Resilience and bioresponse of two marine algae to petroleum fuel pollution

ABSTRACT

Petroleum pollution can upset the ecological stability and health of a marine ecosystem. The physiological, biochemical and morphological responses of Nannochloropsis oculata and Porphyridium cruentum to three different petroleum fuels, kerosene, diesel and gasoline were examined. The effect of water soluble fractions (WSFs) of the three petroleum fuels was investigated at 0%, 25%, 50% and 100%. The growth response of both species was monitored optically every two days for 14 days using a 721 visible spectrophotometer. Chlorophyll a, morphology and antioxidant enzyme activity of the algae were examined using prescribed methods. In both algae, minimum growth was obtained with 100% WSF of the petroleum fuels. In N. oculata, there was growth stimulation and the maximum growth was obtained at different concentrations (25% and 50%) depending on the test fuels. The maximum growth of P. cruentum was obtained at 10% WSF in all the fuels. ANOVA (p < 0.05) showed significant differences in algal growth with changes in concentration of the test fuels. Unpaired t-tests showed that in all the fuels, there was a significant difference (p < 0.05) between the growth of N. oculata and P. cruentum. N. oculata showed more tolerance to petroleum fuel pollution than P. cruentum. Morphological studies showed that petroleum fuel pollution altered the size of N. oculata and caused severe cell clumping in P. cruentum. Antioxidant concentration assessment showed that whereas N. oculata produced high levels of superoxide dismutase, catalase and peroxidase, P. cruentum produced high levels of superoxide dismutase but was less efficient in catalase and peroxidase production. Clumping and inefficiency in antioxidant production affect the physiological and biochemical response of algae. This study showed that the severity of petroleum fuel pollution is reflected in physiological, morphological, and biochemical responses of the test algae. This research provides baseline information that can be used for the evaluation of the effect of petroleum fuel pollution in marine environment and policy making.

KEYWORDS:

• Bio-response
• environment
• enzymes
• marine algae
• petroleum
• pollution

Introduction

Environmental pollution is one of the most important global problems (Reyes, Schueftan, Ruiz, & González, 2018), one source of which is oil spillage. Petroleum is a dark oily liquid formed from the remains of ancient plant and animals (mostly zoo- and phytoplankton) under conditions of high temperature and pressure (Hsu & Robinson, 2006). It is a highly toxic mixture of different hydrocarbons distilled to make fuels and more volatile fractions of higher economic value (Perez, Fernandez, & Beiras, 2010; Sattar et al., 2022). When petroleum spillage occurs in a water body it spreads as a result of diffusion (Keramea, Spanoudaki, Zodiatis, Gikas, & Sylaios, 2021), while the volatile gaseous components evaporate into the air (Keramea, Spanoudaki, Zodiatis, Gikas, & Sylaios, 2021). Factors such as time, temperature and water agitation caused by waves, wind and current cause a part of the liquid components to dissolve and potentially to be absorbed by aquatic organisms (Lu et al., 2021). The dissolved fraction is known as the water soluble fraction (WSF). Some of the oil is oxidized, some undergoes bacterial changes and eventually sinks to the bottom and some is mixed with sediment with serious consequences (Martínez-Gómez et al., 2010). Petroleum pollution has been noted to pose severe ecological issues and imbalances in aquatic environments (Asif, Chen, An, & Dong, 2022); the severity depends on the type of oil, extent and time (season and weather) of the spillage, type of shoreline, and the waves and tides in the area of the incidence (Martínez-Gómez et al., 2010). The volatile components are well known to affect aerial life while the dissolution of the less volatile components results in emulsified water which affects aquatic organisms (Akpofure, Efere, & Ayawei, 2000).

Spillage of petroleum products, especially diesel and gasoline, is a problem in marine ecosystems (López-Rodas et al., 2009). Petroleum pollution is mostly generated from anthropogenic processes (Atlas & Bragg, 2009; Haghighat, Akhavan, Mazaheri Assadi, & Pasdar, 2008), due to its daily usage in marine transportation, but oil thefts, illegal refineries and marine transportation also contribute to petroleum pollution in the marine environment. When petroleum spillage occurs in inland water bodies, this often drains into the marine environment and may affect ecological stability. Petroleum pollution is capable of affecting the composition and sustainability of plankton and other aquatic plants and animals (Sullivan & Currin, 2000). The annual input of petroleum products into the marine environment is estimated to be between 1.1 and 7.2 million metric tons (Unite Nations Environmental Program, 2006). The actual toxic dose of petroleum hydrocarbons for plankton is closely related to the amount of dissolved non-volatile components and the bioavailability of the hydrocarbons (Ito et al., 2013). It is assumed that the WSF is the most environmentally harmful fraction because it is directly available for uptake by organism (Nayar, Gohb, & Choua, 2004). There have been several investigations into the effects of petroleum fuels on algae (Abdul Hameed & Al Obaidy, 2014; Fabregas, Herrero, & Veiga, 2021; Jiang et al., 2022; Nayar, Gohb, & Choua, 2004; Perez, Fernandez, & Beiras, 2010). Different studies have reported both stimulatory and inhibitory effects of petroleum pollution on algae depending on the fuel type and composition, type of algae and environmental conditions. However, although most studies have been focused on freshwater algae and inland water bodies, petroleum pollution has its largest impact in marine ecosystems.

Algae are the base of the food chain in aquatic ecosystems as the primary producers (Jiang et al., 2022). The unicellular marine algae Nannochloropsis oculata and Porphyridium cruentum are useful as bioindicators in the study of the extent and severity of environmental pollution. They are rich source of nutrients for higher organisms and can play a major role in ecosystem stability (Chislock, Doster, Zitomer, & Wilson, 2013; Razaghi, Godhe, & Albers, 2014). This study focuses on the determination of the resilience and bioresponse of Nannochloropsis oculata and Porphyridium cruentum to petroleum fuel pollution, specifically the WSF which is often readily absorbed by phytoplankton (Kadiri & Azomani, 2000). In the aquatic environment, oil spills may cause inhibition of plankton growth, shifts in population structure, affecting the abundance, diversity and distribution of species (Ko & Day, 2004; Martínez-Gómez et al., 2010; Peeb et al., 2022), which will in turn impact other organisms that depend on plankton for food (Akpoghelie, Igbuku, & Osharechiren, 2021). It is therefore imperative to examine the resilience and response of algae to petroleum fuel pollution.

