Inter-annual variability on the physicochemical characteristics of Tuticorin coast, southeast of India

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Long term monitoring is required in order to understand the fluctuating trends of water quality in this area. The present study aimed to monitor the physicochemical characteristics of the coastal water at Tuticorin from 2012 to 2017. Sample collection was conducted for all the seasons of each year throughout the study period to understand the variations in physicochemical characteristics in the coastal water. A regression trend line was plotted based on the data which enables us to understand that water quality is deteriorating in the study area. Such long term studies are essential to determine the actual fate of any coastal area. Currently the area is yet to be termed as polluted; however measures must be taken to reduce the discharge of pollutants. Physicochemical characteristics Tuticorin 2012–2017 Regression trend line Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Water is the principal entity that supports human survival and also contributes to economic growth (Li and Qian, 2018 ). Hence it is essential to determine the quality of water on a regular basis. The water quality is defined by the physicochemical and biological characteristics present in it (Jayaraju et al., 2010 ) which is constantly declining due to several anthropogenic discharges such as municipal and industrial effluents. The rivers bring these wastes to the coastal environment where it affects the marine organisms (Dhirendra et al., 2009 ). These intertidal zones have gained much importance in research to determine the changes in the coastal environment due to anthropogenic discharges (Kress et al., 2002 ). Continuous monitoring of coastal water enables the protection and sustainable use of water resources (Khan et al., 2021 ; Qian et al., 2013 ). Coastal zones host more than 40% of the global population (Gnanachandrasamy et al., 2019 ) since they profusely provide mineral resources, aids transportation, facilitates trade, recreational and cultural activities (Neumann et al., 2015 ). Coastal ecosystem is a reservoir of renewable resources which is on the decline due to a descent in water quality over the years (Gopinath et al., 2013 ). In recent times, human growth has affected the water resources which include the coastal waters (Rosegrant et al., 2009 ; Adimalla and Li, 2019 ). This decline along the southeastern coast of India could possibly be due to the high amount of freshwater incursion which brings in a large amount of nutrients and other waste discharges (Yu and McCreary, 2004 ; Sardessai et al., 2007 ). Coastal zone along the Indian coastline receives effluents from industries, municipal sewage discharges and recreational activities which minimizes the coastal water quality (Anand et al., 2015 ). Due to these activities it is essential to monitor the water quality by carrying out physicochemical studies which are the rudiments for any hydrological system (Damotharan et al., 2010 ). Balasubramanian et al., ( 2022 ) suggested that seawater influences the groundwater also. Hence, it can be understood that polluted seawater may also pollute the groundwater which are essential sources of drinking water to humans. Physicochemical parameters are dependent on each other and any changes to one of the parameters may affect the other parameters also (Iyer et al., 2003 ). The rise in nutrient levels may contribute to eutrophication which consequently increases the frequency of algal blooms (Cloern, 2001 ). Due to fluctuations in the amount of physicochemical parameters across seasons, monitoring the quality of water for a lesser period of time may not provide long term benefits. Hence, perennial studies enable to understand the changes of physicochemical parameters in the coastal zones. Therefore this study gains significance as it projects the physicochemical parameters across all seasons over six years from January, 2012 to December, 2017. For this study, Tuticorin has been chosen as it hosts a diversified system of chemical and other industries, a number of thermal power plants as well as a network of salt pans. Studies on physicochemical characteristics of Indian coastal waters have been on the rise in recent years. Numerous studies have projected the physicochemical variations on a seasonal or monthly basis however, there are no reports exhibiting these variations over a period of 6 years in this area. Study Area Description Tuticorin is a port town located in the Gulf of Mannar sandwiched between 8.7642° N latitude & 78.1348° E longitude. The area has developed rapidly in the recent years due to various establishments such as aquaculture, industries, refineries, chemicals, fertilizers and caustic soda manufacturers, salt pans and thermal power plants. There are no facilities to treat the effluents released from these establishments. Hence, it is understood that a large amount untreated effluents may be released from these establishments which subsequently reaches the coastal waters (Bharathi et al., 2018 ). The sampling stations were classified into 3 categories namely i) coastal – 0.5, 2 and 5 km from the shore (6 samplings-3 along the old harbour and 3 along the new harbour) ii) hotspot-diurnal collection on the shore at every 3 hours interim for a total of 36 hrs in succession (13 samplings) iii) tidal-0.5 km on either side of the hotspot once each during high and low tides (4 samplings) as shown in Fig. 1 . To predict the deteriorating rate of water quality in any area, elaborate studies on the coasts and shorelines are required. The sampling locations remained constant throughout the study and samplings were carried out during all the four seasons across six years from January, 2012 to December, 2017. Methods Sample collection Water samples were collected in triplicates at each study station using a Niskin’s water sampler. The collected samples were preserved in refrigerated conditions (4ºC) until further analysis in the laboratory. Physical parameters Sea surface temperature, salinity and pH The parameters like sea surface temperature, pH and salinity were measured at the collection spot using calibrated instruments such as digital thermometer (TP-101 model), pH pen (pH Ep-3 Model) and Hand Refractometer (Erma Company, Japan) respectively. Dissolved oxygen (DO) and Biochemical Oxygen Demand (BOD) The modified Winkler’s method described by Strickland and Parson ( 1972 ) was adopted for the estimation of dissolved oxygen and biochemical oxygen demand. The values for both parameters were expressed as mg/l. Total Alkalinity Total alkalinity was assessed by adopting the method described by Strickland and Parson ( 1972 ). Carbonate and Bicarbonate values were determined and total concentration of these substances yield total alkalinity which was expressed as ppm. Chemical Parameters Nutrients All nutrient parameters such as nitrite, nitrate, ammonia, total nitrogen, inorganic phosphate, total phosphorous and reactive silicate were determined using the procedure described by Grasshoff et al., ( 1999 ). The units were expressed as µmol/l. Statistical Interpretation Statistical tools such as PRIMER 6, ORIGIN 8 and Graphpad Prism 8.2.0 were used to perform regression trend line studies, Bray Curtis cluster and Multi Dimension Scaling plots and Spearman’s correlation. Results Physical parameters During the study period, the maximum average sea surface temperature (33.33 ± 2.02°C) was recorded in the tidal water during 2016 while the minimum sea surface temperature (27.72 ± 2.82°C) was recorded in the coastal water during 2013. The trend line showed an increasing pattern (R 2 = 0.239; slope = 0.399) visible in Fig. 2 A. The highest average sea water pH (8.22 ± 0.096) was reported in the coastal water during 2012 while the lowest sea water pH (7.90 ± 0.15) was reported in the tidal water in 2016. The trend line showed a decreasing pattern (R 2 = 0.667; slope = − 0.4) exhibited in Fig. 2 B. The maximum average salinity (35.18 ± 0.18 PSU) was observed in the hotspot region during 2017 while the least salinity (32.12 ± 1.22 PSU) was observed in the coastal water in 2013. The trend line showed an increasing pattern (R 2 = 0.048; slope = 0.279) shown in Fig. 2 C. The maximum average total alkalinity (150.19 ± 10.89 ppm) and minimum average alkalinity (113.90 ± 14.80 ppm) were noted in the tidal water during 2015 and 2016 respectively. The trend line showed an increasing pattern (R 2 = -0.182; slope = 1.449) witnessed in Fig. 2 D. The highest average dissolved oxygen (5.79 ± 0.14 mg/l) and the lowest average dissolved oxygen (4.89 ± 0.15 mg/l) were recorded in the coastal water during 2014 and 2016 respectively. The trend line was straight (R 2 = -0.249; slope = 9.669) seen in Fig. 2 E. The highest average BOD (1.53 ± 0.028 mg/l) was observed in the tidal water during 2017 and the lowest average BOD (0.31 ± 0.6 mg/l) was observed in the hotspot sampling during 2013. The trend line showed an increasing pattern (R 2 = 0.082; slope = 0.0948) observed in Fig. 2 F. Chemical parameters The maximum average nitrite concentration (0.71 ± 0.35 µmol/l) was reported in the coastal water during 2016 while the least average nitrite concentration (0.12 ± 0.19 µmol/l) was recorded in the coastal waters during 2017. The trend line was decreasing (R 2 = 0.615; slope = -0.057) as expressed in Fig. 2 G. The maximum average nitrate concentration was observed as 7.54 ± 1.02 µmol/l during 2016 in the coastal water and the minimum average nitrate concentration was observed as 0.25 ± 0.91 µmol/l during 2015 in the tidal water. The trend line was decreasing at R 2 = -0.203; slope = -0.230 as exhibited in Fig. 2 H. The highest average ammonia concentration (23.04 ± 2.24 µmol/l) was noted during 2016 in the coastal water and the lowest average ammonia concentration (0.066 ± 0.21 µmol/l) was noted during 2012 in the coastal water. The trend line showed an increasing pattern (R 2 = 0.178; slope = 1.404) visible in Fig. 2 I. The maximum average Total Nitrogen (TN) concentration was recorded as 52.795 ± 5.32 µmol/l during 2016 in the coastal water and the minimum average Total Nitrogen concentration (7.49 ± 2.33 µmol/l) was recorded during 2013 in the hotspot water. The trend