Multivariate regression studies for the investigation of the COD, BOD, and TOC concentrations in the water of the Styr River within the zone of influence of the power plant discharge

non-radioactive impact, nuclear power plant, surface water, water discharge

The discharge of cooling water into surface waters containing residual heat energy is a pressing issue in the environmental impact of nuclear power. Cooling water flow can cause negative changes in the water and ecological processes of natural waters, in particular an imbalance of organic matter, in combination with a complex of natural factors. The purpose of the article was to perform a statistical analysis of the dynamics of organic matter content in the water of the Styr River in the Rivne Nuclear Power Plant (Rivne NPP) impact zone to assess the manifestation of the environmental impact of the discharge of cooling water with residual heat. The monitoring data series consisted of the results of monthly monitoring of the rate of chemical oxygen demand, the rate of biochemical oxygen demand in 5 days, and total organic carbon in the Styr River sections before and after the discharge of Rivne NPP cooling water in 2018–2022.

HIGHLIGHT

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The purpose of the article was to perform a statistical analysis of the dynamics of organic matter content in the water of the Styr River in the Rivne Nuclear Power Plant impact zone to assess the manifestation of the environmental impact of the discharge of cooling water with residual heat.

INTRODUCTION

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The thermal regime of a water body is a crucial factor for the quality of the ecosystem, and the thermal impact on water bodies caused by nuclear power plant (NPP) cooling water discharges can significantly affect the aquatic environment and its biota (Barescut et al. 2009). Because water treatment technologies can be used to minimize water consumption (Kuznietsov et al. 2024), recirculating cooling water systems (RCWS) are the most commonly used cooling systems in the power sector. Chemical and temperature effects occur when RCWS effluent is discharged into a body of water (Kuznietsov et al. 2023a). In the RCWS of NPPs, water-soluble components of the cooling water are heated, evaporated, and concentrated. National regulations and permits regulate the maximum allowable temperature increase, and chemical content of NPP cooling water discharges (Zhang et al. 2023). Water discharge into natural bodies of water can lead to changes in the chemical equilibrium of their constituents and pose a potential risk to human health (Zak et al. 2021).

The temperature effects of water discharges are determined by direct and indirect effects: direct effects include increased activity with accelerated digestion, increased food demand, reproductive disorders, and destruction of sensitive tissues of the nervous system of aquatic organisms; indirect effects cause negative changes in aquatic ecological processes, in particular, disturbance of the nutrient balance (Sandstrom 1997). There have been studies on the impact of the temperature effects of cooling water discharge on aquatic ecosystems, but the studies that have been conducted at real sites have only looked at the spatial and temporal interpretation of temperature (Jiang et al. 2018). In addition to thermal discharges, aquatic ecosystems face the impact of chemicals discharged from NPP with cooling water return flows, which should also be considered (Vries et al. 2008). Changing chemical balances in surface waters can be considered important indicators of aquatic ecological responses to NPP water discharges (Kuznietsov & Biedunkova 2023a).

Rivers are important sites for the transport of organic matter and are critical components of the global carbon (C) cycle. However, little is known about the longitudinal and temporal changes in organic matter in rivers (Soria-Reinoso et al. 2022). The implementation of measures to protect water resources from pollution and their rational use is an urgent task, which is also essential for the sustainable development of the entire energy sector (Kuznietsov & Biedunkova 2023b). Studies usually consider the temperature and chemical effects of cooling water discharge separately (Kuznietsov et al. 2023b). Our study links chemical to temperature effects, and the established correlations allow predicting the content of chemical indicators after cooling water discharge and focus on the potential impact and relationship of temperature with the content of organic matter in terms of chemical oxygen demand (COD), biochemical oxygen demand (BOD5), and total organic carbon (TOC).

BOD5 represents the oxygen demand needed microorganisms for the decomposition of organic matter, COD reflects the oxygen consumption during the chemical decomposition of organic matter, and TOC represents the concentration of carbon dioxide produced during the catalytic combustion of organic matter. Aguilar-Torrejón et al. (2023) show that the values of BOD5, COD, and TOC vary and depend on the type and parameters of the water analysed. COD and BOD5 are the most accurate measures of the increase in the amount of organic matter that can be oxidized by chemical or biological processes (Costa et al. 2018), and recommend using them together to assess organic pollution in water bodies, with COD values generally higher than BOD5 values and depending on the type of water analysed (Recoules et al. 2019). BOD5 is directly related to the amount of microbial contamination, and TOC is the most comprehensive analysis if the aim is to detect all types of organic matter present in water (Si et al. 2019). Despite the use of toxic chemicals for COD and very long analysis times (5 days) for BOD5, these methods are widely used and have not been completely replaced by other measurements, including TOC (Aguilar-Torrejón et al. 2023).

