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Multivariate Approaches to Find the Significant Influence and Impact of Oil Sources on Phytoplankton Ecology

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Multivariate approaches to find the significant influence and impact of oil sources on phytoplankton ecology


Although many experimental studies reveal that the impact of oil pollution on the marine environment have both positive and negative manner, nevertheless it is still questionable. Therefore, we try to understand the impact of PHC on the aquatic environment, especially on phytoplankton ecology based on the field observation of two years’ data at the oil exploratory site which consists three seasons of pre-drilling, drilling, and post-drilling from the 2012 April to 2013 December. Along with PHC sample, we have also collected the physio-chemical and biological parameters in order to conclude the correlation between other parameters. The Pearson correlation analysis has been carried out, in that we observe the PHC have astrong relationship with phytoplankton and chlorophyll-a than nutrients have. Moreover, Varimax rotated factor analysis was performed in order to understand the positive and negative loading influence in the parameters. In that, we examine that PHC has a strong positive loading on phytoplankton and chlorophyll-a than other parameters. Further, the Kaiser-Meyer-Olkin (KMO) decisive factor is followed to find out the sampling adequacy and we observe our sample variable has 0.723 % of adequacy. The box plot was plotted for all the parameters in order to understand the seasonal variation of the pattern. Moreover, based on the standard calculation, the dominant species of phytoplankton have examined, among all groups diatoms were the dominant followed by dinoflagellates, and  Skeletonema costatum, Melosira sulcata, and Ditylum brightwellii were significant dominant species during drilling period.


The coastal marine environment healthiness is a serious problem concern all over the earth (Rabalis and Nixon 2002; Smith et al.,1999; Elmgren and Larsson 2001), whereas there is serious require to understand the activities between pollution and aquatic environment health. Further, phytoplankton reveals immediate response to fast changing environmental conditions (Stolte et al., 1994; Reynolds, 1984) therefore this organism has been considered as anexcellent bio-indicator to assessseasonal, natural and humanmade changes in the coastal ecosystem (Rimet and Bouchez,2012 and Harris, 1986). Among all humanmade pollution affect most to the coastal environment are increasing since the formation of more industries through sewage, heavy metal, and oil spill in the aquatic environment. Amongst crude oil spill is often occurring and which brings a lot of troubles to the marine environments (NOAA) and petroleumhydrocarbon (PHC) are the primary constitute of crude oil (Shaik et al., 2015). Moreover, with enhancing population globally, the usage length of petroleum and it effects has been increased in many periods in the coastal region (Saramum and Wattayakorn, 2000; Tahir et al., 1997; Wattakayakorn, 2012) which can make a serious damage effect on the biological community of coastal region ( Hylland, 2006; Venkatachalapathy, 2012).  In consequence, since the beginning of 20th century there is a lot of studies have been conducted  in both experimentally and observationally (Karina et al., 2001; Gonza´ lez et al., 2009; Alan et al., 2010; Kendra et al., 2015) to better understand the impact of oil pollution in aquatic environment. Further, some studies showing a harmful effect of PHC on phytoplankton community  (Nayar et al., 2005; Parab et al., 2008). While several studies showed, it enhances the phytoplankton growth (Parsons et al., 1976; Miller, 1978; Carmen, 1995; Ozhan et al., 2014). Therefore, PHC pollution in the coastal waters is an important anxiety internationally (Mackay and Hodgkinson, 1996) and necessary to be monitored in the vulnerable area where PHC occur more than other places. Althoughabove  studies were well reported about PHC pollution in theaquaticenvironment, it still needs some attention to study the PHC pollution specifically on phytoplankton. Because these are not only a bio-indicator it also plays a significant role in global carbon system and may lead to change the carbon cycle. Since then many experimental studies have conducted to understand impact of oil pollution on phytoplankton assemblages by (Bruce et al., 1980;Kjetil et al., 1982;Tamarys et al., 2010;Huang et al., 2011)  it is still needed to understand more based on field observational studies  to conclude  the impact of  oil pollution on aquatic environment. As a result, the present investigation carried out in the oil drilling exploratory site, which is located in the Krishna-Godavari basin, Kakinada coastal waters, southeast coast of India, which gave the opportunity to analyze the PHC along with physio-chemical parameters on phytoplankton ecology during different periods of two years. Even though the physio-chemical parameters of this region are well investigated before, there was no attention paid to the monitoring of oil exploratory site and fuel sources possibleimpacts onthe phytoplankton  withother environment parameters in which particular motivated us to carry out the current exploration.

