, diverse sampling strategy for accurate water quality assessment. The collection, transport, storage preparation, and analysis of the water samples were carried out based on established guidelines and protocol (Beutler et al. 2014). The treated drinking water sample for THM concentration analysis was taken from a point in Adama City with a long residence time history. THMs were extracted from water samples with pentane according to the procedures of Standard Methods.

Apparatus and reagents

The study used sophisticated analytical instruments including an inductively coupled plasma mass spectrometer, a gas chromatography-mass spectrometer (GC-MS), a total organic carbon (TOC) analyzer, a UV–Vis spectrometer, a filter membrane, and an electronic analytical balance with a remarkable sensitivity of 0.0001 (AA-200DS).

The experiment utilized various reagents and chemicals sourced from reputable suppliers. The certified THM standard solution with 99.9% purity was purchased from Supelco, a company based in Germany. Sodium sulfate, sodium hydroxide, pentane, potassium hydrogen phthalate, phosphoric acid, and standard solutions containing iron, manganese, aluminum, copper, chromium, and zinc were among the chemicals purchased from Sigma-Aldrich, a well-known chemical supplier. Working and calibration standard solutions were prepared from these ingredients.

Preparation of standard solution

The THM standard solution was prepared from certified reference materials of 200 μg/L. The organic carbon stock solution was prepared using 2.1254 g of anhydrous potassium phthalate (KC8H5O4). Working standards were then prepared by dilution from the prepared stock solution of known concentrations, following the procedure reported in the literature (Kumari & Gupta 2022). For the stock solution of metals and TOC (1,000 ppm), a specific amount of each standard was weighed and dissolved in deionized water. Following this, the working standards were prepared by diluting stock standard solutions of metals and TOC. Finally, the standard solutions were stored in brown glass bottles at a low temperature of −20 °C to further slowdown the degradation process.

Analytical methods

Various water quality parameters including turbidity, water temperature, and pH were measured using standardized methods and appropriate instruments. Turbidity levels, water temperature, and pH were determined using a turbid meter, a thermometer, and a pH meter, respectively (Lipps et al. 2023). The water quality parameters affecting THM formation were investigated with samples collected at the inlet of the Koka water treatment plant. The extracted THM was analyzed using Agilent Technologies gas chromatograph (GC-MS) type 7890A single quad based on EPA Method 551.1 (Hautman & Munch 1997). The prepared water samples were analyzed for metals using atomic absorption spectroscopy and inductively coupled plasma mass spectrometry (iCAP™ 7600 ICP-OES) (Sadeghi et al. 2019). TOC and dissolved organic carbon were analyzed using a UV–Vis spectrophotometer (Agilent Technologies 60 UV-Vis spectrophotometer) at 254 nm in 1 cm quartz cells according to the USEPA 415.3 standard method (Potter & Wimsatt 2012). A comprehensive mathematical model was developed to predict THM levels in Adama's drinking water supply, accounting for variables such as water temperature turbidity, metals concentration, and TOC.

Validation and quality control

To ensure the accuracy and reliability of the analysis results, validation and quality control measures were implemented. This included evaluating the method parameters in terms of accuracy and detection limit using standard solutions as well as calculating statistical parameters such as mean and standard deviations. The concentrations of metals, TOC, and THM in spiked and unspiked water samples were subjected to mean, standard deviation, and other statistical calculations. The analytical procedure implemented a rigorous approach to determine the lower limits of detection for four THM species. The method employed ensured a detection limit of 0.3 μg/L for each of the THM species analyzed. The extraction and calibration curves were validated by preparing THM samples at 40, 60, and 140 μg/L to ensure that they were within ±10% of their known concentrations. This included the use of blank samples, calibration standards, and replicate samples. The goodness of fit of the regression model was assessed using metrics such as the coefficient of determination (R2), adjusted R2 and p-values, and the mean square error. The significance level (p ≤ 0.05) was used to determine the statistical significance of the model.

