Research Paper Biological Assessment of Water Pollution ... · Many assessments of water pollution in aquatic ecosystem have focused mainly on physical and chemical characteristics

© 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim 1434-2944/12/205-0124

Internat. Rev. Hydrobiol. 97 2012 2 124–156

DOI: 10.1002/iroh.201111456

IBRAHIM NATHER KHAN*, 1 and BEGHAM, M. FIRUZA*, 2

1Ecotone Environmental Management Sdn. Bhd., Suite 912, Block A, Kelana Centre Point, Kelana Jaya, 47301 Petaling Jaya, Selangor, Malaysia, e-mail: [email protected]

2Department of Geography, Faculty of Arts and Social Sciences, University of Malaya, 50603 Kuala Lumpur, Malaysia, e-mail: [email protected]

Research Paper

Biological Assessment of Water Pollution Using Periphyton Productivity and Standing Crop in the Linggi River, Malaysia

key words: biological assessment, periphyton, diatom, chlorophyll-a, water pollution, water quality, aquatic ecology, tropical river

Abstract

Many assessments of water pollution in aquatic ecosystem have focused mainly on physical and chemical characteristics. However, until recently, biological aspects have been given little attention. Although physical and chemical methods of assessing water pollution are relatively simple to interpret, biological assessments have many strong merits. Therefore an attempt was made to use periphyton productivity (in terms of biomass ash-free dry weight, AFDW) and chlorophyll-a content (measured from periphyton colonized on glass microscope slides) to assess water pollution in the Linggi river. The Linggi River is a tropical lotic system in the country of Malaysia. As a result of increased nutri-ent enrichment due to sewage and agro-industrial wastes, analyses of accumulated periphyton on glass slides showed increased biomass AFDW from an unpolluted upstream reach to the highly polluted downstream reach of the river. In contrast to biomass, the chlorophyll-a content of the accumulated periphyton was not always directly related to the AFDW of the biomass. Though the highly polluted Sta-tion 4 showed high biomass AFDW and chlorophyll-a, due to increased nutrient enrichment. The chlo-rophyll-a values at slightly polluted Station 2 were lower than at the unpolluted Station 1. Meanwhile, the mean chlorophyll-a content observed in Linggi river was relatively high as compared to previous studies carried out in Malaysia. When the Water Quality Index (WQI) was calculated using key chemical parameters linked to organic pollution, there was a significant correlation between chemical parameters, biomass AFDW, and chorophyll-a. Though the chlorophyll-a content increased with decreases in the WQI, similar to the biomass AFDW, the chlorophyll-a values were found to be lower in slightly pol-luted Station 2 than unpolluted Station 1. Therefore it was not necessary that an increase in the biomass AFDW, due to nutrient enrichment, would always increase the chlorophyll-a in accumulated periphyton.

1. Introduction

Water pollution assessment is generally focused towards physical and chemical parameters whereas biological aspects were given little attention until recently. CAIRNS and DICKSON (1971) summarized various reasons for exclusion of biological assessment in water pollution

* Corresponding author

Biological Assessment of Water Pollution Using Priphyton Productivity 125

© 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim www.revhydro.com

studies. Although physical and chemical methods of assessing water pollution are relatively simple to interpret, biological assessment has many strong merits (CAIRNS, et al., 1976; CAIRNS and VAN DER SCHALIE, 1980). As biological organisms are somewhat interdependent, pollution affecting a particular group of organisms could alter or even destroy the balance of life in an aquatic ecosystem. Since pollutants basically affect living organisms, collection of biological data with physical and chemical data had been emphasized in water pollution assessment (WARREN, 1971; WILHM, 1975; CAIRNS, et al., 1976, and CAIRNS et al., 1982).

Various group of aquatic organisms such as algae, bacteria, invertebrates and fish have been used for in-stream biological assessment of water pollution. Algae are mostly autotrophic organisms and receive their nutrition from dissolved chemicals in water. Therefore, many researchers used algae for biological assessment of water pollution. Among algal groups attached diatoms (Aufwuchs or periphyton) are generally favoured as indicators of water pollution, especially for lotic system. Use of periphyton, particularly diatoms in biological assessment of water pollution had been reviewed by CAIRNS et al., 1972; PATRICK, 1973; WILHM, 1975 and STEVENSON, 1984.

Periphyton comprise an assemblage of organisms growing on free-surfaces of any objects such as plants, old leaves, woods, stones, rocks and plastic sheets submerged in water. Morphological and topographical variability of natural substrates prevented successful quan-titative determination of periphyton colonized on them. Periphyton colonization on natural substrates is continuous, obscuring seasonal variation in species composition and abun-dance. The rates of colonization and population turnover are difficult to estimate on natural substrates because of the difficulty in obtaining periphyton-free natural substrates. For this reasons, in the past many scientists generally measured standing crop of periphytic popula-tion and not true productivity (TIPPET, 1970). In order to overcome difficulty in studying true productivity from natural substrates, artificial substrates such as pieces of wood, sterilized smooth stones, plastic sheets, styrofoam or glass slides were used for quantitative assessment of periphyton growth (SLADECEK and SLADEKOVA, 1964; KING and BALL, 1967).