Study area

The Krishna Godavari basin (KG) is located in the Bay of Bengal along the Kakinada coast  (16°41′–16°53′ N and 82°14′–82°21′ E) in Andhra Pradesh India. It spreads more than 50000 square kilometers, the Bay also received a large volume of freshwater from several river opening into it, particularly during the monsoon (November – February) which drastically reduce the salinity in the Godavari basin up to 4PSU (Saisastry and Chandramohan 1990). Moreover, India’s second largest mangrove swamp after Sundarbans is  Coringa which located in the Southern part of KakinadaBay and covers approximately 235 km2 with  24 recorded mangrove species. This place under heavy anthropogenic activities for the reason of widespread agricultural and the Kakinada city nicknamed as Fertilizer city due to many fertilizer industries located in the nearby  locality, for instance, the (Nagarjuna Fertilizers and Chemicals) which considered one of the largest urea complexes in India. It is strategically located in the Kakinada coast, which is spread over 1130 acres. Further, several small channels have connected between the Coringa mangrove and the Bay and transport a significant quantity of organic and inorganic materials into the bay in during monsoon season.


Fig.1. Sampling site and stations at Kakkinada coast, Bay of Bengal. (RF: Reference Stations)

Sampling and methods.

The field sampling was carried out in 11 locations from 2012 (April) – 2013 (December) covering two pre-drilling, drilling and post-drilling seasons. Each station consisted three places  sub-surface (1 m), mid-depth (25 m) and near thebottom (65m) from all the stations. The water samples were collected using pre-cleaned 5L Niskin samplers (Hydrobios-Kiel) during pre-drilling (December) drilling (April) and post drilling period (August) at the exploratory site. The vertical salinity and water temperature were measured by using Portable Conductivity–Temperature–Depth profiler (CTD; SBE-19 plus, Sea-Bird Electronics, USA). Further, for water quality analysis 1 L of water samples was collected in pre-acid cleaned plastic Tarsion containers and kept closed till analysis following standard methods (Grasshoff et al., 1999). The (DO) Dissolved Oxygen and (BOD) Biological Oxygen Demand were analyzed by standard Winkler’s titration method (Winkler, 1888). The pH measured onboard using the calibrated pH meter. The Chlorophyll-a (Chl-a) and pheophytin were analyzedwere done by calibrated Turner designed fluorometer (UNESCO, 1994) and based on the standard protocol (Strickland & Parsons, 1984).For Petroleumhydrocarbon (PHC) were analyzed and expressed in terms of µg/L following the methodology of IOC-UNESCO manual (UNESCO, 1984). For phytoplankton composition, 1L of water samples were collected from all the stations and  different depths (surface, mid-depth and near thebottom) and was fixed with Logol’s  iodine solution and by following siphoning procedure the sample concentrated from 1L to 10 ml (Utermohl, 1958). From this one ml of sample was examined microscopically (Olympus XI 51) in a Sedgewick-Rafter counting cell at 60 x magnification by following the procedure (Tomas, 1997). In addition, as much as achievablethe phytoplankton groups were identified to the species level, though some were identified to genera.

Data analysis

Factor analysis (FA) is a mathematical approach which finds the relationship where variables are maximally correlated with one another. Further (Wunderlin et al., 2001) have said that FA which consists a Principal Component Analysis (PCA) is the most powerful and common technique which reduce the large data set without loss of information. FA takes variables in a correlation matrix way as well as reset them in a way to better explain the construction of the underlying, system which produced data. Besides,initially, the FA was done by principal components extraction method. After that, the factor loading matrix is rotated to a orthogonal simple arrangement according to the Varimax rotation technique. The Kaiser-Meyer-Olkin (KMO) decisive factor is followed to find out the sampling adequacy. The factors which best express the variance of the investigated data (eigenvalue > 1) can be taken for further interpretation analysis.

