1 Introduction

Climate change has presented significant challenges to oceans and estuaries, intricate systems with complex dynamic interactions. Ecosystem collapse can occur when these components are damaged or fail at different levels [1]. A healthy ecosystem operates successfully within the bounds of natural variability and exhibits resistance to stress up to a certain threshold [2,3,4]. Environmental factors such as pollution, changes in productivity, oceanic carbon dioxide (CO2) levels, and biodiversity patterns contribute to the system's complexity [5,6,7]. Various sources of pollution, including polluted river tributaries, urban sewage, agricultural and aquaculture runoff, port activities, and oil spills, contribute to increased marine pollution [8], exacerbating decomposition processes and adversely affecting coastal populations and ecology [9,10,11]. The degradation of estuarine ecosystems often manifests as a simultaneous decline in multiple structures and functions, necessitating comprehensive assessments prior to implementing restoration techniques [12, 13]. Regular assessment and monitoring are crucial to evaluate potential environmental damage resulting from anthropogenic activities that can cause large-scale release of harmful compounds, non-toxic organic matter, and nutrients, leading to marine life mortality due to local nutrient accumulation and hypoxia [14,15,16,17,18,19].

Risks associated with the transportation of various chemicals, fertilizers, petroleum products, coal, and iron ore, resulting in significant pollutant discharges during loading and unloading operations [20,21,22,23]. The presence of major industries, and urban in the catchment, contributes to potential discharge of pollutants into the Mahanadi River, which eventually flows into the Bay of Bengal at Paradeep [24, 25]. Additionally, agricultural runoff [15, 16], fisheries [26], flooding [27], recreational activities, and sewage inflows [28] also contribute to marine pollution in the Paradeep area. Continuous strategic monitoring is therefore essential to manage the cumulative impacts of these activities over time.

This study focuses on the temporal and spatial variability influenced by tides and various anthropogenic activities in the Mahanadi Estuary near Paradeep Harbour. The Water Quality Index (WQI) is used to assess the pollution status of Paradeep Port related to port activities. This approach aims to provide a comprehensive, long-term assessment of ecosystem health, valuable information on the Paradeep coastal area (estuarine and sea), and raise awareness among stakeholders and decision-makers.

2 Materials and methods

2.1 Description of study area

Paradeep, situated at the confluence of the Mahanadi River and the Bay of Bengal, holds significant strategic importance as a port. It is positioned approximately 210 nautical miles south of Kolkata and 260 nautical miles north of Visakhapatnam, marked by coordinates 20°15’55.44 north latitude and 86°40’34.62 east longitude. The study area covers a coastal stretch of about 45 km, extending from the mouth of the Jatadhari River to Hukitola Bay (Fig. 1 & Table1). This region boasts remarkable biodiversity, showcasing diverse mangrove and aquatic life. The ecosystem of Paradeep faces various challenges, largely driven by anthropogenic activities that exert substantial pressure on the environment. This study aims to assess the fundamental characteristics of ecosystem health and their impact on the Paradeep coastal area. To facilitate the assessment, the study area has been systematically divided into four zones based on the inflow from the Mahanadi River to the sea. These zones include the Estuary, the Mixed Zone, the southbound downstream of the Mixed Zone, and the northbound upstream of the Mixed Zone. Each of these designated zones serves as a sampling site for data collection and analysis.

Fig. 1
figure 1

Study area showing sampling location points

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Table 1 Zone wise sampling locations in the study area with probable influential factors
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The area is characterized by large industrial clusters, including oil refineries (IOCL), iron and steel plants (ESSAR), ports (PPT), steel and thermal power facilities (ESSAR), and fertilizer and brewery establishments (IFFCO, PPL, and SKOL) (Fig. 1). These activities generate a significant amount of wastewater, with a capacity of 32.72 million liters per day (MLD), resulting in potential anomalies within the estuarine ecosystem. Furthermore, the river system contributes to the pollution load through agricultural runoff, industrial leaching, mine discharges, and municipal effluents from the catchment area. The Mahanadi Basin houses 15 major industries, including aluminum and thermal power plants in Hirakud and Jharsuguda, chromium and power plants in Chowdwar, paper mills in Jagatpur, and fertilizer plants in Paradeep. These industrial units discharge liquid waste directly into the Mahanadi Basin, with larger units in Sambalpur, Cuttack, and Paradeep accounting for effluent volumes of 736 thousand liters per day (KLD), 2780 KLD, and 5280 KLD, respectively. Additionally, numerous medium and small industries in the Mahanadi Basin release approximately 100,000 cubic meters of wastewater daily, while 12 coal mines discharge 14,000 cubic meters of mine water per day during non-monsoon months [29,30,31].