line showed an increasing pattern (R 2 = -0.15; slope = 0.883) as witnessed in Fig. 2 J. The highest average inorganic phosphate concentration (2.104 ± 0.92 µmol/l) was reported during 2015 in the tidal water and the least average inorganic phosphate (IP) concentration (0.34 ± 0.57 µmol/l) was reported during 2013 in the tidal waters. The trend line showed an increasing pattern (R 2 = 0.182; slope = 0.155) as seen in Fig. 2 K. The maximum average total phosphorous (TP) concentration was observed as 3.05 ± 1.12 µmol/l during 2016 in the tidal water and the minimum average total phosphorous concentration (0.87 ± 0.23 µmol/l) was observed during 2013 in the tidal water. The trend line showed an increasing pattern (R 2 = 0.422; slope = 0.122) observed in Fig. 2 L. The highest average reactive silicate concentration was noted as 21.67 ± 1.38 µmol/l during 2016 in the tidal water and the least average Reactive Silicate concentration (0.798 ± 2.34 µmol/l) was noted during 2013 in the coastal water. The trend line showed an increasing pattern (R 2 = -0.103; slope = 0.735) expressed in Fig. 2 M. Discussion Sea surface temperature (SST) is generally governed by atmospheric temperature as its gradual rise will influence the water temperature of any area (PuthiyaSekar et al., 2009 ). The water temperature may also be influenced by other factors such as solar radiation, freshwater influx, cooling and mixing due to ebb and flow of neritic waters (Balakrishnan et al., 2017 ). SST is known to alter the biochemical and physical processes of any living organism (Dupuis and Hann 2009 ). Temperature plays an important role in reproduction, photosynthesis, metabolism, growth of phytoplankton and also influences microbial activity (Hossain et al., 2007 ; Shah et al., 2008 ). Tuticorin area has a large number of chemical industries and thermal power plants. Chemical wastes and coolants discharged from these power plants are also a major reason for the increased sea surface temperature in this area. The increasing trend line of SST (slope = 0.399) during the study period indicates that the atmospheric and water temperature in this area is constantly on the rise. The pH concentration of the coastal water is dependent on a number of factors such as salinity, temperature, carbon dioxide, carbonate, bicarbonate and hydroxyl ion concentration, as well as climate and buffering activity of the sea in the particular area. Additionally, fluctuations in dissolved oxygen can also persuade pH levels in the coastal water (Anand et al., 2015 ). The presence of carbon dioxide and water leads to the formation of carbonic acid which may conveniently reduce the pH levels (Singaraja, 2017 ). The other reasons which influence pH concentration include acidity of the bottom sediment and certain biological activities (Balasubramanian and Kannan, 2005 ). pH highlights environment suitability and can be associated with species composition and chemical changes involving biological processes (Rani et al., 2012 ). In the present study, pH levels have shown a decrease in the trend line (slope = − 0.4) over the years. The increasing water temperature makes the water to be more buffering thereby increasing the carbonate and bicarbonate ratio meanwhile reducing the pH. This reduction in pH indicates the increase in buffering capacity of the water to hold more bicarbonate ions which increases the alkalinity (Petrucci, 2007 ). Salinity is the basic physicochemical characteristic which is assessed regularly by marine ecologists due to its influence on marine biota (Sreenivasulu et al., 2015 ). Salinity is determined by the amount of salt inflow and the rate of evaporation as well as the persuasion of intertidal organisms (Balasubramanian and Kannan, 2005 ). In our study, salinity showed an increasing trend line (slope = 0. 279) which could be correlated with SST as increased temperature leads to higher evaporation thereby increasing the salinity concentration. Yet another reason for elevated salinity in this region is the presence of numerous salt pans and their subsequent brine outlet. Balasubramanian et al., ( 2022 ) opined that increased salinity in the seawater can affect the groundwater in the surrounding areas which makes such groundwater unfit for drinking and irrigation purposes. Total alkalinity is the measure of weak acids and cations that neutralize these acids (Singh et al., 2010 ). Therefore total alkalinity could be simply defined as the total amount of carbonates and bicarbonates present in the water (Ouyang et al., 2006 ). The increase in the amount of carbonates and bicarbonates could also be attributed to degradation of plants and animals as well as the organic input from the estuary (Wang et al., 2006 ). The trend line of total alkalinity showed an increasing pattern (slope = 1.449) which could be correlated with the increasing salinity levels. Increasing alkalinity could be due to the strong alkaline contents present in the industrial discharges and sewages (Safari et al., 2012 ). Increased carbonate and bicarbonates may also be present as a result of plant degradation and excess organic wastes (Wang et al., 2006 ). This increasing alkalinity trend can be correlated with the decreasing trend of pH which occurs due to the buffering of water in order to accommodate excess bicarbonate ions (Petrucci, 2007 ). Dissolved oxygen is of utmost importance in determining the physical condition of an aquatic body thereby ensuring a balanced network of plants and animals (Raffaelli, 2000 ). The higher DO levels could be the result of freshwater mixing and low metabolic activities while the reduced DO levels might occur due to higher temperature and availability of bulk quantity of untreated waste in addition to the elevated metabolic activity of the organisms (Vasanthi and Sukumaran, 2017 ). In the present study, DO exhibited a straight trend line indicating that the DO levels have remained constant over the years. Even though there is higher organic load that depletes the oxygen levels, DO concentration were maintained constantly due to freshwater mixing and photosynthetic activity. Biochemical oxygen demand is the measure of oxygen required to stabilize organic water and it evaluates the proficiency in the removal of biodegradable organic load by certain natural processes (Vasanthi and Sukumaran, 2017 ; Simon et al., 2011 ). Rise in temperature subsequently decreases DO which in turn elevates the oxygen demand (Faragallah et al., 2009 ) therefore sea water temperature and DO are inversely proportional (Sheathe and Kazama, 2007 ). In the current work, BOD showed an increasing pattern (slope = 0.0948) and this could be due to the constant discharge of organic load from the terrestrial sources (Wu et al., 2009 , 2010 ). Nutrients are essential commodities in the marine environment as they influence growth, reproduction and metabolic activities of the organisms (Kalaiarasi et al., 2012 ; Nedumarn and Perumal, 2009 ). The distribution of these nutrients is dependent on the freshwater incursion from terrestrial sources (Damotharan et al., 2010 ) while several biological processes are reliant on the nitrogen and phosphorous concentrations in the water (Sahugouri et al., 2012 ). Nitrite is the intermediate species between ammonia and nitrate where oxidation of ammonia as well as reduction of nitrates leads to nitrite formation (Satpathy et al., 2010 ). The decreasing nitrite trend (slope = -0.057) over the years in our study could be due to the increasing temperature and salinity patterns across the study period (Saravanakumar et al., 2008 ). The presence of nitrites is associated with the concentrations of nitrates (Yogeshkumar and Geetha, 2012 ), hence in our study, nitrite shows a decreasing trend due to the decreasing trend of nitrate. Nitrates are the highest oxidized form of nitrogen and one of the essential indicators of pollution which may be present in the water as a result of freshwater inflow containing fertilizers, chemicals, domestic and municipal discharges as well as organic decomposition (Rajaram et al., 2005; Ashokprabu et al., 2005 ). The oxidation of ammonia may also be a criterion for the presence of nitrogen in water (Govindasamy et al., 2000 ). The decreasing trend of nitrates (slope = -0.230) in our study could be due to the increased photosynthetic activity of phytoplankton over the years since phytoplankton are voracious consumers of nitrates. This was explained by Rippeth and Jones ( 1997 ) in their seasonal nitrate model where phytoplankton consumes high amounts of nitrate during elevated temperatures. Therefore in accordance with our study, increasing temperature trend was noticed over the years which can be considered as a criterion for increased consumption of nitrate by phytoplankton which causes this decreasing trend. Ammonia is the output of breakdown of organic materials such as excreta of organisms. During lofty ammonia levels, the abundance of NH 3+ ions enhances phytoplankton productivity (Dugadale et al., 2007 ). Another reason attributed to the increasing pattern could be the regular release of ammonical excreta by plankton (Godhantaraman, 2002 ). The increasing trend line of ammonia (slope = 1.404) in the present study highlights the constant accumulation of ammonia over the years which may result in algal blooms. Total Nitrogen is the magnitude of all forms of nitrogen available in the water in dissolved or suspended forms. These compounds are generally available in low quantities but its quantum may increase as a consequence of freshwater inflow containing high amounts of organic and industrial effluents (Malarvannan and Balamurugan, 2018 ). The trend line of total nitrogen (slope = 0.883) in the present study indicates the increasing discharge of industrial effluents over the years. Even though, nitrite and nitrate yielded a decreasing trend line, ammonia showed a significantly increasing trend which increases the trend line of total nitrogen. Several biological activities of marine biota are dependent on the inorganic phosphate concentration (Mackey et al., 2007 ). Phosphate concentration in the water is dependent on a number of factors such as upwelling, freshwater incursion and breakdown of organic materials (Paytan and Mclaughlin, 2007 ). The primary source of phosphates in