South African Journal of Botany 90 (2014) 137–145

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South African Journal of Botany

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Influence of seasonal variation in water quality on the microalgaldiversity of sewage wastewater

Nirmal Renuka a, Anjuli Sood b, Radha Prasanna b, Amrik Singh Ahluwalia a,⁎a Department of Botany, Panjab University, Chandigarh 160014, Indiab Division of Microbiology, Indian Agricultural Research Institute, New Delhi 110012, India

⁎ Corresponding author. Tel.: +91 172 2534001, +92779510.

E-mail address: aas.aca2012@gmail.com (A.S. Ahluwa

0254-6299/$ – see front matter © 2013 SAAB. Published bhttp://dx.doi.org/10.1016/j.sajb.2013.10.017

a b s t r a c t

a r t i c l e i n f o

Article history:Received 18 May 2013Received in revised form 11 October 2013Accepted 25 October 2013Available online 4 December 2013

Edited by WA Stirk

Keywords:Sewage wastewaterEutrophicationMicroalgaeDiversitySeasonal variation

An investigation was undertaken to assess the variation in microalgal diversity vis a vis physicochemical character-istics of sewage wastewater at monthly time intervals. Diversity analyses revealed the presence of algal membersbelonging to all major divisions, with a predominance of Cyanophyta. Shannon-Wiener and Simpson's diversityindices illustrated low microalgal diversity in sewage wastewater. Highest chemical oxygen demand (COD) of14,000 mg L−1 was recorded in December 2012. Spectrometric analyses of sewage wastewater revealed the pres-ence of heavymetals,with Cr ranging from3 to 4 mg L−1 being detected in all the samples collected over the year. Apositive correlation was found between COD and total heavy metal concentration (r = 0.77). The indices ofmicroalgal diversity showed a positive correlation with nutrients and a negative correlation with COD and heavymetal concentrations, implying the significant role of these factors in influencing the algal population. Phormidiumsp. was the dominant genus present throughout the year.

© 2013 SAAB. Published by Elsevier B.V. All rights reserved.

1. Introduction

Water is indispensible for the sustenance of the life of mankind. Indeveloping countries like India; many communities or tribes residenear various water resources and are directly or indirectly dependenton them for their livelihood. The increasing population load andmixingof contaminatedwastewater fromdifferent sources e.g. industries or ag-ricultural fields emphasize the need to preserve these water resourcesfor future generations. Additionally, the impact of contaminated waste-water release on the overall health of aquatic bodies is also extremelyimportant (Oliveira et al., 2007; Senthil et al., 2012; Yang et al., 2008),as untreated wastewater is usually very rich in nutrients (nitrogen,phosphorus) along with other contaminants (heavy metals, pesticidesetc.). Discharge of untreated sewage wastewater into the water bodiesleads to increased nutrient load and eutrophication, formation of algalblooms and imbalance in the ecology of such environments (Heisleret al., 2008). Heavy metals released in the environment enter into thefood chain, and exert toxic effects on living organisms by bioaccumula-tion and biomagnification; which may also lead to loss of key species(Atici et al., 2008; Doshi et al., 2008; Ogoyi et al., 2011). A thoroughknowledge about the impact of mixing of contaminated wastewaterand development of cost effective technologies of its treatment isemerging as one of the major issues related to wastewater management.

1 172 2534002; fax: +91 172

lia).

y Elsevier B.V. All rights reserved.

The biota of an aquatic ecosystem comprises micro-/macrofauna,besides a wide range of organisms including micro-/macrophytes.Microalgae present in wastewater systems can be used as indicators ofwater pollution (Torres et al., 2008), as they are the primary producersand have a key role in biotic and abiotic interactions of aquatic systemsand possess the ability to survive in oligotrophic to eutrophic environ-ments. They also play a role in nutrient sequestration and removal ofother contaminants from wastewaters. Studies on microalgal diversityand their associations in thewater bodies as biological indicators are help-ful in the assessment of water quality (Shanthala et al., 2009). Microalgaldiversity of wastewater systems has been studied (Bernal et al., 2008;Chinnasamyet al., 2010). Temporal assessment studies canhelp to under-stand the extent of damage caused bymixing of untreatedwastewater onthe biota, and also enhance our knowledge of species diversity of contam-inatedwastewater, besides identifyingmicroalgaewhich can tolerate andphytoremediate such contaminated sites (Renuka et al., 2013a, 2013b).

