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Article

Exploring Seasonal Changes in Coastal Water Quality: Multivariate Analysis in Odisha and West Bengal Coast of India

by
Pravat Ranjan Dixit
1,†,
Muhammad Saeed Akhtar
2,†,
Rakesh Ranjan Thakur
3,
Partha Chattopadhyay
4,
Biswabandita Kar
5,
Dillip Kumar Bera
6,
Sasmita Chand
7,* and
Muhammad Kashif Shahid
8,*
1
Advanced Chemistry Department, Chitalo Degree Mohavidyalaya, Chitalo, Jajpur 755062, India
2
School of Chemical Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea
3
Odisha Space Applications Centre, Bhubaneswar 751023, India
4
CSIR—Institute of Minerals and Materials Technology, Bhubaneswar 751013, India
5
School of Applied Sciences, KIIT Deemed to Be University, Bhubaneswar 751024, India
6
School of Civil Engineering, KIIT Deemed to Be University, Bhubaneswar 751024, India
7
Manipal School of Architecture and Planning, Manipal Academy of Higher Education, Manipal 576104, India
8
Faculty of Science & Engineering, Macquarie University, Sydney, NSW 2109, Australia
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Water 2024, 16(20), 2961; https://doi.org/10.3390/w16202961
Submission received: 15 August 2024 / Revised: 8 October 2024 / Accepted: 15 October 2024 / Published: 17 October 2024
(This article belongs to the Section Oceans and Coastal Zones)
Browse Figures
Versions Notes

Abstract

:
Marine pollution poses significant risks to both human and marine health. This investigation explores the limnological status of the Odisha and West Bengal coasts during the annual cruise program, focusing on the influence of riverine inputs on coastal marine waters. To assess this impact, physicochemical parameters such as pH, salinity, total suspended solids (TSS), dissolved oxygen (DO), biochemical oxygen demand (BOD), and dissolved nutrients (NO2-N, NO3-N, NH4-N, PO4-P, SiO4-Si, total-N, and total-P) were analyzed from samples collected along 11 transects. Multivariate statistics and principal component analysis (PCA) were applied to the datasets, revealing four key factors that account for over 70.09% of the total variance in water quality parameters, specifically 25.01% for PC1, 21.94% for PC2, 13.13% for PC3, and 9.99% for PC4. The results indicate that the increase in nutrient and suspended solid concentrations in coastal waters primarily arises from weathering and riverine transport from natural sources, with nitrate sources linked to the decomposition of organic materials. Coastal Odisha was found to be rich in phosphorus-based nutrients, particularly from industrial effluents in Paradip and the Mahanadi, while ammonia levels were attributed to municipal waste in Puri. In contrast, the West Bengal coast exhibited higher levels of nitrogenous nutrients alongside elevated pH and DO values. These findings provide a comprehensive understanding of the seasonal dynamics and anthropogenic influences on coastal water quality in Odisha and West Bengal, highlighting the need for targeted conservation and management efforts.

1. Introduction

Coastal environments represent one of the most dynamic interfaces on Earth, consisting of the interconnected realms of land, sea, and atmosphere [1]. Coastal areas are vital for global sustainability, acting as transition zones between land and sea. Effective management of these regions necessitates a comprehensive understanding of the interrelations between socioeconomic, political, and environmental aspects [2]. These regions are vital due to their diverse uses, including higher ecosystem productivity, dense human populations, industrial suitability, waste disposal, and transportation [3].
Approximately 40% of the world’s population resides in a narrow coastal band [4]. In India, coastal regions are particularly significant, with nearly 49% of the population living in these areas, which face significant stress from both human and natural factors. Stretching over 7200 km, these areas are impacted by development, urbanization, groundwater extraction, land use changes, urban land expansion, and pollution [5,6]. Additionally, climate change-induced events like cyclones, extreme rainfall, flooding, and droughts further strain these regions. Unplanned and unsustainable development for socioeconomic growth exacerbates the degradation of these fragile coastal ecosystems [7,8,9,10]. Globally, coastal pollution has reached critical levels due to various human activities. Industrial effluents, infrastructure expansion, agricultural runoff with fertilizers and pesticides, untreated sewage disposal, and oil spills are among the major contributors to coastal pollution. These pollutants often enter coastal waters through rivers, estuaries, and direct discharges, leading to contamination of sediments, water, and marine organisms [6,11,12].
Coastal pollution has multifaceted impacts on ecosystems, particularly when considering the discharge of municipal waste water and industrial effluents into these waters. This practice leads to numerous environmental consequences, posing hazards to aquatic biota and compromising the overall health of the marine environment due to the influx of contaminants [13,14,15]. It can result in the degradation of habitats such as coral reefs, mangroves, and seagrass beds, which serve as crucial breeding grounds and nurseries for marine species. Pollution can also lead to eutrophication, harmful algal blooms, and oxygen depletion, further stressing marine life and disrupting food chains [16,17]. The concentration of nutrients, such as nitrogen and phosphorus, in coastal waters is a key factor that influences the productivity and ecological balance of these ecosystems. Nutrients act as essential building blocks for marine organisms and play a vital role in supporting the growth of phytoplankton, which forms the base of the marine food web, and its levels can impact water quality parameters such as turbidity, pH, and oxygen levels. Understanding nutrient behavior helps in evaluating water quality status and implementing measures for pollution control and management [18,19,20].
In addition to environmental impacts, coastal pollution poses significant risks to human health. Contaminated coastal waters can harbor pathogens and disease-causing organisms, increasing the risk of waterborne illnesses. Consumption of contaminated seafood can also expose humans to harmful chemicals and toxins present in polluted waters [21,22].
Physicochemical parameters offer insights into the environmental characteristics prevailing in the area. Lately, multivariate analyses have been employed to gauge the levels of contamination and identify variations in the physicochemical parameters of coastal waters [23].
The coastal stretch of Odisha and West Bengal spans approximately 650 km, constituting about 10% of India’s total coastline. Surface waters are particularly susceptible to pollution due to their accessibility for waste disposal. The quality of surface water in an area is influenced by a combination of natural processes like precipitation inputs, weathering of rocks, and erosion, as well as anthropogenic activities, including urban development, industrial operations, and agricultural practices. Additionally, the growing demand for water resources contributes to these influences [24].
The major rivers of Odisha, like Mahanadi, Rushikulya, Brahmani–Baitarani, and Dhamra, along with the rivers in West Bengal, such as Hoogly, Saptamukhi, and Matla, play a major role in assimilating or carrying municipal and industrial waste water as well as waste from domestic, agricultural, and aqua-cultural sources. Municipal and industrial waste water discharge serves as a constant source of pollution, while surface runoff is a seasonal phenomenon heavily influenced by basin conditions [25]. Changes in precipitation patterns, surface runoff, interflow, groundwater flow, water inflow, and outflow rates exhibit strong seasonal variations. These fluctuations have a notable impact on river discharge levels and consequently influence the concentration of pollutants present in river water [26]. The concentration of dissolved parameters in coastal waters exhibits spatial variation with higher levels closer to the shore, compared to offshore areas. Seasonal analysis reveals that elevated concentrations are observed during the pre-monsoon period, whereas lower concentrations are seen during the monsoon period with few exceptions that indicate influences from terrestrial runoff, river inflows, and evaporation. Investigating the contributions of natural and anthropogenic fluxes is essential for understanding and characterizing water quality along the coastal line [27].
Numerous industries, including mineral processing plants, mining activities, steel manufacturing units producing ferro-alloys and thermal and power generating plants, as well as other sectors such as paper, petrochemicals, agrochemicals, fertilizers, and cement, discharge their effluents into the coastal waters of the Bay of Bengal. This discharge occurs along the coasts of Odisha and West Bengal. Major ports like Paradip in Odisha and Haldia and Diamond Harbour in West Bengal are particularly impacted. Pollution in these areas arises from cargo loading and unloading activities, ship movements, dredging operations, and crude oil spills. Despite occasional reports on the distribution of physicochemical parameters in coastal waters of the Bay of Bengal, specific information regarding the Odisha and West Bengal coasts is lacking. This is concerning, especially considering the region’s valuable estuarine biotopes and the significant anthropogenic activities taking place along these coasts.
The present study was conducted using the evaluation of the current environmental status pertaining to the physical, chemical, and biological aspects of seawater. It also considers the temporal fluctuations in water quality along the coasts of Odisha and West Bengal. The variations in dissolved inorganic nutrients offer insights into processes influencing nutrient concentrations in seawater and may show a potential correlation with Chlorophyll-a (Chl-a) levels. The study would contribute to scientific knowledge by providing data on coastal water quality and ecosystem dynamics, which can be used for further research, modeling, and predictive analyses. This information is valuable for advancing our understanding of coastal processes and their interactions with human activities. The findings will also help in the better management of coastal resources, including water quality management, fisheries, biodiversity conservation, and public health protection.