The range, mean and standard deviation of the physio-chemical and biological variables in the from the eleven stations are given as season wise in the table(1). The bivariate Pearson Correlation was used in (SPSS.18) to understand the sample correlation coefficient, r, which measures the strength and relations of among variables given in the table (2). The box-and-whisker plots also called box plot are using (SPSS.18) to explain the physio-chemical and biological variables in a simple way of representing data on a plot in which rectangle is drawn to represent the mean and median values of the present data set fig(1). Moreover, to determine the dominant species in the phytoplankton community, the following formula have been used

Yi=(Ni/N) × f i

In this Yi is the dominant of species ,Ni is the number of individual species i in all stations (surface, mid-depth, and bottom). Further, N is the number of individual of all species in all stations, and fi is the frequency stations where ioccur. However according to (Yang et al., 1999; Lin et al., 2011; Lee et al., 2009) species of plankton with Y values of greater than or equal to 0.02 were defined as dominant species.

Results and Discussions.

Physiochemical parameters.

The range , mean and standard deviation of all physio-chemical as well as biological parameters, are given in three seasons in this table.

Pre-drilling (monsoon) Drilling  (post monsoon) Post drilling (pre-monsoon)
Range Mean ±  Std Range Mean ±  Std Range Mean ±  Std
Water Temperature (°C) 20.2 – 25.3 23.2 ± 1.10 23.1 – 34.89 28.44 ± 3.31 21.48 – 28.40 24.77 ± 1.54
Salinity (PSU) 24.38 – 35.01 33.19 ± 1.50 20.08 – 34.89 31.49 ± 3.02 23.54 – 35.32 30.85 ± 2.74
pH 7.10 – 8.56 7.86 ± 0.40 7.5 – 8.6 8.16 ± 0.26 7.23 – 8.80 7.98 ± 0.30
DO (mg/L) 1.99 – 5.92 4.13 ± .83 1.45  – 6.54 4.97 ± 0.86 1.89 – 6.07 4.18 ± 0.94
BOD (mg/L) .381 – 4.87 1.06 ± 1.02 0.33 – 2.35 0.89 ± 0.51 0.372 – 2.54 1.08 ±    0.61
Nitrite (µmol/L) BDL – 1.67 0.45 ± 0.41 0.1 – 1.43 0.41 ± 0.38 0.01 – 0.65 0.14 ± 0.15
Nitrate (µmol/L) BDL – 3.24 0.93 ± 0.80 0.2 – 2.71 1.33 ± 0.72 0.14 – 1.70 1.28 ± 0.57
Ammonia (µmol/L) 0.14 – 1.92 0.62 ± 0.53 0.2 – 1.67 0.57 ± 0.48 BDL – 1.70 0.58 ± 0.42
IP (µmol/L) 0.04 – 2.13 0.91 ± 0.68 0.4 – 1.35 0.47 ± 0.36 0.1 – 1.43 0.33 ± 0.36
Silicate (µmol/L) 1.73 – 17.3 8.32 ± 3.84 1.93 – 21.63 8.92 ± 4.36 2.41 – 21.17 8.01 ± 4.52
PHC (µg/L) 0.23 – 2.20 0.98 ± 0.40 1.03 – 9.63 4.56 ± 1.84 0.12 – 4.63 2.25 ± 1.09
Chlorophyll-(mg/m3) 0.04 – 1.83 0.78 ±  0.41 0.40 – 2.88 1.65 ± 0.59 0.06 – 1.89 0.93 ± 0.45
Phaeophytin (mg/m3) 0.19 – 0.72 0.21 ± 0.19 0.022 – 0.98 0.56 ± 0.20 0.015 – 0.98 0.34 ± 0.33
Phytoplankton (Nos/L) 2174 – 9030 518 ± 158 3870 – 16748 9885 ± 3431 4328 – 11300 7151 ± 1678

Table: 1.         Season wise, Range, mean ± SD of Environmental variables recorded during the year 2012-2014

