Science of the Total Environment 346 (2005) 99 – 111
www.elsevier.com/locate/scitotenv
Use of the aquatic protozoa to formulate a community biotic index
for an urban water system
Jian-Guo Jianga,*, Yun-Fen Shenb
a
College of Food and Bioengineering, South China University of Technology, Guangzhou, 510640, China
b
Institute of Hydrobiology, Chinese Academy of Sciendes, Wuhan, 430072 China
Received 4 August 2004; accepted 1 December 2004
Available online 28 January 2005
Abstract
Protozoan were collected from 16 stations in water system of Changde City (China) using the PFU method. Sampling
programs were conduced on a yearly basis, with seasonal frequency at diverse sites in the water system and 488 species of
protozoa was identified. At the same time, Water sampling from these stations was conducted and various water chemical
parameters, including DO, COD, BOD5, NH3, TP, and Volatile Phenol, were analyzed. The aim of the research was, on one
hand, using chemical method to take an investigation to the water pollution status of Changde City; on the other hand, using
protozoan to make an evaluation to the water quality. With the chemical water parameters and protozoa data, a biotic index was
derived for the investigated region. The species pollution value (SPV) of 469 protozoa species was established, and the
community pollution value (CPV) calculated from SPV was used to evaluate water quality. The method of the biotic index was
tested and the result showed that CPV calculated from SPV had a close correlation with the degree of water pollution
( pb0.00001). This indicated that the method of the biotic index is reliable. The water quality degrees divided by CPV were
suggested.
D 2004 Elsevier B.V. All rights reserved.
Keywords: Biotic index; Protozoa; Species pollution value (SPV); Community pollution value (CPV); Water pollution; Water systems
1. Introduction
Protozoa has long been used as a bioindicator of
water pollution and widely applied for the biological
evaluation of water quality (Sladeckova and Sladecek,
1966; Cairns et al., 1968; Cairns et al., 1969;
* Corresponding author. Tel.: +86 20 87595610; fax: +86 20
87113842.
E-mail address: [email protected] (J.-G. Jiang).
0048-9697/$ - see front matter D 2004 Elsevier B.V. All rights reserved.
doi:10.1016/j.scitotenv.2004.12.001
Sladecek, 1973; Madoni and Ghetti, 1981; Grabacka,
1985; Albrecht, 1986; Foissner, 1988; Madoni, 1993;
Foissner, 1997; Thongchai and Orathaim, 1997;
Johanna et al., 1999; Pascoe et al., 2000; Nicolau et
al., 2001; Xu et al., 2002; Luiz et al., 2003).
Therefore, protozoa communities could provide valuable information on ecosystem health since: (a)
protozoans are comparatively world widely distributed organisms that make them more applicable; and
(b) because protozoa are characterized by relatively
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J.-G. Jiang, Y.-F. Shen / Science of the Total Environment 346 (2005) 99–111
short generation times and react rapidly to the
changing of water environment. Hence, protozoa are
a better indicator of water quality in indicating the
abrupt change and continuing changes over a short
period of time.
The potential of faunal communities to serve as
environmental quality indicators has long been
recognized by freshwater biologists and particularly
fluvial ecologists have a long tradition in application
of biotic indices based on protozoa community
characteristics (Sladecek, 1973).
Over recent decades, there has been considerable
interest in the development of meaningful indices to
express, evaluate, and monitor the environmental
quality of aquatic ecosystems (Fano et al., 2003).
However most biotic indices are suitable for the areas
from which they were devised; their application to
other areas has often resulted in an incorrect conclusion (Washington, 1984).
With this aim in mind, the present study develops
a new biotic index, the species pollution value (SPV)
and community pollution value (CPV) which is
based on the water chemical parameters and protozoa
community and is intended to overcome the above
cited problems, and to provide a tool for environmental managers and policymakers who require
simple, manageable methodologies for the classification, evaluation and monitoring of the ecological
condition of natural and degraded urban water
system.
2. Materials and method
2.1. Description of the water system
Changde City is in the province of Hunan in China
with about 200,000 population. Water system of the
city has several kinds of biotopes including river,
pond, lake, ditch, etc. (Fig. 1). Except sanitary waste,
other kinds of industrial wastes of textile, printing and
dyeing, tobacco, pharmaceuticals industry, and food
handling were discharged into moat, from there
diffused to whole water system of the city. The
releases of industrial effluent and sanitary waste were
5104–6104 t/d and 2.0104 t/d, respectively, most
of them were untreated and directly input to the three
drainage systems of moat, River wulong and Lake
Jiajiahu into corresponding lake and river, with little
into River Yuanjiang. The total coliform (TC) density
exceeded the state standard III degree of surface water
quality by 4 orders of magnitude, and according to
WHO recreational water standard, the FC density
exceeded by 3 orders of magnitude. Furthermore, the
enteric pathogenic bacteria such as Salmonella sp.
were found in moat. Bacterial contamination in other
Fig. 1. Water system of Changde City and sampling stations.