2. Materials and Methods

2.1. Study Area

The study area (Figure 1) spans the Odisha coast, located within latitudes 19°18′11″–21°27′09″ N and longitudes 84°58′01″–87°02′47″ E and West Bengal, with latitudes 21°32′56″–21°37′23″ N and longitudes 87°31′41″–88°39′32″ E. Water samples were collected from various points along this stretch, including Gopalpur, Rushikulya, Chilika, Puri, Konark, Paradip, Mahanadi and Chandipur transects of Odisha and Digha, Sandheads and Saptamukhi transects of West Bengal. The coastal region spanning between Odisha and West Bengal is an industrialized zone with a mix of small, medium, and large industries discharging their effluents into the coastal waters, posing potential environmental risks. To gauge this impact, five fixed locations for each transect were selected at a distance of 0 km, 1 km, 3 km, 5 km, and 10 km from the shoreline. Surface water samples were collected from all stations, and additional bottom samples were taken from stations with depths exceeding 10 m.

2.2. Methodology

Water samples were collected from 11 transects along the coasts of Odisha and West Bengal during the year 2023. Water samples from both the surface and bottom (with a depth exceeding 10 m) were obtained using a Niskin water sampler. In situ measurements of air and water temperature, pH, and dissolved oxygen (DO) were performed using a WTW kit and Winkler’s titrimetric method [28]. Samples for biological oxygen demand (BOD) were incubated at 20 °C for 5 days before measurement. Salinity was determined via Knudsen’s method [28]. For suspended solids analysis, one liter of seawater was filtered through pre-weighed 0.45 μm Whatman membrane filter paper, followed by an analysis of dissolved micronutrients such as nitrate, nitrite, ammonia, phosphate, silicate, total nitrogen (TN), and total phosphorus (TP) using colorimetry [28]. The dissolved micronutrients such as nitrate, TN, ammonia, TP, inorganic phosphate, and silicate were measured in µmol/L unit by spectrophotometer analysis (Perkin-Elmer model no.-Lambda 35, USA). SURFER software 10.0 was used to analyze variations in salinity (in practical salinity units, PSU) for surface and bottom coastal water. Each analysis was performed in duplicate for accuracy, and the mean value was reported.
Chlorophyll-a (Chl-a) determination involved filtering water samples through Whatman GF/C filter paper with the addition of MgCO3 solution for chlorophyll preservation. After extraction with 90% acetone, chlorophyll pigments were measured using a spectrophotometer after 24 h [29], using a double beam UV-visible spectrophotometer (Perkin-Elmer model no.-Lambda 35, USA) for all spectrophotometric analyses.
The study involved evaluating variations in hydrographic parameters, including mean values and standard deviations (Table 1). SPSS 10.0 software was used for statistical analyses, including correlation matrix and principal component analysis (PCA). The analysis utilized data collected throughout the year from stations at Gopalpur (19.25° N, 84.9° E), Rushikulya (19.36° N, 85.06° E), Chilika (19.73° N, 85.64° E), Puri (19.79° N, 85.82° E), Konark (19.86° N, 86.11° E), Paradip (20.25° N, 86.67° E), Mahanadi (20.29° N, 86.71° E) and Chandipur (21.43° N, 87.02° E) in Odisha and Sandheads (21.97° N, 88.02° E), Digha (21.61° N, 87.50° E) and Saptamukhi (21.95° N, 88.16° E) transects of West Bengal.

3. Results and Discussion

The results of the study reveal complex interactions among various physicochemical parameters in the coastal waters of Odisha and West Bengal. Correlation and regression analyses identified significant relationships between water temperatures, salinity, dissolved oxygen, biochemical oxygen demand, and several nutrients, including nitrite, nitrate, ammonia, and TN. The multivariate analysis further highlighted the primary factors influencing water quality and quantified their contributions to overall variance.

3.1. Hydrographic Parameters

3.1.1. pH

The pH of surface water in the study ranged from 7.78 to 8.42, while bottom water samples had a pH range of 8.02 to 8.24. Notably, both the highest and lowest pH values were recorded at the Mahanadi transects during October. Throughout the study, no significant fluctuations in pH values were observed either among stations or between surface and bottom samples. This pH stability is attributed to the extensive buffering capacity of seawater, which helps maintain pH within a narrow range [30].
The pH levels exhibited very weak correlations with most variables, showing no significant correlation with water temperature (r = −0.001) and salinity (r = −0.009). A positive correlation was noted with dissolved oxygen (DO) (r = 0.126), while a weak negative correlation was found with BOD (r = −0.288). Moderate negative correlations were also observed with nitrate (r = −0.377) and suspended solids concentration (SSC) (r = −0.548) (Table 2).

3.1.2. Salinity

The salinity of coastal water ranged from 7.71 to 31.87 PSU in surface water and from 17.56 to 32.14 PSU in bottom water. The highest salinity levels were observed during the pre-monsoon season, likely due to the reduction in freshwater influx after the monsoon and during the summer months. Conversely, lower salinity values observed in October and November can be attributed to the dilution of coastal waters by freshwater discharges from rivers during the monsoon season. Rivers such as the Ganga and Hoogly play a significant role in reducing salinity levels in the northern Bay of Bengal during this period [31]. This study’s findings are consistent with the general trend of decreasing salinity during the monsoon, followed by a gradual increase in salinity levels from the north (Sandheads) to the south (Puri) along the coast (Figure 2). This pattern has also been observed in previous studies, reflecting the influence of riverine discharge on coastal water salinity [32].
Salinity exhibited a moderate negative correlation with water temperature (r = −0.327) and BOD (r = −0.330). Additionally, weaker negative correlations were observed with DO (r = −0.288) and nitrate (r = −0.568). These correlations highlight that higher salinity is typically associated with lower DO and nitrate levels (Table 2 and Figure 2).

3.1.3. Dissolved Oxygen

Dissolved oxygen levels ranged from 5.74 to 7.94 mg/L in surface water and from 5.42 to 7.48 mg/L in bottom water (Figure 3). Across all stations, surface waters generally had higher DO concentrations compared to bottom waters, indicating better aeration at the surface. This is likely due to the mixing of air with water, as well as the process of photosynthesis occurring in the upper layers. The study found a negative correlation between DO and temperature, suggesting that higher water temperatures are associated with lower DO levels. The oxygenation of aquatic systems is influenced by multiple factors, including photosynthesis, the degradation of organic matter, re-aeration processes, and the physicochemical characteristics of the water [33,34].
DO exhibited no significant correlation with water temperature (r = −0.120), BOD (r = −0.307), and salinity (r = −0.288), and has a positive correlation with pH (r = 0.126). Furthermore, a significant negative correlation was also observed with ammonia (r = −0.395) (Figure 3 & Table 2).

3.1.4. Biochemical Oxygen Demand

BOD values ranged from 0.33 to 3.84 mg/L in surface water and from 0.52 to 2.84 mg/L in bottom water. Particularly elevated BOD values were observed in the Puri and Sandheads transects (Figure 4). A negative correlation between BOD and salinity (r = −0.33) was noted. On the other hand, there were strong positive correlations observed with nitrate (r = 0.535), ammonia (r = 0.453), total nitrogen (r = 0.495), and water temperature (r = 0.532) (Figure 4 and Table 2), indicating that higher levels of these nutrients contribute to increased BOD levels. The positive correlation with suspended solids also suggests a contribution to BOD from riverine sources. Moreover, the strong positive correlation with nutrients indicates inputs of organic waste through river runoff, further impacting BOD levels in the coastal water.

