https://orcid.org/0000-0003-4356-4124
ABSTRACT
This study sets out to examine a variety of approaches to the assessment of organic pollution, and to consider the potential application of the results in the educational process of students specializing in the environment. Water samples from the Styr River were collected and analyzed for physicochemical parameters such as nitrogen, phosphorus, carbon components, and dissolved oxygen from 2018 to 2022. Four equations were used to calculate the Organic Pollution Index (OPI). Statistical analyses included Pearson correlation, multiple linear regression, principal component analysis (PCA), and network analysis (NA). Results indicated variations in OPI levels depending on the methodology, with pollution classifications ranging from ‘pure’ to ‘moderate’. Seasonal trends were observed, with higher pollution levels in summer and lower levels in winter. PCA and NA identified key variables contributing to pollution. Some methods showed stable OPI levels, while others exhibited periodic fluctuations. This study provides a comprehensive comparison of OPI calculation methods and integrates statistical techniques for river water quality assessment. It offers a valuable framework for environmental education, enhancing students' understanding of water quality assessment, data analysis, and statistical applications in environmental science.
HIGHLIGHTS
This study presents a novel comparative analysis of multiple organic pollution index (OPI) calculation methods to assess river water quality, using statistical techniques like PCA and NA.
The research provides a valuable framework for integrating real-world water quality assessment into environmental education, enhancing students’ practical skills in data analysis.
INTRODUCTION
Sustainable energy is increasingly crucial due to climate change and fossil fuel depletion (Chang et al. 2024). Environmental pollution is a growing problem and environmental education is a key factor in improving the natural environment (Haque & Sharif 2021). Water is widely used to meet human needs for drinking, irrigation, fisheries and energy production, and the purpose of its use depends on the quality of the water, making the maintenance of its quality indicators relevant. Water quality is determined by physical, chemical and biological indicators (Kuznietsov & Biedunkova 2024). The relevant documents regulating the impact of water discharge on the environment are the Water Code of Ukraine (WCU 2002) and the Water Framework Directive (WFD 2000). The WCU in Ukraine establishes the analogue of the WFD environmental quality standards, the maximum permissible concentration (MPC) of a substance in water, which determines the suitability of water for specific water use purposes. The MPCs are divided into two groups: Group H ‘for water used to meet drinking, domestic and other needs of the population’ (Standard 2022) and Group F ‘for water used for fisheries’ (Standard 1990). The river waters within the boundaries of a settlement are classified as domestic waters and are subject to the MPC, while other sections are classified as fisheries waters. Consequently, different MPCs may be applied to different sections of a river, so it is important to assess the ecological status of a water body according to different standards. Surface water organic pollution is the excessive anthropogenic input of organic substances (e.g., nutrients) to water bodies, often causing undue bacterial and algal growth, rendering these waters anoxic, thereby perturbing the ecological communities within them. These include river bed communities as these beds are altered through organic deposits (James 2003). The generation of organic pollution can, to a large extent, be predicted by the content of biogenic elements of nitrogen, carbon, phosphorus and oxygen (Hoque et al. 2023) and the corresponding chemical indicators of nitrate ions (
), ammonium nitrogen (N-NH3), phosphate ions (
), dissolved inorganic nitrogen (DIN), the rate of chemical oxygen demand (COD), the rate of biochemical oxygen demand in 5 days (BOD) and dissolved oxygen (DO). Organic pollution of surface waters can be monitored using the organic pollution index (OPI). To understand the water quality situation and the impact of anthropogenic activities on the possible deterioration of water quality in the river, a study was carried out using time series data of water quality parameters from OPI, whose numerical values were compared with each other at different sampling sites using statistical methods. Mitigation of оrganic pollution is an urgent task that involves the use of remediation strategies (Karthik et al. 2019; Balaji et al. 2023).
Carrying out work on the assessment of the ecological state of natural entities and their components allows students to combine research and educational activities. A comprehensive assessment of this ecological state involves the use of systematic indices, different calculation methods and values for the same indices, and the interpretation of their numerical values. The OPI can be used as an immediate measure of the overall health of a river ecosystem. Different calculation methods are known as OPIs (Anny et al. 2017; Son et al. 2020; Chen et al. 2023; Makki et al. 2023).