freshwater is due to domestic and agricultural discharge containing a high amount of detergents and pesticides respectively (Liu et al., 2009 ). The increasing trend line of inorganic phosphates (slope = 0.155) in our study could be due to frequent discharge of alkyl phosphates and detergents from the household, fertilizers and super phosphate residues from agricultural fields, turbulence and mixing of total phosphorous from the bottom sediments (Saravanakumar et al., 2008 ). Total Phosphorus is the measure of all forms of phosphorus available in the water and is capable of inducing cyanobacterial blooms. Naturally, total phosphorous may be present in low quantities in coastal water however increased levels may be present as a result of agricultural deposits which may prove to be toxic to the marine environment (Malarvannan and Balamurugan, 2018 ). The increased trend line (slope = 0.122) of total phosphorous in the present study could be associated with the rising trend of inorganic phosphates. Silicates are essential in regulating the distribution of phytoplankton in the coastal water. The alteration of reactive silicates in water could be due to factors such as saline and freshwater mixing, chemical interface with clay and biological elimination by diatoms (Satpathy et al., 2010 ). The increasing trend line of reactive silicate (slope = 0.735) in our study could be attributed to the turbulence of water bringing the bottom sediments to the surface as well as the mixing of freshwater containing leached rock material comprising large amounts of silicate (Govindasamy et al., 2000 ; Rajasegar, 2003 ). Statistical interpretation of our findings The Spearman correlation coefficient was plotted for all the parameters in the coastal, hotspot and tidal samplings. In the coastal samplings, SST showed the highest correlation with BOD (p = 0.83; <0.01 level of significance) and this was predicted as rise in SST may lead to increase in BOD. IP exhibited the highest correlation with TP (p = 0.94; <0.01 level of significance) and this was also predicted since IP and TP are different measures of phosphates (Fig. 3 A ) . In the hotspot samplings, SST expressed a very high correlation with salinity (p = 0.89; <0.01 level of significance) and this was foreseen since increasing SST may lead to higher rate of evaporation ultimately increasing the salinity as seen in Fig. 3 b. In the tidal samplings, SST had a high correlation with salinity, DO and TP (p = 0.77; <0.01 level of significance). Nitrate had a very high correlation with TN (p = 0.71; <0.05 level of significance) and this was also expected since TN is the measure of all forms of nitrogen as seen in Fig. 3 C. The Bray Curtis similarity cluster for the coastal sampling showed three groups with a distantly related fourth group of alkalinity. The first group comprised of the highest similarity parameters where ammonia had the highest similarity to silicate (99.4%). This group in turn had a very high similarity to DO (96.9%). In the second group, water temperature and salinity showed a very high similarity (94.07%) while in the third group, IP and BOD showed a higher similarity (94.09%). The remaining parameters showed insignificant similarities with the other parameters (Fig. 4 A ) . Likewise in the similarity cluster for the hotspot sampling, three main groups with sub groups were noted. The highest similarity was observed between pH and silicate (98.97%) while SST and salinity showed a very high similarity (95.08%) whereas BOD and IP also showed a significantly higher similarity (90.08%) shown in Fig. 4 B. The tidal samplings cluster similarity showed two closely associated groups and a distantly associated alkalinity group. The highest similarity was noticed between ammonia and TP (98.13%). SST and salinity also showed a very high similarity (96%) exhibited in Fig. 4 C. The Bray Curtis MDS plot (Multi-Dimensional Scaling) for the coastal sampling (Fig. 5 A) was also in line with the cluster graph as alkalinity resembled other parameters distantly. The resemblance exhibited three groups where ammonia and silicate had the highest resemblance. The MDS plot for hotspot sampling (Fig. 5 B ) showed the highest resemblance between pH and silicate. The MDS plot for tidal samplings (Fig. 5 C) showed the highest resemblance between ammonia and TP. These results are in line with the cluster plots. Suitable nutrient concentrations and physicochemical characteristics are required to maintain a good biodiversity in any aquatic environment (Kristensen et al., 2018 ). Increased nutrient concentrations lead to eutrophication affecting the plankton community and subsequently the corresponding food chain (Taipale et al., 2019 ). Holopainen and Lehikoinen ( 2022 ) observed that reduced water quality affected the wetland biodiversity. The authors understand that such changes occur in the coastal zones also which can drastically affect its biodiversity. A former study (Bharathi et al., 2018 ) also carried out studies on physicochemical changes of the coastal waters at Tuticorin and presented higher values compared to the present study. This further confirms that the region has a deteriorating water quality. Ayyandurai et al., ( 2022 ) recommended some suggestions to reduce discharges that pollute the water resources at Cuddalore district, Tamilnadu India. The recommendations include the recycling of water in industrial processes and the use of wastewater in cooling processes. The above recommendations are also applicable at Tuticorin, hence the authors recommend the same. Further it is suggested that the unavoidable effluents that remain after processing must be released away from the continental shelves to reduce its impact on the coastal regions. Conclusions Physicochemical characteristics are the most essential components in determining the quality of any aquatic system. These characteristics need to be monitored regularly for longer durations to understand the pattern and causes of water pollution. A large number of organisms including humans are dependent on coastal waters for food. A major share of a country’s economy is also dependent on the coastal water. Hence polluting the marine environment may prove to be harmful to the society. Sea surface temperature was on the rise throughout the study which also influenced the increasing salinity. The nutrients showed an increasing pattern due to the excessive discharge of effluents from various sources paving the way for eutrophication which may permit the formation of harmful algal blooms. This could have drastic effects on the coastal zones. From this study, it can be concluded that Tuticorin coastal waters have become polluted due to the effluent discharges from the industries, thermal power plants and salt pans. However, the pollution levels have not yet reached intolerable levels. There needs to be a regulation in these discharges if not, there could be drastic impacts on the marine biota which could indirectly harm the human life. Declarations Ethics approval and consent to participate: Not applicable Consent to publication: Not applicable Authors Contributions: A. Nithin: Methodology, Investigation, and Writing - original draft, Visualization, Validation. A. Sundaramanickam: Research hypothesis, Conceptualization, Supervision, Data curation, Data validation, Writing - review & editing. K. Manimaran : sample analysis, data validation. K. Saranya : sample analysis, data validation. M. Sathish: sample analysis, data validation. P. Surya: sample analysis, Data validation. K. Balachandar: sample analysis, data validation. R.S. Sathishkumar : Sample Analysis, data validation. M. Meena : Sample Analysis, data validation. Funding Information: This study was supported by a research project “Seawater Quality Monitoring (SWQM)” 317 from Ministry of Earth Sciences (MoES), Government of India (Project File No.MoES / ICMAM-PD/ME/CAS318 MB/53/2017). Competing of interest: The authors declare that they have no conflict of interest Availability of data and materials: The current work was funded by the Ministry of Earth Sciences, Government of India. Therefore, the financial institution holds the rights. Data can be available from the authors on a reasonable request. Acknowledgements This study was supported by a research grant from Ministry of Earth Sciences (MoES), National Centre for Coastal Research, Government of India (Project File No.MoES / ICMAM-PD/ME/CAS-MB/53/2017).We would like to acknowledge authorities of Annamalai University for providing necessary facilities. 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Resour. 34 (1), 205–222. https://doi.org/10.1146/annur ev.envir on.03030 8.090351 Safari D, Mulongo G, Byarugaba D, Tumwesigye W (2012) Impact of Human Activities on the Quality of Water in Nyaruzinga Wetland of Bushenyi District – Uganda. Int Res J Environ Sci 1(4):1–6 Sahugouri KK, Satpathy AK, Mohanty, Sarkar SK (2012) Variations in community structure of phytoplankton in relation to physicochemical properties of coastal waters, southeast coast of India. I J Mar Sci 41(3):223–241 Sardessai S, Ramaiah N, Kumar SP (2007) Influence of environmental forcings on the seasonality of dissolved oxygen and nutrients in the Bay of Bengal. J Mar Res 65(2):301–316. https://doi.org/10.1357/002224007780882578 Saravanakumar A, Rajkumar M, Seshserebiah J, Thivakaran GA (2008) Seasonal variations in Physico – chemical characteristics of water, sediment and soil texture in mangroves of Kach – Gujarat. J Environ Biol 29:725–732 Satpathy KK, Mohanty AK, Gourisahu, Sarkar SK, Natesan U, Venkatesan R, Prasad MVR (2010) Variations of physicochemical properties in Kalpakkam coastal waters, east coast of India, during southwest to northeast monsoon transition period. Environ Mon Assess 171:411–424. https://doi.org/10.1007/s10661-009-1287-9 Sheathe JO, Kazama F (2007) Assessment of surface water quality using multivariate statistical techniques: A case study of the Fuji river basin. Japa Environ Model Software 22(4):464–470 Shah MMR, Hossain MY, Begum M (2008) Seasonal variations of phytoplanktonic community structure and production in relation to environmental factors of the Southwest Coastal waters of Bangladesh. J Fish Aquat Sci 3:102–113 Simon FX, Penrue Y, Guastalli RA, Llorens J, Vaig S (2011) Improvement of