Ecotoxicological Techniques and Assessment of Environmental Samples

Chapter

Abstract

In this chapter the primary aspect was to dissect and determine the toxic nature of effluent and environmental samples (river sediments and water). This also involved sample manipulation coupled to a biosensor toxicity assay for the purpose of identifying possible remediation strategies for future environmental conservancy. Traditionally, the tanning industry has been associated with odours and water pollution from partially or untreated discharges. Sparging was used to identify toxicity associated with volatile organic compounds. This type of ­toxicity testing technique was found to be ideal for samples collected from tannery ­effluent treatment pits or pans and anaerobic lagoons. For example the toxicity of ­contaminants removed by treatment with activated charcoal, was identified for all the sampling points (tannery effluent treatment pits, anaerobic lagoons and riverine sampling points) except for the points upstream. A similar result was observed when filtration, as a technique, was used to identify toxicity associated with suspended solids. The approach used highlighted the complex nature of the toxic ­pollutants in the tannery effluent. Moreover the results strongly indicated the polluting sources and also the possible remediation strategies for effluents at various stages of the tanning industry.

Keywords

Starch Dust Filtration Phenol Sedimentation 

3.1 Introduction

The many diverse environmental impacts of tanneries have made them subject to relatively sophisticated pollution control policies in many countries. Factory sites, lagoons, storage areas and temporary waste dumps may severely contaminate the underlying soil and water systems if appropriate precautions are not taken. As the first line of environmental audit, an ecotoxicological risk assessment is taken by use of multiple criteria or multiple lines of evidence (Suter 1993; USEPA 1991). Ecotoxicological assessment entails understanding the categories of hazardous waste, hazard identification, exposure assessment, ecological effects and risk characterisation. Ecological risk assessment (ERA) is a relatively new approach to quantifying the risk of significant harm to organisms and their ecosystems, but it is already a requirement in the developed world. For example, Part IIA of the Environmental Protection Act 1990 and the Habitats Directive (UK) (UK Habitat 2004). Ecological risk assessment will be used during this study for a quantitative and qualitative evaluation of the tanning industry.

The need to use biological techniques for the monitoring of environmental ­pollution by industry and regulators alike has become a necessity (Cervantes et al. 2000; Saxena et al. 2000). Therefore the integration of both chemical and biological approaches to underpin ecotoxicity testing is essential. While chemical methods have been traditionally used to determine total concentrations of the pollutants, biologically linked measurements on the other hand have been used to assess the bioavailable fractions of the pollutants (Steinberg et al. 1995; Paton et al. 1997). In this chapter both chemical and biological approaches using lux-based biosensors (Escherichia coli HB101 pUCD607), a dehydrogenase activity test and a set of water chemistry parameters to investigate river health at the study site (Fig. 3.10) to provide a stressor (pollutants) profile is discussed. For example, whole cell microbial biosensors for monitoring environmental contamination by heavy metals (Knight et al. 1999) and organic contaminants (e.g. chlorinated phenolics) (Heitzer et al. 1992) as well as toxicity in soils and water contaminated by industrial effluents have previously been used (Brown et al. 1996; Duncan et al. 1994) (Fig. 3.1).
Fig. 3.1

(a,b) Tannery effluent showing the free wish blue chromium effect on the surrounding environment (a) along the draining canal and (b) within the discharge points at the river

There is an increasing trend towards the use of biological techniques as indicated earlier for monitoring the hazards associated with environmental pollution by industry and regulators alike. This is particularly applicable to components of tannery waste when disposed in environmental systems. Chromium toxicity has been investigated using biological techniques involving microorganism (Cervantes et al. 2000; Saxena et al. 2000; Chen and Hao 1998; Chirwa and Wang 1997; Shen and Wang 1993) and plants (Prasard et al. 1991). The toxicity of chlorinated phenols has also been assessed by bioluminescence-based ecotoxicity tests (Lagido et al. 2001; Mwinyihija et al. 2005a, 2006).

