The Upstream Citarum Watershed is the largest watershed in West Java Province that supports the activities and livelihoods of its surrounding communities. Thus, continuous monitoring of the Citarum River's water quality is essential, with the water quality index (WQI) as an effective tool for assessing its status. Various methods can be used, such as the National Sanitation Foundation WQI (NSF-WQI), the Prati index, and the Dinius index. This study aims to compare these three methods based on secondary data in the form of water quality data from the Regional Environmental Management Agency of West Java Province (2013–2022) collected at four monitoring points, namely, Wangisagara, Koyod, the area after the wastewater treatment plant (WWTP) at Cisirung, and Nanjung. Additionally, primary data from sampling conducted at the same monitoring points were analyzed. The data obtained were categorized based on wet–dry months, wet–dry years, monitoring points, and monitoring years. Based on this study, the water quality status of the Upper Citarum River Basin was obtained in the form of 'Good' and 'Moderate' by the NSF-WQI method; 'Excellent', 'Acceptable', 'Slightly Polluted', and 'Polluted' by the Prati index method; and 'Excellent', 'Good', 'Moderate', 'Bad', and 'Very Bad' by the Dinius index method.

  • Water quality status was calculated using the NSF-WQI, Prati index, and Dinius index methods.

  • Water quality analysis was performed using primary and secondary data.

  • Water quality status of the Upper Citarum Watershed was obtained to range from ‘moderate’ to ‘bad’ using the NSF-WQI method, from ‘excellent’ to ‘polluted’ using the Prati index method, and from ‘excellent’ to ‘very bad’ using the Dinius index method.

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.

Study location and data collection

This study was conducted in the Upper Citarum River at four monitoring points. These points were selected to align with the sampling locations of secondary data taken from the Regional Environmental Management Agency of West Java Province, Indonesia. The Citarum River, the longest in West Java Province, spans 297 km, with its headwaters located in Situ Cisanti (Syamsiah et al. 2023). The four monitoring points were Wangisagara, Koyod, the area downstream of the Cisirung WWTP, and Nanjung (Figure 1). Primary data collection was carried out at these four points in two periods, namely, in February 2023, representing the rainy season, and June 2023, representing the dry season. Secondary data in this study consisted of rainfall data and water quality monitoring data for the Upper Citarum River carried out for 10 years, from 2013 to 2022, by the Regional Environmental Agency of West Java Province, Indonesia.
Figure 1

(a) Map of watershed area in Indonesia, and (b) map of monitoring points.

Figure 1

(a) Map of watershed area in Indonesia, and (b) map of monitoring points.

Close modal

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.

Standardization and categorization of secondary data

To ensure accuracy and avoid errors in conclusions, secondary data used in this study were carefully standardized. Datasets with missing, null, or censored values were excluded. Outlier data were identified and addressed to test the validity and reliability of the dataset, employing the z-score equation as shown in Equation (1) (Muslimin & Saraswati 2013):
(1)
where Zi is the standardized value of the ith data point, Xr is mean of the dataset, Xi is I observed value of the ith data point, and S is the standard deviation.
Outlier testing was carried out to determine data points significantly different from the rest of the dataset, often appearing as extreme values (Ghozali 2013). Such data arise from data entry errors, sampling issues, or extreme conditions. After standardization, data were considered outliers if they satisfied the condition expressed in the following equation (Muslimin & Saraswati 2013):
(2)
where xi is the observed value of the ith data point; xr is the mean of the dataset; S is the standard deviation; and k is the outlier threshold, set to 3. Values exceeding this threshold were categorized as outlier data and corrected by replacing them with the average value of water quality monitoring data for the corresponding parameter, location, and season (Muslimin & Saraswati 2013). The standardized secondary data were subsequently used to calculate the WQI.

Grouping of water quality data

Water quality data were grouped based on wet and dry months as well as wet and dry years. These classifications were determined using rainfall calculations derived from the Thiessen Polygon method, which incorporated data from five rainfall monitoring stations as shown in Equation (3) (Ningsih 2012):
(3)
where Pn is the rainfall at the nth station and An is the area represented by the nth station.
Rainfall data were classified into wet and dry months according to the Schmidt–Ferguson theory and into wet and dry years according to the Markov theory (Schmidt & Ferguson 1951; Stroock 2005). The percentage of rainfall data for determining wet and dry years, based on Markov theory, is calculated using Equation (4):
(4)
where P is the percentage (%); n is the sequence of data; and N is the total number of data points.

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.

WQI calculation method

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.