the analysis of the Biochemical Oxygen Demand (BOD) of Mediterranean Sea by seeding control. Talanta 85:527–532. https://doi.org/10.1016/j.talanta.2011.04.032 Singaraja C (2017) Relevance of water quality index for groundwater quality evaluation: Thoothukudi District, Tamil Nadu, India. Appl Water Sci 7:2157–2173. https://doi.org/10.1007/s13201-017-0594-5 Strickland JDH, Parson TR (1972) A practical handbook of seawater analysis. J Fish Res Board Can 167:310 Singh MR, Gupta A, Beeteswari KH, India (2010) J Appl Sci Environ Manag, 4(4):85–89. https://doi.org/10.4314/jasem.v14i4.63263 Sreenivasulu G, Jayaraju N, Sundararaja BCR, Lakshmi TP (2015) Physico-chemical parameters of coastal water from Tupilipalem coast, Southeast coast of India. J Coast Sci 2(2):34–39 Taipale SJ, Vuorio K, Aalto SL, Peltomaa E, Tiirola M (2019) Eutrophication reduces the nutritional value of phytoplankton in boreal lakes. Environ Res 179:108836 Part B). https://doi.org/10.1016/j.envres.2019.108836 Vasanthi P, Sukumaran M (2017) Physicochemical analysis of coastal water of east coast of Tamil Nadu (Muthupet estuary). I J Zool Stu 2(5):15–21 Wang YS, Lou ZP, Sun CC, Wu ML, Han SH (2006) Multivariate statistical analysis of water quality and phytoplankton characteristics in Daya Bay, China, from 1999 to 2002. Oceanol 48:193–211 Wu ML, Wang YS, Sun CC, Wang H, Dong JD, Yin JP, Han SH (2010) Identification of coastal water quality by statistical analysis methods in Daya Bay, South China Sea. Mar Pollut Bull 60(6):852–860. https://doi.org/10.1016/j.marpolbul.2010.01.007 Wu ML, Wang YS, Sun CC, Wang H, Dong JD (2009) Using chemometrics to identify water quality in Daya Bay. China Oceanol 52:217–232 Yogeshkumar JS, Geetha S (2012) Seasonal changes of hydrographic properties in sea water of Coral reef islands, Gulf of Mannar, India. I.J. Plant Animal Environ. Sci 2(2):135–159 Yu Z, McCreary JJP (2004) Assessing precipitation products in the Indian Ocean using an ocean model. J Geophys Res 109(C5):C05013. https://doi.org/10.1029/2003JC002106 Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3488144","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":257903619,"identity":"ae9d62a5-c503-42d6-bae3-2071b0941d07","order_by":0,"name":"Ajith Nithin","email":"","orcid":"","institution":"Annamalai University Faculty of Marine Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ajith","middleName":"","lastName":"Nithin","suffix":""},{"id":257903620,"identity":"dd53604e-d0d3-4b8a-80f7-acfa0e113030","order_by":1,"name":"Arumugam Sundaramanickam","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0ElEQVRIiWNgGAWjYBAC9mYGNgaGAgYGfgYQgxjAcxik0oCBQbKBaC0HoFoMDhCthZ392QMGA7t84xvJzx58qGCQ5xc7QEALM4+5AYNBsuW2G2nmhjPOMBjOnJ2AX4s9Mw+bBIMBs4HZjQQzad42hgSD2wS08DCzPwNqqTcwnpH+jVgtDGZALYcNDCRyiLaFB6TluIHEmTdlkjPOSBD2Cw//caDDKqoN+NvTt0l8qLCR55cmoAUEmP+ASAGwSgnCyhGA/wApqkfBKBgFo2AkAQAx3TTmnQSKmQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-2682-445X","institution":"Annamalai University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Arumugam","middleName":"","lastName":"Sundaramanickam","suffix":""},{"id":257903621,"identity":"11db195b-73c0-41b0-895d-e5e5a767f163","order_by":2,"name":"Kuppusamy Manimaran","email":"","orcid":"","institution":"Annamalai University Faculty of Marine Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Kuppusamy","middleName":"","lastName":"Manimaran","suffix":""},{"id":257903622,"identity":"43e601f0-af03-4682-aa4a-2af11e7159f5","order_by":3,"name":"Kailasam Saranya","email":"","orcid":"","institution":"Annamalai University Faculty of Marine Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Kailasam","middleName":"","lastName":"Saranya","suffix":""},{"id":257903623,"identity":"adf3191e-394a-4239-84bb-a4beb8510091","order_by":4,"name":"Manupoori Sathish","email":"","orcid":"","institution":"Annamalai University Faculty of Marine Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Manupoori","middleName":"","lastName":"Sathish","suffix":""},{"id":257903624,"identity":"d721d308-a56b-4e05-a998-e3cdec117a2d","order_by":5,"name":"Parthasarathy Surya","email":"","orcid":"","institution":"Annamalai University Faculty of Marine Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Parthasarathy","middleName":"","lastName":"Surya","suffix":""},{"id":257903625,"identity":"fe66278c-7b98-4476-998d-b98714d3ac08","order_by":6,"name":"Kumar Balachandar","email":"","orcid":"","institution":"Annamalai University Faculty of Marine Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Kumar","middleName":"","lastName":"Balachandar","suffix":""},{"id":257903626,"identity":"a14d7a7e-cda7-4b15-84d1-19c13e1503b4","order_by":7,"name":"Rengasamy Subramaniyan Sathishkumar","email":"","orcid":"","institution":"Annamalai University Faculty of Marine Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Rengasamy","middleName":"Subramaniyan","lastName":"Sathishkumar","suffix":""},{"id":257903627,"identity":"68ede14b-612d-4772-8f46-d3bcf37f27ae","order_by":8,"name":"Moorthy Meena","email":"","orcid":"","institution":"Annamalai University Faculty of Marine Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Moorthy","middleName":"","lastName":"Meena","suffix":""}],"badges":[],"createdAt":"2023-10-25 01:55:01","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3488144/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3488144/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":48071625,"identity":"ef592be6-16ac-4b11-88f8-adf8e39c678c","added_by":"auto","created_at":"2023-12-12 15:38:54","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":81509,"visible":true,"origin":"","legend":"\u003cp\u003eThe collection spots at Tuticorin i) Coastal sampling – Old Harbour (TOH-0.5,TOH-2,TOH-5) and New Harbour (TNH-0.5,TNH-2, TNH-5) which are 0.5, 2 and 5 km distances from the respective harbors ii) Hotspot sampling (TUT-HS) iii) Tidal sampling (TUT-HS-L and TUT-HS-R) which are 0.5 km on either side of the hotspot\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3488144/v1/a72faed12bed01619cf84c9e.jpg"},{"id":48071610,"identity":"b6f27f44-91aa-4226-b7de-55cbf16e57b2","added_by":"auto","created_at":"2023-12-12 15:38:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3885556,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003eTrend line of Sea Surface Temperature in the Coastal, Hotspot and Tidal samplings at Tuticorin from 2012-2017.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003eTrend line of pH in the Coastal, Hotspot and Tidal samplings at Tuticorin from 2012-2017.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003eTrend line of Salinity in the Coastal, Hotspot and Tidal samplings at Tuticorin from 2012-2017.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eD\u003c/strong\u003eTrend line of Alkalinity in the Coastal, Hotspot and Tidal samplings at Tuticorin from 2012-2017.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eE\u003c/strong\u003eTrend line of Dissolved Oxygen in the Coastal, Hotspot and Tidal samplings at Tuticorin from 2012-2017.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003eTrend line of Biochemcial Oxygen Demand in the Coastal, Hotspot and Tidal samplings at Tuticorin from 2012-2017.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eG\u003c/strong\u003eTrend line of Nitrite in the Coastal, Hotspot and Tidal samplings at Tuticorin from 2012-2017.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH\u003c/strong\u003eTrend line of Nitrate in the Coastal, Hotspot and Tidal samplings at Tuticorin from 2012-2017.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eI\u003c/strong\u003eTrend line of Ammonia in the Coastal, Hotspot and Tidal samplings at Tuticorin from 2012-2017.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJ\u003c/strong\u003eGraph showing the Trend line of Total Nitrogen in the Coastal, Hotspot and Tidal samplings at Tuticorin from 2012-2017.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eK\u003c/strong\u003eTrend line of Inorganic Phosphate in the Coastal, Hotspot and Tidal samplings at Tuticorin from 2012-2017.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eL\u003c/strong\u003eTrend line of Total Phosphorous in the Coastal, Hotspot and Tidal samplings at Tuticorin from 2012-2017.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eM\u003c/strong\u003eTrend line of Reactive Silicate in the Coastal, Hotspot and Tidal samplings at Tuticorin from 2012-2017.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3488144/v1/3fb34c0286ba6af2e3102923.png"},{"id":48071545,"identity":"a0a687c0-e30e-45ee-99c9-4bf38cfa62ae","added_by":"auto","created_at":"2023-12-12 15:38:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2077337,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA \u003c/strong\u003eSpearman Correlation of the physicochemical parameters in the Coastal samplings at Tuticorin from 2012-2017.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB \u003c/strong\u003eSpearman Correlation of the physicochemical parameters in the Hotspot samplings at Tuticorin from 2012-2017.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC \u003c/strong\u003eSpearman Correlation of the physicochemical parameters in the Tidal samplings at Tuticorin from 2012-2017.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3488144/v1/169dd9d354a7b78ee26378d9.png"},{"id":48071588,"identity":"7e74ca0f-23e1-479c-a00c-31daf880c889","added_by":"auto","created_at":"2023-12-12 15:38:45","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":140103,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA \u003c/strong\u003eBray Curtis cluster graph of the physicochemical parameters in the Coastal samplings at Tuticorin from 2012-2017.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003eBray Curtis cluster graph of the physicochemical parameters in the Hotspot samplings at Tuticorin from 2012-2017.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003eBray Curtis cluster of the physicochemical parameters in the Tidal samplings at Tuticorin from 2012-2017.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3488144/v1/be851b3d2e9cc5d7243a0dbc.png"},{"id":48071560,"identity":"96d2ba15-27ca-4557-bef2-bdb2886babd1","added_by":"auto","created_at":"2023-12-12 15:38:45","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":505193,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA \u003c/strong\u003eBray Curtis MDS plot of the physicochemical parameters in the Coastal samplings at Tuticorin from 2012-2017.