COD, TOC, and BOD5 can be considered complementary methods for monitoring organic matter content (Dubber & Gray 2010) Considering the relationship and different natures of COD and BOD5, the ratio of BOD5:COD is used to indicate biodegradability. If the ratio of COD:BOD5 is less than 0.1, it indicates the presence of organic matter that is difficult to biodegrade. The lower limit of the BOD5:COD ratio for organic matter to be biodegradable is 0.4 (Farraji et al. 2015). The BOD5:TOC ratio is also proposed as a measure of biodegradability (Ledakowicz 1998). Thus, the degree of biodegradation calculated from the BOD5:COD or BOD5:TOC ratio is commonly used for wastewater but is much more universal (Kowalski 1987). In our study, the BOD5:COD and BOD5:TOC ratios were used to assess the biodegradability of organic matter at different sites before and after the Rivne NPP discharge to determine the possible change in the nature of organic matter due to the possible impact of the cooling water discharge.

The purpose of our study is to investigate the spatial and temporal changes in the contents of BOD5, COD, and TOC and to establish correlation dependencies based on subject-specific approaches to the distribution of their content data in surface waters. The study was carried out on the example of the Styr River in the zone of influence of the water discharges of the Rivne NPP. The novelty of our study lies in the application of statistical regression analysis to the assessment and impact of cooling water discharges on the natural waters of the river. The practical value of this research lies in the possibility of applying this model to other RCWS power plants with cooling water discharges into water bodies.

METHODS

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The study is carried out using the example of the RCWS of the Rivne NPP. The Styr River is the source of the technical water supply of the Rivne NPP. Sampling and monitoring of parameters were carried out by the certified measuring laboratory of the Rivne NPP (Certificate of Recognition of Measuring Capabilities No. R-8/11-57-5 dated 22.12.17), using measuring instruments verified by the State Metrological Supervision of Ukraine. Standard measurement methods were used to control the concentration, for TOC using an Elementar liqui TOC II analyser, the thermocatalytic oxidation method at 680 °C with nondispersive infrared (NDIR), and COD and BOD5 using titrimetric methods (Table 1). COD was determined by the acidic permanganate, and the sample with H₂SO₄ and KMnO₄ was heated for 30 min in a water bath at 96–98 °C. The BOD5 of water samples was determined by getting the variation between dissolved oxygen on day 1 and day 5. Dissolved oxygen was determined by the Winkler method with KI-KOH-NaN solution. The scheme of the sampling of Styr River water before intake (A−) and after discharge (B−) of cooling water from Rivne NPP is shown in Figure 1. Water samples were collected from the subsurface, with no air bubbles, directly to dark glass vessels according to DSTU ISO 5667-6:2009. They were stored at +4 °C. Sampling was carried out once a week during 2018–2022. The preservation and handling of samples were carried out in accordance with DSTU ISO 5667-3-2001.

Table 1

Characterization of methods for measuring the concentrations of BOD5, COD, and TOC used in the study (CI is the measurement range)

Indicator	CI	Relative measurement error δ (%)	Method of measurement (standard in Ukraine)
TOC (mgC/dm³)	0.3–100*	From 0.3 to 10: δ = ±10; more than 10: δ = ±5	DSTU ISO 1484:2003
BOD5, (mgO₂/dm³)	0.5–15	From 0.5 to 2 inclusive: δ = ±(90–27); over 2 to 5 inclusive: δ = ±(27–11); over 5 to 15 inclusive: δ = ±(11–5)	KND 211.1.4.024 − 95
COD (mgO/dm³)	5–100	From 5 to 10 inclusive: δ = ±(65–34); over 10 to 30 inclusive: δ = ±(34–14); over 30 to 100: δ = ±(14–9)	KND 211.1.4.021 – 95

Indicator	CI	Relative measurement error δ (%)	Method of measurement (standard in Ukraine)
TOC (mgC/dm³)	0.3–100*	From 0.3 to 10: δ = ±10; more than 10: δ = ±5	DSTU ISO 1484:2003
BOD5, (mgO₂/dm³)	0.5–15	From 0.5 to 2 inclusive: δ = ±(90–27); over 2 to 5 inclusive: δ = ±(27–11); over 5 to 15 inclusive: δ = ±(11–5)	KND 211.1.4.024 − 95
COD (mgO/dm³)	5–100	From 5 to 10 inclusive: δ = ±(65–34); over 10 to 30 inclusive: δ = ±(34–14); over 30 to 100: δ = ±(14–9)	KND 211.1.4.021 – 95

Note: According to DSTU EN 1484:2003, sample preparation for TOC determination includes pre-filtering the sample through a 0.45 ηm pore size filter.

Figure 1

Location of water sampling and monitoring sites of the water of the Styr River before (А−) and after (В−) water discharge from the Rivne NPP (Ukraine).

