Environmental Factors and Biodiversity Reflecting Water Quality: Case Study of Raising Livestock
Journal of Life Sciences and Technology, 2012, 1(1):1-7
Ecology and Comparative Biology - Research Paper
1* 2
Hertien Koosbandiah Surtikanti , and Syamsul Bahri
1Study Program of Biology, Universitas Pendidikan Indonesia, Jl. Dr. Setiabudhi 229, Bandung
2Pusat Penelitian dan Pengembangan Sumber Daya Air, Bandung
Abstract
The ecological health of water-sediment quality of the Cikapundung river has been evaluated since 2000 up to 2009. Previously, Cikapundung river is under stress as result of the pressures of anthropogenic activities such as residential area and textile industry. Now days, the pressure is increasing since most of the riverbank of Cikapundung river has been changed into agriculturesand grazing livestock land. As a consequency, animal, agricultural, and domestic wastes are discharging into Cikapundung river. In this research, organic pollution due to raising livestock and agriculture activity are studied in order to evaluate water quality of Cikapundung water. Macrozoobenthos were sampled from 7 location sites of Cikapundung River using travelling-kick net method. Water-sediment were sampled for chemical analysis and for ICP index (Index of chemistry and physic) determination. Macrozoobenthos data were analysed by BMWP(Biological Monitoring Working Party)score and Shannon-Wiener Index. The ICP index showed that nutrient concentration and physical water characteristics relates to the water pollution status.
High water pollution may decrease the biodiversity level and the number of sensitive organisms (based on BMWPscore and Shannon-Wiener Index). It can be concluded that organic waste from dairy local farmhas a high contribution to the water quality decreases.
Keywords:BMWP score, ICP index,macrozoobenthos, organic pollution,Shannon-Wiener index, water quality
Introduction
Recently, macrozoobenthos has been used routinely in biomonitoring and bioassessment of water quality.
As Odum (1993) explained that biotic component may illustrate the environmental condition physically, chemically and biologically. Some macrozoobenthos are very sensitive animals to environmental condition alteration. These animals give a respon due to environmental condition and it reflectsto their distribution and composition.
Riverbank of Cikapundung river, mainly upstream part, has been changed gradually into raising livestock land and agricultural activities since(Surtikanti &
Priyandoko 2005). Furthermore, Bukit Tunggul region (Cikapundung up stream), known as a conservation area (Surtikanti et al. 2001) and act as
water catches area that provides water for Cikapundung river, has been developed into local dairy farm fueled with around 1000 of cows. About 30% of local people in Bukit Tunggul worked for raising livestock (Setiawan 2005). These activities may reduce water quality of Cikapundung due to accumulation of livestock by waste and production of methane gas that contribute to the increasing of green house gasses (Surtikanti 2008,Goodland & Anhang 2009). In this research, macrozoobenthos is used in evaluating water pollution due to raising livestock and agricultural waste in Cikapundung catchment.
Materials and Methods
Study site location of Cikapundung catchment Selection of study site location is determined by some consideration. The site should be able to give a clear reflection from environmental disturbance. The location is also has a good access and safety for human to conduct the research. Detail condition of research
*Correspondence author: E-mail: hertien_surtikanti@yahoo.com Received: 25 November 2011/Accepted: 2 December 2011/Published online: 20 April 2012
area explained in Surtikanti et al. (2001). Studycarried out during dry season when low water debit and high concentration of pollutants are commonly found in most sampling points. These conditions are representative for maximal stress of biology community in water system. Seven study site locations have been selected (Table 1, Fig.1).
Macrozoobenthos sampling method
The macrozoobenthos samples were obtained by Surber net (surface area 0.1m and mesh size 0.5 x 0.5 2
mm ). The Surber net was placed on the bottom of 2
shallow river (opposite to water stream). Sampling carried out bytraveling kick-netmethod (Sudarso 2007). Five to twenty metres travelling is done side by side (zig zag movement) for 30 minutes. All materials collected in Surber net transferred into labelled plastic bags then preserved with 40% formaline. In the laboratory, samples were sieved with 500-µm mesh size-sieve and rinsed with tap water in a fume cupboard. The macrozoobenthos samples then hand sorted from debris in a white tray. The recovered
macrozoobenthos transferred into a 10 mL vial and preserved in 70% alcohol prior to identification. All samples were identified to the family level with the aid of a diseccting microscope using taxonomic keys of Merrit & Cummin (1996) and Edmonson (1959).