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003eBray Curtis MDS plot of the physicochemical parameters in the Hotspot samplings at Tuticorin from 2012-2017.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003eBray Curtis MDS plot of the physicochemical parameters in the Tidal samplings at Tuticorin from 2012-2017.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-3488144/v1/5d506a6c736bea7c797d4373.png"},{"id":51892495,"identity":"e4041255-d331-47ba-bbc8-8030ce8c7f00","added_by":"auto","created_at":"2024-03-02 08:10:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1024198,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3488144/v1/56836591-3eea-4bff-a1cc-c67784e6c34b.pdf"}],"financialInterests":"","formattedTitle":"Inter-annual variability on the physicochemical characteristics of Tuticorin coast, southeast of India","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWater is the principal entity that supports human survival and also contributes to economic growth (Li and Qian, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Hence it is essential to determine the quality of water on a regular basis. The water quality is defined by the physicochemical and biological characteristics present in it (Jayaraju et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) which is constantly declining due to several anthropogenic discharges such as municipal and industrial effluents. The rivers bring these wastes to the coastal environment where it affects the marine organisms (Dhirendra et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). These intertidal zones have gained much importance in research to determine the changes in the coastal environment due to anthropogenic discharges (Kress et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Continuous monitoring of coastal water enables the protection and sustainable use of water resources (Khan et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Qian et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCoastal zones host more than 40% of the global population (Gnanachandrasamy et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) since they profusely provide mineral resources, aids transportation, facilitates trade, recreational and cultural activities (Neumann et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Coastal ecosystem is a reservoir of renewable resources which is on the decline due to a descent in water quality over the years (Gopinath et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In recent times, human growth has affected the water resources which include the coastal waters (Rosegrant et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Adimalla and Li, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This decline along the southeastern coast of India could possibly be due to the high amount of freshwater incursion which brings in a large amount of nutrients and other waste discharges (Yu and McCreary, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Sardessai et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Coastal zone along the Indian coastline receives effluents from industries, municipal sewage discharges and recreational activities which minimizes the coastal water quality (Anand et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Due to these activities it is essential to monitor the water quality by carrying out physicochemical studies which are the rudiments for any hydrological system (Damotharan et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Balasubramanian et al., (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) suggested that seawater influences the groundwater also. Hence, it can be understood that polluted seawater may also pollute the groundwater which are essential sources of drinking water to humans.\u003c/p\u003e \u003cp\u003ePhysicochemical parameters are dependent on each other and any changes to one of the parameters may affect the other parameters also (Iyer et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). The rise in nutrient levels may contribute to eutrophication which consequently increases the frequency of algal blooms (Cloern, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Due to fluctuations in the amount of physicochemical parameters across seasons, monitoring the quality of water for a lesser period of time may not provide long term benefits. Hence, perennial studies enable to understand the changes of physicochemical parameters in the coastal zones. Therefore this study gains significance as it projects the physicochemical parameters across all seasons over six years from January, 2012 to December, 2017. For this study, Tuticorin has been chosen as it hosts a diversified system of chemical and other industries, a number of thermal power plants as well as a network of salt pans. Studies on physicochemical characteristics of Indian coastal waters have been on the rise in recent years. Numerous studies have projected the physicochemical variations on a seasonal or monthly basis however, there are no reports exhibiting these variations over a period of 6 years in this area.\u003c/p\u003e"},{"header":"Study Area Description","content":"\u003cp\u003eTuticorin is a port town located in the Gulf of Mannar sandwiched between 8.7642\u0026deg; N latitude \u0026amp; 78.1348\u0026deg; E longitude. The area has developed rapidly in the recent years due to various establishments such as aquaculture, industries, refineries, chemicals, fertilizers and caustic soda manufacturers, salt pans and thermal power plants. There are no facilities to treat the effluents released from these establishments. Hence, it is understood that a large amount untreated effluents may be released from these establishments which subsequently reaches the coastal waters (Bharathi et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe sampling stations were classified into 3 categories namely i) coastal \u0026ndash; 0.5, 2 and 5 km from the shore (6 samplings-3 along the old harbour and 3 along the new harbour) ii) hotspot-diurnal collection on the shore at every 3 hours interim for a total of 36 hrs in succession (13 samplings) iii) tidal-0.5 km on either side of the hotspot once each during high and low tides (4 samplings) as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. To predict the deteriorating rate of water quality in any area, elaborate studies on the coasts and shorelines are required. The sampling locations remained constant throughout the study and samplings were carried out during all the four seasons across six years from January, 2012 to December, 2017.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSample collection\u003c/h2\u003e \u003cp\u003eWater samples were collected in triplicates at each study station using a Niskin\u0026rsquo;s water sampler. The collected samples were preserved in refrigerated conditions (4\u0026ordm;C) until further analysis in the laboratory.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003ePhysical parameters\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003eSea surface temperature, salinity and pH\u003c/h2\u003e \u003cp\u003eThe parameters like sea surface temperature, pH and salinity were measured at the collection spot using calibrated instruments such as digital thermometer (TP-101 model), pH pen (pH Ep-3 Model) and Hand Refractometer (Erma Company, Japan) respectively.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eDissolved oxygen (DO) and Biochemical Oxygen Demand (BOD)\u003c/h2\u003e \u003cp\u003eThe modified Winkler\u0026rsquo;s method described by Strickland and Parson (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e1972\u003c/span\u003e) was adopted for the estimation of dissolved oxygen and biochemical oxygen demand. The values for both parameters were expressed as \u003cb\u003emg/l.\u003c/b\u003e\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003eTotal Alkalinity\u003c/h2\u003e \u003cp\u003eTotal alkalinity was assessed by adopting the method described by Strickland and Parson (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e1972\u003c/span\u003e). Carbonate and Bicarbonate values were determined and total concentration of these substances yield total alkalinity which was expressed as \u003cb\u003eppm.\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eChemical Parameters\u003c/h2\u003e \u003cdiv id=\"Sec10\" class=\"Section4\"\u003e \u003ch2\u003eNutrients\u003c/h2\u003e \u003cp\u003eAll nutrient parameters such as nitrite, nitrate, ammonia, total nitrogen, inorganic phosphate, total phosphorous and reactive silicate were determined using the procedure described by Grasshoff et al., (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). The units were expressed as \u003cb\u003e\u0026micro;mol/l.\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Interpretation\u003c/h2\u003e \u003cp\u003eStatistical tools such as PRIMER 6, ORIGIN 8 and Graphpad Prism 8.2.0 were used to perform regression trend line studies, Bray Curtis cluster and Multi Dimension Scaling plots and Spearman\u0026rsquo;s correlation.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePhysical parameters\u003c/h2\u003e \u003cp\u003eDuring the study period, the maximum average sea surface temperature (33.33\u0026thinsp;\u0026plusmn;\u0026thinsp;2.02\u0026deg;C) was recorded in the tidal water during 2016 while the minimum sea surface temperature (27.72\u0026thinsp;\u0026plusmn;\u0026thinsp;2.82\u0026deg;C) was recorded in the coastal water during 2013. The trend line showed an increasing pattern (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.239; slope\u0026thinsp;=\u0026thinsp;0.399) visible in Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e2\u003c/span\u003eA. The highest average sea water pH (8.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.096) was reported in the coastal water during 2012 while the lowest sea water pH (7.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15) was reported in the tidal water in 2016. The trend line showed a decreasing pattern (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.667; slope\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.4) exhibited in Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e2\u003c/span\u003eB. The maximum average salinity (35.