Introduction

This paper proposes a system for determining water contamination along Chanomi Creek, a distributary of the Escravos and Forcados rivers, in the Niger Delta, Nigeria. The creek is the busiest area for oil production and shipping in Western Africa.

This paper is divided into three areas: Research objective, research hypothesis and research methodology. It first addresses the water quality problem, followed by a case study where two different techniques are applied. After defining the scenario, the paper deals with the principles of the two techniques and the results derived from using data collected from 10 locations along the creek. It follows an analysis of the results from the two procedures and, finally, a conclusion.

Research problem

Nigerian ecosystems are still figuring out the best way to quantify water quality. The Chanomi Creek and the Niger Delta's waters are lacking in terms of environmental standards.

Research objective

The research discusses real-time indicators and factors affecting the complex condition of water quality for various water needs and the development of a methodology for a more comprehensive assessment of Chanomi's water health using the SIMUS decision-making method taking into account 25 water quality parameters.

Research hypothesis

  • The Creek in the study area is unfit for human consumption and poses ecological risks due to organic and oil pollution.

  • Water quality varies geographically due to physical and chemical changes in surface water induced by contaminants.

  • WQI compresses a huge number of parameters into a single value.

  • SIMUS integrates all water quality metrics and assesses their cumulative effect on study stations.

Research methodology

  • Emphasize the significance of utilizing the SIMUS in assessing water quality, which enables comprehension and dissemination among the various fields interested in this subject area.

  • Identify the many water quality indicators and then the most contaminated site and the most critical pollutant.

Main issue: water quality

Water pollution and the resultant limitation of water resources present critical challenges for the adequate understanding of an integrated water quality assessment and sustainability (Su et al. 2017; Sheffield et al. 2018; Benson et al. 2020). The status of surface waters depends on the integrity and function of the biological, chemical, and physical parameters (Onyena et al. 2021).

These water parameters are often affected by natural and anthropogenic stressors such as atmospheric conditions, soil erosion, industrial, urbanization, and agricultural activities (Khatri and Tyagi 2015; Akhtar et al. 2021). Water quality identification and assessments become clumsy due to different environmental parameters used in defining water quality (Mladenović-Ranisavljević et al. 2018). However, Water Quality Indices (WQI) solves this challenge by summarizing several parameters into a single index value (Odia and Nwaogazie 2017).

The Nigeria Standard for Drinking Water Quality (NSDWQ) uses the WQI indicator to inform the citizens about water resources quality. Furthermore, literature also shows that researchers have used WQI to detect changes in the quality of water bodies (Rocha et al. 2015; Sener et al. 2017; Nwaogazie et al. 2018) and also to evaluate its appropriateness for different water supply purposes (Ochuko et al. 2014; Akter et al. 2016; Odia and Nwaogazie 2017). Notwithstanding, WQI may give a false description of the factual status of particular water. Thus, it has a limitation of exaggerating temporary water quality problems, which does not provide accurate information of water status (Abbasi and Abbasi 2012). Also, while the index can indicate the status of water in an observed temporal period, Multi-Criteria Decision-Making (MCDM) analysis offers a holistic evaluation of water quality (Mladenović-Ranisavljević et al. 2018).