Nutrient composition of water bodies also determines the phyto-plankton community structure. Imbalance in the nutrient ratio (N:P)can lead to the growth of certain allelochemical producing species,which suppresses the growth of other organisms (Graneli et al., 2008).Water quality affects the abundance, species composition, productivityand physiology of these organisms (El-Sheekh et al., 2000). However,the complex inter-relation between algal communities and nutrientlevels inwastewater still needs in depth analyses. Community structuregenerally fluctuates with the change in the nutrient composition of thewastewater (Borchardt, 1996). Therefore, it is important to study themicroalgal dynamics in response to different environmental conditions

Table 1Monthly variation in the physicochemical characteristics of the sewage wastewater of canal during the year 2012.

Month pH EC(μS cm−1)

TDS(mg L−1)

Salinity(mg L−1)

Alkalinity(mg L−1)

Hardness(mg L−1)

Ca(mg L−1)

DO(mg L−1)

BOD(mg L−1)

January 8.1 ± 0.12bc 1745 ± 8b 1240 ± 14b 898 ± 11b 330.0 ± 14.6cd 437.0 ± 15.0a 137.0 ± 2.5a ND 2.00 ± 0.01d

February 8.4 ± 0.11a 1917 ± 19a 1360 ± 19a 997 ± 16a 376.7 ± 12.0a 443.0 ± 13.0a 141.3 ± 7.5a ND 2.04 ± 0.01d

March 7.9 ± 0.12d 1615 ± 15c 1150 ± 25c 838 ± 13c 366.7 ± 13.2a 310.0 ± 15.0d 63.3 ± 2.4fg ND 1.83 ± 0.01e

April 8.2 ± 0.11b 1501 ± 13d 1060 ± 12d 774 ± 16d 341.7 ± 15.8b 309.7 ± 12.1d 141.0 ± 5.1a ND 3.20 ± 0.09a

May 8.0 ± 0.11cd 1603 ± 15c 1130 ± 11c 823 ± 19c 340.0 ± 15.0b 395.0 ± 15.0b 57.5 ± 1.5h ND 2.98 ± 0.09b

June 8.0 ± 0.15cd 1404 ± 18f 1010 ± 18f 734 ± 17ef 283.3 ± 12.0e 360.0 ± 12.0c 65.7 ± 2.6e 1.08 ± 0.001c 2.32 ± 0.08c

July 8.4 ± 0.10a 1498 ± 19d 1040 ± 14de 752 ± 16de 235.0 ± 18.0f 299.3 ± 11.1d 79.3 ± 1.2d 1.82 ± 0.002b 1.97 ± 0.09d

August 8.0 ± 0.10cd 1390 ± 23f 982 ± 9g 713 ± 15f 218.3 ± 17.6g 264.0 ± 3.5f 88.7 ± 1.2c 2.55 ± 0.01a 1.40 ± 0.01g

September 8.4 ± 0.10a 1045 ± 8g 739 ± 16h 530 ± 16g 256.7 ± 11.2f 222.7 ± 1.2e 58.6 ± 1.9gh 0.33 ± 0.01e 3.30 ± 0.09a

October 8.0 ± 0.10cd 1908 ± 9a 1035 ± 13def 990 ± 14a 308.3 ± 17.6d 306.7 ± 14.2d 114.0 ± 4.0b ND 0.77 ± 0.05i

November 8.0 ± 0.10cd 1450 ± 13e 1020 ± 19ef 741 ± 12e 283.3 ± 15.3e 272.0 ± 7.21e 56.7 ± 1.2h 0.41 ± 0.001d 0.91 ± 0.05h

December 8.2 ± 0.10b 1611 ± 16c 1140 ± 12c 822 ± 11c 310.0 ± 12.0d 377.5 ± 12.0bc 65.6 ± 3.9f ND 1.70 ± 0.02f

ND — not detected; values given are mean of n samples ± S.D., where n = 6 and superscripts (a, b…) indicate DMRT ranking within a column.

