Comparative analysis of water quality index based on seasonal differences in the upstream Citarum watershed

Water is essential to sustaining life, encouraging socioeconomic development, and supporting ecosystems. Furthermore, energy and food systems are intricately linked to water availability and quality. Therefore, it is important to ensure an adequate, secure, accessible, and available water supply for all parties (Fenner 2017; Perveen & Amar-Ul-Haque 2023; Kedida & Arsano 2024). One-third of the global population consumes surface water for domestic, agricultural, and industrial purposes, making water central to urban development. Clean and safe water is an important resource for improving and maintaining human health and well-being. Hence, it is essential to maintain the water quality to ensure its sustainable use in daily life for human activities (Romimohtarto & Juwana 2001; Gule et al. 2023; Kujiek & Sahile 2023).

Human population growth inevitably leads to increased water consumption. This sustained growth, coupled with the expansion of anthropogenic activities and increased climate variability, has exacerbated water quality degradation. The pollution level is expected to increase further, putting enormous pressure on already dwindling resources. As a result, water quality has become an increasingly important issue, as the quantity of water available for consumption often cannot be independently assessed for its quality (Boyd 2019; du Plessis 2022).

Assessing water quality in rivers and lakes is important for understanding pollution levels and overall water health. The water quality index (WQI) serves as a useful assessment method to evaluate water quality by converting various parameters into a single, comprehensive index (Tyagi et al. 2013; Wu et al. 2023). There are many types of WQIs that can be used to assess water quality, and each WQI has a different calculation formula, expert assessment, and location, leading to variations in the resulting indices (Fernandez 2014).

The Citarum River Watershed is designated as a national priority and identified as a critical watershed according to The National Medium-Term Development Plan for 2020–2024 (Sapan et al. 2022). The upstream area of the Citarum watershed, located in West Java Province, includes several administrative areas such as Bandung City, Cimahi City, Bandung Regency, West Bandung Regency, Sumedang Regency, and Garut Regency (Fadhil et al. 2021). The Citarum River supplies 80% of Jakarta's raw water needs and serves over 28 million people across Java Island (Fitriana et al. 2023). However, the water quality of the Citarum River has significantly deteriorated due to high pollutant loads stemming from human activities, such as agricultural, animal husbandry, fisheries, industry, and domestic waste activities (Utami 2019).

Records from the Ministry of Environment and Forestry in 2018 showed that 54% of the Citarum River water was classified as heavily polluted, 23% as moderately polluted, 20% as lightly polluted, and only 3% met the quality standards. Several other studies have further highlighted the river's water quality issues. For example, a 2017 study using the National Sanitation Foundation WQI (NSF-WQI) categorized the upstream Citarum River's water quality as poor to moderate, primarily due to pollution from surrounding activities. Research in 2023 observed worsening concentrations of ammonia (NH₃), biochemical oxygen demand (BOD), and chemical oxygen demand (COD), further exacerbating the river's water quality problems (Nufutomo & Muntalif 2017; Fitriana et al. 2023).

This research was conducted to achieve three objectives. First, it seeks to compare water quality parameters in the Upper Citarum Watershed with Class 2 water quality standards according to Government Regulation Number 22 of 2021. Second, it determines the water quality status of the Upper Citarum Watershed using three water quality indices, namely, NSF-WQI, Prati index, and Dinius index. Third, it compares the water quality classifications generated by these three indices to understand how each method represents the water quality status.

Water sampling for primary data is carried out based on the Indonesian National Standard (SNI) 6989.57:2008 concerning Water and Wastewater (Standar Nasional Indonesia 2008). In situ testing was carried out at the sampling sites to test physical–chemical parameters such as pH, dissolved oxygen (DO), and temperature. Additional parameters, such as conductivity, total dissolved solid (TDS), total suspended solid (TSS), NH₃, total phosphate (PO₄), BOD₅, COD, nitrate (NO₃), chloride, fecal coli, coliform, iron, manganese, turbidity, permanganate, alkalinity, hardness, and color were analyzed through ex situ testing in the laboratory.