Lux (reporter genes encoding marine bacterial luminescence (i.e. light production)) bacterial biosensor technology has been used to measure the toxicity of heavy metals in a number of matrices ranging from aqueous solutions of single compounds to industrial effluents (Sinclair 1999). The light emission intensity is proportional to the concentration of the toxic analyte over a certain concentration range, allowing one to perform a quantitative analysis. For example, Paton et al. (1995) used the luminescence response of a chromosomally lux-marked bacterium, P. fluorescens, to assess the toxicity of metal salts. Chaudri et al. (1999) used the luminescence response of lux-marked bacteria to assess the toxicity of zinc in pore water in a long-term sewage sludge field trial. Galli et al. (1994) used naturally luminescent marine bacteria (Microtox) to test the toxicity of soil from a site contaminated with various pesticides, dyes and other chemicals. Mwinyihija et al. (2005a, b, 2006) carried out the assessment of toxicity levels in various samples form the tannery effluent, dust and river sediment using E. coli HB101pUCD based biosensor.

The advantages associated with the use of genetically modified bacterial biosensors over other forms of ecotoxicity testing are that they are rapid, sensitive, easy to culture and maintain, flexible in terms of selecting for environmental relevance, and represent reliable tools which integrate the many factors contributing to environmental toxicity (Wild et al. 1993).

Lux bacterial biosensor assays of toxicity can be linked to sample manipulation to assess the scope and the nature of possible remediation strategies. Sousa et al. (1998) used sample manipulation (coupled to bioassay with lux- marked bacteria) to examine the toxicity of a site contaminated with BTEX (benzene, toluene, ethylbenzene, xylene) compounds. The use of biosensors enabled reporting on site toxicity characteristics and contaminant bioavailability. The manipulations included ­sparging, filtration, muffle furnace and pH adjustment of the sediment samples. This enabled any constraints to bioremediation (such as adverse pH, heavy metals or volatile organics) to be identified (Sinclair 1999). In the case of environmental constraints, success is only likely to be attained if these constraints are identified and means are devised to alleviate them to an extent where bioremediation can ­effectively proceed (Allan-King et al. 1994). The use of the lux biosensor E. coli HB101 pUCD607 in relation to sample manipulation, allowing dissection and classification of sample toxicity, has not previously been applied to tannery waste (Fig. 3.2).
Fig. 3.2

Schematic representation of toxicity dissection VOC’s (Volatile organic compounds), non-VOC’s, inorganic and pH constraints in Kenya tannery effluents and riverine samples

Triplicate 900 μl aliquots of each sample were taken using a Gilson pipette (Pipetman Model P1000) for biosensor test immediately after opening the bottles and the bioassay carried out immediately. Additional aliquots were taken for pH measurement and submitted subsequently to a series of protocols in order to identify underlying toxic constraints to remediation and assess the scope for their alleviation.

3.2 Toxicity Dissection

3.2.1 Sparging

Aliquots (900 μl) of effluent and sediment extract were pipetted into luminometer cuvettes and sparged with air (N2) for 10 min (at 1,650 mL/min). The high flow of air allowed for rapid removal of volatiles present.

3.2.2 Activated Charcoal Treatment

Charcoal was first conditioned by placing 100 g of charcoal in a Duran bottle filled with double deionised water and allowing it to stand for 48 h, prior to multiple (×10) deionised water rinsing and recovery by filtration. Ten milliliter of the effluent or sediment extract was placed in a centrifuge tube. Charcoal (0.1 g) was added and shaken for 30 min. Samples were centrifuged (3,000g) for 20 min (MSE Coolspin 2) at 48°C and toxicity of the supernatant tested by transferring 900 μL of the sample into a luminometer cuvette (Clinicon, Petworth, West Sussex, U.K.) and adding 100 μL of resuscitated biosensor suspension.

3.2.3 Filtration

Aliquots of water samples and sediment extracts (50 mL) were filtered through a cellulose acetate membrane filter (0.22 μm pore diameter) in order to determine toxicity related to suspended solids. Triplicates of each sample were submitted to the bioassay immediately after filtration.

3.2.4 pH Adjustment

Aliquots of the water samples (pH 5.5) and effluent/sediment extracts were adjusted to pH 4.0, 6.0 and 8.0 with 0.1 M sodium hydroxide and 0.1 M hydrochloric acid. Triplicates of the samples were submitted to the bioassay immediately after pH adjustment.