Each parameter in the NSF-WQI method is assigned a specific weight, with the total weight of all parameters summing to 1. The weight values for each parameter are as follows: DO, 0.17; fecal coliform, 0.16; pH, 0.11; BOD5, 0.11; temperature, 0.1; total phosphorus, 0.1; nitrate, 0.1; turbidity, 0.08; TS, 0.07. In this study, the parameter weights were modified to accommodate the secondary data available as follows: DO, 0.185; fecal coliform, 0.174; pH, 0.12; BOD5, 0.12; temperature, 0.109; total phosphorus, 0.109; nitrate, 0.109; TS, 0.076 (Tyagi et al. 2013; Sutadian et al. 2016). The subindex values of each parameter were determined using subindex curves (Wills & Irvine 1996). The overall WQI was then calculated using Equation (5) (Brown et al. 1970):
(5)
where Qi is the subindex value for the ith water quality parameter; Wi is the weighted value for the ith water quality parameter; and n is the total number of water quality parameters.

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).

Table 1

Water quality status classification

Water quality classWater quality index calculation method
NSF-WQI
Prati index
Dinius index
IndexWater quality statusIndexWater quality statusIndexWater quality status
91–100 Excellent 0–1 Excellent 91–100 Excellent 
71–90 Good 1–2 Acceptable 81–90 Good 
51–70 Moderate 2–4 Slightly polluted 60–80 Moderate 
26–50 Bad 4–8 Polluted 50–59 Bad 
0–25 Very bad >8 Heavily polluted 0–49 Very bad 
Water quality classWater quality index calculation method
NSF-WQI
Prati index
Dinius index
IndexWater quality statusIndexWater quality statusIndexWater quality status
91–100 Excellent 0–1 Excellent 91–100 Excellent 
71–90 Good 1–2 Acceptable 81–90 Good 
51–70 Moderate 2–4 Slightly polluted 60–80 Moderate 
26–50 Bad 4–8 Polluted 50–59 Bad 
0–25 Very bad >8 Heavily polluted 0–49 Very bad 

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).

There are 11 parameters used to calculate primary data with the Prati index method, namely, pH, DO, 5-day biological oxygen demand (BOD-5), COD, permanganate, suspended solids, ammonia (NH3), NO3, chlorine (Cl), iron, and manganese. Meanwhile, secondary data are calculated based on nine parameters, consisting of pH, DO, BOD-5, COD, TSS, NH3, NO3, Cl, and methylene blue active substances. The calculation of the pollution level for each parameter is determined using Equation (6), with the classification of results shown in Table 1 (Prati et al. 1971):
(6)
where n is the number of parameters and Ii is the subindex value.

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 Dinius index calculation requires different weights for each parameter, with the sum of all parameters’ weights equaling 1. The weight values for each parameter are as follows: DO, 0.109; BOD5, 0.097; coli, 0.09; E. coli, 0.116; alkalinity, 0.063; hardness, 0.065; chloride, 0.074; specific conductance, 0.079; pH, 0.077; nitrate, 0.09; temperature, 0.077; color, 0.063 [26] (Dinius 1987). For secondary data calculations, the weights were modified as follows: DO, 0.13; BOD5, 0.12; coli, 0.11; E. coli, 0.14; chloride, 0.09; pH, 0.1; nitrate, 0.11; temperature, 0.1; specific conductance, 0.1. The equation used for calculating this index is shown in Equation (7) (Dinius 1987; Kumar et al. 2024):
(7)
where IWQ is the water quality index, expressed as a value between 0 and 100; Ii is the pollutant variable subindex, with a value between 0 and 100; wi is the pollutant variable weight, with a value between 0 and 100; ; and n is the number of pollutant variables.

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.

Table 2

Primary and secondary data parameters for NSF-WQI, Prati index, and Dinius index methods

No.NSF-WQI
Prati index
Dinius index
ParametersPrimary dataSecondary dataParametersPrimary dataSecondary dataParametersPrimary dataSecondary data
Temperature ✓ ✓ pH ✓ ✓ DO ✓ ✓ 
Total solids ✓ ✓ DO ✓ ✓ BOD ✓ ✓ 
Total phosphate ✓ ✓ BOD ✓ ✓ Total coli ✓ ✓ 
BOD ✓ ✓ COD ✓ ✓ Fecal coli ✓ ✓ 
Nitrate (NO)3 ✓ ✓ TSS ✓ ✓ Chloride ✓ ✓ 
DO ✓ ✓ NH3 ✓ ✓ pH ✓ ✓ 
pH ✓ ✓ NO3 ✓ ✓ Nitrate ✓ ✓ 
Fecal coli ✓ ✓ ABS ✓ ✓ Temperature ✓ ✓ 
Turbidity ✓  Iron ✓  Specific conductance ✓ ✓ 
10    Manganese ✓  Alkalinity ✓  
11    Permanganate ✓  Hardness ✓  
12    CCE   Color ✓  
No.NSF-WQI
Prati index
Dinius index
ParametersPrimary dataSecondary dataParametersPrimary dataSecondary dataParametersPrimary dataSecondary data
Temperature ✓ ✓ pH ✓ ✓ DO ✓ ✓ 
Total solids ✓ ✓ DO ✓ ✓ BOD ✓ ✓ 
Total phosphate ✓ ✓ BOD ✓ ✓ Total coli ✓ ✓ 
BOD ✓ ✓ COD ✓ ✓ Fecal coli ✓ ✓ 
Nitrate (NO)3 ✓ ✓ TSS ✓ ✓ Chloride ✓ ✓ 
DO ✓ ✓ NH3 ✓ ✓ pH ✓ ✓ 
pH ✓ ✓ NO3 ✓ ✓ Nitrate ✓ ✓ 
Fecal coli ✓ ✓ ABS ✓ ✓ Temperature ✓ ✓ 
Turbidity ✓  Iron ✓  Specific conductance ✓ ✓ 
10    Manganese ✓  Alkalinity ✓  
11    Permanganate ✓  Hardness ✓  
12    CCE   Color ✓  