Statistical analysis

To examine the association between the formation of THMs and various predictor variables, correlation, and regression analyses were used. Utilizing the multiple linear regression (MLR) model, the concentration of THMs was predicted using independent variables including water temperature, turbidity, metal concentration, and TOC. In addition, the statistical regression analysis was performed to observe the impact of independent variables on the effectiveness of the water treatment plant in TOC removal. The performance of the MLR in predicting the concentration of THMs was assessed through R2 and comparative analysis of predicted versus actual values.

Developing a predictive model for THM formation

A predictive model for THM formation was developed utilizing parameters such as water temperature, turbidity, concentration of metals, and TOC. MLR was employed as the statistical tool to develop the predictive model and identify the most significant factors affecting THM formation following the methodology outlined by Tsitsifli & Kanakoudis (2020). To ensure the validity and reliability of the predictive model for THM formation, extensive testing was performed to evaluate model assumptions, including linearity, homoscedasticity, and multicollinearity.

Water quality analysis

The results of the study presented in Table 1 provide the average analytical results of the water samples. It includes various parameters such as metal concentration, water temperature, turbidity, pH, and UV absorption. Analysis revealed that the concentrations of metals, particularly Fe, Mn, Cr, Cu, Zn, and Al, were elevated both in the upper reaches of the Awash River and in the corresponding drinking water samples. This finding strongly suggests that the presence of metals is directly attributable to the combined effects of industrial, urban, domestic, and agricultural runoff (Yimer & Geberkidan 2020). The results showed that Modjo and Awash Rivers had higher metal concentrations, higher pH, higher turbidity, higher water temperature, and higher UV absorption before their convergence with Koka Lake. On the contrary, when the Awash River flows into Koka Lake, the average values of the aforementioned parameters decrease due to dilution effects and the absence of industrial activities after Koka Lake. In addition, the pollutants present in water showed a slight decrease after passing through the Koka water treatment plant. The observed reduction in pollutants during the sedimentation and filtration treatment stages indicates the effectiveness of these treatment processes. This finding aligns with the research conducted by García-Avila et al. (2021) which similarly demonstrated that conventional water treatment methods play a crucial role in improving the overall quality of drinking water by reducing pollutants. In conclusion, the results showed that the average values of parameters such as metals, pH, and water temperature met drinking water quality standards. In addition, it is important to note that the concentration of THM in drinking water remains within the permissible limit set by the World Health Organization (WHO 2022). This shows that the water supply meets the recommended standards for THM levels, ensuring the safety and quality of drinking water.

Table 1

The average values of sources’ water quality analysis

LocationFe (μg/L)Mn (μg/ L)Cr (μg/L)Cu (μg/L)Zn (μg/L)Al (μg/L)Turbidity (NTU)pHUV (cm−1)Temperature (°C)THMs (μg/L)
Modjo river 2,320 1,210 1,850 1,120 2,100 279 231.00 8.5 0.344 22.5 a 
Koka lake 1,100 980 760 1,100 1,000 770 143.00 7.8 0.216 21.4 a 
Awash before Koka 3,450 242 3,100 2,410 2,200 3,640 215.00 8.7 0.322 22.3 a 
Awash at treatment plant 2,900 1,935 1,335 1,950 2,120 3,210 141.87 8.5 0.214 21.6 a 
Drinking water 1,012 990 1,200 990 1,550 1,380 11.22 7.7 0.085 20.3 127.3 
LocationFe (μg/L)Mn (μg/ L)Cr (μg/L)Cu (μg/L)Zn (μg/L)Al (μg/L)Turbidity (NTU)pHUV (cm−1)Temperature (°C)THMs (μg/L)
Modjo river 2,320 1,210 1,850 1,120 2,100 279 231.00 8.5 0.344 22.5 a 
Koka lake 1,100 980 760 1,100 1,000 770 143.00 7.8 0.216 21.4 a 
Awash before Koka 3,450 242 3,100 2,410 2,200 3,640 215.00 8.7 0.322 22.3 a 
Awash at treatment plant 2,900 1,935 1,335 1,950 2,120 3,210 141.87 8.5 0.214 21.6 a 
Drinking water 1,012 990 1,200 990 1,550 1,380 11.22 7.7 0.085 20.3 127.3 

aNot observed.