Materials and methods

Procedure for the preparation of water soluble fraction of petroleum fuels

Gasoline, kerosene and diesel were the test fuels investigated in this study. The procedure of Phaterpekar & Ansari (2000) was followed to obtain the 100% stock solution of the WSF of each of the fuels. The stock solution was prepared by mixing 1 part of fuel to 9 parts of seawater. The mixture was stirred using a magnetic stirrer hotplate for 24 h. The solution was allowed to stand for 24 h in a separating funnel after which the aqueous phase was separated and regarded as 100% stock solution of WSF. The seawater used for the preparation of media was collected from the Atlantic Ocean in Delta State, Nigeria. It was filter-sterilized through 0.2 µm omnipore membrane filters (Millipore). F/2 medium was added to the filter-sterilized seawater in a ratio of 1:1000 ml of seawater (Kadiri & Azomani, 2000).

Table 1 shows the physico-chemical condition of the seawater. Salinity, pH, conductivity and TDS were determined using APERA salinity metre (manufactured by APERA Instruments LLC, Model number: salt20), HANNA pH metre (manufactured by Hanna Instruments Inc., Model number: HI96107), ATC conductivity metre (manufactured by Hanna Instrument Inc., HI98304P) and HANNA TDS metre (manufactured by Hanna Instrument Inc., Model number: HI98301), respectively.

Table 1. Physico-chemical condition of the seawater used for the preparation WSF of the fuels and culture medium.

Parameter	Concentration
pH	7.69
Salinity	23.5 mg l^−1
Conductivity	36 050 µS cm^−1
Total dissolved solid (TDS)	19 085 mg l^−1

Procedure for the preparation of culture medium

F/2 medium (Guillard, 1975) was used. The F/2 medium was obtained from (Algae Research Supply, Carlsbad, California, USA). The natural seawater, which was collected from the Atlantic Ocean, Delta State, Nigeria was filter-sterilized through 0.2 µm omnipore membrane filters (Millipore) and used as diluent water in the preparation of the culture medium. One ml of f/2 medium was added to filter-sterilized seawater to make up 1 l of culture medium.

Algal cultures

A unialgal culture of Nannochloropsis oculata (Droop) D.J.Hibberd was obtained from Algae Research Supply. This small (2–5 µm in diameter) non-motile species of tropical green algae grows in both marine and brackish environments (Assaf-Sukenik, 1989). Nannochloropsis oculata is considered promising for industrial applications, used as an energy-rich food source for fish larvae and rotifers (Kandilian et al., 2013).

A unialgal culture of the unicellular red alga Porphyridium cruentum (S.F.Gray) Nageli was also obtained from Algae Research Supply. Like N. oculata, this species occurs in both marine and brackish tropical environments, it is spherical (3–12 µm in diameter) and non-motile (Assaf-Sukenik, 1989). P. cruentum is a good source of lipids, carbohydrates (up to 57% of biomass) and pigments for a broad range of industrial applications and is a good source of energy for aquatic organisms (Razaghi, Godhe, & Albers, 2014).

Experimental setup

The experimental cultures were grown inside the botany screen house of the Department of Plant Biology, University of Benin, Benin City, Nigeria in natural conditions (average light intensity: 40 μmol m⁻² s⁻¹, temperature of the screen house maintained at 28°C). A 2 ml of each algal culture was used to inoculate each 200 ml experimental sample and these were kept in 500 ml cylindrical culture vessels, corked using cotton wool. The cultures were agitated every morning and evening to improve circulation, aeration and to prevent clumping (Kadiri & Azomani, 2000). Stock solutions (100% WSF) were diluted using 0.2 µm filter-sterilized seawater (culture medium) to 25% and 50% WSF, while the control was 0% WSF (n = 3). The growth of test algae was monitored optically using a 721 visible spectrophotometer (produced by PEC MEDICAL, USA) at 750 nm (Bianchini, Vieira, & Toledo, 1985; Kadiri & Azomani, 2000) every 2 d for 14 d (see eq. 1),

eq. 1 $\mathrm{G}\mathrm{r}\mathrm{o}\mathrm{w}\mathrm{t}\mathrm{h}\left(\mathrm{A}\mathrm{b}{\mathrm{s}}_{750\mathrm{n}\mathrm{m}}\right)=\mathrm{A}\mathrm{b}{\mathrm{s}}_{\mathrm{t}}-\mathrm{A}\mathrm{b}{\mathrm{s}}_0$eq. 1

where Abs_t = Abs (time), Abs_o = Abs (initial) Abs = Absorbance.

Morphological studies

Morphology of all test algae in the treatments was examined at the end of the experiment (day 14). Cells were examined and photographed using Olympus Trinocular Microscope at Magnification × 100. The sizes (length and width) of the algae were examined using ocular micrometry. The five largest cells were identified and measured, and five smallest cells were also identified and measured in each mount. The largest and smallest lengths and widths were noted to give the full size range of the cells.

Chlorophyll a determination

This was determined using the method of Talling (1974). This procedure involved filtration, extraction, homogenization and spectrophotometry. The sample was filtered using Whatman No. 1 filter paper and extracted using 90% acetone at room temperature in the dark to avoid exposure to high light intensity. The sample was left for 24 h and reading was taken using 721 visible spectrophotometer at 630 nm, 645 nm, and 665 nm,

eq. 2 $\mathrm{C}\mathrm{h}\mathrm{l}a(\mathrm{m}\mathrm{g}{\mathrm{l}}^{-1})=\frac{11.6\mathrm{A}\mathrm{b}{\mathrm{s}}_{665}-1.31\mathrm{A}\mathrm{b}{\mathrm{s}}_{645}-0.14\mathrm{A}\mathrm{b}{\mathrm{s}}_{630}}{\mathrm{V}\times \mathrm{I}}\times \mathrm{v}$eq. 2

where v = volume of acetone (extractor), V = volume of sample filtered, I = path length of the cuvette (cm).

Peroxidase determination (GPx)

Peroxidase concentration was determined spectrophotometrically using the method of Kim & Yoo (1996). The formation of tetraguaiacol was estimated at 470 nm. The reaction mixture (3.0 ml) contained 0.9 ml of 0.1 M phosphate buffer (pH 6.0), 1.0 ml of 15 mM guaiacol, 0.1 ml of test sample and 1.0 ml of 3 mM hydrogen peroxide. One unit (U) of peroxidase is the amount (concentration) of enzyme that can convert 1 μmol of substrate into product (tetraguaiacol) per time (1 min). The activity of peroxidase was estimated as shown below.

eq. 3 $\mathrm{G}\mathrm{P}\mathrm{x}(\mathrm{U}\mathrm{m}{\mathrm{l}}^{-1})=\left(\mathrm{\Delta }\mathrm{O}\mathrm{D}/\mathrm{m}\times \mathrm{T}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l}\mathrm{A}\mathrm{s}\mathrm{s}\mathrm{a}\mathrm{y}\mathrm{V}\mathrm{o}\mathrm{l}\mathrm{u}\mathrm{m}\mathrm{e}\right)/\text{break}\left(\mathrm{E}\times \mathrm{I}\times \mathrm{E}\mathrm{n}\mathrm{z}\mathrm{y}\mathrm{m}\mathrm{e}\mathrm{e}\mathrm{x}\mathrm{t}\mathrm{r}\mathrm{a}\mathrm{c}\mathrm{t}\mathrm{v}\mathrm{o}\mathrm{l}\mathrm{u}\mathrm{m}\mathrm{e}\right)$eq. 3

where, ΔOD min^-1 = change in absorbance per minute, E = Extinction Coefficient = 2.8 mM⁻¹ cm⁻¹, EV = enzyme extract volume (ml), and I = diameter of cuvette.