, 
and DOi are the monitored pollution concentrations in different reaches and BODо, CODо, 
, 
and DOо are the MPCs.
) in (1), the concentration of ammonium nitrogen (N-NH3) is used in calculation (2), and the concentration of phosphate 
is not taken into account. Their equation is given as follows:
and 
. Thus, the following levels have been established that characterize organic matter pollution: none <9; weak 10–29; medium 30–39; poor 40–49; deteriorated 50–59 and bad 60–69. Their equation is given as follows:
and 
; Cmi is the MPC and n = 3 is the number of Ci.
. DIN is the total concentration of N-NH3, 
and nitrite ions (
). The BOD values of Son et al. (2020), unlike those of Chen et al. (2022), Anny et al. (2017) and Makki et al. (2023), are not considered. OPIs by Son et al. (2020) are classified into four categories: excellent OPI < 0, good OPI = 0–1, polluted OPI = 1–4 and extremely polluted OPI = 4–5. Their equation is as follows:
and DOi are the monitored pollution concentrations in different reaches, and CODo, DINo, 
and DOo are the MPCs.It is well known that statistical methods are widely used in studies of the impact of pollution on the environment (Kuznietsov et al. 2024). Data set analysis was performed using Pearson correlation analysis to evaluate the relationships between water quality variables (Barakat et al. 2016). This minimizes the effect of between-station correlations and between-sampling campaign relationships. A correlation coefficient near −1 or 1 indicates a strong negative or positive relationship between two variables, and a value close to 0 means no linear relationship between them. Multiple linear regression was utilized as a statistical technique to ascertain the factors that contributed to control indicators (Hajigholizadeh & Melesse 2017). Principal component analysis (PCA) (Cai et al. 2022; Samuel et al. 2023) and network analysis (NA) (Dsikowitzky et al. 2017; Azli et al. 2022) are effective in elucidating complex relationships in environmental data.
The present study examines different approaches to calculating the OPIs of water. The study focuses on the hydrochemical indicators of water quality, namely those that are used to detect organic pollution. The educational component of student training in this study encompasses the analysis of data sets, the comparison of statistically derived data with one another and the spatial characteristics of sampling sites. This approach enables students to synthesize, systematize, interpret and publish the obtained data while developing practical skills in assessing surface water pollution from anthropogenic sources and applying different statistical methods. The main purpose of this study was to demonstrate different approaches to the assessment of OPIs and the possibility of using these results in the educational process of environmental students. This study presents a novel comparative analysis of four OPI calculation methods, integrating advanced statistical techniques such as PCA and NA to assess surface water quality.
METHODS
Study location
Location of water sampling and monitoring sites of the water of the Styr River before (A) and after (B) water discharge from the RNPP.
Location of water sampling and monitoring sites of the water of the Styr River before (A) and after (B) water discharge from the RNPP.