Data were classified as ‘dry’ if the P value exceeded 50% and as ‘wet’ if the P value was less than 50%. Monitoring data were further categorized into two dimensions, namely, monitoring year data and monitoring point data. Monitoring year data show the annual averages across four monitoring points from 2013 to 2022, whereas monitoring point data show annual averages for each monitoring point from 2013 to 2022. Using these determinations, water quality data were summarized into average values for wet and dry months, wet and dry years, per monitoring point, and per monitoring year.

As mentioned previously, the WQI in this study was determined using three different methods, namely, the NSF-WQI method (Brown et al. 1970), the Prati Index method (Prati et al. 1971), and the Dinius Index method (Dinius 1987). Among these, the NSF-WQI method, developed in the 1970s, is widely used to calculate and evaluate water quality in various regions such as Asia, Africa, and Europe (Chidiac et al. 2023).

The NSF-WQI method classifies water quality parameters into five categories, such as physical parameters (temperature, turbidity, and total solids (TS), chemical parameters (pH and DO), microbiological parameters (fecal coliforms and BOD), nutritional parameters (total phosphates and nitrates), and toxic parameters (pesticides and toxic compounds) (Uddin et al. 2021). The method calculates water quality based on nine key parameters, namely, BOD, DO, nitrate, total phosphate, temperature, turbidity, TS, pH, and fecal coliform (Effendi et al. 2015). However, due to limitations in the available secondary data, this study used eight parameters, excluding turbidity.

The resulting index from the NSF-WQI method ranges from 0 to 100, where a value of 100 represents perfect water quality conditions, while a value closer to zero indicates poor water quality requiring further treatment. Table 1 shows the classification of water quality based on the NSF-WQI index (Effendi et al. 2015; Chidiac et al. 2023).

The Prati index was selected to measure the ecological health of freshwater ecosystems through a set of key physical–chemical parameters. Several studies have shown the potential application of this index assessment method in different environments, in both temperate and tropical river systems (Chrea et al. 2023). This index is useful for describing the pollution level of a water body and numerical evaluation of the qualitative characteristics of water by converting pollutant concentration level into a new unit called the pollution measurement unit. The Prati index provides a quantitative evaluation of the water quality unit known as the pollution measurement unit (Prati et al. 1971).

The Dinius index is considered an effective method for describing water quality to the general public, especially to those with limited technical knowledge about water quality (Dinius 1987). This index is developed based on a multiplicative aggregation method with a decreasing scale, where the resulting value is expressed as a percentage, with 100% representing perfect water quality (Shah & Joshi 2017).

In this study, 12 parameters were used to calculate primary data using this method. These parameters are DO, BOD-5, coli, Escherichia coli, chloride, pH, nitrate, temperature, specific conductance, alkalinity, hardness, and color. However, for secondary data calculations, only nine parameters were used, excluding alkalinity, hardness, and color.

The parameters compared across the NSF-WQI, Prati index, and Dinius index are presented in Table 2. For primary data, all relevant parameters are included. However, some parameters are missing in the secondary data due to data limitations provided by the West Java Provincial Environment Office.

The analysis of Upper Citarum River water quality data includes 22 parameters from primary data in 2023 and 15 parameters from secondary data collected over 10 years (2013–2022). The values of these parameters were compared with the Class 2 quality standards for river water and similar categories as outlined in Appendix VI of Government Regulation Number 22 of 2021 concerning the Implementation of Environmental Protection and Management.

Based on the parameter comparison with quality standards, parameters that met the quality standards are solutes or TDS, detergents (ABS), NO₃, and pH. Meanwhile, several water quality parameters did not meet the quality standards, including suspended solids (TSS), ammonia (NH₃), total PO₄, BOD, COD, DO, free chlorine, fecal coli, and total coli.