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18 PSU) was observed in the hotspot region during 2017 while the least salinity (32.12\u0026thinsp;\u0026plusmn;\u0026thinsp;1.22 PSU) was observed in the coastal water in 2013. The trend line showed an increasing pattern (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.048; slope\u0026thinsp;=\u0026thinsp;0.279) shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e2\u003c/span\u003eC. The maximum average total alkalinity (150.19\u0026thinsp;\u0026plusmn;\u0026thinsp;10.89 ppm) and minimum average alkalinity (113.90\u0026thinsp;\u0026plusmn;\u0026thinsp;14.80 ppm) were noted in the tidal water during 2015 and 2016 respectively. The trend line showed an increasing pattern (R\u003csup\u003e2\u003c/sup\u003e = -0.182; slope\u0026thinsp;=\u0026thinsp;1.449) witnessed in Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e2\u003c/span\u003eD. The highest average dissolved oxygen (5.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14 mg/l) and the lowest average dissolved oxygen (4.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15 mg/l) were recorded in the coastal water during 2014 and 2016 respectively. The trend line was straight (R\u003csup\u003e2\u003c/sup\u003e = -0.249; slope\u0026thinsp;=\u0026thinsp;9.669) seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e2\u003c/span\u003eE. The highest average BOD (1.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.028 mg/l) was observed in the tidal water during 2017 and the lowest average BOD (0.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6 mg/l) was observed in the hotspot sampling during 2013. The trend line showed an increasing pattern (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.082; slope\u0026thinsp;=\u0026thinsp;0.0948) observed in Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e2\u003c/span\u003eF.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eChemical parameters\u003c/h2\u003e \u003cp\u003eThe maximum average nitrite concentration (0.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35 \u0026micro;mol/l) was reported in the coastal water during 2016 while the least average nitrite concentration (0.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19 \u0026micro;mol/l) was recorded in the coastal waters during 2017. The trend line was decreasing (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.615; slope = -0.057) as expressed in Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e2\u003c/span\u003eG. The maximum average nitrate concentration was observed as 7.54\u0026thinsp;\u0026plusmn;\u0026thinsp;1.02 \u0026micro;mol/l during 2016 in the coastal water and the minimum average nitrate concentration was observed as 0.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.91 \u0026micro;mol/l during 2015 in the tidal water. The trend line was decreasing at R\u003csup\u003e2\u003c/sup\u003e = -0.203; slope = -0.230 as exhibited in Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e2\u003c/span\u003eH. The highest average ammonia concentration (23.04\u0026thinsp;\u0026plusmn;\u0026thinsp;2.24 \u0026micro;mol/l) was noted during 2016 in the coastal water and the lowest average ammonia concentration (0.066\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21 \u0026micro;mol/l) was noted during 2012 in the coastal water. The trend line showed an increasing pattern (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.178; slope\u0026thinsp;=\u0026thinsp;1.404) visible in Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e2\u003c/span\u003eI. The maximum average Total Nitrogen (TN) concentration was recorded as 52.795\u0026thinsp;\u0026plusmn;\u0026thinsp;5.32 \u0026micro;mol/l during 2016 in the coastal water and the minimum average Total Nitrogen concentration (7.49\u0026thinsp;\u0026plusmn;\u0026thinsp;2.33 \u0026micro;mol/l) was recorded during 2013 in the hotspot water. The trend line showed an increasing pattern (R\u003csup\u003e2\u003c/sup\u003e = -0.15; slope\u0026thinsp;=\u0026thinsp;0.883) as witnessed in Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e2\u003c/span\u003eJ. The highest average inorganic phosphate concentration (2.104\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92 \u0026micro;mol/l) was reported during 2015 in the tidal water and the least average inorganic phosphate (IP) concentration (0.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.57 \u0026micro;mol/l) was reported during 2013 in the tidal waters. The trend line showed an increasing pattern (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.182; slope\u0026thinsp;=\u0026thinsp;0.155) as seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e2\u003c/span\u003eK. The maximum average total phosphorous (TP) concentration was observed as 3.05\u0026thinsp;\u0026plusmn;\u0026thinsp;1.12 \u0026micro;mol/l during 2016 in the tidal water and the minimum average total phosphorous concentration (0.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23 \u0026micro;mol/l) was observed during 2013 in the tidal water. The trend line showed an increasing pattern (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.422; slope\u0026thinsp;=\u0026thinsp;0.122) observed in Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e2\u003c/span\u003eL. The highest average reactive silicate concentration was noted as 21.67\u0026thinsp;\u0026plusmn;\u0026thinsp;1.38 \u0026micro;mol/l during 2016 in the tidal water and the least average Reactive Silicate concentration (0.798\u0026thinsp;\u0026plusmn;\u0026thinsp;2.34 \u0026micro;mol/l) was noted during 2013 in the coastal water. The trend line showed an increasing pattern (R\u003csup\u003e2\u003c/sup\u003e = -0.103; slope\u0026thinsp;=\u0026thinsp;0.735) expressed in Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e2\u003c/span\u003eM.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eSea surface temperature (SST) is generally governed by atmospheric temperature as its gradual rise will influence the water temperature of any area (PuthiyaSekar et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The water temperature may also be influenced by other factors such as solar radiation, freshwater influx, cooling and mixing due to ebb and flow of neritic waters (Balakrishnan et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). SST is known to alter the biochemical and physical processes of any living organism (Dupuis and Hann \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Temperature plays an important role in reproduction, photosynthesis, metabolism, growth of phytoplankton and also influences microbial activity (Hossain et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Shah et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Tuticorin area has a large number of chemical industries and thermal power plants. Chemical wastes and coolants discharged from these power plants are also a major reason for the increased sea surface temperature in this area. The increasing trend line of SST (slope\u0026thinsp;=\u0026thinsp;0.399) during the study period indicates that the atmospheric and water temperature in this area is constantly on the rise.\u003c/p\u003e \u003cp\u003eThe pH concentration of the coastal water is dependent on a number of factors such as salinity, temperature, carbon dioxide, carbonate, bicarbonate and hydroxyl ion concentration, as well as climate and buffering activity of the sea in the particular area. Additionally, fluctuations in dissolved oxygen can also persuade pH levels in the coastal water (Anand et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The presence of carbon dioxide and water leads to the formation of carbonic acid which may conveniently reduce the pH levels (Singaraja, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The other reasons which influence pH concentration include acidity of the bottom sediment and certain biological activities (Balasubramanian and Kannan, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). pH highlights environment suitability and can be associated with species composition and chemical changes involving biological processes (Rani et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). In the present study, pH levels have shown a decrease in the trend line (slope\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.4) over the years. The increasing water temperature makes the water to be more buffering thereby increasing the carbonate and bicarbonate ratio meanwhile reducing the pH. This reduction in pH indicates the increase in buffering capacity of the water to hold more bicarbonate ions which increases the alkalinity (Petrucci, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSalinity is the basic physicochemical characteristic which is assessed regularly by marine ecologists due to its influence on marine biota (Sreenivasulu et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Salinity is determined by the amount of salt inflow and the rate of evaporation as well as the persuasion of intertidal organisms (Balasubramanian and Kannan, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). In our study, salinity showed an increasing trend line (slope\u0026thinsp;=\u0026thinsp;0. 279) which could be correlated with SST as increased temperature leads to higher evaporation thereby increasing the salinity concentration. Yet another reason for elevated salinity in this region is the presence of numerous salt pans and their subsequent brine outlet. Balasubramanian et al., (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) opined that increased salinity in the seawater can affect the groundwater in the surrounding areas which makes such groundwater unfit for drinking and irrigation purposes.