3.1.5. Nitrite

Nitrite concentration varied from 0.10 to 2.09 µmol/L in surface and from 0.11 to 1.44 µmol/L in bottom samples (Figure 5). The highest concentration for surface water was found during February at Sand heads and that for bottom water during October at Puri station. The near-shore (station 1) water samples were found to contain higher nitrite concentrations compared to offshore (station 5) water samples. This could be due to the dilution of nitrite content in water as it goes from station 1 to station 5, 10 km far from the shore.
Nitrite is the partial form of nitrogen occurring as an intermediate form during denitrification and nitrification reactions. Nitrite in water is formed through the oxidation of ammonia by aerobic nitrifying bacteria such as Nitromonas or the reduction of nitrate by facultative anaerobic denitrifying bacteria like Pseudomonas. This process renders nitrite a highly unstable dissolved inorganic nitrogen species found in seawater. No significant variation in nitrite concentration was noticed in the transects of West Bengal, as the waters are well mixed due to the relatively shallow depth (5.5 m–11.0 m).
Dissolved nitrate shows a positive correlation of nitrite with suspended solids, BOD (r = 0.113), and a significant correlation with nitrate (r = 0.447), and a negative correlation with water temperature (r = −0.249), pH (r = −0.117), and salinity (r = 0.170) (Figure 5 and Table 2), suggesting that fresh water from rivers is the primary source of nitrite. Nitrite also showed a positive correlation with nitrate, ammonia, and TN, indicating their common source and close association between these parameters.

3.1.6. Nitrate

Nitrate concentration ranged from 0.41 to 14.29 µmol/L in surface water and from 0.63 to 8.91 µmol/L in bottom water (Figure 6). The highest surface concentration of nitrate was recorded at Puri, while the highest bottom concentration occurred at Sandheads. Comparatively lower values of nitrate were observed in February compared to October. Notably, near-shore water at Puri exhibited the highest nitrate values, likely due to municipal effluents/sewage discharge from Puri town. This region also reported relatively higher values of ammonia and total nitrogen, along with BOD exceeding 3 mg/L. These findings suggest that terrestrial inputs are minimal, and localized effects such as municipal effluents play a more significant role in influencing nitrate and other nutrient concentrations in this area.
In the presence of oxygen, nitrate is the only nitrogen compound with the highest oxidation state (−3 to +5) that is thermodynamically stable in seawater [35]. Its primary source in water stems from the biological oxidation of nitrogen-rich organic matter, originating from both autochthonous (within the ecosystem) and allochthonous (external sources) sources. Allochthonous nitrogenous organic matter mainly comes from domestic sewage and agricultural runoff. Meanwhile, autochthonous nitrogenous organic matter comes from metabolic waste from aquatic communities and deceased organisms.
Nitrate plays a key role as a micronutrient that controls primary production in surface water layers. Its concentration in these layers is influenced by factors such as the transport of nitrate into surface layers, microbial conversion of ammonia, and the utilization of nitrate by primary producers. This intricate balance of processes shapes the availability and distribution of nitrate, ultimately influencing primary productivity in marine ecosystems.
Nitrate showed significant correlations with several variables. A weak positive correlation with water temperature (r = 0.246) and moderate positive correlations with BOD (r = 0.535), nitrite (r = 0.447), and total nitrogen (r = 0.542) were observed. Nitrate showed a moderate negative correlation with pH (r = −0.38), salinity (r = −0.57), and a strong positive correlation with suspended solids (r = 0.61), indicating the fresh riverine water contribution toward the source of this nutrient (Figure 6 and Table 2). The strong positive correlation observed among NO2, NH4, and TN suggests a shared source of origin, while their strong negative correlation with salinity indicates an allochthonous origin [31]. A strong positive correlation with suspended solids suggested that riverine water is the chief source of NO3.

3.1.7. Ammonia

The concentration of ammonia in surface water ranged from 0.14 to 33.21 μmol/L, and in bottom water from 0.19 to 10.83 μmol/L (Figure 7). The Puri transects recorded the highest surface and bottom water concentrations of ammonia due to the high load of municipal effluents of Puri town. Other transects recorded ammonia values lower than 1.0 μmol/L, except for Puri, Paradip, Mahanadi, and Sandheads. Relatively lower values of NH4 were recorded in the month of February than those recorded during October.
Ammonia has a positive correlation with water temperature (r = 0.164) with a moderate positive correlation with BOD (r = 0.453), nitrite (r = 0.372), nitrate (r = 0.325), and total nitrogen (r = 0.371), indicating that ammonia levels tend to increase with higher nutrient concentrations and BOD (Figure 7 and Table 2).
Ammonia undergoes rapid biological conversion, such as oxidation into nitrite and nitrate and fixation as organically bound nitrogen in organisms. The concentration of ammonium in the marine environment shows considerable variation and can change rapidly. This could explain why ammonia does not show a consistent trend of variation.

3.1.8. Total Nitrogen

The Total Nitrogen (TN) in the study region varied from 12.50 to 99.13 μmol/L on the surface and from 20.48 to 94.15 μmol/L in the bottom samples. Bottom water samples were found to have higher concentrations of TN compared to surface samples (Figure 8). The variation in TN mirrored that of nitrate and nitrite. Total nitrogen showed a moderately positive correlation with water temperature (r = 0.300) and significant positive correlations with BOD (r = 0.495), nitrite (r = 0.339), nitrate (r = 0.542), and ammonia (r = 0.371) (Figure 8 and Table 2). These correlations suggest that total nitrogen levels are heavily influenced by other nutrient parameters and BOD. The concentration of TN gradually decreased from shore station (S-1) to offshore station (S-5) as the nitrogenous contents become diluted with an increase in distance from the shore to offshore.

3.1.9. Inorganic Phosphate

The concentration of phosphate ranged from 0.61 to 64.06 μmol/L for surface water and from 1.79 to 54.17 μmol/L for bottom water. The highest phosphate concentration was observed in October, while the lowest was noted in February. Phosphate concentrations in bottom water remained relatively high in comparison to the values observed in surface water (Figure 9).
Phosphorus, an abundant trace element and key nutrient, is released in significant amounts during weathering from minerals. The weathering solution contains alkali phosphate and dissolved or colloidal calcium phosphate, but most of it is carried to the sea. In the uppermost layer of the euphotic zone, the phosphorus levels are usually lower as phytoplankton uptake and incorporation into the marine food chain occurs [33]. Upon the decomposition of organisms and plants, a portion of this phosphorus is released back into the water. Together with water movements, these processes of removal and return give rise to seasonal variation in the distribution of elements. Inorganic phosphate exists in the sea in the form of ions of orthophosphoric acid (H3PO4). About 10% is present as PO43− ions, and nearly all the remaining phosphate exists as HPO42− ions. According to a study of the coastline [36], seawater of average salinity at pH 8.0 contains 3.5% phosphate, with 87% of it as HPO42−, 12% as PO43−, and 1% as H2PO42−. Additionally, 99.6% of the PO43− and 44% of the HPO42− are in the form of ion pairs, presumably with calcium and magnesium.
Inorganic phosphate has weak correlations with most variables. There is no significant correlation with water temperature (r = −0.033), but it shows a positive correlation with pH (r = 0.157). It has a negative correlation with salinity and a moderate positive correlation with ammonia (r = 0.307), and a very strong positive correlation with total phosphorus (r = 0.864) is observed (Figure 9 and Table 2). This indicates that nitrogen-rich surface runoff contributes to the dilution of coastal water and serves as an external source of phosphate in the coastal belt. A comparison of the present concentration of phosphate with previously recorded values (surface: 0.11–1.81 μmol/L, bottom: 0.36–1.22 μmol/L) from the coastline [32] showed that the present values were much higher, particularly at the Mahanadi transect of the Odisha coast indicating the activities of the fertilizer industrial units located in Paradip, which discharge high-nutrient rich effluents into the river.