The application of MCDM in water quality assessments offers outstanding outcomes (Stefanopouls et al. 2014; Salehi et al. 2017; Mladenović-Ranisavljević et al. 2018). The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS—developed by Hwang and Yoon 1981) MCDM was used to establish a model for restoration planning of water networks. Here, criteria and weights were based on the local conditions and experts' group decision-making, respectively (Salehi et al., 2017). Mladenović-Ranisavljević et al. (2018) used a combined approach (Preference Ranking Organization Method for Enrichment Evaluation) – (PROMETHEE (Brans and Vincke 1985) and GAIA) (Geometrical Analysis for Interactive Assistance) in simplifying the process of decision-making in the choice of selection of particular locations with critical water quality. The most dominant variables that affect water quality at specific locations in the Danube River were identified as polluting parameters (Suspended matter, BOD-5, Total N, NH4-N, and PO4-P). Sutadian et al. (2017) used AHP (Analytic Hierarchy Process) MCDM analysis to determine weights and parameters in the Indonesian WQI index. The MAVT (Multi-Attribute Value Theory) analysis for Ground Water Protection by (Stefanopouls et al. 2014) led to a high consensus among stakeholders on the alternative favouring water recycling from wastewater treatment plants combined with the option that tends water recycling small dams for rainwater harvesting. MCDM analysis has been used to evaluate various Nigerian environmental problems, including Petroleum Hydrocarbon contamination (Sam et al. 2017; Okparanma et al. 2018), Waste Management (Olatona and Nduka 2017; Akinluyi 2019), and mangrove vulnerability (Omo-Irabor et al. 2011), among others.

Chanomi creek in the Niger Delta is a typical disturbed brackish ecosystem from continuous inputs of anthropogenic activities and oil explorations and exploitation. Recently, evaluation of water quality in the Nigerian water ecosystem has emerged in scientific discourse, most of which has used various physical, biological, and chemical means of water monitoring (Nkwoji et al. 2020). None of the published works has developed an MCDM approach in assessing water quality in Nigeria. MCDM presents a detailed evaluation of water quality, applicable to an area and any surface water (Mladenović-Ranisavljević et al. 2018).

A river, its pollutants, reasons for pollution, and diverse contamination parameters constitute a system. Thus, the problem of a water body cannot be addressed by examining each site independently but as part of a whole, considering simultaneously all aspects that interact. In other words, what is needed is to design a mathematical model with all the intervening factors and their interrelationships. This paper aims to suggest such a model and use it for determining the result for each site, with all components, including the influence of other sites or stations.

This modelling involves the construction of an initial matrix with all data and solving it using a Multi-Criteria Decision Making (MCDM) method called SIMUS (Sequential Interactive Modelling for Urban Systems) (Munier 2011). The use of the SIMUS method in the current investigation is preferred instead of other MCDM methods. SIMUS was used because there is no need to use weights for criteria to decide. It considers the scenario as a whole, not individually, by acknowledging that the level of pollution of a site also influences others.

SIMUS is a hybrid method based on Linear Programming, Weighted Sum Method (WSM), and Outranking (Munier 2011, 2016) to solve decision-making problems with multiple objectives. SIMUS was tested in many diverse types of projects (Munier 2016). The application of the SIMUS method does not use subjective assessments from experts. The study aims to develop a methodology for a better and more complete evaluation of the water health in the Chanomi Creek for every possible use by using the SIMUS method for making a decision and taking into account 25 water quality parameters (See Table 1).

Table 1 Initial data from 10 stations along the Chanomi Creek
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Methodology

Case Study

Chanomi (Figs. 1, 2) is in Warri Southwest Local Government Area in Delta State, Nigeria. It is located at the left bank of the Escravos River, noted for shipping and inland waterborne transportation, and is the preferred route for ocean-going vessels engaged in the oil and gas industry. Chanomi creek is also commonly used for industrial and domestic water supply, irrigation, and electricity generation. The Creek has a low-lying mean height of 6 m above sea level with an extensive flood plain and is tide influenced. The vegetation is dominated by red mangroves (Rhizophora racemosa and Rhizophora mangle). Some other plant species are Paspalum viginatum and Achrostichum aureum. The Creek is a tropical equatorial type, dominated by two seasons; wet season (May–October) and dry season (November–April). However, there is a brief wet and dry condition in April and August.

Fig. 1
figure 1

Study area: Escravos River and Chanomi Creek

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Fig. 2
figure 2

Map showing oil activity as well as pipelines

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The average temperature is 26 °C in the wet months to 33 °C in the unnoticed dry season as it frequently rains even in the dry season while the humidity is about 80%. The region's annual rainfall is usually approximately and sometimes over 3000 mm, as no month of the year is entirely devoid of rain. Data acquired from 10 measuring stations (S1–S10) on the Chanomi Creek shown in Fig. 1 was used for this research. This investigation includes 25 water quality parameters monitored monthly in ten measuring from 2019 to 2020.