Some results of the water quality parameters that did not meet the quality standards such as the suspended substance parameter (TSS) are attributed to the extensive human activities around the Citarum River (Kamajaya et al. 2021). The low concentrations of DO in some data points are likely due to human activities such as agricultural practices and waste disposal (Blume et al. 2010). For the free chlorine parameter, elevated values were observed in certain cases, such as in secondary data from the Nanjung monitoring point in April 2014, where the concentration reached 1.22 mg/L, attributed to industrial waste near the river. The consistently high concentrations of COD at all four monitoring points are due to the presence of residential, commercial, and industrial areas around the water body. Additionally, the very high levels of total and fecal coliform observed at all observation points, in both primary and secondary data, are linked to industrial wastewater, livestock manure waste, and the use of the river for washing and other domestic purposes by the local community (Asrini et al. 2017).

The calculation of the WQI using the three methods employed in this study reveals differences across various aspects, such as functions, water quality parameters, parameter selection methods, transformation processes, standardization, weighting, aggregation, scale, institutions/countries origins, aggregation equations, interpretation frameworks, and intended applications. These differences are presented more clearly in Table 3.

Table 3

Differences in NSF-WQI, Prati index, and Dinius index methods

Aspects .	Methods .
	NSF-WQI .	Prati index .	Dinius index .
Function	General evaluation of surface water quality	Used to assess water pollution, showing the pure qualitative characteristics of water	Evaluation of pollution level in freshwater
Water Quality Parameters	Total of 9 parameters: Physical parameters: temperature, total solids, total phosphate, turbidity Chemical parameters: BOD, nitrate, DO, pH Biological parameters: fecal coli	Total of 12 parameters: Physical parameters: pH, TSS Chemical parameters: DO, BOD, COD, NH3, NO3, ABS, iron, manganese, permanganate, and CCE	Total of 12 parameters: Physical parameters: temperature, electrical conductivity, color Chemical parameters: DO, BOD, free chlorine, pH, nitrate, alkalinity, hardness Biological parameters: total coli, fecal coli
Parameter selection method	Delphi method	Literature	Delphi method
Transformation process	Based on the subindex curve compiled by Wills & Irvine (1996)	There is a subindex equation for each parameter	There is a subindex equation for each parameter
Standardization	Rating curve: expert opinion	Linear and parabolic functions	Linear and nonlinear functions
Weighting	Questionnaire survey from experts	None	Delphi method
Aggregation	Geometric mean	Arithmetic mean	Multiplicative aggregation function
Scale	River	Regional or national surface water	Not specified
Institution/country of origin	National Sanitation Foundation, United States	English	American Water Resources Association, United States
Aggregation equation	where Qi = subindex for the ith water quality parameter, Wi = weighted value for the ith water quality parameter, n = total number of water quality parameters	where n = number of parameters and Ii = subindex value	where Ii = subindex of pollutant variables, wi = weight of pollutant variables, and n = number of pollutant variables
Interpretation	0 = very bad 100 = very good	1 = excellent 8 = heavily polluted	0 = very bad 100 = very good
Usage	Commonly used: 21% of water quality assessment studies used NSF-WQI	Not commonly used	Not commonly used
Reference	Brown et al. (1970)	Prati et al. (1971)	Dinius (1987)

