ECO-CHRONICLE 117

ECO CHRONICLE ISSN: 0973-4155 Vol. 12, No. 4, December, 2017 PP: 117- 135

ASSESSMENT OF SURFACE WATER QUALITY OF WETLAND ADJACENT TO THE SEAFOOD PROCESSING FACILITIES

Vidya, V. and G. Prasad

Department of Zoology, University of , Kariavattom, , Kerala, . Corresponding author: [email protected]

ABSTRACT

The seafood processing units found along the coast of Vembanad Lake are posing serious threat to water bodies by unloading their waste without any treatment. In the coastal belt of Vembanad lake, there are several seafood processing plants especially in the areas of Cherthala-Aroor- Edakochi belt. The present study analyzes the extent of degradation caused by the discharge of effluent from seafood processing units on physico chemical qualities of water. The physical- chemical parameters examined from the samples drawn from ten preselected locations for two years on monthly basis. The first nine stations are situated near the seafood processing plants whereas the last station is kept as the reference station. For the physico-chemical analysis, water samples were collected in plastic bottles, taken to the laboratory, refrigerated at 4° C and were analysed using standard procedures. The physico-chemical parameters (atmospheric and water temperature, TDS, pH, EC, salinity, DO, BOD, alkalinity, COD, hardness and nutrients such as nitrate, phosphate, ammonia and silica were studied. The mean and standard error of mean (SEM) values were calculated for all parameters in each station and season. The mean values of physico-chemical parameters except DO were low in the reference station when compared to the other nine (S1-S9) stations. The high values of BOD in nine stations (S1-S9) would be due to the presence of high biological waste in the water. Parameters like TDS, alkalinity, BOD, COD and hardness exceeded the permissible limits in polluted stations. EC, TDS, hardness, alkalinity, BOD, COD and nitrate are found to be high during monsoon whereas high

phosphate, ammonia, and silicate are reported in post monsoon season. The increased levels of free CO2, BOD, phosphate, nitrate, and ammonia in the selected stations confirms degrading lake water because of seafood processing effluents. The waste discharge from the seafood processing industry is a major reason for the alarming rate of organic pollution and eutrophicationThis will adversely affect the seafood industry.The study suggests that it would be undesirable for the further expansion of seafood industry in the Cherthala- Aroor-Edakochi coastal belt of Vembanad lake.

INTRODUCTION India is blessed with abundant marine and inland fishery resources and the Indian fishery sector has now eked out Seafood is a cottage industry which significantly an honourable place in the world marine product export contributed to the world’s food supply and an important market (Dixitulu and Paparao, 1994). India is the third protein-providing food in terms of per capita consumption. largest fish producer in the world and is second in inland The global seafood industry comprises activities relating fish production (Singh et al., 1997). India has 388 to the culturing, catching, preserving, processing, selling processing plants with 191 of them approved by European and distribution of fish or fishery products. Seafood demand Union (SEAI, 2010; MPEDA, 2015). continues to increase globally and significant population growth in countries such as China, India and Brazil is an The fisheries sector has been recognized as a powerful important demand driver for the seafood industry (Anon, income and employment generator as it stimulates growth 2013). of a number of subsidiary industries and is a source of cheap and nutritious food, at the same time it is an The seafood exports from India began in 1955 (SEAI, instrument of livelihood for a large section of economically 2009). Indian seafood Industry has come a long way; backward population of the country (SEAI, 2011). Another shipping seafood products to more than 100 countries. key factor is the high women labour constituent in the total Today Indian factories have grown to have world class employment requirement of this sector. The fisheries facilities, with better quality control; meeting the stringent sector, including aquaculture, which employs more than 5 international norms. million people, both direct and indirect, and affects the 118 ECO-CHRONICLE livelihood of four times this number, with an income amines, diamines, and sometimes ammonia), and generation of nearly 50,000 crores of rupees annually, has temperature (Tay et al., 2006). Both liquid (effluent) and been not given the desired importance by the Government solid wastes are generated by most seafood processing of India (SEAI, 2010; MPEDA, 2015). technologies. Untreated effluents often contain varying amounts of solid matter including offal, skin, and bone The seafood industry consists primarily of many small (AMEC, 2003; Morry et al., 2003; Adams et al., 2005; processing plants, with a number of larger plants located Lalonde et al., 2007; Theriault et al., 2007). near industry and population centres. Seafood products form a considerable segment of the post-harvest utility of The negative impact of seafood processing effluent on marine fish resources. There has been a considerable water quality was investigated by Walden (1991); Park et structural change in the seafood processing and export al., (2001); Morry et al., (2003); Tchoukanova et al., (2003); industry for the last few years. There is a growing demand Akan et al., (2008); Sankpal and Naikwade (2012). Thus, for “ready to cook” or “ready to serve” type of seafood, it is obvious that, the seafood associated pollutants led to hygienically prepared and attractively packed foods to the destabilization of aquatic ecosystem (DWAF and WRC, match the changing needs of urban population. The 1995; Morrison et al., 2001). The percent composition of seafood processing and marketing has become insoluble to soluble compounds in seafood processing competitive all over the world and exporters are switching effluent is important as this will dictate the effectiveness of over to value addition to increase profit (Sathiadhas and potential wastewater treatment technologies (Lim et al., Salim, 2012). 2003; Metcalf and Eddy, 2003). Several studies have characterized the effluent from fish processing plants As in most processing industries, seafood-processing (Riddle and Shikaze, 1973; Claggett and Wong, 1974; operations produce wastewater containing substantial NovaTec and EVS, 1994; Jamieson, 2006). contaminants in soluble, colloidal, and particulate forms. The degree of the contamination depends on the particular The tremendous growth in the resources and operation; it may be small (e.g., washing operations), mild infrastructure of seafood industry across the country is (e.g., fish filleting), or heavy (e.g., blood water drained from also being observed in Kerala. The development of fish fish storage tanks). A related factor, aggravating the and fishery products export from Kerala has gone hand pollution problem, is that the receiving water bodies do in hand with the evolution of the fish processing sector. A not have sufficient water for dilution. The problem of distinct feature of the processing sector in Kerala is its pollution of rivers caused by untreated urban sewage is dependence on the pre-processing sector, which is compounded by the discharge of industrial effluents popularly known as “peeling sheds”. The seafood (Goldar and Banerjee, 2004). products exported from Kerala is dominated by frozen shrimp, cuttle fish and squids. Pre-processing is an Seafood-processing wastewater characteristics that raise extremely labour intensive operation, the success of concern include pollutant parameters, sources of process which depends on the availability of experienced labour waste, and types of wastes. In general, seafood- and raw material which in turn depends on with season processing wastewater can be characterized by its (Sathyan et al., 2014). physicochemical parameters, organics, nitrogen, and phosphorus contents. As in most industrial wastewaters, Kerala is one of the most important maritime states in the the contaminants present in seafood-processing country contributing significantly to the Indian seafood wastewaters are an undefined mixture of substances, Industry. About 1/5th of its total landmass is wetlands (Kokkal mostly organic in nature (Tay et al., 2006). Important et al., 2008). There are about 287 sea food exporters in pollutant parameters of the wastewater are five-day Kerala with 124 processing plants and 169 cold storages. biochemical oxygen demand (BOD5), chemical oxygen About 119 pre-processing units are located in Alappuzha demand (COD), total suspended solids (TSS), fats, oil (Alleppey) district, Kerala (Sathyan et al., 2014). Kerala and grease (FOG), and water usage (Carawan et al., accounted for 10, 8616 tonnes valued at Rs.1524.12 crores 1979). The degree of pollution of a wastewater depends sharing 17.7% in quantity and 18.2% in value of marine on several parameters. The most important factors are export from India (KSIDC, 2013). the types of operation being carried out and the type of seafood being processed. In addition to it, the waste water Vembanad wetlands have been acclaimed as the ‘Inland may have pH (effluent pH from seafood processing plants fish basket’ of the State (Padmakumar, 2003). In Kerala, is usually close to neutral), solid content (dissolved solids seafood industry is growing over the years and is and suspended solids), odour (caused by the dominated by export of shrimps, cuttlefish, squids and decomposition of the organic matter, which emits volatile finfish varieties. ECO-CHRONICLE 119 Fishing is practiced all along the coastal line but intensive are highly organic in nature and are highly polluted and fishing activity is centered on Kochi mainly due to the port can affect the aquatic ecosystem if it is released without facilities for processed fish (Iyer et al., 1994). The Kochi adequate treatment. region has a vast majority of export processing plants within the state. 173 out of the 206 registered exporters in Kerala The coastal belt of Vembanad lake is bordered by a number are situated within the Kochi and Cherthala taluks (MPEDA, of seafood processing units. All the processing plants 2015). discharge their effluents together with solid waste into this lake. The volume of effluent discharged into this wetland In India especially Kerala, the environmental problems system is unknown still. No routine monitoring is done to associated with the discharge of wastewater from seafood check the uncontrolled discharge of the seafood processing facilities are gaining attention. Unnithan et al. processing waste into the Ramsar site. (1998) estimated the production and capacity utilization in the fish processing plants in Kerala. The impact of waste An integrated approach relating biological as well as discharge into the water system and hence phytoplankton physicochemical aspects with respect to seasons are not from three important cottage industries namely, retting of available for this unique backwater ecosystem (Radhika, husks, fish peeling and liming of shells were 2013). Because of the aforementioned reasons, it was documented by Alex, (2005). She reported that the highest decided worthwhile to undertake a detailed study on this pollution was recorded at retting zone followed by peeling aspect of pollution. Hence, the present study has been shed zone and liming zone. Geethalakshmi, (2011) while formulated to understand the influence of seafood studying the utilization capacity in fish processing industry processing waste on the physico-chemical properties of in Gujarat reported that there was a gross under utilization water receiving the effluent discharge. of capacity installed for fish processing in Gujarat in the year 2006-07 to 2008-09. Sankpal and Naikwade, (2012) MATERIALS AND METHODS studied the physicochemical analysis of effluent discharge of fish processing industries in Ratnagiri and found that The present study conducted in Cherthala-Aroor-Edakochi effluents form fish processing industries are the major coastal belt, where most seafood processing plants are sources of coastal environmental pollution and all the functioning. The study was conducted for two years samples are polluted and having the values higher than (October 2010-September 2012) from ten preselected the permissible limits. stations (S1 - S10) on monthly basis. They are Pattanakadu