Water quality data analysis of the Upper Citarum Watershed

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).

Water quality index method analysis

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

AspectsMethods
NSF-WQIPrati indexDinius 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)  
AspectsMethods
NSF-WQIPrati indexDinius 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)  

Results of calculating the primary data water quality index

Wet months and dry months

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).

Table 4

Water quality index and water quality status in primary data for wet months and dry months

Monitoring pointNSF-WQI
Prati index
Dinius index
Month typeIndexWater quality statusClassIndexWater quality statusClassIndexWater quality statusClass
Wangisagara Wet month 62.86 Moderate 2.25 Slightly polluted 61.72 Moderate 
Koyod 55.10 Moderate 4.77 Polluted 57.92 Bad 
After WWTP Cisirung 47.39 Bad 4.10 Polluted 52.25 Bad 
Nanjung 46.22 Bad 3.25 Slightly polluted 50.07 Bad 
Wangisagara Dry month 58.62 Moderate 1.70 Acceptable 62.59 Moderate 
Koyod 54.20 Moderate 2.66 Slightly polluted 55.50 Bad 
After WWTP Cisirung 40.83 Bad 4.62 Polluted 49.33 Very bad 
Nanjung 42.27 Bad 5.16 Polluted 47.71 Very bad 
Monitoring pointNSF-WQI
Prati index
Dinius index
Month typeIndexWater quality statusClassIndexWater quality statusClassIndexWater quality statusClass
Wangisagara Wet month 62.86 Moderate 2.25 Slightly polluted 61.72 Moderate 
Koyod 55.10 Moderate 4.77 Polluted 57.92 Bad 
After WWTP Cisirung 47.39 Bad 4.10 Polluted 52.25 Bad 
Nanjung 46.22 Bad 3.25 Slightly polluted 50.07 Bad 
Wangisagara Dry month 58.62 Moderate 1.70 Acceptable 62.59 Moderate 
Koyod 54.20 Moderate 2.66 Slightly polluted 55.50 Bad 
After WWTP Cisirung 40.83 Bad 4.62 Polluted 49.33 Very bad 
Nanjung 42.27 Bad 5.16 Polluted 47.71 Very bad 

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.

As shown in Figure 2, the WQI results of primary data during wet months and dry months obtained ‘bad’ and ‘moderate’ quality statuses using the NSF-WQI method; ‘polluted’, ‘slightly polluted’, and ‘acceptable’ quality statuses with the Prati index methods; and ‘moderate’, ‘bad’, and ‘very bad’ quality statuses according to the Dinius index method.
Figure 2

Results of water quality index of primary data for wet months and dry months using (a) NSF-WQI method, (b) Prati index, and (c) Dinius index.

Figure 2

Results of water quality index of primary data for wet months and dry months using (a) NSF-WQI method, (b) Prati index, and (c) Dinius index.

Close modal
Figure 3 demonstrates that the NSF-WQI method generally produces water quality classifications that tend toward the higher end of the spectrum, predominantly within class ranges of 2 and 3. Furthermore, the Prati index also produces slightly more favorable water quality compared with the NSF-WQI method, spanning class ranges from 2 to 4. By contrast, the Dinius index tends to generate classifications that reflect poorer water quality, primarily falling within class ranges 1–3.
Figure 3

Comparison of water quality class in primary data for wet months and dry months.

Figure 3

Comparison of water quality class in primary data for wet months and dry months.

Close modal

Calculation results of secondary data water quality index

Wet months and dry months

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.