The region also hosts over 60 coastal aquaculture firms operating on land areas exceeding 36 hectares for multiple years. Therefore, the present study aims to assess the fundamental characteristics related to the health of the ecosystem and investigate the resulting impacts on the bio-system in the coastal area of Paradeep. This region is subjected to various activities that potentially stress the coastal ecosystem, thus posing a significant threat. The overall condition of the study area plays a crucial role in shaping both the structural and functional aspects of the ecosystem, and conversely, these aspects have an influence on the overall condition. This approach adopts a holistic perspective to evaluate the ecosystem health of the Paradeep Coastal Area, distinguishing it from previous studies [24, 32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47] (Table 2).

Table 2 Past studies on sea-estuarine environment near Mahanadi River confluence on Bay of Bengal near Paradeep
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Intensive studies have been accomplished by different researchers to recognize the dynamics of sea current circulation in Bay of Bengal (BOB). It is compelled through southwest monsoon, with an anti-cyclonic gyre pattern in summer, and also influenced by the north eastern East Indian Coast Current (EICC) [48, 49]. The southwest monsoon current, develops in the summer season near east of Sri Lanka from local wind [50] have also impact on the coast. The northeast monsoon created in winter, dominates the entire BOB and generates a cyclonic motion, whereas the EICC flows southwest along the western boundary [51, 52]. The Paradeep coast is turbulent, experiencing with the highly unstable East Indian Coast current (EICC) along the coast. The major as well as high intra seasonal eddy kinetic energy centers are also part of Odisha coast [53]. The eddy current, EICC, and local turbulence with Ekman pump Combinely may play a contributory role in distribution of pollutant in the study area.

To facilitate the analysis, the study area has been systematically divided into four zones based on the inflow from the Mahanadi River to the sea. These zones include (1) the Estuary, (2) the Mixed Zone, (3) the Downstream of the Mixed Zone towards the South, and (4) the Upstream of the Mixed Zone towards the North. Appropriate sampling sites for the Estuary, the Mixed Zone, the Mixed Zone towards the north, and the Mixed Zone towards the south were considered based on the confluence point of the Mahanadi River and the salinity gradient (Fig. 1).

2.2 Sample collection and data analysis

The study covered 32 fixed GPS locations (Fig. 1) spanning the Mahanadi River estuary, including the estuarine and mixing zones, as well as the adjacent sea with mixing zones both upstream and downstream, along the Paradeep coast in the Bay of Bengal. These locations were strategically selected to capture the influential factors identified in Table 1.

Surface water samples were systematically collected at all sites between 2013 and 2020 during the pre-monsoon, monsoon, and post-monsoon periods. A Niskin vertical sampler was used to collect samples at depths ranging from 0.5 to 1.5 m. The physical and biochemical parameters were determined following the methods outlined by the American Public Health Association [54].

On-site measurements were conducted using a portable multiprobe (Make,model YSI-556) to assess parameters such as pH, sea surface temperature (SST), and salinity. Total suspended solids (TSS) were quantified gravimetrically using GF/C glass microfiber filter paper with a diameter of 47 mm. Turbidity was assessed through nephelometric method outlined by APHA in 2010 (Systronics, Model 135), while dissolved oxygen (DO) levels were determined using the Winkler's method with iodometric titration. Biological oxygen demand (BOD) was determined by incubating samples for 3 days at 27 °C, following the Winkler's method. The concentration of phosphate (PO4) and silicate (SiO4) were analyzed calorimetrically using an auto analyzer (Manufacturer: Skalar, Model: San++, Germany). Nitrite (NO2) levels were measured spectrophotometrically using a Perkin Elmer-Lambda 45 UV/VIS spectrophotometer. Total organic carbon (TOC) was estimated following the APHA 2010 guidelines using a TOC analyzer (Manufacturer: OI Analytical Xylem, model: 1030D, USA) based on NDIR principles.

Heavy metals (Fe, Mn, Cd, Pb) were classified according to the method developed by Satyanarayan and his research group [55] and analyzed by ICP-MS (Make: Perkin Elmer, Model: NexION 300X). Mercury (Hg) levels were assessed using the cold vapor atomic absorption spectrophotometry method outlined by APHA in 2010 [54], employing a mercury analyzer (Make: ECIL brand, Model: MA5840). Chlorophyll (Chl-a) concentrations were determined spectrophotometrically, following the methodology described by Parson and his coworkers [56]. Fecal coliforms (FC) were enumerated using the most probable number (MPN) method outlined in the APHA 2010 guidelines [54].

