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Use of the aquatic protozoa to formulate a community biotic index for an urban water system
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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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.
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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)
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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
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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
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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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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)
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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)
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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)
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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)
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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
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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
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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.
References Albrecht J. Periphyton (aufwuchs) communities of ciliated protozoa in salt-polluted running water of the Weser River basin. Their structure and indicator value. Int Rev Gesamten Hydrobiol 1986;71:187 – 224. Borja A, Franco J, Perez V. A marine biotic index to establish the ecological quality of soft-bottom benthos within European estuarine and coastal environments. Mar Pollut Bull 2000;40: 1100 – 14. Cairns J, Douglas WA, Busey F, Chaney MD. The sequential comparison index—a simplified method for nonbiologists to estimate relative differences in biological diversity in stream pollution studies. J-Water Pollut Control Fed 1968;40:1607 – 13. Cairns Jr J, Dahlberg ML, Dickson KL, Smith N, Waller WT. The relationship of freshwater protozoan communities to the
MacArthur–Wilson equilibrium model. Am Nat 1969;103: 439 – 54. Chandler JR. A biological approach to water quality management. Water Pollut Control 1970;69:415 – 22. Chessman BC, Growns JE, Kotlash AR. Objective derivation of macroinvertebrate family sensitivity grade numbers for the SIGNAL biotic index: application to the hunter river system, New South Wales. Mar Freshw Res 1997;48:159 – 72. China EPA. Analytical method for monitoring water and wastewater. Beijing7 Chinese Environmental Protection Press; 1989. p. 576 – 7. Ebina J, Tsutsui T, Shirai T. Simultaneous determination of total nitrogen and total phosphorus in water using peroxodisulfate oxidation. Water Res 1983;17:1721 – 6. Engle VD, Summers JK. Refinement, validation, and application of a benthic condition index for Northern Gulf of Mexico estuaries. Estuaries 1999;22:624 – 35. Fano EA, M istri M, Rossi R. The ecofunctional quality index (EQI): a new tool for assessing lagoonal ecosystem impairment. Estuar Coast Shelf Sci 2003;56:709 – 16. Foissner W. Taxonomic and nomenclatural revision of Sladecek’slist of ciliates (Protozoa: Ciliophora) as indicators of water quality. Hydrobiologia 1988;166:1 – 64. Foissner W. Protozoa as bioindicators in agroecosystems, with emphasis on farming practices, biocides, and biodiversity. Agric Ecosyst Environ 1997;62:93 – 103. Grabacka E. Ecology of some waters in the forest agriculture basin of the River Brynica near the Upper Silesian industrial region. Acta Hydrobiol 1985;27:521 – 33. Grall J, Glemarec M. Using biotic indices to estimate macrobenthic community perturbations in the Bay of Brest. Estuar Coast Shelf Sci 1997;44:43 – 53. Johanna LP, Janelle B, Pamela R. The role of flagellated and ciliated Protozoa in lagoon and grass filter sewage treatment systems. Water Res 1999;33:2971 – 7. Lawrence TM, Harris TL. A quantitative method for ranking the water quality tolerances of benthic species. Hydrobiologia 1979;67:193 – 6. Luiz FMV, Fa´bio AL, Luis MB. Influence of environmental heterogeneity on the structure of testate amoebae (Protozoa, Rhizopoda) assemblages in the plankton of the upper Parana´ River floodplain. Braz Int Rev Hydrobiol 2003;88:154 – 66. Madoni P. Ciliated protozoa and water quality in the Parma River (Northern Italy): long-term changes in the community structure. Hydrobiologia 1993;264:129 – 35. Madoni P, Ghetti PF. Ciliated protozoa and water quality in the Torrente Stirone (Northern Italy). Acta Hydrobiol 1981; 23:142 – 54. Majeed SA. Organic matter and biotic indices on the beaches of North Brittany. Mar Pollut Bull 1987;18:490 – 5. Nicolau A, Dias N, Mota M, Lima N. Trends in the use of protozoa in the assessment of wastewater treatment. Res Microbiol 2001;152:621 – 30. Pascoe D, Wenzel A, Janssen C, Girling AF, Jqttner I, Fliedner A, et al. The development of toxicity tests for freshwater pollutants and their validation in stream and pond mesocosms. Water Res 2000;34:2323 – 9.
J.-G. Jiang, Y.-F. Shen / Science of the Total Environment 346 (2005) 99–111 Sladecek V. System of water quality from the biological point of view. Arch Hydrobiol 1973;7:1 – 218. Sladeckova A, Sladecek V. Indicator value of some sessile protozoans. Arch Protistenkd 1966;109:223 – 5. Thongchai P, Orathaim C. Water quality and occurrences of protozoa and metazoa in two constructed wetlands treating different wastewaters in Thailand. Water Sci Technol 1997;36: 183 – 8. Walley WJ, Hawkes HA. A computer-based reappraisal of the Biological Monitoring Working Party scores using data from the 1990 river quality survey of England and Wales. Water Res 1996;30:2086 – 94. Wan DB, Qiu CQ, Ma N, Sun XX. Assessment of urban water bodies contamination using pollution indication bacteria. Acta
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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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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.
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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)
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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
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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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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)
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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)
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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)
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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)
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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
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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
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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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