Table 5

Water quality index and water quality status using secondary data for wet months and dry months

Monitoring pointType monthNSF-WQI
Prati index
Dinius index
IndexWater quality statusClassIndexWater quality statusClassIndexWater quality statusClass
Wangisagara Wet month 50.91 Bad 1.71 Acceptable 75.34 Moderate 
Koyod 44.60 Bad 2.64 Slightly polluted 67.39 Moderate 
Setelah IPAL Cisirung 38.39 Bad 3.45 Slightly polluted 64.56 Moderate 
Nanjung 38.10 Bad 3.69 Slightly polluted 61.28 Moderate 
Wangisagara Dry month 51.02 Moderate 1.67 Acceptable 74.89 Moderate 
Koyod 35.64 Bad 4.00 Polluted 59.36 Bad 
Setelah IPAL Cisirung 35.18 Bad 4.34 Polluted 57.35 Bad 
Nanjung 32.64 Bad 4.94 Polluted 53.50 Bad 
Monitoring pointType monthNSF-WQI
Prati index
Dinius index
IndexWater quality statusClassIndexWater quality statusClassIndexWater quality statusClass
Wangisagara Wet month 50.91 Bad 1.71 Acceptable 75.34 Moderate 
Koyod 44.60 Bad 2.64 Slightly polluted 67.39 Moderate 
Setelah IPAL Cisirung 38.39 Bad 3.45 Slightly polluted 64.56 Moderate 
Nanjung 38.10 Bad 3.69 Slightly polluted 61.28 Moderate 
Wangisagara Dry month 51.02 Moderate 1.67 Acceptable 74.89 Moderate 
Koyod 35.64 Bad 4.00 Polluted 59.36 Bad 
Setelah IPAL Cisirung 35.18 Bad 4.34 Polluted 57.35 Bad 
Nanjung 32.64 Bad 4.94 Polluted 53.50 Bad 

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.

Figure 4 shows the results of the WQI for wet and dry months derived from secondary data. The NSF-WQI method produces water quality statuses classified as ‘moderate’ and ‘bad’; the Prati index method produces statuses of ‘polluted’, ‘slightly polluted’, and ‘acceptable’, while the Dinius index yields water quality statuses of ‘moderate’ and ‘bad’. The observed improvement in water quality during wet months compared with dry months is attributed to the water quality parameters, which tend to deteriorate during dry months. Based on the previous research, lower water flow and higher pollutant concentrations during the dry season are significant factors contributing to poorer water quality compared with the rainy season (Gossweiler et al. 2019).
Figure 4

Results of water quality index of secondary data for wet months and dry months using (a) NSF-WQI, (b) Prati index, and (c) Dinius index methods.

Figure 4

Results of water quality index of secondary data for wet months and dry months using (a) NSF-WQI, (b) Prati index, and (c) Dinius index methods.

Close modal
The secondary data WQI results for wet and dry months, calculated using the three methods, were then compared based on the water quality classification, as shown in Figure 5. The NSF-WQI and the Dinius index methods generally produce a good WQI within the range of Grades 2 and 3, indicating relatively better water quality. Meanwhile, the Prati index produces a relatively high-grade classification, predominantly within class ranges of Grades 1–4. This shows that the NSF-WQI method adopts a more stringent approach in classifying water quality status.
Figure 5

Comparison of water quality class in secondary data for wet months and dry months.

Figure 5

Comparison of water quality class in secondary data for wet months and dry months.

Close modal

Wet years and dry years

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.

Table 6

Water quality index and water quality status using secondary data of wet and dry years

Monitoring pointYear typeNSF-WQI
Prati index
Dinius index
IndexWater quality statusClassIndexWater quality statusClassIndexWater quality statusClass
Wangisagara Wet year 49.70 Bad 1.90 Acceptable 74.14 Moderate 
Koyod 41.91 Bad 3.07 Slightly polluted 63.90 Moderate 
Setelah IPAL Cisirung 37.55 Bad 3.73 Slightly polluted 59.43 Bad 
Nanjung 37.19 Bad 3.99 Slightly polluted 57.09 Bad 
Wangisagara Dry Year 56.43 Moderate 1.22 Acceptable 82.02 Good 
Koyod 38.32 Bad 3.41 Slightly polluted 63.87 Moderate 
Setelah IPAL Cisirung 36.97 Bad 3.52 Slightly polluted 64.25 Moderate 
Nanjung 34.35 Bad 4.37 Polluted 59.40 Bad 
Monitoring pointYear typeNSF-WQI
Prati index
Dinius index
IndexWater quality statusClassIndexWater quality statusClassIndexWater quality statusClass
Wangisagara Wet year 49.70 Bad 1.90 Acceptable 74.14 Moderate 
Koyod 41.91 Bad 3.07 Slightly polluted 63.90 Moderate 
Setelah IPAL Cisirung 37.55 Bad 3.73 Slightly polluted 59.43 Bad 
Nanjung 37.19 Bad 3.99 Slightly polluted 57.09 Bad 
Wangisagara Dry Year 56.43 Moderate 1.22 Acceptable 82.02 Good 
Koyod 38.32 Bad 3.41 Slightly polluted 63.87 Moderate 
Setelah IPAL Cisirung 36.97 Bad 3.52 Slightly polluted 64.25 Moderate 
Nanjung 34.35 Bad 4.37 Polluted 59.40 Bad 

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).