The water quality of the study area has been assessed according to Coastal Water Quality Standards [57,58,59] through a comprehensive analysis of 17 physico-biochemical parameters. The investigation revealed distinct patterns that are commonly observed in subtropical marine environments (Table 3). Indicators such as temperature, pH, dissolved oxygen (DO), biochemical oxygen demand (BOD), total suspended solids (TSS), turbidity, total organic carbon (TOC), nitrite, phosphate, silicate, chlorophyll-a, fecal coliform (FC), iron (Fe), manganese (Mn), mercury (Hg), lead (Pb), and cadmium (Cd) were compared to appropriate thresholds.

Table 3 Studies on nutrients in different estuaries of India
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In this study, all collected data underwent analysis to calculate the Water Quality Index (WQI) using the aforementioned parameters. The calculation was performed in accordance with the Coastal Water Quality Standards established by the Central Pollution Control Board (CPCB) in 2010 [59], the Australian and New Zealand Environment and Conservation Council (ANZECC) in 1992 and 2000 [57, 58], and the methods outlined by the Canadian Council of Ministers of the Environment (CCME) in 2001 [60].

2.3 Statistical calculation

The assessment followed the guidelines of the Canadian Council of Ministers of the Environment Water Quality Index (CCME) [60], which compares to baseline conditions on the basis of severity (F1: proportion of indicators missing one or more targets) and frequency (F2: Proportion) included all comparisons in which the targets were not met) and amplitude (F3: normalized degree of target exceeding). These indicators have been integrated to calculate an overall health index, represented by a score and a corresponding color code. Using these values, a Geographical Information System (GIS) was used to create a colour-coded representation of the Health Index.

Brown and his research group developed an assessment to determine the level of pollution in the sea water (Table 3) [61]. Table 4 provides a high-level overview of seventeen parameters included in this methodology, along with the corresponding thresholds assigned to each parameter. Equation (1) represents the WQI equation that quantifies overall water quality by assigning a numerical value based on various water quality parameters [60, 62, 63]. The Water Quality Index (WQI) is a dimensionless value that summarizes multiple water quality factors into a single number by normalizing their values using subjective rating curves [64]. It serves as an appropriate and specific indicator for a broader assessment of the ecological health of ecosystems [65, 66]. In addition, the WQI provides a standard metric for assessing the effectiveness of water quality management strategies [67, 68]. The choice of factors to include in a WQI model depends on the specific water use and local preferences. This model condenses large amounts of water quality data into simplified terms (e.g., “excellent,” “good,” or “poor”), facilitating consistent reporting for managers and the public [69, 70].

Table 4 Water quality standards and threshold limit taken for calculation
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Index score in CCME WQI was calculated by the relation

$$WQI=100-\frac{\sqrt{{F}_{1}^{2}+{F}_{2}^{2}+{F}_{3}^{2}}}{1.732}$$
(1)

where, Scope (F1), frequency (F2) and amplitude (F3) was calculated as.

F1 =\(\left(\frac{\text{number of failed variables}}{\text{total number of variables}}\right)\)  × 100.

F2 = \(\left(\frac{\text{number of failed tests}}{\text{total number of tests}}\right)\)  × 100.

F3 is calculated in three steps:

  1. 1.

    “Excursion” is the number of times by which an individual concentration is greater than the objective.When the test value must not exceed the objectives

    • When the test value must not exceed the objectives

  1. a)

    \({excursion}_{i}=\left(\frac{{Failedtestvalues}_{i}}{{Objectives}_{j}}\right)\)

    • When the test value must not fall below the objective,

  1. b)

    \({excursion}_{i}=\left(\frac{{Objectives}_{j}}{{Failedtestvalues}_{i}}\right)\)

  1. 2.

    Normalized sum of excursion (nse) is calculated as:

$$Nse\hspace{0.17em}=\hspace{0.17em}\frac{{\sum }_{\text{i=1}}^{\text{n}}{excursion}_{i}}{\text{number of tests}}$$
  1. 3.

    \(\mathrm{F}3\hspace{0.17em}=\hspace{0.17em}\frac{nse}{0.01nse+0.01}\)

The WQI score obtained is compared with grading scale, which is broken down into the five classifications listed in Table 5. Water quality is considered improved when WQI values increase from 0 to 100. Based on the value obtained, the WQI rating was compared with the general conditions: Excellent (A): 80–100, Good (B): 60–80, Fair (C): 40–60, Poor (D): 20–40 and Very Poor (F): 0–20 as indicated [71, 72].