(S1), Parayakadu (S2), Shankaranthodu (S3), Konkeri

Quality management practices adopted in seafood bridge (S4), Chandiroor (S5), Edakochi I (S6), Edakochi II processing sector in Cochin region was reported by (S7), Aroormukkam (S8), Aroorkutti bridge (S9), Panavally

Balasubramaniam et al. (2012). The study was conducted (S10). Nine of the selected stations near the effluent outlet in 34 fish processing units in Ernakulam and Alleppey of the seafood processing plants and one kept as the districts of Kerala and the results of the study revealed the reference site (S10) which is free from seafood effluent general profile of the seafood processing units and the discharge (Table 1 and Fig.1). The first five stations are in extent of adoption of various quality management interconnected channels and the remaining including the practices. The current status of the seafood pre-processing reference station is in the main water body. The collections facilities in Alleppey district was envisaged by Sathyan et were made on a monthly basis in all stations. al. (2014). He conducted a survey to understand the socio- economic status and challenges faced by owners and Water quality analysis women workers of shrimp pre-processing units located in Alleppey district of Kerala and to assess the presently For the physico-chemical analysis, water samples were available facilities at these units. The physico-chemical collected in plastic bottles, taken to the laboratory and characteristics of fish processing industry effluent refrigerated at 4°C. Atmospheric and water temperature, discharge into veraval harbour waters of Gujarat were pH, electrical conductivity and total dissolved solids were studied by Vaghela et al. (2015). He found that the fish measured using microprocessor based portable water processing industry effluent was eutrophic in nature and quality testing meters. Salinity was measured using a hand the increased contaminants in the fish waste volume refractometer. Transparency was measured using Secchi resulted in environmental problems. The physico chemical Disc of 20cm in diameter. The titration method was adopted analysis of the effluents from seafood processing industries for testing dissolved oxygen, alkalinity, hardness, chemical are the major cause of water pollution in and around Aroor oxygen demand and biological oxygen demand as gramapanchayath, Alappuzha District was done by recommended by Adoni (1985) and APHA (2005). The Thomas et al. (2015) and reported that all the samples nutrients namely nitrate, phosphate, silicate, and ammonia 120 ECO-CHRONICLE were examined using a Bench photometer (Hanna significant variation in air temperatures between stations Instruments C 200 series). (Pd” 0.05) and also among seasons (Pd”0.01).