3.3 Toxicity Testing Using Escherichia coli HB101 pUCD607

Determination of toxicity was based on the bioluminescence response of the ­lux-modified biosensor, E. coli HB101 pUCD607, which had previously been, marked with the lux CDABE genes, (isolated from Vibrio fischeri) using the multi-copy plasmid pUCD607 (Amin-Hanjani et al. 1993). The biosensor was stored at −20°C and resuscitated from freeze dried condition prior to bioassay. Results from the toxicity dissection is shown in Tables 3.13.3.
Table 3.1

Percentage maximum bioluminescence of effluent treatment pits untreated and after treating with sparging, activated charcoal, filtration and pH adjustment calculated against a blank of double-deionised water. Figures in parentheses are standard errors of the mean (SEM) (n = 9)

 

No Treatment

N2 Sparged

Activated charcoal

Filtration

pH adjustment

Samples

Means

Means

Means

Means

4.00

6.00

8.00

Beam house

0.06 (0.04)

101.82 (8.2)

125.14 (1.06)

63.15 (0.83)

4.32 (0.72)

83.42 (1.68)

0.04 (0.04)

General sedimentation

2.40 (1.02)

141.21 (2.40)

167.53 (2.20)

105.56 (1.61)

4.72 (0.81)

202.00 (23.37)

2.40 (1.02)

Strip Chrome Tank

0.004 (0.004)

84.67 (1.40)

65.26 (0.90)

0.01 (0.003)

1.83 (1.05)

43.29 (2.20)

0.00 (0.001)

Chrome sedimentation

26.26 (12.70)

120.84 (8.3)

34.36 (1.50)

0.43 (0.02)

169.62 (33.7)

284.10 (8.10)

26.26 (12.67)

Equalisation Tank

6.83 (2.60)

46.36 (46.4)

39.14 (0.9)

5.88 (0.34)

0.03 (0.02)

36.77 (1.81)

6.83 (2.56)

Reference (Ddw)

104.85 (5.36)

94.75 (0.49)

101 (1.61)

99.97 (1.13)

102.87 (3.82)

107.29 (4.06)

114.24 (2.70)

LSD (5%)

18.27

18.23

4.39

2.61

42.71

31.80

16.66

Ddw Double deionised water

The choice of the lux-marked biosensor in this work offered great environmental relevance in dissecting and categorising into broad groups the toxic nature of the effluent from the tanning industry. For example Kenyan tannery samples from the effluent treatment pits (Table 3.5) and anaerobic lagoons (lagoons 1, 2, 4 and 5) (Table 3.2) were associated with this type of toxicity.
Table 3.2

Percentage maximum bioluminescence of anaerobic effluent treatment lagoons untreated and after treating with sparging, filtration and pH adjustment. Figures in parentheses are standard errors of the mean (SEM) (n = 9)

 

No Treatment

N2 Sparged

Activated charcoal

Filtration

pH adjustment

Samples

Means

Means

Means

Means

4.00

6.00

8.00

Lagoon1

27.86 (7.0)

90.89 (9.93)

70.92 (3.60)

149.32 (1.70)

2.10 (0.78)

4.86 (0.11)

115.11 (21.24)

Lagoon2

34.07 (21.2)

87.07 (8.00)

122.43 (22.55)

139.58 (6.64)

1.40 (0.36)

56.00 (1.45)

116.49 (23.14)

Lagoon3

77.08 (23.1)

89.39 (13.5)

154.07 (3.44)

145.62 (1.67)

1.02 (0.35)

4.89 (0.49)

91.76 (0.88)

Lagoon4

8.39 (0.90)

100.54 (7.70)

104.92 (20.30)

94.56 (1.50)

15.17 (0.91)

182.65 (0.70)

138.23 (0.50)

Lagoon5

1.83 (0.50)

95.59 (11.70)

69.88 (10)

104.77 (1.22)

0.62 (0.62)

3.16 (0.70)

110.74 (0.39)

Reference (Ddw)

104.85 (5.36)

94.75 (0.49)

101 (1.61)

99.97 (1.13)

102.87 (3.82)

107.29 (4.06)

114.24 (2.70)

LSD (5%)

45.37

32.72

45.52

10.29

1.85

2.43

44.29

The response of sparged (N2) samples reflected the toxicity of the samples once volatile organics had been removed (untreated samples showing total toxicity). This residual toxicity would be caused by inorganic and/or non-volatile organics in the sample. However, the increase of luminescence in this study in all the effluent treatment pits and anaerobic lagoons suggested that considerable toxicity was caused by volatile organics. The river samples showed no difference between untreated and sparged (N2), demonstrating the “self sparging effect” inherent in high flow, active rivers. Alleviation of toxicity in sparged, effluent treatment pit and anaerobic lagoon ­tannery samples highlights sparging as a potential remediative technique for tannery effluent, which would be based on proven technology (Boyd et al. 1998; Mwinyihija et al. 2006).