Aspects .	Methods .
	NSF-WQI .	Prati index .	Dinius index .
Function	General evaluation of surface water quality	Used to assess water pollution, showing the pure qualitative characteristics of water	Evaluation of pollution level in freshwater
Water Quality Parameters	Total of 9 parameters: Physical parameters: temperature, total solids, total phosphate, turbidity Chemical parameters: BOD, nitrate, DO, pH Biological parameters: fecal coli	Total of 12 parameters: Physical parameters: pH, TSS Chemical parameters: DO, BOD, COD, NH3, NO3, ABS, iron, manganese, permanganate, and CCE	Total of 12 parameters: Physical parameters: temperature, electrical conductivity, color Chemical parameters: DO, BOD, free chlorine, pH, nitrate, alkalinity, hardness Biological parameters: total coli, fecal coli
Parameter selection method	Delphi method	Literature	Delphi method
Transformation process	Based on the subindex curve compiled by Wills & Irvine (1996)	There is a subindex equation for each parameter	There is a subindex equation for each parameter
Standardization	Rating curve: expert opinion	Linear and parabolic functions	Linear and nonlinear functions
Weighting	Questionnaire survey from experts	None	Delphi method
Aggregation	Geometric mean	Arithmetic mean	Multiplicative aggregation function
Scale	River	Regional or national surface water	Not specified
Institution/country of origin	National Sanitation Foundation, United States	English	American Water Resources Association, United States
Aggregation equation	where Qi = subindex for the ith water quality parameter, Wi = weighted value for the ith water quality parameter, n = total number of water quality parameters	where n = number of parameters and Ii = subindex value	where Ii = subindex of pollutant variables, wi = weight of pollutant variables, and n = number of pollutant variables
Interpretation	0 = very bad 100 = very good	1 = excellent 8 = heavily polluted	0 = very bad 100 = very good
Usage	Commonly used: 21% of water quality assessment studies used NSF-WQI	Not commonly used	Not commonly used
Reference	Brown et al. (1970)	Prati et al. (1971)	Dinius (1987)

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The primary water quality data were calculated using three WQI methods. The calculations were categorized as the wet month and dry month for each monitoring point. The results of the WQI calculations are presented using the NSF-WQI, Prati index, and Dinius index methods.

Based on the index results in Table 4, the WQI values derived from the NSF-WQI, Prati index, and Dinius index methods have different water quality statuses. These discrepancies arise from variations in the parameters, equations, subindices, and classification systems employed by each method (Zotou et al. 2018).

The WQI at the Wangisagara monitoring point tends to be better compared with the other three monitoring points. This finding can be attributed to the upstream location of Wangisagara, where there is a minimal discharge of domestic or industrial pollutants into the Citarum River.

Secondary data were grouped into wet and dry months using Thiessen's polygon method, which was then classified according to the Schmidt–Ferguson theory. Based on the Schmidt–Ferguson theory, months with a rainfall amount below 100 mm/month are categorized as dry months, while those exceeding 100 mm are classified as wet months (Schmidt & Ferguson 1951).

After grouping into wet and dry months, the WQI was calculated using the NSF-WQI, Prati index, and Dinius index methods. The results of the WQI for the Upper Citarum River, stratified by wet and dry months, are presented in Table 5.

Based on Table 5, it can be concluded that the WQI during the wet months tends to be better compared with dry months. This is because rainfall during wet months is higher. Additionally, during the rainy season, nutrient concentrations are generally lower than in the dry season with low plankton density (Moyle & Cech 2004). This natural dilution process reduced pollution levels, leading to improved river water quality.

Grouping of secondary data into wet years and dry months was done using the Thiessen polygon method, followed by classification based on Markov's theory. This theory sorts rainfall values from highest to lowest and divides the data into two categories, such as wet and dry (Stroock 2005).

Data with a P value greater than 50% are classified as dry, while data with a P value less than 50% are classified as wet. Based on the classification, the WQI was calculated using the NSF-WQI, Prati index, and Dinius index methods, with the results presented in Table 6.

The grouping found that the years 2013, 2014, 2016, 2021, and 2022 fell into the wet years category, while 2015, 2017, 2018, 2019, and 2020 were included in the dry years category. As shown in Table 7, the WQI for wet years tends to be better than that for dry years when evaluated using the NSF-WQI and Prati index methods. Conversely, the Dinius index results show that dry years have a slightly better index than wet years. These discrepancies can be attributed to differences in equations, subindices, and classification systems utilized by each method. The NSF-WQI and Prati index methods, according to the previous studies, state that water, except wastewater, can help dilute and destroy organic matter (Slamet 2004).