Table 5 International Water quality rating
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The application of various multivariate statistical techniques such as cluster analysis (CA), principal components analysis (PCA) and factor analysis (FA) allows the identification of significant components or factors responsible for most of the variances in a system [73, 74]. These techniques aim to reduce the number of variables to a small set of indices while preserving the relationships present in the original data [75]. To assess the variability of the parameters, multidimensional scaling (MDS) and hierarchical cluster analysis were conducted using IBM SPSS (Version: 22) and Minitab 17 software packages. The MDS and hierarchical cluster analysis aimed to determine the similarity index between different sampling stations based on the measured parameters, as well as the similarity index among the parameters themselves. The dissimilarity between the stations and parameters was calculated using the Euclidean distance metric. Moreover, a cluster analysis (CA) was carried out to evaluate the similarity index, considering the Euclidean distance, among all stations with respect to the measured parameters. Similarly, the similarity index among all parameters was determined using the same cluster analysis approach. To present the seasonal variations, boxplots were generated using Origin 8.5 software, incorporating the seasonal data. Principal Component Analysis (PCA) is a robust statistical technique widely used to analyse the variance present in a large dataset consisting of inter-correlated variables, thereby reducing the number of independent variables employed [76]. The PCA process involves optimizing various aspects, such as selecting relevant variables and determining optimal weights to categorize the index variables [77].

Qualitative examination of the association between principal components and manifest variables, measured by stress (i.e., the correlation between principal component values and manifest variables), can contribute to the interpretation of a principal component. The PCA technique involves extracting eigenvalues and eigenvectors from the covariance matrix of the original variables. The principal components (PCs) are uncorrelated (orthogonal) variables obtained by multiplying the original correlated variables by the eigenvector, which represents a set of coefficients (loadings or weights). As a result, the PCs are weighted linear combinations of the original variables. PCs provide valuable information about the most significant parameters that describe the entire dataset, enabling data reduction with minimal loss of original information [78, 79].

The first principal component corresponds to the linear combination of the original variables that contributes the most to their overall variance, while the second principal component, uncorrelated with the first, contributes the most to the remaining variance. This process continues iteratively until the entire variance is analysed. It is important to note that the suitability of this method relies on all variables being measured in the same units due to its dependence on the total variance of the original variables [80, 81].

3 Result and discussion

3.1 Seasonal and temporal variation

The present study reveals significant temporal and spatial differences in the physicochemical and biological properties that are considered as key drivers of the marine environment. To allow for better comparison and analysis of the data, a categorization based on three-year phases (i.e. 2013–2015, 2015–2017 and 2017–2020) has been adopted. The parameter contribution was observed using a logarithmic transformation and the results are presented in Fig. 2 (a, b, c).

Fig. 2
figure 2

a Contribution of different parameters in the estuarine-sea conditions at Paradeep in 2013–2015. b Contribution of different parameters in the estuarine-sea conditions at Paradeep in 2015–2017. Figure c Contribution of different parameters in the estuarine-sea conditions at Paradeep in 2017–2020

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To assess the variability of each parameter over different years, the data were analysed using boxplots as shown in Fig. 3a–r. The box plots were labelled PR (pre-monsoon), MO (monsoon), PO (post-monsoon), and Y (year) for the respective years. The analysis showed that most parameters showed significant deviations in 2019 and 2020, with the exception of nitrite (Fig. 3i), salinity (Fig. 3g), faecal coliform bacteria (Fig. 3l), mercury (Fig. 3n) and chlorophyll (Fig. 3m). This observation is of concern and suggests that the discrepancies may be due to municipal and river discharges from land into the estuarine-sea ecosystem.

