Second International Conference on Coastal Zone Engineering and Management (Arabian Coast 2010), November 1-3, 2010, Muscat, Oman ISSN: 2219-3596

Impact of Water Quality Changes on Harbour Environment Due to Port Activities along the West Coast of

*P.V. Shirodkar, U.K. Pradhan2 and P. Vethamony3

National Institute of Oceanography (CSIR), Dona Paula, -403 004, India * Corresponding Author, email: [email protected]

Abstract

Physico-chemical and biological studies were carried out in 3 major harbours (Kandla, Mormugao and Mangalore) along the west coast of India over different periods from 2002 to 2007. R-mode factor analyses, water quality index (WQI) and hydrodynamic modeling were used to understand the dominant parameters influencing water quality, trace the sources of contaminants, their impact on harbour environments and their fate.

Factor analyses showed the dominance of anthropogenic nutrients, petroleum hydrocarbons and phenols, while the dominance of suspended solids, turbidity and salinity from natural effects in Kandla harbour. Despite large availability of nutrients, a significant decrease in chlorophyll a and primary productivity during monsoon suggested detrimental effects of the dominant contaminants. Mormugao harbour showed year round dominance of microbes, anthropogenic nitrogen compounds, petroleum hydrocarbons and heavy metals from sewage, boat traffic and port activities, with significant negative effect on chlorophyll a during post-monsoon. New Mangalore harbour showed the dominance of anthropogenic nitrite, ammonia, petroleum hydrocarbons, cadmium and mercury from sewage, industrial discharges and port activities.

Overall Index of Pollution (OIP values) of harbour waters, evaluated from WQI of each water quality parameter showed increases (OIP>4) in Kandla harbour suggesting polluted water during all seasons. Mormugao harbour showed acceptable to slightly polluted water (OIP: 2.51-2.75) mostly during post-monsoon, while New Mangalore harbour showed acceptable water quality (OIP: 1 - 2).

Hydrodynamics indicated that strong ebb currents (0.09–2 m/s) in Kandla harbour transport anthropogenic contaminants to the inner Gulf of Kutch; while in Mormugao harbour, the coastal water enters the estuarine mouth from north and flows out towards south and transports the contaminants to the coastal water, augmented by E-W flowing tidal currents. In New Mangalore harbour, the strong seasonal currents and seasonal winds keep the water well mixed and aerated and help in driving the contaminants away from the shore.

Keywords: Physico-Chemical Parameters; Biological Parameters; Harbour Waters; West Coast of India; Factor Analyses; Water Quality Index (WQI); Hydrodynamics.

1. Introduction

Ports are important means of cheap transportation of cargo, oil, chemicals, machinery, etc and are highly economical to the nation. India has many small, medium and large ports along its coast; some of which are provided with natural harbours. Port activities generate various kinds of waste that contaminate harbour waters. Substantial amounts of the generated waste are mostly assimilated by water depending upon its capacity, while the hydrodynamics of the region play a greater role in further dispersion of waste contaminants. Recent past studies have shown that increasing port activities in India have contributed to increases in the concentration of toxic heavy metals, petroleum hydrocarbons, phenols, and many other toxic compounds in harbours, which can affect the environments to varying degree.

We present here the results of our impact assessment studies carried out in 3 different harbour environments along the west coast of India during various developmental activities from 2002 to 2007. These harbours envisage; i) Kandla harbour in Gujarat, ii) Mormugao harbour in Goa and iii) New Mangalore harbour in Karnataka. The aim was to understand the dominant physico-chemical and biological characteristics influencing the water quality & tracing of their sources using factor analyses; evaluate changes in water quality using water quality index (WQI) and to assess the impact of contaminants on harbour environments. The study also aimed to understand how the hydrodynamics help in containing the contaminants in harbour environments (the fate of contaminants). 1 i) Geographical Features of the area

Kandla Port & its harbour: Kandla port is located along the western bank of Kandla creek (220 55’ - 230 05’ N and 700 05’ - 700 02’), which is one of the major creeks along the NW coast of India supplying water to the inner Gulf of Kutch (GoK). Away from the Arabian Sea towards east, the GoK narrows down into a constriction at Satsaida Bet (70o20’E), and then bifurcates into a creek system called Little Raan (Figure-1). This Little Rann has a network of many small and large creeks, intermingled with marshy tidal flats rich in fine clays. Kandla creek, being a major tributary of this creek system, the water brought by creek tributaries is discharged by Kandla creek into the inner GoK. Kandla creek is a main harbour area of Kandla Port. The tidal height in the creek ranges from 0.83 to 7.2 m, while tidal currents vary from 0.08 to 2 m/s. Various industrial-chemical manufacturing units, fertilizer-manufacturing industry (IFFCO) and the salt manufacturing units with saltpans rich in brines occur around Kandla creek.