The mean and standard error of mean (SEM) values were The temperature plays an important role in physico- calculated for all parameters in each station and season chemical and biological performance of water ecosystem and represented in graphs (Fig.2 -18). (Dwivedi and Pandey, 2002). No other factor has so much influence as temperature (Welch, 1952). The rise in RESULTS AND DISCUSSION atmospheric temperature caused enhancement in the Physico-chemical parameters of water highly influence the evaporation rate, which resulted in a reduction in water occurrence and abundance of aquatic organisms. Each depth. Atmospheric temperature showed significant physico-chemical parameter has its own role and the variation among stations and seasons, whereas water interaction of factors finally produces the cumulative effect. temperature showed significant variation between Observations on the different parameters investigated are seasons. The difference in the water temperature might detailed below. be due to difference in sampling time and the influence of season as suggested by Desai (1995) and Jayaraman et Atmospheric temperature (°C) al. (2003). The station wise and season wise variations of atmospheric temperature are shown in figure 2. The total mean value Water temperature (°C) of atmospheric temperature was noted to be minimum in The station wise and season wise values of surface water

S1 (30.02 ± 0.82) and maximum in S7 (33.04 ± 0.51). In the temperatures are shown in figure 3. The total mean value pre-monsoon period, the lowest mean temperature was of water temperature was observed to be low in S1 (29.00 noted in S (28.75 ± 1.36) and the highest in S (31.81 ± 1 10 ± 0.31) and high in S7 (30.85 ± 0.40). 1.00). Throughout monsoon season, the lowest temperature was reported in S1 (32.31 ± 1.13) and the For the duration of pre monsoon season, the lowest surface highest in S (34.75 ± 0.45) while in post monsoon period 7 water temperature was reported in S3 (27.50 ± 0.38) and the lowest temperature was reported in S (29.00 ± 1.54) 1 the highest in S9 (29.56 ± 0.65). During the monsoon and the highest in S (33.00 ± 0.76). ANOVA showed a 7 season, the lowest water temperature was reported in S1 Table 1. Characteristics of study sites

Sl. No Name Geographic position Source of pollution Land utilisation Colour Remarks

Discharge from Sea food Panchayath Interconnected 1 Pattanakadu 9o44'22 N, 76o19'07E Turbid water processing industry area/Residential canal Discharge from Sea food Aquaculture Interconnected 2 Parayakadu 9o47'13N,76o18'20E Turbid water processing industry farm canal Shankaran Discharge from Sea food Panchayath Interconnected 3 9047'22N,76o18'12E Heavily turbid water thodu processing industry area/Residential canal Discharge from Sea food Panchayath Interconnected 4 Konkeri Bridge 9049'22N,76o18'22E Turbid water processing industry area/Residential canal Discharge from Sea food Panchayath Interconnected 5 Chandiroor 9o50'28N,76o18'29E Heavily turbid water processing industry area/Residential canal Discharge from Sea food Panchayath Main water 6 Edakochi I 9o54'47N,76o17'06E Heavily turbid water processing industry area/Residential body Discharge from Sea food Boat building Main water 7 Edakochi II 9o54'07N,76o17'42E Heavily turbid water processing industry yard body Discharge from Sea food Main water 8 Aroormukkam 9o53'23N,76o17'49E Fallow land Turbid water processing industry body Discharge from Sea food Panchayath Main water 9 Aroorkutti 9o52'12N,76o19'01E Heavily turbid water processing industry area/Residential body Panavally/ Free from Sea food Agriculture and Main water 10 (Reference 9o49'13 N, 76o21'33E processing waste Clear water Aquaculture body site) discharge ECO-CHRONICLE 121

Figure 1. Location map and maximum in S1 (8.09 ± 0.25). During the pre monsoon

season, the lowest pH value was reported in S5 (6.69 ±

0.11) and highest in S1 (7.91 ± 0.53). Throughout the

monsoon season, the lowest pH was reported in S5 (7.21

± 0.18) and the highest in S2 (8.08 ± 0.22) and in the post

monsoon season, the lowest value was reported in S8 (7.32

± 0.33) and highest in S1 (8.99 ± 0.20). The ANOVA results showed significant variation of pH between stations (Pd”0.01) and also between seasons (Pd”0.01).

pH of water has given the idea of the intensity of pollution (Verma and Shukla, 1970) and polluted zones can be identified by monitoring pH values (Clarks et al., 1977). The pH during the study period was alkaline and it showed significant variation between stations, and seasons. The

pH in the S1 and S2 were high when compared to the

reference station (S10). The low pH in the polluted stations

(S6-S9) situated in the main water body might be because of the waves and its connection to the sea, which diluted the waste upon its discharge. While studying, the physico- chemical characteristics of seafood effluents from Aroor Grama Panchayath, Thomas et al., (2015) reported the pH variation from 6.8 to 7.5. Effluents from fish processing establishments are seldom acidic and are usually close to seven or alkaline (Alex, 2005; Chowdhury et al., 2010; Sankpal and Naikwade, 2012; Vaghela et al., 2015; Thomas et al., 2015) that is generally due to the decomposition of proteinaceous matter present in the effluent (Alex, 2005). On the contrary, Iyer et al. (1994)

(30.38 ± 0.26) and the highest in S 4 (32.25 ± 0.38). reported that the pH of effluents from eight seafood- Throughout the post monsoon season, the lowest processing factories in Cochin varied from 5 to 7.5. If the temperature was reported in S1 (28.38 ± 0.56) and the current situation of waste disposal continues, the water highest in S6 (31.69 ± 0.48). The ANOVA results showed quality shifted to alkaline range over time, which affects significant variation of water temperatures between stations aquatic life. Either highly acidic or alkaline water would kill (Pd”0.05) and also between seasons (Pd”0.01). marine life (Lokhande et al., 2011). Changes occurring in pH due to chemical and other industrial effluents render a Water temperature plays a vitally important role in studying stream unsuitable not only for recreational purpose but the distribution, metabolism, and life histories of aquatic also for the rearing of fish and other aquatic life (Webb, organisms (Newell, 1965; Kinne, 1970; Hines, 1978). In the 1982). present study water temperature and atmospheric temperature did not show much variation and the values Total dissolved solids (ppt) recorded were within a narrow range. Studies conducted in The station wise and season wise variations of TDS are the retting zones by Remani (1979) in Cochin backwaters, shown in figure 5. The total mean value of TDS was found to be minimum in S (2.58 ± 0.53) and maximum in S Azis and Nair (1986) in -Nadayara estuary and in 1 7 - estuarine system Nandan (5.42 ± 0.61). The seasonal variation of TDS is shown in (1991) and Nandan and Aziz, (1994) showed that higher table 7. During the pre monsoon period, the lowest TDS value was reported from S (2.36 ± 0.83) and the highest values of water temperature in the retting zones than non- 1 from S (6.38 ± 0.77). For the monsoon season, the lowest retting zones. On the contrary, in the present study, the water 8 value was reported from S (4.14 ± 0.73) and the highest temperature did not show such differences between polluted 10 from S (7.16 ± 0.82) whereas in the post monsoon season, stations and the reference station. 7 the lowest value was reported from S1 (0.58 ± 0.19) and pH the highest from S7 (2.94 ± 0.83). Statistical analysis using The variations with respect to stations and seasons in pH ANOVA showed that there was significant variation of TDS are shown figure 4. Amongst the stations, the total mean between stations (Pd”0.01) and also between seasons value of pH was found to be minimum in S5 (7.10 ± 0.14) (Pd”0.01). 122 ECO-CHRONICLE Wastewater contains a high fraction of dissolved solids ANOVA test showed significant variation of electrical (Sankpal and Naikwade, 2012). The high TDS level is conductivity between stations (Pd”0.01) and also between generally due to the accumulation of different pollutants seasons (Pd”0.01). (Rani et al., 2003; Reginaa and Nabi, 2004). In the present study, TDS expressed in ppt were well above the Electrical conductivity is a measure of ionic concentration. permissible limits prescribed by WHO (2004). Thomas et According to Kadam and Tiwari, (1990) a high value of al., 2015 reported the total solids from the processing EC designates the pollution status of the lake. The effluents ranged from 2211.5 mg/l to 3779.9mg/l depending conductivity showed significant variation between stations upon the type of fish processed. Sankpal and Naiwade, and seasons. In the present study, conductivity was high