The water temperature is showing logical differences as changing the season. The highest water temperature (34.89 °C) were recorded during drilling period as it is a post monsoon period with the mean value of (28.44 ± 3.31°C). There are no huge  changes in salinity system among seasons, more interestingly we recorded high salinity in monsoon (35.01 PSU) than summer (34.89 PSU), even though the study area received a lot of freshwater influence each year.   For the reason of this during monsoon  the cooled high saline surface waters can become dense enough to sink in bottom when it receives a large amount of freshwater influence, due to dissolve saline in the sea water which is  denser than freshwater, which give buoyancy to fresh water float in surface than deep sea water, as we collected sample from the depth of (65m) where there is less possibility of freshwater influence to reduce the salinity as in surface water, this is the reason beyond the high salinity in monsoon in the study area. A low pH was recorded in the monsoon period which might be due to freshwater influence, no variation was observed in the mean of pH between the seasons which may be the reason of buffering capacity of the seawater (Riley and Chester 1971; Moresco et al., 2012). The high concentration of Dissolved Oxygen (DO) was recorded (4.97 ± 0.86) during post monsoon, due to high phytoplankton production in the summer season with good light availability enhance the DO level because in the ocean phytoplankton produces oxygen as a byproduct of the photosynthesis process. The high level of BOD were recorded in the monsoon period (1.06 ± 1.02) while the low level (0.89 ± 0.51) were observed  in post monsoon due to freshwater discharging which brings organic debris that led enhance the microbial metabolic process and subsequently affects the BOD values (Dilip Kumar Jha et al., 2015). The nutrients like nitrite, ammonia and inorganic phosphate mean were recorded high in monsoon season, which assumes that nutrients are brought to the coastal bays by rivers under certain conditions (Xu, 1989) while the nitrate and silicate were recorded high mean in post monsoon. The highest PHC mean (4.56 ± 1.84) have been registered  during post monsoon period than other seasons due to oil drilling activity in post monsoon season, (Shaik et al., 2015) recorded similar ranges of PHC 1.49 to 17.60 µg/L when compared to our records in the Kakinada coast. Also, he indicated that PHC values recorded during our study revealed a fair range of values when compared to the range found in other Asian countries: China, Pakistan, Malaysia, and Thailand. Nevertheless, (Chouksey, et al. 2004) reported that PHC values within the range of 2.9-39.9 µg/L from off Mumbai basin (west coast of India) surface waters which are faintly high value to our observed range in India. The high chlorophyll-a and pheophytin were recorded high in the post monsoon as well as low in the monsoon, likewise (Redekar et al., 2000) recorded high chlorophyll-a in post monsoon lower value in monsoon, the main  reason of this is influenced by wind-driven mixing of seawater column which brought bottom bounded nutrients to the surface for the flourishment of phytoplankton in the Bay of Bengal (Thangaradjou et al., 2013).

Phytoplankton species composition

In our investigation as we observe Diatoms are the dominant group than other groups. Moreover, (Madhupratap et al.,2003; Paul et al., 2008; Naik et al., 2011) described Bacillariophyceae also called diatoms are the dominating community of the Bay of Bengal coastal region our results are also supporting in this view by indicating a dominant of diatoms than other groups of phytoplankton.Besides, overall an 89 species were observed during the entire study period, among them Coscinodiscus radiates, Pleurosigma elongatum and Podolampas palmipeswere the dominantin the pre-drilling (monsoon) while during drilling (post-monsoon) Skeletonema costatum, Melosira sulcata, Rhizosolenia setigera, Bacteriastrum delicatulum, Ditylum brightwelliiare the some of the  among dominant species. Further, in post drilling (pre-monsoon) Biddulphia mobiliensis,Navicula directa, Cylindrotheca closteriumand Ceratium fususwere the dominant species. Most interestingly,Pleurosigma elongatum,Thalassionema nitzschiodes, and Thalassiosira subtilisarethecommon dominant species in all the seasons. Remarkably,In all of these assemblage diatoms are the dominant than the other species, as they can be tolerating the changing hydrographical conditions to grow more (Mani, 1992; Kannan and Vasantha 1992; Rajasekar et al.,  2000).