3.1.10. Total Phosphorous

Total phosphorous concentration ranged from 3.75 to 121.87 μmol/L for the surface water and from 9.45 to 66.14 μmol/L for the bottom water. The lower nutrient values on the surface were due to utilization by phytoplankton. The observed variation might be caused by river water draining into the Bay of Bengal with a timely increase in concentration of nutrients (Figure 10).
Total phosphorus shows a weak correlation with water temperature (r = −0.127) and pH (r = 0.149). A moderate positive correlation is seen with ammonia (r = 0.381). The variation in total phosphorus follows a similar trend to that of inorganic phosphate, as evidenced by the strong positive correlation (r = 0.864) between the two variables. The notable difference between inorganic phosphate and total phosphorus at Puri, Paradip, and Mahanadi in all seasons suggests a greater contribution of the organic form of phosphate from municipal sewage and nearby industrial effluents.

3.1.11. Silicate

The Silicate (SiO4-Si) concentration ranged from 1.30 to 85.20 μmol/L in surface and from 1.13 to 84.37 μmol/L in bottom water samples (Figure 11). The magnitude of silicate concentration in bottom samples remained high throughout the study period. In all transects, silicate values were found to be higher at shore stations compared to stations farther from the shore, with the lowest values found at station 5. At shore stations, the riverine inputs play a major role in higher silicate concentration, which gradually becomes diluted with river water as the distance from the shore increases. The Mahanadi transect recorded the highest silicate concentration in both surface and bottom water samples, likely due to the input of siliceous sediment collected from its catchments [37].
Silicon is an abundant element that enters ionic solution during the weathering of silicate materials. The spatial and temporal variability of silicate in coastal water is affected by several factors, including the physical blending of seawater and fresh water [38], adsorption of reactive silicate onto suspended sediment particles [39], reactions with clay minerals [34,40] and the biological uptake by phytoplankton [41]. Silicate has a moderate negative correlation with temperature (r = −0.448), and salinity shows a moderate positive correlation with ammonia (r = 0.267), inorganic phosphate, and TP (r = −0.394) (Table 3 and Figure 11). This indicates an influence from rivers, suggesting that nutrients are supplied by river sources [42].

3.1.12. Chlorophyll-a

The Chlorophyll-a concentration ranged from 0.38 to 8.84 mg/L in surface water and from 0.17 to 1.96 mg/L in bottom water (Figure 12). The highest concentration of Chlorophyll-a was recorded in February at the Puri shore station, likely due to higher nutrient levels. The concentration of surface Chlorophyll-a was higher compared to the bottom across all stations in coastal waters. Chlorophyll-a showed a moderate positive correlation with ammonia (r = 0.512), inorganic phosphate (r = 0.213), total phosphate (r = 0.450), and silicate, and a weak negative correlation with water temperature (r = −0.255) [42] (Figure 12 and Table 3). This indicates that chlorophyll-a levels are influenced by these nutrient parameters.

3.2. Multivariate Analysis

There has been a recent surge in the application of statistical techniques utilizing multivariate data from coastal water systems to establish environmental classifications [23]. These methods aid in gaining a deeper insight into the biological and chemical processes occurring within coastal environments. Water quality parameters, along with their mean values (M) and standard deviations (SD), were assessed at 11 different transects (Table 1). Multivariate statistical approaches like principal component analysis (PCA), regression analysis, and correlation matrix analyses were conducted to assess the significance of specific parameters derived from the collected data (Table 2 and Table 3). These analytical techniques help in identifying key factors and relationships among various environmental parameters, which facilitate a more comprehensive understanding of coastal ecosystem dynamics.

3.2.1. Correlation between Variables

The correlation coefficient quantifies the strength of the relationship between variables. It measures how closely the values vary around their respective means and the direction of their covariation. A correlation coefficient matrix was computed among 14 variables for the coastal regions of Odisha and West Bengal, as shown in Table 2.
Chlorophyll-a exhibits strong positive correlations with ammonia, TP, and silicate, while showing a negative relationship with water temperature. Salinity shows strong negative correlations with DO, BOD, nitrate, total nitrogen, and suspended solids. BOD shows strong positive correlations with nitrate, ammonia, and TN.

3.2.2. Principal Component Analysis

The application of multivariate statistical techniques, such as principal component analysis (PCA) and factor analysis (FA), is instrumental in comprehensively interpreting intricate data matrices. These methods contribute to a deeper understanding of water quality and the ecological status of the studied systems. These e methods facilitate the identification of potential factors and sources impacting water systems, providing valuable insights for the effective management of water resources [43].
Principal Component Analysis (PCA) is a mathematical technique that identifies and extracts linear relationships among variables, helping to understand inter-parameter relationships and explain correlation structures in detail [44,45], effectively summarizing the data while minimizing the loss of original information [42,46]. PCA transforms potentially correlated variables into a smaller set of uncorrelated variables, referred to as principal components. The first principal component captures as much variability in the data as possible, with subsequent components accounting for the remaining variability. In this study, PCA was conducted using SPSS-10.0, considering all parameters to explore direct relationships and sources of environmental variables in the coastal area. The Scree plot in Figure 13 displays the eigenvalues for each component, guiding the selection of components. Factor analyses with varimax rotation were applied, selecting eigenvalues greater than 1. Results were organized to include only those with values exceeding 0.4 [32,47].
Factor Analysis (FA) identifies underlying relationships between variables by grouping them into factors, thereby reducing data dimensionality and aiding in the understanding of data structure. As categorized by Liu et al. [48], factor loadings denote the strength of association between variables and factors. Factor loadings greater than 0.75 are considered “strong”, between 0.75 and 0.50 as “moderate”, and between 0.50 and 0.30 as “weak”; this provides insights into the relative importance of variables in the PCA results.
Eigenvalue and Scree plot analysis indicated that the first four principal components were the most significant, explaining 70.09% of the total variance (Table 3). PC-1, which explained 25.01% of the variance, is strongly associated with TP (0.891) and inorganic phosphorus (0.831), while showing moderate positive loadings for silicate (0.619), Chlorophyll-a (0.622), and ammonia (0.535). This reflects the natural relationship where ecosystem productivity directly depends on chlorophyll-a concentration, which in turn is governed by nutrient availability essential for plankton growth. Hence, this factor can be referred to as the productivity factor. PC-2, which accounts for 21.94% of the total variance, is marked by strong positive loadings for water temperature (0.837) and BOD (0.774), with moderate positive loadings for TN (0.633) and nitrate (0.511), and a moderate negative loading for salinity (r = −0.592). In this factor, the positive correlation of water temperature with BOD (r = 0.53), TN (r = 0.30), and nitrate (r = 0.25) (Table 2) indicates that water temperature plays a key role in the decomposition process, thereby enhancing BOD and nitrate levels [49].
Nitrate primarily originates from the decomposition of organic matter rather than from anthropogenic sources [50]. The positive correlation between nutrients (nitrate, ammonia, TN, and phosphate) and their correlation with BOD suggest a shared origin for these elements. The elevated nutrient levels in near-shore, less-saline waters suggest a significant riverine contribution to the coastal region, indicating a strong influence from river inputs [51].
Principal Component 3 (PC-3), accounting for 13.13% of the total variance, shows strong positive loading for suspended solids (0.848), moderate positive loadings for nitrate (0.737), nitrite (0.677), and another nitrate (0.558), and a moderate negative loading for pH (−0.618). The positive loadings of nitrite and nitrate with suspended solids suggest that these nutrients are primarily land-derived. The negative loading of pH suggests that nitrite, nitrate, and suspended solids are more concentrated at shore stations, while pH tends to increase further offshore [52,53].
Principal Component 4 (PC-4), accounting for 9.99% of the total variance, exhibits a positive loading for salinity (0.547) and a strong negative loading for DO (−0.810). No other significant correlations were identified within this component.