(2012) reported TDS from the fish processing industries in the interconnected channels (S2-S5) and in the main in Ratnagiri ranged from 8200mg/l to 14,700mg/l. Vaghela water body (S6-S9) when compared to the reference station. et al. (2015) showed that the total dissolved solids from In lake, the increase in conductivity also may be due to the fish processing effluent samples varied from 12750mg/ inflow of wastewater from the surroundings (Hardikar, l to 13128mg/l. In this study, the TDS in the main water 2013). Sankpal and Naikwade, (2012) reported that the conductivity in the effluent discharge of fish processing body (S6-S9) was high, compared to the interconnected channels and the reference station. Higher values of TDS industries ranged from 113.42mhs to 18.02mhs. In the indicate maximum disturbance due to human activities as present study, conductivity in the polluted stations ranged suggested by Thakur et al., 2013. The tidal influence and from 4.63ms (S1) to 11.95ms (S8). The mixing of the effluent current patterns along with the effluent discharge in the discharge into the water may have lowered the conductivity in the polluted stations. The electrical conductivity is due polluted stations (S6-S9) might be the reason for its high TDS. According to Kataria et al. (1996), the rise in the value to mainly the dissolved ions such as Bicarbonates, of TDS indicated pollution from unrelated sources. Chlorides, Sodium, Potassium, Magnesium and Sulphate Khatavkar and Trivedy, (1993) proved that high (Peavy et al., 1986). The presence of high amount of concentration of suspended solids was not only due to chlorides, nitrate, ammonia in the fish processing waste high concentration of algae but also due to organic matter were already established (Sankpal and Naikwade, 2012; and silt load. The high organic matter in the seafood effluent Vaghela et al., 2015). The discharge of waste from the discharge might be the reason for the higher total dissolved nearby seafood processing plants might have increased solids in the polluted stations. The total dissolved solids the conductivity. (TDS) affect the water quality by increasing the density of Salinity (ppt) water and thereby retarding the palatability of water. In the The station wise and season wise variations of salinity are present study, TDS was high in the monsoon season. shown and figure 7. Among the stations, the total mean According to Trivedy and Goel, (1984) an excess amount value of salinity was found to be minimum in S (4.29 ± of TDS in water tends to disturb the ecological balance 1 0.59) and maximum in S (11.17 ± 1.01). In the pre due to suffocation in aquatic fauna even in the presence 8 monsoon season, the lowest salinity was reported from S of a fair amount of dissolved oxygen. The maximum values 1 (4.00 ± 0.60) and the highest from S (15.38 ± 1.77). During of total dissolved solids during monsoon might due to the 6 the monsoon season the lowest value was reported from gradual increase in the entry of domestic sewage, S (4.88 ± 0.44) and the highest from S (10.38 ± 2.19) detergents, and industrial waste to the river stream as 10 6 and throughout the post monsoon season, the lowest reported earlier by (Gonzalves and Joshi, 1946). The salinity was reported from S (1.38 ± 0.94) and the highest surface runoff during the rainy season might be the reason 10 from S (8.38 ± 1.74). The ANOVA results showed for increased TDS. On the contrary, Scaria (2004) reported 8 significant variation of salinity between stations (Pd” 0.01) the higher values of TDS in pre-monsoon season, and also between seasons (Pd” 0.01). river, Chitrapuzha river and Cochin estuary.

Electrical conductivity (ms) Salinity acts as a limiting factor in the distribution of The station wise and season wise variations of EC are organisms and its variation may influence various fauna shown in figure 6. Among the stations, the total mean value in the coastal ecosystem (Balasubramanian and Kannan, 2005; Sridhar et al., 2006). The salinity showed significant of EC was found to be minimum in S1 (4.63 ± 0.92) and variation between stations and seasons. The lowest value maximum in S8 (11.95 ± 1.26). Throughout the pre monsoon reported from the reference station, whereas the highest season, the lowest EC was reported from S1 (2.59 ± 0.72) and the highest from S8 (14.75 ± 1.12). During monsoon value reported from Station 8, which is in the main water body. This may be due to the proximity to the sea. Fish season, the lowest EC value was reported from S10 (7.76 ± processing industries use salt for the preservation of 1.03) and the highest from S8 (15.52 ± 1.82) while in the post monsoon season, the lowest value was reported from seafood products and hence the chloride content is increased in the discharged water. The average value of S10 (0.70 ± 0.30) and the highest from S7 (5.99 ± 1.79). ECO-CHRONICLE 123 chloride reported in the fish processing effluent discharge surface runoff. This is in agreement with the findings of was 8350mg/l (Sankpal and Naikwade, 2012). Vaghela et Saksena et al. (2008) that high current in the monsoon al. (2015) reported that the chloride concentration in the season erodes the bank of the river due to turbid floodwater. fish processing effluent discharge ranged from 8000 to 10,000mg/l. The salinity was found to be high during Free Carbon dioxide (mg/l) summer season. The recorded higher values could be The station wise and season wise variations of free CO2 attributed to the low amount of rainfall, higher rate of are shown in figure 9. Amongst the stations, the total mean evaporation, neritic water dominance as reported by earlier value of free carbon dioxide was found to be minimum in workers in other areas (Govindasamy et al., 2000; Gowda S10 (16.98 ± 2.08) and maximum in S5 (118.28 ± 16.31). et al., 2001; Rajasegar, 2003) and due to the discharge of During the pre-monsoon months, the lowest value was waste from the fish processing industries. The extreme reported from S10 (19.69 ± 4.62) and the highest from S9 drop in salinity with near freshwater conditions observed (73.12 ± 14.06). Throughout the monsoon season, the due to the dilution by large amounts of fresh water lowest value was reported from S10 (11.38 ± 1.45) and the