(ii) Mormugao Port & its harbour: Mormugao Port is located at the estuarine mouth of Zuari River in Goa (15o 22’ - 15o 28’ N and 73o 44’ - 73o 51’ E; Figure 2). Zuari is one of the major rivers connected to by Cumbarjua canal, forming the major estuarine system in Goa. Zuari River originates at Hemad-Barshem in the and after passing through Goa, opens out into the Arabian Sea near Mormugao in south Goa. The estuarine mouth of Zuari forms the main harbour area of Mormugao port, with a maximum water depth of 20 m (av. 3 m). Two Sewage Treatment Plants (STPs) are located in its surrounding region, one at Mormugao Headland, at Zuari mouth, closer to Mormugao port, while the other one is located outside the estuarine mouth, towards its south at Baina (Figure 2). The southern bank of Zuari hosts various shipbuilding industries, yards, workshops, recreational activities and anthropogenic setups.

(iii) Mangalore Port & its harbour: Mangalore Port is a all weather port situated at Panambur, towards north of the confluence of Gurupur river to Arabian sea in Karnataka state, about 170 nautical miles south of Mormugao (Figure 3). It ranks India's ninth largest port in terms of cargo handling as it handles 75% of India’s coffee exports and bulk of cashew nuts. The major commodities exported through this Port are iron ore concentrates & pellets, POL products, granite stones, containerized cargo, etc. The major imports include crude and POL products, LPG, wood pulp, timber logs, finished fertilizers, liquid ammonia, phosphoric acid, other liquid chemicals, containerized cargo, etc. Various types of small, medium and large industries located in this area discharge their effluents directly into the coastal water off Kulai. Available information indicates that Mangalore Refineries and Petrochemical Ltd.,(MRPL) discharges 7200 m3/d, BASF India Ltd, discharges 3600 m3/d north of harbour, while Mangalore Chemicals & Fertilizers (MCF) discharges 13,000 m3/d, south of the harbour. All these wastes are likely to impinge on the coastal marine environment of Karnataka.

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2. Material and methods

Water samples were collected regularly from Kandla harbour, twice every season – pre-monsoon (Feb. – May), monsoon (June – Sept.) and post-monsoon (Oct. - Jan.) from surface, mid and near bottom levels at 4 key locations – the cargo jetty, oil jetty, mouth of the creek and the junction, where Sara and Phang creeks meet the Kandla creek (Figure-1) by using a hired boat. From Mormugao harbour region, the surface and bottom water samples were regularly collected every month during high tide and low tide from 8 different locations (Figure 2) from September 2003 to April 2004, comprising 3 seasons, while surface and bottom water samples were collected from 15 locations in the coastal waters from Suratkal to New Mangalore harbour during 2007 (Figure 3) using a Niskin water sampler. Soon after collection, the water samples were fixed for dissolved oxygen and measured by Winkler’s method, while the BOD was measured after 5 days of incubation at 20oC in BOD incubator. Temperature and pH were measured on board using a thermometer and Eutech pH meter, respectively. Techniques used for the analyses of various water quality parameters are indicated in table below.

Table-1: Techniques used for physico-chemical and biological parameter determination. Parameter Abbreviation Techniques / Instruments used Unit Temperature Temp. CTD (SeaBird) 0C Salinity Sal. CTD (SeaBird) Psu pH pH pH probe (Eutech) pH units Filtration (Aspirator Vacuum)(Preston et Total Suspended Solids TSS mgl-1 al.,1997) Dissolved Oxygen DO Titration (Strickland and Parsons, 1979) mg l-1 Biochemical Oxygen BOD Titration (Strickland and Parsons,1979) mg l-1 Demand Petroleum Hydrocarbons PHc Spectrofluorometry (Grasshoff ,1999) µgl-1 Phenolic Compounds PhOH Spectrophotometry (APHA,1992) µgl-1 Lead (seawater) Pb Flame AAS µg l-1 Cadmium (seawater) Cd AAS µg l-1 Mercury (seawater) Hg Cold Vapour AAS ng l-1 -1 Ammonia NH3-N Spectrophotometry (Grasshoff ,1999) µmol l -1 Nitrite NO2-N Spectrophotometry (Grasshoff ,1999) µmol l -1 Nitrate NO3-N Spectrophotometry (Grasshoff ,1999) µmol l -1 Phosphate PO4-P Spectrophotometry (Grasshoff ,1999) µmol l -1 Silicate SiO4-Si Spectrophotometry (Grasshoff ,1999) µmol l Chlorophyll – a Chl-a Turner Fluorometer mgm-3 -3 Pheophytene pheo Turner Fluorometer mgm

Primary productivity PP 14C technique by Liquid Scintillation mg C m-3 day-1

Coliforms (Bacteria) TV, TC,TVC Microbiological techniques Nos.