The 25 water quality parameters recorded monthly from these stations include air temperature (aT), water temperature (wT), pH, salinity, electrical conductivity (EC), dissolved oxygen (DO), total dissolved solids (TDS), total suspended solids (TSS), turbidity, biochemical oxygen demand (BOD5), chemical oxygen demand (COD), oil and grease, silicate, nitrate, phosphate, sulphate, iron (Fe), manganese (Mn), cadmium (Cd), lead (Pb), zinc (Zn), arsenic (As), copper (Cu), chromium (Cr). Water quality parameters were sampled and determined according to standard prescribed procedures and methods.

The concept of the research includes several important steps. Firstly, an overall assessment of the water quality of the Creek was done using the WQI index. Independently, the water quality analysis is extended with SIMUS to identify significant criteria (parameters) and their interdependence with the other stations and different pollutants.

This investigation includes 25 water quality parameters monitored monthly in the ten measuring stations from 2019 to 2020. Table 1.

Background on the WQI method and result

The water quality index (WQI) offers a value that explains the general water quality on a certain period, based on several water quality parameters. In WQI, water quality data is transformed into information accessible to the public. WQI provides a method for presenting a cumulatively-derived numerical expression, thus, defining a certain level of water quality (Nwaogazie et al. 2018). Aggregating “n” quality parameters obtain WQI into a single value that expresses the quality indicator of the observed water body. This aggregation of numbers makes information much more easily understood than a long list of numerical values for ambiguous parameters studied (Odia and Nwaogazie 2017). In WQI methodology, the index places water quality into the following categories: excellent (0–25), good (26–50), poor (51–75), very poor (76–100), and > 100 unsuitable water quality according to the points scored (Table 2). These classifications serve particular uses, namely,

Table 2 Initial Table normalized and water quality classification
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excellent-for water supply, food industry, and aquatic life; good water quality-for swimming and recreation, aquatic life or, after regularized water purification methods, for water supply and food industry; poor-for irrigation and industries other than food; very poor-use only after treatment and purification procedures.

The calculation and evaluation of WQI have been used by Odia and Nwaogazie (2017) and Nwaogazie et al. (2018), and the equation is given as follows:

$${\text{WQI}} = \frac{\sum QiWi}{{\sum wj}}$$
(1)

The quality rating scale (Qi) for each parameter is calculated via Eq. (2):

$${\text{Qj}} = \frac{Vi - Vo}{{Si - Vo }} \times { }100$$
(2)

where vi is the estimated concentration of the nth parameter in the analyzed water samples.

v0 is the ideal value of analyzed water parameter in pure water sample which is usually zero except pH = 7.0 and dissolved oxygen, DO = 14 mg/l, si is the recommended standard value of the nth parameter which for this study was World Health Organization (WHO 2011) and National Environmental Standards and Regulations Enforcement Agency (NESREA 2011).

The unit weight (wi) for each water quality parameter is evaluated using:

$${\text{Wj}} = \frac{K}{Si}$$
(3)

where k = proportionality constant and is evaluated by:

$${\text{K}} = \frac{1}{{\sum \frac{1}{Si}}}$$
(4)

The obtained index value is a number between 0 and 100, depending on the scored points of individual parameters, where the sum of 100 shows minimum quality. The water quality index results and classification rating are presented in Table 2.

Background on the SIMUS method and result

SIMUS principle works by taking each criterion or parameter one at a time and using it as an objective, and finding the optimal solution through the Simplex Linear Programming (LP) algorithm (Dantzig 1948), and thus, building a matrix that has all its rows optimal, called ERM (Efficient Results Matrix). From the ERM, SIMUS uses two different MCDM methods, and those arriving at the same best solution and ranking.