Table 7

Water quality index and water quality status using secondary data from monitoring points

Monitoring pointNSF-WQI
Prati index
Dinius index
IndexWater quality statusClassIndexWater quality statusClassIndexWater quality statusClass
Wangisagara 51.74 Moderate 1.59 Acceptable 76.15 Moderate 
Koyod 40.08 Bad 3.23 Slightly polluted 63.41 Moderate 
Setelah IPAL Cisirung 37.65 Bad 3.63 Slightly polluted 61.19 Moderate 
Nanjung 35.99 Bad 4.18 Polluted 57.33 Bad 
Monitoring pointNSF-WQI
Prati index
Dinius index
IndexWater quality statusClassIndexWater quality statusClassIndexWater quality statusClass
Wangisagara 51.74 Moderate 1.59 Acceptable 76.15 Moderate 
Koyod 40.08 Bad 3.23 Slightly polluted 63.41 Moderate 
Setelah IPAL Cisirung 37.65 Bad 3.63 Slightly polluted 61.19 Moderate 
Nanjung 35.99 Bad 4.18 Polluted 57.33 Bad 

Based on the results of the WQI from secondary data for wet and dry years, the Citarum River in the Upper Citarum watershed obtained ‘bad’ and ‘moderate’ index results using the NSF-WQI method; ‘acceptable’, ‘slightly polluted’, and ‘polluted’ index results using the Prati index method; and ‘good’, ‘moderate’, and ‘bad’ index results using the Dinius index method, as shown in Figure 6. The index results during wet years tend to be better than those during dry years, primarily due to higher rainfall, which increases river discharge. This condition naturally dilutes pollutants in water bodies, resulting in reduced pollutant levels and improved river WQI (Nurjanah 2018).
Figure 6

Results of water quality index of secondary data for wet years and dry years using (a) NSF-WQI method, (b) Prati index, and (c) Dinius index.

Figure 6

Results of water quality index of secondary data for wet years and dry years using (a) NSF-WQI method, (b) Prati index, and (c) Dinius index.

Close modal
A comparison of water quality, as viewed by water quality classes across the three methods used, is illustrated in Figure 7. The comparison reveals that the Dinius index and Prati index methods tend to produce better water quality classifications compared with the NSF-WQI method.
Figure 7

Comparison of water quality classifications in secondary data for wet years and dry years.

Figure 7

Comparison of water quality classifications in secondary data for wet years and dry years.

Close modal

Based on monitoring points

The WQI calculation, water quality status, and river water quality class classification in the Upper Citarum watershed was carried out using secondary data for each monitoring point. The results of these calculations, using the NSF-WQI, Prati index, and Dinius index methods, are presented in Table 7 and illustrated in Figure 8. The monitoring points, arranged from upstream to downstream, include Wangisagara, Koyod, the area after WWTP Cisirung, and Nanjung. Based on the monitoring locations, the best river water quality is observed at the Wangisagara point, with the quality progressively deteriorating toward the downstream point at Nanjung.
Figure 8

Results of water quality index of secondary data per monitoring points using (a) NSF-WQI, (b) Prati index, and (c) Dinius index methods.

Figure 8

Results of water quality index of secondary data per monitoring points using (a) NSF-WQI, (b) Prati index, and (c) Dinius index methods.

Close modal

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).

Figure 9 shows a comparison of water quality indices based on the water quality class. The comparison indicates that the Prati index and Dinius index methods produce better water quality classifications compared with the NSF-WQI method.
Figure 9

Comparison of water quality class in secondary data for wet years and dry years.

Figure 9

Comparison of water quality class in secondary data for wet years and dry years.

Close modal
Figure 10

Results of water quality index of secondary data per monitoring years using (a) NSF-WQI, (b) Prati index, and (c) Dinius index methods.

Figure 10

Results of water quality index of secondary data per monitoring years using (a) NSF-WQI, (b) Prati index, and (c) Dinius index methods.

Close modal

Based on the year of monitoring

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’.