138 N. Renuka et al. / South African Journal of Botany 90 (2014) 137–145

and fluctuations in the nutrient level of these wastewaters. However,reports on modulation of microalgal community structure due to qual-itative and quantitative changes in nutrients in wastewater are scarce.

The present investigation describes a systematic study in whichphysicochemical and nutrient characteristics of a wastewater canaland their inter-relationships with algal diversity were analyzed atmonthly intervals, over a period of one year.

2. Materials and methods

2.1. Study area and sampling of wastewater

Sewage wastewater samples were collected in clean plastic bottlesfrom a canal, near the fields, belonging to the research farm of theIndian Agricultural Research Institute (IARI), New Delhi, situated at alatitude of 28°40′N and longitude of 77°12′E, altitude of 228.6 m abovethe mean sea level (Arabian sea) from January–December 2012, atmonthly intervals from several points. Pooled samples were transportedto laboratory, stored at 4 °C and used for further analyses. The meanannual rainfall of Delhi is 650 mm, more than 80% of which generallyoccurs during the south-west monsoon season (July–September) with amean annual evaporation of 850 mm.

2.2. Collection, identification and their diversity analyses

Microalgae were collected in sampling bottles (aminimum of six al-iquots from different points within the canal and pooled) and 4% form-aldehyde was added to preserve the samples. Three such replicateswere taken for each monthly sampling. In a separate sampling bottle,1% Lugol's iodine solution was added for quantification of algae.Microalgae were studied using the Neubauer hemocytometer under alight microscope (Axio Cam Cc1 Carl Zeiss Scope.A1) and calculated as

Table 2Nutrient load and temperature of the sewage wastewater of the canal taken at monthly interv

Month Water TEMPERATURE Chlorides(mg L−1)

Carbonate(meq L−1)

January 16.3 ± 2.1g 292.5 ± 3.7c 0.41 ± 0.001a

February 20.5 ± 1.5f 339.1 ± 2.9b NDMarch 26.0 ± 2.6de 274.8 ± 3.9d NDApril 27.3 ± 1.1cd 254.9 ± 5.4e NDMay 32.9 ± 2.9ab 252.7 ± 2.9e NDJune 34.0 ± 2.4a 240.2 ± 1.1f NDJuly 27.2 ± 1.7cd 242.8 ± 2.8f NDAugust 29.8 ± 1.3bc 251.3 ± 1.4e 0.167 ± 0.01b

September 29.1 ± 1.8cd 90.4 ± 0.8i NDOctober 23.0 ± 2.3ef 371.1 ± 5.4a NDNovember 20.9 ± 1.9f 197.4 ± 2.8h NDDecember 15.6 ± 2.2g 229.0 ± 4.9g ND

ND — not detected; values given are mean of n samples ± S.D., where n = 6 and superscripts

number of organisms mL−1. Total number of different genera wasconsidered for calculating total species richness and determined as de-scribed by Kindt and Coe (2005). Two diversity indices — Shannon-Wiener and Simpson's diversity index were used to analyze themicroalgal diversity (Nayak et al., 2009; Shanthala et al., 2009). Identifi-cation of microalgae was done using standard monographs; Fritsch(1965a, 1965b); Cyanophyta (Desikachary, 1959; Komárek andAnagnostidis, 1999, 2005); Chlorophyta (Komárek and Fott, 1983;Krishnamurthy, 2000) and Bacillariophyta (Round et al., 1990).

2.3. Selection of sampling time

In a preliminary study, sampling of the sewage wastewater was per-formed at time intervals of 2 h from 10.00 a.m. to 4.00 p.m. in a day, toascertain the progressive changes in the nature and amount of chemicalconstituents in the wastewater for selected physicochemical parame-ters. On the basis of data generated, the time duration between11.30 a.m. and 12.30 noonwas selected for the sampling of wastewaterat monthly time intervals, when highest nutrient loading was detected.Sampling of water andmicroalgaewere performed at the same time forthe analyses of physicochemical and biological parameters respectively.