Catalase determination (CAT)

Catalase was estimated using the method of Korolyuk, Ivanova, and Majorova (1988) (eq. 4). A 0.1 ml of cell sample was added to 1 ml of 4% ammonium molybdate and 2 ml of 0.03% H₂O₂ solution. One unit of catalase concentration is defined as the amount of enzyme required to clear 1 μmol of H₂O₂ min⁻¹ ml⁻¹ of sample. The breakdown of hydrogen peroxide in the reaction mixture was measured spectrophotometrically at 410 nm,

eq. 4 $\mathrm{C}\mathrm{A}\mathrm{T}(\mathrm{U}\mathrm{m}{\mathrm{l}}^{-1})=\left(\mathrm{S}\mathrm{o}/{\mathrm{S}}_3\right)\mathrm{X}203/1$eq. 4

where So = Abs (std) – Abs (b), where b = blank, std = ammonium molybdate + Hydrogen peroxide S₃ = Abs (std) – Abs (t), where t = ammonium molybdate + hydrogen peroxide + test sample after 3 min.

Superoxide dismutase determination (SOD)

This was carried out using the method described by Misra & Fridovich (1972). A 0.2 ml of distilled water was added to the reference tube, while 0.2 ml of the test sample was added to the sample test tube. To each of these, 2.5 ml of the carbonate buffer was added and allowed to reach equilibrium, then 0.3 ml of 0.3 mM adrenaline solution was then added to the reference and each of the test solutions and allowed by mixing. Absorbance reading was taken at 420 nm using UV spectrophotometer,

eq. 5 $\mathrm{S}\mathrm{O}\mathrm{D}\left(\mathrm{U}/\mathrm{m}\mathrm{l}\right)=\mathrm{\%}\mathrm{i}\mathrm{n}\mathrm{h}\mathrm{i}\mathrm{b}\mathrm{i}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}/50\times \mathrm{Y}$eq. 5

eq. 6 $\mathrm{\%}\mathrm{i}\mathrm{n}\mathrm{h}\mathrm{i}\mathrm{b}\mathrm{i}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}=\frac{\left(\mathrm{O}{\mathrm{D}}_{\mathrm{R}\mathrm{e}\mathrm{f}}-\mathrm{O}{\mathrm{D}}_{\mathrm{T}\mathrm{e}\mathrm{s}\mathrm{t}}\right)}{\mathrm{O}{\mathrm{D}}_{\mathrm{R}\mathrm{e}\mathrm{f}}}\times 100$eq. 6

where y is the volume of sample extract, OD_Ref = absorbance reading of reference tube containing distilled water, and OD_Test = absorbance reading of test tube containing algal sample.

Determination of hydrocarbon content of the WSF of the different petroleum fuels

WSFs of the various petroleum fuels were analysed using the USEPA 8015 method for the Gas Chromatography analysis of Diesel Range Organics (DRO) (USEPA, 1996). HP5890 PLUS (manufactured by Agilent Technologies Inc.) was used for this analysis. Sample extraction was carried out using n-hexane and was concentrated using blow down. Sample cleanup was immediately carried out using silica gel. Detail of procedures used was FID (Flame Ionization Detector), HP 7353 autosampler, HP-5, 0.32 mm ID × 0.5 UM, 30 column, 250°C injector temperature, nitrogen carrier gas, hydrogen and compressed air (other gas), 350°C detector temperature, 1 µl injector volume, FID detector and oven programme (oven temperature was set at 50°C then ramped at a rate of 5°C min⁻¹ to 280°C and held for 6 min), with a total run time of 52 min. Agilent ChemStation quantitative software was used to analyse petroleum hydrocarbons.

Data analysis

PAST (version 4.03) statistical software and Microsoft Excel were used to analyse data from three replicates of each treatment. Means and standard errors were derived using Microsoft Excel, and graphs were plotted. A two-way analysis of variance (ANOVA) was used to test for significant differences in growth response between treatments using PAST. Tukey’s pairwise comparison test was used to compare means. Principal component analysis biplots were used to show the relationship between growth, chlorophyll a (Chl a), peroxidase (POD), superoxide dismutase (SOD) and catalase (CAT) activity. Unpaired t-tests were used to determine significant differences (p < 0.05) between growth of N. oculata and P. cruentum.

Results

Physiological response (growth) of test algae to the WSF of the different petroleum fuels

The growth response of N. oculata and P. cruentum in different fuels is shown (Figs 1-2). Much faster growth in all the fuels was observed in N. oculata than P. cruentum (p < 0.05 at day 14). In kerosene (Fig. 1a), maximum growth of N. oculata was in 25% WSF, and minimum growth in 100% WSF. In diesel (Fig. 1b), maximum growth of N. oculata was obtained in 50% WSF, and minimum growth in 100% WSF. In gasoline (Fig. 1c), growth of N. oculata was maximum in 25% WSF, and minimum in 100% WSF. An extended lag phase was observed until day 4 in all the fuels. Peak growth was observed in N. oculata on day 14 in all the fuels at 0% (control) to 50% WSF, but it was observed on day 12 in all the fuels at 100%. There was growth stimulation in N. oculata at 25% and 50% in all the fuels, whereas there was growth reduction at 100% WSF. There was a significant difference (ANOVA; p < 0.05) in the growth response of N. oculata at 100% with time in all the fuels.

Figure 1. Physiological response (growth) of test algae to the WSF of the different petroleum fuels with time: a) growth response of Nannochloropsis oculata in kerosene, b) growth response of N. oculata in diesel, c) growth response of N. oculata in gasoline.

In kerosene (Fig. 2a), maximum growth of P. cruentum was obtained in 25% WSF, with minimum growth in 100% WSF. In diesel (Fig. 2b), maximum growth of P. cruentum was in 25% WSF, and minimum at 100% WSF. In gasoline (Fig. 2b), maximum growth of P. cruentum was at 25% WSF, and minimum at 100% WSF. An extended lag phase was observed up to day 4 and growth reduction and retardation were observed in all the fuels, the latter more severe with increased concentration of fuels (severe at 100%). ANOVA showed a significant difference (p < 0.05) in the growth response of P. cruentum at different concentrations with time.