Table 8

Water quality index and water quality status using secondary data from monitoring years

YearNSF-WQIPrati indexDinius index
IndexWater quality statusClassIndexWater quality statusClassIndexWater quality statusClass
2013 38.34 Bad 4.73 Polluted 60.46 Moderate 
2014 37.72 Bad 4.77 Polluted 58.97 Bad 
2015 51.49 Moderate 1.56 Acceptable 100.00 Very good 
2016 43.39 Bad 2.92 Slightly polluted 69.36 Moderate 
2017 38.61 Bad 3.74 Slightly polluted 68.66 Moderate 
2018 40.02 Bad 3.46 Slightly polluted 63.32 Moderate 
2019 37.87 Bad 4.29 Polluted 60.14 Moderate 
2020 46.66 Bad 1.81 Acceptable 78.64 Moderate 
2021 50.20 Bad 1.68 Acceptable 74.05 Moderate 
2022 49.13 Bad 1.46 Acceptable 74.50 Moderate 
YearNSF-WQIPrati indexDinius index
IndexWater quality statusClassIndexWater quality statusClassIndexWater quality statusClass
2013 38.34 Bad 4.73 Polluted 60.46 Moderate 
2014 37.72 Bad 4.77 Polluted 58.97 Bad 
2015 51.49 Moderate 1.56 Acceptable 100.00 Very good 
2016 43.39 Bad 2.92 Slightly polluted 69.36 Moderate 
2017 38.61 Bad 3.74 Slightly polluted 68.66 Moderate 
2018 40.02 Bad 3.46 Slightly polluted 63.32 Moderate 
2019 37.87 Bad 4.29 Polluted 60.14 Moderate 
2020 46.66 Bad 1.81 Acceptable 78.64 Moderate 
2021 50.20 Bad 1.68 Acceptable 74.05 Moderate 
2022 49.13 Bad 1.46 Acceptable 74.50 Moderate 

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).

An improvement in the WQI was observed in 2015, likely due to government-led initiatives aimed at revitalizing the Citarum River. Nevertheless, the WQI declined again between 2016 and 2019, before showing signs of improvement in 2020 and 2021. These fluctuations in WQI are likely influenced by changes in industrial and residential effluents. The water quality class for each monitoring year is shown in Figure 11. Figure 11 further illustrates that the NSF-WQI, the Prati index, and the Dinius index methods yield water quality classifications ranging from optimal to suboptimal conditions.
Figure 11

Comparison of water quality classes in secondary data by monitoring years.

Figure 11

Comparison of water quality classes in secondary data by monitoring years.

Close modal

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

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

AspectsMethods
NSF-WQIPrati indexDinius 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. 2016Not commonly used Not commonly used 
AspectsMethods
NSF-WQIPrati indexDinius 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. 2016Not commonly used Not commonly used 

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.