Studies of periphyton productivity in temperate lotic systems are numerous (ODUM, 1957; HOHN and HELLERMAN, 1963; CUSHING 1967, KING and BALL, 1967, WETZEL, 1975). How-ever, only a few publications concern tropical lotic systems and these have been reviewed by BISHOP (1973), HO (1976) and NATHER KHAN et al. (1987). Most of these limited studies in tropic systems focused on periphyton productivity as part of general limnological studies and were not specifically designed for biological assessment of water pollution. Therefore, NATHER KHAN (1985) carried out an extensive physical, chemical and biological assessment of water pollution in the highly disturbed Linggi river basin. This river is an important water resource for the state of Negri Sembilan in Malaysia. Several research papers were subse-quently published from this study (NATHER KHAN, et al., 1986a & 1986b; NATHER KHAN et al., 1987; WEE TEE TAN, et al., 1988; NATHER KHAN, 1990a, 1990b, 1990c, 1991a, 1991b, NATHER KHAN & LIM, 1991 and NATHER KHAN, 1992a, 1992b).

Periphyton productivity can be estimated as biomass (dry and ash-free dry weight), in terms of phyto-pigment content, determination of selected elements or element ratios, organ-ic matter, and caloric content, etc. Some researchers estimated periphyton biomass or pro-ductivity through microscopic counting methods (BROWN, 1976; CATTAENO and KALF, 1978). As the periphyon accumulated on artificial substrates contains considerable amount of inor-ganic and organic detritus, as well as heterotrophic organisms, direct gravimetric analysis of accumulated matter may not give a good estimate of periphyton biomass (JONES, 1978). For this reason, the content of chlorophyll-a in accumulated periphyton was estimated because it is more closely linked to photosynthetic activity and was a partial function of cell surface area or volume (SMAYDA, 1978). Net periphyton productions estimated on artificial substrates excluded decomposition, excretion, grazing and loss during currents (VOLLENWEIDER, 1974).

As diatoms are numerically abundant in periphyton community of Linggi River, quantita-tive assessments of the diatom community were also estimated by using microscopic count-

126 I. N. KAHN and B. M. FIRUZA


Figure 1. Location of sampling stations (Station 1, 2 and 4), urban and industrial centres in the Linggi river basin. Sungai (Sg.) = rivers, streams, tributaries.



ing methods. The results were published elsewhere (NATHER KHAN, 1990a, 1990b, 1991a). The presend paper covers an additional investigation carried out during this period on peri-phyton productivity and standing crop estimated by measuring biomass ash-free dry weight (AFDW), and chlorophyll-a content with reference to existing pollution and river water quality at three stations located at main channel of the upper Linggi River. The assumption during the investigation was that, although tested in a temperate lotic system, changes in the physical-chemical properties of the river water would also influence periphyton production and standing crop. An attempt was made to use periphyton biomass AFDW and chlorophyll-a data to assess water pollution particularly in the Linggi river of Malaysia and a tropical river in general.

2. Study Area

The Linggi river basin is one of the major polluted river basins in Malaysia, located 2°24ʹ–2 °50ʹ N latitude and 101°53ʹ–102 °12ʹ E longitude at south-western part of the state of Negeri Sembilan in Malaysia. There are two distinct sub-basins, Linggi (sensu stricto) and Rembau. The Linggi sub-basin with, about 523 sq. km, covers the entire area from its source until where it joins Rembau sub-basin (Fig. 1). The Linggi basin has more than 21 major tributaries of which seven are located above the town of Seremban, the state capital. There are two urban centres (100,000 persons or more) in the study area including Seremban and Port Dickson. The later is an important coastal tourism town. Predominant types of land uses in the basin were rubber and oil palm cultivation, small areas of rice fields, urban and industrial areas. The area under investigation received mostly treated and untreated urban and industrial wastes from Seremban municipality area. Domestic sewage from Seremban ranks highest among all pollutants. Water from the Linggi river was used extensively for domestic, industrial, irrigation, drinking and bathing purposes. Water was extracted and treated at three locations within the basin. The water treatment plant (WTP) located near the town of Kuala Sawa on a highly polluted reach of the river, provided most of domestic and industrial water supply requirements for the Seremban and Port Dickson municipalities.

3. Materials and Methods

3.1 Periphyton and Water Quality Sampling Stations

Three stations (Stations 1, 2 and 4) were established at the main channel of upper Linggi river (see in Fig. 1). The stations were selected based on the nature and degree of pollution, for monthly water quality and periphyton productivity studies, over a period of 13 months. The Station 1 was established at the Batang Penar River. The main upstream channel of this river situated at an altitude of about 272 m asl, 7 km below its apparent source from the Berembun forest reserve and about 40 m downstream of the Pantai water treatment plant near the village of Pantai. The average width and depth of the river at Station 1 was 5.7 m and 0.23 m respectively. The substrate consisted predominantly of boulders, stones, gravel and sand. The water there was clean and unpolluted and had no discharge from any point source of pollutants. The Station 1 was located at a typical forest stream with uniform riffles and pools, and was considered as an unpolluted control station. The Station 2 was established downstream of Batang Penar river at an elevation of 258 m asl, immediately below the confluence of Batang Penar and Jera-lang rivers near the village of Bukit Kubot. The average width and depth of the river at Station 2 was 8.1 m and 0.22 m respectively. Sandy substrates predominated. The water was slightly polluted owing to occasional discharges of household sewage and refuse from nearby villages. Station 4 was established downstream of the Linggi river (sensu stricto), at an altitude of 132 m asl, near the bridge at the town of Membau, within the Seremban municipality area, and about 4 km upstream of Linggi water treatment



intake point. The substrates at Station 4 were generally sandy with scattered stones all along the river banks. The water here was highly polluted with urban and industrial wastes, the former being more dominant and discharged mainly from the Seremban municipality area.