Figure 2. Physiological response (growth) of test algae to the WSF of the different petroleum fuels with time: a) Growth response of Porphyridium cruentum in kerosene, b) growth response of P. cruentum in diesel, c) growth response of P. cruentum in gasoline.

There was more similarity in the growth trend of both N. oculata and P. cruentum in kerosene and gasoline than in diesel. The negative values for P. cruentum (Fig. 2a–c) indicate death and reduction of cell number below day 0 (Initial).

Morphology of test algae in the WSF of the different petroleum fuels

Figs 3–8 includes micrographs showing alteration in size and clumping of test algae in the different petroleum fuels. Both N. oculata and P. cruentum were spherical in shape and uniform in size before the experiment.

Figure 3. Morphological alteration of Nannochloropsis oculata in kerosene at the end of the experiment; a) N. oculata before experiment, b) 0% WSF, c) 25% WSF, d) 50% WSF and e) 100% WSF of kerosene, respectively. Magnification × 100, arrows highlight algal cells.

Figure 4. Morphological alteration of Nannochloropsis oculata in diesel at the end of the experiment; a) N. oculata before experiment, b) 0% WSF, c) 25% WSF, d) 50% WSF and e) 100% WSF of kerosene, respectively. Magnification × 100, arrows highlight algal cells.

Figure 5. Morphological alteration of Nannochloropsis oculata in kerosene at the end of the experiment; a) N. oculata before experiment, b) 0% WSF, c) 25% WSF, d) 50% WSF and e) 100% WSF of kerosene, respectively. Magnification x 100, arrows highlight algal cells.

Figure 6. Morphological alteration of Porphyridium cruentum in kerosene at the end of the experiment; a) P. cruentum before experiment, b) 0% WSF, c) 25% WSF, d) 50% WSF and e) 100% WSF of kerosene, respectively. Magnification x 100, arrows highlight algal cells.

Figure 7. Morphological alteration of Porphyridium cruentum in diesel at the end of the experiment; a) P. cruentum before experiment, b) 0% WSF, c) 25% WSF, d) 50% WSF and e) 100% WSF of kerosene, respectively. Magnification x 100, arrows highlight algal cells.

Figure 8. Morphological alteration of Porphyridium cruentum in gasoline at the end of the experiment; a) P. cruentum before experiment, b) 0% WSF, c) 25% WSF, d) 50% WSF and e) 100% WSF of kerosene, respectively. Magnification x 100, arrows highlight algal cells.

The microscopic examination after the experiment showed that N. oculata reduced in size and was deformed in shape from spherical to obovoid and oblong shape at 25%, 50% and 100% WSF of the different petroleum fuels. The reduction in size was not observed at 50% and 100% WSF of diesel, where minor clumping was observed. Reduction in cell size became more severe with increase in concentration of WSF of fuels (Table 2). Change in shape of cells from spherical to obovoid and oblong also became more severe with increase in concentration of WSF of fuels.

Table 2. Cell size range and shape of Nannochloropsis oculata at the end of experiment (day 14).

		Cell size range in (µm)
Conc		Kerosene	Diesel	Gasoline	Cell clumping	Cell shape
0%	Length	2.08–4.57	2.08–4.57	2.08–4.57	None	Spherical
	Width	2.08–4.57	2.08–4.57	2.08–4.57
25%	Length	2.08–3.74	2.08–3.32	2.08–3.32	None	Obovoid, oblong
	Width	1.25–2.08	1.25–2.08	1.25–2.08
50%	Length	1.66–3.32	1.25–3.32	2.08–3.32	Minor clumping in 50% diesel	Obovoid, oblong
	Width	1.25–2.08	2.08–4.98	1.25–2.08
100%	Length	1.66–2.08	1.25–4.98	1.66–2.08	Minor clumping in 100% diesel	Obovoid, oblong
	Width	0.83–2.08	2.08–4.98	0.83–2.08

For P. cruentum, there was increase in cell size (Table 3) accompanied with severe clumping of cells at 100% WSF of the different fuels. Cell clumping was also observed in 25% WSF and 50% WSF of the petroleum fuels except in 25% WSF of kerosene. There was increase in severity of clumping of cells with increasing concentration. There was change in shape of cells from spherical to ovoid.

Table 3. Cell size range of Porphyridium cruentum at the end of experiment (day 14).

		Cell size range (µm)
Conc		Kerosene	Diesel	Gasoline	Cell clumping	Cell shape
0%	Length	3.74–9.96	3.74–9.96	3.74–9.96	None	Spherical
	Width	3.74–9.92	3.74–9.92	3.74–9.92
25%	Length	3.32–9.96	3.32–9.96	2.91–9.92	Clumping in 25% WSF of the various fuels	Ovoid
	Width	2.91–7.47	2.91–9.96	2.91–7.47
50%	Length	3.74–10.79	3.74–10.79	2.91–10.79	Clumping in 50% WSF of the various fuels	Ovoid
	Width	2.91–9.96	2.91–9.96	2.91–9.96
100%	Length	3.74–11.62	3.74–11.62	3.74–11.96	Severe clumping in 100% WSF of all the fuels	Ovoid
	Width	2.91–10.62	2.91–10.62	2.91–11.96

Antioxidant enzymes concentration of test algae in the WSF of the different petroleum fuels

Fig. 9a, b show the peroxidase (POD) concentrations in N. oculata and P. cruentum in the different petroleum fuels respectively. In kerosene, peroxidase concentration of N. oculata was highest at 50% WSF (3.46 U ml⁻¹) and lowest at 100% (1.52 U ml⁻¹), in diesel, it was highest at 50% (2.74 U ml⁻¹) and was lowest at 100% (0.82 U ml⁻¹), and in gasoline, it was highest at 50% (2.72 U ml⁻¹) and was lowest at 100% (1.24 U ml⁻¹). There was increase in peroxidase concentration with concentration of petroleum fuels but was decreased at 100%.

Figure 9. a) Peroxidase concentrations of Nannochloropsis oculata in the different petroleum fuels at the end of the experiment (day 14), b) peroxidase concentration of Porphyridium cruentum in the different petroleum fuels at the end of the experiment (day 14).

In kerosene, the peroxidase concentration of P. cruentum was highest at 0% WSF (2.73 U ml⁻¹) and lowest at 100% (1.53 U ml⁻¹), in diesel, it was highest at 25% (3.27 U ml⁻¹) and was lowest at 100% (1.24 U ml⁻¹), and gasoline, it was highest in 0% (2.73 U ml⁻¹) and was lowest at 100% (0.48 U ml⁻¹). There was decrease in peroxidase concentration with increase concentration of petroleum fuels.