Identification up to family level provided enough information for analysis, because other studies have shown that similar patterns of community distribution were obtained using both family and species level data (Wright et al. 1995).
Water sample analysis
Physical and chemical parameters of the water were taken within each sampling site. The measurment of DO (dissolved oxygen), temperature, pH, turbidity and conductivity of water was done using water quality Horiba. Collected water samples were sent and analysed to the PusAir Laboratory, Bandung for chemical and nutrients analysis. Sediment samples were collected for chemical analysis (Cu, Zn and Pb), Total Organic Carbon (TOC) and particle size analysis.
Figure 1. Cikapundung catchment areas Table 1. Study location on Cikapundung catchment area
Data analysis
Macrozoobenthos data were analysed using BMWP score and Shannon Wiener index. Based on Mandaville (2002), BMWP score (1-10) applicable for organism, at family level, which is tolerant to organics pollutants. BMWP score (Table 2) also more sensitive than other diversity index in evaluating organic water pollution (Bahri 2007). The higher tolerance score is reflected to the level of aquatic organism sensitivity to pollutants.
In order to understand the correlation between biological community and environmental condition, Shannon-Wiener index diversity was used. Based on Bahri (2007), Shannon-Wiener index diversity may illustrate the community structure and predict the aquatic ecosystem degradation. The lower score of diversity index means that biotic community is not suitable in environment condition (Odum 1993).
Where H is diversity index, pi is important probability for each species = ni/N, ni is important score for each
species, and N is total important score. According to Lee et al. (1978) in Bahri (2007), pollution criteria of river ecosystem using Shannon-Wiener index diversity is divided into 4 levels (Table 3).
Further more, in order to determine the status of water quality based on water chemistry score, LAWA Score (Bach 1980 in Tontowi et al. 1993) is used. Water quality was classified based on saprobic classification
(Table 4) (Sharma & Moog 2006). Water quality parameter such as water temperature, pH, conductivity, dissolved oxygen (DO), turbidity, biological oxygen demand (BOD), ammonium, nitrat, and orthofosfat. These water parameter has score and calculated to determined ICP (Index of chemistry and physic) using this formula :
With :
I k-f = Index of chemistry and physic is ranging from 0 (very bad) to 100 (very good).
n =Number of analysed water parameter
Qi =Sub-index is ranging from 0 to 100. Score Qi = 0 means bad water quality due to parameter i, meanwhile Qi = 100 means good water quality due to parameter i.
Wi =Factor for parameter i ranging from 0 to. Score wi = 0, means water quality is not depending on parameter i, meanwhile wi = 1, means water quality is depending on parameter i.
Result and Discussion Water chemistry characteristic
Measurement of water quality in the field (Table 5) showed that level of turbidity, DO, BOD, disssolved
particle and alkalinity were varied. It indicated low water quality mainly at site 2 (dairy farm), site 5 (dairy farm and agriculture) and site 7 (urban area). Each parameters of water quality has an interaction to others. For example, low DO was reflected to high BOD and high dissolved particle at site 7.
Biomass of Trichoptera
Trichoptera, Hydrophyceae was found in clean water Table 2. Classification of water quality in evaluating status of water pollution (Bahri 2007)
Table 3. Criteria of water pollution by Shannon-Wiener index diversity (Lee et al., 1978 in Bahri 2007)
Table 4. Relationship between water chemistry score and status of water quality
as well as water polluted by organic pollutants. It seems, these benthos were not a good bioindicator, however biomass and numbers of these (Table 6) affected by water pollution. In the clean water (site 1), biomass of Trichoptera was higher than in polluted waters (site 5, 6, and 7) while the numbers much higher at polluted water. This characteristic was studied by Yovitner (2005) that environmental factors of water may affect to the biomass of Mytilus viridis.