Activated charcoal treatment to the effluent treatment pit samples showed toxicity associated with organics after the removal of certain inorganics and organics (particularly chlorinated hydrocarbons). Samples from the Beam-house, general sedimentation and the anaerobic lagoon responded by showing an increased percentage luminescence (stimulation). This observation was probably related to the charcoal-mediated removal of the high total phenols load in the samples. Toxicity from chlorinated phenolics has been reported by Sinclair (1999), and most chlorinated and non chlorinated phenolics are considered to be narcotics (Cronin and Schultz 1997). Phenolic compounds are known to be toxic through a protonophoric mechanism by acting as uncouplers, and/or inhibiting electron flow in the electron transfer chain. Phenols can accumulate in the membrane and disturb membrane function, causing narcotic effects (Escher et al. 1996). The observed impact of charcoal filtration on the toxicity of key tannery and associated environmental samples suggests that it may provide an important remediative step, exploiting established and cost-effective technologies.

The removal of particulate matter and colloidal materials through filtration was critical for samples from the Beam-house, general sedimentation and all the anaerobic lagoons. In relation to this observation, studies by Thanikaivelan et al. (2003) reported that activities such as soaking, liming, reliming (including fleshing) and deliming (Beam-house activities) account for 15–20% total solids containing lime sludge, fleshing and hair. Chrome sedimentation, chrome stripping and the equalisation tank showed the lowest response to filtration, suggesting that toxicity was not bound within the particulate and colloidal content of the samples. However, as the effluent flows towards the general sedimentation tank, an effect of filtration was observed, suggesting aggregation of the effluent contents to particulate matter. Coagulation and flocculation are envisaged to be the main activities in sedimentation tanks (UNEP 1994). Filtering of chrome sedimentation samples was associated with a slight increase in bioluminescence (not necessarily indicating a decrease of toxicity) in comparison to other samples within the effluent treatment pits. This phenomenon was also observed when the river samples were filtered, with the discharge point indicating stimulation. Along with the results from charcoal treatment, the effects of filtration on sample toxicity also highlighted this treatment as likely to have an important role in the remediation of tannery effluents, again using proven technologies.

Available metals are generally in the form of soluble cations and their tendency to be present in ionic form increases with increasing acidity (Sposito 1989). Sarin (2000) reported the toxicity response of lux-marked E. coli HB101 to a range of metals. In this study, metal toxicity and bioavailability patterns were identified through pH adjustment (4.0, 6.0 and 8.0) in the tannery effluent (Table 3.1), anaerobic lagoons (Table 3.2) and riverine sampling points (Table 3.3). The increase in % luminescence (>80%) on adjustment from pH 4.0 to pH 6.0 was demonstrated for all the tannery related samples tested (Tables 3.1 and 3.2). This suggested the presence of metal toxicity and the response to pH variation on bioavailability, which is imparted by changes in speciation and portioning effects of the metals (Ritchie et al. 2001; van Leeuwen 1999; McGrath et al. 1999; Knight and McGrath 1995). The tannery treatment effluent showed increased % luminescence at pH 6.0 (representing typical environmental conditions) (Mwinyihija et al. 2006) where the majority of the metals are limited in their bioavailability (Gadd 1990). Because of the alleviation in toxicity of all tannery samples through adjustment to pH 6, pH treatment (along with charcoal treatment and filtration) offers a potentially useful remediative option for tannery effluents.
Table 3.3

Percentage maximum bioluminescence of riverine sediments untreated and after treating with sparging, activated charcoal and pH adjustment calculated against a blank of double-deionised water. Figure in parentheses are standard error of the means (SEM)(n = 9)

 

No Treatment

N2 Sparged

Activated charcoal

Filtration

pH adjustment

Samples

Means

Means

Means

Means

4.00

6.00

8.00

200 m upstream

97.13 (2.5)

96.33 (7.4)