Tides, winds and currents were studied in GoK, Kandla creek and at Vengurla, Mormugao and Karwar. The bathymetry data were generated by digitizing the Naval Hydrographic chart of the regions, while the wind data was collected from Autonomous Weather Station (AWS) installed at Kandla Port and at the National Institute of Oceanography, Goa and the data was reduced to 10 m level for simulation. For water level simulation, the currents of the harbour regions were taken into account for running the Hydrodynamics module of MIKE 21 numerical modeling software (Anonymous 2001 - MIKE 21 user manual; Chubarenko and Tehpikova, 2001; Madsen and Jacobsen, 2004; Babu et al., 2005, Babu et al., 2006). Hydrodynamic module calculated the hydrodynamic behavior of water in response to a variety of forcing functions. 3 i) Multivariate Statistics (MS): Data collected was subjected to multivariate statistical analysis to understand the influence of various parameters on Chlorophyll a, considered here as an index of biological productivity. MS approaches such as principle component analysis (PCA), also called the factor analysis, multiple regression analysis (MRA), and cluster analysis (CA) are widely used for deriving the significance of specific parameters among the data generated (Vega etal.,1998; Helena etal., 2000; Simenovaetal.,2003; Singh etal.,2004, Wunderlin et al, 2001; Richman, 1986). R-mode (sorted) factor analysis resulted in eigen values, percentage of variance and cumulative percentage for the total data, allowing inter-parameter relation and variation at surface and bottom water layers. The loadings of principal components were considered as strong (>0.75), moderate (0.5-0.75) and weak (0.4-0.5) as per Liu et al (2003). ii) Water Quality Index (WQI): Water quality index is a mathematical tool, which transforms the bulk of water quality data into a single digit, cumulatively derived numerical expression, indicating the level of water quality. It is normally used for measuring the quality of riverine water and is essential for comparing the water quality of different regions and sources and for monitoring the changes in water quality of a given source as a function of time and other influencing factors. Since water quality is influenced by physical, chemical, biological and bacteriological factors, a total of nine parameters are normally measured and the index (WQI) is developed. These nine parameters include dissolved oxygen, fecal coliform, pH, biochemical oxygen demand, temperature change, total phosphates, nitrates, etc and are selected as per the classification scheme by Inland Surface Water in India for classification of the types of water, accepted by Central Pollution Control Board (CPCB), for recommending the suitability of water for specific use. For each water quality parameter, the WQI is first measured individually by using the equation obtained from the value function curve in which, the concentration of parameter is taken on Y-axis and the index value on X-axis and plotted for each of the parameter as per Sargaonkar and Deshpande (2003). Measured WQI values are then transformed into a single number called the Overall Index of Pollution (OIP), representing the overall quality of water at that particular station. OIP is thus an average of all the pollution indices (Pi) for individual water quality parameters considered in this study and is th given by the mathematical expression OIP = Ʃ i Pi / n; where Pi = pollution index for i parameter; where, i = 1, 2, . n and n = number of parameters.

3. Results and Discussion

Factor analyses indicated significant differences in loadings between the 3 harbour waters, indicating dominant parameters influencing the water quality and their impact on biological characteristics. i) Kandla port and its harbour:

Pre-monsoon: In Kandla harbour (Kandla Creek), a total of 5 Factors explained 84.2% of total variance (Table 2a). Factor 1 explained 26.4% of the variance and indicated strong positive loadings of primary productivity (PP), phosphates, nitrite; moderate of ammonia, temperature, pH and low of pheophytin, with strong negative loading of salinity. PP correlated positively with phosphate, nitrite and ammonia and negatively with nitrate suggesting phytoplankton growth and utilization of nitrate and its strong positive loading indicated a Productive condition. Factor 2 explained 51.1% of cumulative variance with strong positive loadings of suspended solids, silicates, phenol and pH; moderate positive loading of turbidity, temperature, nitrate and a weak positive loading of PHc. Significant positive correlations of TSS and Turbidity with silicates, phenols and nitrate and negative with DO solubility suggested that strong tidal currents in the creek increase TSS, turbidity and silicates and decrease DO solubility in water. The currents also bring in phenols from a point source and add to the creek water, indicating a significant Tidal influence. Factor 3 explained 68% of cumulative variance with strong positive loadings of AOU and Chl.a; moderate of DO solubility and pheophytin with moderate negative loading of DO and a weak of turbidity. Significant positive correlation of Chl.a with pheophytin and negative with turbidity revealed that the production of Chl.a and its proportionate degradation to pheophytin is a continuous process, mediated by high turbidity in creek water and hence this factor showed Turbidity condition. Factor 4 explained 77.3% of cumulative variance and indicated strong positive loading of BOD and moderate of ammonia and nitrate. Ammonia and nitrate correlated negatively with each other indicating their different source contribution and nitrate utilization in preference to ammonia, suggesting that the concentrations of anthropogenic nitrate and ammonia in the Creek from loading and unloading activities and from the fall out from fertilizer industry, reflect on their accumulation or utilization by photosynthetic organisms, suggesting Industrial contribution. Lastly, Factor 5 explained 84.2% of cumulative variance with strong positive loading of transparency (light penetration by Sacchi disk) and moderate of PHc, with no correlation between the two, suggesting that the PHc in creek water arises from the fall out from loading and unloading of oil and petroleum products, boat traffic and other external additions, indicating Petroleum hydrocarbon (PHc) contamination.