In the clean water, larvae of Hydrophyceae keep their survival in increasing their body biomass. Meanwhile larvae in low polluted organic water keep their generation in producing their offspring. On the other hand, there is possibility that high water velocity increase their biomass (Surtikanti 2004) as shown from larvae collected from Site 1 where water velocity much higher that other sampling sites. It indicated, that larvae of Hydrophyceae have developed different strategies to survive in different water quality condition.
BMWP Score
Based on BMWP score (Table 7), site 5 and 7 were classified as heavy polluted that may caused by discharged of organic and domestic wastes into the water system. On the other hand, site 1, the conservation area, was catagorized as sllighly polluted water.
Shannon – Wiener Index
Shannon-Wiener index was used to determine the diversity of aquatic biota in water ecosystem. High Shannon-Wiener index (Table 8) was detected at site 1 (0.89). This site was classified as clean water.
TOC and ICP Index
Based on this study, livestock wastes directly increase the level of pollution by developed high organic carbon content on the water. We found that sampling site located nearest with dairy farm (site 2) has the highest Total Organic Carbon (TOC) (Table 9).
In order to find more about the level and possible Table 5. The observed data of Cikapundung River
Table 6. Ratio between biomass and number of Hydrophyceae
Table 7. BMWP score at different Cikapundung catchment area
Table 8. Indeks Shannon-Wiener of benthos found at sampling sites
Table 9. Total organic Carbon at sampling sites
Table 10. Index of chemical-physical water quality (ICP) Cikapundung catchment
Table 11. Sediment chemistry of Cikapundung catchment area
Table 12.Two sediment quality criteria (mg/g dry weight) proposed by the Ontario Ministry Development (Persaud et al. 1989) and ANZECC (Batley and Maher 1999)
factor caused the pollution, we calculated Index of Chemical-Physical Water Quality (ICP) of all sampling sites (Table 10). ICP itself consider as simple yet powerful index to measure water quality. This value showed that total number of parameter value positively correlated with water quality. Site 1 has high ICP number (92.41), so the location was categorized
as clean water. Low ICP numbers were detected at 2 (68.53) and this area characterized by low DO level (2.14mg/L). Same condition also found at site 5 (ICP number=60.43 and DO level=0.043 mg/L). On the other hand, the lowest ICP number was recorded at Site 7 (urban area) (ICP number=56.60) that may indicated that inorganic waste from urban area may
become the main pollutant of as the TOC much lower than other site. This fact may also indicated that some mechanisms maybe responsible for reducing the amount of organic wastes when water flow from upper area to lower area.
Sediment chemistry
Based on sediment chemistry analysis, all heavy metals (Cu, Zn, Pb) were detected at all sites (Table 11). The highest concentration of these heavy metals found on site 3 and 4 (Conservation and agriculture are). However, the level of concentration still lower than recommended value of Ontario Ministry Development and ANZECC (Table 12). Although high concentration of heavy metals was detected in the sediment, it will not caused toxicity problems to biological system since the chemical structure in a complex bound.
Conclusion
Degradation of water quality, caused by raising livestock and agricultural waste, has been occurred at upstream to downhill of Cikapundung river. Water pollution of Bukit Tunggul to Babakan Siliwangi was reflected by macrozoobenthos distribution and community and by physical and chemical characteristics of water. These data were analysed using BMWP score, Shannon-Wiener index and ICP score to assess level of water pollution. We found differences classifications among BMWP Score, Shannon-Wiener Index (Table 8), and IKF (Table 10) in which indicated that site 1 has clean water.
Because BMWP Score consider to all biotic and abiotic component. Therefore, using BMWP score methods to asses the water quality, is more comprehensive than ICP index supported the study result of Bahri & Bambang (2006). This study showed, that livestock waste direct and indirectly degraded water quality. We also found that the level of pollution much higher than expected as augmentation
by clean water from other springs and other sourcesunable to improve thewater quality at lower area.
Acknowledgement
This project was funded by DIKTI (Hibah Kompetensi 2008). Also we would like to thank PusAir Laboratory for water-sediment chemical analysis, and Widi for technical assistance.
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