BUFFALO RIVER, EASTERN CAPE
4.3 S TUDY SITE
4.6.3 Effluent, organism and experimental variability
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on textile industries and Sewage Treatment Works. The Sewage Treatment Works were forced to reduce the levels of colour permitted in their trade effluent standards in order to comply. Colour is one of the most pressing problems facing the textile industry, and has the highest public profile. Up to 50% of the initial dye load will be found in the effluent (ENDS Report, 1993), giving rise to highly coloured effluent, which is difficult to treat.
The textile mill in this study is discharging coloured effluent into the receiving water via the Mlakalaka stream, as there are no strict regulations prohibiting the discharge.
The strong colour of the textile effluent caused some change in the river water below the point of discharge. The colour change can cause a considerable disturbance to the ecological system of the receiving water.
Although GTE samples for this study were collected from the same mill, utilizing the same manufacturing process, effluent chemical profile and subsequent organism responses were different.
Effluent variability
The complex composition and the continuos changing nature of the effluent (Rand, 1995), make minimizing the variability between organism responses over time difficult. Effluents can be highly variable over time, making toxic evaluations difficult (Warren-Hicks and Parkhurst, 1992; Dorn, 1996). There are currently no established criteria stipulating the acceptable levels of effluent variability in a WET test (Parkhurst and Mount, 1991), due to the inherent variability in effluent composition experienced over time. Average intra-laboratory coefficients of variation for acute WET test have been recorded as 17%, with as much as 135% variability being recorded between the experiments (Parkhurst and Mount, 1991). Such high variability between experiments can be expected if the composition of the effluent is highly variable. Understanding the effluent’s variability may be more important than evaluating one toxic result as a significant event (Roux, 1994, Grothe et al., 1996).
In this study, selected physico-chemical constituents were used to compare the composition of different effluent batches (Tables 4.12 and 4.13). Chemical analysis of individual effluent showed no significant changes between the initial and the stored effluent over a four-day period. This indicates little variability within the effluent sample, which shows stability during storage. There was however, variability between the different effluent batches. This effluent was more variable, and produced more variable toxicity results than the kraft effluent used in Chapter 3. This effluent results show that it would not be possible to set the criteria or numerical value for effluents such as textile, as effluent produced differ all times.
Test organism variability
Riverine organisms from “unpolluted” water were used as test organisms, as suggested in APHA (1992). However, the organisms genetic structure was unknown, thereby introducing the variability associated with using a wild population of
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organisms such as baetids. The use of such populations does incorporate natural variability, and therefore a measure of environmental realism that is not present in toxicity testing using standard laboratory organisms (Rosenberg and Resh, 1993).
However, the test organisms were collected from one sampling site, thereby attempting to reduce a potential source of variability. The baetid population used for this study comprised a species complex; different species may respond differently to contaminant exposure (US EPA, 2000). As shown in Table 4.17, it is not possible to collect the same test species in the field, but an effort was made to try and select similar-looking organisms during sorting. Occasionally, it was necessary to use smaller-sized organisms due to low abundance of larger organisms, and this could have contributed to variability. The differences in species tolerance within a complex species may have contributed to variability.
Experimental variability
The channel temperatures fluctuated by more than ±3°C. A fluctuation of ±3°C over 24 hrs is considered acceptable under semi-controlled conditions (DWAF, 2000).
These temperature fluctuations were due to sudden changes in laboratory temperature, which were influenced by the outside ambient temperatures. Although the recommended temperature fluctuation rate was exceeded, it did not appear to detrimentally affect test orgainsms, as control mortalities were below the 10% limit of DWAF (2000). The temperatures were still within the range that the organisms were naturally exposed to in the field, and suggests that test organisms are tolerant of a relatively wide temperature range. The river water temperatures fluctuate during the day, ranging from 13-18°C. Generally, if temperatures are allowed to fluctuate above the 18-20°C range, the life-cycle of mayflies becomes rapid, and as a result, emergence takes place (DWAF, 2000). In this study, there seems to be little correlation between poor temperature control and toxicity results (Experiments 2 and 3; Table 4.3 and Section 4.5.3). The use of a site-specific laboratory e.g. the Zwelitsha laboratory) for toxicity testing, reduces the control over physical parameters such as laboratory temperature. The effluent pH can influence the bioavailability of some metals (e.g. Cu and Zn), and therefore contribute to variability (US EPA, 2000). Strict control over abiotic factors must be exercised to reduce variability. Experimental
variability may be affected by dilution water quality, hence the use of river water as diluent in this study. In this study, the baetids percentage frequency data (Table 4.17) suggests that B. harrisoni is less sensitive than A. parvum.
Tolerance data
From the data tables and figures, it is apparent that test responses to each effluent batch was different. Out of eight different effluent samples, only four GTE showed acute toxic responses over 96 hrs. The effects varied in different batches, some showing immediate effects, while others showed delayed acute responses. Individual organisms of the same population could have responded differently from each other;
effluent variability could also have contributed to different responses. Baetids showed less tolerance to GTE than PITE, indicating that PITE is more stable. The ameliorative effect of Ca2+ may also have contributed to the reduced toxicity of PITE.
4.6.4 Application of WET testing in South African water quality