Monsoon: A total of 5 Factors explained 79.6% of total variance (Table 2 b). Factor 1 explained 35.4% of the variance with strong positive loadings of PHc, TSS, turbidity, salinity, phenol; moderate of Chl a and weak of phosphate. Stronger loadings of turbidity and TSS in this factor indicated their increase, which decrease the

4 solubility of DO and weaken the photosynthetic process, suggesting an influence of Monsoonal flow. Factor 2 explained 50.7% of cumulative variance with moderate positive loading of nitrate, weak of silicate, with strong negative loadings of PP and light penetration (Sacchi disk). Nitrate and silicates did not correlate well with PP, but correlated significantly with each other suggesting a common source and their addition to the creek water from upstream. Decreases in PP due to lack of water transparency for light penetration reflected in strong negative loading of PP. Low transparency arises from turbid waters and this factor showed the Turbidity effect. Factor 3 explained 61.7% of cumulative variance and indicated strong positive loading of ammonia, weak of nitrite with a strong negative loading of pH. Positive correlation of ammonia with nitrite indicated their association, while their negative correlations with pH indicated their addition to the Creek from upstream. This showed a cumulative effect of the fallout from fertilizer industry; loading and unloading of fertilizers and raw materials at IFFCO jetty and of oil and oil products at oil jetty, suggesting an Industrial effect. Factor 4 explained about 72% of cumulative variance and showed strong positive loading of DO, weak of pheophytin and a strong negative loading of AOU. Negative correlations of DO with AOU and pheophytin, and positive of AOU with pheophytin indicated less degradation of Chl a to pheophytin in well-oxygenated Creek waters, suggesting the Dissolved Oxygen effect. Lastly, Factor 5 explained 79.6% of cumulative variance with moderate positive loadings of temperature and pheophytin and a weak of nitrate. The significant correlations of pheophytin with nitrate suggested that the presence of nutrients, generation of Chl a and its degradation to pheophytin is a process mediated by high turbidity and TSS and this factor showed Chl. a Degradation effect.

Post-monsoon: Total of 5 Factors explained 83.6% of total variance during post-monsoon (Table 2 c). Factor 1 explained 34% of the total variation and indicated strong positive loadings of temperature, PP, pH, Chl.a; moderate of ammonia; strong negative loadings of salinity and DO solubility; moderate of phenol, BOD and TSS and weak of DO. Good correlations of ammonia with Chl.a and PP indicated that ammonia activates the photosynthetic process in the presence of sunlight, while negative correlations of PP and Chl.a with TSS and phenols though suggest negative impacts, the strong positive loading of PP in this factor shows a Productive condition. Factor 2 accounted for 57.8% of cumulative variance and indicated strong positive loading of silicate, nitrite, nitrate and phosphate; moderate of phenol, with moderate negative loading of pheophytin and a weak of pH, indicating nutrient enrichment in creek water with moderate amount of phenols. Negative correlations of pH with nutrients and phenols and positive among the nutrients indicated soluble nitrogenous species in creek water to arise from port activities suggesting Nutrient enrichment. Factor 3 explained 68.7% of cumulative variance and indicated strong positive loading of DO, moderate of BOD and a strong negative loading of AOU, indicating well-oxygenated water with low organic load, suggesting good Oxygenation of Creek water. Factor 4 explained 77% of cumulative variance with strong positive loadings of turbidity and TSS, with a significant positive correlation between the two. Turbidity in Creek water arises from the re-suspension of fine sediment particles brought about by tidal currents and the tributaries flow, this factor suggested a Turbid condition. Factor 5 explained 83.6% of cumulative variance with a strong positive loading of transparency and weak of PHc and pheophytin, with no significant correlation between PHc and pheophytin, suggesting partial Petroleum hydrocarbons contamination in Creek water. ii) Mormugao port and its harbour

Pre-monsoon: A total of 4 Factors explained 82.7% of total variance during high tide. Factor 1 accounted for 37% of the variance and indicated strong positive loadings of TVC, TC and TV with significant correlations between them, suggesting bacterial dominance. Moderate positive loadings of pH, Chl.a and Hg with good correlations of pH with Chl.a and Hg, suggested addition of Hg to harbour water and moderate Chl.a generation. Significant negative correlations of TVC, TC and TV with silicates, PHc and Pb indicated their contributions from sources near the estuarine mouth, which include STP discharge located closer to the estuarine mouth. They enter the harbour water during high tide as seen from higher values of TVC, TC and TV during the high tide relative to low tide. Negative correlations of Chl.a with Pb though indicated its detrimental effect on Chl.a, the good nutrients supply maintained moderate Chl.a generation with this factor suggesting a Productive condition. Factor 2 explained 61% of cumulative variance with strong positive loadings of phosphate, silicates and nitrite, moderate of nitrate and BOD and a weak of Pb, with weak negative loadings of pheophytin and pH. Positive correlations of nutrients with each other and with Pb and BOD and negative with DO suggested their common source and their generation from oxidation of organic matter from STP. Anthropogenic nutrients released support Chl.a generation in harbour water. Factor 3 explained 74.5% of cumulative variance with strong positive loading of PP, moderate of DO, with moderate negative loading of nitrate. PP correlated significantly with DO and negatively with nitrate, indicating that the nutrients released from oxidation maintained the productivity of harbour water by utilization of nitrate. Factor 4 explained 82.7% of cumulative variance with strong positive loading of Cd and moderate of pheophytin, with no significant correlation between the two, indicating the dominance of Cd, suggesting Cd contamination in harbour water.