Water monitoring and performance
, N-NH3, 
, COD, BOD and DO, were measured. The parameters were monitored by the RNPP certified measuring laboratory. The concentration parameters were monitored using standard measurement methods recognized in Ukraine and listed in Table 1. The water temperature (t) and water flow (D) of the Styr River were measured according to Biedunkova et al. (2024). The range from 10 to 63 m3/s characterizes the water flow of the Styr River in the intake area of the RNPP. The water flow of the Styr River in the water discharge zone of the RNPP was determined to range from 10 to 63 m3/s (Figure 2(a)), the minimum value of D was observed during 2018 and the maximum was observed during 2021 (Figure 2(c)), with a spring maximum and autumn minimum water flow (Figure 2(e)). The water temperature of the Styr River in the water discharge zone of the RNPP ranged from 0.3 to 24.6 °С (Figure 2(b)), and the water temperature over those years looked fairly constant during the period of 2018–2022 (Figure 2(d)), which reflects seasonal changes in indicators with a maximum in summer and a minimum in winter (Figure 2(f)). Figure 2(c) and 2(e) demonstrates significant variations in river water flow both throughout the year and between different years. These fluctuations are primarily driven by seasonal flooding and freezing processes. Inundation typically begins with rising temperatures and snowmelt, occurring between February and April, depending on climatic conditions. Additionally, variations in snow cover across different years contribute to the observed differences in water flow dynamics. Methods for measuring the concentration of chemical parameters in the study (CI is the measurement range)
| Indicators | CI | Relative measurement error δ (%) | Method |
|---|---|---|---|
| BOD (mgO2/dm3) | 0.5–15 | 0.5–2: δ = ±(90–27); 2–5: δ = ±(27–11); 5–15: δ = ±(11–5) | KND 211.1.4.024-95 (KND 211.1.4.024-95 1995) |
| COD (mgO/dm3) | 5–100 | 5–10: δ = ±(65–34); 10–30: δ = ±(34–14); 30–100: δ = ±(14–9) | KND 211.1.4.021-95 (KND 211.1.4.021-95 1995) |
Nitrate ions ( ) | 0.5–1000 | 0.5–100: δ = ±25; 100: δ = ±16 | MVV 081/12-0651-09 (МVV 081/12-0651-09 2009) |
Nitrite ions ( ) | 0.03–10 | δ = ±10 | KND 211.1.4.023-95 (KND 211.1.4.023-95 1995) |
| Ammonium nitrogen N-NH3 (mgN/dm3) | 0.5–10 | 0.1–0.5: δ = ±20; 0.5: δ = ±9 | MVV 081/12-0106-03 (МVV 081/12-0106-03 2003) |
Orthophosphate ions  ( ) | 0.05–100 | 0.05–0.5: δ = ±15; 0.5: δ = ±10 | MVV 081/12-0005-01 (МVV 081/12-0005-01 2001) |
| DO (mgO2/dm3) | 0.5–50 | 0.5–5: δ = ±25; 5–20: δ = ±20; 20–50: δ = ±10 | MVV 081/12-0008-01 (МVV 081/12-0008-01 2001) |
| Indicators | CI | Relative measurement error δ (%) | Method |
|---|---|---|---|
| BOD (mgO2/dm3) | 0.5–15 | 0.5–2: δ = ±(90–27); 2–5: δ = ±(27–11); 5–15: δ = ±(11–5) | KND 211.1.4.024-95 (KND 211.1.4.024-95 1995) |
| COD (mgO/dm3) | 5–100 | 5–10: δ = ±(65–34); 10–30: δ = ±(34–14); 30–100: δ = ±(14–9) | KND 211.1.4.021-95 (KND 211.1.4.021-95 1995) |
| Nitrate ions () | 0.5–1000 | 0.5–100: δ = ±25; 100: δ = ±16 | MVV 081/12-0651-09 (МVV 081/12-0651-09 2009) |
| Nitrite ions () | 0.03–10 | δ = ±10 | KND 211.1.4.023-95 (KND 211.1.4.023-95 1995) |
| Ammonium nitrogen N-NH3 (mgN/dm3) | 0.5–10 | 0.1–0.5: δ = ±20; 0.5: δ = ±9 | MVV 081/12-0106-03 (МVV 081/12-0106-03 2003) |
| Orthophosphate ions () | 0.05–100 | 0.05–0.5: δ = ±15; 0.5: δ = ±10 | MVV 081/12-0005-01 (МVV 081/12-0005-01 2001) |
| DO (mgO2/dm3) | 0.5–50 | 0.5–5: δ = ±25; 5–20: δ = ±20; 20–50: δ = ±10 | MVV 081/12-0008-01 (МVV 081/12-0008-01 2001) |
Water flow (a) and water temperature (b), along with their density distribution over the years (c, d) and by months (e, f) in the Styr River within the Rivne NPP discharge zone.
Water flow (a) and water temperature (b), along with their density distribution over the years (c, d) and by months (e, f) in the Styr River within the Rivne NPP discharge zone.
The OPIs were calculated using Equations (1)–(4). The following methods were used: mathematical modelling of the parameter justification, classification and generalization, system analysis in the development of methodological bases for assessment, analysis and technical calculations, and calculation and statistical methods. The results of the study are initial data for further monitoring of possible abnormal changes in ОРІ levels in the water of the Styr River, including changes due to the influence of anthropogenic factors of discharge waters.