3.2 Water Quality Measurement

Over 29 physical and chemical parameters including river flow, river discharge, organic, inorganic and heavy metals were collected monthly at each station over a period of 13 months. Water samples were collected from middle of water column using prewashed polyethylene bottles. A Van Dorn sampler was used to collect water from the deeper downstream Station 4. The temperature, pH, and conductivity were measured in the field and dissolved oxygen was determined immediately upon returning to the laboratory. All analytical samples were analysed within 48 hours after collection except BOD. All deter-minations were made in duplicate and repeated when precision was needed. The physical and chemical parameters analysed were based on methods outlined in APHA (1975) and MACKERETH et al., (1978). The detailed methods and result were also discussed in NATHER KHAN (1992a and 1992b).

3.3 Periphyton Sampling and Identification

Periphyton samples were collected at all three stations using glass microscope slides as artificial substrates. Glass microscope slides are readily available in convenient sizes and easy to handle for bio-mass estimation. A special periphyton collection device was constructed using wooden frames to hold 12 microscope slides of 75 × 25 mm. At each station two such devices, one at each bank of river were placed and held vertically parallel to current and just below water level by means of iron stakes and wire (NATHER KHAN, 1985, NATHER KHAN et al., 1987). The frames were always sited in the same location and maintained at constant depth, where light exposure and current conditions were judged to be aver-age for the site. Prior to placement, the slides were marked at both ends to limit the area to be assessed to 15 cm2 (total colonization area of 30 cm2) and washed thoroughly in 90% aqueous acetone. After approximately one month, the exposed frames were removed and replaced with new frames for the next period of colonization. Two colonised slides were chosen from each frame and preserved immediately in 80ml of a 4% formalin solution for species identification. Additional slides were taken to the labora-tory in bottles containing river water to examine periphyton in fresh condition. As far as possible, the colonized periphyton were identified to species level mostly under oil immersion (× 1000 magnification) using taxonomic keys, drawings and descriptions given in HUSTEDT (1959), PROWSE (1962a), SLADECEK (1963), MIZUNO (1964), PROWSE and RATNASABAPATHY (1970), RATNASABAPATHY (1971) and HO (1978).

3.4 Chlorophyll-a and Biomass Estimation

For biomass and chlorophyll-a determination, four colonized slides were randomly chosen from each frame and placed directly in aluminium foil-wrapped bottles containing 80 ml of 99% methanol (extractant). These bottles were kept in an ice box for transportation to laboratory. In the laboratory, the bottles contained four colonized slides in methanol were placed in dark at 2–4 °C overnight. Then the extracts were filtered through Whatman glass-fibre filters, to remove suspended materials, and made up to 100 ml with methanol in a volumetric flask. The phaeophytin-corrected chlorophyll-a content was then determined spectrophotometrically at 665 nm (HO, 1978). Dilute hydrochloric acid (0.1M) at 0.1 ml /25 ml extract was used for phaeophytinisation of chlorophyll-a (RIEMANN, 1976). Subsequently, this was neutralised with magnesium carbonate. The equation of MARKER (1972) was used to calculate the chlorophyll-a concentration of periphyton samples. In order to estimate dry and ash-free dry weight of periphyton, the methanol extract and particulate matter of each sample were combined, placed in a porcelain crucible, evaporated on a steam bath to remove methanol, dried to a constant weight at 105 °C, and then weighed to obtain dry weight. The sample was then ignited for 1 hour at 500 °C. Before meas-uring ash weight, the ash was re-wetted with distilled water and dried to constant weight at 105 °C. This step was taken to reintroduce water of dehydration of clay and other minerals not driven off at 105 °C but lost during ashing (APHA, 1975).



4. Results and Discussion

4.1 River Water Quality – Physical and Chemical Characteristics

The water quality measurements carried out monthly over a year, indicated that Stations 1 and 2 were characterised by relatively low ionic content, slightly neutral pH, high silica and oxygen contents, and low BOD and COD. The ammoniacal-nitrogen was very low and nitrites were also low or in undetectable levels. Nitrates and phosphates also showed low values (Tables 1 and 2). Drastic changes in the physical and chemical constituents above were found at Station 4. Very high specific conductivity, ammoniacal-nitrogen, nitrate, BOD and consistently low oxygen and acidic pH were recorded at the Station 4 (Table 3). More detailed water quality data including spatial and temporal variation over a period of 13 months were discussed in detail in previous publications (NATHER KHAN and LIM, 1991, NATHER KHAN, 1992a and 1992b, NATHER KHAN and FIRUZA, 2010).

4.2 Periphyton Species composition and Relative abundance

The periphyton colonised on glass slides showed a great diversity of life forms, with outer loose growth of unbranched or branched filamentous algae and an inner layer of strongly attached sessile algae. The filamentous algae were mostly species of Spirogyra, Ulothrix, Oedogonium and the sessile algae were mostly diatoms and members of Chaetophorales. In addition there were lots of detritus, planktonic or pseudo-epiphytic unicellular or colonial algae (desmids, volvocales, etc.), flagellates, stalked ciliates, fungi and bacteria. The occur-rence and abundance of these organisms varies from station to station. Bacteria are the first colonizers followed by diatoms, green and blue green algae. Diatoms are the most rapid and efficient colonizers in the glass slides (NATHER KHAN, 1985, 1990a, 1990b; NATHER KHAN et al., 1987).

Diatoms showed the most diversity in periphyton colonized on the glass slides, particu-larly at Stations 1 and 2. A total of 70 diatoms species were recorded at all three stations; 43 from Station 1, 42 from Station 2 and 27 from Station 4. Thirty and seventeen common species were found between Stations 1 and 2 and Stations 1 and 4 respectively. Between Stations 1 and 2, twelve new but rare species were recorded at Station 2. Seventeen com-mon species were recorded between Stations 2 and 4 and 15 new but rare species appeared at Station 4 that were not recorded at all at Stations 1 and 2. Most of the species recorded at Station 1 were epilithic diatoms commonly found in clean and rocky streams (Table 4 and Figure 2).