Statistical processing of the study results included determination of the range of data series (min-max), arithmetic mean (M), standard deviation (±SD), coefficient of variation (CV), Pearson coefficient (r), significance of the connection (p), and coefficient of determination (R²) of the respective sample and factor analysis of data using the Minitab software package (Version 21.4.1, Minitab, LLC). Dataset analysis was performed using Pearson correlation analysis to evaluate the relationship between water quality variables (Barakat et al. 2016; Kuznietsov & Biedunkova 2024). Тhe principal component analysis (PCA) was applied efficiently to our data to identify the underlying interrelationship among the parameters (Hajigholizadeh & Melesse 2017). Partial least-squares regression (PLSR), as a multivariate regression method, was used to determine the linear relationship between a set of dependent response variables (X) and a set of predictor variables (Y) (Kahaer & Tashpolat 2019).

RESULTS AND DISCUSSION

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Changes in the concentration of BOD5, COD, TOC, and temperature in the water of the Styr River in the area of influence of the Rivne NPP water discharges show a wide range of fluctuations of these indicators (Table 2). The statistical analysis of the laboratory control data shows that the average values of the water content of the Styr River differ slightly in the sections of the river before and after the Rivne NPP water discharges. The difference in the temperature of the Styr River water before and after the discharge of cooling water from the Rivne NPP did not exceed the established temperature increase effect under the conditions of the Permit of the State Agency for Water Resources of Ukraine (SAWRU) (Permit SAWRU 2020) of 3 °C and had an average value of 0.9 °C, with m = ± 0.54 °C.

Table 2

Statistical descriptive the concentrations of BOD5, COD, and TOC and water temperature in different monitoring sites of the Styr River for 2018–2022

Indicator	M	± SD	min	max	CV
The water intake of the Rivne NPP
BOD5 (mgO₂/dm³)	1.18	0.19	0.86	3.86	25.6
COD (mgO/dm³)	42.6	20.3	17.6	83.2	43.9
TOC (mgC/dm³)	11.6	4.2	5.23	20.0	56.5
Temperature (°C)	11.3	10.6	0.8	24.3	92.5
After the water discharge of the Rivne NPP
BOD5 (mgO₂/dm³)	1.22	0.22	0.80	3.96	27.6
COD (mgO/dm³)	40.6	22.3	15.1	82.4	43.8
TOC (mgC/dm³)	12.2	3.9	6.22	25.4	42.3
Temperature (°C)	12.2	11.2	1.7	25.6	94.5

Indicator	M	± SD	min	max	CV
The water intake of the Rivne NPP
BOD5 (mgO₂/dm³)	1.18	0.19	0.86	3.86	25.6
COD (mgO/dm³)	42.6	20.3	17.6	83.2	43.9
TOC (mgC/dm³)	11.6	4.2	5.23	20.0	56.5
Temperature (°C)	11.3	10.6	0.8	24.3	92.5
After the water discharge of the Rivne NPP
BOD5 (mgO₂/dm³)	1.22	0.22	0.80	3.96	27.6
COD (mgO/dm³)	40.6	22.3	15.1	82.4	43.8
TOC (mgC/dm³)	12.2	3.9	6.22	25.4	42.3
Temperature (°C)	12.2	11.2	1.7	25.6	94.5

The concentrations of BOD5, COD, and TOC increase slightly at the site after the Rivne NPP outfall, and the average concentrations of BOD5, COD, and TOC remain at the same level during the years of observation, although when comparing their maximum values, higher values are observed at the site after the Rivne NPP outfall in the warm season (Figure 2).

Figure 2

Dynamics of temporal change in the concentrations of BOD5, COD, TOC, and temperature in the water of the Styr River before (a) and after (b) water discharge from the Rivne NPP for 2018–2022.

Changes in the climatic pattern of water bodies due to natural factors such as precipitation and temperature can affect the type and concentration of BOD5, COD, and TOC in the water system (Dubber & Gray 2010). Therefore, it is important to separate anthropogenic factors influencing changes in the organic matter balance when evaluating them. During the seasons of the year, the minimum values of BOD5, COD, and TOC in the water of the Styr River occurred in winter, with the maximum content occurring in July–October (Figure 3). Most of the carbon compounds entering the river originate from soil mobilization and rock weathering, including plant carbon compounds, soil microbial carbon compounds, and petrogenic carbon compounds. In rivers, carbon compounds can be transported as particulate or dissolved organic matter, with particulate carbon compounds often containing old, degraded plant and soil material, while dissolved organic matter is generally younger (Wang et al. 2023). The seasonal increase in organic matter content during warm periods of the year is associated with production and destruction processes that occur during phytoplankton photosynthetic activity (Si et al. 2019). The detected decrease in the content of BOD5, COD, and TOC in the autumn-winter period (Figure 3) indicates the natural ability of the river to self-purify (Dеbska et al. 2021).

Figure 3

Seasonal variability of the temperature (a) and the concentrations of BOD5 (b), COD (c), and TOC (d) in the water of the Styr River for 2018–2022.

Figure 4

Relative variable importance components of BOD5, COD, ТОС, and temperature in the water of the Styr River before (а) and after (b) water discharge from the Rivne NPP for 2018–2022.