3.2.3. Regression Analysis

Regression analyses were performed to examine the relationship between salinity, expressed in PSU, and various parameters, including suspended solids, chlorophyll-a, DO, BOD, nitrite, nitrate, ammonia, inorganic phosphate, and silicate, as illustrated in Figure 14. The corresponding R2 values are presented in Table 4, demonstrating that a negative correlation exists in all cases except for chlorophyll-a. This lack of correlation highlights the independence of salinity from the other parameters chosen for the study. Generally, salinity levels in coastal waters increase with depth. Rising salinity poses significant threats to water quality and public health, particularly for recreational activities at nearby beaches and municipalities. Many regions worldwide are affected by this global issue, making it important to consider all parameters related to salinity [54,55]. Consequently, the origin of any increase in the concentration of these parameters should be investigated to effectively mitigate its effects. For instance, the elevated phosphate concentration at Mahanadi and Paradip could be addressed by implementing measures to prevent untreated discharges from phosphatic industries at these sites.
Chlorophyll-a is a key parameter for assessing the primary productivity of coastal waters and is indicative of the presence of phytoplankton [56]. The distribution and concentration of chlorophyll-a are closely linked to phytoplankton biomass, which is influenced by factors such as water temperature, pH, salinity, and DO levels [57]. Regression analysis of suspended solids (mg/L) against chlorophyll-a (mg/m3) showed a negative correlation, indicating that chlorophyll-a concentration is not related to suspended solids. Moreover, DO exhibited strong negative correlations with several parameters, including BOD (mg/L), nitrite (µmol/L), nitrate (µmol/L), ammonia (µmol/L), inorganic phosphate (µmol/L), and silicate (µmol/L). These negative correlations are attributed to increased water temperature, biological activity, and the rate of organic matter decomposition, along with nutrient dynamics [58].

4. Conclusions and Recommendations

The current investigation concludes that significant changes and trends in various hydrographic parameters have been observed across the coastal regions over the last decade. Notably, the DO content has decreased, with surface levels ranging from 7.50 to 8.38 mg/L and bottom levels from 5.63 to 8.37 mg/L. In the present study, DO levels were found to be 5.74 to 7.94 mg/L for surface water and 5.42 to 7.48 mg/L for bottom water, highlighting potential environmental stressors affecting water quality. Virtually no change in Chl-a content was observed across all transects except for the Puri transect. Both nitrite and nitrate concentrations showed a significant increase and rose to two and a half times their previous levels in this transect, indicating potential shifts in nutrient dynamics and ecological processes. However, no notable variation was observed in ammonia levels along the coast. Additionally, phosphate concentrations in the Paradip and Mahanadi transect have significantly increased, which are attributed to the recent activities of phosphatic plants in the area, highlighting the impact of these activities on nutrient loading in coastal waters. Other parameters studied showed only marginal increases.
The findings of this study highlight the increasing influence of anthropogenic activities on coastal environments, emphasizing the need for a comprehensive database and monitoring strategy. Such measures are essential for enhancing planning, conservation, and management efforts aimed at formulating effective strategies to address and mitigate environmental impacts. It is recommended that numerical hydrodynamic and environmental studies be conducted to monitor and track pollutant movement in coastal waters, evaluate the effectiveness of offshore outfalls in mitigating marine pollution, and assess their overall impact on coastal water quality. Additionally, implementing comprehensive monitoring programs that focus on physicochemical parameters and nutrient levels is important for continuously evaluating the limnological status of coastal regions. Collaborating with stakeholders and local communities to raise awareness about the risks of marine pollution and promote sustainable coastal development practices is essential for long-term environmental conservation and management.

Author Contributions

All authors contributed to the work. P.R.D., M.S.A. and R.R.T.; conceptualization, writing—original draft, P.C., B.K. and D.K.B.; contributed to all sections and reviewed, and S.C. and M.K.S.; supervision, contributed to all sections, reviewed, and edited the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

Some or all data that support the findings of this study are available from the corresponding author upon reasonable request.

Acknowledgments

The authors express their sincere thanks to senior professors, scientists, and colleagues for providing adequate guidance, scientific and technical discussions, and necessary support for this manuscript.

Conflicts of Interest

The authors declare no competing interests.