\u003c/p\u003e \u003cp\u003eTotal alkalinity is the measure of weak acids and cations that neutralize these acids (Singh et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Therefore total alkalinity could be simply defined as the total amount of carbonates and bicarbonates present in the water (Ouyang et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The increase in the amount of carbonates and bicarbonates could also be attributed to degradation of plants and animals as well as the organic input from the estuary (Wang et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The trend line of total alkalinity showed an increasing pattern (slope\u0026thinsp;=\u0026thinsp;1.449) which could be correlated with the increasing salinity levels. Increasing alkalinity could be due to the strong alkaline contents present in the industrial discharges and sewages (Safari et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Increased carbonate and bicarbonates may also be present as a result of plant degradation and excess organic wastes (Wang et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). This increasing alkalinity trend can be correlated with the decreasing trend of pH which occurs due to the buffering of water in order to accommodate excess bicarbonate ions (Petrucci, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDissolved oxygen is of utmost importance in determining the physical condition of an aquatic body thereby ensuring a balanced network of plants and animals (Raffaelli, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). The higher DO levels could be the result of freshwater mixing and low metabolic activities while the reduced DO levels might occur due to higher temperature and availability of bulk quantity of untreated waste in addition to the elevated metabolic activity of the organisms (Vasanthi and Sukumaran, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In the present study, DO exhibited a straight trend line indicating that the DO levels have remained constant over the years. Even though there is higher organic load that depletes the oxygen levels, DO concentration were maintained constantly due to freshwater mixing and photosynthetic activity.\u003c/p\u003e \u003cp\u003eBiochemical oxygen demand is the measure of oxygen required to stabilize organic water and it evaluates the proficiency in the removal of biodegradable organic load by certain natural processes (Vasanthi and Sukumaran, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Simon et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Rise in temperature subsequently decreases DO which in turn elevates the oxygen demand (Faragallah et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) therefore sea water temperature and DO are inversely proportional (Sheathe and Kazama, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). In the current work, BOD showed an increasing pattern (slope\u0026thinsp;=\u0026thinsp;0.0948) and this could be due to the constant discharge of organic load from the terrestrial sources (Wu et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2009\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNutrients are essential commodities in the marine environment as they influence growth, reproduction and metabolic activities of the organisms (Kalaiarasi et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Nedumarn and Perumal, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The distribution of these nutrients is dependent on the freshwater incursion from terrestrial sources (Damotharan et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) while several biological processes are reliant on the nitrogen and phosphorous concentrations in the water (Sahugouri et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Nitrite is the intermediate species between ammonia and nitrate where oxidation of ammonia as well as reduction of nitrates leads to nitrite formation (Satpathy et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The decreasing nitrite trend (slope = -0.057) over the years in our study could be due to the increasing temperature and salinity patterns across the study period (Saravanakumar et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The presence of nitrites is associated with the concentrations of nitrates (Yogeshkumar and Geetha, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), hence in our study, nitrite shows a decreasing trend due to the decreasing trend of nitrate.\u003c/p\u003e \u003cp\u003eNitrates are the highest oxidized form of nitrogen and one of the essential indicators of pollution which may be present in the water as a result of freshwater inflow containing fertilizers, chemicals, domestic and municipal discharges as well as organic decomposition (Rajaram et al., 2005; Ashokprabu et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). The oxidation of ammonia may also be a criterion for the presence of nitrogen in water (Govindasamy et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). The decreasing trend of nitrates (slope = -0.230) in our study could be due to the increased photosynthetic activity of phytoplankton over the years since phytoplankton are voracious consumers of nitrates. This was explained by Rippeth and Jones (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1997\u003c/span\u003e) in their seasonal nitrate model where phytoplankton consumes high amounts of nitrate during elevated temperatures. Therefore in accordance with our study, increasing temperature trend was noticed over the years which can be considered as a criterion for increased consumption of nitrate by phytoplankton which causes this decreasing trend.\u003c/p\u003e \u003cp\u003eAmmonia is the output of breakdown of organic materials such as excreta of organisms. During lofty ammonia levels, the abundance of NH\u003csup\u003e3+\u003c/sup\u003e ions enhances phytoplankton productivity (Dugadale et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Another reason attributed to the increasing pattern could be the regular release of ammonical excreta by plankton (Godhantaraman, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). The increasing trend line of ammonia (slope\u0026thinsp;=\u0026thinsp;1.404) in the present study highlights the constant accumulation of ammonia over the years which may result in algal blooms.\u003c/p\u003e \u003cp\u003eTotal Nitrogen is the magnitude of all forms of nitrogen available in the water in dissolved or suspended forms. These compounds are generally available in low quantities but its quantum may increase as a consequence of freshwater inflow containing high amounts of organic and industrial effluents (Malarvannan and Balamurugan, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The trend line of total nitrogen (slope\u0026thinsp;=\u0026thinsp;0.883) in the present study indicates the increasing discharge of industrial effluents over the years. Even though, nitrite and nitrate yielded a decreasing trend line, ammonia showed a significantly increasing trend which increases the trend line of total nitrogen.\u003c/p\u003e \u003cp\u003eSeveral biological activities of marine biota are dependent on the inorganic phosphate concentration (Mackey et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Phosphate concentration in the water is dependent on a number of factors such as upwelling, freshwater incursion and breakdown of organic materials (Paytan and Mclaughlin, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The primary source of phosphates in freshwater is due to domestic and agricultural discharge containing a high amount of detergents and pesticides respectively (Liu et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The increasing trend line of inorganic phosphates (slope\u0026thinsp;=\u0026thinsp;0.155) in our study could be due to frequent discharge of alkyl phosphates and detergents from the household, fertilizers and super phosphate residues from agricultural fields, turbulence and mixing of total phosphorous from the bottom sediments (Saravanakumar et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTotal Phosphorus is the measure of all forms of phosphorus available in the water and is capable of inducing cyanobacterial blooms. Naturally, total phosphorous may be present in low quantities in coastal water however increased levels may be present as a result of agricultural deposits which may prove to be toxic to the marine environment (Malarvannan and Balamurugan, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The increased trend line (slope\u0026thinsp;=\u0026thinsp;0.122) of total phosphorous in the present study could be associated with the rising trend of inorganic phosphates.\u003c/p\u003e \u003cp\u003eSilicates are essential in regulating the distribution of phytoplankton in the coastal water. The alteration of reactive silicates in water could be due to factors such as saline and freshwater mixing, chemical interface with clay and biological elimination by diatoms (Satpathy et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The increasing trend line of reactive silicate (slope\u0026thinsp;=\u0026thinsp;0.735) in our study could be attributed to the turbulence of water bringing the bottom sediments to the surface as well as the mixing of freshwater containing leached rock material comprising large amounts of silicate (Govindasamy et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Rajasegar, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eStatistical interpretation of our findings\u003c/h2\u003e \u003cp\u003eThe Spearman correlation coefficient was plotted for all the parameters in the coastal, hotspot and tidal samplings. In the coastal samplings, SST showed the highest correlation with BOD (p\u0026thinsp;=\u0026thinsp;0.83; \u0026lt;0.01 level of significance) and this was predicted as rise in SST may lead to increase in BOD. IP exhibited the highest correlation with TP (p\u0026thinsp;=\u0026thinsp;0.94; \u0026lt;0.01 level of significance) and this was also predicted since IP and TP are different measures of phosphates (Fig.\u0026nbsp;\u003cspan refid=\"Fig17\" class=\"InternalRef\"\u003e3\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eIn the hotspot samplings, SST expressed a very high correlation with salinity (p\u0026thinsp;=\u0026thinsp;0.89; \u0026lt;0.01 level of significance) and this was foreseen since increasing SST may lead to higher rate of evaporation ultimately increasing the salinity as seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig17\" class=\"InternalRef\"\u003e3\u003c/span\u003eb.