J.-G. Jiang, Y.-F. Shen / Science of the Total Environment 346 (2005) 99–111
101
Table 1
Chemical characteristics and measurements (mg/L1) of each sampling station
Stations
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
DO
COD
BOD
NH3
Grade II
N6
b15
b3
b0.02
b0.1
b0.002
spring
7.10
8.30
6.50
6.50
8.50
6.20
1.40
0.15
0.70
5.50
11.90
0.20
8.86
3.10
0.90
0.90
4.21
3.65
2.76
3.62
7.58
5.77
5.48
6.65
0.50
5.37
3.28
0.50
5.53
5.90
0.50
0.50
7.90
7.10
2.10
7.10
7.90
7.70
7.10
7.70
0.50
5.60
5.70
0.50
4.80
1.60
0.50
2.50
14.05
18.73
13.27
13.27
36.68
23.41
46.05
88.97
184.19
30.44
84.29
69.46
24.19
38.24
41.37
55.41
8.69
26.08
12.88
32.99
41.85
24.95
51.51
32.19
130.41
30.82
84.51
86.99
18.97
27.66
77.40
45.05
21.60
17.60
46.70
54.40
21.60
29.40
25.30
31.40
329.00
43.10
29.20
36.90
50.50
42.80
43.80
19.40
0.460
0.880
0.880
0.600
4.200
6.400
9.400
10.500
2.360
0.760
1.140
13.000
3.500
4.000
4.400
7.200
0.070
0.550
0.120
0.160
0.120
0.330
1.050
1.250
10.500
1.200
0.500
14.600
0.550
1.000
2.720
2.880
0.504
0.148
0.136
0.290
0.164
0.192
0.028
0.036
7.500
1.360
0.720
7.780
1.408
2.560
1.640
2.526
0.050
0.040
0.040
0.110
0.340
0.400
0.780
0.580
1.350
0.680
0.080
1.300
0.920
0.430
0.490
0.330
0.055
0.135
0.042
0.071
0.110
0.122
0.280
0.263
2.250
0.175
0.240
2.390
0.138
0.295
0.920
0.608
0.097
0.116
0.050
0.064
0.094
0.087
0.236
0.206
1.236
0.030
0.100
0.933
0.034
0.184
0.432
0.500
0.000
0.000
0.000
0.004
0.000
0.000
0.008
0.002
0.020
0.003
0.000
0.002
0.000
0.000
0.005
0.002
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.004
0.005
0.003
0.004
0.003
0.003
0.000
0.003
0.020
0.002
0.030
0.110
0.003
0.003
0.005
0.008
summer
autumn
1.34
2.00
1.90
1.90
15.60
13.90
15.40
21.75
83.00
2.90
7.25
26.60
5.50
7.70
9.60
7.00
0.10
0.80
3.40
4.00
2.30
5.00
19.60
9.10
42.80
20.70
7.40
15.90
11.20
8.40
9.10
8.90
3.10
1.20
7.50
1.20
1.90
3.40
4.20
3.00
78.50
5.20
5.30
7.60
10.02
6.10
6.70
17.90
TP
Volatile
Phenol
Pb
ln(10Pb/n)
CPV
25.02
47.04
46.84
35.84
221.75
330.85
494.29
584.98
1252.00
50.39
66.34
707.50
188.32
211.35
240.00
377.00
6.09
32.50
10.59
13.90
11.45
22.08
63.36
71.21
582.46
71.82
37.33
777.00
34.96
586.10
165.39
168.05
31.40
13.48
17.27
21.31
13.47
15.84
7.69
9.23
457.46
124.97
26.77
470.32
81.56
139.98
105.97
144.96
3.73
4.36
4.35
4.09
5.91
6.31
6.71
6.88
7.64
4.43
4.70
7.07
5.75
5.86
5.99
6.44
2.31
3.99
2.87
3.14
2.94
3.60
4.66
4.77
6.87
4.78
4.13
7.16
4.06
4.58
5.62
5.63
3.95
3.11
3.36
3.57
3.11
3.27
2.55
2.73
6.63
5.33
3.79
6.66
4.91
5.44
5.17
5.48
4.80
4.75
4.75
4.75
4.91
4.96
5.38
5.58
5.01
4.92
4.81
5.10
4.98
4.91
5.01
5.03
4.39
4.42
4.25
4.43
4.37
4.48
4.57
4.67
4.85
4.60
4.49
4.80
4.48
4.48
4.64
4.77
4.53
4.48
4.46
4.46
4.60
4.42
4.51
4.48
4.99
4.70
4.53
4.81
4.64
4.67
4.63
4.62
(continued on next page)
102
J.-G. Jiang, Y.-F. Shen / Science of the Total Environment 346 (2005) 99–111
Table 1 (continued)
Stations
1
2
4
5
6
7
8
9
10
11
12
13
14
15
16
DO
COD
BOD
NH3
Grade II
N6
b15
b3
b0.02
b0.1
b0.002
winter
10.40
10.20
9.20
10.00
1.40
0.10
0.40
1.30
2.90
7.90
0.10
6.10
3.80
1.50
1.00
5.00
6.80
13.00
8.90
56.70
84.70
178.30
327.30
41.70
17.80
25.30
7.50
19.10
41.00
35.30
0.90
2.10
2.20
1.60
29.60
21.30
17.00
127.00
17.80
5.10
21.60
1.70
8.70
28.00
13.20
0.024
0.008
0.120
0.760
9.680
17.720
21.520
16.400
10.400
1.120
16.800
3.320
6.960
6.640
5.680
0.040
0.032
0.040
0.316
0.648
1.280
1.770
2.090
1.680
0.092
1.220
0.340
0.628
0.528
0.478
0.000
0.000
0.000
0.000
0.000
0.031
0.029
0.017
0.002
0.000
0.016
0.000
0.000
0.002
0.003
water bodies including a lake in Bin Lake Park was
serious. Only suburban Lake Liuye was one of the
slightly polluted localities (Wan et al., 1994).