References

  1. Wieneke, F. The Use of Remote Sensing in Coastal Research. GeoJournal 1991, 24, 71–76. [Google Scholar] [CrossRef]
  2. de Alencar, N.M.P.; Le Tissier, M.; Paterson, S.K.; Newton, A. Circles of Coastal Sustainability: A Framework for Coastal Management. Sustainability 2020, 12, 4886. [Google Scholar] [CrossRef]
  3. Sudha Rani, N.N.V.; Satyanarayana, A.N.V.; Bhaskaran, P.K. Coastal Vulnerability Assessment Studies over India: A Review. Nat. Hazards 2015, 77, 405–428. [Google Scholar] [CrossRef]
  4. McGranahan, G.; Balk, D.; Anderson, B. The Rising Tide: Assessing the Risks of Climate Change and Human Settlements in Low Elevation Coastal Zones. Environ. Urban 2007, 19, 17–37. [Google Scholar] [CrossRef]
  5. Dhiman, R.; Kalbar, P.; Inamdar, A.B. Spatial Planning of Coastal Urban Areas in India: Current Practice versus Quantitative Approach. Ocean Coast. Manag. 2019, 182, 104929. [Google Scholar] [CrossRef]
  6. Sterzel, T.; Lüdeke, M.K.B.; Walther, C.; Kok, M.T.; Sietz, D.; Lucas, P.L. Typology of Coastal Urban Vulnerability under Rapid Urbanization. PLoS ONE 2020, 15, e0220936. [Google Scholar] [CrossRef]
  7. Saravanan, S.; Jegankumar, R.; Selvaraj, A.; Jennifer, J.; Parthasarathy, K.S.S. Utility of Landsat Data for Assessing Mangrove Degradation in Muthupet Lagoon, South India. In Coastal Zone Management: Global Perspectives, Regional Processes, Local Issues; Elsevier: Amsterdam, The Netherlands, 2019; pp. 471–484. ISBN 9780128143506. [Google Scholar]
  8. Chinnasamy, P.; Parikh, A. Remote Sensing-Based Assessment of Coastal Regulation Zones in India: A Case Study of Mumbai, India. Environ. Dev. Sustain. 2021, 23, 7931–7950. [Google Scholar] [CrossRef]
  9. Tjahjo, D.W.H.; Wiadnyana, N.N.; Purnamaningtyas, S.E.; Arifin, T.; Purbani, D.; Syam, A.R.; Wisha, U.J. Assessment of Water Quality Status, Nutrients, and Phytoplankton Communities in the Coastal Zone of East Aceh Regency, Indonesia. J. Ecol. Eng. 2023, 24, 112–129. [Google Scholar] [CrossRef]
  10. Shampa, M.T.A.; Shimu, N.J.; Chowdhury, K.M.A.; Islam, M.M.; Ahmed, M.K. A Comprehensive Review on Sustainable Coastal Zone Management in Bangladesh: Present Status and the Way Forward. Heliyon 2023, 9, e18190. [Google Scholar] [CrossRef]
  11. Tripathy, J.; Mishra, A.; Pandey, M.; Thakur, R.R.; Chand, S.; Rout, P.R.; Shahid, M.K. Advances in Nanoparticles and Nanocomposites for Water and Wastewater Treatment: A Review. Water 2024, 16, 1481. [Google Scholar] [CrossRef]
  12. Bansal, N. Industrial Development and Challenges of Water Pollution in Coastal Areas: The Case of Surat, India. In Proceedings of the IOP Conference Series: Earth and Environmental Science; Institute of Physics Publishing: Bristol, UK, 2018; Volume 120. [Google Scholar]
  13. Rafiq, F.; Techetach, M.; Achtak, H.; Boundir, Y.; Kouali, H.; Sisouane, M.; Mandri, B.; Cherifi, O.; Dahbi, A. First Assessment of Domestic and Industrial Effluents Impact on Intertidal Zone of Safi Coastline (West of Morocco): Physicochemical Characteristics and Metallic Trace Contamination. Desalination Water Treat. 2022, 245, 167–177. [Google Scholar] [CrossRef]
  14. Kumar, B.; Mukherjee, D.P.; Kumar, S.; Mishra, M.; Prakash, D.; Singh, S.K.; Sharma, C.S. Bioaccumulation of Heavy Metals in Muscle Tissue of Fishes from Selected Aquaculture Ponds in East Kolkata Wetlands. Sch. Res. Libr. Ann. Biol. Res. 2011, 2, 125–134. [Google Scholar]
  15. Dixit, P.R.; Kar, B.; Chattopadhyay, P.; Panda, C.R. Seasonal Variation of the Physicochemical Properties of Water Samples in Mahanadi Estuary, East Coast of India. J. Environ. Prot. 2013, 4, 843–848. [Google Scholar] [CrossRef]
  16. Häder, D.P.; Banaszak, A.T.; Villafañe, V.E.; Narvarte, M.A.; González, R.A.; Helbling, E.W. Anthropogenic Pollution of Aquatic Ecosystems: Emerging Problems with Global Implications. Sci. Total Environ. 2020, 713, 136586. [Google Scholar] [CrossRef]
  17. Khalid, N.; Aqeel, M.; Noman, A.; Hashem, M.; Mostafa, Y.S.; Alhaithloul, H.A.S.; Alghanem, S.M. Linking Effects of Microplastics to Ecological Impacts in Marine Environments. Chemosphere 2021, 264, 128541. [Google Scholar] [CrossRef]
  18. Shahid, M.K.; Choi, Y. Synthesis of Magnetite Particles for Enhanced Environmental Performance: Comparative Analysis of Three Schemes and Their Applications for Phosphorus Recovery from High-Strength Wastewater. Mater. Chem. Phys. 2024, 317, 129136. [Google Scholar] [CrossRef]
  19. Herawati, E.Y.; Darmawan, A.; Valina, R.; Khasanah, R.I. Abundance of Phytoplankton and Physical Chemical Parameters as Indicators of Water Fertility in Lekok Coast, Pasuruan Regency, East Java Province, Indonesia. In Proceedings of the IOP Conference Series: Earth and Environmental Science; IOP Publishing Ltd.: Bristol, UK, 2021; Volume 934. [Google Scholar]
  20. Thayer, G.W. Identity and Regulation of Nutrients Limiting Phytoplankton Production in the Shallow Estuaries Near Beaufort, N.C. Oecologia 1974, 14, 75–92. [Google Scholar] [CrossRef]
  21. Landrigan, P.J.; Stegeman, J.J.; Fleming, L.E.; Allemand, D.; Anderson, D.M.; Backer, L.C.; Brucker-Davis, F.; Chevalier, N.; Corra, L.; Czerucka, D.; et al. Human Health and Ocean Pollution. Ann. Glob. Health 2020, 86, 151. [Google Scholar] [CrossRef]
  22. Sonone, S.S.; Jadhav, S.; Sankhla, M.S.; Kumar, R. Water Contamination by Heavy Metals and Their Toxic Effect on Aquaculture and Human Health through Food Chain. Lett. Appl. NanoBioSci. 2021, 10, 2148–2166. [Google Scholar]
  23. Ratnam, K.; Jha, D.K.; Prashanthi Devi, M.; Dharani, G. Evaluation of Physicochemical Characteristics of Coastal Waters of Nellore, Southeast Coast of India, by a Multivariate Statistical Approach. Front. Mar. Sci. 2022, 9, 857957. [Google Scholar] [CrossRef]
  24. Jarvie, H.P.; Whitton, B.A.; Neal, C. Nitrogen and Phosphorus in East Coast British Rivers: Speciation, Sources and Biological Significance. Sci. Total Environ. 1998, 210–211, 79–109. [Google Scholar] [CrossRef]
  25. Sundaray, S.K.; Panda, U.C.; Nayak, B.B.; Bhatta, D. Multivariate Statistical Techniques for the Evaluation of Spatial and Temporal Variations in Water Quality of the Mahanadi River-Estuarine System (India)—A Case Study. Environ. Geochem. Health 2006, 28, 317–330. [Google Scholar] [CrossRef]
  26. Vega, M.; Pardo, R.; Barrado, E.; Deba, L. Assessment of Seasonal and Polluting Effects on the Quality of River Water by Exploratory Data Analysis. Water Res. 1998, 32, 3581–3592. [Google Scholar] [CrossRef]
  27. Upendra, B.; Ciba, M.; Arun, V.; Sreelesh, R.; Anoop Krishnan, K. Appraisal of Coastal Water Quality of Two Hot Spots on Southwest Coast of India: A Case Study of Multi-Year Biogeochemical Observations. In Coasts, Estuaries and Lakes Implications for Sustainable Development; Springer International Publishing: Cham, Switzerland, 2023; pp. 41–62. [Google Scholar]
  28. Grasshoff, K.; Ehrhardt, M. Methods of Seawater Analysis; Verlag Chemie GmbH: Hoboken, NJ, USA, 1999. [Google Scholar]
  29. Clesceri, L.S.; Greenberg, A.; Trussell, R. Standard Methods for Examination of Water and Wastewater; American Water Works Association, and Water Environment Federation: Washington, DC, USA, 1998. [Google Scholar]
  30. Riley, J.P.; Chester, R. Introduction to Marine Chemistry; Academic Press: Cambridge, MA, USA, 1971. [Google Scholar]
  31. Satpathy, K.K.; Mohanty, A.K.; Natesan, U.; Prasad, M.V.R.; Sarkar, S.K. Seasonal Variation in Physicochemical Properties of Coastal Waters of Kalpakkam, East Coast of India with Special Emphasis on Nutrients. Environ. Monit. Assess. 2010, 164, 153–171. [Google Scholar] [CrossRef]