Furthermore, the observations revealed that the Awash and Modjo rivers have a significant impact on the pollution of the water source before mixing with Lake Koka. However, after the mixing process, there was a gradual change in turbidity, metal concentration, pH, UV radiation, and temperature due to the dilution effect. Recognizing the variability of contaminants over time and under different environmental conditions in water sources emphasizes the importance of continuous monitoring of these sources and the improvement of treatment processes (Wijesiri et al. 2018). This is essential for ensuring the provision of clean water and mitigating the potential risks associated with the impacts of water sources variability in pollutant levels.

Effects of water quality parameters

Effects of water temperature

The correlation coefficient demonstrated the strength and direction of a linear relationship between water treatment plants to remove TOC and THM formation and water temperature. The data presented in Table 2 show a slight correlation between raw water temperature and THMs in chlorinated drinking water from Adama City. Furthermore, the results clearly showed that changes in water temperature have an impact on the water treatment plant's ability to effectively remove TOC. Correlation analysis indicated that the effect of water temperature on THM formation is relatively limited, with the relationship represented by a correlation coefficient (R2 = 0.331) and a statistically significant p-value (≤0.05). However, it is important to consider that while water temperature plays a noticeable role in THM formation, other factors may have a greater influence.

Table 2

Regression analysis of raw water quality and THM formation

MonthsSampling frequencyWater temperature (°C)Turbidity (NTU)Metals (μg/L)TOC (mg/L)TOC removal eff. (%)THMs (μg/L)
September 21.00 120.56 1,297.54 17.50 62.49 106.21 
October 22.00 176.02 2,209.15 19.60 61.01 126.60 
November 22.30 134.76 2,816.89 21.00 60.03 140.20 
December 22.00 161.96 3,250.99 22.00 59.33 149.91 
January 23.40 186.32 3,685.09 23.00 58.62 159.62 
February 21.30 139.86 2,165.74 19.50 61.08 125.63 
March 23.00 137.01 2,816.89 21.00 60.03 140.20 
April 19.50 144.98 1,948.69 19.00 61.43 120.78 
May 24.00 153.61 3,250.99 22.00 59.33 149.91 
June 20.00 117.41 1,514.59 18.00 62.14 111.07 
July 19.00 118.85 1,080.49 17.00 62.84 101.36 
August 21.50 111.14 863.44 16.50 63.19 96.50 
Average 21.58 141.87 2,241.70 19.68 60.90 127.30 
MonthsSampling frequencyWater temperature (°C)Turbidity (NTU)Metals (μg/L)TOC (mg/L)TOC removal eff. (%)THMs (μg/L)
September 21.00 120.56 1,297.54 17.50 62.49 106.21 
October 22.00 176.02 2,209.15 19.60 61.01 126.60 
November 22.30 134.76 2,816.89 21.00 60.03 140.20 
December 22.00 161.96 3,250.99 22.00 59.33 149.91 
January 23.40 186.32 3,685.09 23.00 58.62 159.62 
February 21.30 139.86 2,165.74 19.50 61.08 125.63 
March 23.00 137.01 2,816.89 21.00 60.03 140.20 
April 19.50 144.98 1,948.69 19.00 61.43 120.78 
May 24.00 153.61 3,250.99 22.00 59.33 149.91 
June 20.00 117.41 1,514.59 18.00 62.14 111.07 
July 19.00 118.85 1,080.49 17.00 62.84 101.36 
August 21.50 111.14 863.44 16.50 63.19 96.50 
Average 21.58 141.87 2,241.70 19.68 60.90 127.30 

Furthermore, the analysis of water temperature's effects on the efficiency of TOC removal in the Koka water treatment plant resulted in a correlation coefficient of R2 = 0.187 and a statistically significant p-value of 0.01. These findings suggest that the impact of water temperature on TOC removal is relatively small. Therefore, it becomes vital to take into account other relevant factors to optimize the water treatment process and achieve the desired level of TOC reduction.

Previous studies by Ramavandi et al. (2015) and his research group have consistently shown that higher temperatures can accelerate the chemical reactions involved in THM formation. However, an interesting finding challenged the common assumption that higher temperatures after chlorination lead to increased THM levels. Instead, it was observed that higher temperatures actually improved TOC removal, resulting in a reduction of THM formation during the chlorination process (Lee & Lee 2015; Chu et al. 2016). These results emphasize the significant influence of water temperature on TOC removal efficiency in the Koka water treatment plant, surpassing the impact on THM formation after chlorination in the treated drinking water.