For the whole year of sampling, the most abundant diatom species at Station 1 were Achnanthes saxonica (38%) and Achnanthes minutissima (35%), Achnanthes saxonica (59%) and Eunotia vanheurckii (22%) at Station 2 and Nitchzia palea (43%) and Gomphonema parvulum (37%) at Station 4 (Table 4). The abundance of Achnanthes minutissima in Sta-tion 1 and abundance of Eunotia vanheurckii in Station 2 are different from each other. The increased urban and industrial wastes at downstream Station 4 are reflected in the absence of many clean water species found in Stations 1 and 2, and the abundance of three pollutant tolerant diatom species of Nitchzia palea, (43%), Gomphonema parvulum (37%) and Pin-nularia braunii (7.2%). The number of species recorded at Station 4 was less (27) compared to the slightly polluted Station 2 (42) and unpolluted Station 1 (43) (NATHER KHAN, 1990a and 1990b).



Tabl

e 1.

W

ater

qua

lity

at s

ampl

ing

Stat

ion

1 in

the

Ling

gi R

iver

bet

wee

n Ja

nuar

y 19

83 to

Jan

uary

, 198

4.

Para

met

er24

.1.8

32.

3.83

22.3

.83

26.4

.83

29.5

.83

26.6

.83

30.7

.83

27.8

.83

26.9

.83

28.1

0.83

25.1

1.83

29.1

2.83

30.1

.84

Tim

e–

17.0

011

.26

10.4

011

.00

18.4

012

.55

4.40

12.0

511

.45

11.0

510

.06

11.0

5W

idth

(m

)8.

17–

7.45

7.45

6.89

3.42

4.77

3.85

3.89

4.40

4.85

5.5

4.36

Dep

th (

m)

0.18

–0.

090.

130.

090.

210.

280.

200.

180.

310.

420.

260.

38Ve

loci

ty (

m/s

ec)

––

0.16

0.36

0.13

0.15

0.10

0.38

0.40

0.33

0.29

0.31

0.37

Dis

char

ge (

cum

ec)

––

0.92

3.35

0.69

1.03

1.21

2.66

2.51

4.39

5.51

4.38

5.78

Air

Tem

p. °C

30.0

30.0

30.0

29.0

31.5

31.0

32.5

27.0

31.5

29.0

29.5

26.0

27.0

Wat

erTe

mp.

°C25

.031

.028

.026

.529

.027

.027

.026

.025

.525

.023

.523

.023

.0pH

6.56

5.88

5.26

6.7

6.55

5.81

6.04

6.20

5.82

6.05

5.38

5.60

6.75

Con

duct

ivity

25.0

31.0

44.0

25.0

32.0

36.0

27.0

32.0

23.0

25.0

25.0

31.0

25.0

Alk

alin

ity–

–39

.035

.07.

051

.038

.038

.036

.012

.08.

011

.010

.0C

hlor

ide

––

1.90

2.4

1.5

1.6

0.9

7.48

4.5

5.5

5.0

3.45

2.5

DO

8.33

6.5

7.31

8.50

8.43

7.87

8.45

8.20

8.91

8.30

9.2

8.46

9.1

BO

D2.

030.

980.

680.

321.

821.

120.

991.

131.

920.

830.

991.

211.

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Tes

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23.

41.

42.

20.

821.

41.

71.

21.

20.

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4O

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70.

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0.00

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033

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mon

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103

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110

0.11

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137

0.12

40.

092

0.10

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231

0.08

20.

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0.15

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158

0.05

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0.09

10.

087

0.06

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059

0.05

00.

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0.06

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lica

21.2

15.8

20.8

18.7

18.7

17.4

18.7

20.0

22.0

17.4

18.3

17.0

16.6

TSS

57.1

124.

043

.259

.662

.665

.758

.654

.879

.838

.644

.655

.651

.8SS

4.7

75.0

4.5

3.6

2.6

2.7

3.6

2.8

2.8

3.6

1.6

1.6

1.8

DS

52.4

49.0

38.7

56.0

60.0

63.0

55.0

52.0

77.0

35.0

43.0

54.0

50.0



Tabl

e 2.

W

ater

qua

lity

at s

ampl

ing

Stat

ion

2 in

the

Ling

gi R

iver

bet

wee

n Ja

nuar

y 19

83 to

Jan

uary

, 198

4.

Para

met

er24

.1.8

32.

3.83

22.3

.83

26.4

.83

29.5

.83

26.6

.83

30.7

.83

27.8

.83

26.9

.83

28.1

0.83

25.1

1.83

29.1

2.83

30.1

.84

Tim

e–

17.3

012

.40

11.4

511

.50

12.2

513

.50

16.0

012

.25

12.4

511

.30

11.0

511

.30

Wid

th (

m)

5.65

–6.

425.

97.

598.

168.

6210

.84

9.87

8.2

7.91

8.8

8.97

Dep

th (

m)

0.25

–0.

660.

110.

090.

130.

140.

180.

180,

260.

200.

190.

24Ve

loci

ty(m

/sec

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620.

510.

410.

540.

520.

550.

570.

930.

570.

540.

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415.

245.

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9.42

19.2

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318.

1019

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Air

Tem

p. °C

30.0

30.0

30.0

32.0

32.0

28.0

30.5

27.0

29.5

30.5

27.5

24.0

27.0

Wat

erTe

mp.