During low tide, only 2 Factors explained 100% of variance in Mormugao harbour water. Factor 1 explained 58.6% of the variance due to strong positive loadings of silicates, Pb, PHc, nitrate and Hg; moderate of nitrite,

5 pheophytin, pH and DO with a weak of Cd, followed by strong negative loadings of PP, TC, TVC and TV. Silicate indicates its dominance in harbour water, while its positive correlation with nitrate and Pb; that of nitrate with nitrite and pheophytin; and positive correlation of Pb with PHc all suggested a riverine contribution. Significant negative correlation of Hg with silicate and insignificant with nitrate, nitrite, DO, etc., suggested no Hg addition by riverine water. Similarly, the positive correlations of TC, TV and TVC with each other, and negative with nitrate, silicate and Pb and positive with Hg suggested the contribution of TV, TC and TVC to harbour water by Mormugao STP, with Hg additions from external sources. Factor 2 explained 100% of cumulative variance with strong positive loadings of BOD, Chl.a, DO and pH; moderate of pheophytin, with strong negative loadings of phosphate, Cd and nitrite, and moderate of Hg. Chl.a correlated well with DO and BOD and negatively with pH suggesting good Chl.a generation in well oxygenated, nutrient rich riverine water and its transportation to harbour, as evident from increasing Chl.a during low tide relative to high tide. Negative correlation of Hg with Cd indicated a different source, while positive correlation of BOD with pH indicated BOD contribution from Mormugao STP.

Monsoon: A total of 5 Factors explained 78.3% of variance during high tide. Factor 1 accounted for 23.8% of the total variance with moderate positive loading of DO, weak of Chl.a and nitrate, followed by strong negative loadings of phosphate, lead and silicates and a weak of PP and Cd. Weak positive loading of Chl.a of this factor suggested low Chl.a while weak negative loading of PP suggested low PP in harbour water. High tide showed low Chl.a values of 1.4 to 3.4 mgm-3, while low tide showed high values of 1.7 to 19.3 mgm-3. Factor 2 accounted for 44.3% of cumulative variance with strong positive loadings of nitrite and BOD; moderate of Chl.a and pheophytin; followed by strong negative loading of pH and a weak of nitrate. Negative correlation of Chl.a, pheophytin and nitrite with pH indicated generation of Chl.a and pheophytin in brackish riverine water and its transportation to harbour water rich in nitrate and nitrite contributed by Mormugao STP. Factor 3 accounted for 57.8% of cumulative variance and indicated moderate positive loading of DO and a weak of pheophytin with strong negative loadings of Hg and phenol. Factor 4 accounted for 68.5% of cumulative variance and indicated a strong positive loading of PHc, moderate of TC, PP and Chl.a and a weak of BOD. PHc correlated positively with TC, PP, Chl.a and BOD; TC correlated positively with BOD suggesting its contribution from Mormugao STP, while strong positive loading of PHc indicated PHc contamination. Factor 5 accounted for 78.3% of cumulative variance with strong positive loading of TVC, moderate of TC and strong negative loading of TV. TVC and TC correlated significantly with each other but negatively with TV, indicating their dominance in harbour water relative to TV.

During low tide, a total of 5 factors explained 89.5% of variance. Factor 1 accounted for 46.64% of the variance with moderate positive loading of phosphate, weak of silicate and PHc; strong negative loadings of DO and PP, moderate of pH and weak of nitrate. Phosphate correlated negatively with pH and DO and positively with silicates; silicate correlated negatively with pH, indicating phosphate and silicates to be the riverine contributions. Negative correlations of PHc with pH and DO, and positive with phosphate and silicates, indicated PHc also to be a riverine source. Factor 2 accounted for 61.27% of cumulative variance with moderate positive loading of TV, weak of Chl.a; strong negative loading of TVC and moderate of nitrite. Chl.a showed insignificant relationship with TV indicating Chl.a to be independent of TV. Factor 3 accounted for 73.2% of cumulative variance and indicated strong positive loading of pheophytin and nitrate, moderate of pH and a weak of Chl.a, followed by strong negative loadings of Hg, phenol, moderate of silicate, Pb, phosphate and nitrite. Chl.a correlated positively with pheophytin, nitrate and pH and negatively with phenol, phosphate, silicates, nitrite, Hg and Pb, all of which are contributed by riverine water, indicating the generation of Chl.a in harbour water containing nitrates and its degradation to pheophytin mediated by phenol, Hg and Pb. Factor 4 accounted for 81.74% of cumulative variance and indicated moderate positive loading of TV and strong negative loading of TC, with negative correlations between them indicating a reverse trend of their occurrence in harbour water. Factor 5 accounted for 89.5% of cumulative variance with strong positive loading of Cd, moderate of BOD and PHc and weak of silicate and phenol. All parameters correlated significantly with each other indicating their coexistence in harbour water, while the strong positive loading of cadmium suggested Cd contamination.