S.No Phytoplankton species composition PRD DRI POD
1 Melosira sulcata * **** ***
2 Cyclotella sp. * ** **
3 Skeletonema costatum *** ** *
4 Skeletonema pseudocostatum ** **** ***
5 Thalassiosira subtilis **** **** ****
6 Thalassiosira decipiens ** *** **
7 Coscinodiscus marginatus ** ** *
8 Coscinodiscus rothii ** * **
9 Coscinodiscus concinnus *** ** **
10 Coscinodiscus radiatus **** ** *
11 Stephanopyxis palmeriana * * **
12 Lauderia sp. ** * *
13 Lauderia annulata ** **
14 Rhizosolenia sp. * ** **
15 Rhizosolenia simplex ** * **
16 Rhizosolenia imbricata * *** ***
17 Rhizosolenia setigera * **** **
18 Rhizosolenia calcaravis * ***
19 Bacteriastrum sp. *** * **
20 Bacteriastrum delicatulum ** **** **
21 Bacteriastrum hyalinum * **** **
22 Bacteriastrum furcatum ** *** *
23 Chaetoceros coarctatus ** *
24 Chaetoceros eibenii ** *** **
25 Guinardia striata ** * *
26 Guinardia flaccida * * **
27 Guinardia indicus **
28 Ditylum brightwellii ** **** ***
29 Biddulphia sinensis ** **** *
30 Biddulphia mobiliensis *** **** ****
31 Cerataulina bergonii * ** **
32 Hemialus indicus ** *** *
33 Hemiaulus sinensis ** *
34 Hemiaulus hauckii **
35 Asterionella japonica *** **** **
36 Thalassionema nitzschioides **** **** ****
37 Thalassiothrix longissima
38 Grammatophora marina ** *** **
39 Leptocylindrus danicus * ** **
40 Leptocylindrus minimus *
41 Licomorpha sp. *
42 Climacosphenia moniligera ** ** *
43 Thallassiothrix bacillare ** *** **
44 Thallassiothrix frauenfeldii * **** **
45 Gyrosigma balticum *** * *
46 Pleurosigma sp. *** *** ***
47 Pleurosigma elongatum **** **** ****
48 Pleurosigma normanii *** **** ***
49 Phaeodactylum tricornutum *** **
50 Navicula sp. ** **** ***
51 Navicula directa ** **** ****
52 Navicula membranacea * *** **
53 Navicula septentrionalis ** *
54 Navicula angusta * *
55 Navicula distans ** ***
56 Amphiprora gigantean ** **** **
57 Amphora sp. *** * *
58 Amphora hyalina *** ** ***
59 Cylindrotheca closterium *** **** ****
60 cylindrotheca fusiformis * ** **
61 Nitzschia angularis * ** *
62 Nitzschia longissima ** **** ***
63 Nitzschia pungens ** *
64 Nitzschia delicatissima, ** ** *
65 Nitzschia seriata * *** **
66 Synedra undulata ** ** *
67 Surirella ovalis ** **** **
68 Dinophysis hastata ** * **
69 Dinophysis caudata ** * ***
70 Dinophysis sp. * * **
71 Prorocentrum micans **** **** ****
72 Prorocentrum triestinum * *** **
73 Prorocentrum gracile ** *** ***
74 Dinophysis caudata * **** *
75 Dinophysis hastata ***
76 Dinophysis rotundata * **
77 Noctiluca scintillans * ** *
78 Ceratium extensum **
79 Ceratium fusus * ** ***
80 Ceratium tripos ** *** **
81 Ceratium trichoceros *** **** ****
82 Gonyaulax sp. * *
83 Oxytoxum sp. ** **** **
84 Pyrophacus sp. ** *** **
85 Podolampas palmipes **** *
86 Protoperidinium sp. *** ****
87 Protoperidinium oceanicum ** ** *
88 Protoperidinium divergens ** *** **
89 Oscillatoria limosa *** *

Table: 2. Species composition of phytoplankton recorded in the oil exploratory site (2012-2014).  The minus represents no recording and (*) indicate <1 % contribution, ** indicates 1-3 % of contribution, *** indicates 3-5 % of contribution **** indicates 5 – 10 % of contribution in the population structure (Jagadeesan et al.,2013) and abbreviations: PRD –Pre-drilling, DRI –Drilling  and POD – Post drilling .