Fig. 3
figure 3

Boxplots for seasonal and annual variation for (a) pH, b Temperature, c DO, d BOD, e TSS, f Turbidity, g Salinity, h TOC, i Nitrite, j Phosphate, k Silicate, l Silicate, m Chlorophyll-a, n Mercury, o Manganese, p Iron, q Lead, r Cadmium of Mahanadi estuary near Paradeep

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On the other hand, parameters such as total suspended solids (TSS) (Fig. 3e), turbidity (Fig. 3f), total organic carbon (TOC) (Fig. 3h), nitrite (Fig. 3i) and faecal coliform (Fig. 3l) showed maximum variability in 2014, 2015 and/or 2016 (Fig. 3). This variability is due to the Mahanadi River floods in 2014–2016. Furthermore, the boxplots comparing seasonal data (Fig. 3a–r) showed that in 2014, 2015, 2016 and 2019 pH (Fig. 3a) showed a typical trend of pre-monsoon < monsoon < post-monsoon while showing another trend in other years. Salinity (Fig. 3g) consistently showed lower values ​​during the monsoon season throughout the study period. Nitrite (Fig. 3i) had unusually high levels during the 2015 and 2016 pre-monsoons compared to other years, which had a significant impact on the dataset. This anomaly can be attributed to uncontrolled discharges from fertilizer plants in those particular years, later regulated by the State Pollution Control Board (OSPCB). Phosphate (Fig. 3j) showed higher levels during the 2019 pre-monsoon, suggesting that special attention and limitations are needed due to possible contributions from fertilizer crops.

Chlorophyll-a (Fig. 3m) showed seasonal variations with a trend of pre-monsoon > monsoon > post-monsoon in 2013, 2015 and 2017, possibly due to upwelling and nutrient enrichment. However, in 2019 and 2020 the trend shifted towards pre-monsoon < monsoon < post-monsoon. The cadmium (Fig. 3r) concentrations did not show any significant seasonal fluctuations, but the concentration was significantly higher in the period 2019–2020 compared to the previous years (2013–2018). Whereas, iron (Fig. 3p), manganese (Fig. 3o), and lead (Fig. 3q) showed strong seasonal variation during 2019, 2020, 2016, 2020, and 2018, 2019, respectively. However, their concentrations were significantly higher in 2019–2020 compared to 2013–2018. In contrast, mercury (Fig. 3n) concentrations were minimal in 2019–2020 compared to 2013–2018. It showed strong seasonal variation during 2014, 2015, 2016, 2017, and 2018. The significant change in the chlorophyll-a trend during the pre-monsoon and post-monsoon periods in the first phase (2013–17) compared to the second phase (2019–20) can be attributed to physical phenomena such as upwelling processes. The term "upwelling" pertains to the vertical transport of nutrient-laden waters from the deep regions of the ocean to its surface, leading to heightened levels of primary productivity and chlorophyll-a concentration. The process of upwelling facilitates the upward movement of nutrient-laden waters from lower depths to the uppermost layers, thereby serving as a vital nutrient supply for the proliferation of phytoplankton. The increase in nutrient levels serves as a catalyst for the proliferation of phytoplankton, resulting in elevated concentrations of chlorophyll-a. Coastal regions are notably impacted by upwelling phenomena as a result of the presence of coastal upwelling systems. These systems facilitate the upwelling of nutrient-rich waters in coastal regions, resulting in elevated levels of chlorophyll-a and enhanced primary productivity. Numerous investigations [82,83,85] have been conducted to explore the relationship between the intensity of upwelling and the concentrations of chlorophyll-a in various geographical areas. These studies have yielded compelling evidence supporting the impact of upwelling on primary productivity and the dynamics of chlorophyll-a.

3.2 Tidal variation

Upon analysis, it was observed that some environmental parameters exhibited noteworthy variations when comparing HT and LT conditions (Fig. 4a–r). Notably, parameters such as pH, salinity, and TOC consistently showed higher values during high tide events. The increase TOC could be linked to enhanced organic matter input, possibly facilitated by the lower water volume evacuation during HT. The increase values may be attributed to the influx of seawater and associated factors during high tides, leading to higher concentrations of these parameters. Dissolved oxygen concentrations were also elevated during high tide cycles, could be due to reduced water movement during high tide, allowing for increased gas exchange at the water–air interface [99].

Fig. 4
figure 4

a-r Tidal variations of different physicochemical parameters of Mahanadi estuary near Paradeep during the study period (2013–2020)

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However, TSS, turbidity, BOD, NO2, PO4, SiO4, FC, Chlorophyll and Heavy metals levels were consistently higher during low tide events [86,87,88,89,90]. This observation might be due to increased sediment resuspension during lower tidal energy conditions, leading to a cloudier water column. Interestingly, pH and temperature did not exhibit significant differences between high and low tide conditions. This observation suggests that tidal fluctuations might not exert a substantial influence on these two parameters, likely owing to other prevailing factors that dominate their variation. The observed variations in environmental parameters during tidal cycles highlight the complex interactions between tidal dynamics and biogeochemical processes in coastal ecosystems. The findings underscore the need to consider the multifaceted nature of these systems when evaluating the impacts of tidal influences on water quality and ecosystem health.