PCA provides information on the most significant variables by reducing high-dimensional data with minimal loss of information. PCA was applied to extract the most significant principal components (PCs) and reduce the contribution of variables with the least significance (Figure 4). In this study, the multivariate dataset comprised three chemical variables (BOD5, COD, and TOC) and 48 water samples collected from two sampling locations (before water intake and after discharge of the Rivne NPP) in 12 sampling periods (January to December). The number of significant PCs was retained based on the Kaiser criterion with an eigenvalue greater than 1. Тhe cluster analysis of variables (Figure 5) shows the distribution of BOD5, COD, and TOC parameters for water sampled in the sampling locations before (А−) and after (В−) water discharge from the Rivne NPP, and indicators are grouped according to the sampling locations, which reflects the correlation of control indicators before water intake and after discharge of the Rivne NPP.

Figure 5

Dendrogram data of ВOD5, COD, and ТОС and temperature in the water of the Styr River before (А−) and after (В−) water discharge from the Rivne NPP for 2018–2022.

The k-means clustering on the generating cluster graph (Figure 6) has three clusters, which correspond to the control indicator. The uniformity of the control data is confirmed by the fact that the сluster analysis method did not identify the corresponding clusters corresponding to the before (A−) and after (B−) water discharge from the Rivne NPP. The PCA score plot shows that the percentages of total variance explained by the first three PCs (PC₁, PC₂) are 98.52 and 1.48%, respectively, which accounts for accumulative explained variance of 100% (Figure 7).

Figure 6

Dendrogram data of ВOD5, COD, ТОС, and temperature in the water of the Styr River before (А−) and after (В-) water discharge from the Rivne NPP for 2018–2022.

Figure 7

PCA рlot data of BOD5, COD, and TOC contents of the group in the water of the Styr River before (А−) and after (В-) water discharge from the Rivne NPP for 2018–2022.

The normalized object scores and variable loadings on each PC were scaled proportionally to the root of the variance accounted for by that PC, as shown in Figure 7. The PC consists of moderate positive loadings for BOD5, COD, and TOC. The variables (Styr River before water intake and after discharge of the Rivne NPP) were found to be highly correlated and their contribution was significant in grouping the samples collected from the Styr River upstream and downstream sampling sites. It is important to note that although PCA can provide insight into the data on the distribution of water discharge components, it is not necessarily an optimal method for feature extraction, and other methods such as correlation analysis with subject-specific data partitioning approaches may be to complement PCA (Aguilar-Torrejón et al. 2023).

The spatial distribution of the chemical monitoring data shows that the average values of organic matter (BOD5, COD, and TOC) in the Styr River water differ slightly in the Styr River watercourse sections before (A−) and after (B−) water discharge from the Rivne NPP, as seen in the contour plot of their contents (Figure 8). The regression analysis revealed a statistically significant direct relationship between the water concentrations of BOD5, COD, and TOC in both study sites of the Styr River (Figure 9).

Figure 8

Contour plot the concentration of BOD5, COD, and ТОС in water of the Styr River before (а) and after (b) water discharge from the Rivne NPP for 2018–2022.

Figure 9

The relationship correlations of BOD5, COD, and TOC content in the water of the Styr River before (A−) and after (B−) water discharge from the Rivne NPP for 2018–2022: (a) COD, (b) BOD5, and (c) TOC.

The relationship correlations between the concentrations of BOD5, COD, and TOC in the Styr River water were established using topic-specific data distribution approaches using PLSR. The values of COD concentration in the Styr River sections before and after the water discharge from the Rivne NPP (Figure 9(a) had a statistically significant (p = 0.0005) direct correlation at the level of very strong to functional (r = 0.93, R² = 85.90%) as described by Equation (). There was a statistically significant (p = 0.0002) direct correlation at the level of very strong to functional (r = 0.99, R² = 81.72%) between the values of BOD5 concentration in the Sty River before and after water discharge from the Rivne NPP (Figure 9(b) as described by Equation (). There was a statistically significant (p = 0.0002) direct correlation at the level of very strong to functional (r = 0.90, R² = 96.83%) between the values of TOC concentration in the Styr River before and after water discharge from the Rivne NPP (Figure 9(c) as described by Equation ().

(1)

(2)

(3)

where COD(A-), BOD5(A-), and TOC(A-) are the concentrations of BOD5, COD, and TOC in the water of the Styr before the water discharge of the Rivne NPP (in mgO/dm³, mgO₂/dm³, and mgC/dm³, respectively); COD(В-), BOD5(В-), and TOC(В-) are the concentrations of BOD5, COD, and TOC in the water of the Styr after the water discharge of the Rivne NPP (in mgO/dm³, mgO₂/dm³, and mgC/dm³, respectively).