Correlation analysis

Correlation analysis was made for the present study to assess a possible association between physio-chemical and biological parameters. Pearson’s Correlation coefficients are estimated by using SPSS package.  It demonstrates the WT is positively correlated with almost all the physio-chemical parameters, especially a strong positive correlation with Chl-a, due to a higher temperature, which increases the phytoplankton growth to ultimately shows a higher concentration of  Chl-a in the open sea (Klapper, 2001). While salinity has a negative correlation with IP which believes the signifying nutrients sources from freshwater origin into coastal waters (Todd et al., 2008; Vijayakumar et al., 2014; Shanthi et al., 2014). Similar kind of observation was reported by (Saisastry and Chandramohan 1990) in the Godavari basin where they observed a 4 PSU reduction in salinity due to freshwater inflow during the monsoon period. Moreover, the negative correlation of DO with BOD, ammonia, and IP showed organic pollution could be attributable to rainfall and anthropogenic activities (Sundaray et al.,2009). There is a positive correlation between BOD and ammonia due to an input of inorganic compounds of land runoff into the Bay of Bengal. Furthermore, there is significant negative correlation was observed between chlorophyll-a and nutrients sources: nitrite, ammonia and IP, this may be favor by uptake and absorption of inorganic nutrients by phytoplankton, which enhanced the higher growth of phytoplankton and resulted in lower nutrient concentration (Yuangen Yang et al., 2008). Likewise silicate concentration hasshown a positive correlation with CHL-a, and this is due to the dominant diatoms species we observe, (Sarthou et al., 2005) have said that diatoms growth in the coastal environment canstraightly reply on the silicate concentration, because diatoms are the group which uses silicate to built their cell wall.Most interestingly the PHC have shown  a strong significant correlation with phytoplankton, chlorophyll, and pheophytin when comparing to most renowned limiting factor of nutrients, salinity and water temperature for the phytoplankton growth, however, (Koray Ozhan et., 2014) said that oil sources stimulated the phytoplankton, although with a low salinity water, and the phytoplankton small cells are more tolerant to oil products in lower level of concentration. For example, (Gonzales and colleagues 2009) report that small diatoms less  than 20 mm) were not only more tolerant than bigger diatoms, but their growth was stimulated under a lower concentration of crude oil products. Moreover, (Huang et al., 2011) reported that the relatively smaller phytoplankton Skeletonema coastatum and Melosirasp became the dominant species and showed greater tolerance to crude oil products than did the larger phytoplankton. Moreover, (Jiyalal Ram et al., 2013; Gordon and Prouse 1973; Parsons 1984) have suggested that lower level of PHC concentration have stimulated planktonic photosynthesis. According to (Yi –Jun Huang et al., 2011) the high concentrations (≥2.28 mg l−1) of oil pollution would greatly restrain the phytoplankton growth, decreased chlorophyll-a content and cell density, whereas, the lower concentration (≤1.21 mg l−1) did not restrain the growth but rather promoted the phytoplankton growth. As we observe the maximum concentration of PHC in our study maximum was (9.63 µg/L) which is lower than  (≤1.21 mg l−1) concentration, it is therefore understood that the growth of phytoplankton stimulated andthere is no astonishing that PHC hasa strong correlation with phytoplankton, chlorophyll-a, and pheophytin. And finally the phytoplankton have a strong positive relation with Chl-a reveals that micro-phytoplankton has main contributing contained by the phytoplankton community; also the phytoplankton has a positive correlation with DO and negative relation with IP have been  recorded.  It means that DO is a promoting factor and phosphate is the  limiting factors for the phytoplankton population.

WT 1
SAL .166* 1
pH .388** 0.106 1
DO .444** 0.008 .239** 1
BOD -0.139 0.028 -0.03 -0.004 1
NO2 .160* 0.049 0.084 -0.017 -.140* 1
NO3 .176* 0.115 .147* .203** 0.085 .340** 1
NH3 -.211** 0.037 -0.106 -0.114 .379** -0.094 -0.054 1
IP -0.123 -0.094 -0.088 -0.094 -.258** .491** -0.032 -0.075 1
SIL .409** .283** .235** .164* -0.092 .425** .386** -0.039 .255** 1
PHC .484** 0.015 .228** .416** 0.039 -.144* .167* -.192** -.286** -0.047 1
CHL .578** .179* .268** .421** 0.034 -0.033 .180* -.170* -.156* .209** .776** 1
PHEO .371** -0.097 .181* .258** 0.111 0.013 .175* -.154* 0.007 0.105 .560** .436** 1
PHYTO .403** .172* .227** .318** 0.032 -.210** .142* -.175* -377** 0.073 .661** .697** .284** 1
Table:3.Corelation analysis between environmental parameters. **. Correlation is significant at the 0.01 level . *. Correlationis significant at the 0.05 level

The measured data of physio-chemical and biological parameters from the current study iswerefitted into box plot to know the seasonal variation pattern are present in the Fig (1). In this notably, the water temperature, dissolved oxygen (DO), PHC, and Chlorophyll-a were higher during drilling period (post monsoon).While, nitrite, nitrate, and IP have recorded high during pre-drilling (monsoon).Besides, some parameters are shown prominent variation among seasons for silicate, BOD, and pH during the study period.