This research provides a valuable framework for integrating real-world water quality assessment into environmental education, enhancing students’ practical skills. It includes a database for monitoring the physicochemical parameters of water, using the Styr River as a case study; a series of OPI calculations to evaluate water оrganic pollution and a model for comparative statistical analysis against established standards to the interpretation and comparison of water quality data.
Statistical analysis
Statistical analysis of the study results involved determining the range of the data series (min–max), the arithmetic mean (M), the standard deviation (SD), the Pearson coefficient (r), the significance of the connection (р), the coefficient of determination (R2) of the respective sample and statistical analysis of the data using the Minitab software package (version 21.4.1). Moreover, PCA according to Yan et al. (2023) was performed to understand the underlying relationships between the water quality variables of all OPIs and to identify their characteristics. Additionally, the contribution of the components to the OPI according to PCA was assessed by water flow and water temperature of the Styr River in the RNPP water discharge zone. This analysis has been used to extract principal components (PCs) from sampling points as well as to evaluate possible sources and variations in water quality parameters in water samples. NA was conducted in accordance with Epskamp et al.’s (2012) model to examine the interactions between multiple variables for OPIs. Using NA, we estimated the relationships between all variables directly for the obtained OРI levels. The nodes in the NA are positioned using the Fruchterman–Reingold algorithm which organizes the network based on the strength of the connections between nodes. The JASP software package (version 0.14.3) was used for PCA and NA calculations, and the network graphs generated by JASP were generated with the R package.
RESULTS
Analysis of water quality parameters
The data on the monitoring of chemical parameters of the Styr River in the area influenced by the RNPP water discharge show a wide range of fluctuations in the concentrations of controlled parameters (Table 2).
Changes in concentrations of controlled parameters in the water of the Styr River (2018–2022)
| Parameter | min–max (mg/dm3) | Arithmetic mean (M) (mg/dm3) | Standard deviation (SD) (mg/dm3) |
|---|---|---|---|
 | 2.03–20.07 | 15.81 | ±0.49 |
 | 0.02–0.22 | 0.09 | ±0.06 |
| N-NH3 | 0.25–2.13 | 0.56 | ±1.18 |
| DIN | 0.39–4.32 | 1.74 | ±2.09 |
| COD | 17.6–83.2 | 46.72 | ±22.5 |
| BOD | 0.86–3.87 | 1.34 | ±0.17 |
 | 0.09–0.61 | 0.301 | ±0.134 |
| DO | 7.56–14.65 | 10.32 | ±2.11 |
| Parameter | min–max (mg/dm3) | Arithmetic mean (M) (mg/dm3) | Standard deviation (SD) (mg/dm3) |
|---|---|---|---|
| | 2.03–20.07 | 15.81 | ±0.49 |
| | 0.02–0.22 | 0.09 | ±0.06 |
| N-NH3 | 0.25–2.13 | 0.56 | ±1.18 |
| DIN | 0.39–4.32 | 1.74 | ±2.09 |
| COD | 17.6–83.2 | 46.72 | ±22.5 |
| BOD | 0.86–3.87 | 1.34 | ±0.17 |
| | 0.09–0.61 | 0.301 | ±0.134 |
| DO | 7.56–14.65 | 10.32 | ±2.11 |
concentration was preferentially observed in the spring months (March–May) and early summer (June), especially in 2018 and 2019, and the maximum concentration was observed in the winter months. The minimum 
concentration was generally observed in the winter months (September–February) in all years, and the maximum concentration was observed in the summer months (June–August) and autumn months (September–November), especially in August in 2020, 2021 and 2022. The maximum DIN, N-NH3 and DO concentrations were observed in the winter months (December–February), and the minimum concentration was observed in the summer months (June–August). Among the annual variations, the maximum concentrations of DIN, N-NH3 and 
were occurred in 2020. 
Сolorgrams of temporal variations of water quality parameters in the Styr River.