Fig. 10a, b show the SOD concentrations in N. oculata and P. cruentum in the different petroleum fuels respectively. In kerosene, SOD concentration of N. oculata was highest at 100% WSF (16.53 U ml⁻¹) and lowest at 0% (12.54 U ml⁻¹), in diesel the activity was highest at 100% (17.55 U ml⁻¹) and was lowest at 0% (12.54 U ml⁻¹), in gasoline the activity was highest at 100% (14.56 U ml⁻¹) and was lowest at 0% (12.54 U ml⁻¹). There was increase in SOD concentration with concentration of petroleum fuels up to 100%.

Figure 10. a) Superoxide dismutase concentration in Nannochloropsis oculata in the different petroleum fuels at the end of the experiment (day 14), b) Superoxide dismutase concentration of Porphyridium cruentum in the different petroleum fuels at the end of the experiment (day 14).

In kerosene, SOD concentration of P. cruentum was highest at 50% WSF (8.58 U ml⁻¹) and lowest at 0% (4.96 U ml⁻¹); in diesel the concentration was highest at 50% (7.55 U ml⁻¹) and was lowest at 0% (4.96 U ml⁻¹); in gasoline the concentration was highest at 50% (8.26 U ml⁻¹) and was lowest at 0% (4.96 U ml⁻¹). There was increase in SOD concentration with concentration of petroleum fuels but there was a slight reduction at 100%.

Fig. 11a, b show CAT concentration in N. oculata and P. cruentum in different petroleum fuels respectively. In kerosene, CAT concentration in N. oculata was highest at 50% WSF (39.09 U ml⁻¹) and lowest at 0% (27.63 U ml⁻¹), in diesel the concentration was highest at 25% (38.50 U ml⁻¹) and was lowest at 0% (27.63 U ml⁻¹), in gasoline the concentration was highest at 25% (64.73 U ml⁻¹) and was lowest at 0% (27.63 U ml⁻¹). There was higher concentration of CAT in all the concentration compared to 0% in all the fuels. An increase in fuel concentration resulted in lowered concentration of CAT in gasoline.

Figure 11. a) Catalase concentration of Nannochloropsis oculata in the different petroleum fuels at the end of the experiment (day 14), b) catalase concentration of Porphyridium cruentum in the different petroleum fuels at the end of the experiment (day 14).

In kerosene, CAT concentration of P. cruentum was highest at 0% WSF (38.73 U ml⁻¹) and lowest at 100% (36.11 U ml⁻¹), in diesel the concentration was highest at 0% (38.73 U ml⁻¹) and was lowest at 100% (37.31 U ml⁻¹), in gasoline the concentration was highest at 0% (38.73 U ml⁻¹) and was lowest at 100% (36.52 U ml⁻¹). There was lower concentration of CAT in all the concentration compared to 0% in all the fuels and an increase in fuel concentration resulted in lowered concentration of catalase.

Comparatively, for N. oculata, there was slightly higher production of POD in kerosene than diesel and gasoline. There was more efficient production of catalase in gasoline than in kerosene and diesel. For P. cruentum, there was higher production of POD in diesel than in kerosene and gasoline. There was also slightly higher production of CAT in diesel than in gasoline and kerosene. However, less overall production of SOD was observed in diesel.

Effect of fuels on chlorophyll a of test algae in the WSF of the different petroleum fuels

Fig. 12a shows the chlorophyll a concentration in N. oculata in the different fuels. The Chl a concentration of N. oculata was highest at 50% WSF of kerosene and diesel. In gasoline, the highest concentration was recorded at 25% WSF and the lowest concentration was recorded at 100%. Comparatively, the overall highest Chl a concentration was recorded at 50% WSF of kerosene and the overall lowest was recorded at 100% WSF of gasoline.

Figure 12. a) Chlorophyll a concentration of Nannochloropsis oculata in the different petroleum fuels at the end of the experiment (day 14), b) chlorophyll a concentration of Porphyridium cruentum in the different petroleum fuels at the end of the experiment (day 14).

Fig. 12b shows the Chl a concentration in P. cruentum in the different fuels. The Chl a concentration in P. cruentum was highest at 25% WSF in all the fuels, while the lowest concentration was recorded at 100% in all the fuels. The overall highest chlorophyll a concentration was recorded at 25% WSF of kerosene, while the overall lowest was recorded at 100% WSF of the same kerosene. Comparatively, there was no significant difference (p < 0.05) in the chlorophyll a concentration in the different fuels for both algae.

Multivariate analysis

Principal component analysis (PCA) was done to show the relationship between growth, Chl a, POD, SOD and CAT activity of test algae in the different petroleum fuels. Figs 13a-c are PCA biplots showing the relationship between growth, Chl a, POD, CAT and SOD of N. oculata in kerosene, diesel and gasoline, respectively.

Figure 13. a) Principal component analysis biplot showing the relationship between growth, chlorophyll a (Chl a), peroxidase (POD), superoxide dismutase (SOD) and catalase (CAT) activity of Nannochloropsis oculata in kerosene, b) principal component analysis biplot showing the relationship between growth, chlorophyll a (Chl a), peroxidase (POD), superoxide dismutase (SOD) and catalase (CAT) activity of Nannochloropsis oculata in diesel, c) Principal component analysis biplot showing the relationship between growth, chlorophyll a (Chl a), peroxidase (POD), superoxide dismutase (SOD) and catalase (CAT) activity of Nannochloropsis oculata in gasoline.

There was positive relationship between growth and CAT, POD, Chl a in all the petroleum fuels. There was a negative relationship between growth and SOD, and between Chl a and SOD in all the petroleum fuels. However, a strong positive relationship was observed between growth, CAT, and POD in kerosene; growth, Chl a and POD in diesel; growth and CAT in gasoline.

The eigen value for component 1, 2 and 3 were 26.574, 3.163 and 0.019, respectively, while percentage variability explained were 89.31%, 10.63% and 0.06%, respectively, in kerosene. In diesel, the eigen values were 29.688, 3.882 and 0.154, respectively, while the percentage variability explained were 88.03% 11.51% and 0.46%, respectively. In gasoline, eigen values were 28.532, 1.927, 0.059, respectively, while the percentage variability explained were 99.31%, 0.67% and 0.02%, respectively. Figs 14a–c are PCA biplots showing the relationship between growth, Chl a, POD, CAT and SOD of P. cruentum in kerosene, diesel and gasoline, respectively. There was positive relationship between growth and POD, CAT and Chl a in all the petroleum fuels. A negative relationship was observed between growth and SOD in all the petroleum fuels. However, a strong positive relationship was observed between Chl a, CAT, and POD in kerosene; growth, Chl a and CAT in diesel; and Chl a, POD and CAT in gasoline.