Post-monsoon: A total of 4 Factors explained 72% of total variance during high tide. Factor 1 explained 22% of the variance with strong positive loadings of phosphate, PP, silicates and Chl.a; moderate of pH with moderate negative loading of Hg. All parameters correlated insignificantly with each other, except for negative relationships of phosphate with silicate and Chl.a. Silicates are contributed by riverine water, so this relationship indicated the contribution of phosphate to harbour water by a source within the harbour. Similarly, a significant negative correlation of Chl.a with phosphate indicated the generation of Chl.a in brackish riverine water and its transportation to the harbour. Moreover, the strong positive loadings of PP and Chl.a in this factor suggested their dominance in harbour water. Factor 2 explained 40.6% of cumulative variance and indicated strong positive loading of TV, moderate of DO, salinity and temperature, weak of BOD, Hg and phenol followed by moderate negative loading of pheophytin and weak of TVC. TV indicated its dominance in harbour water, while, its insignificant relationships with salinity, pH, DO and BOD indicated no contribution of TV by riverine

6 water. Positive correlation of TV with phenol indicated their association in the harbour. Factor 3 explained 57% of cumulative variance and indicated strong positive loading of TVC, moderate of phenol and DO, weak of Cd and TC followed by strong negative loading of nitrite and moderate of nitrate. Strong positive loading of TVC indicated its dominance, while its positive correlation with TC indicated their association. Similarly, the positive correlation between TVC and nitrate suggested the TVC, TC and nitrate to be the contributions from Mormugao STP release. Significant positive correlation of phenol with DO and insignificant with TVC showed that phenol was not contributed by STP but its presence in the harbour water was from other waste discharges. Factor 4 explained 72% of cumulative variance and indicated strong positive loading of PHc, moderate of Pb and TC with moderate negative loadings of BOD and Cd, with no significant correlation between them, suggesting PHc contamination, which mainly occurs from boat traffic and port activities.

During low tide, altogether 4 Factors explained 92.6% of total variance. Factor 1 explained 33% of the variance with strong positive loadings of nitrate, pheophytin and Hg; moderate of salinity and Chl.a with strong negative loadings of PHc, Pb and temperature followed by moderate of TV. Strong positive loading of nitrate and Hg indicated their dominance in harbour water, while a significant negative correlation of nitrate with PHc and insignificant with Hg, salinity, Pb, and TV suggested their different source contributions. Strong negative correlations of salinity with Chl.a, pheophytin and Hg and positive with Pb, indicated that Chl.a, pheophytin and Hg were also contributed by riverine water while Pb was a contribution from the sources within the harbour water. The negative correlation of Chl.a with PHc indicated PHc and Pb to be harbour contributions, with Hg from riverine source. Factor 2 explained 56% of cumulative variance and indicated strong positive loadings of TC and TVC; moderate of Chl.a and weak of PP and Hg followed by strong negative loadings of nitrite and phosphate and a weak of DO. TVC and TC correlated positively with each other and with DO, while negatively with phosphate, nitrite and PHc, indicating their contribution from Mormugao STP. Significant positive correlation of phosphate with nitrite indicated a common source, while positive correlation of TVC with Chl.a, pheophytin and PP suggested their transportation and association in harbour water. This factor thus showed the dominance of TVC and TC with good Chl.a and PP in harbour water. Factor 3 explained 76% of cumulative variance with strong positive loadings of pH and PP; moderate of TV, DO and BOD with moderate negative loading of phosphate and weak of temperature, with no significant correlations among them, except for positive correlations of PP with DO and pH with DO, suggesting that the harbour water under the influence of oxygen rich riverine water acts as a productive zone. Factor 4 explained 92.6% cumulative variance and indicated strong positive loading of silicate; moderate of BOD and DO with strong negative loading of Cd and moderate of salinity. BOD correlated well with DO but negatively with salinity, while, silicate correlated negatively with salinity suggesting that the silicates were brought into the harbour water by well oxygenated riverine water during low tide with some oxidisable organic load. iii) Mangalore port and its harbour

In this case, the Factor analyses were applied separately to surface and bottom water data. In surface water, altogether 6 Factors explained 86.9% of total variance. Factor 1 accounted for 26% of the total variance with strong positive loadings of silicate, phosphate, nitrite, nitrate; moderate of TSS and weak of PHc; with a strong negative loading of pH. Phosphate, nitrite, nitrate and silicate correlated significantly with each other and with TSS suggesting a common source while, their negative correlations with pH, indicated a riverine input. Factor 2 explained 42% of cumulative variance with strong positive loadings of Hg and Pb, weak of pheophytin; moderate negative loading of PHc and a weak of TSS. Mercury correlated positively with lead, suggesting their association, while, their strong positive loadings indicated their dominance, suggesting Metal contamination. Factor 3 explained 58% of cumulative variance with strong positive loadings of BOD and DO, weak of Chl.a and a strong negative loading of salinity. DO and BOD correlated positively with each other and negatively with salinity, while their strong positive loadings indicated well oxygenated near shore waters, with riverine additions of organic matter. Weak correlations of Chl-a with salinity, DO and BOD suggested Chl.a to be independent of other loadings and thus indicated a Productive condition. Factor 4 explained 69% of cumulative variance and indicated strong positive loading of ammonia and moderate of pheophytin with a moderate negative loading of temperature. Neither ammonia correlated well with pheophytin nor the pheophytin or ammonia correlated well with temperature, suggesting the dominance of ammonia, indicating Ammonia contamination. Factor 5 explained 79% of the total variance and indicated strong positive loading of cadmium, moderate negative loading of Chl.a and a weak of salinity. Strong positive loading of cadmium indicated its dominance and this factor suggested Cadmium contamination. Factor 6 explained 86.9% of the total variance and indicated strong positive loading of phenol and a weak of temperature, suggesting Phenol contamination.