2.4. Analytical procedures

Six samples of sewage wastewater were collected from differentpoints within the canal and pooled for the analyses of physicochemicalcharacteristics and heavy metals at monthly intervals. Quantification ofphysicochemical parameters viz. temperature, pH, electrical conductiv-ity (EC), total dissolved solids (TDS), salinity, alkalinity, acidity, chlo-rides, carbonate, bicarbonate, calcium, hardness, free CO2, dissolvedoxygen (DO), biological oxygen demand (BOD), chemical oxygen de-mand (COD), nitrate (NO3–N), nitrite (NO2–N), ammonia (NH3–N)

als (2012).

Bicarbonate(meq L−1)

NO2–N(mg L−1)

PO4–P(mg L−1)

NH4–N(mg L−1)

0.9 ± 0.01d ND 3.16 ± 0.06d 8.46 ± 0.50de

0.9 ± 0.001d ND 2.78 ± 0.01e 35.39 ± 1.45a

1.4 ± 0.001a ND 3.88 ± 0.05b 35.69 ± 1.90a

1.3 ± 0.02b ND 4.56 ± 0.02a 9.40 ± 0.35d

0.8 ± 0.03e 0.030 ± 0.00d 3.84 ± 0.02b 20.03 ± 12.0c

0.8 ± 0.012e 0.025 ± 0.001e 3.44 ± 0.04c 29.20 ± 1.6bc

1.0 ± 0.01c 0.070 ± 0.003a 1.11 ± 0.03h 23.68 ± 1.1bc

0.8 ± 0.03e 0.017 ± 0.001f 1.72 ± 0.04g 2.21 ± 0.06e

0.8 ± 0.001e 0.037 ± 0.001c 3.11 ± 0.13d 24.27 ± 1.76bc

0.8 ± 0.01e ND 2.78 ± 0.09e 2.13 ± 1.28e

0.8 ± 0.01e 0.059 ± 0.003b 3.92 ± 0.09b 19.18 ± 1.86c

0.9 ± 0.01d ND 2.6 ± 0.02f 17.66 ± 1.36c

(a, b…) indicate DMRT ranking within a column.

139N. Renuka et al. / South African Journal of Botany 90 (2014) 137–145

and phosphate (PO4–P) was carried out using the standardmethods forwater and wastewater (APHA, 2005). Parameters viz. temperature, pH,EC, TDS, salinity and DO were analyzed in situ. Heavy metal analyses[Chromium (Cr), Cobalt (Co), Lead (Pb), Arsenic (As) and Cadmium(Cd)] were carried out by using WD-XRF spectrometer (WavelengthDispersive X-Ray Fluorescence Spectrometer); Model: S8 TIGER, MakeBruker, Germany. All the calibration curves for heavy metal analyseswere made using standard heavy metal solutions (Merck, Germany).

2.5. Statistical analyses

The statistical analyses were performed using the software Statisti-cal package for Social Sciences (SPSS Version 10.0). One-way analysisof variance (ANOVA) was used to evaluate the differences among thetreatments. The triplicate sets of data were evaluated in accordancewith the experimental design (Completely Randomized Design) withANOVA (Analysis of Variance). S.D. (standard deviation) values aredepicted in the graphs as bars. DMRT (Duncan's Multiple Range Test)analysis was depicted as alphabets (superscripts) in tables and figureswith ‘a’ representing highest values. Similar alphabets indicate non-significant (p b 0.05) mean values. Correlation coefficients were calcu-lated using Microsoft Excel package and analyzed for their significanceusing Pearson's tables.