Analysis of the OPI levels
in the cold season. The III-А(В)-OPI-H(F) levels, calculated according to Equation (3), were characterized as ‘none’ or ‘weak’ (Figure 4(c)), and the results obtained by Equation (3) did not characterize the water as polluted with organic matter during the period of the year. The ІV-А(В)-OPI-H(F) levels, calculated according to Equation (4), were characterized as ‘good’ mainly in spring and autumn and ‘lightly polluted’ and ‘moderately polluted’ in autumn and winter (Figure 4(d)). The range (min–max) and M index were almost comparable for I-A(B)-OPI-H(F) and II-A(B)-OPI-H(F) and identical for III-A(B)-OPI-H(F). This is due to the different calculations used and the use of the same MPCs, and for III-A(B)-OPI-H(F), the limited indicators used for the calculation (Table 3), as the MPC values are the same for BOD, DO, 
and slightly different for 
, СOD and N-NH3 according to the standards (Standard 1990, 2022). Characterization of methods for measuring the concentration of chemical parameters in the study (CI is the measurement range)
| OPIa for river water | Equation | min–max | Arithmetic mean (M) | Standard deviation (SD) |
|---|---|---|---|---|
| ОРІ before the water discharge and H standard (І-A-ОРІ-H) | (1) | −2.45 to 3.32 | 0.54 | ±1.34 |
| ОРІ after the water discharge and H standard (І-B-ОРІ-H) | (1) | −2.45 to 3.01 | 0.53 | ±1.43 |
| ОРІ before the water discharge and F standard (І-A-ОРІ-F) | (1) | −2.55 to 3.40 | 0.56 | ±1.38 |
| ОРІ after the water discharge and F standard (І-B-ОРІ-F) | (1) | −2.50 to 3.05 | 0.55 | ±1.21 |
| ОРІ before the water discharge and H standard (IІ-A-ОРІ-H) | (2) | −1.92 to 3.52 | 1.08 | ±1.11 |
| ОРІ after the water discharge and H standard (ІI-B-ОРІ-H) | (2) | −1.91 to 3.05 | 0.99 | ±1.27 |
| ОРІ before the water discharge and F standard (ІI-A-ОРІ-F) | (2) | −1.85 to 3.45 | 1.05 | ±1.16 |
| ОРІ after the water discharge and F standard (IІ-B-ОРІ-F) | (2) | −1.94 to 3.08 | 0.87 | ±1.09 |
| ОРІ before the water discharge and H standard (IIІ-A-ОРІ-H) | (3) | 4.71–22.25 | 11.52 | ±6.85 |
| ОРІ after the water discharge and H standard (ІII-B-ОРІ-H) | (3) | 4.92–21.05 | 10.31 | ±6.81 |
| ОРІ before the water discharge and F standard (ІII-A-ОРІ-F) | (3) | 4.71–22.25 | 11.52 | ±6.85 |
| ОРІ after the water discharge and F standard (IIІ-B-ОРІ-F) | (3) | 4.92–21.05 | 10.31 | ±6.81 |
| ОРІ before the water discharge and H standard (IV-A-ОРІ-H) | (4) | 1.23–3.94 | 2.25 | ±1.42 |
| ОРІ after the water discharge and H standard (ІV-B-ОРІ-H) | (4) | 1.51–3.97 | 2.14 | ±1.35 |
| ОРІ before the water discharge and F standard (ІV-A-ОРІ-F) | (4) | 1.04–3.89 | 1.95 | ±1.50 |
| ОРІ after the water discharge and F standard (IV-B-ОРІ-F) | (4) | 1.51–3.07 | 1.97 | ±1.50 |
| OPIa for river water | Equation | min–max | Arithmetic mean (M) | Standard deviation (SD) |
|---|---|---|---|---|
| ОРІ before the water discharge and H standard (І-A-ОРІ-H) | (1) | −2.45 to 3.32 | 0.54 | ±1.34 |
| ОРІ after the water discharge and H standard (І-B-ОРІ-H) | (1) | −2.45 to 3.01 | 0.53 | ±1.43 |
| ОРІ before the water discharge and F standard (І-A-ОРІ-F) | (1) | −2.55 to 3.40 | 0.56 | ±1.38 |
| ОРІ after the water discharge and F standard (І-B-ОРІ-F) | (1) | −2.50 to 3.05 | 0.55 | ±1.21 |