(Dehadrai and Bhargava, 1972; Nasnolkar et al., 1996), highest from S5 (130.88 ± 39.01). In the post monsoon and the higher values of salinity usually depend on the season, the lowest value was reported from S10 (19.88 ± intrusions of seawater through bar mouth. Salinity is also 3.55) and the highest from S5 (163.00 ± 13.45). The ANOVA high in interconnected channel (S3, S5), where wastewater results showed significant variation of free CO2 between from seafood processing plants enters. Salinity may arise stations (Pd” 0.01) and also between seasons (Pd” 0.01). as a by-product of degradation. This is in agreement with The amount of free CO in water is generally maintained the finding of Singh and Singh (1995) that, higher level of 2 by diffusion from the atmosphere, respiration of animals salinity might be due to increase in decomposition of organic matter. along with plants and bacterial decomposition of organic matter (Misra et al., 1993). The free CO2 showed significant

Transparency (sdd) variation between stations and seasons. The free CO2 was

The station wise variation of transparency is shown figure comparatively high in the polluted stations (S1-S9) when 8. Among the stations, the total mean value was found to compared to the reference station. Yakub, (2010) reported be minimum in S5 (28.10 ± 1.76) and maximum in S10 (79.91 that the higher value of free CO2 in waste water from the ± 3.27). In the pre monsoon season, the lowest value was milk processing unit was due to presence of a greater reported from S3 (29.35 ± 6.78) and the highest from S10 quantity of organic matter and its decomposition. Similarly (83.56 ± 3.71). During the monsoon season, the lowest Nandan, (2004) reported the highest concentration of free value was reported from S5 (23.31 ± 1.31) and the highest carbon dioxide in the rooting zone, which is due to the from S10 (73.75 ± 5.39). Throughout the post monsoon process of decomposition of organic matter. The free season, the lowest value was reported from S5 (28.10 ± carbon dioxide was low in the reference station (S10) during

1.76) and the highest from S10 (82.41 ± 7.37). The ANOVA all the seasons, which reflects less load of organic matter results showed significant variation of transparency only in water as reported by Pathani and Upadhyay (2006). The between stations (Pd” 0.01). seafood processing effluent has resulted in the rise of free carbon dioxide in the selected study stations of the wetland. Transparency of water is a measure of light penetration (Pickard and Emery, 1982; Goldman and Horne, 1983), Alkalinity (mg/l) which mainly depends on the turbidity of water. The The station wise and season wise fluctuations of alkalinity transparency showed significant variation only between are shown in figure 10. The total mean value of alkalinity stations. In the present study, the highest transparency was found to be minimum in S10 (29.83 ± 2.92) and was reported in the reference station. Transparency of maximum in S5 (186.46 ± 30.63). In the pre- monsoon water is also affected due to total solids partly or fully season, the lowest alkalinity was reported from S10 (35.25 decomposed organic matters, silts and turbulence caused ± 4.32) and the highest from S9 (130.75 ± 10.91). During by the currents, waves, human and cattle activities (Singh the monsoon season, the lowest value was reported from et al., 1999). The high organic matter in the seafood S10 (26.88 ± 3.19) and the highest from S5 (234.38 ± 75.05) discharge reduced the transparency in the polluted while in the post monsoon season, the lowest alkalinity stations. Fat, oil and grease are important parameters of was reported from S10 (29.83 ± 2.92) and the highest value the fish processing, wastewater (Chowdhury et al., 2010; was reported from S9 (186.46 ± 30.63). Statistical analysis Sankpal and Naikwade, 2012) hindered transparency in using ANOVA showed significant variation between stations the seafood waste discharge affected stations. (Pd” 0.01) and also between seasons (Pd” 0.05). Transparency oscillated on a seasonal basis, and was less during the monsoon season due to high current and The high value of alkalinity indicates the presence of suspended matter and dissolved particles associated with bicarbonate, carbonate, and hydroxide in the water body 124 ECO-CHRONICLE (Jain, 2000). Adding waste in water bodies may also cause Kannan, (1991) has classified water based on hardness higher alkalinity level in that medium (Mulani et al., 2009). values in the following –60 mg/L, soft, 61–120 mg/L, Alkalinity showed significant variation between stations and moderately hard, 121–160 mg/L, hard and greater than seasons. Alkalinity was low in the reference station (S10) 180 mg/L as very hard. The hardness in the stations (S1- during all seasons signalling less pollution load. In the S9) exceeded the permissible limits described by BIS of present investigation, the values of alkalinity at different sites 300mg/l (BIS, 2003). The high hardness in the were high during summer season and reduced during rainy interconnected channels (S1-S5) and in the main water body periods due to dilution of effluents. High values of total (S6-S9) even after tidal mixing, points out the impact of alkalinity may be attributed to the increase in organic seafood processing waste discharge on the receiving water decomposition during which carbon dioxide is liberated. This body. Using these criteria, the water of the stations (S1-S9) reacts to form bicarbonate thereby increasing total alkalinity can be considered as very hard. Similarly Cong (1999) in summer (Mahadev et al., 2010). Jain et al. (1996) and and Junshum et al., (2007) observed a high value of water Scaria, (2004) while studying Cochin backwaters and Saikia hardness in wastewater treatment plant, as compared with and Lohar, (2012) while studying the effect of pulp and paper natural reservoirs. mill effluent discharge on wetlands in Assam, observed similar results. The alkalinity values more than 100 mg/L is Dissolved Oxygen (mg/l) listed as eutrophic, and those with less than 50 mg/L as The station wise and season wise variations of dissolved oligotrophic (Anon, 2001). As per this, the stations S2, S3, oxygen are shown in figure 12. Among the stations, the