Altogether, 6 factors explained 82.4% of total variance in bottom water layer. Factor 1 explained 20.7% of the total variance with strong positive loadings of phosphate, silicate and TSS; weak of pheophytin and nitrite and weak negative loadings of phenol and salinity. Phosphate, silicate, nitrite and TSS correlated positively with each other, but their negative correlations with salinity, and that of pheophytin with phenol suggested riverine input of nutrients to the coastal water indicating a Riverine influence. Factor 2 explained 36.7% of cumulative 7 variance with strong positive loadings of DO, nitrate and PHc, moderate of pheophytin and a weak negative loading of salinity. Strong positive loading indicated the dominance of PHc from shipping and port activities, suggesting PHc contamination. Factor 3 explained 50.2% of cumulative variance with strong positive loading of Chl-a, moderate of pH; moderate negative loading of ammonia and a weak of cadmium. The insignificant correlations of Chl.a with pH, Cd and ammonia and significant negative correlations of Cd and ammonia with pH indicated their sources towards the shore. Strong positive loading of Chl.a showed its dominance and independent of external contaminations suggesting Productive water. Factor 4 explained 63% of cumulative variance with strong positive loading of lead, moderate of nitrite and weak negative loadings of temperature, phenol and ammonia. Lead and nitrite neither correlated well with each other nor did they correlate well with temperature, phenol and ammonia, suggesting the dominance of lead due to its strong loading thereby indicating Lead contamination. Factor 5 explained 75% of cumulative variance with strong positive loadings of BOD and cadmium and weak negative loadings of phenol and pH. Cadmium correlated positively with BOD and negatively with pH, indicating the association of cadmium with waste discharges in near shore coastal water. Such waste discharges particularly off the industrialized belt off Kulai, could contain Cd and BOD, indicating Cadmium contamination. Factor 6 explained 82.4% of cumulative variance with weak positive loading of temperature and strong negative loading of mercury.

Evaluation of changes in the quality and types of water at each location in harbour waters using overall index of pollution (OIP) values indicated that in Kandla harbour, the OIP values range from 4-5 at junction location, oil jetty and cargo jetty suggesting polluted water, while at the creek mouth, the OIP values range from 2- 4, suggesting slightly polluted water during pre- monsoon and monsoon. During post-monsoon, the OIP values at all the 4 locations vary from 2 - 3, suggesting slightly polluted water (Figure 4). In Mormugao harbour, the OIP values at 8 selected locations showed lowest values during high and low tide of the monsoon (OIP: 1.35 – 1.65), while the post-monsoon showed values from 2.51-2.75 during high and low tide and from 1.60-2.44 during low and high tide of the pre-monsoon, indicating slightly polluted water during post-monsoon, suggesting increasing contaminations in harbour water. This increase was greater during the low tide relative to high tide, because the contaminants from the upstream get accumulated in harbour water during low tide, making it slightly polluted. In Mangalore harbour and adjacent regions, the OIP values at almost all stations varied within 1.5–2, indicating acceptable water quality, except for one station off Kulai, where OIP showed an increase above 2, indicating slightly polluted water. Hydrodynamics indicated strong tidal currents in Kandla Creek, with a current speed of 0.08 to 1.7 m/s during spring, increasing to 0.09 to 2.0 m/s during ebb. The strong ebb tidal flow transport contaminated water from Kandla creek to the inner GoK. In Mormugao harbour the coastal from the north enters the estuary and flushes out of the estuary primarily towards south (schematically shown in Fig.2), with a stronger force inside the estuary, while the east-west current propagation within the estuary is mostly influenced by tides. At the mouth of the estuary, the northward flow takes a cyclonic reversal and flows again southwards without entering the estuary driving the contaminants out of the estuary into the coastal water towards south. In Mangalore harbour and Karnataka coast, the southerly currents with a maximum speed of 0.4 m/s were observed off Suratkal and Mulki. Frequent spells of current reversal with weak northerly currents also observed during April–May 2007. South to north current direction observed during November – January reverses during February with strong north to south currents from May to October (Mandal, 1996). These strong seasonal currents and the seasonal winds keep the coastal waters well mixed and aerated and help to disperse the contaminants, without significantly affecting the Chlorophyll-a.