3. Results

3.1. Water quality

Preliminary studies on the variation in sewage wastewater physico-chemical characteristics at 2 h intervals, revealed highest EC, TDS, salinity

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Fig. 1. Physicochemical characteristics of sewagewastewatermeasured atmonthly intervals A.Nvalues and lower case alphabets represent DMRT ranking for COD values); B. COD and Heavy m

(1930 μS cm−1, 1350 mg L−1 and 983 mg L−1 respectively) between11:30 a.m. and 12.30 noon. Similarly, the amount of nutrients (NO3–N,NH4–N and PO4–P) was also found to be highest (71.93, 30.64 and3.76 mg L−1 respectively) at this time (Supplementary Table 1). There-fore, this time interval was selected for collection of samples, on amonth-ly basis, during the present study.

The various physicochemical characteristics showed a significantvariation in terms of concentration and composition of different chem-ical constituents studied. Colour of the sewagewastewaterwas turbid inmost months of the study period. The pH of wastewater showed valuesranging from 7.9 to 8.4. The highest EC, TDS, salinity, alkalinity, chloride,water hardness and Ca content were measured in February and lowestin September (except alkalinity and Ca content) as given Tables 1 and2. Highest free CO2 and acidity were recorded in December (Supple-mentary Table 2).

COD levels in sewage wastewater samples in our study showed awide variation ranging from 1200 to 14,000 mg L−1 (Fig. 1A) and washigher in the winter months i.e. October to December (Fig. 1A). On theother hand, an opposite trendwas recorded in BOD levels with relative-ly low values from October to December (Table 1). The DO in sewagewastewater did not exhibit any distinct trend and was not detectable,in most of the months during the year of investigation (Table 1).

The sewage wastewater in our study was found to be rich in nutri-ents and reflected a high variation in its enrichment levels during thestudy period. PO4–P levels of sewage wastewater did not show muchvariation during the study period, however, highest values were mea-sured in April (Table 2). NO2–N levels in the sewage water were verylow and detectable only during the summer season (Table 2). NH4–Nand NO3–N levels in sewage wastewater revealed a wide variation(Table 2, Fig. 1A). Highest NH4–N levels were observed in March

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O3–N, COD and species richness (upper case alphabets represent DMRT ranking for NO3–Netal concentration.

140 N. Renuka et al. / South African Journal of Botany 90 (2014) 137–145

(Table 2), while, highest NO3–N level was recorded in June (Fig. 1A).High species richness in summer months (particularly in June) wascorrelated with the high NO3–N level and low COD levels (Fig. 1A).

The sewage wastewater in this study also showed the presence ofheavy metals viz. Cr, Co, Pb, As and Cd. The level of these heavy metalsvaried in terms of their presence and concentration throughout theyear (Fig. 1B). Cr was observed throughout the year, with its concentra-tion varying from 3 to 4 mg L−1. Cd was present in all the monthsexcept December; higher Cd concentrations were observed in September

B

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Fig. 2. Photomicrographs of dominant cyanobacteria present in sewage wastewater A. AphanocF. Lyngbya sp.; G.Oscillatoria sp.; H. Phormidium sp.; I. Limnothrix sp.; J.Anabaena sp.; K. Lyngbya

and November. The highest heavymetal concentrations for Cr, Co, Pb andAs were recorded in December. On the other hand, higher number ofheavy metals was recorded in March, September, October and December(Fig. 1B).

3.2. Microalgal diversity

The sewagewastewater samples in this study harbouredmicroalgaebelonging to the different divisions viz. Cyanophyta, Chlorophyta,

C D E

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apsa sp.; B. Chroococcus sp.; C. Synechocystis sp.; D. Planktothricoides sp.; E. Planktothrix sp.;sp.; L. Fischerella sp.; M.Westiellopsis sp.; scale Bar represents 20 μmexcept panel B (5 μm).

A B

DC E

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Fig. 3. Photomicrographs of dominant microalgae present in sewage wastewater A. Chlorococcum sp.; B. Chlorella sp.; C. Scenedesmus sp.; D. Ulothrix sp.; E. Spirogyra sp.; F. Vaucheria sp.;G. Navicula sp.; H. Nitzschia sp.; I–J. Euglena sp.; scale Bar represents 20 μm except panels A (10 μm), B and C (5 μm).