2.2. Protozoa sampling and water chemical analysis
Sixteen stations were set up in the water system
of the city (Fig. 1). Protozoans were collected by
PFU (Polyurethane foam Unit) method (Cairns et al.,
1969). The PFU block was about 6.56.57.5 cm
in size and blocks were soaked in distilled water for
24 h and squeezed before use. The blocks were tied
with thin ropes and placed at the depth of 1 m
below the surface water for 15–20 days. Sampling
was conducted four times for a whole year, once in
the spring, summer, autumn, and winter, respectively. At each station, two PFU blocks were
collected and each one was observed by one person
in order to obtain a complete picture of the
protozoans for each station. Samples were examined
in the laboratory within 5 h after collection. Three
slides from each sample were examined sequentially
under high, medium, and low amplifications for
species identification in order to observe more
species. Sampling was carried out in the morning
and living protozoans were identified and enumerated immediately after squeezing the PFU into a
beaker. For a whole year’s qualitative observation
and identification, the species error between two
people could not exceed 10%.
TP
Volatile
Phenol
Pb
ln(10Pb/n)
CPV
28.10
2.46
9.02
42.87
508.41
987.05
1140.75
918.17
548.58
60.66
929.09
171.45
360.03
354.35
303.00
1.54
1.41
2.71
4.27
6.74
7.40
7.55
7.33
6.81
4.61
7.34
5.65
6.39
6.38
6.22
4.55
4.69
4.67
4.79
4.89
4.98
5.01
5.16
4.99
4.74
4.99
4.82
4.84
4.90
5.00
At meantime of protozoa sampling, water samples
were collected at the depth of 1 m and chemical
measurements of water quality were taken using
standard methods. Each sample was a mixture of
several sub-samples collected from the surface to the
bottom at 0.5-m intervals. Chemical oxygen demand
(COD) and DO were measured by the national
standard of environmental protection by China EPA
(China EPA, 1989). Total phosphate (TP) in the lake
water was measured by colorimetry after digestion of
the total samples with K2S2O8+NaOH to orthophosphate (Ebina et al., 1983). Ammonium (NH4+) was
determined by the Nessler method, and nitrite (NO2)
by the a-naphthylamine method. In situ dissolved
oxygen, the concentrations of nitrate (NO3), was
determined using the automated Korolev (Dionex-100
Ion Chromatography).
2.3. The biotic index
Comprehensive chemical pollution index (PB) was
calculated by the following formulas:
n
X
CD
PB ¼
ð1Þ
Pi
Pi ¼
CO
i¼1
where Pi is the chemical pollution index for a single
chemical parameter based on grade II standard for
surface water (Environmental Quality standard of
People’s Republic Of China for surface water,
GB3838-88, see Table 1). CD is the concentration
J.-G. Jiang, Y.-F. Shen / Science of the Total Environment 346 (2005) 99–111
of tested chemical parameter in sampling station. CO
is the upper limit of the concentration of the chemical
parameter in the grade II standard for surface water. n
is the number of contributing parameters . Here n=6
(Table 1).
SPV is calculated by the following formula:
n
P
ðln10Pb=nÞi
SPV ¼ i¼1
ð2Þ
N
n is the items number of chemical parameters; and N
is the number of stations.
CPV or the biotic index of each sampling station is
calculated:
n
P
SPVi
ð3Þ
CPV ¼ i¼1
n
n is the number of species in a community.
3. Result and discussion
488 species of protozoa were classified from the
samples collected at 16 stations. In these species, 128
were Phytomastigophora, 47 were Zoomastigophora,
76 were Sarcodina, and 254 were Ciliophora. Protozoans with their distribution in different stations are
not listed here but form the basis of the present
research. The water chemical data of each station is
listed in Table 1.
3.1. SPV of species from water system of Changde
City
PB and ln(10 PB/n) are calculated according to (1).
SPVs of 469 protozoa species from water system of
Changde City are calculated according to (2) and
listed in Table 2. Some unidentified species are not
listed in Table 2 because their SPV is of little use in
application. CPV of each station is calculated according to (3) and is listed in Table 1.
The correlation analysis between comprehensive
chemical pollution index and CPV is:
CPV ¼ 4:16547 þ 0:122006 lnð10PB=nÞ;
r ¼ 0:77268;
n ¼ 63
The coefficient between CPV and PB is significant
at pb0.00001 level. This result demonstrates that the
103
method of the biotic index is reliable in the water
system of Changde City.
3.2. Water qualities divided by CPV
There is no doubt that the higher the CPV, the
higher the degree of water pollution. According to the
relationship between the CPV obtained from the final
SPV (Table 2) and the PB, the water qualities divided
by CPV for water system of Changde City is proposed
as Table 3:
The annual average CPV of each station is showed
in Table 4, according to the evaluation standard of
water quality (Table 3), the overall annual pollution
status for each station is assessed and showed in
Table 4.