Observation was made for higher-than-expected concentrations of nitrate and silicate, along with an elevation of phosphate levels in the specific region. The nutrient concentrations in the previous studied found to be greater than expected, corroborating previous researches carried out in the estuarine regions of the country (Table.3) [41, 91,92,93,94,95,96,97,98,99]. The unusual weather conditions, characterized by heavy rainfall (> 1638 mm) in 2019 and 2020 in comparison to previous years, could have led to runoff from the nearby phosphate fertilizer plants. This runoff might have carried phosphate compounds from the factories into local water bodies, thereby increasing the phosphate concentration. This phenomenon has ecological implications, emphasizing the need for further research and potential management strategies to mitigate adverse impacts on the aquatic ecosystem.

3.3 Water quality index (WQI)

To assess the overall water quality, the Water Quality Index (WQI) was calculated for different zones in different phases (2013–15, 2015–17 and 2017–20) as well as for the whole period. Figs. 5, 6 show the values of different zones in relation to the WQI. The total value of coastal waters fell below Class C in 2017–20, indicating the impact of river inflows, port activities and industrial discharges in and around Paradeep. This finding clearly indicates that the water quality of the Mahanadi River and its estuary falls below Class D. The WQI ratings for the estuary zone and mixed zone were 30 and 46, respectively, indicating the impact of loading in the estuary, which may have been diluted beyond the confluence.

Fig. 5
figure 5

Grades of different zones (Estuary, Mixing, Mixing up and mixing Dn.) with respect to WQI in different years and as a whole (2013–2020)

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Fig. 6
figure 6

Grades of different zones with respect to WQI

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In 2013–15, the WQI scores for the Estuarine Zone (E), Mixing Zone (MZ), Mixing Zone North (MZN), and Mixing Zone South (MZS) were 44, 47, 65, and 59, respectively. In the subsequent 2017–20 period, these scores dropped to 32, 39, 45, and 38. This decrease, particularly in the estuary zone (from 44 in 2013–15 to 30 in 2017–20), indicates a 32% deterioration in water quality. Although the mixed zone status declined from 47 in 2013–15 to 39 in 2015–17, it improved again to 46 in 2017–20. The mixed zone also showed a downward trend, with the WQI 65 in 2017 drop to 43 in 2020, a 32% decrease. The mixed zone south, influenced by the Jatadhari River and IOCL activities, saw a decline from a WQI score of 59 to 38 (36%) in 2015–17 and further to 41 (30%) in 2017–20. Rainwater, which carries sediment, nutrients and heavy metals, is a major contributor to the deterioration of the Mahanadi River estuary. In addition, the presence of port stockpiles with ore dumps, extensive storage sites for industrial waste (e.g. IFFCO and PPL gypsum), discharges from boats at the fishing dock, agricultural effluents and industrial discharges from Paradeep and the catchment area aggravate the decline in water quality in the estuary and in the mixed zone (class C or D). Therefore, appropriate mitigation plans are required to regulate the flow of pressures and improve ecosystem quality.

3.4 Trend analysis

A comprehensive analysis was performed considering multiple studies conducted earlier in the same area (Table 2). In the present study, 18 parameters were examined and various trends analyzed over different study periods. Among the parameters considered was TSS (Total Suspended Solids), turbidity, FC (faecal coliform bacteria), nitrite, phosphate, silicate, mercury and iron were found to be the most important factors for the route studied. These parameters showed significant differences during their respective phases from 2013 to 2020. However, despite their significant contributions, TSS and turbidity remained relatively stable throughout the study period (Fig. 2 a, b, c). During 2017–20 (Fig. 2c), the dominance of iron and mercury in the water samples can be attributed to under management of the Paradeep Harbor ore stock, which allowed storm water intrusion into the study area, thereby affecting water quality. The increase in nitrite and phosphate levels in the study area over time may be influenced by nearby fertilizer plants such as IFFCO and PPL.

Data from 32 stations monitored from 2013 to 2020 was considered to observe the trends of individual parameters at each station. The regression value (R2) was used to determine the increasing or decreasing trend of each parameter. The percentage of stations showing an increasing or decreasing trend was then calculated and expressed as a percentage of the total 32 stations as shown in Table 6. The aim of this analysis was to provide site-specific trends in pollution levels for important water quality parameters. It underscores the need for constant monitoring to predict pollutant properties, their presence, changes, movement or leakage in complex ecosystems where time is of the essence.