Fig.: 2. Seasonal variations box plots for selected parameters (DO: Dissolved Oxygen, PHC: Petroleum Hydro Carbon,  IP :  Inorganic Phosphate ). In each box plot, the  central point represents the median, the whisker indicates the range. The outliers values are given as dots.

Factor analysis

Factor analysis  was carried out on the presence data set of physiochemical and biological parameters to understand the compositional patterns between the parameters and their influence.Kaisere–Meyere–Olkin (KMO) values was undertaken to measure the sampling adequacy which indicates the proportion of variance. In this current study, KMO is 0.723 showing  that PCA can achieve a significant reduction of the dimensionality of the original dataset (Wu et al., 2010). The (Eigenvalue> 1) explained 62.60% of total variance of the dataset; four factors were taken for the result elucidation.

The first vari factor, with an eigenvalue of 3.88, explained 27.7% of the total variance. It is clearly dominated by  PHC, Chl-a,  and phytoplankton, pheophytin ,water temperature and  DO which exhibited significant positive loadings ,along with reasonable negative loading of ammonia, IP, and nitrite. It showed the common source of phytoplankton properties have reasonable negative loading with nutrients, it is, therefore, clear that the main source of phytoplankton uptake is PHC in the particular season and the source of PHC study region is either oil splitting or leaking in the oil exploratory site during drilling season.

The Second (PC2) varifactor explained 15.9 % variance with 2.23 % of the eigenvalue. It indicated that nitrite has strong positive loading with a moderate positive loading of nitrate, IP, and silicate. It showed general and organic loads of nutrients sources NO3-N sources from the riverine freshwater direction. The most likely causes of this anthropogenic sources of water discharge into the coastal water, mainly from fertilizer industry (Nirmal Kumar et al., 2012) from the Kakinada-Krishna Godavariestuary, this area is under heavy anthropogenic activities due to an extensive proximity of several fertilizer industries ( Shaik et al., 2015). Hence it is clear that the main nutrient source is land run off to the study area, (Balliarsingh et al., 2015 and Prasanna Kumar et al.,) have recorded high nutrient loading in the Bay of Bengal from river influx due to monsoonal precipitation. In our study area Krishna-Godavari basin, Kakinada (Sooria et al., 2011) recorded relatively high nutrient content mainly  nitrate during monsoon due to excess run off by Krishna Godavari river into the Bay of Bengal. The third (PC3) have revealed that BOD has strong positive loading with a moderate loading of ammonia, which makes clear the normal biological processes due to phytoplankton and microorganism (Biraja Kumar Sahu et al., 2013).Besides  (PC4) describe with the strong positive loading of salinity with a moderate negative load of phytoplankton.

Parameters PC PC 2 PC 3 PC4 Communality
Water temperature .697 .244 -0.2 .237 .642
Salinity .06 .077 .054 .82 .684
pH .405 .181 -.078 .273 .277
DO .601 .109 -.015 -.013 .373
BOD .064 -.082 .854 -.065 .745
Nitrite -.74 .826 -.122 -.026 .703
Nitrate .298 .546 .277 .115 .478
Ammonia -.257 -.02 .718 .082 .589
Silicate .188 .718 -.024 .398 .711
IP -.295 .616 -.309 -.302 .652
PHC .879 -.178 -.033 -.117 .818
Chlorophyll-a .857 -.008 -.033 .123 .751
Pheophytin .656 .163 .096 -.425 .647
Phytoplankton .749 -.269 -.023 .246 .694
Eigen values 3.883 .234 1.462 1.186
% of Variance 27.735 15.957 10.439 8.475
Cumulative % 27.735 43.692 54.131 62.606
KMO Sampling adequacy 0.723

Table:3. Varimax rotated factor loadings and commonality of Environmental parameters recorded at Krishna-Godavari basin, Kakinada Bay, east coast India.   Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. A rotation coverged in 7 lierations.


We conclude, by Pearson correlation and factor analysis, it is revealed that the PHC have more interaction with phytoplankton than other physio-chemical parameters in a particular season. Further, as recorded earlier we observe Skeletonema costatumand  Melosira sulcataas a dominant species in drilling season which are the evidence of PHC influence of phytoplankton species richness. Therefore it is assumed that phytoplankton has fascinated by PHC rather than nutrients,  particularly when their concentration is < 12 µg/L, also it is presumed that the PHC has the power to stimulate phytoplankton metabolism to grow more and fast reproduction than nutrients does.


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