3.3. The reliability of the CPV in water quality
evaluation
The selected sampling stations were essentially
the same as those sampled routinely by respective
local monitoring stations. It was decided to use and
incorporate data collected by these local stations.
According to the degrees of taken on wastewater,
the 16 stations were set-up as five classes by the
local environmental manager. Class I included
stations 9 and 12, which was the most centralized
area of wastewater. Class II included stations 10,
15, and 16, which was the centralized area of
wastewater. Class III included stations 6, 7, 8, 11,
13, and 14, which was the diffusing area of
wastewater. Class IV included stations 2, 3, 4,
and 5, which was the slightly or unpolluted area.
Station 1 was set-up on River Yuanjiang (class V)
which was clean water area. With the data of Table
1, the mean CPV of each class for the overall year
are calculated and listed in Table 5. Table 5 clearly
shows that the CPV correctly divide the water
quality of the five classes, which indicated the
application of the biotic index is reliable. From the
data of Table 4 we can reach a same conclusion. In
Table 4, stations 9, 12, and 8 were evaluated by
CPV as severely polluted water; stations 16 and 7
were heavily polluted water; stations 10 and 15
were moderately polluted water; stations 5, 6, 11,
13, and 14 were slightly polluted water; station 4
was slightly to moderately polluted water; stations
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J.-G. Jiang, Y.-F. Shen / Science of the Total Environment 346 (2005) 99–111
Table 2
List of protozoan species and their SPV
Table 2 (continued)
Species
SPV
Acanthocystis aculeata
Acanthocystis brevicirrhis
Acanthocystis erinaceus
Acanthocystis spinifera
Acineria incurvata
Acineta grandis
Acineta cuspidata
Acineta tuberosa
Actinomonas mirabilis
Actinophrys sol
Actinosphaerium eichhorni
Amoeba gorgonia
Amoeba limicola
Amoeba proteus
Amoeba radiosa
Anisonema acinus
Anisonema dexiotaxum
Anisonema ovale
Anisonema prosgeobium
Anthophysis vegetans
Arcella discoides
Arcella hemisphaerica
Arcella rotundata
Arcella vulgaris
Askenasia volvox
Aspidisca costata
Aspidisca lynceus
Astasia harrisii
Astrodisculus radians
Bodo alexeieffii
Bodo amoebinus
Bodo angustus
Bodo caudatus
Bodo celer
Bodo compressus
Bodo edex
Bodo fusiformis
Bodo globosus
Bodo lens
Bodo minimus
Bodo mutabilis
Bodo obovatus
Bodo ovatus
Bodo ovum
Bodo putrinus
Bodo repens
Bodo triangularis
Bodo variabilis
Bryophyllum aramatum
Bursaria difficile
Caenomorpha aculeata
Caenomorpha capucina
Caenomorpha medusula
4.17
4.26
3.81
3.56
5.14
2.87
3.82
4.27
5.86
4.48
4.19
5.71
6.66
5.20
1.54
4.69
5.57
5.40
3.89
4.83
5.19
4.10
5.43
4.66
4.21
4.98
4.72
4.42
4.17
4.24
3.57
5.00
5.00
4.66
4.35
4.89
6.00
4.93
5.27
4.81
3.68
4.95
4.74
3.27
5.31
2.94
4.68
2.87
3.60
4.78
4.66
5.82
6.79
Species
SPV
Caenomorpha uniserialis
Carchesium polypinum
Carteria globosa
Carteria multifilis
Cashia limacoides
Cercobodo agilis
Cercobodo longicauda
Cercobodo rodiatus
Cercomastix parva
Cercomonas agilis
Cercomonas ovatus
Chilodonella acuta
Chilodonella algivora
Chilodonella aplanata
Chilodonella cucullulus
Chilodonella labiata
Chilodonella nana
Chilodonella turgidula
Chilodonella uncinata
Chilodontopsis depressa
Chilodontopsis pseudonassula
Chilomonas paramecium
Chlamydomonas braunii
Chlamydomonas globosus
Chlamydomonas komma
Chlamydomonas microsphaera
Chlamydomonas mutabilis
Chlamydomonas ovalis
Chlamydomonas reinhardi
Chlamydomonas simplex
Chlamydomonas stellata
Chlamydophrys minor
Chlorobrachis gracillima
Chlorogonium elegans
Chlorogonium elongata
Chromulina ovalis
Chroomonas acuta