SIMUS works in two stages:

1st: Using the original matrix in Table 1, SIMUS transposes it and computes the most important contaminants, considering all relationships (Table 3).

Table 3 SIMUS last screen with result (shortened for legibility reasons) of most contaminants
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2nd: Using the original matrix in Table 1, SIMUS determines the most important sites regarding contamination, considering all relationships (Table 5).

First stage: Result (Table 3) from the first stage indicates that the most important contaminants are:

$$Conductivity \, {-} \, SO4 \, {-} \, Turbidity \, - \, Salinity \, {-} \, DO.$$

The real scenario confirms these findings:

Large conductivity and salinity: Due to brackish water, i.e., water somewhat salty, produced by tides, in the Escravos and Forcados rivers, at both ends of the Chanomi Creek,

Sulphates from oil leaks.

Turbidity, due to the heavy traffic of ocean-going oil tankers, and the relatively high Dissolved oxygen in the creek may mean healthy water for aquatic life. These high values could be from the water turbulence produced by the large screws of oil tankers, shipping, boat transportation, and mangrove services that often aerate the water (Onyena et al. 2021).

Second Stage: The second stage in SIMUS involves determining the most contaminated stations. Table 4 shows the modelling of the initial decision-making or original data construction. As can be seen, stations from S1 to S10 are placed in columns and parameters, i.e., the different contaminants from C1 to C25 in rows. The values on the BOD5 row are only illustrative. This is the normal initial decision making used in all multi-criteria decision-making methods and represents the mathematical modelling of a scenario.

Table 4 Modelling the scenario in the Chanomi Creek (original model)
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At each intersection, the value of the corresponding contaminant and for each station is placed. For instance, for BOD5 and station S7, the contamination is 3 mg/l. The 10 × 25 matrix contains all data gathered by the equipment of technicians in that section of the river during a specific time.

However, the SIMUS method works differently and uses inequations for each row instead of equations; it is an algebraic format with a Left-Hand Side (LHS) or data, and a Right-Hand Side (RHS), for restrictions or limits in each row. i.e., that bound their scope. In this case, these are the maximum limits allowed for each type of contamination established for a river by international organizations like the United Nations Development Agency, the World Bank, the Organization for Economic Co-operation and Development (OECD), the World Health Organization (WHO), and other institutions, as well as experts that adjust these limits to the particular conditions of each scenario and country. The limits used in this study were from WHO and NESREA (National Environmental Standards and Regulations Enforcement Agency).

For this reason, Table 4 also shows three additional columns that correspond to action, mathematical symbols and RHS. These columns contain values untimely related to the respective rows, i.e., indicating a maximization, as when the row pertains to benefits, minimized when the row addresses costs, or in this case, contamination, that must be reduced. This explains the series of minimizations (MIN) since we want the contamination as low as possible.

Symbols are the mathematical characters to indicate how both sides of the inequation are related. Thus, ‘ ≥ ’ is used for minimization, ‘ ≤ ’ for maximization, and ‘ = ’ for equalization, for instance, if we want that LHS = RHS. When processed by the software, all this information is considered, as well as their interrelationships. This makes it possible to record the effects of contamination in a station (n) on station (n + 1). For instance, an oil spill in station S4 influences the state of the water in station S5. Since each station gets a score relative to its contamination level, it is possible to rank the station from the worst to the ‘best’, in decreasing order. The method follows a complex mathematical algorithm based on matrix analysis and finds a solution that is impossible to get by other means.

Analysis from the WQI result

This study is based on the average values of 25 water quality parameters in the Chanomi, collected monthly from ten measuring stations between 2019 and 2020. The entire sample period's mean values and mathematical analysis were done using the NESREA and WHO drinking water standards.

This analysis aims not to discuss the critical individual chemical, physical, and biological representations of the water quality measures used in this investigation. However, to use WQI to assign a single value to the water quality based on its overall features. The WQI was calculated for each location (Table 2).