141N. Renuka et al. / South African Journal of Botany 90 (2014) 137–145

Bacillariophyta, Xanthophyta and Euglenophyta (Figs. 2, 3 and 4A).Microalgae belonging to Cyanophyta and Chlorophyta, were present inall themonths (Fig. 4A). A total of 27 genera belonging to different divi-sions were recorded during the study period. The commonly presentgenera were Phormidium, Oscillatoria, Lyngbya, Limnothrix, Anabaena,Chroococcus, Ulothrix, Chlorella, Chlorococcum, Nitzschia and Navicula.Phormidium was the dominant genus, which was present throughoutthe year (Figs. 2 and 3). Vaucheria sp. was present only in December,while Bacillariophytamembers were absent in this month. Highest spe-cies richness (17) was observed in June. Cyanophyta was the dominantdivision throughout the study period, accounting for 34–67% of totalspecies richness, with an average of 46% (Fig. 4A).

Only three divisions; Cyanophyta, Chlorophyta and Bacillariophytawere taken into account for indices, since only these three groupswere the most predominant throughout the study period. Shannon-Wiener and Simpson's diversity indices ranged from 0.56–1.078 and

0.375–0.653 respectively, indicative of low microalgal diversity(Fig. 4B). The lowest values of Shannon-Wiener index (0.56) andSimpson's diversity index (0.375) were recorded in December.

3.3. Relationships between physicochemical characteristics, speciesrichness and indices

The study revealed that heavy metals (number and/or their concen-tration) had a significant effect on the COD, and exhibited a strong pos-itive correlation of COD level with Cr concentration (r = 0.73) and totalheavy metal concentration (r = 0.77) (Fig. 5A and B). COD levels nega-tively correlated with Shannon-Wiener (r = 0.76), Simpson's diversityindex (r = 0.79) and species richness (r = 0.67) (Fig. 5B,C andD). Spe-cies richness positively correlated with the NO3–N (r = 0.68) andwater temperature (r = 0.63) (Fig. 5E and F). Interestingly, a strong

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Fig. 4.Microalgal diversity evaluated at monthly intervals and represented as A. Percent contribution of different divisions of microalgae to the total species richness; B. Diversity indices(Shannon–Wiener and Simpson's diversity index).

142 N. Renuka et al. / South African Journal of Botany 90 (2014) 137–145

negative correlation (r = 0.81) between Cr concentration and speciesrichness was recorded (Supplementary Fig. 1).

4. Discussion

The release of untreated industrial and domestic wastewater intonatural water systems is leading to eutrophication and proliferation ofalgal blooms, resulting in the deterioration of our aquatic resources(Yang et al., 2008). Remediation of these sites by any physical, chemicalor biologicalmethod requires thorough knowledge of the physicochem-ical and biological characteristics. Microalgae are an important part ofsuch eutrophic water systems and their composition/diversity is greatlyinfluenced by the composition of nutrients and contaminants in the sys-tem. Additionally, the composition and concentration of the chemicalconstituents in the wastewater vary throughout the day and accordingto the season, as well as depends on the time of release of wastewaterfrom the industries or domestic drainage into the water bodies. Theuse of native microalgae for the remediation of wastewaters is welldocumented (Chinnasamy et al., 2010; Zhou et al., 2012). Therefore,efforts towards bioremediation, especially combating aquatic pollutionshould be coupled with deriving in-depth understanding of the micro-/macrocommunity dynamics and nutrient fluctuations as well as theirinter-relationships. However, the reports on temporal change inmicroalgal community structure of wastewaters are scarce (Bernalet al., 2008; Chinnasamy et al., 2010). The present study deals withthe fluctuations in the physicochemical characteristics and microalgaldiversity of sewage wastewater and their inter-relationships.