°C28

.031

.029

.029

.029

.526

.028

.528

.527

.527

.025

.524

.024

.0pH

5.56

5.70

5.06

6.65

6.50

5.57

5.80

5.76

5.49

5.20

4.64

5.04

5.86

Con

duct

ivity

30.0

30.0

45.0

35.0

37.0

36.0

31.0

30.0

33.0

25.0

30.0

33.5

30.0

Alk

alin

ity–

–36

.049

.027

.050

.045

.040

.046

.011

.012

.011

.011

.0C

hlor

ide

––

2.8

3.0

2.9

2.7

1.0

7.48

4.50

4.50

4.0

3.45

2.50

DO

7.1

6.0

6.5

7.2

7.37

7.87

7.56

6.73

7.66

8.03

8.8

8.05

8.10

BO

D2.

51.

010.

650.

421.

121.

020.

821.

421.

550.

921.

012.

022.

12C

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/PV

Tes

t2.

22.

23.

41.

42.

02.

459.

43.

521.

212.

61.

210.

20.

32O

rthop

hosp

hate

0.00

70.

016

0.01

60.

007

0.00

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007

0.00

70.

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0.00

0.03

30.

033

0.24

Am

mon

iaca

l-N0.

150

0.28

40.

131

0.11

00.

137

0.09

70.

127

0.06

20.

007

0.10

0.10

0.08

0.11

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itrite

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001

0.00

0.00

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0007

0.00

070.

000.

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0.00

030.

0007

0.00

Nitr

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079

0.13

40.

140

0.09

30.

069

0.11

60.

128

0.12

20.

063

0.13

30.

087

0.02

50.

105

Silic

a22

.514

.920

.418

.717

.917

.418

.719

.121

.715

.820

.017

.016

.6TS

S19

2.5

3388

.092

.771

.875

.75

99.7

72.4

105.

672

.085

.470

.055

.685

.0SS

138.

333

22.0

25.4

4.8

7.75

28.7

10.4

35.6

6.0

26.4

10.0

1.6

19.0

DS

54.2

56.0

67.3

67.0

68.0

71.0

62.0

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Tem

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30.0

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4.3 Periphyton Biomass and Chlorophyll-a Content

The periphyton biomass, in terms of ash-free dry weight (AFDW) at all three stations, ranged from 167.0 mg/m2 – 28782.0 mg/m2. Low values were always recorded at the unpol-luted Station 1 (mean 95 mg/m2), followed by slightly polluted Station 2 (mean 192 mg/m2) and highly polluted Station 4 (mean 1211 mg/m2). Consistently high biomass AFDW was recorded at highly polluted Station 4, which also showed highest ash content (Table 5 and Fig. 2). Increased biomass AFDW at the downstream reach as seen in Station 4 of Linggi River was due to increased nutrient enrichment due to sewage and other organic pollutants. Though based on biomass accumulation in terms of AFDW, the slightly polluted Station 2 showed slightly higher values than unpolluted Station 1. Both the stations showed somewhat similar temporal variations throughout the sampling period as compared to highly polluted Station 4 which showed wider temporal fluctuation during our one year investigation. Simi-larly the rate of biomass AFDW per day showed similar pattern as total biomass AFDW for the full incubation period. The rate of AFDW per day ranged from 5.96 mg/m2 to 846.56 mg/m2/day for all three stations with mean values of 30.75 mg/m2/day at unpolluted Station 1, 60.95 mg/m2/day at slightly polluted Station 2 and 384.6 mg/m2/day at highly polluted Station 4. The values of biomass AFDW, and rates of AFDW, were generally higher in the months of April in all three stations, although at Station 4 they were more pronounced dur-ing the dry low flow period.

Table 4. The mean constancy value (C in %) and mean abundance (A in %) of 17 major diatom taxa recorded at Stations 1, 2 and 4 in the Linggi River.

No. Diatom Species Linggi river (s.s.)

Station 1 Station 2 Station 4

C A C A C A

1 Achnanthes minutissina 100 35 75 2 16 + 2 Achnanthes linearis 100 4.0 92 1.3 25 + 3 Achnanthes saxonica 100 38 100 59 58 3 4 Cymbella javanica 100 7 92 1 8 + 5 Cymbella sumatrensis 92 1 50 2 – – 6 Cymbella turgida 100 1.5 75 + – – 7 Eunotia vanheurckii 92 1 100 22 67 + 8 Gomphonema parvulum 100 4 83 3 100 37 9 Navicula cryptocephala 92 + 92 2 67 + 10 Navicula globosa 25 + 17 + 16 + 11 Melosira italica – – 42 + 58 + 12 Nitzschia palea 50 + 67 + 100 4313 Pinnularia microstauron 25 + 58 + 42 + 14 Pinnularia braunii 42 + 33 + 100 7.215 Pinnularia biceps – + 50 + 5 + 16 Synedra rumpens 100 4.2 83 1.5 – –17 Surirella tenuissima 83 + 67 + 8 +

Note: C – Constancy value in percentage; A – Relative Abundance in percentage; + less than 1% abundance; – the species was absent; Sg. – sungai = river, stream; s.s – sensu stricto(Constancy is defined as the number of occurrences of a particular species in the samples and expressed as a percentage of total number of samples examined (William and Soltero, 1978)).



Station 1: Achnanthes saxonica (38%) and Achnanthes minutissima ( 35%).

Station 2: Achnanthes saxonica (59% ) and Eunotia vanheurckii (22%).

Station 4: Nitzschia palea var. braunii (43%) and Gomphonema parvulum ( 37%).

Figure 2. Two most common and abundant species at Stations 1, 2 and 4 in the Linggi river, Malaysia (based on average values of 13 months data).