82.54 (15.0)

100.67 (4.04)

86.38 (1.4)

71.83 (3.1)

49.80 (11.1)

100 m upstream

87.40 (9.3)

85.86 (1.0)

46.80 (3.0)

99.26 (3.02)

85.84 (4.6)

74.37 (8.1)

35.75 (2.6)

0 m discharge point

64.01 (6.0)

57.06 (7.3)

85.30 (8.6)

115.68 (1.81)

102.16 (3.2)

74.26 (3.9)

1.15 (0.2)

100 m downstream

90.86 (3.8)

88.93 (1.92)

65.56 (22.3)

105.07 (2.10)

68.65 (1.7)

72.73 (12.3)

24.28 (1.8)

200 m downstream

78.49 (3.6)

81.50 (4.6)

74.57 (9.0)

104.83 (8.3)

53.36 (1.3)

71.03 (1.7)

10.85 (0.4)

400 m downstream

69.91 (3.9)

87.25 (3.6)

100.13 (4.0)

103.17 (2.70)

68.67 (2.8)

68.60 (19.4)

26.17 (1.8)

800 m downstream

87.21 (9.0)

83.51 (2.7)

103.09 (6.1)

100.14 (0.70)

73.95 (2.6)

78.66 (12.9)

30.66 (2.7)

Reference (Ddw)

104.85 (5.36)

94.75 (0.49)

101 (1.61)

100.80 (4.40)

92.25 (1.97)

104.85 (1.79)

80.58 (3.14)

LSD (5%)

18.51

14.23

35.08

9.47

7.41

32.20

12.26

Toxicity in samples such as treatment effluents, anaerobic lagoons and downstream riverine sampling points (Tables 3.13.3) was attributed to high concentrations of chromium and phenols. For example, although in tannery wastewater Cr3+ is the most expected Cr form, the Redox reactions occurring in the sludge can increase the concentration of the hexavalent form (Kotaś and Stasicka 2000). Most metals show increased solubility with decreased pH (Artiola 1996), ­indicating increased bioavailability (chemical assimilation and possible toxicity) of organic/inorganic compounds (Steinberg et al. 1995; Shaw et al. 2000; Alexander 2000). Under slightly acidic or neutral pH conditions in this type of wastewater, the poorly soluble (Cr(OH)3 aq should be the preferred form, but a high content of organic matter originating from the hide material processing is effective in forming soluble organic Cr3+ complexes (Stein and Schwedt 1994; Walsh and O’Halloran 1996a, b). Samples from the discharge points showed higher toxicity when the samples were adjusted from pH 6.0 to pH 8.0. Other related studies investigating the fractionation of chromium toxicity in water using E. coli HB101 pUCD607 showed that speciation of chromium at different pH levels and a synergistic effect with other metals (e.g. copper and zinc) contributed to its toxicity (Wararatananurak 2000). This observation suggested that chromium is frequently a constraint to bioremediation in contaminated environments (Killham Pers. Comm. 2004).

3.3.1 Resuscitation of Freeze Dried Cultures

Freeze dried cultures of Escherichia coli HB101 pUCD607 were resuscitated in 10 mL of sterile 0.1 M KCl (contained in a Universal). 1 mL of KCl was added and the culture resuspended by mixing (drawing up and down five times into a PI000 Gilson pipette). The resuspended culture was transferred back to the universal and the culture placed in a shaking (200 rpm) incubator (25°C) for 1 h.

3.3.2 Sample Addition and Luminometry Measurements

One hundred microliters of the resuscitated biosensor suspension was added to the samples at 15 s intervals, accurately timed for measurement in the Bio Orbit 1,253 luminometer (Labtech International, Uckfield, U.K). Each sample was exposed to the sensor for exactly the same time. Samples were incubated for 15 min before light output measurements were carried out at 15 s intervals. This ensured the same exposure time to the potentially toxic elements for cells in each of the cuvettes.