Figure 14. a) Principal component analysis biplot showing the relationship between growth, chlorophyll a (Chl a), peroxidase (POD), superoxide dismutase (SOD) and catalase (CAT) activity of Porphyridium cruentum in kerosene, b) principal component analysis biplot showing the relationship between growth, chlorophyll a (Chl a), peroxidase (POD), superoxide dismutase (SOD) and catalase (CAT) activity of Porphyridium cruentum in diesel, c) principal component analysis biplot showing the relationship between growth, chlorophyll a (Chl a), peroxidase (POD), superoxide dismutase (SOD) and catalase (CAT) activity of Porphyridium cruentum in gasoline.

The eigen value of component 1, 2 and 3 were 3.569, 0.355 and 0.055, respectively, while percentage variability explained were 89.69%, 8.93% and 1.38%, respectively, in kerosene. In diesel, the eigen value were 1.562, 0.650, 0.086, respectively, while percentage variability explained were 67.95%, 28.29% and 3.76%, respectively. In gasoline, the eigen value were 3.219, 0.804 and 0.0385 respectively, while the % variability explained was 79.31%, 19.79% and 0.95%, respectively.

Discussion

This study investigated the resilience and bioresponse of N. oculata and P. cruentum to petroleum fuel pollution. The starting total petroleum hydrocarbon (TPH) concentration shown in Table 4 can be compared to background levels in polluted sites in Southern Nigeria. Daniel & Nna (2016) reported that a part of Cross River Estuary, Niger Delta, Nigeria has TPH concentration ranging between 13,161.81 to 24,854.62 µg l⁻¹. Olufemi, Tunde, & Temitope (2011) reported a higher TPH concentration at Ubeji River, Warri, Nigeria which was 73,500.00 µg l⁻¹. In Indonesia, Sari, Trihadiningrum, Ni’matuzahroh, & Ni’matuzahroh (2018) reported a very high TPH concentration (211 025.73 µg l⁻¹) from a surface water sample that was collected from a small river in Wonocolo public crude oil mining. Some studies have shown that some algae including green, brown and red algae have the potential to degrade or use some hydrocarbons as a carbon source, indicating their ability to withstand crude oil pollution (Amran et al., 2022; Naeem & Qazi, 2020).

Table 4. Petroleum hydrocarbon component of the WSF of the different petroleum fuels.

Petroleum hydrocarbon	Concentration (µg l^–1)
	Kerosene (B. EXP.)	Diesel (B. EXP.)	Gasoline (B. EXP.)
N-octane	<0.01	<0.01	22.99
n-nonane	656.15	50.57	260.51
n-decane	2256.17	268.11	2034.09
n-undecane	1563.64	389.99	1239.29
n-dodecane	5676.2	2083.16	2322.04
n-tridecane	<0.01	152.31	<0.01
n-tetradecane	2777.86	2966.49	2846.44
n-pentadecane	206.45	193.92	43.94
n-hexadecane	1654.67	2121.15	1159.47
n-heptadecane	176.15	1068.64	114.95
Pristine	137.05	615.57	55.14
n-octadecane	911.01	1868.29	761.72
Phytane	184.81	1120.91	178.11
n-nonadecane	138.65	250.45	71.21
n-eicosane	442.03	110.63	392.11
n-heneicosane	63.95	57.78	32.33
n-docasane	197.41	382.14	166.88
n-tricosane	22.89	56.2	<0.01
n-tetracosane	95.15	321.61	80.68
n-pentacosane	5.6	32.55	354.83
n-haxecosane	20.92	39.28	12.41
n-heptacosane	20.21	268.61	21.71
n-octacosane	19.09	45.14	19.4
n-nonacosane	23.09	97.05	29.1
n-tricotane	18.35	<0.01	31.32
n-hentriacotane	7.42	24.22	9.44
n-dotriacotane	<0.01	<0.01	<0.01
n-tritriacontane	24.63	5.27	<0.01
∑TPH	17299.57	15590.08	16444.46

Growth response of test algae in WSFs of the different petroleum fuels

In this study, after careful analysis of data, result showed significant responses of the investigated algae as expressed by applied biological parameters which may be positive or negative. The different fuels were found to have considerable effect, stimulatory or inhibitory on the growth of the different test algae, depending on concentration, type of fuel and type of algae. Some other researchers have also reported similar findings. Laura et al. (2018) reported species dependence in their study of the physiological response of phytoplankton species exposed to macondo oil and the dispersant, COREXIT1. The effects of petroleum spillage in marine environment are gross. There is inadequate real-time data on the impacts of petroleum spillage in marine ecosystem (Asif, Chen, An, & Dong, 2022; Wang et al., 2021).

In N. oculata, growth stimulation was observed in concentrations up to 50% WSF. This could be as a result of its ability to degrade, accumulate and use petroleum products as source of carbon. This corroborates the report of Dhull, Soni, Rahi, & Soni (2014) that concentrations of petroleum products in a medium increase the growth and biomass production of some algae. Stimulation of growth and photosynthesis in microalgae exposed to low concentrations of hydrocarbons has also been noted in Wang, Tang, Li, & Liu (2002). In P. cruentum, there was reduction and retardation of growth. This could be as a result of the high toxicity of petroleum fuels above the tolerance level of the algae or its inefficiency in metabolizing petroleum hydrocarbon for heterotrophy. This corroborates the result of Fabregas, Herrero, & Veiga (2021) who observed that petroleum oil stimulated the growth of Tetraselmis suesica at low concentration and inhibited the growth at high concentrations. Jiang et al. (2022) reported that the specific growth rates of Skelotenema costatum were significantly inhibited by a hydrocarbon component (phenanthrene) at both low and high concentrations. The resilience and efficient growth of N. oculata in WSF of the different petroleum fuels suggests that it could serve as a potential agent for the bioremediation of petroleum hydrocarbon. The negative values recorded the growth response of P. cruentum depict death and reduction of cell number over time.

Extended lag phase observed at high concentration can be attributed to cell adaptation as a result of the change in medium condition. This is in line with the result of Rajabnasab, Khavari-Nejad Ra, Shokravi, Nejadsattari, & Khavari-Nejad (2018) where they reported that extended lag phase in some cyanobacteria species as a result of change in environmental conditions. This is further substantiated by the study of Sushama, Reena, Arun, & Madhavi (2008) on the effect of Bombay crude oil on Thalassiosira sp where WSF fraction was capable of having negative growth rate on 10%, 20% and 40% WSF at 24 h, indicating effect, and after 24 h, growth rate in 10% and 20% increased, while that of 40% remain low for 6 days. Lim et al. (2012) in the isolation and evaluation of oil-producing microalgae from subtropical coastal and brackish waters also reported extension of the lag phase.