Effects of raw water turbidity

The results presented in Table 2 indicate a clear relationship between higher levels of raw water turbidity and reduced TOC removal efficiency at the Koka water treatment plant. Analysis revealed a negative correlation (R2 = 0.373) and a statistically significant p-value (<0.05) between raw water turbidity and TOC removal efficiency in the water treatment plant. These results indicate that higher turbidity levels in raw water are associated with reduced TOC removal efficiency during the treatment process. Consequently, this leads to increased concentrations of THMs in the chlorinated drinking water supply.

Interestingly, a positive correlation (R2 = 0.609) and a statistically significant p-value (<0.005) were observed between raw water turbidity and THM formation in the drinking water of Adama City after chlorination. This highlights the significant influence of turbidity on THM formation and underlines the crucial importance of monitoring and controlling the turbidity levels to mitigate THM formation in drinking water.

Previous studies have indicated that higher turbidity in raw water may require a higher coagulant dosage and longer flocculation times during the treatment process to effectively remove NOMs (Ramavandi et al. 2015). However, it is important to consider potential health risks associated with the use of higher dosages, such as those related to Alzheimer's disease (Klotz et al. 2017). These findings highlighted the critical significance of efficiently managing raw water turbidity to address concerns regarding THM formation and to ultimately ensure the safety of the treated water supply.

Furthermore, the study demonstrated that the significant association between the discharge of industrial, urban, and agricultural wastes into the Awash River during dry seasons was characterized by reduced dilution and the resulting increase in turbidity levels. This highlights the need for effective management and control of waste discharges to mitigate turbidity-related issues in water sources.

Effects of metal concentrations

Table 2 presents the impact of varying metal concentrations in the source water on the overall performance of the Koka water treatment plant in eliminating TOC and the formation of THMs during the chlorination process. The Awash Basin, known for its industrial, urban, and agricultural activities, faces the challenge of discharging metal-bearing wastes directly into the Awash River (Assegide et al. 2022a). Analysis showed a strong positive relationship between metal concentration in the source water and THM content in chlorinated drinking water, indicated by a high correlation coefficient of R2 = 0.832 and a p-value below the significance threshold (p-value ≤ 0.05). These results highlight the influential role of metal contaminants in raw water, which directly affect the formation of THMs during the chlorination process. In addition, the study results showed that the presence of metals in raw water has a significant impact on the effectiveness of the water treatment plant in removing TOC. This is evident from the correlation coefficient R2 = 0.716 and the statistically significant p-value (p ≤ 0.05). The results show that higher concentrations of metals, including Fe, Zn, Cr, Al, Mn, and Cu, in raw water negatively affect the TOC removal efficiency of the water treatment plants. The presence of high concentrations of metals in raw water leads to the formation of complexes with organic materials, resulting in larger and more stable particles that are resistant to the coagulation process (Moni et al. 2010).

This poses challenges to the effectiveness of coagulation processes in removing TOC and results in elevated levels of THMs in chlorinated drinking water supplied to Adama City.

This is similar to the findings of other researchers who have observed that metals compete with coagulants for binding sites on organic matter, resulting in reduced effectiveness of coagulants in removing organic matter (Kim & Jang 2017; Pontoni et al. 2021; Karwowska et al. 2022). These results highlight the need to consider metal concentration in raw water as a critical factor in the water treatment process to improve THM reduction and TOC removal, thereby improving the overall quality of treated drinking water.

Effects of TOC concentration

This was to examine the relationship between TOC concentrations in Awash River water at the inlet of the Koka water treatment plant and THM formation in chlorinated drinking water. In addition, the effects of TOC concentration on the efficiency of the water treatment plant to remove TOC itself were observed. The results presented in Table 2 show a strong negative correlation between TOC concentration and the efficiency of the water treatment plant in removing TOC. This correlation was supported by a high coefficient of determination (R2 = 0.856) and a statistically significant p-value (p ≤ 0.05), indicating a reliable relationship between these variables. This suggests that higher TOC concentrations in raw water pose a challenge to the water treatment plant's ability to remove TOC during the water treatment process.