Table 6 Station wise decreasing and increasing trend of different parameters
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Observed trends indicate that TSS, BOD (Biochemical Oxygen Demand), turbidity and silica are of particular concern, with 44%, 36%, 28% and 28% of stations showing an upward trend respectively. Although high levels of nitrite were expected due to the presence of large fertilizer plants (IFCCO and PPL) nearby, 40% of the stations experienced a decrease. This decrease is due to strict regulations by the regulator on industrial discharges and modernization of household and ETP (Waste Water Treatment Plants) plants in industry.

3.5 Multidimensional scaling analysis (MDS)

To characterize the variability of the parameters, a multidimensional scaling analysis (MDS) was performed with IBM SPSS (version: 22). The data set from 2013 to 2020 was subjected to this analysis and revealed four different groups of parameters with similarities (Fig. 7). TSS and pH formed a separate group while nitrite, temperature and mercury merged into another group. Another group formed fecal coliforms, turbidity, silicates, phosphates and DO (dissolved oxygen). Chl-a (chlorophyll-a), BOD, TOC (Total Organic Carbon), salinity, iron (Fe) and cadmium (Cd) formed the third distinct group. Remarkably, metals such as manganese (Mn) and lead (Pb) did not belong to any group with a strong similarity. However, in the cluster analysis (Fig. 8) three distinct groups were observed with a similarity of 48.2%. Group one included temperature, salinity, Cd, Hg, BOD, TOC and Chl-a. Group two consisted of Mn, Fe and Pb. Group three included parameters such as pH, phosphate, turbidity, nitrate, TSS, silicate, FC and DO, which were partially consistent with MDS analysis. Mn and Pb, which were independent of each other in MDS, showed strong resemblance to Fe in cluster analysis and formed a group. At ~ 70% similarity level, a narrower micro group consisting of pH, PO4, turbidity and nitrate emerged, which showed differences to TSS, silicate, FC and DO within the third group. This clearly demonstrates the interdependence of parameters such as DO, PO4, silicate, TSS, turbidity and nitrite, which control ecosystem biological community parameters and consequently affect coastal health. Likewise, BOD and chlorophyll-a are interdependent and play a crucial role in the health of the coastal environment [17, 29, 100]. Hierarchical cluster analysis (Fig. 8) shows three distinct groups, with metals (Fe, Mn and Pb) forming a separate group [101]. Cd and Hg, which share similarities with salinity, are in a higher range of distinguishable similarity [29]. BOD, TOC and Chl-a are grouped together but differ significantly at a higher similarity range and are closely related [102]. Phosphate, DO, silicate, TOC, turbidity, FC and nitrite are also grouped together, indicating a higher degree of similarity compared to other groups [103].

Fig. 7
figure 7

MDS plotting taking data of different parameters from 2013 to 2020

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Fig. 8
figure 8

Cluster analysis of different parameters (2013–2020)

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In the multi-dimensional scaling (MDS) analysis (Fig. 9), six distinct groups were identified upon comparing various stations. The aforementioned groups are linked to four distinct zones, which are referred to as the Estuary (E), Mixing Zone (MZ), Mixing Zone South (MZS), and Mixing Zone North (MZN). It is worth mentioning that the estuary stations, ES1 to ES6, constituted a distinct group. Further, MZ9 of M zone and MZS7 of MS zone exhibited a close association with the E zone (Fig. 10). The mixing zone stations displayed a range of distinct characteristics and were classified into various categories. Within the set of Mixing Zone stations, MZ2 is associated with the MZN zone, MZ1 is affiliated with the MZS Zone, and MZ5 has formed a distinct new group with MZS4 of MZS zone. Upon analysis of the MN zones' stations, it was observed that MZN3, MZN4, MZN5, and MZN6, in addition to MZ2 of the Mixing Zone, exhibited a discernible cluster. Nevertheless, MZN1 and MZN2 exhibited a noticeable clustering pattern with MZ8 of the Mixing Zone. The stations within the Mixing Zone South, have been classified into four distinct categories. Specifically, MZS11, MZS3, and MZS9 have been grouped together, while MZS1, MZS6, MZS10, MZS2, MZS5, and MZS9 have been grouped with the mixing zone. Eventually, MZS4 of the Mixing Zone South zone and MZ5 of the Mixing Zone separated into their own distinct group.