Regression analysis showed a statistically significant direct relationship between the water concentrations of BOD5, COD, and TOC and temperature (Figure 10). The r-Pearson correlation coefficients were calculated to assess the strength of the relationship between BOD5, COD, and TOC concentrations and temperature (Figure 11). BOD5, TOC, and temperature concentrations were correlated at a moderate level (r = 0.5–0.7) and COD and temperature were correlated at a strong level (r = 0.7–0.9). The correlations between TOC, COD, and BOD5 concentrations were weak (r = 0.3–0.5) for COD versus TOC and BOD5 versus TOC and strong for BOD5 versus COD. Taking into account the dependencies between the water concentrations of BOD5, COD, TOC, and temperature, equations describing the relationship between each and temperature were obtained (Table 3). The obtained regressions have medium (R² = 70–85%) and high (R² = 85–100%) coefficients of determination and can be used to calculate the content of BOD5, COD, and TOC concerning the temperature factor. COD and BOD5 measure oxygen demand, TOC is a direct measure of carbon content, and the ratio between TOC, COD, and BOD5 can vary depending on the composition of organic matter. Therefore, trends and correlations are general observations and may not be universally applicable to all water bodies or regions.

Table 3

Summary statistics of correlations of BOD5, COD, and TOC concentrations and temperature (t, °C) in the water of the Styr River before (X) and after (Y)

Indicator (Y)	Correlation between (X)	R²	p	Equation of correlation
COD (0–80 mgО/dm³)	TOC (0–20 mgС/dm³)	70.10	0.0003	Y = 14.53 + 0.12X + 2.438t
COD (0–80 mgО/dm³)	BOD5 (0–6 mgО/dm³)	79.18	0.0004	Y = 6.82 + 9.31X + 1.243t
TOC (0–20 mgС/dm³)	BOD5 (0–6 mgО₂/dm³)	72.27	0.0002	Y = 6.82 + 4.31X + 1.251t
TOC (0–20 mgС/dm³)	COD (0–80 mgО/dm³)	82.23	0.0002	Y = 6.45 + 0.64X + 0.184t
BOD5 (0–6 mgО₂/dm³)	COD (0–80 mgО/dm³)	71.30	0.0005	Y = 9.71–0.367X + 0.152t
BOD5 (0–6 mgО₂/dm³)	TOC (0–20 mgС/dm³)	85.40	0.0009	Y = −5.37 + 0.86X + 0.135t

Indicator (Y)	Correlation between (X)	R²	p	Equation of correlation
COD (0–80 mgО/dm³)	TOC (0–20 mgС/dm³)	70.10	0.0003	Y = 14.53 + 0.12X + 2.438t
COD (0–80 mgО/dm³)	BOD5 (0–6 mgО/dm³)	79.18	0.0004	Y = 6.82 + 9.31X + 1.243t
TOC (0–20 mgС/dm³)	BOD5 (0–6 mgО₂/dm³)	72.27	0.0002	Y = 6.82 + 4.31X + 1.251t
TOC (0–20 mgС/dm³)	COD (0–80 mgО/dm³)	82.23	0.0002	Y = 6.45 + 0.64X + 0.184t
BOD5 (0–6 mgО₂/dm³)	COD (0–80 mgО/dm³)	71.30	0.0005	Y = 9.71–0.367X + 0.152t
BOD5 (0–6 mgО₂/dm³)	TOC (0–20 mgС/dm³)	85.40	0.0009	Y = −5.37 + 0.86X + 0.135t

Figure 10

Matrix plot for indicators (temperature (Т) in °C, COD in mgO/dm3, BOD5 in mgO2/dm3, and TOC in mgC/dm3) in water of the Styr River before (А−) and after (В-) water discharge from the Rivne NPP for 2018–2022.

Matrix plot for indicators (temperature (Т) in °C, COD in mgO/dm³, BOD5 in mgO₂/dm³, and TOC in mgC/dm³) in water of the Styr River before (А−) and after (В-) water discharge from the Rivne NPP for 2018–2022.

Figure 11

Correlation coefficient r-Pearson for indicators (temperature (Т) in °C, COD in mgO/dm3, BOD5 in mgO2/dm3, and TOC in mgC/dm3) in water of the Styr River before (А−) and after (В-) water discharge from the Rivne NPP for 2018–2022.

Correlation coefficient r-Pearson for indicators (temperature (Т) in °C, COD in mgO/dm³, BOD5 in mgO₂/dm³, and TOC in mgC/dm³) in water of the Styr River before (А−) and after (В-) water discharge from the Rivne NPP for 2018–2022.