  32. Panigrahy, P.K.; Das, J.; Das, S.N.; Sahoo, R.K. Evaluation of the Influence of Various Physico-Chemical Parameters on Coastal Water Quality, around Odisha, by Factor Analysis. Indian J. Mar. Sci. 1999, 28, 360–364. [Google Scholar]
  33. Garnier, J.; Billen, G.; Palfner, L. Understanding the Oxygen Budget and Related Ecological Processes in the River Mosel: The RIVERSTRAHLER Approach; Springer: Berlin/Heidelberg, Germany, 2000; Volume 410. [Google Scholar]
  34. Aston, S.R. Nutrients, Dissolved Gases, and General Biogeochemistry in Estuaries; Wiley: Hoboken, NJ, USA, 1980. [Google Scholar]
  35. Sillen, L.G. The Physical Chemistry of Sea Water. Lectures at the International Oceanographic Congress in New York, Septemper 1959. In Methodes Sea Water Analysis; John Wiley & Sons Inc.: Hoboken, NJ, USA, 1961. [Google Scholar]
  36. Kester, D.R.; Pytkowicx, R.M. Determination of the apparent dissociation constants of phosphoric acid in seawater. Limnol. Oceanogr. 1967, 12, 243–252. [Google Scholar] [CrossRef]
  37. Pal, R.; Reddy, P.M. Distribution of Nutrients Off Malpe, South Kanara Coast. Indian J. Mar. Sci. 1981, 10, 322–326. [Google Scholar]
  38. Purushothaman, A.; Venugopalan, V.K. Distribution of Dissolved Silicon in the Vellar Estuary. Indian J. Mar. Sci. 1972, 1, 103–105. [Google Scholar]
  39. Lal, D. Transfer of Chemical Species through Estuaries to Oceans. In Biogeochemistry of Estuarine Sediments: Proceedings of a Unesco/SCOR Workshop, Melreux, Belgium, 29 November–3 December 1976; UNESCO: Paris, France, 1978. [Google Scholar]
  40. Gouda, R.; Panigrahy, R.C. Seasonal Distribution and Behavior of Silicate in the Rushikulya Estuary, East Coast of India. Indian J. Mar. Sci. 1992, 21, 111–115. [Google Scholar]
  41. Liss, P.S.; Spencer, C.P. Abiological Processes in the Removal of Silicate from Sea Water. Geochim. Cosmochim. Acta 1970, 34, 1073–1088. [Google Scholar] [CrossRef]
  42. Sahu, B.K.; Begum, M.; Khadanga, M.K.; Jha, D.K.; Vinithkumar, N.V.; Kirubagaran, R. Evaluation of significant sources influencing the variation of physico-chemical parameters in Port Blair Bay, South Andaman, India by using multivariate statistics. Mar. Pollut. Bull. 2013, 66, 246–251. [Google Scholar] [CrossRef]
  43. Reghunath, R.; Sreedhara Murthy, T.R.; Raghavan, B.R. The Utility of Multivariate Statistical Techniques in Hydrogeochemical Studies: An Example from Karnataka, India. Water Res. 2002, 36, 2437–2442. [Google Scholar] [CrossRef]
  44. Shrestha, S.; Kazama, F.; Nakamura, T. Use of Principal Component Analysis, Factor Analysis and Discriminant Analysis to Evaluate Spatial and Temporal Variations in Water Quality of the Mekong River. J. Hydroinform. 2008, 10, 43–56. [Google Scholar] [CrossRef]
  45. Sârbu, C.; Pop, H.F. Principal Component Analysis versus Fuzzy Principal Component Analysis: A Case Study: The Quality of Danube Water (1985–1996). Talanta 2005, 65, 1215–1220. [Google Scholar] [CrossRef]
  46. Helena, B.; Pardo, R.; Vega, M.; Barrado, E.; Fernandez, J.M.; Fernandez, L. Temporal evolution of groundwater composition in an alluvial aquifer (Pisuerga River, Spain) by principal component analysis. Water Res. 2000, 34, 807–816. [Google Scholar] [CrossRef]
  47. Siddha, S.; Sahu, P. A Statistical Approach to Study the Evolution of Groundwater of Vishwamitri River Basin (VRB), Gujarat. J. Geol. Soc. India 2020, 95, 503–506. [Google Scholar] [CrossRef]
  48. Liu, C.W.; Lin, K.H.; Kuo, Y.M. Factor Analysis in the Assessment of Groundwater Quality in a Blackfoot Disease Area in Taiwan. Sci. Total Environ. 2003, 313, 77–89. [Google Scholar] [CrossRef]
  49. Panda, U.C.; Sundaray, S.K.; Rath, P.; Nayak, B.B.; Bhatta, D. Application offactor and cluster analysis for characterization of river and estuarine watersystems—A case study: Mahanadi River (India). J. Hydrol. 2006, 331, 434–445. [Google Scholar] [CrossRef]
  50. Roy, D.S.; Krishnan, P. Mangrove stands of Andamans vis-à-vis tsunami. Curr. Sci. 2005, 89, 1800–1804. [Google Scholar]
  51. Shirodkar, P.V.; Mesquita, A.; Pradhan, U.K.; Verlekar, X.N.; Babu, M.T.; Vethamony, P. Factors controlling physic-chemical characteristics in the coastalwaters off Mangalore—A multivariate approach. Environ. Res. 2009, 109, 245–257. [Google Scholar] [CrossRef]
  52. Simeonova, P.; Simeonov, V.; Andrew, G. Analysis of the Struma river water quality. Cent. Eur. J. Chem. 2003, 2, 121–126. [Google Scholar]
  53. Simeonov, V.; Simeonova, P.; Tsitouridou, R. Chemometric quality assessmentof surface waters two case studies. Chem. Eng. Ecol. 2004, 11, 449–469. [Google Scholar]
  54. Singh, K.P.; Malik, A.; Mohan, D.; Sinha, S. Multivariate statistical techniquesfor the evaluation of spatial and temporal variations in water quality of Gomtiriver (India)—A case study. Water Res. 2004, 38, 3980–3992. [Google Scholar] [CrossRef]
  55. Sarakar, S.K.; Rudra, R.R.; Nur, M.S.; Das, P.C. Partial least Squares regression for soil salinity mapping in Bangladesh. Ecol. Indic. 2023, 154, 110825. [Google Scholar] [CrossRef]
  56. Marlin, N.; Damar, A.; Effendi, H. The Horizontal Distribution Chlorophyll-a Phytoplankton as indicator of Tropic state in waters of Meulaboh Bay, West Aceh. J. IImu Pertinian Indones. JIPI 2015, 20, 272–279. [Google Scholar]
  57. Maslukah, L.; Setiawan, R.Y.; Nurdin, N.; Helmi, M.; Widiaratih, R. Phytoplankton Chlorophyll-a Biomass and the Relationship with Water Quality in Barrang Caddi, Spermonde, Indinesia. Ecol. Eng. Environ. Technol. 2022, 23, 25–33. [Google Scholar] [CrossRef]
  58. Okbah, M.; Hussein, R.N. Impact of Environmental Conditions on the Phytoplankton Structure in Mediterranean Sea Lagoon, Lake Burullus, Egypt. Water Air Soil Pollut. 2006, 172, 129–150. [Google Scholar] [CrossRef]
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Figure 1. Sampling location of 11 different transects along Odisha and West Bengal Coasts.
Figure 1. Sampling location of 11 different transects along Odisha and West Bengal Coasts.
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Figure 2. Contours showing variation in salinity in PSU unit.
Figure 2. Contours showing variation in salinity in PSU unit.
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Figure 3. Contours showing variation in dissolved oxygen in mg/L unit by SURFER Analysis method.
Figure 3. Contours showing variation in dissolved oxygen in mg/L unit by SURFER Analysis method.
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Figure 4. Contours showing variation in BOD in mg/L by SURFER Analysis.
Figure 4. Contours showing variation in BOD in mg/L by SURFER Analysis.
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Figure 5. Contours showing variation in dissolved nitrite (µmol/L) by SURFER Analysis method.
Figure 5. Contours showing variation in dissolved nitrite (µmol/L) by SURFER Analysis method.
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Figure 6. Contours showing variation in nitrate in µmol/L unit by SURFER Analysis method.
Figure 6. Contours showing variation in nitrate in µmol/L unit by SURFER Analysis method.
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Figure 7. Contours showing variation in ammonia in µmol/L unit by SURFER Analysis method.
Figure 7. Contours showing variation in ammonia in µmol/L unit by SURFER Analysis method.
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Figure 8. Contours showing variation in total nitrogenin µmol/L unit by SURFER Analysis method.