| ОРІ before the water discharge and H standard (IІ-A-ОРІ-H) | (2) | −1.92 to 3.52 | 1.08 | ±1.11 |
| ОРІ after the water discharge and H standard (ІI-B-ОРІ-H) | (2) | −1.91 to 3.05 | 0.99 | ±1.27 |
| ОРІ before the water discharge and F standard (ІI-A-ОРІ-F) | (2) | −1.85 to 3.45 | 1.05 | ±1.16 |
| ОРІ after the water discharge and F standard (IІ-B-ОРІ-F) | (2) | −1.94 to 3.08 | 0.87 | ±1.09 |
| ОРІ before the water discharge and H standard (IIІ-A-ОРІ-H) | (3) | 4.71–22.25 | 11.52 | ±6.85 |
| ОРІ after the water discharge and H standard (ІII-B-ОРІ-H) | (3) | 4.92–21.05 | 10.31 | ±6.81 |
| ОРІ before the water discharge and F standard (ІII-A-ОРІ-F) | (3) | 4.71–22.25 | 11.52 | ±6.85 |
| ОРІ after the water discharge and F standard (IIІ-B-ОРІ-F) | (3) | 4.92–21.05 | 10.31 | ±6.81 |
| ОРІ before the water discharge and H standard (IV-A-ОРІ-H) | (4) | 1.23–3.94 | 2.25 | ±1.42 |
| ОРІ after the water discharge and H standard (ІV-B-ОРІ-H) | (4) | 1.51–3.97 | 2.14 | ±1.35 |
| ОРІ before the water discharge and F standard (ІV-A-ОРІ-F) | (4) | 1.04–3.89 | 1.95 | ±1.50 |
| ОРІ after the water discharge and F standard (IV-B-ОРІ-F) | (4) | 1.51–3.07 | 1.97 | ±1.50 |
aOPI abbreviation, e.g. I-A-OРI-H, where the first digit indicates the calculation method, e.g. I calculated according to Equation (1), II calculated according to Equation (2), etc.; the second letter is the sampling location A – before water intake, B – after discharge; the last letter H or F is the MРС, where H is for water used to meet drinking, domestic and other needs of the population, F is for water used for fisheries, respectively.
OPI levels in the Styr River water calculated using different methods: (a) based on Equation (1), (b) Equation (2), (c) Equation (3), and (d) Equation (4).
OPI levels in the Styr River water calculated using different methods: (a) based on Equation (1), (b) Equation (2), (c) Equation (3), and (d) Equation (4).
Density plots of OPI levels in the Styr River water calculated using different methods: (a) based on Equation (1), (b) Equation (2), (c) Equation (3), and (d) Equation (4).
Density plots of OPI levels in the Styr River water calculated using different methods: (a) based on Equation (1), (b) Equation (2), (c) Equation (3), and (d) Equation (4).
Estimation of the numerical levels and distribution of the OPIs
, 
and N-NH3 were identified as PC4 and PC5 (Figure 6). The IІІ-A(B)-OPI-H(F) levels showed strong positive loadings on PC1 for BOD. It is important to note that due to the limited number of water quality parameters used for calculating OPI according to Equation (3), PC2 comprises a physical water discharge parameter (D). For IV-A(B)-OPI-H(F) levels, discrepancies in the formation of PCs are noted, with a strong negative loading on PC1 for water temperature (t) under standard H and for DO under standard F. This may be attributed to the higher MPC value for DO in water bodies designated for fish farming. COD, BOD and DO had the greatest contributions to PC1 and PC2, which often exhibited strong negative and positive loadings, respectively (Figure 6). Moreover, parameters t (water temperature) often significantly contributed to the PC3, albeit with varying signs across different categories (H and F). In addition, other water quality parameters (DIN, 
, N-NH3) are defined as PC4 and PC5. 