3.3.3 Data Analysis

The output from the luminometer resulting from each assay carried out was recorded in relative light units (RLU’s) (equating to mV/10 s/mL). The light output was then converted to percentage maximum bioluminescence. This was calculated against a blank of double deionised water adjusted to pH 5.5, the optimum pH for bioluminescence.
$$ \%\text{maximum bioluminescence}={\text{I}}_{\text{S}}/{\text{I}}_{\text{C*100}}$$
(3.1)
where IS = RLU’s emitted by the cells exposed to the sample IC = RLU’s emitted by the cells exposed to the control.
The percentage (%) maximum bioluminescence was determined for the three sample replicates. A mean of this determination was then calculated. The assay performance was monitored by reference to the response to the control, the reproducibility of the response to the three replicates and the response to a standard of trichlorophenol (TCP) (Fig. 3.3a, b). Effect of exposure time on toxicity to a range of standard solutions of Zn and Cu were prepared by dilution with double deionised water at pH 5.5 (Fig. 3.4a, b).
Fig. 3.3

(a, b) Quality control of E. coli HB101pUCD607 IMS (15 min bioassay) on TCP and Cu. NB (All error bars <17.6% of the largest bar show as points in the graph). All standards were prepared by dilution with double deionised water at pH 5.5. Curves were fitted using Sigma plot 9.0 and an equation and r-squared determined

Fig. 3.4

(a, b) Quality control of E. coli HB101 pUCD607 IMS (15 min bioassay) on (a) Zn standard solutions (b) Cu standard solutions. NB (All error bars <17.6% of the largest bar show as points in the graph). All standards were prepared by dilution with double deionised water at pH 5.5. Curves were fitted using Sigma plot 9.0 and an equation and r-squared determined

3.4 Dehydrogenase Assay

The method was adapted from the protocol of Packard (1971), Benefield et al. (1977) and Paton et al. (1995). A volume of 2 mg of sediment was immediately transferred to light proof, universal vials containing 2 mL of buffer solution (TES 0.5 M, Sigma-UK), 0.1% INT (p-Iodonitrotetrazolium violet, Sigma-UK) and the samples. All preparations were carried out in triplicate for all the samples and ­performed under aseptic conditions. The mixture was vortexed and incubated for 6 h at 25°C, 225 rpm in an orbital shaking incubator. After 6 h of incubation, 10 mL of ethanol was added to stop bacterial activity and fix the colour. The universal vials (20 mL) were vortexed and absorbance of formazan measured at 490 nm (Fig. 3.5).
Fig. 3.5

Samples showing different colour intensity (e.g. Control samples cleaner than those from the impacted areas are progressively darker) due to the effect of INT dehydrogenase activity

Dehydrogenase is an oxidoreductase enzyme and depends on oxygen as a ­terminal acceptor. The relationship between BOD and DO was therefore measured and a positive correlation was established between INT-dehydrogenase and DO (r = 0.3, p < 0.01) while a negative correlation was noted as expected between ­INT-dehydrogenase and BOD (r = −0.6, p < 0.001). The BOD and DO showed a strong negative correlation (r = −0.9), indicating that increased oxygen demand in the breakdown of organic/inorganic matter chemically or biologically in an aquatic system results in lowered dissolved oxygen (Fig. 3.6). This result further demonstrated that the depletion of oxygen in the river had an effect on the microbial ­activity. The biotic stress was highest in all cases at the discharge point rather than upstream. However, a gradual recovery was observed downstream implicating the tannery effluent as the source of contamination.
Fig. 3.6

BOD and DO levels upstream and downstream (River Sagana as impacted by the effluent from tanning industry in Kenya

The experimental data in relation to percentage bioluminescence and dehydrogenase activity (Table 3.4) indicated that river health is impacted markedly by effluent from the tanning industry. This observation conforms to a study by Ros and Ganter (1998) who reported that tannery waste was a potential environmental pollutant. The impact of the tannery effluent was identified when the biological sensors showed a significant difference between the up and downstream data. The effectiveness of these sensors during the study was demonstrated when the biological effects (through analysis of biomass activity, bioluminescence and BOD parameters), effects of the tannery pollutants and other interacting environmental factors were predicted (Atlas and Bartha 1993; Cairns and Prat 1989; Gersberg et al. 1995, Mwinyihija et al. 2006).
Table 3.4

Percentage bioluminescence of the control, pH, dehydrogenase (ugTFg−1 sediment 6 h−1) and their correlations at different river sediment sampling points

  

% Bioluminescence

Dehydrogenase μgTFg Sed −1 . 6 h

  