The ratio of BOD5:COD is also studied, and if the BOD5:COD ratio is less than 0.5, it indicates that the effluent contains a large proportion of non-biodegradable matter. The ratio of BOD5:COD obtained from the results of the present study ranges from 0.05 to 0.11 (Figure 12(a)). The regression analysis revealed a statistically significant (p = 0.0003) direct strong relationship between the BOD5:COD ratio (Figure 12(b)) in the Styr River sections before (A−) and after (B−) water discharge from the Rivne NPP (r = 0.89, R² = 81.45%) using PLSR. Since TOC and COD are similar indicators of the content of oxidisable organic matter, the BOD5:TOC ratio can also be estimated. The BOD5:TOC ratio obtained from the results of the present study ranges from 0.06 to 0.63 (Figure 12(a)) and is higher than the BOD5:COD ratio. The obtained correlation for the BOD5:TOC ratio in the Styr River before and after water discharge from the Rivne NPP is very strong (r = 0.91, R² = 95.12%) and statistically significant (p = 0.0001) (Figure 12(c)), which can more accurately characterize the biodegradability of organic pollution. The values of the ratio of BOD5:TOC and BOD5:TOC in the water of the Styr River before and after water discharge from the Rivne NPP indicate (Ledakowicz 1998) that a significant part of organic matter cannot be easily degraded by biological processes and that the spatial content of organic matter will not decrease spatially along the Styr River. This indicates that the effluent contains a large portion of non-biodegradable matter and is not related to the Rivne NPP activity, as the ratio is less than 0.5 recorded in the water of the Styr River before (A−) water discharge from the Rivne NPP.

Figure 12

Relationship, importance, and development of analytical techniques: COD, BOD, and, TOC in water—an overview through time

Changes in the BOD5:COD, BOD5:TOC ratio (a) and correlation relationships between the ratio of BOD5:COD (b), BOD5:TOC (c) indicates in water the Styr River before (А− and after (В-) water discharge from the Rivne NPP for 2018–2022.

In addition, the identified seasonal variations of COD, BOD5, and TOC (Figures 2(a) and 3) in the Styr River section up to the Rivne NPP outfall may be the initial data for identifying natural influencing factors. The data analysis methodology used in this study can separate the influencing anthropogenic factor is to assess the difference (increase) in COD, BOD5, TOC concentrations in the water of the Styr River before (A−) and after (B−) water discharge from the Rivne NPP (Figure 9 and Equations (1)–(3)). Moreover, taking into account the temperature increase in the Styr River water before (A−) and after (B−) due to the discharge of heated water from the Rivne NPP, the anthropogenic factor can be identified by water temperature (Table 3) when only one COD or BOD5 or TOC indicator and the corresponding temperature at the site before (A−) and after (B−) water discharge are known. Thus, it is proposed to use the geometric sum of the measurement error according to the applied measurement methodology as a criterion for the presence of anthropogenic influence on the formation of COD, BOD5, and TOC concentrations as a result of water discharge. Identification and separate influencing factors may be the subject of further studies. It is known (Kuznetsov & Tichomirov 2017) that considerable attention is being paid to the problems of NPP operation, so further research will be important in the future.

CONCLUSIONS

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Based on the results of long-term measurements of BOD5, COD, and TOC concentrations in the Styr River water in the zone of influence of Rivne NPP wastewater, the dynamics of changes in the content of organic substances, the factors of variability, and correlations were determined. Currently, the results of the study are the initial data for further monitoring of possible abnormal changes in BOD5, COD, and TOC content and trends in the C cycle in the Styr River water, including changes due to the impact of anthropogenic factors of Rivne NPP effluent.

It should be noted that the seasonal variability of BOD5, COD, and TOC concentrations is characterized by minimum values in the cold season and maximum values in the warm season. The values of water content in the Styr River differ slightly in the sections of the watercourse before and after the Rivne NPP discharges. Statistically significant correlations were found between the indicators at different sites and between BOD5, COD, TOC, and temperature. The regression analysis revealed a statistically significant (p = 0.0005) direct relationship between the concentrations of COD and TOC in the water before (A− and after (B−) water discharge (r = 0.89, R² = 81.45%). The calculated equations allow predicting the organic matter input with return cooling water based on the initial BOD5, COD, and TOC content in the Styr River water before water discharge from the Rivne NPP, taking into account temperature effects.

The calculated BOD5:TOC and BOD5:TОС ratios can provide useful information on the organic matter content of the water, but this is only one of many factors that need to be considered when assessing water quality and organic pollution potential, which will be the subject of further research. The obtained correlation for the BOD5:TOC ratio in the Styr River before and after water discharge from an NPP is very strong (r = 0.91, R² = 95.12%) and statistically significant (p = 0.0001).

In general, the results of the study indicate that there is no negative impact of the Rivne NPP water discharge on the organic matter balance and therefore no aquatic ecological reactions to the thermal effects of the Rivne NPP cooling water discharge. Methodology of this study can be used to assess the aquatic content of carbon forms in water bodies and to evaluate the water discharges of any NPP with RCWS.

DATA AVAILABILITY STATEMENT

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

CONFLICT OF INTEREST

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The authors declare there is no conflict.

REFERENCES

Aguilar-Torrejón

J. A.

,

Balderas-Hernández

P.

&

Roa-Morales

G.

2023

.

SN Applied Sciences

5

,

118

.

https://doi.org/10.1007/s42452-023-05318-7

.

Assessment of spatial and seasonal water quality variation of Oum Er Rbia River (Morocco) using multivariate statistical techniques

Barakat

A.

,

Baghdadi

M.

,

Rais

J.

,

Aghezzaf

B.

&

Slassi

M.

2016

.