The study revealed a wide variation in the physicochemical charac-teristics and microalgal diversity of sewage wastewater during thestudy period. Krishnan et al. (2007) reported similar observations insewage wastewater samples collected from different sites in Sivakasi,

Tamil Nadu, India. In the present investigation, lowest water hardnessand chloride content were recorded in the month of September,which may be due to heavy precipitation during this period. DO isanother important parameter in water quality assessment and the3–5 mg L−1 of DO are an indicator of healthy state of water (Patil andPatil, 2010). In the present study, DOwasnot detectable inmostmonthsduring the study period, indicating a high pollution load in the sewagewastewater.

Sewage wastewater in this study also exhibited high NO3–N, NH4–Nand COD levels. The amount of NO3–Nwas much above the permissiblelimit (5 mg L−1) given by WHO (World Health Organisation) (Kumarand Puri, 2012), which can be attributed to the proximity of the sam-pling site to the run-off from the agricultural fields of IARI, New Delhi,India. Similar high NO3–N and NH4–N levels have been recorded in arubber industry effluent (Senthil et al., 2012).

The sewage wastewater also showed the presence of heavymetals (Cr, Co, Pb, As and Cd), which were above than the permissi-ble limits for drinking water, as prescribed by the WHO (Kumar andPuri, 2012). However, the level of these heavy metals varied interms of their presence and concentration throughout the year.The observed variation in heavy metal concentrations may be dueto the fluctuations in the nature of discharge during the study peri-od. Previous workers have also reported the occurrence of heavymetals in contaminated freshwater bodies — Cr, Cd, Hg and Zn(Ogoyi et al., 2011). Oliveira et al. (2007) reported the presence ofCr, Cd, Cu, Pb, Hg, Mn, and Zn in urban effluents. Chen et al.(2000) found that high concentration of Pb in eutrophic lakescould be due to adjacent agricultural areas or road traffic and ex-haust gases. In the present study, the presence of Pb in sewagewastewater may be due to the run-off from the adjacent agriculturalareas into the canal. Analyses of water quality parameters, nutrient

y = -1E-05x + 0.6554R² = 0.621r = 0.788

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Fig. 5. Correlation between physicochemical parameters and microalgal diversity A. COD and total heavy metal concentration; B. COD and Simpson's diversity index; C. CODand Shannon-Wiener index; D. COD and Species richness; E. NO3–N and Species richness; F. Water temperature and species richness.

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levels and heavy metal concentration of sewage wastewater showeda high degree of variation, which is indicative of fluctuations in theinput of contaminants and precipitation during the time period ofstudy.

Microalgal diversity of sewage wastewater in the present studyrevealed the presence of members belonging to the different divisionsviz. Cyanophyta, Chlorophyta, Bascillariophyta, Xanthophyta andEuglenophyta. Cyanophyta was the dominant group throughout thestudy period and accounted for 34–67% of total species richness withan average of 46%. This is in agreement with the published work of var-ious researchers, who observed the predominance of Cyanophyceanmembers in different wastewater environments (Badr et al., 2010;Martins et al., 2010; Vasconcelos and Pereira, 2001). Vasconcelos andPereira (2001) reported that cyanobacteria frequently dominated thefacultative and maturation pond of wastewater treatment plant andconstituted 15.2–99.8% of the total phytoplankton density. Similarly,Badr et al. (2010) also observed the frequent dominance of cyano-bacteria in wastewater treatment plant with 2.2–97.8% of total phyto-plankton density. On the other hand, Chlorophyta was observed as thedominating group in carpetmill effluent, in which pH ranged from acid-ic to neutral 6.54–7.18 (Chinnasamy et al., 2010). Ghosh et al. (2012)observed that 34% of the population were Chlorophycean members,

followed by cyanobacteria (28%) in Santragachi lake (pH 6.8–7.2),West Bengal, India.

pH is one of themajor factors controlling the growth, establishmentand the overall diversity of different groups. The pH (neutral-alkaline)is known to support faster growth and establishment of Cyanophyta,than that of other microalgal groups (Nayak and Prasanna, 2007). Inthe present investigation, the predominance of Cyanophycean generacan be directly related to the neutral to alkaline pH (7.4–8.5) of sewagewastewater. Senthil et al. (2012) also reported cyanobacteria as oneof the dominant groups of algae in rubber industry effluent with pHranging from 7.28 to 7.85.