Periphyton production, in terms of chlorophyll-a content measured at all the three stations, ranged from 5 mg/m2 to 155 mg/m2 with mean values of 28.58 mg/m2 at unpolluted Station 1, 12.5 mg/m2 at polluted Station 2, and 98.50 mg/m2 at highly polluted Station 4 (Table 6 and Fig. 3). Interestingly, chlorophyll-a values were consistently higher at the unpolluted Sta-tion 1 as compared to the slightly polluted Station 2 which showed higher biomass AFDW compared to Station 1. Both Stations 1 and 2 showed a similar pattern of temporal variation during the one year sampling period. The highly polluted Station 4 showed consistently higher chlorophyll-a content but the temporal variation was wider compared to unpolluted Station 1 and slightly polluted Station 2. The pattern was somewhat similar to biomass AFDW values obtained for Station 4. The chlorophyll-a content ranged from 0.185 mg/m2/day to 5.536 mg/m2/day with mean value of 0.919 mg/m2/day at Station 1, 0.400 mg/m2/day at Station 2, and 3.182 mg/m2/day at Station 4.

The increased productions of periphyton, in terms of both biomass AFDW and chlo-rophyll-a at the highly polluted Station 4 (as compared to Stations 1 and 2), was due to increased growth of pollution tolerant filamentous algae as well as diatoms. However the total number of diatom species recorded at Station 4 was low compared to Stations 1 and 2 (NATHER KHAN, 1990a and 1990b). Station 1, with the lowest mean biomass AFDW, showed a higher chlorophyll-a content than that of Station 2. This indicated that the major portion

Table 5. Biomass AFDW (mg/m2) and rate of AFDW (mg/m2/day) of periphyton accumulated on glass slides.

Period ending

No. of days

Biomass ash-free dry weight Rate of biomass ash-free dry weight

Station 1 Station 2 Station 4 Station 1 Station 2 Station 4

24.01.83 28 167.0 208.0 1066.0 5.96 7.43 38.0722.03.83 29 1292.0 1858.0 8291.0 44.55 64.07 285.8926.04.83 34 1821.0 3183.0 28782.0 53.56 93.62 846.5329.05.83 31 1124.0 1818.0 8833.0 36.25 58.65 284.9425.11.83 27 958.0 1449.0 12810.0 35.48 53.67 474.4429.12.83 34 845.0 2080.0 10100.0 24.85 61.18 297.0630.01.84 32 467.0 2817.0 14857.0 14.59 88.03 464.28

Mean 953.0 1916.0 12106.0 30.75 60.95 384.46

Table 6. Chlorophyll-a (mg/m2) and rate of chlorophyll-a (mg/m2/day) of periphyton accu-mulated on glass slides.

Period ending

No. of days Chlorophyll-a Rate of Chlorophyll-a

Station 1 Station 2 Station 4 Station 1 Station 2 Station 4

24.01.83 28 22.5 10.0 155.0 0.804 0.357 5.53622.03.83 29 n.d n.d n.d n.d n.d n.d26.04.83 34 22.5 5.0 50.0 0.662 0.147 1.47129.05.83 31 57.5 25.0 93.75 1.855 0.806 3.02425.11.83 27 15.0 5.0 35.0 0.556 0.185 1.29629.12.83 34 26.75 17.5 131.25 0.787 0.515 3.86030.01.84 32 27.25 12.5 125.0 0.852 0.391 3.906

Mean 28.58 12.5 98.50 0.919 0.400 3.182

Note: n.d. – no data collected due to weather.



Figure 3. Biomass AFDW (mg/m2) of periphyton accumulated on glass slides.

of organic content in the glass slides, at unpolluted Station 1, was comprised of photosyn-thetic organisms compared to slightly polluted Station 2. Difference in biomass AFDW and chlorophyll-a between Stations 1 and 2 might also due to substratum, though this has to be investigated further. The whole stretch of the river, where Station 1 was located, contained rocky substrates with abundant boulders and stones, whereas the region where Station 2 was located contained mainly sandy substrates with scattered stones with no big boulders. The organisms with strong attachment abilities including some filamentous algae were found more in greater number at Station 2 compared to Station 1. Moreover, the river conditions, including mild sewage pollution, had facilitated an increased growth of filamentous algae and other heterotrophic organisms especially vorticellids, protozoans, and sphaerotilus. This resulted in greater biomass over chlorophyll-a content at downstream slightly polluted Sta-tion 2. HO (1976) also reported that relationships between pigment content, biomass and pro-ductivity were seldom consistent. They may vary depending upon species involved, physi-ological state of the community and environmental conditions such as light and temperature (WETZEL, 1963; HICKMAN, 1973). In contrast, BREHMER and GREZENDA (1960) and CUSHING (1967) reported a significant correlation between chlorophyll-a and biomass AFDW.

WEBER and MCFARLAND (1969) proposed an index using the biomass AFDW and chloro-phyll-a content of periphyton community. The Index (Iq) values were calculated by divid-ing biomass AFDW (mg/m2) by chlorophyll-a content (mg/+m2) (WILHM, 1975). Normally, the Index values should be lower in unpolluted stations, or only slightly polluted stations, where most of the organic content accumulated on the glass slides would be that of algae. In contrast values is the highly polluted reaches should be higher because the accumulated biomass would be mainly composed of filamentous bacteria and other non-chlorophyllous organisms. However in the present study, the Index values were consistently lower at the unpolluted Station 1. Surprisingly the values obtained at slightly polluted Station 2, and highly polluted Station 4, had somewhat similar values and patterns as shown in graph in Figure 5. This suggests that there was no clear distinction between slightly polluted Station 2 and highly polluted Station 4 based on Iq values.