Chrysococcus rofescens
Cinetochilum margaritaceum
Clathrostoma vininale
Clautriavia parva
Coccomonas orbicularis
Cochliopodium actinophorum
Cochliopodium bilimbosum
Cochliopodium minutum
Codonocladium umbellatum
Codosiga botrytis
Codosiga uticulus
Coleps hirtus
Collodictyon triciliatum
Colpidium campylum
Colpoda cucullus
Colpoda steinii
Colponema loxodes
5.64
5.21
4.14
3.47
5.51
4.40
5.75
6.67
5.00
5.42
4.05
5.15
4.32
4.58
5.35
4.01
4.56
2.31
3.87
3.56
3.95
5.64
4.79
4.74
4.91
5.00
4.88
5.59
3.50
4.83
4.31
4.72
6.04
2.55
4.12
4.45
4.48
4.59
4.64
4.77
4.33
4.19
3.80
4.37
3.89
3.76
4.04
5.52
4.53
4.89
5.77
5.52
4.67
4.55
J.-G. Jiang, Y.-F. Shen / Science of the Total Environment 346 (2005) 99–111
Table 2 (continued)
105
Table 2 (continued)
Species
SPV
Species
SPV
Cristigera phoenix
Cristigera setosa
Cryptomonas erosa
Cryptomonas marssonii
Cryptomonas ovata
Ctedoctema acanthocryptum
Cyathomonas ovata
Cyathomonas truncata
Cyclidium flagellatum
Cyclidium glaucoma
Cyclidium libellus
Cyclidium litomesum
Cyclidium muscicola
Cyclidium simulans
Cyclidium singularis
Cyclidium uncinata
Cyclidium versatile
Cyclogramma trichocystis
Cyrtolophosis acuta
Cyrtolophosis bursaria
Cyrtolophosis elongata
Cyrtolophosis mucicola
Dendromonas laxa
Didinium nasutum
Difflugia globulosa
Difflugia gramen
Difflugia oblonga
Dileptus anser
Dileptus conspicus
Dileptus cygnus
Dileptus falciformis
Dileptus monilatus
Dinobryon sociale
Dinomoeba mirabilis
Diplophrys archeri
Discamoeba guttula
Discamoeba tenella
Discosoma tenella
Distigma protens
Dysmorphococcus variabilis
Enchelyodon elegans
Enchelyodon lasius
Enchelys mutans
Entosiphon obliqum
Entosiphon sulcatum
Epalxella striata
Epistylis lacustris
Epistylis plicatilis
Epistylis urceolata
Epistylis viridis
Eudorina elegans
Euglena acanthophora
Euglena acus
Euglena caudata
3.60
4.54
4.27
4.42
4.40
4.10
3.60
4.79
4.86
5.06
4.66
4.54
6.08
3.93
5.98
2.87
4.00
3.50
4.18
4.66
4.66
4.18
5.63
4.68
4.79
4.67
4.25
4.14
3.52
4.77
4.77
4.02
3.45
4.72
4.47
4.03
4.19
2.87
5.99
4.49
3.57
4.68
4.62
4.28
4.59
3.57
4.92
4.04
4.40
4.72
4.25
6.58
4.29
5.02
Euglena deses
Euglena ehrenbergii
Euglena elastica
Euglena gasterosteus
Euglena geniculata
Euglena gracilis
Euglena intermedia
Euglena mutabilis
Euglena oxyuris
Euglena piciformis
Euglena polymorpha
Euglena proxima
Euglena sanguinea
Euglena tripteris
Euglena viridis
Euglypha laevis
Euplotes affinis
Euplotes eurystomus
Euplotes muscicola
Euplotes patella
Eutreptia viridis
Frontonia acuminata
Frontonia leucas
Furgasonia trichscystis
Glaucoma macrostoma
Glaucoma maupasi
Glaucoma scintillans
Glaucoma setosa
Glenodinium gymnodinium
Glenodinium pulvisculus
Gonium formosum
Gonium pectorale
Gonium sociale
Gymnodinium aeruginosum
Halteria grandinella
Hartmannella amoebae
Hartmannella cantabrigiensis
Hartmannella vermiformis
Hastatella radians
Hemiophrys anser
Hemiophrys fusidens
Hemiophrys pleurosigma
Hemiophrys procera
Heteronema acus
Heteronema discomorphum
Heterophrys radiata
Hexamastix batrachorum
Hexamita inflata
Hexamita pusillus
Hexamitus fusiformis
Hexamitus inflatus
Histiobalantium natans
Histriculus histrio
5.06
4.52
4.72
5.33
4.72
3.36
4.42
4.81
4.76
4.67
5.33
4.62
2.95
4.68
4.94
5.38
4.66
4.52
4.70
4.66
4.59
4.34
4.23
4.46
5.07
4.67
4.60
3.60
4.03
3.87
4.32
4.47
2.94
3.97
4.69
3.65
4.54
5.15
5.56
4.11
5.53
4.45
4.18
4.97
5.00
4.07
5.41
5.33
5.79
5.63
5.39
3.47
3.44
(continued on next page)
106
J.-G. Jiang, Y.-F. Shen / Science of the Total Environment 346 (2005) 99–111
Table 2 (continued)
Table 2 (continued)
Species
SPV
Species
SPV
Histriculus muscorum
Histriculus similis
Holophrya atra
Holophrya simplex
Holophrya sulcata
Holosticha kessleri