The PC of the OРI levels of the water of the Styr River: (а) I-A(B)-OPI-H(F), (b) II-A(B)-OPI-H(F), (c) III-A(B)-OPI-H(F) and (d) IV-A(B)-OPI-H(F).
The PC of the OРI levels of the water of the Styr River: (а) I-A(B)-OPI-H(F), (b) II-A(B)-OPI-H(F), (c) III-A(B)-OPI-H(F) and (d) IV-A(B)-OPI-H(F).
Network graph of the OРI levels of the water of the Styr River.
The correlation relationships for water in the Styr River and the OPIs calculated using different methods and sampling locations (В depending on А) and MPCs (H – a, c, e, g; F – b, d, f, h).
The correlation relationships for water in the Styr River and the OPIs calculated using different methods and sampling locations (В depending on А) and MPCs (H – a, c, e, g; F – b, d, f, h).
DISCUSSION
When implementing the methods in the educational sphere using this research framework, students are asked to analyze the database of monitoring results of DIN, 
, N-NH3, 
, COD, BOD and DO concentrations in the river surface water. In this study, the results of chemical monitoring of the Styr River in the area influenced by water discharge from the RNPP for 2018–2022 are used. As part of their training, students will carry out numerical calculations of the OPI using different methods (Anny et al. 2017; Son et al. 2020; Chen et al. 2023; Makki et al. 2023). These calculations will use two types of environmental standards: MPC for fisheries and domestic purposes, for different sampling points from a point source of anthropogenic pollution (Figure 4).
The seasonal variations in water quality parameters from 2018 to 2022 revealed distinct patterns (Figure 3). Higher BOD and COD values are associated with warmer summer months, reflecting increased organic pollution and microbial activity. 
concentrations peak in spring, likely due to agricultural runoff, while phosphate levels are highest in autumn and winter, possibly due to runoff and reduced uptake by aquatic plants (Saavedra et al. 2024). DO shows an inverse relationship, peaking in winter when temperatures are lower and solubility is higher. N-NH3 and DIN values are highest in winter, indicating possible decreased microbial activity and slower nitrification processes (Klymenko et al. 2018; Böllmann & Martienssen 2024). These seasonal trends provide valuable insights for managing and mitigating water pollution. Consequently, based on the OPI levels, students are asked to identify the existing causes of OPI deterioration. The primary benefit of this study is the utilization of an array of environmental standards, which enables students to better comprehend environmental standards and MPCs in water bodies. The distribution of OPIs by year, as calculated using different methods (Figure 5), is determined by the weight of water quality parameters (Chen et al. 2022). Moreover, the different interpretations of the OPI levels obtained by the different calculation methods in this study include the interpretation of results from ‘none’ for categories (3,4) to ‘pure’ for (1,2). Students can acquire a systematic approach to the environmental assessment of pollution and avoid ambiguity in the interpretation of results (Damalas & Ilias 2011; Samuel et al. 2018; Trach et al. 2024). When studying this topic, students can use the lessons learned from the OPI calculations to make informed safety recommendations.
The PCA and NA performed using JASP software can be used for the interpretation of complex data for students in their studies. In particular, using this material, PCA revealed distinct patterns in OPI levels of variation (Figure 6) and these components help in understanding the underlying factors affecting OPIs in different MPCs (H and F), which might represent different habitats or sampling conditions. PCA is frequently used in environmental studies (Biedunkova & Kuznietsov 2024; Shams et al. 2024) to identify patterns and reduce the dimensionality of data, thereby making it easier to interpret complex data sets. The network graph provides a visual and quantitative representation of the relationships between various OPIs calculated using different methods (Figure 7). The strong connections between certain indices suggest that they share similar underlying factors or conditions affecting organic pollution and imply that the methods used for these indices capture similar aspects of water quality. To address organic pollution in the Styr River, effective remediation strategies should be considered. One of the primary approaches is improving wastewater treatment processes (Ethiraj et al. 2024). Advanced treatment technologies, including biofiltration and constructed wetlands, can enhance the removal of organic pollutants, minimizing their impact on aquatic ecosystems. Integrating toxicity assessment with statistical modelling could enhance predictive capabilities, allowing for the development of more effective water quality management policies that safeguard river ecosystems and public health (Martyniuk et al. 2018; Samuel et al. 2020).