Figure 4. Chlorophyll-a (mg/m2) of periphyton accumulated on glass slides.

Table 7. Ratio (Iq value) and percentage (%) of biomass AFDW and chlorophyll-a.

Period Station 1 Station 2 Station 4

Iq value % Iq value % Iq value %

24.01.83 7.42 13.47 20.80 4.81 6.88 14.5426.04.83 80.93 1.24 636.60 0.16 575.64 0.1729.05.83 19.55 5.12 72.72 1.38 94.22 1.0625.11.83 63.87 1.57 289.80 0.35 366.00 0.2729.12.83 31.59 3.17 118.86 0.84 76.95 1.3030.01.84 17.14 5.84 225.36 0.44 118.86 0.84Mean 36.75 5.07 227.36 1.33 206.43 3.03

The mean chlorophyll-a content, expressed as percentage of biomass AFDW, showed 2.9% for unpolluted Station 1, 0.66% at slightly polluted Station 2 and 0.81% at highly pol-luted Station 4 (Table 7 and Fig. 6). SLADECEK and SLADECKOVA (1964) reported a range of 0.09 – 2.4% while ROUND (1965) reported at even wider range of 0.01 – 6.0%. The relatively low percentage of chlorophyll-a in the Linggi river, as compared to temperate rivers, was due to the overall poor growth of algae. In addition, the accumulated algae on slides were predominantly diatoms, except at the highly polluted Station 4, which contributed relatively low chlorophyll-a content compared to other periphytic green algae. However, the mean chlorophyll-a content observed in the Linggi River was relatively high in comparison to previous studies carried out in Malaysia (BISHOP, J. E. 1973; HO, 1976).



Figure 6. Percentage of periphyton biomass AFDW and chlorophyll-a at three stations.

Figure 5. Ratio between periphyton biomass AFDW and chlorophyll-a at three stations.



Table 8. Correlation Co-efficient between AFDW with water quality parameters.

Variables Station 1 Station 2 Station 4

AFDW & Conductivity 0.303467 0.169357 0.457146AFDW & BOD –0.7653 –0.4553 0.87308AFDW & DS 0.030258 0.609075 0.767209AFDW & DO –0.28095 0.089021 –0.6049AFDW & Ammoniacal-N 0.346457 –0.52222 0.501121AFDW & COD 0.247306 –0.40443 –0.36986

4.4 Relationship between Chemical and Biological Data

The biomass AFDW data from each station were compared with some key organic pollu-tion (water-quality-linked) parameters like conductivity, dissolved solids, dissolved oxygen, BOD, COD, and ammonical-nitrogen. Within stations, there were few correlations between chemical parameters with biomass AFDW at unpolluted Station 1 (Table 8). No positive correlations were recorded within Station 2 except for dissolved solids. Increases in BOD, due to minor sewage pollution at Station 2, resulted in corresponding increase in biomass in AFDW and decrease in chlorophyll-a. However, correlations between chemical and bio-mass AFDW were very distinct at highly polluted Station 4. Increased organic pollution at downstream Station 4 had increased biomass AFDW of accumulated periphyton. Most of the chemical parameters, including conductivity, dissolved solids, BOD and ammonical-nitrogen positively correlated with positively biomass AFDW and negatively with dissolved oxygen.The above chemical parameters were compared with chlorophyll-a recorded at all the sta-tions. Interestingly, most of the chemical parameters were correlated positively with chlo-rophyll-a at unpolluted Station 1 (Table 9). Similarly, at slightly polluted Station 2, the conductivity, BOD, COD and ammonical-nitrogen correlated positively with increases in chlorophyll-a, although the pattern was not as perfect as like Station 1. However, at the highly polluted Station 4, the relationship between chemical parameters and chlorophyll-a, (unlike biomass AFDW), was somewhat haphazard and did not show any pattern, even though the chlorophyll-a values were higher than at Stations 1 & 2 (Table 10).

Table 9. Correlation Co-efficient between Chlorophyll-a and water quality parameters.


Chlorophyll-a & Conductivity 0.7656226 0.5733014 – 0.5909962Chlorophyll-a & BOD 0.4644904 0.2299698 – 0.6836255Chlorophyll-a & DS 0.75971103 0.12918358 – 0.3699888Chlorophyll-a & DO – 0.4011279 – 0.2100828 0.7237215Chlorophyll-a & Ammoniacal-N 0.6016849 0.1656569 – 0.6935213Chlorophyll-a & COD 0.55883724 0.02874522 0.27456942

Table 10. Correlation Coefficient between biomass AFDW and Chlorophyll-a.


AFDW & Chlorophyll-a 0.1233981 – 0.052142873 – 0.66491



Figure 7a. Relationship between AFDW (mg/m2) and conductivity at Stations 1, 2 and 4.



Figure 7b. Relationship between biomass AFDW (mg/m2) and dissolved solids (DS) at Stations 1, 2 and 4.



Figure 7c. Relationship between biomass AFDW (mg/m2) and dissolved oxygen (DO) at Stations 1, 2 and 4.



Figure 7d. Relationship between biomass AFDW (mg/m2) and BOD at Stations 1, 2 and 4.



Figure 7e. Relationship between biomass AFDW (mg/m2) and Ammoniacal-N at Stations 1, 2 and 4.



Figure 7f. Relationship between biomass AFDW (mg/m2) and COD at Stations 1, 2 and 4.



Figure 8a. Relationship between Chlorophyll-a (mg/m2) and conductivity at Stations 1, 2 and 4.



Figure 8b. Relationship between Chlorophyll-a (mg/m2) and dissolved solids (DS) at Stations 1, 2 and 4.