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4. Conclusions i) Physico-chemical and biological data collected from 3 different harbour waters of Kandla, Mormugao and Mangalore over different seasons from 2002–2007 were subjected to multivariate statistics using factor analyses, water quality index and hydrodynamic modeling for understanding the impact of port activities on harbour waters. Aim was to elucidate the type and extent of contaminants, tracing of their sources as well as understanding the effect of contaminants on the quality of harbour waters and their fate. ii) Data indicated that port activities contaminated the harbour waters to a varying extent. Significantly high values of nutrients (phosphates, nitrates and ammonia), PHc and phenols observed in Kandla harbour were from anthropogenic activities related to industrial fall outs, loading and unloading activities of port, boat traffic, etc. iii) The existing strong tidal currents in Kandla harbour increase turbidity and suspended solids throughout the year, with a large increase during the monsoon season as compared to other seasons. The cumulative effect of anthropogenic and natural forces significantly affect the quality of water rendering it polluted, and hampering Chl.a and primary productivity in Creek water, more significantly during monsoon, followed by that during pre- monsoon. The strong ebb currents in the creek transport the contaminants to the inner Gulf of Kutch. iv) In Mormugao harbour, various port and other anthropogenic activities, and the associated waste discharges from Mormugao STP show year round dominance of TV, TVC and TC; heavy metals, PHc and nitrogenous substances. Their increase during the post-monsoon season deteriorates harbour water quality making it slightly polluted, affecting Chl.a generation and PP. However, the coastal inflow into the harbour region from the north and its outflow towards south, with a stronger force inside the harbour, drives the contaminants out to the coastal region. v) In Mangalore harbour, the port activities, domestic & industrial discharges into the coastal water increase heavy metal contaminations, PHc and nutrients (nitrates and ammonia) making it slightly polluted off the industrialized areas. The strong seasonal currents and seasonal winds however help in well oxygenation and dispersion of contaminants into the offshore waters, rendering it non-polluted, thereby maintaining good Chl.a generation in harbour water.

Acknowledgements

Authors express their sincere gratitude to Kandla Port Trust, and MSEZ, Mangalore and for sponsoring the work and to the Director NIO for extending the facilities to carry out the work.

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Legend to Figures

Figure 1: Location of Kandla Port Trust (KPT ) & its Facilities in Kandla creek. Figure 2: Location of Mormugao Port and the Flow Dynamics in the Zuari estuary. Figure 3: Location of Manglore Harbour, Karnataka along the west coast of India. Figure 4: Overall Index of Pollution values of Harbour waters.

Legend to Tables

Table 1: Techniques used for physico-chemical and biological parameter determination. Table2 (a-c): Factor analyses of physico- chemical and biological data of Kandla Harbour.

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Factor Loadings of Kandla Harbour Data shown as an Example:

Table-2 (a-c): Factor analysis of Physico-chemical and biological data of Kandla Harbour

a) Pre monsoon season b) Monsoon season c) Post monsoon season Variable PC1 PC2 PC3 PC4 PC5 Variable PC1 PC2 PC3 PC4 PC5 Variable PC1 PC2 PC3 PC4 PC5 PP 0.93 PHc 0.98 Temp 0.97 PO4-P 0.91 TSS 0.96 Salinity -0.95 NO2-N 0.89 Turbidity 0.9 PP 0.94 Salinity -0.84 Salinity 0.9 DO Sol. -0.88 NH3-N 0.65 0.58 DO Sol. -0.89 pH 0.87 -0.4 TSS 0.89 Phenol 0.81 Chla 0.79 SIO4-Si 0.88 SIO4-Si -0.64 0.45 NH3 0.74 Phenol -0.46 0.8 Chla 0.62 Phenol -0.57 0.53 PH 0.52 0.76 NO2-N -0.59 0.44 SIO4-Si 0.97 Turbidity 0.75 -0.47 BOD -0.55 NO2-N 0.95 Temp. 0.6 0.68 PO4-P 0.44 NO3-N 0.92 NO3-N -0.51 0.62 0.5 PP -0.94 PO4-P 0.91 AOU 0.84 Sacchi -0.89 Phaeo -0.69 0.43 Chl-a 0.77 NO3-N 0.7 0.41 AOU -0.87 DO Sol. -0.41 0.72 pH -0.87 DO -0.46 0.83 DO -0.7 NH3-N 0.84 BOD -0.52 0.6 Phaeo 0.47 0.69 AOU -0.9 Turbidity 0.96 BOD 0.86 DO -0.48 0.83 TSS -0.5 0.74 Sacchi 0.85 Temp. 0.75 Sacchi 0.82 PHc 0.41 0.66 Phaeo 0.43 0.6 PHc 0.48 Eigen 5.3 4.9 3.4 1.9 1.4 Eigen 7.1 3.1 2.2 2.1 1.5 Eigen 6.8 4.8 2.2 1.7 1.3 % Var. 26.4 24.7 16.9 9.3 6.9 % Var. 35.4 15.3 11.1 10.3 7.5 % Var. 34 23.8 10.8 8.3 6.6 Cumm% 26.4 51.1 68 77.3 84.2 Cumm% 35.4 50.7 61.7 72.1 79.6 Cumm% 34 57.8 68.7 77 84

Factor loadings: Strong (> 0.75), Moderate (0.5 - 0.75) and Weak (0.4 - 0.5).

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