Hyalodiscus actinophorus
Keronopsis gracilis
Keronopsis monilata
Khawhinea quatana
Khawkinea viriabilis
Lacrymaria elegans
Lacrymaria olor
Lagynophrya conifera
Lagynophrya rostrata
Lagynophrya simplex
Lembadion lucens
Lembadion magnum
Lepocinclis ovum
Litonotus anser
Litonotus carinatus
Litonotus cygnus
Litonotus fasciola
Litonotus hirundo
Litonotus lamella
Litonotus obtusus
Loxodes magnus
Loxodes striatus
Loxodes vorax
Loxophyllum uninucleatum
Mastigella polyvacuolata
Mastigella vitrea
Mayorella ambulans
Mayorella bicornifrons
Mayorella bigemma
Mayorella bulla
Mayorella limacis
Mayorella penardi
Mayorella riparia
Menoidiun pellucidum
Mesodinium pulex
Metopus acidiferus
Metopus acuminatus
Metopus es
Metopus fuscus
Metopus intercedens
Metopus minimus
Metopus setosus
Microthorax pusillus
Microthorax simulans
Monas amoebina
Monas elongata
Monas guttula
Monas minima
3.69
4.89
5.55
4.72
4.23
4.64
5.20
3.76
4.31
7.33
5.63
5.05
5.12
4.14
5.43
5.22
5.22
4.45
6.44
4.09
4.56
3.66
4.47
4.13
4.52
4.27
4.80
4.41
4.22
4.59
4.57
3.72
3.14
3.86
4.73
3.95
3.94
4.47
4.42
5.63
3.97
6.63
5.64
5.86
6.40
5.53
5.06
5.43
4.56
4.59
5.11
5.91
4.76
4.89
Monas sociabilis
Monas socialis
Monas vivipara
Monosiga ovata
Monosiga robusta
Naegleria gruberi
Nassula elegans
Nassula exigua
Nassula flava
Nassula gutturata
Nassula picta
Nassula sorex
Nassula tumida
Nephroselmis olvacea
Notosolenus orbicularis
Notosolenus sinuatus
Ochromonas acuta
Oikomonas excavata
Oikomonas ocellata
Oikomonas socialis
Oikomonas termo
Opisthotricha similis
Oxytricha fallax
Oxytricha hymenostoma
Oxytricha saprobia
Oxytricha setigera
Palychaos discoides
Pandorina movum
Paramecium aurelia
Paramecium bursaria
Paramecium caudatum
Paramecium multimicronucleatum
Paramecium putrinum
Pateriodendron petlolatum
Pedinomonas minor
Pelomyxa lacustris
Pelomyxa palustris
Pelomyxa villosa
Penardia mutabilis
Peranema cuneatum
Peranema deflexum
Peranema furcatum
Peranema trichophorum
Peridinium bipes
Petalomonas mediocanellata
Petalomonas obliqum
Petalomonas pusilla
Petalomonas steinii
Peteriodendron petlolatum
Phacotus acuminata
Phacotus lenticularis
Phacus acuminata
Phacus hamatus
Phacus helicoides
4.22
4.96
4.77
4.23
4.99
4.19
4.66
3.96
3.36
5.30
4.66
4.66
4.61
7.33
4.79
4.77
2.94
3.95
4.61
5.01
4.75
4.68
4.60
3.99
3.83
4.40
3.99
4.05
5.88
5.40
5.64
4.66
4.47
5.09
4.51
5.85
5.82
4.70
3.99
4.41
4.59
3.92
3.96
3.14
4.64
3.47
4.66
4.77
3.90
4.56
4.02
4.29
4.28
5.19
J.-G. Jiang, Y.-F. Shen / Science of the Total Environment 346 (2005) 99–111
Table 2 (continued)
107
Table 2 (continued)
Species
SPV
Species
SPV
Phacus longicauda
Phacus oscillans
Phacus petoloti
Phacus platalea
Phacus pleuronectes
Phacus pyrum
Phacus stokesii
Phacus tortus
Phyllomitus amylophagus
Pithothorax rotundus
Plagiocampa mutabilis
Plagiopyla nasuta
Platyamoeba placida
Platycola longicollis
Platyophrya spumacola
Pleuromonas jaculans
Pleuronema coronatum
Podophorya fixa
Podophrya nollis
Prorodon armatus
Prorodon discolor
Prorodon margaritifer
Prorodon marinus
Prorodon ovum
Holoprya teres
Prorodon teres
Protochrysis phacophycearum
Pseudodifflugia gracilis
Pseudomicrothorax agilis
Pseudoprorodon armatus
Pteridomonas scherffellii
Pteromonas aculeata
Pteromonas golenhiniana
Pyrobotrys gracilis
Pyrobotrys minima
Pyxidium vernale
Quasillagilis contanciensis
Raphidiophrys elegans
Raphidiophrys pallida
Rhagadostoma nudicaudatum
Rhynchomonas nasuta
Saccamoeba gongornia
Saccamoeba limax
Salpingoeca convallaria
Spathidium elegans
Spathidium falciforme
Spathidium muscicola
Spathidium scalpriforme
Spathidium spathula
Spathidium viride
Sphaerellopsis elongata
Sphaerophrya magna
Sphenomonas quadrangularis