Sampling points

Ph

Mean

SEM

Mean

SEM

r values

p values

400 m up

6.96

74.2

8.2

0.0294

0.02

0.07

**

200 m up

6.94

97.1

2.5

0.0318

0.024

−0.9

***

100 m up

6.98

87.4

9.3

0.0346

0.028

0.5

**

0 m

6.49

64

6

0.0058

0.002

0.1

**

100 m down

7.05

90.9

3.8

0.0046

0.001

0.98

***

200 m down

7.05

81.5

4.6

0.0069

0.001

−0.76

**

400 m down

7.01

87.3

3.6

0.0204

0.002

−0.84

***

600 m down

7.1

84.8

7

0.0235

0.021

−0.56

**

800 m down

7.16

87.2

9.2

0.0269

0.03

0.89

**

LSD 0.05

11.1

 

0.032

    

0.01

 

15.2

 

0.0439

   

0.001

 

20.7

 

0.0598

   

SEM Standard errors of mean

Sed Sediment

*p ≤ 0.05, ** p ≤ 0.01, *** p ≤ 0.001

Dehydrogenase activity effectively provided the status of the river sediment as a positive correlation with the dissolved oxygen level was established. This suggested that the increase in organic matter (Table 3.5) in the river resulted in depressed oxygen levels (Fig. 3.7) (due to biochemical degradation) affecting the DHA, which uses oxygen as a terminal electron acceptor (Skujins 1978). Similarly, a decrease in DHA was observed for metal contaminated soils, indicating reduced microbial activity in the soils (Cenci and Morozzi 1979; Ruhling and Tyler 1973; Doleman and Haanstra 1979; Schinner et al. 1980; Brookes et al. 1984). However in comparing DHA with oxygen status, a large proportion of O2 uptake may not be accounted for due to the presence of alternate electron acceptors (Burns 1978; Sommervile et al. 1978) or DAH inhibition by humic acids (Pflug and Ziechman 1982), high levels of inorganic nitrogen (Trevors et al. 1981) and reducing substances in anaerobic soils (Okazaki et al. 1983). To address this constraint, INT-DHA measurement instead of TTC-DHA was preferred due to INT competing well with O2 liberated electrons and has a rapid and high sensitivity (Benefield et al. 1977; Hongwei et al. 2002).
Table 3.5

Results showing heavy metals total concentration analysed using Atomic Absorption Spectrometry (Perkin Elmer 100) and total phenol concentration (gas chromatography) within the riverine sediment sampling points

Description

Cr mg kg−1

Pb mg kg−1

Fe mg kg−1

Cu mg kg−1

Cd mg kg−1

Ni mg kg−1

Zn mg kg−1

Chlorinated phenols (mg L−1)

400 m up

0.89

0.4

948

0.46

0.03

0.49

1.03

ND

200 m up

0.93

0.27

772

0.16

0.02

0.43

0.79

ND

100 m up

1.14

0.39

1303

0.46

0.04

0.72

1.63

ND

0 m

1.31

0.69

1010

0.4

0.03

0.65

1.15

30.3

100 m down

1.41

0.36

1048

0.44

0.03

0.62

1.37

17.7

200 m down

1.65

0.58

1362

0.69

0.03

0.96

1.92

11.4

400 m down

1.76

0.58

1349

0.52

0.04

1.01

2.3

5.5

600 m down

1.78

0.71

1359

0.96

0.03

1.02

2.45

ND

800 m down

1.22

0.41

1234

0.48

0.03

0.71

1.34

ND

ND None detected

Fig. 3.7

OD550 value of water samples from (River Sagana) indicating turbidity and colour

Turbidity levels are associated with colour, colloidal and particulate matter, and, in this study, a close relationship was established with bioluminescence (positively correlated), INT-DHA and DO which were negatively correlated. The result demonstrated that high particulate matter (Fig. 3.7) increased demand of oxygen in biochemical degradation, which leads to stress in the aquatic system, eventually affecting the water quality. The impact of the tannery effluent on oxygen demand, colour and chemical compound concentration was also observed by Song et al. 2000.

3.4.1 Data Analysis

A calibration curve was prepared using standard solutions of INTF (Iodonitrotetra­zolium formazan, Sigma-UK) to translate the absorbance values to concentration (μgTFg−1 Sediment 6 h−1).