Asrini
N. K.
,
Adnyana
I. W. S.
&
Rai
I. N.
(
2017
)
Water quality analysis study in the Pakerisan River Basin of Bali Province
,
Ecotrophic
,
11
(
2
),
101
107
.
https://dx.doi.org/10.24843/EJES.2017.v11.i02.p01
.
Blume
K. K.
,
Macedo
J. C.
,
Meneguzzi
A.
,
Silva
L. B.
,
Quevedo
D. M.
&
Rodrigues
M. A. S.
(
2010
)
Water quality assessment of the Sinos River, Southern Brazil
,
Brazilian Journal of Biology
, 70 (4 Suppl), 1185–1193.
https://doi.org/10.1590/s1519-69842010000600008
.
Boyd
C. E.
(
2019
)
Water Quality: An Introduction
.
Auburn, AL: Springer Nature
.
Brown
R. M.
,
McClelland
N. I.
,
Deininger
R. A.
&
Tozer
R. G.
(
1970
)
A water quality index-do we dare
,
Water and Sewage Works
,
117
,
339–343
.
Chidiac
S.
,
El Najjar
P.
,
Ouaini
N.
,
El Rayess
Y.
&
El Azzi
D.
(
2023
)
A comprehensive review of water quality indices (WQIs): history, models, attempts and perspectives
,
Reviews in Environmental Science and Bio/Technology
,
22
,
349
395
.
https://doi.org/10.1007/s11157-023-09650-7
.
Chrea
S.
,
Tudesque
L.
&
Chea
R.
(
2023
)
Comparative assessment of water quality classification techniques in the largest north-western river of Cambodia (Sangker River-Tonle Sap Basin)
,
Ecological Indicators
,
154
,
110759
.
1–12. https://doi.org/10.1016/j.ecolind.2023.110759
.
Dinius
S. H.
(
1987
)
Design of an index of water quality
,
AWRA
, 23,
833
843
.
https://doi.org/10.1111/j.1752-1688.1987.tb02959.x
.
du Plessis
A.
(
2022
)
Persistent degradation: global water quality challenges and required actions
,
One Earth
,
5
(
2
),
129
131
.
https://doi.org/10.1016/j.oneear.2022.01.005
.
Effendi
H.
,
Romanto
&
Wardiatno
Y.
(
2015
)
Water quality status of Ciambulawung River, Banten Province, based on Pollution Index and NSF-WQI
,
Procedia Environmental Sciences
,
24
,
228
237
.
https://doi.org/10.1016/j.proenv.2015.03.030
.
Fadhil
M. Y.
,
Hidayat
Y.
,
Murtilaksono
K.
&
Baskoro
D. P. T.
(
2021
)
The landuse and hydrological characteristic changes of the Upper Citarum Watershed
,
Jurnal Ilmu Pertanian Indonesia (JIPI)
,
26
(
2
),
213
220
.
https://doi.org/10.18343/jipi.26.2.213
.
Fenner
R. A.
(
2017
)
Water: an essential resource and a critical hazard
. In:
Bishop
J.
(ed.)
Building Sustainable Cities of the Future. Green Energy and Technology
,
Cham
:
Springer
, pp. 75–97.
https://doi.org/10.1007/978-3-319-54458-8_5
.
Fernandez
N.
(
2014
)
Physico-chemical water quality indices a comparative review
,
Bistua Revista de la Facultad de Ciencias Bá
, 2 (1),
19
30
.
Fitriana
F.
,
Yudianto
D.
,
Sanjaya
S.
,
Roy
A. F. V.
&
Seo
Y. C.
(
2023
)
The assessment of Citarum River Water Quality in Majalaya District, Bandung Regency
,
Rekayasa Sipil
,
17
(
1
),
37
46
.
https://doi.org/10.21776/ub.rekayasasipil.2023.017.01.6
.
Ghozali
I.
(
2013
)
Model of Structural Equations: Concept and Application with the AMOS 21.0 Program
.
Semarang, Indonesia
:
Badan Penerbit UNDIP
.
Gitau, M. W., Chen, J. & Ma, Z. (2016) Water quality indices as tools for decision making and management. Water Resources Management, 30 (8), 2591–610.
Gossweiler
B.
,
Wesström
I.
,
Messing
I.
,
Romero
A. M.
&
Joel
A.
(
2019
)
Spatial and temporal variations in water quality and land use in a semi-arid catchment in Bolivia
,
Water
,
11
,
2227
.
doi:10.3390/w11112227
.
Government Regulation of Indonesia No. 22 of 2021 on Environmental Protection, Organisation and Management. Available at: https://peraturan.bpk.go.id/Details/161852/pp-no-22-tahun-2021.
Gule
T. T.
,
Lemma
B.
&
Hailu
B. T.
(
2023
)
Implications of land use/land cover dynamics on urban water quality: Case of Addis Ababa city, Ethiopia
,
Heliyon
,
9
,
e15665
.
https://doi.org/10.1016/j.heliyon.2023.e15665
.
Kamajaya, G. Y., Putra, I. D. & Putra, I. N. (2021) Analisis sebaran Total Suspended Solid (TSS) berdasarkan citra landsat 8 menggunakan tiga algoritma berbeda di perairan Teluk Benoa, Bali. Journal of Marine and Aquatic Sciences, 7 (1), 18–24.
Kujiek
D. C.
&
Sahile
Z. A.
(
2023
)
Water quality assessment of Elgo River in Ethiopia using CCME WQI and IWQI for domestic and agricultural usage
,
Heliyon
,
10
,
e23234
.
https://doi.org/10.1016/j.heliyon.2023.e23234
.
Kumar
D.
,
Kumar
R.
,
Sharma
M.
,
Awasthi
A.
&
Kumar
M.
(
2024
)
Global water quality indices: Development, implications, and limitations
,
Total Environment Advances
,
9
,
200095
.
https://doi.org/10.1016/j.teadva.2023.200095
.
Moyle
P. A.
&
Cech, J. J. (2004) Fishes:
An Introduction to Ichthyology
.
USA
:
Prentice Hall, Inc
.
Muslimin
&
Saraswati
S. P.
(
2013
)