S5, S6 and S9 are eutrophic. This suggests that over time, total mean value of DO was found to be minimum in S5 the other polluted stations might become eutrophic if the (5.36 ± 0.35) and maximum in S10 (8.60 ± 0.19). During present situation of wastewater from the seafood industries the pre monsoon season, the lowest value was reported persists. Thomas et al. (2015), while analysing the seafood from S1 (5.47 ± 0.52) and maximum in S10 (8.38 ± 0.31). In processing effluents in Aroor reported that the alkalinity the monsoon season, the lowest value was reported from varied from 410 mg/l to 555.3mg/l. The discharge of S5 (4.14 ± 0.52) and the highest from S10 (8.57 ± 0.30). untreated processing effluent into the wetland influenced Throughout the post monsoon season, the lowest value the alkalinity value. In station 5, alkalinity exceeded the was reported from S5 (6.21 ± 0.46) and the highest value permissible limit of 200mg/l as described by BIS, (2003). from S10 (8.85 ± 0.37). The ANOVA results showed Higher values of the alkalinity indicate the pollution load. significant variation of hardness between stations (Pd” According to Nayak et al. (1982) and Ghosh and George 0.01) and between seasons (Pd” 0.01). (1989) the higher alkalinity indicates pollution. Therefore, alkalinity values obtained during the study period confirmed The effect of wastewater released into a water body largely that the water quality is deteriorated in the seafood waste determined by the discharge of oxygen demanding waste discharge stations. and oxygen balance of the system. (Krishnaram et al., 2007). The dissolved oxygen showed significant variation Hardness (mg/l) between stations and seasons. The dissolved oxygen was The station wise and season wise variations of hardness less in the effluent discharge stations compared to the are shown in figure 11. Amongst the stations, the total mean reference station. The introduction of oxygen demanding value of hardness was found to be minimum in S10 (154.33 material either organic or inorganic, into wetland causes

± 18.88) and maximum in S 5 (1402.58 ± 232.24). During depletion of the dissolved oxygen (Ramana et al., 2007). the pre monsoon season, the lowest value was reported The accumulation of organic matter from seafood waste from S10 (146.75 ± 34.34) and the highest in S 3 (1043.50 ± will lead to the depletion of dissolved oxygen in the water 94.24). Throughout the monsoon season, the lowest value column (Mazik et al., 2005). High DO was reported in the was reported from S10 (214.00 ± 36.72) and the highest from post monsoon season and low DO was reported during

S5 (2827.50 ± 253.18) while during the post monsoon the monsoon season. High amount of Dissolved Oxygen season, the lowest value was reported from S10 (102.25 ± in the post monsoon may be due to the intensive

9.08) and the highest from S8 (743.25 ± 101.89). ANOVA photosynthetic activity as reported by (Ganpati, 1962). Low results revealed that the hardness demonstrated significant DO in the monsoon season may be due to the surface variations between stations (Pd” 0.01) and also between runoff, which reduced transparency and hence resulted in seasons (Pd” 0.01). low photosynthesis. Low DO was reported in the

interconnected channels (S1-S5) when compared to the

Hardness in water is derived from CO2 released in bacterial main water body (S6-S9). In the present study, high DO action and by metabolic ions dissolved in wastewater was reported in the post monsoon when compared to the (Chauhan and Sagar, 2013). The hardness showed monsoon season. The overall decrease in DO indicates significant variation between stations and seasons. the increase in eutrophic conditions (Sheela et al., 2011). ECO-CHRONICLE 125 The low temperature of surface water during the post- 1999). Prasannakumari et al. (2003) also stated that the monsoon season enhances the solubility of atmospheric higher values of BOD during rainy was also due to input of oxygen in the surface water (Riley and Chester, 1971). organic wastes and enhanced bacterial activity. The low The tidal mixing and the current pattern in the main water biological oxygen demand in all seasons in the reference body might have diluted the waste. The cooler water station suggests less organic pollution there. The low value discharged along with the seafood waste might have of the BOD may be due to the lesser quantity of total solids, increased the dissolved oxygen content of the surface suspended solids in water as well as to the quantitative water in the polluted stations. A reduction in dissolved number of microbial population (Avasan and Rao, 2001). oxygen because of nutrient load was also reported Thomas et al. (2015) reported that the overall mean value (Johannessen and Dahl, 1996). Singh and Singh (2001) of BOD from seafood industry located in Aroor Grama and Medhi et al. (2011) also noticed water quality Panchayath varied from 964mg/l to 2250mg/l. Sankpal and degradation due to changes in values of dissolved oxygen Naikwade, (2012) reported that the BOD in the effluent of river Ami and in Nagaon paper mill effluents respectively. discharge of the fish processing industry ranged from 10mg/l to 266mg/l. Vaghela et al. (2015), reported that Biological Oxygen Demand (mg/l) the BOD in the effluent discharge of the fish processing The station wise and season wise variations of BOD are industry varied from 90mg/l to 105mg/l. Seafood shown in figure 13. Amongst the stations, the total mean processing operations produce wastewater containing value of BOD was found to be minimum in S10 (5.42 ± substantial contaminants in soluble, colloidal and

0.48) and maximum in S5 (44.48 ± 4.05). During the pre particulate forms, high organic matter, fat, oil and grease monsoon season, the lowest value was reported from S10 and ammoniacal-nitrogen (Tchoukanova et al., 2003;

(4.58 ± 1.01) and the highest from S5 (38.66 ± 5.98). Goldar and Banerjee, 2004; Islam et al., 2004; Sohsalam Throughout the monsoon season, the lowest value was et al., 2008). Their high organic matter content frequently reported from S10 (5.02 ± 0.93) and the highest from S9 contributes to pollution and high-level biological oxygen (42.70 ± 7.43). During the post monsoon season, the lowest demand of water bodies near seafood processing units value was reported from S10 (6.38 ± 0.45) and the highest (Morry et al., 2003; Tchoukanova et al., 2003; Ferjani et from S5 (64.85 ± 4.60). The ANOVA results showed that al., 2005; Sirianuntapiboon and Srikul, 2006; Moens et al., significant variation of BOD exists only between stations 2007; Sohsalam et al., 2008). An elevated value of BOD (Pd” 0.01). in polluted water body was reported earlier (Nandan and Aziz, 1990; Adermoroti, 1996; Usha et al., 2006; Elmaci et In the present study, BOD showed significant variation al., 2008; Meera and Nandan, 2010). Singh and Rai (1999) among the stations and it exceeded the permissible limits observed that high BOD was indication of organic pollution prescribed by WHO (2004). A high BOD value occurs due in the river Ganga at Varansi. According to Mulani et al., to the discharge of domestic sewage and anthropogenic 2009, increasing trend of the BOD and the decreasing trend activity (Maheshwari, 2011). The lowest value reported from of DO clearly indicate the addition of pollution load. This is the reference station in main water body, and the highest in conformity with the fact that the discharge of organic from the station (Station 5) in interconnected canal. Raised waste from the nearby seafood processing plants resulted BOD values may be due to dead organic matter and in the high BOD in the polluted stations. As the primary stagnant water condition. Similar results were reported water quality of bathing water prescribed by the Central (Chaudhary et al., 2004; Sanap et al., 2006). The greater Pollution Control Board, biological oxygen demand shall the decomposable matter present, the greater the oxygen not exceed 3 mg/l. However, the average biological oxygen demand and greater the BOD values (Adermoroti, 1996). demand is more than 9 times than the standard values. In seafood-processing effluent, biological oxygen demand The high values of BOD indicated high pollution levels due originates from the carbon compounds, which are used to discharge of industrial effluents and sewage wastes by microorganisms as their substrate, and from the (Khan et al., 2005). nitrogenous compounds such as proteins and volatile amines (Thomas et al., 2015). The decomposable waste Chemical Oxygen Demand (mg/l) from the seafood industry might be the reason for the high The station wise and season wise variations of COD are BOD in the seafood effluent discharge affected stations. shown in figure 14. Amongst the stations, the mean value