International Soil and Water Conservation Research

4

,

284

–

292

.

http://dx.doi.org/10.1016/j.iswcr.2016.11.002

.

A Screening Level Ecological Risk Assessment and ranking method for liquid radioactive and chemical mixtures released by nuclear facilities under normal operating conditions

Barescut

J.

,

Garnier-Laplace

J.

,

Beaugelin-Seiller

K.

,

Gilbin

R.

,

Della-Vedova

C.

,

Jolliet

O.

&

Payet

J.

2009

.

Radioprotection

44

(

5

),

903

–

908

.

https://doi.org/10.1051/radiopro/20095161

.

Microfluidic chemiluminescence system with yeast Saccharomyces cerevisiae for rapid biochemical oxygen demand measurement

Costa

S. P.

,

Cunha

E.

,

Azevedo

A. M.

,

Pereira

S. A.

,

Neves

A. F.

,

Vilar

A. G.

&

Saraiva

M. L.

2018

.

ACS Sustainable Chemistry and Engineering

6

(

5

),

6094

–

6101

.

https://doi.org/10.1021/acssuschemeng.7b04736

.

Changes in selected water quality parameters in the Utrata river as a function of catchment area land use

Dеbska

K.

,

Rutkowska

B.

,

Szulc

W.

&

Gozdowski

D.

2021

.

Water

13

(

21

),

2989

.

https://doi.org/10.3390/w13212989

.

Replacement of chemical oxygen demand (COD) with total organic carbon (TOC) for monitoring wastewater treatment performance to minimize disposal of toxic analytical waste

DSTU ISO 1484:2003

.

Water Research. Guidelines for Determining Total and Dissolved Organic Carbon. State Standard of Ukraine

.

Available from: https://online.budstandart.com

DSTU ISO 5667-3-2001

.

Water Quality. Sampling. Guidance on the Preservation and Handling of Samples. State Standard of Ukraine

.

Available from: https://online.budstandart.com

DSTU ISO 5667-6:2009

.

Water Quality. Sampling. Guidance on Sampling of Rivers and Streams. State Standard of Ukraine

.

Available from: https://online.budstandart.com

Dubber

D.

&

Gray

N.

2010

.

Journal of Environmental Science and Health, Part A

45

,

1595

–

1600

.

https://doi.org/10.1080/10934529.2010.506116

.

Enhancing BOD/COD ratio of POME treatment in SBR system

Farraji

H.

,

Zaman

N. Q.

,

Abdul Aziz

H.

,

Ashraf

M. A.

,

Mojiri

A.

&

Mohajeri

P.

2015

.

In Applied Mechanics and Materials

802

,

437

–

442

.

https://doi.org/10.4028/www.scientific.net/amm.802.437.

Assortment and spatiotemporal analysis of surface water quality using cluster and discriminant analyses

Hajigholizadeh

M.

&

Melesse

A. M.

2017

.

Catenia

151

,

247

–

258

.

https://doi.org/10.1016/j.catena.2016.12.018.

Microbial fuel cell sensors for water quality early warning systems: Fundamentals, signal resolution, optimization and future challenges

Jiang

Y.

,

Yang

X.

,

Liang

P.

,

Liu

P.

&

Huang

X.

2018

.

Renewable and Sustainable Energy Reviews

81

,

292

–

305

.

https://doi.org/10.1016/j.rser.2017.06.099

.

Estimating salt concentrations based on optimized spectral indices in soils with regional heterogeneity

Kahaer

Y.

&

Tashpolat

N.

2019

.

Journal of Spectroscopy

15

,

2402749

.

https://doi.org/10.1155/2019/2402749

.

KND 211.1.4.021- 95

.

Method for Determination of Chemical Oxygen Demand (COD) in Surface and Waste Water. State Standard of Ukraine

.

Available from: https://online.budstandart.com

KND 211.1.4.024-95

.

Method for Determination of Biochemical Oxygen Demand After N Days (BOD) in Natural and Waste Water

.

State Standard of Ukraine

.

Available from: https://online.budstandart.com.

Kowalski

T. A.

1987

Determining the resistance of organic pollutants to biodegradation in water and wastewater

.

Environment Protection Engineering

13

(

2

),

5

–

16

.

Kuznetsov

P. N.

&

Tichomirov

A. U.

2017

Water-chemistry operating condition of the second circuit power units№ 1–4 Rivne NPP with ethanolamines corrective treatment

.

Problems of Atomic Science and Technology

2

(

108

),

109

–

113

.

Evaluating the impact of dispersed particles in the water of a power plant recirculating cooling system on the discharge of suspended solids into a natural water body

Kuznietsov

P.

&

Biedunkova

O.

2023a

.

Eastern-European Journal of Enterprise Technologies

6

(

10 (126)

),

6

–

16

.

https://doi.org/10.15587/1729-4061.2023.292879

.

Technological and environmental problems in the stabilization treatment of the main condenser cooling circuit by sulfuric acid

Kuznietsov

P. M.

&

Biedunkova

O. O.

2023b

.