The results of the water quality index indicate that the Chanomi creek water is unsuitable for drinking at all ten stations and over two years. The quality of the Chanomi creek in the Niger Delta ranged from 2717 to 5045 index points, which corresponds to descriptive indicators of unsuitable water quality for possible use for swimming and water sports, cultivating certain species, aquatic life protection and for water supply. The obtained WQI values indicate no location with excellent water quality; the study shows that the Chanomi creek recorded values greater than 100, indicating unsuitable water quality.

S6 is the location with the poorest water quality scored with 5045 index points, followed by S1 (4866 WQI points). S8 showed the lowest index point of 2717 (See Table 2). However, the station was still above the required minimum for sustainable water quality. The water quality index of Chanomi creek ranked stations as:

WQI ranking: S6 > S1 > S4 > S3 > S2 > S9 > S7 > S10 > S5 > S8

Thus, S6 is termed the worst station and S8 the less contaminated. Although Station 5 has a pipeline across Station 8 carrying various oil and gas products, the two stations recorded a water quality index that was almost twice less than in Station 6. The concentration of contaminants in S5 and S8 could mean that the stations recorded less cumulative anthropogenic activities, including open defecation, sewage disposal and oil spills. Apart from possible oil spills and prevailing contaminants in the creek that degraded the water quality, the S5 and S8 have lower human settlements. In contrast, the S6, S1, and S4 have inputs from sawdust, accumulation of human stressors from waste dumping, public toilet and bathroom structures, and open market. The stations with higher WQI points have a more frequent passage of speed boats, ships that constantly cause resuspension of sediments and regurgitation of water.

Furthermore, the resultant high values of the WQI could be attributed to contaminants from oil spills from crude oil pipelines, inputs of anthropogenic stressors from the increased human population with consequent waste generation and disposal, cumulative toxic action of pollutants over the years in the study area. It also is attributed to the high surface runoffs and organic matter debris, domestic sewage, and open defecation. The Creek exhibited low species diversity and richness, which reflected the present unsuitable water quality index of the Creek.

The study's findings indicate little to no substantial distinction in the water quality at the study stations that support the increasing concentrations. WQI of Chanomi creek presents similar water contamination problems  observed in other Nigerian ecosystems. After conducting the study, it was determined that the water quality of Chanomi creek is very unsuitable for drinking and other water purposes.

As a bottom line, water in the study stations is unfit for consumption and poses ecological risks. The purpose of the WQI was to assign a single value to the water quality while also condensing a large number of parameters into a simple expression, allowing for a straightforward interpretation of water quality monitoring data. Water quality levels vary spatially due to differences in surface water's physical and chemical features caused by prevalent pollutants in certain stations.

Analysis from the first stage of SIMUS values

Given a set of sites, or stations, alternatives, projects, parameters, indicators, etc., and a set of criteria (Table 3 is used to evaluate them), MCDM tries to find the parameter that better satisfies all criteria. These criteria vary from problem to problem since they are dependent on the alternatives.

In this case, the parameters are evaluated by the values encountered in each station. A parameter such as ‘Salinity’ is assessed by a linear combination of the values for salinity in each of the ten stations and by considering the influence that other parameters such as ‘Conductivity’ may or may not have. This is done following an algorithm included in the SIMUS method. Consequently, salinity is evaluated by the salinity content in each station and considering the influence on salinity from all other parameters.

A parameter is not considered if it has minor importance because of the magnitude of its values in all stations and considering all of the other parameters; in this way, the research analysis focuses on the most relevant pollutants. The above is determined pollution-wise, which is the most critical parameter in the ten sites. In our case, it is parameter number 6 corresponding to ‘Conductivity’ (6).

Table 3 shows the snapshot of SIMUS’ screen with final results. Observe that parameters are in columns and stations in a row in this matrix because we are interested in determining the most important pollutants. It is followed by Turbidity (8), SO4 (13), Salinity (4), and Dissolved oxygen (5). The importance is related to the most influential parameter, followed in a ranking for contaminants in decreasing order of importance.