\u003c/p\u003e \u003cp\u003eIn the tidal samplings, SST had a high correlation with salinity, DO and TP (p\u0026thinsp;=\u0026thinsp;0.77; \u0026lt;0.01 level of significance). Nitrate had a very high correlation with TN (p\u0026thinsp;=\u0026thinsp;0.71; \u0026lt;0.05 level of significance) and this was also expected since TN is the measure of all forms of nitrogen as seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig17\" class=\"InternalRef\"\u003e3\u003c/span\u003eC.\u003c/p\u003e \u003cp\u003eThe Bray Curtis similarity cluster for the coastal sampling showed three groups with a distantly related fourth group of alkalinity. The first group comprised of the highest similarity parameters where ammonia had the highest similarity to silicate (99.4%). This group in turn had a very high similarity to DO (96.9%). In the second group, water temperature and salinity showed a very high similarity (94.07%) while in the third group, IP and BOD showed a higher similarity (94.09%). The remaining parameters showed insignificant similarities with the other parameters (Fig.\u0026nbsp;\u003cspan refid=\"Fig20\" class=\"InternalRef\"\u003e4\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eLikewise in the similarity cluster for the hotspot sampling, three main groups with sub groups were noted. The highest similarity was observed between pH and silicate (98.97%) while SST and salinity showed a very high similarity (95.08%) whereas BOD and IP also showed a significantly higher similarity (90.08%) shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig20\" class=\"InternalRef\"\u003e4\u003c/span\u003eB.\u003c/p\u003e \u003cp\u003eThe tidal samplings cluster similarity showed two closely associated groups and a distantly associated alkalinity group. The highest similarity was noticed between ammonia and TP (98.13%). SST and salinity also showed a very high similarity (96%) exhibited in Fig.\u0026nbsp;\u003cspan refid=\"Fig20\" class=\"InternalRef\"\u003e4\u003c/span\u003eC.\u003c/p\u003e \u003cp\u003eThe Bray Curtis MDS plot (Multi-Dimensional Scaling) for the coastal sampling (Fig.\u0026nbsp;\u003cspan refid=\"Fig23\" class=\"InternalRef\"\u003e5\u003c/span\u003eA) was also in line with the cluster graph as alkalinity resembled other parameters distantly. The resemblance exhibited three groups where ammonia and silicate had the highest resemblance. The MDS plot for hotspot sampling (Fig.\u0026nbsp;\u003cspan refid=\"Fig23\" class=\"InternalRef\"\u003e5\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e showed the highest resemblance between pH and silicate. The MDS plot for tidal samplings (Fig.\u0026nbsp;\u003cspan refid=\"Fig23\" class=\"InternalRef\"\u003e5\u003c/span\u003eC) showed the highest resemblance between ammonia and TP. These results are in line with the cluster plots.\u003c/p\u003e \u003cp\u003eSuitable nutrient concentrations and physicochemical characteristics are required to maintain a good biodiversity in any aquatic environment (Kristensen et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Increased nutrient concentrations lead to eutrophication affecting the plankton community and subsequently the corresponding food chain (Taipale et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Holopainen and Lehikoinen (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) observed that reduced water quality affected the wetland biodiversity. The authors understand that such changes occur in the coastal zones also which can drastically affect its biodiversity.\u003c/p\u003e \u003cp\u003eA former study (Bharathi et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) also carried out studies on physicochemical changes of the coastal waters at Tuticorin and presented higher values compared to the present study. This further confirms that the region has a deteriorating water quality.\u003c/p\u003e \u003cp\u003eAyyandurai et al., (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) recommended some suggestions to reduce discharges that pollute the water resources at Cuddalore district, Tamilnadu India. The recommendations include the recycling of water in industrial processes and the use of wastewater in cooling processes. The above recommendations are also applicable at Tuticorin, hence the authors recommend the same. Further it is suggested that the unavoidable effluents that remain after processing must be released away from the continental shelves to reduce its impact on the coastal regions.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003ePhysicochemical characteristics are the most essential components in determining the quality of any aquatic system. These characteristics need to be monitored regularly for longer durations to understand the pattern and causes of water pollution. A large number of organisms including humans are dependent on coastal waters for food. A major share of a country\u0026rsquo;s economy is also dependent on the coastal water. Hence polluting the marine environment may prove to be harmful to the society.\u003c/p\u003e \u003cp\u003eSea surface temperature was on the rise throughout the study which also influenced the increasing salinity. The nutrients showed an increasing pattern due to the excessive discharge of effluents from various sources paving the way for eutrophication which may permit the formation of harmful algal blooms. This could have drastic effects on the coastal zones. From this study, it can be concluded that Tuticorin coastal waters have become polluted due to the effluent discharges from the industries, thermal power plants and salt pans. However, the pollution levels have not yet reached intolerable levels. There needs to be a regulation in these discharges if not, there could be drastic impacts on the marine biota which could indirectly harm the human life.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publication:\u003c/strong\u003e Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors Contributions:\u003c/strong\u003e\u003cstrong\u003eA. Nithin: \u003c/strong\u003eMethodology, Investigation, and Writing - original draft, Visualization, Validation. \u003cstrong\u003eA. Sundaramanickam: \u003c/strong\u003eResearch hypothesis, Conceptualization, Supervision, Data curation, Data validation, Writing - review \u0026amp; editing. \u003cstrong\u003eK. Manimaran\u003c/strong\u003e: sample analysis, data validation. \u003cstrong\u003eK. Saranya\u003c/strong\u003e: sample analysis, data validation. \u003cstrong\u003eM. Sathish: \u003c/strong\u003esample analysis, data validation. \u003cstrong\u003eP. Surya:\u003c/strong\u003e sample analysis, Data validation. \u003cstrong\u003eK. Balachandar: \u003c/strong\u003esample analysis, data validation. \u003cstrong\u003eR.S. Sathishkumar\u003c/strong\u003e: Sample Analysis, data validation. \u003cstrong\u003eM. Meena\u003c/strong\u003e: Sample Analysis, data validation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Information:\u003c/strong\u003e This study was supported by a research project \u0026ldquo;Seawater Quality Monitoring (SWQM)\u0026rdquo; 317 from Ministry of Earth Sciences (MoES), Government of India (Project File No.MoES / ICMAM-PD/ME/CAS318 MB/53/2017).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting of interest:\u003c/strong\u003e The authors declare that they have no conflict of interest\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u003c/strong\u003e The current work was funded by the Ministry of Earth Sciences, Government of India. Therefore, the financial institution holds the rights. Data can be available from the authors on a reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by a research grant from Ministry of Earth Sciences (MoES), National Centre for Coastal Research, Government of India (Project File No.MoES / ICMAM-PD/ME/CAS-MB/53/2017).We would like to acknowledge authorities of Annamalai University for providing necessary facilities.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdimalla N, Li P (2019) Occurrence, health risks, and geochemical mechanisms of fluoride and nitrate in groundwater of the rock-dominant semi-arid region, Telangana State, India. 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Sci 2(2):135\u0026ndash;159\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu Z, McCreary JJP (2004) Assessing precipitation products in the Indian Ocean using an ocean model. J Geophys Res 109(C5):C05013. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1029/2003JC002106\u003c/span\u003e\u003cspan address=\"10.1029/2003JC002106\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Physicochemical characteristics, Tuticorin, 2012–2017, Regression trend line","lastPublishedDoi":"10.21203/rs.3.rs-3488144/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3488144/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTuticorin area is populated with a large number of chemical industries and thermal power plants which contribute to changes in physicochemical characteristics of the coastal waters. Long term monitoring is required in order to understand the fluctuating trends of water quality in this area. The present study aimed to monitor the physicochemical characteristics of the coastal water at Tuticorin from 2012 to 2017. Sample collection was conducted for all the seasons of each year throughout the study period to understand the variations in physicochemical characteristics in the coastal water. A regression trend line was plotted based on the data which enables us to understand that water quality is deteriorating in the study area. Such long term studies are essential to determine the actual fate of any coastal area. Currently the area is yet to be termed as polluted; however measures must be taken to reduce the discharge of pollutants.\u003c/p\u003e","manuscriptTitle":"Inter-annual variability on the physicochemical characteristics of Tuticorin coast, southeast of India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-12-12 15:31:14","doi":"10.21203/rs.3.rs-3488144/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"dca839d6-a46b-417b-9acc-77132e183e59","owner":[],"postedDate":"December 12th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-02T08:01:57+00:00","versionOfRecord":[],"versionCreatedAt":"2023-12-12 15:31:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3488144","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3488144","identity":"rs-3488144","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}