Spirostomum minus
4.55
4.72
3.76
3.04
3.80
4.89
4.13
4.43
5.63
2.94
4.40
5.04
5.24
5.62
5.02
4.90
3.97
3.87
4.34
3.98
4.32
4.09
3.49
4.96
4.66
4.47
5.63
3.97
4.09
4.83
4.43
3.91
5.32
4.07
4.42
3.60
5.28
3.96
4.36
4.66
4.88
3.14
4.80
3.35
4.16
5.63
4.79
3.14
4.61
5.17
4.22
4.11
3.47
5.32
Spirostomum teres
Staurophrya elegans
Stentor coeruleus
Stentor mulleri
Stentor polymorphus
Stentor roeseli
Stichotricha aculeata
Stichotricha saginata
Stokesiella acuminata
Stokesiella lepteca
Stokesiella longipet
Striamoeba striata
Strobilidium gyrans
Strombidium viride
Strombomonas ensifera
Strongylidium crassum
Stylonychia mytilus
Tachysoma pellionellum
Tetrahymena pyriformis
Thecamoeba striata
Thylacomonas compressa
Tintinnidium entzii
Tintinnidium fluviatile
Tintinnopsis cratera
Tintinnopsis potiformis
Tintinnopsis wangi
Tokophrya infusionum
Tokophrya ovifovme
Tokophrya quadripatita
Trachelia minuta
Trachelius ovum
Trachelomonas crebea
Trachelomonas granulosa
Trachelomonas hispida
Trachelomonas oblonga
Trachelomonas volvocina
Trachelophyllum chilense
Trachlomonas allia
Trachlomonas australica
Trachlomonas hispida
Trachlomonas oblonga
Trachlomonas scabra
Trachlomonas superba
Trachlomonas sydneyensis minima
Trachlomonas volvocina
Trepomonas agilis
Trepomonas steinii
Trichamoeba cloaca
Trichamoeba myakka
Trichamoeba osseosaccus
Trichamoeba villosa
Trinema lineare
Trochilia palustris
5.06
4.66
4.76
5.20
5.04
4.92
4.09
3.44
3.01
3.01
6.31
4.04
3.81
4.23
4.77
6.31
4.24
4.76
4.96
4.15
4.87
5.98
3.08
5.12
5.86
4.45
4.61
4.85
4.95
3.94
3.72
5.91
3.72
4.20
4.68
4.18
4.16
2.87
3.60
3.86
4.61
3.55
2.94
4.36
4.40
5.28
6.48
5.07
3.99
3.60
2.77
4.38
1.54
(continued on next page)
108
J.-G. Jiang, Y.-F. Shen / Science of the Total Environment 346 (2005) 99–111
Table 2 (continued)
Species
SPV
Trochilia minuta
Trochilia sulcata
Urceolus gobii
Urceolus pascheri
Urocentrum turbo
Uroleptus caudatus
Uroleptus dispar
Uroleptus halseyi
Uroleptus mobilis
Uronema marinum
Urophagus rostratus
Urostyla muscorum
Urostyla multipes
Urostyla urostyla
Urostyla weissei
Urotricha agilis
Urotricha armata
Urotricha discolor
Urotricha globosa
Urotricha ovata
Vaginicola tincta
Vahlkampfia limax
Vahlkampfia vahlkampfia
Vannella mira
Vannella platypodia
Vannella simplex
Vexillifera bacillipedes
Vorticella campanulla
Vorticella convallaria
Vorticella cupifera
Vorticella extensa
Vorticella fromenteli
Vorticella hamatella
Vorticella mayeri
Vorticella microstoma
Pseudovorticella monilata
Vorticella octava
Vorticella picta
Vorticella putrina
Vorticella similis
Vorticella striata
4.52
4.31
4.76
4.26
4.85
4.35
4.77
4.04
4.93
4.19
4.66
3.99
4.56
3.60
2.87
4.74
4.32
4.81
3.57
4.84
4.28
4.79
5.41
3.86
4.66
5.30
3.51
3.68
4.82
4.23
5.27
4.13
4.50
5.91
5.51
3.99
5.13
4.77
6.04
4.25
5.03
1, 2, and 3 were unpolluted or slightly polluted
water. Comparing to the classes of these stations
showen in Table 5, we can see that pollution
degrees of these stations divided by CPV and by
the classes has a higher consistency. Although
pollution degrees of stations 7 and 8 (Table 4) is
slightly higher than the classes (Table 5), and
station 11 slightly lower than the classes, we could
not exclude the possibility that the results reflected
the real status of sampling time, for example, the
Table 3
Water qualities divided by CPV
CPV
Pollution status of water
b4.61
Unpolluted or clean water generally
suitable for drinking after treatment
Slightly polluted water
Moderately polluted water
Heavily polluted water
Severely polluted water
4.61–4.75
4.75–4.84
4.84–4.90
N4.90
PBs of stations 7 and 8 in winter’s sampling were
the two highest in all stations (Table 1).