The Upper Citarum Watershed has a river typology predominantly affected by domestic and industrial waste, as the river traverses urbanized areas. Pollution from domestic sources typically consists of organic waste from household activities, while industrial waste pollutants are primarily associated with large industries located in the upstream zone of the Citarum (Puslitbang Sumber Daya Air 2018). The main pollution sources at the Wangisagara monitoring point are industrial activities, livestock operations, and agriculture/plantation activities. The Koyod monitoring point is primarily influenced by pollutants from the textile industry and agriculture/plantations. The area after the Cisirung WWTP monitoring point receives domestic waste and industrial waste from Bandung City, Bandung Regency, South Bandung Regency, and the Cisirung WWTP. Finally, the Nanjung monitoring point is heavily impacted by waste from various industrial sectors (Pratiwi & Noviana 2016).

The WQI, water quality status, and river water quality class classification in the Upper Citarum watershed were calculated based on yearly monitoring data. The results can be found in Table 8. Figure 10 depicts the results. The NSF-WQI method produced water quality statuses ranging from ‘bad’ and ‘moderate’; the Prati index method produced statuses of ‘acceptable’, ‘slightly polluted’, and ‘polluted’; and the Dinius index method yielded statuses of ‘bad’, ‘moderate’, and ‘very good’.

The results reveal a decline in the WQI during 2013 and 2014. This is due to an increased pollution load from domestic waste, industrial waste, livestock waste, and agricultural waste (Puslitbang Sumber Daya Air 2018).

The differences among the NSF-WQI, the Prati index, and the Dinius index methods can be analyzed based on various aspects mentioned previously, alongside additional aspects summarized in Table 9.

Table 9

Difference between NSF-WQI, Prati index, and Dinius index methods

Aspects .	Methods .
	NSF-WQI .	Prati index .	Dinius index .
Function	General evaluation of surface water quality	Assessment of water pollution, showing the pure qualitative characteristics of water	Evaluation of pollution level in freshwater
Water quality parameters	Total of 9 parameters: Physical parameters: temperature, total solids, total phosphate, turbidity Chemical parameters: BOD5, nitrate, DO, pH Biological parameters: fecal coli	Total of 12 parameters: Physical parameters: pH, TSS Chemical parameters: DO, BOD5, COD, NH3, NO3, ABS, iron, manganese, permanganate, and CCE	Total of 12 parameters: Physical parameters: temperature, electrical conductivity, color Chemical parameters: DO, BOD5, free chlorine, pH, nitrate, alkalinity, hardness Biological parameters: total coli, fecal coli
Parameter selection method	Delphi method	Literature	Delphi method
Transformation process	Based on the subindex curve compiled by Wills & Irvine (1996)	Subindex equation provided for each parameter	Subindex equation provided for each parameter
Standardization	Rating curve based on expert opinion	Linear and parabolic functions	Linear and nonlinear functions
Weighting	Questionnaire survey from experts	None	Delphi method
Aggregation	Geometric mean	Arithmetic mean	Multiplicative aggregation function
Scale	River	Regional or national surface water	Not specified
Aggregation equation	Qi = sub-index for the ith water quality parameter; Wi = weighted value for the ith water quality parameter; n = total number of water quality parameters	n = number of parameters; Ii = sub-index value	Ii = sub-index of pollutant variables; wi = weight of pollutant variables; and n = number of pollutant variables
Interpretation	0 = very bad 100 = very good	1 = very good 8 = heavily polluted	0 = very bad 100 = very good
Usage	Commonly used: 21% of water quality assessment studies used NSF-WQI (Gitau et al. 2016)	Not commonly used	Not commonly used