Species diversity is usually used in monitoring the ecological chang-es, and expressed using indices. These indices are derived from quanti-tative data and expressed as scores. There are many types of indicesused in ecological studies, but the Shannon-Wiener Index is most com-monly employed, as it is not greatly affected by sample size, and is use-ful in indicating the pollution and trophic status of aquatic bodies(Ghosh et al., 2012; Spellerberg, 2008). In the present study, the lowestShannon-Wiener index (0.56) and Simpson's diversity index (0.375)were recorded in the month of December. This is well supported bythe data on the higher pollution load (COD and total heavy metal con-centration) in this month. Diversity indices were negatively correlated

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with the COD levels. Shanthala et al. (2009) also observed lesser diver-sity of phytoplankton and a negative correlation of diversity indiceswith the pollution level. Although, the NO3–N level was high duringthis period (December), the observed high COD levels might be respon-sible for low species diversity in this month. On the other hand, highestspecies richness was recorded in the summer months (particularly inJune), which is correlated with high NO3–N and low COD level. Bernalet al. (2008) also reported a significant negative correlation ofmicroalgal biomass with COD and a positive correlation with NO3–Nlevel. Similarly, Kim et al. (2004) also observed the existence of a corre-lation between the prevalence of cyanobacterial species and levels inpollution. In this study, species richness was also positively correlatedwith the water temperature, which was reflected by the higher speciesdiversity observed in the summer season. Similarly, Muller (1994)found a positive correlation of biomass and growth of algae withwater temperature. In another study, higher temperature was foundto support the growth and density of epipelic algae in the Balikli DamReservoir (Kolayli and Sahin, 2009). Therefore, factors other than nutri-ent composition can also be responsible for the change in communitystructure. The biology of such complex wastewater systems is probablydependent upon the interactions among the different types of factorssuch as climate, concentration of nutrients and heavy metals, besidesspecies interaction and production of chemicals by algae (Chinnasamyet al., 2010).

The N:P ratio is an indicator of the limiting factors of growth ofalgae in eutrophic water bodies (Yang et al., 2008); when the Pconcentration in water is low vis a vis N, it can be a limiting factorinducing eutrophication symptoms, such as algal blooms (Zhao,2004). Algal species, which have the potential to compete and sur-vive in these nutrient limiting conditions, dominate the scenario.Such nutrient conditions can also stimulate the production of allelo-chemicals in certain microalgal species and exert an adverse effecton other algae, thereby inhibiting their growth (Graneli et al.,2008). The sewage wastewater in our study was found to be veryrich in N and N:P N16 indicates that P is the limiting factor for thegrowth of microalgae. Such conditions can lead to the dominanceof certain microalgal species, particularly cyanobacteria producingallelochemicals and toxins, which enhance their competitive ability(Graneli et al., 2008).

5. Conclusions

In the present investigation, the indices of microalgal diversityshowed a positive correlation with nutrients and a negative correlationwith COD and heavy metal concentrations. Highest microalgal speciesrichness was recorded in the month of June, in which high NO3–N andlow COD levels were recorded. Low values of Shannon-Wiener andSimpson's diversity indices revealed that sewage wastewater washeavily polluted. Phormidium sp. is a robust organism, as it was presentthroughout the year and could tolerate high COD and heavy metal con-centrations. In depth characterization of this strain can help in under-standing its metabolic and biochemical attributes, which can proveuseful for remediation of such wastewaters.

Supplementary data to this article can be found online at http://dx.doi.org/10.1016/j.sajb.2013.10.017.

Acknowledgements

The first author is thankful to the University Grants Commission,New Delhi for her fellowship. All the authors are thankful toChairman, Department of Botany, Panjab University, Chandigarhfor providing the funds and Division of Microbiology, Indian Agricul-tural Research Institute, New Delhi for research facilities to carry outthe present investigation. The authors highly acknowledge theWD-XRF Laboratory, SAIF/CIL, Panjab University, Chandigarh forthe analyses of heavy metals.

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