On the other hand, a positive correlation was observed between the concentration of THMs in chlorinated drinking water and TOC levels, with the coefficient of determination (R2 = 0.823) and the significant p-value (p ≤ 0.05) (Kumari & Gupta 2015; Enrique et al. 2022). In addition, the study highlighted that the higher concentrations of TOC during the water treatment process require higher dosages of coagulants to respond to the negative charges of organic compounds and facilitate their aggregation. Therefore, this finding highlights the significant influence of TOC as the primary factor affecting the removal of organic matter and the potential formation of THMs in treated water (Liu et al. 2022). Higher TOC concentrations in water result in an increased number of negative charges, primarily due to the presence of carboxylic and alcoholic hydroxyl groups. Therefore, a higher dosage of coagulants is required to provide a sufficient number of positive charges that can neutralize the increased number of negative charges associated with organic compounds (Chow et al. 2009; El-taweel et al. 2023). As a result, the presence of higher TOC levels in the Awash River negatively impacts the efficiency of the Koka water treatment plant in removing TOC and reducing THM levels in chlorinated drinking water.

Effects of seasonal variation

In Ethiopia, the climatic conditions are characterized by a dry season, which typically lasts from October to May, and a rainy season, which lasts from June to September (Fazzini et al. 2015). The study's findings, as presented in Table 2, highlight the relationship between seasonal variation and the formation of THMs in drinking water, as well as their impact on TOC removal efficiency. The findings of the study revealed that the TOC removal efficiency of the treatment plant was 59.19% during the dry season and 66.25% during the rainy season. Despite these removal rates, THM levels in drinking water exhibited seasonal fluctuations. During the dry season, the measured THM concentration was 136.6 μg/L, while during the rainy season, it was measured at 108.7 μg/L, both of which are within the permissible limit of the WHO.

The results of this study contradict the previous study that linked higher THM precursor concentrations with rainy seasons. A higher water flow during the rainy season led to higher turbidity levels, which in turn reduced the overall efficiency of TOC removal (Namara et al. 2022). However, as the rainy season progressed, water flow diluted suspended particles, decreasing turbidity and increasing the treatment plant's efficiency, consequently reducing THM formation (Woods et al. 2015; Dong et al. 2022; Zhou et al. 2022).

On the other hand, the slow flow rate during the dry season increased the residence time, resulting in higher turbidity and TOC formation. This extended residence time provided ample opportunity for the decomposition of organic materials, leading to higher turbidity and subsequent TOC formation (Pivokonsky et al. 2021; Wang et al. 2023). The prevention of dense algal blooms during the rainy season played a crucial role in reducing TOC production, thus resulting in comparatively lower THM formation in chlorinated water after treatment. Thus, seasonal variations in the Awash River have a notable impact on the efficiency of TOC removal by the water treatment plant and the levels of THMs in the chlorinated drinking water supply. Therefore, it is important to consider the complex interaction between seasonal fluctuations, raw water quality, and treatment processes to ensure safe drinking water for the public.

Model development and validation

This is used to identify the most significant independent variables that have an impact on THM concentrations, optimize treatment processes, and guide decision-making regarding source water management. Water temperature, turbidity, metal concentration, and TOC were among the variables studied. To develop an effective prediction model, stepwise regression analysis was used. In this analysis, the multicollinearity assumption was carefully examined. The results showed that the TOC variable was found to have a strong correlation with THM formation (Liu et al. 2022). On the other hand, it was more correlated with other independent parameters, which resulted in its exclusion from the analysis. As a result, the remaining variables such as water temperature, turbidity, and metal concentration, were found to be the most important predictors of THMs. The coefficient of determination (R2 = 0.906) indicates the strong relationship between the variables of water temperature, turbidity, and concentration of metals in relation to THM concentrations. Additionally, the p-values for these variables falling below the predetermined significance level of 0.05 further support their significance in influencing THM concentrations. These findings highlight the importance of considering water temperature, turbidity, and metal concentrations as significant factors affecting THM formation (Kelly-Coto et al. 2022). The results of the study provided convincing evidence of the reliability and effectiveness of the mathematical model as a reliable tool for accurately predicting THM levels. In addition, the results highlighted how temperature, turbidity, and metal concentrations affect THM concentrations and highlighted the importance of these factors in determining and controlling water quality. In conclusion, Equation (1) represents the developed predictive model, which proves to be an invaluable tool for effectively monitoring and managing THM levels in the chlorinated drinking water supply of Adama City:
formula
(1)