The result using the first procedure in SIMUS is shown in Table 3, with the scores in the solid row and the ranking immediately below.

The result using the second procedure in SIMUS is shown in Table 5, with the scores in the solid row and the ranking immediately below.

Table 5 Determining the most important stations contamination-wise
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In the result, every station gets a score obtained mathematically by SIMUS, which allows us to measure the relative importance of each one quantitatively.

Analysis from the second stage of SIMUS values

Their scores from the first procedure (Table 5) are respectively:

$${4}.{9 }{-} \, 0.{1 }{-} \, 0.{1 }{-} \, 0.0{8 }{-} \, 0.0{1}$$

Ranking: Conductivity–Turbidity–Sulfates–Salinity–Dissolved Oxygen (1).

Notice the large value of the score for Conductivity (4.9) compared to the other scores. Conductivity is by far the most critical parameter significance-wise.

The last one is in Dissolved Oxygen with as little as 0.01. Since Conductivity measures how well an electrical current passes water, it is affected by sulfates (13) and salinity (4). Notice that these parameters are in the ranking, following Conductivity.

Observe that there are also two critical parameters in the ranking: Turbidity (8) and Dissolved Oxygen (5). Both are related to Conductivity because Conductivity is the most vital measure to gauge water quality. On the other hand, Dissolved Oxygen is responsible for aquatic life, which in different degrees depends on the species and according to the amount of oxygen in the water. Dissolved Oxygen also depends on salinity because the latter is essential to oxygen solubility.

Sunlight is also fundamental since its effects on aquatic plants produce oxygen through photosynthesis. Turbidity, which blocks sunlight, can impair photosynthesis. Plants are less likely to survive if photosynthesis is paused or reduced, resulting in decreased dissolved oxygen. The greater the turbidity, the less light can reach the water's bottom depths (Pivato et al. 2019). For this reason, turbidity (8) plays a central role in water health. Salinity and Conductivity have a significant correlation, the greater the salinity, the larger the Conductivity. The excess ions resulted in a rise in salinity, indicating that the creek is brackish. They are likely to contain toxins and act as a conduit for effluents to enter the creek.

Observe that both, Conductivity and Salinity are in the ranking as 6 and 4. If we now use the transpose of the initial matrix, with stations in columns and parameters in rows, SIMUS gives us the following ranking  SIMUS ranking: S3 > S1 >S2 > S5 > S10 > S4 > S6 > S8 (See Table 5).

Again, results from the second procedure are in the solid row line.

SIMUS did not consider stations S7 and S9 in their ranking, probably because they are insignificant. The software also considers S6 and S8 the least contaminated stations because S6 lies in an open river part of the Chanomi, while S8 belongs to the distributary, which influxes into the Chanomi channel. S6 per se is the most contaminated station according to WQI, and because it is Chanomi's upriver, its influence appears to have little influence on S8.

No exact correspondence should be expected from river direction and contaminants in each station since there are many other factors like the speed of the river flow, the size of the flow, the characteristics of the terrain, etc.

Temporary water quality challenges may have magnified this difference, making it impossible to identify the water's actual state (Abbasi and Abbasi 2012). This also includes possible overestimations involved in the computation process. As a result, WQI alone may provide an inaccurate representation of a water's true condition.

Conclusion

This paper used two different metrics to determine water quality in Chanomi Creek. The analysis of the WQI results showed that the most contaminated was S6, with a ranking for the other stations.

In contrast, the most polluted station from SIMUS is S3. This is understood since WQI considers isolated stations, while SIMUS works with all stations simultaneously. In addition, the second method determines which are the most severe contaminants. In so doing, most methods make a complete assessment of water quality.

It is interesting to check that the most important contaminant is conductivity, which is related to the brackish nature of the river water due to tidal effects. At the same time, S6 is determined as the most polluted individually due to the high content of heavy metals, including Cr+6, Cu, and Cd, and S3 is the most contaminated when all stations are considered together.