High BOD was reported in the pre monsoon season and was found to be minimum in S10 (6.68 ± 0.83) and maximum then in the post monsoon season. High BOD during the in S5 (48.29 ± 6.95). Throughout the pre monsoon season pre monsoon season may be due to the presence of the lowest value was reported from S10 (7.45 ± 1.42) and several microbes in water bodies, which accelerated their the highest value was reported from S9 (52.05 ± 10.47). In metabolic activities with the increase in concentration of the monsoon season, the lowest value was reported from organic matter in the form of municipal and domestic waste S10 (5.94 ± 1.52) and the highest value was reported from pouring into the pond with run off (Kaushik and Saksena, S5 (54.47 ± 13.15) whereas in the post monsoon season, 126 ECO-CHRONICLE value reported in the reference station during all seasons. the lowest value was reported from S10 (6.65 ± 1.51) and High phosphate content was reported in interconnected the highest from S5 (67.86 ± 11.23). Statistical analysis using ANOVA results showed significant variations of COD channels. The higher values of phosphates confirmed the between stations (Pd” 0.01) and also between seasons polluted status, which could be due to the untreated unload (Pd” 0.05). of waste from the nearby seafood industry. Vaghela et al., 2015 reported that the phosphate content in the waste COD is used to measure pollution of domestic and discharge from the fish processing industry ranged from industrial waste. The waste is measured in terms of equality 10mg/l to 16.5mg/l. The high phosphate in the seafood of oxygen required for oxidation of organic matter to effluent discharge stations (S1-S9) points out the role played produce CO2 and water (Sankpal and Naikwade, 2012). by nearby seafood industry in deteriorating water quality. Chemical oxygen demand (COD) showed significant The higher level in monsoon may be because of surface variation between stations and seasons. The COD was runoff carrying untreated effluent. Hutchinson (1957), reported high in the seafood waste discharge affected Welch, (1952) and Ruttner, (1953) found the little amount stations (S1-S9). Jayraman et al. (2003) reported the of phosphates in natural waters free from human influence of organic matter, thermal power, effluents, and interference. anthropogenic activities for high COD. In a fish processing Nitrate (mg/l) industry, the effluent COD is usually higher than BOD 5 The station wise and season wise variations of nitrate are (Chowdhury et al., 2010). In the present study, the BOD shown in figure 16. Between stations, the total mean value and COD values were unusually higher and exceeded the of nitrate was found to be minimum in S (2.17 ± 0.30) permissible limits prescribed by WHO (2004). High amount 10 and maximum in S (14.78 ± 2.17). During the pre monsoon of BOD and COD cause oxygen depletion, which leads to 9 season, the lowest value was reported from S (2.95 ± the suffocation of aquatic life (Verma et al., 1984). The 10 0.70) and highest from S (12.73 ± 2.28). Throughout the high values of BOD and COD reflected high pollution levels 9 monsoon season, the lowest value was reported from S due to discharge of industrial effluents and sewage wastes 10 (2.48 ± 0.32) and highest from S (17.55 ± 6.06) while in (Khan et al., 2005). Thomas et al. (2015) reported that COD 9 the post monsoon season, the lowest value was reported in the seafood processing effluents in Aroor Panchayath from S (1.09 ± 0.08) and the highest from S (14.05 ± varied from 1442 mg/l to 2700mg/l. Sankpal and Naikwade, 10 9 1.61). The ANOVA results showed significant variations in (2012) reported that the COD in the effluent discharge of nitrate between stations (Pd” 0.01) and seasons (Pd” 0.05). the fish processing industry in Ratnagiri varied from 1200mg/ l to 2200mg/l. Similarly, Vaghela et al. (2015) reported that Among the nutrients nitrate play an important role in the COD in the effluent discharge of the fish processing balancing various biological processes occurring in industry in Veravel harbour varied from 100mg/l to 250mg/ aquatic ecosystems. High nitrate concentrations are l. The discharge of the high pollution load from the seafood frequently encountered in treating wastewater, because industry might have increased the COD level in the of ammonium nitrogen. High nitrate levels in wastewater interconnected channels (S -S ) and in the main water body 1 5 could also contribute to eutrophication effects, particularly (S -S ) when compared to the reference station (S ). 6 9 10 in freshwater (OECD, 1982). The nitrate showed Phosphate (mg/l) significant variation between stations and seasons. The The station wise and season wise variations of phosphate lowest value was reported in the reference station are shown in figure 15. The total mean value of phosphate irrespective of the seasons. Nitrate was high in the main was found to be minimum in S (0.56 ± 0.09) and maximum 10 water body (S6-S9) when compared to the interconnected in S (5.66 ± 0.91). During the pre monsoon season, the 5 channels (S1-S5). The nitrate content was higher in the lowest value was reported from S10 (0.47 ± 0.15) and seafood discharge affected stations when compared to highest in S 3 (2.84 ± 0.41). In the monsoon season, the the reference station. The high nitrogen levels in seafood lowest value was reported from S10 (0.59 ± 0.13) and waste are likely due to the high protein content (15–20% highest from S9 (4.04 ± 0.97) and in the post monsoon of wet weight) of fish and marine invertebrate (Sikorski, season, the lowest value was reported from S10 (0.63 ± 1990). Sankpal and Naikwade, (2012) reported that the

0.18) and highest from S5 (10.50 ± 1.10). The ANOVA nitrate in the effluent discharge of the fish processing results showed significant variation between stations (Pd” industry ranged from 126-168mg/l whereas Vaghela et 0.01) and also between seasons (Pd” 0.05). al. (2015) reported that the COD in the effluent discharge of the fish processing industry varied from 28.5-30mg/l. Phosphate acts as nutrient for plant growth and high Higher nitrate points out adding processing effluent into concentration of this nutrient indication of eutrophication. the wetland. Osibanjo et al. (2011) recorded a high nitrate The phosphate showed significant variation between in the river, which receives industrial effluents and urban stations and seasons. While comparing stations, lowest, runoff. Another possible way of nitrate entry might be ECO-CHRONICLE 127 through oxidation of ammonia (Rajasegar, 2003). The highest Silica (mg/l) concentration of nitrate is an indication of organic pollution The station wise and season wise variations of silica are and eutrophication (Maheshwari, 2011). shown in figure 18. Among the stations, the total mean

value was found to be minimum in S10 (1.46 ± 0.26) and Ammonia (mg/l) maximum in S5 (4.43 ± 0.64). During the pre monsoon