Data analysis of the OPI calculations using the results of the Styr River water for this study is carried out using Minitab software, but any software that uses the method with the definition of statistical parameters p, r, and R2 can be used. The advantage of this study is the use of Pearson's correlation analysis, which allows students to acquire practical skills in comparing results and describing correlation relationships. The obtained r levels are comparable for the OPIs, both for those calculated according to the MPCs for the F and H and for different sampling sites before water intake (A) and after discharge (B) with return water from a point anthropogenic source, in this case, the RNPP water discharge (Figure 8). The identification of correlation type facilitates students' exploration of information recall regarding the various relationships, their graphical representation and statistical properties, thereby enabling the identification of correlation relationships (Kahaer & Tashpolat 2019). A well-known study by Rajnish et al. (2021) highlights the role of biogenic synthesis methods in producing nanoparticles capable of degrading organic pollutants, which are key contributors to high OPIs. Similarly, Singh et al. (2024) examined the potential application of these nanoparticles in treating organic dye pollutants in water, further emphasizing their role in mitigating organic contamination. The development of nutrient recovery technologies may be an avenue to consider in the future.
The professional development of ecologists is of paramount importance in order to establish pollution by one of the indicators (Inobeme et al. 2022; Statnyk et al. 2023). This approach to identifying pollution enables students to establish the cause of the deterioration of the indicator and to carry out a comprehensive analysis of pollution using statistical–mathematical methods. This in turn allows them to formulate conclusions of environmental assessments of pollution.
CONCLUSION
This study demonstrated various approaches to assessing organic pollution and explored their applicability in the environmental education of students. By analysing time series data of water quality parameters from the OPI and comparing these values across sampling sites near the RNPP along the Styr River in Ukraine, we employed statistical methods to interpret the results. Over the period of 2018–2022, the water quality parameters in the Styr River demonstrated significant fluctuations, with different OPI calculation methods yielding varying levels of organic pollution. These indices indicate periods of ‘pure’ water with occasional ‘contaminated’ episodes. РCA was utilized to uncover underlying relationships between water quality parameters, revealing distinct patterns in OPI levels before and after the water discharge. For instance, BOD and COD had strong positive loadings on the PCs for OPI levels, while DO typically showed strong negative loadings, suggesting its inverse relationship with pollution levels. The discharge from the RNPP influenced the OPI levels. This suggests that discharge might have a slight effect on certain pollutants or that the river's mitigation processes and dilution are effective in this region. Moreover, integrating PCA with NA provided a more comprehensive understanding of the interrelationships among water quality parameters. Correlation analysis revealed a statistically significant relationship between OPI levels at sampling sites before water intake (A) and after discharge (B) of water discharge from the RNPP, with correlations ranging from very strong (r > 0.9) to strong (r = 0.8–0.9) at the significance level of p < 0.001. The regressions obtained had medium (R2 = 70–85%) and high (R2 = 85–100%) coefficients of determination, indicating the adequacy of the selected assessment approach. Moreover, considering the monitoring data and the practical aspects of this study, students will deepen their analytical and statistical skills whilst getting hands-on practice in environment assessments and pollution mitigation. This student training allows us to combine educational activities with scientific research and provides insight for future work in this area. We believe that this approach is beneficial to and strengthens environmental education. Given the observed seasonal and annual variability, it is essential to maintain a robust data set to anticipate and mitigate potential pollution spikes. Continuous monitoring and diverse methodological approaches are recommended for a more thorough understanding of water quality.
AUTHOR CONTRIBUTIONS
O.B.: conceived the experiment(s), conceptualization, resources, data curation, writing – review and editing, project administration and supervision; P.K.: validation, supervision and writing – original draft preparation; V.K.: investigation and visualization; O.B., P.K. and V.K.: analyzed the results, formal analysis and methodology; P.K. and O.B.: software. All authors reviewed the manuscript.
DATA AVAILABILITY STATEMENT
All relevant data are included in the paper or its Supplementary Information.
CONFLICT OF INTEREST
The authors declare there is no conflict.
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