The comparative assessment of the average growth of test algae in WSF of the different fuels which showed that for N. oculata, lowest growth occurred in diesel corroborates the findings of Kadiri & Enoma (2013) where they reported that diesel caused higher growth inhibition in Selenastrum capricornutum than kerosene and gasoline. For P. cruentum in this study, the same trend was also observed. This effect could be as result of diesel being a heavier petroleum fuel than kerosene and gasoline. This study showed that WSF of different fuels have different potential environmental damage depending on the type of fuel and the concentrations of WSF. Extensive evidence also exists on the effects of petroleum hydrocarbons among some other groups of algae (Kadiri & Eboigbodin, 2012). This study showed that WSF of petroleum fuels have different potential environmental damages depending on the type of fuel and the concentrations of WSF.

Morphological studies of test algae in WSF of the different petroleum fuels

This study showed morphological alterations of test algae and its relationship with concentrations. Kerosene, diesel and gasoline were capable of causing change in the shape of N. oculata from spherical to obovoid and oblong. In P. cruentum, the petroleum fuels were capable of causing increased cell size and heavy clumping. Clumping became more severe at higher concentrations. Increase in size of algae under severe toxicity is accompanied with cell clumping. These present observations are in good agreement with Soltani, Amira, Sifi, & Beldi (2012) where they reported that the microscopic examination of algal cells in response to crude oil pollution indicated that the crude oil led to an increase in algal biomass, although it caused heterocyst separation from the filament of Anabaena sp. The clumping observed in P. cruentum corroborates the observation of Soltani, Amira, Sifi, & Beldi (2012) that the cells of Oscillatoria sp. were aggregated in clusters like a ball covered with oil. Gamila, Ibrahim, & El-Ghafar (2003) also observed the same in Oscillatoria sp. in their study of the effect of crude oil. Soto, Hutchinson, Hellebust, & Sheath (2011) also observed induced morphological abnormalities in the cell of Chlamydomonas angulosa in aqueous crude and fuel oil extracts. Similarly, Gaur & Singh (1990) found that Assam crude oil had a serious effect on heterocyst differentiation in Anabaena doliolum and that microscopic examination of Tetraselmis suecica cells indicated abnormal cellular morphology.

Nechev et al. (2002) postulated that diesel fuel causes a disruption of the optimal physical state of the cytoplasmic membranes of algae, thus increasing the permeability of these membranes, which in turn facilitate the entry of diesel fuel into the cells and the accumulation of a high quantity of hydrocarbons. This could lead to obstruction in biochemical and molecular processes such as cell division, thereby suggesting a reason for the increase in cell size of P. cruentum. The resultant cell clumping then leads to inhibition in growth and productivity of the cell. Abdul Hameed & Al Obaidy (2014) suggested that a clear reduction of the growth and chlorophyll of Microcystis flos-aquae could be due to the cellular structure changes as a result of the toxic effects of crude oil.

The comparative assessment of the morphological effects of the WSF of the different fuels which showed that diesel caused minor cell clumping in N. oculata explains why it could pose higher environmental treat and caused higher growth inhibition than kerosene and gasoline. In P. cruentum, diesel was also observed to cause worst cell clumped than kerosene and gasoline. These resulted in great physiological stress of both algae in WSF of diesel.

Chlorophyll concentration of test algae in WSF of the different petroleum fuels

Chlorophyll is a photosynthetic pigment which serves as a biomass indicator for microalgae and it is the most frequently measured biochemical parameter in toxicity studies (Martínez, Kinet, Bajji, & Lutts, 2005; Somruthai, Rungcharn, & Nuttha, 2021). Chl a study in N. oculata revealed that low (10% WSF) to moderate (50% WSF) petroleum fuel did not have any effect on the chlorophyll concentration. However, at very high concentration (100%), the petroleum fuels enhanced reduction of chlorophyll concentration in the algae. This corroborates the result reported by Nayar, Gohb, & Choua (2004) that hydrocarbon was capable of increasing productivity and may not affect chlorophyll production at certain concentrations. Nagwa, Yean-Chang, Abd-El-Ruhman, & Rania (2005) reported that a positive effect of the oil pollutant was seen on the total yield and production of Chl a of algae. High chlorophyll concentration in alga exposed to petroleum hydrocarbon may be attributed to its utilization of the petroleum products as source of organic compounds.

In P. cruentum, Chl a concentration was inhibited with increase in petroleum fuel concentration. This could be as a result of high toxicity of petroleum fuel to the algae compared to N. oculata. The inhibition of chlorophyll with increase in petroleum fuel concentration in P. cruentum is in agreement with the findings of Nayar et al. (2003) in their study of the impact of petroleum hydrocarbons (diesel) on periphyton in an impacted tropical estuary based on in-situ microcosms, where chlorophyll concentration of the periphyton in the background levels of the petroleum hydrocarbon was severely affected. Comparative assessment of the effect of the different petroleum fuels on Chl a concentration showed that none of the petroleum fuels caused a significant difference in both test algae.

Antioxidant enzyme concentration of test algae in WSF of the different petroleum fuels

Reactive oxygen species (ROS) are reactive chemical species of oxygen that are bi-products of biochemical reactions. They are chemically reactive and pose threat to aerobic organisms. They are of different forms, namely, hydrogen peroxide (H₂O₂), singlet oxygen (O⁻²) and hydroxyl radicals (OH-) (Pinto et al., 2003). Halliwell & Gutteridge (1999) noted that ROS can alter the structure and mutagens of cells. Cellular defence against these harmful oxygen products involves the production of antioxidant enzymes such as SOD, POD and CAT by cells. These are the first line of antioxidant enzymes produced by cells during antioxidant activities (Chia & Kwaghe, 2015). They are involved in the decomposition of H₂O₂ and O⁻² to water and oxygen by the interaction of the amino acids, asparagine at position 147 and histidine at position 74, which causes a proton transfer between the oxygen atoms (Tores, McNeill, Gibson, Wayne, & Yates, 2008). Antioxidant enzymes serve as signals of distress at the molecular level and useful as toxicity biomarkers (Pinto et al., 2003; Tores, McNeill, Gibson, Wayne, & Yates, 2008).