Figure 8. Contours showing variation in total nitrogenin µmol/L unit by SURFER Analysis method.
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Figure 9. Contours showing variation in Inorganic phosphatein µmol/L unit by SURFER Analysis method.
Figure 9. Contours showing variation in Inorganic phosphatein µmol/L unit by SURFER Analysis method.
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Figure 10. Contours showing variation in total phosphorous in µmol/L unit by SURFER Analysis method.
Figure 10. Contours showing variation in total phosphorous in µmol/L unit by SURFER Analysis method.
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Figure 11. Contours showing variation in silicate in µmol/L unit by SURFER Analysis method.
Figure 11. Contours showing variation in silicate in µmol/L unit by SURFER Analysis method.
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Figure 12. Contours showing variation in Chlorophyll-a in mg/L unit by SURFER Analysis method.
Figure 12. Contours showing variation in Chlorophyll-a in mg/L unit by SURFER Analysis method.
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Figure 13. Scree plot for components with its eigenvalue.
Figure 13. Scree plot for components with its eigenvalue.
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Figure 14. Linear regression analysis: (a) Suspended solids and Chl-a; (b) BOD and Salinity; (c) Suspended solids and Salinity; (d) Nitrite and Salinity; (e) Chl-a and Salinity; (f) Nitrate and Salinity; (g) DO and Salinity; (h) Ammonia and Salinity; (i) Inorganic Phosphate and Salinity; (j) Silicate and Salinity.
Figure 14. Linear regression analysis: (a) Suspended solids and Chl-a; (b) BOD and Salinity; (c) Suspended solids and Salinity; (d) Nitrite and Salinity; (e) Chl-a and Salinity; (f) Nitrate and Salinity; (g) DO and Salinity; (h) Ammonia and Salinity; (i) Inorganic Phosphate and Salinity; (j) Silicate and Salinity.
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Table 1. Water quality parameters, with mean value (M), and standard deviation (SD) at 11 different transects.
Table 1. Water quality parameters, with mean value (M), and standard deviation (SD) at 11 different transects.
StationsWTEMPSSCpHSALINDOBODNO2NO3NH4TNIPTPSiO4Chl-a
M ± SDM ± SDM ± SDM ± SDM ± SDM ± SDM ± SDM ± SDM ± SDM ± SDM ± SDM ± SDM ± SDM ± SD
Gopalpur25.6 ± 0.610.62 ± 2.758.17 ± 0.0629.92 ± 1.466.52 ± 0.411.20 ± 0.240.67 ± 0.361.40 ± 0.420.25 ± 0.0953.32 ± 11.116.99 ± 7.6316.02 ± 6.093.12 ± 2.071.29 ± 0.60
Rushikulya26.5 ± 0.312.04 ± 2.518.16 ± 0.0529.23 ± 1.296.38 ± 0.560.78 ± 0.300.37 ± 0.141.47 ± 0.720.36 ± 0.1236.02 ± 16.694.83 ± 2.7413.18 ± 4.4717.18 ± 12.031.49 ± 0.57
Chilika25.9 ± 1.012.17 ± 2.828.14 ± 0.0529.52 ± 1.067.11 ± 0.560.67 ± 0.190.52 ± 0.171.66 ± 0.830.35 ± 0.0924.02 ± 9.525.75 ± 3.4014.44 ± 3.7217.49 ± 13.371.49 ± 0.65
Puri28.3 ± 2.111.14 ± 4.388.21 ± 0.1026.69 ± 4.526.88 ± 0.551.94 ± 0.830.59 ± 0.453.70 ± 3.065.17 ± 8.6669.57 ± 18.1713.13 ± 12.9225.31 ± 24.0315.37 ± 17.881.68 ± 2.05
Konark25.5 ± 0.710.64 ± 2.888.09 ± 0.0428.63 ± 1.596.67 ± 0.521.04 ± 0.250.55 ± 0.091.40 ± 0.640.69 ± 0.1631.03 ± 12.237.90 ± 4.4014.94 ± 4.846.88 ± 2.861.07 ± 0.52
Paradip27.7 ± 2.712.12 ± 3.428.21 ± 0.1125.10 ± 5.227.01 ± 0.441.20 ± 0.580.43 ± 0.212.70 ± 1.551.39 ± 1.2454.77 ± 14.7616.73 ± 9.0233.57 ± 18.368.26 ± 3.201.24 ± 0.88
Mahanadi27.5 ± 3.215.65 ± 8.728.23 ± 0.1123.28 ± 7.037.06 ± 0.461.66± 0.760.41 ± 0.343.04 ± 1.682.06 ± 2.5058.02 ± 22.1538.03 ± 13.7749.20 ± 16.7031.53 ± 27.161.12 ± 0.88
Chandipur23.5 ± 1.840.00 ± 44.268.22 ± 0.1223.89 ± 1.587.20 ± 0.321.98 ± 0.311.02 ± 0.083.81 ± 2.550.75 ± 0.4244.96 ± 5.007.08 ± 4.0710.21 ± 3.445.22 ± 1.821.57 ± 0.23
Digha26.6 ± 1.822.49 ± 11.478.29 ± 0.0424.81 ± 3.027.56 ± 0.142.24 ± 0.400.86 ± 0.351.58 ± 0.571.04 ± 0.7646.21 ± 10.715.37 ± 3.248.25 ± 3.2943.50 ± 6.791.55 ± 0.44
Sandheads26.6 ± 2.641.22 ± 17.308.04 ± 0.1116.82 ± 4.187.44 ± 0.292.09 ± 1.050.87 ± 0.468.88 ± 1.811.52 ± 1.0676.63 ± 14.275.06 ± 3.7212.23 ± 5.1114.18 ± 7.790.88 ± 0.42
Saptamukhi24.5 ± 0.219.18 ± 4.718.02 ± 0.1725.67 ± 0.786.84 ± 0.450.79 ± 0.281.98 ± 0.164.78 ± 0.180.36 ± 0.0943.03 ± 11.314.37 ± 3.548.75 ± 4.4225.61 ± 26.981.74 ± 0.03
Notes: Units: WTEMP in °C. SSC, DO, BOD are in mg/L. Salinity is in PSU. NO2, NO3, NH4, TN, IP, TP, SiO4 are in µmol/L and Chl-a is in mg/L.
Table 2. Correlation matrices of physicochemical parameters with Chlorophyll-a.
Table 2. Correlation matrices of physicochemical parameters with Chlorophyll-a.
WTEMPpHSALINDOBODNO2NO3NH4TNIPTPSiO4Chl-aSSC
WTEMP1
pH−0.0011
SALIN−0.327 **−0.0091
DO−0.1200.126−0.288 **1
BOD0.532 **−0.288 *−0.330 **−0.307 **1
NO2−0.249 *−0.117−0.170−0.1020.1131
NO30.246 *−0.377 **−0.568 **0.0640.535 **0.447 **1
NH40.164−0.129−0.075−0.395 **0.453 **0.372 **0.325 **1
TN0.300 **−0.194−0.428 **−0.0730.495 **0.339 **0.542 **0.371 **1
IP−0.0330.157−0.030−0.1520.1010.081−0.1590.307 **0.1531
TP−0.1270.1490.096−0.2170.0740.211−0.0590.381 **0.1040.864 **1
SiO4−0.448 **0.0330.148−0.301 **−0.0330.142−0.0510.267 *−0.0340.398 **0.394 **1
Chl-a−0.255 *0.0440.203−0.0850.0420.2050.0580.512 **0.0280.253 *0.450 **0.406 **1
SSC−0.065−0.548 **−0.332 **0.290 *0.1950.445 **0.610 **−0.0060.311 **−0.112−0.144−0.046−0.0921
Notes: ** Correlation is significant at the 0.01 level. * Correlation is significant at the 0.05 level.
Table 3. Factor loadings (varimax rotation) rotated component matrices.
Table 3. Factor loadings (varimax rotation) rotated component matrices.
Variable1234
TP0.891
IP0.831
Chl-a0.622
SiO40.619
NH40.535
WTEMP 0.837
BOD 0.774
TN 0.633
SALIN −0.592 0.547
SSC 0.848
NO3 0.5110.737
NO2 0.677
pH −0.618
DO −0.810
Eigenvalues3.503.071.831.39
Variance %25.0121.9413.139.99
Cumulative %25.0146.9660.0970.09
Notes: Loadings with magnitude greater than 0.4 are shown.
Table 4. R2 values for different physicochemical parameters.
Table 4. R2 values for different physicochemical parameters.
ParametersR2 Values
Suspended solids−0.31
Chlorophyll-a0.0388
Dissolved oxygen−15.422
Biochemical oxygen demand−0.5823
Nitrite−0.2336
Nitrate−0.4778
Ammonia−0.0376
Inorganic phosphate−0.1114
Silicate0.0142
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Dixit, P.R.; Akhtar, M.S.; Thakur, R.R.; Chattopadhyay, P.; Kar, B.; Bera, D.K.; Chand, S.; Shahid, M.K. Exploring Seasonal Changes in Coastal Water Quality: Multivariate Analysis in Odisha and West Bengal Coast of India. Water 2024, 16, 2961. https://doi.org/10.3390/w16202961

AMA Style

Dixit PR, Akhtar MS, Thakur RR, Chattopadhyay P, Kar B, Bera DK, Chand S, Shahid MK. Exploring Seasonal Changes in Coastal Water Quality: Multivariate Analysis in Odisha and West Bengal Coast of India. Water. 2024; 16(20):2961. https://doi.org/10.3390/w16202961

Chicago/Turabian Style

Dixit, Pravat Ranjan, Muhammad Saeed Akhtar, Rakesh Ranjan Thakur, Partha Chattopadhyay, Biswabandita Kar, Dillip Kumar Bera, Sasmita Chand, and Muhammad Kashif Shahid. 2024. "Exploring Seasonal Changes in Coastal Water Quality: Multivariate Analysis in Odisha and West Bengal Coast of India" Water 16, no. 20: 2961. https://doi.org/10.3390/w16202961

APA Style

Dixit, P. R., Akhtar, M. S., Thakur, R. R., Chattopadhyay, P., Kar, B., Bera, D. K., Chand, S., & Shahid, M. K. (2024). Exploring Seasonal Changes in Coastal Water Quality: Multivariate Analysis in Odisha and West Bengal Coast of India. Water, 16(20), 2961. https://doi.org/10.3390/w16202961

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