The developed prediction model for estimating THM levels in drinking water supply was evaluated by comparing the calculated and predicted THM concentrations. A comparison was made between the calculated THM concentrations in drinking water and the predicted THM concentrations using the developed prediction model. The comparison results, presented in Table 3, demonstrate a strong correlation between the calculated and predicted THM concentrations. R2 for the predicted and calculated values was 0.906, allowing for the estimation of THM formation with an average deviation of 5.79%.

Table 3

Measured verses predicted THM concentrations

Water temp. (°C)Turbidity (NTU)Metal (μg/L)Calculated THMs (μg/L)Predicted THMs (μg/L)R2Deviation (%)
18.5 123.5 1,390.9 102.0 100.7  1.30 
23.0 126.5 1,418.5 107.8 116.5  −8.08 
21.5 125.0 1,400.6 117.2 112.5  3.99 
22.0 173.7 1,992.1 132.0 135.2 0.906 −2.41 
19.6 143.6 3,389.4 133.2 140.1  −5.17 
24.4 129.7 1,818.4 130.8 125.8  3.79 
22.6 137.4 2,165.8 132.1 128.7  2.61 
22.0 143.9 3,077.3 134.0 141.3  −5.46 
22.3 138.4 2,256.9 135.9 129.5  4.72 
22.9 155.4 3,347.1 148.5 150.1  −1.11 
23.3 150.0 3,498.2 151.6 151.5  0.05 
19.8 139.5 3,054.7 149.9 134.9  10.00 
23.1 149.1 3,381.2 156.1 149.3  4.37 
22.4 187.6 3,541.7 159.6 160.7  −0.66 
24.7 158.3 4,177.1 161.3 166.0  −2.93 
23.9 121.4 2,037.9 126.0 125.4  0.50 
20.2 143.5 2,275.0 127.3 126.5  0.64 
19.8 145.5 2,287.1 130.7 126.3  3.34 
22.2 159.1 2,518.9 139.5 138.6  0.67 
25.8 133.6 2,590.1 141.6 140.3  0.88 
21.0 129.7 2,691.0 141.9 130.0  8.41 
20.2 131.5 1,790.5 119.9 116.7  2.69 
18.7 138.4 2,210.7 120.2 120.9  −0.57 
19.6 136.3 1,998.8 119.9 119.5  0.36 
24.4 146.9 2,339.4 149.0 137.6  7.63 
24.6 154.7 3,598.3 151.6 157.1  −3.61 
23.0 148.4 4,262.3 152.4 160.6  −5.39 
21.1 123.6 2,215.7 106.5 122.1  −14.67 
17.6 119.4 2,003.8 113.0 110.4  2.33 
21.3 118.8 1,180.5 110.5 107.4  2.80 
19.6 120.4 806.0 99.5 99.1  0.42 
19.5 121.7 856.5 97.6 99.9  −2.35 
17.9 123.9 1,037.5 99.9 99.4  0.51 
22.5 120.0 677.8 96.1 103.7  −7.89 
19.0 120.5 702.5 88.7 96.4  −8.68 
23.0 118.0 719.7 106.2 104.8  1.33 
Water temp. (°C)Turbidity (NTU)Metal (μg/L)Calculated THMs (μg/L)Predicted THMs (μg/L)R2Deviation (%)
18.5 123.5 1,390.9 102.0 100.7  1.30 
23.0 126.5 1,418.5 107.8 116.5  −8.08 
21.5 125.0 1,400.6 117.2 112.5  3.99 
22.0 173.7 1,992.1 132.0 135.2 0.906 −2.41 
19.6 143.6 3,389.4 133.2 140.1  −5.17 
24.4 129.7 1,818.4 130.8 125.8  3.79 
22.6 137.4 2,165.8 132.1 128.7  2.61 
22.0 143.9 3,077.3 134.0 141.3  −5.46 
22.3 138.4 2,256.9 135.9 129.5  4.72 
22.9 155.4 3,347.1 148.5 150.1  −1.11 
23.3 150.0 3,498.2 151.6 151.5  0.05 
19.8 139.5 3,054.7 149.9 134.9  10.00 
23.1 149.1 3,381.2 156.1 149.3  4.37 
22.4 187.6 3,541.7 159.6 160.7  −0.66 
24.7 158.3 4,177.1 161.3 166.0  −2.93 
23.9 121.4 2,037.9 126.0 125.4  0.50 
20.2 143.5 2,275.0 127.3 126.5  0.64 
19.8 145.5 2,287.1 130.7 126.3  3.34 
22.2 159.1 2,518.9 139.5 138.6  0.67 
25.8 133.6 2,590.1 141.6 140.3  0.88 
21.0 129.7 2,691.0 141.9 130.0  8.41 
20.2 131.5 1,790.5 119.9 116.7  2.69 
18.7 138.4 2,210.7 120.2 120.9  −0.57 
19.6 136.3 1,998.8 119.9 119.5  0.36 
24.4 146.9 2,339.4 149.0 137.6  7.63 
24.6 154.7 3,598.3 151.6 157.1  −3.61 
23.0 148.4 4,262.3 152.4 160.6  −5.39 
21.1 123.6 2,215.7 106.5 122.1  −14.67 
17.6 119.4 2,003.8 113.0 110.4  2.33 
21.3 118.8 1,180.5 110.5 107.4  2.80 
19.6 120.4 806.0 99.5 99.1  0.42 
19.5 121.7 856.5 97.6 99.9  −2.35 
17.9 123.9 1,037.5 99.9 99.4  0.51 
22.5 120.0 677.8 96.1 103.7  −7.89 
19.0 120.5 702.5 88.7 96.4  −8.68 
23.0 118.0 719.7 106.2 104.8  1.33 