Fig. 9
figure 9

MDS plotting taking data of different sampling stations (2013–2020)

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Fig. 10
figure 10

Cluster analysis of different sampling stations (2013–2020)

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3.6 Principal component analysis (PCA)

The principal component analyses were performed on the parameters of coastal water samples from 2013 to 2020. The results are presented in Table 7, which identifies the correlated variables (principal components) from the extensive data set. The table includes the loadings and eigenvalues for each component, as well as the percentage and cumulative percentage of variance explained by each parameter. The objective is to explain the maximum variance with the fewest number of principal components.

Table 7 Principal component analyses of data of coastal water from 2013 to 2020
Full size table

The analysis revealed that the first eight principal components account for over 90% of the total variance in the dataset. The first principal component accounts for 34.34%, the second for 17.92%, the third for 9.49%, the fourth for 7.83%, and the fifth for 7.02% of the total variance. The eigenvalues of the first five principal components can be used to assess the dominant physicochemical and biological processes of the coastal ecosystem.

In the first principal component (PC1), salinity, turbidity, BOD, TOC, phosphate, silicate, Chl-a, Mn, Fe, and Cd showed strong positive loadings (0.516–0.823), indicating an interdependence between phytoplankton growth, increased chlorophyll levels, and the rise in phosphate and silicate. This, in turn, contributes to the increase in BOD due to the death and decay of phytoplankton [104]. This finding supports the results of previous studies [105, 106]. Turbidity played a significant role with the highest loading in this study.

The second major component (PC2) exhibited high positive loadings for dissolved oxygen (DO) (0.722) and fecal coliform (FC) (0.691), along with negative loadings for pH (-0.747), total suspended solids (TSS) (-0.549), and salinity (-0.607). The inflow of the Mahanadi River into the study area brings surface water and industrial effluent, which may explain these associations. Salinity showed a negative correlation with DO and silicate, while DO solubility decreased with increasing salinity. Freshwater inflow from rivers is considered the primary source of silicate and phosphate in coastal waters [107]. The positive correlation between DO, phosphate, and silicate in this study could be attributed to the intrusion of freshwater with low salinity but high nutrient content (phosphate and silicate) into the coastal waters [108], thus confirming our findings.

The third (PC3) and fourth (PC4) principal components did not exhibit significant positive or negative loadings for any of the parameters. In PC5, BOD showed a negative loading (-0.649) without any notable evidence of positive loading.

The cumulative percentage of variance for the different components, presented in ascending order (34.38, 52.30, 61.8, 69.63, 76.64, 81.9, 86.4, 90.2, etc.), indicates that the first eight components account for over 90% of the total variance. The first five components explain over 70% of the deviations and are influenced by parameters such as salinity, turbidity, BOD, TOC, phosphate, silicate, Chl-a, Mn, Fe, and Cd, which likely play a crucial role in determining the characteristics of this coastal ecosystem.

4 Conclusion

The Paradeep shoreline has been under constant pressure for a long time, from both natural and human sources. The initial trophic level of a coastal environment is affected by variables such as biochemical oxygen demand (BOD), total organic carbon (TOC), turbidity, nitrite, silicate, and phosphate. Discharges from rivers are the primary entry point for pollutants into ecosystems. In particular, pollution from the Mahanadi River basins reaches the estuary carrying agricultural runoff, effluents from industry, mines, and urban settlements. The increasing complexities of the problems need thorough research in order to protect the environment. This study can be used as a resource to advocate for environmentally responsible management and call for more careful planning of the ecological and biological characteristics of the area.

Effective pollution management in the Mahanadi river basin requires regulatory measures as follows.

  • Strengthening the operability of the Effluent Treatment Plant (ETP) or establishing a Common Wastewater Treatment Plant (CWTP) can effectively treat point source pollutants discharged by industry, thereby restoring the health of the coastal ecosystem.

  • The impacts of non-point sources, particularly from agricultural fields to Mahanadi River and their subsequent intrusion into the study area requires a comprehensive plan of action to limit their impact.

  • It is important not to overlook the heavy metal contamination leaking from the Paradeep Port storage yard. The port's open courtyard serves as a storage facility for significant amounts of raw materials, including coal and iron ore, which must be remodelled and stored indoors to contain this source of contamination.

  • Raising awareness among stakeholders and decision-makers is crucial. Which must be adopted through

  • Communication campaigns: Using various channels to disseminate relative information on health of the ecosystem.

  • Stakeholder engagement: Involving stakeholders in decision-making processes in the study area.

  • Data dissemination: Providing access to relevant scientific information.

  • Policy advocacy: Advocating for supportive policies and regulations.

  • Monitoring and feedback: Evaluating effectiveness and gather input for improvement of the site is the need.