Aspects .	Methods .
	NSF-WQI .	Prati index .	Dinius index .
Function	General evaluation of surface water quality	Assessment of water pollution, showing the pure qualitative characteristics of water	Evaluation of pollution level in freshwater
Water quality parameters	Total of 9 parameters: Physical parameters: temperature, total solids, total phosphate, turbidity Chemical parameters: BOD5, nitrate, DO, pH Biological parameters: fecal coli	Total of 12 parameters: Physical parameters: pH, TSS Chemical parameters: DO, BOD5, COD, NH3, NO3, ABS, iron, manganese, permanganate, and CCE	Total of 12 parameters: Physical parameters: temperature, electrical conductivity, color Chemical parameters: DO, BOD5, free chlorine, pH, nitrate, alkalinity, hardness Biological parameters: total coli, fecal coli
Parameter selection method	Delphi method	Literature	Delphi method
Transformation process	Based on the subindex curve compiled by Wills & Irvine (1996)	Subindex equation provided for each parameter	Subindex equation provided for each parameter
Standardization	Rating curve based on expert opinion	Linear and parabolic functions	Linear and nonlinear functions
Weighting	Questionnaire survey from experts	None	Delphi method
Aggregation	Geometric mean	Arithmetic mean	Multiplicative aggregation function
Scale	River	Regional or national surface water	Not specified
Aggregation equation	Qi = sub-index for the ith water quality parameter; Wi = weighted value for the ith water quality parameter; n = total number of water quality parameters	n = number of parameters; Ii = sub-index value	Ii = sub-index of pollutant variables; wi = weight of pollutant variables; and n = number of pollutant variables
Interpretation	0 = very bad 100 = very good	1 = very good 8 = heavily polluted	0 = very bad 100 = very good
Usage	Commonly used: 21% of water quality assessment studies used NSF-WQI (Gitau et al. 2016)	Not commonly used	Not commonly used

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This study shows that, based on secondary data, the Prati index method tends to classify water bodies into higher water quality classes. By contrast, the Dinius index often assigns lower water quality class results. Furthermore, the NSF-WQI, being more ‘stringent’, consistently provides the lowest water quality class results. Meanwhile, the analysis using primary data reveal that the NSF-WQI and Prati index methods tend to classify water bodies into better water quality classes compared with the Dinius index.

The assessment of river water quality status in the Upper Citarum watershed using NSF-WQI, Prati index, and Dinius index methods indicates significant variations in outcomes. These differences are attributed to the distinct parameters, equations used, and classification systems employed by each method. The observed fluctuations in water quality status over monitoring years, based on secondary data, are primarily due to the polluting loads from domestic waste and industrial waste entering the water body.

This study identified the water quality status of the Upper Citarum River Basin as follows: ‘good’ and ‘moderate’ using the NSF-WQI method; ‘excellent’, ‘acceptable’, ‘slightly polluted’, and ‘polluted’ using the Prati index method; and ‘excellent’, ‘good’, ‘moderate’, ‘bad’, and ‘very bad’ using the Dinius index method.

From secondary data calculations, the Prati Index method tends to classify water bodies into higher water quality classes, while the Dinius index method classifies water bodies into lower water quality classes. The NSF-WQI method, being the most rigorous, frequently categorizes water quality into the lowest class.

Conversely, primary data analysis indicated that the NSF-WQI method and Prati index classify water bodies into better water quality classes compared with the Dinius index method.

The authors express their gratitude to the Bandung Institute of Technology, especially the Funding Research Program of 2024, for their support.

MM and SHP conceptualized and designed the study. SHP collected the data. MM, SHP, and SN analyzed and interpreted the results. SHP and SN prepared the paper. All authors contributed to the interpretation of the results and the writing of the paper.

The author(s) acknowledge financial support received for the research, authorship, and/or publication of this paper. This research was funded by P2MI ITB Year 2024.

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

The authors declare there is no conflict.