This study showed that WSFs of petroleum fuels induced low production of peroxidase and catalase with increase in concentration in P. cruentum, thereby reducing the ability of the alga in combatting stress effects of ROS with increase in the fuel concentration. This could have also consequently resulted in the inhibition of certain physiological (growth) and biochemical processes, and also may have caused morphological changes. The reduction of growth in algae under conditions of contamination with petroleum fuel can be attributed to production of ROS such as O⁻² and H₂O₂ (Chia, Cordeiro-Araújo, Lorenzi, & Bittencourt-Oliveira, 2016), and the inefficient response of algae to the stress. SOD is responsible for converting certain reactive oxygen species (super oxides) to peroxides while peroxides and catalase converts H₂O₂, to H₂O and O₂ (Chia & Kwaghe, 2015). As a result of increased ROS, microalgae tend to up-regulate the biosynthesis and activities of ROS combating enzymes (Chia & Kwaghe, 2015). The efficient growth and productivity of N. oculata can be attributed to the efficient production of antioxidant enzymes. This study showed that difference in fuel type had notable effect on the average peroxidase production in P. cruentum. For example, gasoline seems to induce the lowest overall production of peroxidase in P. cruentum which says more about the unique responses from the alga. Difference in fuel type also had notable effect on catalase production in N. oculata which is a unique effect. The observed higher concentration of catalase at all the concentrations compared to 0% (control) in all the fuels for N. oculata which is contrary to the observed decrease of catalase at all the concentrations compared to 0% in all the fuels for P. cruentum explains part of reasons for observed tolerance and growth efficiency of N. oculata.

Multivariate analysis of data

In N. oculata, there was positive relationship between the growth, chlorophyll concentration, POD and CAT. This means that growth and productivity of the test algae in the condition of petroleum pollution depended largely on their ability to produce peroxidase and catalase which are responsible for the conversion of peroxides to water and oxygen. While high concentration (100% WSF) of petroleum fuels slightly inhibited peroxidase and catalase, SOD concentration was not affected by high petroleum fuel concentrations. Therefore, reduction of POD and CAT at high concentration (100% WSF) of the pollutants could be the reason for decrease in growth and productivity at that concentration. This result corroborated the report of Chia & Kwaghe (2015) where it was opined that decrease in peroxidase and catalase concentrations may be related to inhibition of the enzymes by high concentration of contaminant. It is the view of Cordeiro-Araújo, Chia, Hereman, Sasaki, & Bittencourt-Oliveira (2015) that catalase inhibition maybe related to the inactivation via the binding of thiol group with the bioactive compounds of the toxicant investigated. The high overall antioxidant enzymes concentrations in Nannochloropsis oculata can be attributed to be the reason for observed tolerance of the alga in the petroleum fuels.

Positive relationship between growth, chlorophyll concentration, POD and CAT was also observed in P. cruentum. The lower production of antioxidant enzymes in P. cruentum, compared to N. oculata, could have contributed to the reason for lower productivity due to inefficiency in the conversion of super-oxides and peroxides to oxygen and water. This is in line with the finding of (Chia, Cordeiro-Araújo, Lorenzi, & Bittencourt-Oliveira, 2016), where it was observed that, by the 4th day after exposure to contaminant, the microalga was no longer able to withstand the effect of the contaminant (stress source), as it inhibited CAT and POD activity. It was then concluded that that the altering of the peroxidase and catalase activities in S. quadricauda exposed to contaminant was an indication that the microalga suffered severe oxidative stress with increasing aqueous contaminant concentration.

SOD had a huge impact on growth as shown in the PCA plots and this could explain the uniqueness of the role of the enzyme in combatting stress. According to Chia & Kwaghe (2015), SOD is an enzyme that converts super oxides O⁻² to H₂O₂. Thereafter, peroxidase and catalase convert peroxides to water and oxygen. Therefore, the role of SOD is unique and an inadequate production of it will have impact on the algal development.

For N. oculata, as expressed by the eigen values, two principal components (component 1 and 2) explained over 99% of the total variance, in all the fuels. For P. cruentum, as expressed by the eigen values, two principal components (component 1 and 2) explained over 98% of the total variance, in all the fuels.

Comparative assessment

The poor overall growth of P. cruentum, compared to N. oculata in all the fuels, can be attributed to severe cell clumping, poor overall production of antioxidant enzymes and high sensitivity of P. cruentum. The cell clumping according to Nechev et al. (2002) is caused by the disruption of the optimal physical state of the cytoplasmic membranes of algae, thus raising the permeability of these membranes, which in turn facilitate the entry of fuel into the cells and the accumulation of a high quantity of hydrocarbons. This leads to cell enlargement and the subsequent clumping of cells. Clumping of cells could have also affected biochemical and physiological developments, therefore, could have contributed to the overall lower production of antioxidant enzymes observed in P. cruentum.

The observed tolerance of N. oculata to the petroleum fuels could be as a result of its high efficiency in antioxidant production and absence of cell clumping. The lowest overall growth observed in diesel for Nannochloropsis oculata could be attributed to minor clumping observed. The overall higher tolerance of Nannochloropsis oculata to the fuels compared to Porphyridium cruentum could be attributed to :

• its overall higher efficiency in the production of antioxidant enzymes;

• morphological factors such as absence of cell clumping;

• more efficiency in chlorophyll production.

The observed differences in the enzymatic responses of each alga to the different fuels could be as a result of concentration differences in the hydrocarbon components of the petroleum fuels as shown in Table 4. The increased aggregation of cells with increase in concentration of WSF of fuels observed in P. cruentum could be attributed to the increased toxicity (cytoplasmic disruption) of the fuel with increase in concentration.

The most severe clumping and cell deformation which was observed in diesel could have immensely contributed to the overall lowest growth observed in diesel for both algae.

Conclusion

Petroleum fuel pollution could result in growth retardation or stimulation on marine algae depending on the fuel type, extent of the pollution and composition of the algae community or type of algae. There was differential response of the test marine algae to the different fuels. N. oculata displayed tolerance to petroleum fuels, while P. cruentum was more sensitive. High concentrations of WSF of the petroleum fuels caused reduction or retardation of growth, decreased antioxidant enzyme production, as well as reduced Chl a concentration in the test algae. The tolerance of test algae depended largely on antioxidant enzyme activity. Morphological alteration and cell clumping were also observed as part of the effects of petroleum fuel pollution. This study provides useful information for ecological capital, economic consideration of the effect of petroleum fuel pollution in marine environment and policy making.

Acknowledgements

We appreciate Dr (Mrs) J. E. Ukpebor and all staff of Chemistry Department, University of Benin technical staff who assisted in the analysis of hydrocarbons. We appreciate Dr Jeffrey Uyi Ogbebor for his technical assistance. We acknowledge and appreciate the Limnology and Algology Laboratory, Department of Plant Biology and Biotechnology, University of Benin, Nigeria where this research was carried out.

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.