4. Discussion
There is an urgent need for the development of
environmental indices for the assessment of environmental quality. Such tools must be simple, manageable methodologies for the classification, evaluation
and monitoring of the ecological condition of natural
water system (Fano et al., 2003). The majority of
quality bioindices developed so far (Woodiwiss, 1964;
Cairns et al., 1968; Chandler, 1970; Majeed, 1987;
Grall and Glemarec, 1997; Weisberg et al., 1997;
Engle and Summers, 1999; Borja et al., 2000) were
designed to differentiate between impacted and
reference sites (Fano et al., 2003). However, environmental managers and policymakers also require tools
capable of distinguishing the degree of degradation to
Table 4
Average CPV over a year and degree of pollution of each sampling
station
Stations
CPV
Pollution classes
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
4.57
4.59
4.53
4.61
4.69
4.71
4.87
4.94
5.00
4.80
4.64
4.93
4.73
4.73
4.80
4.86
Unpolluted or slightly polluted water
Unpolluted or slightly polluted water
Unpolluted or slightly polluted water
Slightly to moderately polluted water
Slightly polluted water
Slightly polluted water
Heavily polluted water
Severely polluted water
Severely polluted water
Moderately polluted water
Slightly polluted water
Severely polluted water
Slightly polluted water
Slightly polluted water
Moderately polluted water
Heavily polluted water
J.-G. Jiang, Y.-F. Shen / Science of the Total Environment 346 (2005) 99–111
109
Table 5
Overall chemical analysis and CPV of each area for the whole year
Classes
I
II
III
IV
V
Level of
pollution
Most centralized
area of wastewater
Centralized area
of wastewater
Diffusing area
of wastewater
Slightly or
unpolluted area
River
Yuanjiang
Stations
CPV
9, 12
4.97
10, 15, 16
4.82
6, 7, 8, 11, 13,14
4.77
2, 3, 4, 5
4.58
1
4.57
the biotic community. The advantage of the biotic
index presented here, the SPV and CPV, is that water
pollution can be clearly identified along an environmental quality scale. Thus, this index provides a
greater degree of sensitivity to degradation in water
system quality, compared to some other currently
available methods.
As discussed by Washington (1984), all biotic
indices have its weaknesses that limit its application in
a wider area. In general, all the biotic indices have a
common weakness of being more or less a subjective
one, mainly depending on the feeling and empiricism
of investigators. This may explain why the evaluation
results with these indices often do not coincide with
the actual situation. For this reason several papers
presented objective methods for assigning pollution
sensitivity values (Lawrence and Harris, 1979; Walley
and Hawkes, 1996; Chessman et al., 1997). We
present a new method of using the correlation of
communities with chemical water quality and treat
biotic data directly with chemical parameters in which
these problems may be minimized. It is hoped that the
method of handing biotic data according to chemical
parameter will be useful to workers in the other parts
of the world.
The proposed biocriteria were able to give accurate, consistent, and repeatable bioclassifications
throughout a range of water qualities and habitat
types. Bioclassifications made using these criteria
have been demonstrated to be accurate over a wide
range of water inhabits including river, pond, lake,
ditch, etc., over square kilometers of area.
It is appropriate to select the PFU method to collect
protozoa when applying SPV and CPV since the SPV
is based on the identification of species collected by
PFUs. CPV is based on all the protozoa occurring in
the community as all species identified in a sample
contribute to the assessment. The index has no fixed
levels but expresses the biological quality of the
sampling stations as a CPV that depends on the
animals present, which makes the index a continuous
gradation from clean to polluted water. It is not
impossible that completely different communities may
have the same CPV.
The biotic index present here takes no account of
the abundance of the organisms because the CPV is
the average of the SPVs for all the species identified
and therefore the presence of single individual cannot
greatly alter a station’s index. In addition, we often see
that certain taxa frequently occurs in very large
number in samples and so would dominate numerical
assessment so that the absolute abundance would
obviously be of little use. Relative abundance is on the
one hand subjective and on the other hand unnecessary because the CPV is given by dividing the total
SPV obtained for a station by the number of species.
In general, the higher the SPV of a species is, the
higher the tolerance of the species to pollution. But
SPV could not simply be used to compare the
pollution tolerant ability between species. Because
every species has a tolerant scope to pollution, SPV is
just the mean value of the scope and the scope
between species is very different. Traditionally, those
organisms whose adaptive scope to water quality is
narrow were defined as indicator organism (Washington, 1984). While CPV considers all protozoa in a
community include both of the wide and narrow
pollution tolerant species, therefore SPV is designed
for all protozoa species, not just for the indicators,
thus, SPV is an attribute of species reacted to
pollution. All aquatic organisms should have such a
attribute.
Although the biotic index presented here can be
debatable due to the selection of the standard of grade
II for surface water, we think, no matter what standard
the biotic index is based on, the evaluating result to
water pollution by CPV from SPV is similar. Of
course, the chemical standard should best be internationally admissive; this is what we are considering for
the future modification of SPV. But the basic method
110
J.-G. Jiang, Y.-F. Shen / Science of the Total Environment 346 (2005) 99–111
of the biotic index is fixed. Others biogroups of easier
identification, such as Rotifera, Cladocera, and
Copepoda, could be given SPV values using the
same method if their distribution in relation to the
chemical quality is available.
In this study, we establish a new method of biotic
index using the data from the investigation to the
Changde city’s water system, and obtained a
classification of these sites that corresponded well
with a subjective classification of the same sites
based on the results of the opinions of local experts
and best judgment. Thus, the results indicate that the
biotic index is able to give an accurate evaluation of
the environmental quality of freshwater environments. However, while encouraging, these data must
be considered as being preliminary and the general
applicability of the biotic index for the evaluation of
urban water habitats still needs to be validated. In
subsequent years we intend to investigate other
freshwater system, such as large rivers and lakes
to acquire data in order to validate and, if necessary,
modify some species’ SPV. Therefore, until such a
validation process has been completed we recommend that the SPV should only be used with
caution.
Acknowledgment
This project was partly supported by Guangdong
Provincial Natural Science Foundation of China
990847.
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