Figure 8c. Relationship between Chlorophyll-a (mg/m2) and dissolved oxygen (DO) at Stations 1, 2 and 4.



Figure 8d. Relationship between Chlorophyll-a (mg/m2) and BOD at Stations 1, 2 and 4.



Figure 8e. Relationship between Chlorophyll-a (mg/m2) and Ammoniacal-N at Stations 1, 2 and 4.



Figure 8f. Relationship between Chlorophyll-a (mg/m2) and COD at Stations 1, 2 and 4.



Figure 9. Relationship between WQI and biomass AFDW.

Figure 10. Relationship between WQI and chlorophyll-a



The Water Quality Index (WQI) was calculated using six key water quality parameters commonly used to detect organic pollutants in Malaysian rivers. The WQI was derived using pH, Suspended Solids (SS), Dissolved Oxygen (DO), Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD) and ammoniacal nitrogen (AN). The sub-index values of 0.12 for pH, 0.16 for SS, 0.22 for DO, 0.19 for BOD, 0.16 for COD and 0.15 for AN were totalled to obtain the WQI. A higher WQI means a better water quality. WQI values were calculated for each station and month and provided in Table 11. The WQI was consistently higher at the unpolluted Station 1, followed by slightly polluted Station 2 and lowest at highly polluted Station 4. When WQI data was compared with biomass AFDW, there was a clear correlation between WQI and AFDW values (Fig. 8). Decreases in the WQI due to organic pollution, increased the biomass AFDW. When WQI values increased at unpolluted or less polluted station, the biomass AFDW values were decreased. When WQI values were compared with chlorophyll-a the pattern more or less similar to biomass AFDW. Interestingly, however the chlorophyll-a values were lower in the slightly polluted Station 2 as compared to unpolluted Station 1. This showed that it was not necessary that increases in the biomass AFDW would always increases in the chlorophyll-a content of colonized periphyton.

5. Conclusion

This study of periphyton productivity, over one year period in the Linggi river in Malay-sia, recorded consistently high biomass AFDW at the highly polluted Station 4, followed by the slightly polluted Station 2 and then the unpolluted Station 1. Increased biomass AFDW at downstream reaches as seen in Station 4 was due to increased nutrient enrichment owing to sewage and other organic pollutants. The highly polluted Station 4 consistently recorded high biomass AFDW and high chlorophyll-a content. However, the chlorophyll-a values, unlike biomass AFDW, were generally lower at the slightly polluted Station 2 than at the unpolluted Station 1. The index values (Iq) calculated using biomass AFDW with chlorophyll-a showed low values at unpolluted Station 1 compared to slightly polluted Sta-tion 2. Meanwhile, the index values for Station 2, although lower than at Station 4, were insignificant. When chemical parameters and biomass AFDW (biological parameters) were compared within Station 1 and Station 2, very little correlation was found between them except for conductivity (Station 1) and dissolved solids (Station 2). However, the relation-ship between some chemical and the biomass AFDW was more pronounced at highly pol-luted Station 4. Increases in conductivity, dissolved solids, BOD, ammonical-nitrogen, and decreases in dissolved oxygen tended to create a corresponding increase in biomass AFDW at this highly polluted Station 4. Meanwhile, when chemical parameters and chlorophyll-a values (biological parameters) were compared, the unpolluted Station 1 showed positive correlation with most of the parameters compared to other two stations. When WQI values were compared with the biomass AFDW, there was a clear correlation. When WQI values increased at unpolluted or less polluted stations, the biomass AFDW values decreased also. When WQI values were compared with chlorophyll-a, the pattern was more or less similar

Table 11. Water Quality Index (WQI) at Stations 1, 2 and 4 at Linggi River.

Jan’83 March April May Nov Dec Jan’84

Station 1 95.38 94.38 96.91 95.17 95.25 96.08 96.79Station 2 86.14 90.00 96.71 95.27 92.55 94.2 93.67Station 4 70.23 51.54 39.18 46.19 50.39 52.06 56.41



to biomass AFDW, however interestingly, the chlorophyll-a values were lower in slightly polluted Station 2 as compared to unpolluted Station 1. This showed that it was not neces-sarily that increases in biomass AFDW would always increase in chlorophyll-a content in colonized periphyton in glass slides. There was also dependent upon the type and rate of pollution and also upon other physical factors such as river flow and the substrate which collectively determinded the biomass AFDW and chlorophyll-a content.

6. Acknowledgements

We would like to express our sincere thanks to United Nations University (UNU), Japan, the Inter-national Foundation for Sciences (IFS), and Sweden for financing this project. Thanks are also due to Prof. J. I. FURTADO, former Science Advisor and Secretary, Commonwealth Science Council, United Kingdom, Prof. R. P. Lim, University of Technology Sydney, Australia, and Professor EMERITUS HAJI MOHAMED, University of Brunei Darussalam, Brunei and Prof. JOTHI PANANDAM, University of Putra, Malaysia, for their help, support and encouragement while we were conducting extensive research in the Linggi River Basin, Malaysia. Thanks are also due to Prof. JOHN CAIRNS, Jr., former Distinguished.

Professor and Director, Virginia Polytechnic Institute and State University, USA, for his encourage-ment, Mr. Md. Faizal Mohamed and Miss Nurul Safwah Mohd Yusoff for their support while preparing this paper.

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Manuscript submitted April 4th, 2011; revised November 25th, 2011; accepted November 30th, 2011

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Research Paper Biological Assessment of Water Pollution ... · Many assessments of water pollution in aquatic ecosystem have focused mainly on physical and chemical characteristics