The close agreement between the predicted and calculated values affirms the accuracy and reliability of the model in estimating THM levels in Adama City's chlorinated drinking water supply. The developed prediction model proves its effectiveness, providing reliable estimates of THM levels.

Inadequate understanding of the factors affecting the efficiency of the conventional water treatment systems in removing TOC and reducing the formation of THMs in drinking water poses a significant challenge in improving the efficiency of the water treatment processes. The efficiency of the Koka water treatment plant in removing TOC and reducing THMs in drinking water was found to be significantly affected by factors such as metal concentration, turbidity, water temperature, TOC concentration, and seasonal variations. Considering that, the influence of these factors and their possible interactions are of paramount importance to achieve efficient TOC removal and to effectively reduce the formation of THMs in the Koka water treatment plant. This study found that the significant negative correlation between turbidity, metals, and TOC concentration in raw water affects the Koka water treatment plant's efficiency in TOC removal. The developed model can be used to inform decision-makers about appropriate measures to control THM levels and maintain safe drinking water quality for the community. To effectively address the challenges faced by the Koka water treatment plant, this study strongly recommends the implementation of alternative treatment methods, particularly aimed at improving turbidity, as well as metal and TOC removal, prior to the chlorination process.

The authors acknowledge Addis Ababa University for providing access to their laboratory facilities, which played a crucial role in the successful completion of this study. In addition, the authors would like to acknowledge the Chemistry Department of Haramaya University for providing access to their laboratory equipment. The authors sincerely thank the Adama City Water supply staff for their invaluable assistance in onsite sampling and onsite measurements, which contributed significantly to the successful completion of this study. Furthermore, the authors would like to thank all other individuals and organizations who provided support and resources throughout the research process.

EA contributed to the conceptualization, investigation, methodology, writing, and review. AJ contributed to formal analysis, review, and editing. EM contributed to the conceptualization, methodology, and review. MD contributed to the conceptualization and methodology. ET contributed to the conceptualization, methodology, editing, and review. All authors have read and agreed to the published version of the manuscript.

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

The authors declare there is no conflict.

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