The station wise and season wise variations of ammonia season, the lowest value was reported from S10 (0.64 ± are shown in figure 17. Among the stations, the total mean 0.10) and highest from S5 (2.80 ± 0.65). In the monsoon

season, the lowest value was reported from S10 (1.47 ± value of ammonia was found to be minimum in S10 (0.68 ± 0.39) and highest from S9 (4.18 ± 0.91). During the post 0.10) and maximum in S5 (9.22 ± 1.57). During the pre monsoon season, the lowest value was reported from S10 monsoon season, the lowest value was reported from S10 (2.28 ± 0.55) and highest from S5 (6.93 ± 1.18). The (0.80 ± 0.13) and highest from S5 (6.52 ± 1.72). In the computation of ANOVA showed significant variations monsoon season, the lowest value was reported from S10 between stations (Pd” 0.01) and seasons (Pd” 0.01). (0.52 ± 0.19) and highest from S5 (6.42 ± 1.84) whereas in the post monsoon season, the lowest value was reported Silica showed significant variation between stations and from S10 (0.74 ± 0.22) and highest from S5 (14.72 ± 3.41). The ANOVA results showed significant variations in seasons. Comparatively high values were observed in the ammonia between stations (Pd” 0.01) and seasons (Pd” seafood waste discharge stations. According to Livingstone 0.01). (1963), worldwide mean value is 13.0 mg/l for silicates. In the present study, average value did not exceeded 4.60 The ammonia showed significant variation between mg/1, and this could be because of its use by diatoms. stations and seasons. The high amount of ammonia was Among the stations, the lowest value was reported in the reported from the seafood waste discharge affected reference station during all seasons. The observed lower stations. The lowest value reported from the reference values of silicate could be attributed to uptake of by station. The ammonia is also formed as a result of the phytoplankton for their biological activity (Mishra et al., decomposition of organic nitrogen by ammonfication. So, 1993; Ramakrishnan et al., 1999). The higher value of silica the high amount of ammonia can be used to understand is a sign of waste from industrial effluents and of the death the duration of exposure of waste. High levels of ammonia of diatoms. exist in the interconnected canal and in the main water body suggest that these stations were experiencing SUMMARY AND CONCLUSION pollution for a while. Ammonia in natural waters is generally absent or present at very low levels In India especially in Kerala, the environmental problems (Maheshwari, 2011). Water pollution by sewage or associated with the discharge of wastewater from seafood industrial wastes containing nitrogenous organic water processing facilities are gaining attention only recently. The may contain high concentration of ammonia (Goel, 1997). processing of seafood products can require significant Davies and Jaja, (2014) reported that if the ammonia in volume of water, which has a direct consequence in the the natural water bodies exceeded international generation of contaminated effluents. The seafood acceptable levels of 0.10 mg/l, which indicated high industries in Kerala are particularly limited to the narrow nutrient status, organic matter and potential pollutants. coastline areas of the Vembanad Lake aremainly due to Thomas et al., (2015) reported that the ammonia the ease of transport. All the processing plants discharge concentration in the seafood processing effluents in their effluents together with solid waste into this lake. The Grama Panchayath varied from 29.1mg/l to 36.2mg/l. The volume of effluent discharged into this wetland system is degradation of the waste discharged from the seafood unknown and no routine monitoring is done to check the plants resulted in the rise in ammonia. The high content uncontrolled discharge of the seafood processing waste. of ammoniacal nitrogen in seafood processing effluent is The purpose of the present study was to understand the mainly responsible for its rise in the wetland. The decay influence of seafood waste dicharge on the physico- of fish and shellfish has also released significant amounts chemical properties of water. of ammonia and nitrate (Leffler, 1997). The death and subsequent decomposition of phytoplankton and the The extent of pollution impact of seafood waste discharge excretion of ammonia by planktonic organism may also on nearby water bodies was done by studying the result in a rise in ammonia (Segar and Hariharan, 1989). physicochemical parameters. Parameters like TDS, The high ammonia concentration in fish processing, alkalinity, BOD, COD and hardness exceeded the wastewater is due to the high blood and slime content in permissible limits in polluted stations. Among polluted wastewater streams (FREMP, 1994; Chowdhury et al., stations, the effect was pronounced in the interconnected 2010). channels because of less surface runoff. The stagnated 128 ECO-CHRONICLE Fig. 2. Station wise and season wise variation of atmospheric Fig. 7. Station wise and season wise variation of salinity temperature

Fig.3. Station wise and season wise variation of water Fig. 8. Station wise and season wise variation of transparency temperature

Fig. 4. Station wise and season wise variation of pH Fig. 9. Station wise and season wise variation of free CO2

Fig. 5. Station wise and season wise variation of TDS Fig. 10. Station wise and season wise variation of alkalinity

Fig. 6. Station wise and season wise variation of EC Fig. 11. Station wise and season wise variation of hardness ECO-CHRONICLE 129

Fig.12. Station wise and season wise variation of DO Fig. 17. Station wise and season wise variation of ammonia

Fig. 13. Station wise and season wise variation of BOD Fig. 18. Station wise and season wise variation of silica

Fig. 14. Station wise and season wise variation of COD nature of the lake may add on the ruinous effect. EC, TDS, hardness, alkalinity, BOD, COD and nitrate are found to be high during monsoon whereas high phosphate, ammonia, and silicate are reported in post monsoon season. Hypereutrophic status is high in the interconnected channels than the main water body.The increased levels

of free CO2, BOD, phosphate, nitrate, and ammonia in the selected stations confirms degrading lake water because of seafood processing effluents. High content of nutrients namely, nitrate and ammonia is associated with Fig. 15. Station wise and season wise variation of eutrophication. High levels of ammonia existed in the phosphate interconnected canals and in the main water body.

The study revealed that hyper eutrophication existed near the effluent discharge points because of the indiscriminate waste dumping.The physico-chemical characters of selected stations evaluated in the present work demonstrates that the changes in the properties are due to both monsoon and direct discharge of effluents from surrounding environment. The nutrient loading into the water from the nearby seafood industry has been considered as one of the major cause for eutrophication. Fig.16. Station wise and season wise variation of nitrate The parameters like TDS, alkalinity, BOD, COD and hardness nutrients and pollution indicators showed wide fluctuations according to the pollution load.

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