Journal of Engineering Sciences (Ukraine)

10

(

2

),

H1

–

H8

.

https://doi.org/10.21272/jes.2023.10(2).h1

.

Experimental bench tests on the corrosion resistance and the environmental safety of structural materials of the cooling system of nuclear power plant

Kuznietsov

P.

&

Biedunkova

O.

2024

.

Key Engineering Materials

973

,

3

–

10

.

https://doi.org/10.4028/p-S6MjB7.

Monitoring of phosphorus compounds in the influence zone affected by nuclear power plant water discharge in the Styr River (Western Ukraine): Case study

Kuznietsov

P.

,

Biedunkova

O.

&

Trach

Y.

2023a

.

Sustainability

15

(

23

),

16316

.

https://doi.org/10.3390/su152316316.

Experimental study of transformation of carbonate system components cooling water of Rivne Nuclear Power Plant during water treatment by liming

Kuznietsov

P. N.

,

Biedunkova

O. О

&

Yaroshchuk

O. V.

2023b

.

Problems of Atomic Science and Technology

2

(

144

),

69

–

73

.

https://doi.org/10.46813/2023-144-069

.

Optimization of the anti-scale corrective treatment of water by organic phosphonate

Kuznietsov

P. М

,

Biedunkova

O. О

,

Yaroshchuk

O. V.

&

Рryshchepa

A. M.

2024

.

Science and Innovation

20

(

2

),

79

–

90

.

https://doi.org/10.15407/scine20.02.079

.

Ledakowicz

S.

1998

Integrated processes of chemical and biological oxidation of wastewaters

.

Environment Protection Engineering

24

(

1

),

35

–

47

.

Towards a miniaturized device to evaluate the BOD parameter of wastwater

Permit SAWRU

2020

Permit for Special Water use of SS Rivne NPP No. 53/RV/49d-20. 2020. The State Agency for Water Resources of Ukraine (SAWRU)

.

Available from: https://e-services.davr.gov.ua/

Recoules

L.

,

Jouanneau

S.

,

Thouand

G.

,

Guе

A. М

&

Boukabache

A.

2019

.

International Journal of Environmental Science and Development

10

(

6

),

178

–

182

.

https://doi.org/10.18178/ijesd.2019.10.6.1168

.

Temperature effects on spawning and egg development in Eurasian perch

Sandstrom

O.

1997

.

Journal of Fish Biology

51(5), 1015–1024. Nov. Wiley. https://doi.org/10.1006/jfbi.1997.0506.

A real-time on-line photoelectrochemical sensor toward chemical oxygen demand determination based on field-effect transistor using an extended gate with 3D TiO₂ nanotube arrays

Si

H.

,

Pan

N.

,

Zhang

X.

,

Liao

J.

,

Rumyantseva

M. N.

,

Gaskov

A. M.

&

Lin

S.

2019

.

Sensors and Actuators. B: Chemical

289

,

106

–

113

.

https://doi.org/10.1016/j.snb.2019.03.071

.

The seasonal dynamics of organic and inorganic carbon along the tropical Usumacinta River Basin (Mexico)

Soria-Reinoso

I.

,

Alcocer

J.

,

Sánchez-Carrillo

S.

,

García-Oliva

F.

,

Cuevas-Lara

D.

,

Cortés-Guzmán

D.

&

Oseguera

L. A.

2022

.

Water

14

(

17

),

2703

.

https://doi.org/10.3390/w14172703

.

Development and application of a species sensitivity distribution for temperature-induced mortality in the aquatic environment

Vries

P.

,

Tamis

J. E.

,

Murk

A. J.

&

Smit

M. D.

2008

.

Environmental Toxicology and Chemistry

27

,

2591

–

2598

.

https://doi.org/10.1897/08-056.1

.

Phytoplankton carbon utilization strategies and effects on carbon fixation

PubMed

Wang

X.

,

Chen

Z. Y.

&

Liu

J.

2023

.

Water

15

(

11

),

2137

.

https://doi.org/10.3390/w15112137

.

Sulphate in freshwater ecosystems: A review of sources, biogeochemical cycles, ecotoxicological effects and bioremediation

Zak

D.

,

Hupfer

M.

,

Cabezas

A.

,

Jurasinski

G.

,

Audet

J.

,

Kleeberg

A.

,

McInnes

R.

,

Kristiansen

S. M.

,

Petersen

R. J.

,

Liu

H.

&

Goldhammer

T.

2021

.

Earth-Science Reviews

212

,

243

–

256

.

https://doi.org/10.1016/j.earscirev.2020.103446

.

Optimization of the circulating cooling water mass flow in indirect dry cooling system of thermal power unit using artificial neural network based on genetic algorithm

Zhang

W.

,

Ma

L.

,

Jia

B.

,

Zhang

Z.

,

Liu

Y.

&

Duan

L.

2023

.

Applied Thermal Engineering

223

,

1125

–

1132

.

https://doi.org/10.1016/j.applthermaleng.2023.120040

.