Study of water quality status in Gajahwong River using several water quality indices
,
Lingkungan Tropis
,
6
(
2
),
91
103
.
Ningsih
D. H. U.
(
2012
)
Thiessen Polygon method for forecasting the distribution of rainfall over a given period in areas where rainfall data is not available
,
Dinamik
,
17
(
2
),
154
163
.
https://doi.org/10.35315/dinamik.v17i2.1664
.
Nufutomo
T. K.
&
Muntalif
B. S.
(
2017
)
Cryptosporidium as a biological indicator and NSF-WQI index to evaluate water quality (case study: Upper Citarum River, Bandung Regency)
,
Jurnal Presipitasi
,
14
(
2
),
45
53
.
https://doi.org/10.14710/presipitasi.v14i2.45-53
.
Nurjanah
P.
(
2018
)
Analysis of the Effect of Rainfall on Water Quality Microbiological Parameters and Water Quality Status in Code River, Yogyakarta
.
Undergraduate dissertation
.
Universitas Islam Indonesia
,
Yogyakarta, Indonesia
.
Perveen
S.
&
Amar-Ul-Haque
(
2023
)
Drinking water quality monitoring, assessment and management in Pakistan: a review
,
Heliyon
,
9
,
e13872
.
https://doi.org/10.1016/j.heliyon.2023.e13872
.
Prati
L.
,
Pavanello
R.
&
Pesarin
F.
(
1971
)
Assessment of surface water quality by a single index of pollution
,
Water Research
,
5
(
9
),
741
751
.
https://doi.org/10.1016/0043-1354(71)90097-2
.
Pratiwi
R.
&
Noviana
L.
(
2016
)
Evaluation of Citarum River Water Quality. Lecturer Research Report
.
Jakarta, Indonesia
:
Universitas Sahid Jakarta
.
Puslitbang Sumber Daya Air
(
2018
)
Restoration of Upper Citarum: Realizing a Healthy Citarum River for Community Welfare
.
Bandung, Indonesia
:
ITB Press
.
Romimohtarto
K.
&
Juwana
S.
(
2001
)
Identification of Marine Plants and Animals of Indonesia
.
Jakarta
:
Djambatan
.
Sapan
E. G. A.
,
Riandasenya
S. A. R.
,
Yulianingsani
A.
,
Ilmi
M. K.
&
Habibie
M. I.
(
2022
)
Health assessment of the Upper Citarum Watershed, West Java, Indonesia
,
IOP Conf. Series: EES
,
1109
,
012082
.
doi:10.1088/1755-1315/1109/1/012082
.
Schmidt
F.
&
Ferguson
J.
(
1951
)
Verhandelingen No.42 rainfall types based on wet and dry period rations for Indonesia with Western New Guinee
.
Jakarta, Indonesia
:
Kementrian Perhubungan Djawatan Meteorologi
.
Shah
K. A.
&
Joshi
G. S.
(
2017
)
Evaluation of water quality index for River Sabarmati, Gujarat, India
,
Applied Water Science
,
7
,
1349
1358
.
https://doi.org/10.1007/s13201-015-0318-7
.
Slamet
J. S.
(
2004
)
Environmental Health
.
Yogyakarta, Indonesia
:
Gadjah Mada University Press
.
Standar Nasional Indonesia (Indonesian National Standard), SNI 6989.57:2008 concerning Water and Wastewater Part 57: Surface Water Sampling Method, National Standardization Agency of Indonesia, Jakarta, Indonesia
.
Stroock
D. W.
(
2005
)
An Introduction to Markov Processes
.
Cambridge, USA: Springer
.
https://doi.org/10.1007/978-3-642-40523-5
.
Sutadian
A. D.
,
Muttil
N.
,
Yilmaz
A. G.
&
Perera
B. J. C.
(
2016
)
Development of river water quality indices – a review
,
Environmental Monitoring and Assessment
,
188
(
58
),
1
29
.
https://doi.org/10.1007/s10661-015-5050-0
.
Syamsiah
N.
,
Sulistyowati
L.
,
Noor
T. I.
&
Setiawan
I.
(
2023
)
The sustainability level of an ecovillage in the Upper Citarum Watershed of West Java Province, Indonesia
,
Sustainability
,
15
,
15951
.
https://doi.org/10.3390/su152215951
.
Tyagi
S.
,
Sharma
B.
,
Singh
P.
&
Dobhal
R.
(
2013
)
Water quality assessment in terms of water quality index
,
American Journal of Water Resources
,
1
, 34–38.
doi:10.12691/ajwr-1-3-3
.
Uddin
M. G.
,
Nash
S.
&
Olbert
A. I.
(
2021
)
A review of water quality index models and their use for assessing surface water quality
,
Ecological Indicators
,
122
,
107218
.
https://doi.org/10.1016/j.ecolind.2020.107218
.
Utami
A. W.
(
2019
)
Citarum River Water Quality
.
Indonesia
.
https://doi.org/10.31227/osf.io/m3ha2
.
Wills
M.
&
Irvine
K. M.
(
1996
)
Application of the National Sanitation Foundation Water Quality Index in the Cazenovia Creek, NY, Pilot watershed management project
,
Middle States Geographer
, 1996,
95
104
.
Wu
L.
,
Zhang
Y.
,
Wang
Z.
,
Geng
M.
,
Chen
Y.
&
Zhang
F.
(
2023
)
Method for screening water physicochemical parameters to calculate water quality index based on these parameters’ correlation with water microbiota
,
Heliyon
,
9
,
e16697
.
https://doi.org/10.1016/j.heliyon.2023.e16697
.
Zotou
I.
,
Tsihrintzis
V. A.
&
Gikas
G. D.
(
2018
)
Comparative assessment of various water quality indices (WQIs) in Polyphytos Reservoir–Aliakmon River, Greece
,
MDPI Proceedings
,
2
(
611
),
1
8
.
https://doi.org/10.3390/proceedings2110611
.
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