3.5 Daphnia Test

Daphnia magna (a micro-crustacean) was used in the test as an invertebrate to represent the primary consumer level and identify short-term acute effects. This was carried out using ten freshly bred (neonates) Daphnia magna exposed to the riverine and tannery effluent samples for 24 h (Stuhlfauth 1995). The same clone of D. magna was used to ensure the same sensitivity during the testing.

The bioassays were conducted in a disposable multiwell test plates with 30 test wells (Fig. 3.8). Each plate was provided with four wells for the controls and four wells (A,B,C,D) for each toxicant concentration/sample. Additionally, the plates were provided on the left side with a column of “rinsing wells” to prevent dilution of the toxicant during the transfer of the neonates from the hatching petri dish to the test wells. Each well of the test plates was filled with 10 mL toxicant solution (or standard freshwater in the control column).
Fig. 3.8

A multiwell plate showing the rinsing well and test wells to carryout Daphnia magna test

The hatching petri dish was placed on the transparent stage of a light table provided with a black strip to enhance the contrast. Ten (actively swimming) neonates were transferred with a micropipette into each rinsing cup in the sequence: row X (control), row 1 to row 5 (increasing concentrations of toxicant) (Fig. 3.8).

The data were scored on the results Sheet and calculated the % effect. The number of immobile Daphnia magna after 24 h was noted and expressed as a lethal dose (LD) given as a percentage.

The neonates in the multiwell plate were placed on the transparent stage of a light table provided with a black strip to enhance the contrast (Fig. 3.9) during the counting of dead and immobile Daphnia magna. Besides all other specific validity conditions prescribed in standard Daphnia bioassay protocols, the number of dead and immobile D.magna in the controls should not exceed 10%.
Fig. 3.9

Light table with transparent stage provided with a black strip to enhance the contrast during the counting of dead and immobile D. magna

In order to check the correct execution of the test procedure and the sensitivity of the test animals, it is advised to perform a reference test from time to time. For example quality control tests can be performed with the reference toxicant ­potassium dichromate (K2Cr2O7), using the following dilution series: 3.2–1.8–1–0.56–0.32 mg L−1.

An example of related results (Table 3.6) obtained from a case study carried out in Kenya was as follows.
Table 3.6

Lethal dose (LD) values of Daphnia magna on treatment pits, riverine and anaerobic lagoons of a Kenyan tannery site

Samples

LD values D magna

Treatment pits

Beam-house

LD 50

General sedimentation

LD 50

Chrome stripping

LD 100

Chrome sedimentation

LD 100

Equalisation tank

LD 90

Anaerobic lagoons

Lagoon1

LD 90

Lagoon2

LD 85

Lagoon3

LD 85

Lagoon4

LD 80

Lagoon5

LD 80

Riverine

400 m upstream

NE

200 m upstream

NE

100 m upstream

NE

50 m upstream

NE

Discharge at 0 m

LD 85

50 m downstream

LD 80

100 m downstream

LD 80

200 m downstream

LD 80

400 m downstream

LD 60

600 m downstream

LD 60

800 m downstream

LD 50

NE No effect

3.5.1 Invertebrate Trophic Level (Daphnia Magna)

Higher LD values were observed at the treatment pits (Chrome stripping, ­sedimentation and equalisation pits) (Table 3.6). The anaerobic pits showed a reduction on LD values indicating a reduction in toxicity as the effluent flows from lagoon 1 (LD 90) to lagoon 5 (LD 80). Similarly, the trend was observed in the riverine sampling points, with lower LD values noted downstream. The dilution effect of the river (reduction of LD value observed progressively) downstream and the source of toxicity at the discharge point was demonstrated. There was no effect observed upstream for D. magna upstream (50 m, 100 m, 200 m and 400 m upstream), indicating that the source of toxicity was from the tannery effluent at the discharge point (LD 85).

3.6 Chemical Analysis

In order to complement the bioassay tests carried out during this study, total concentration of Cr, Ni, Cu, Zn, Cd, and Fe in each tannery effluent sample, was determined (acidified with 1% HNO3) by Atomic Absorption spectrometry (Perkins Elmer Analyst 100).

3.6.1 Sample Digestion (Sediments and Dust Samples)

Aliquots (2.5 mL) of concentrated HNO3 (69% Analar grade) were added to 200 mg dry dust samples, which had been weighed into 75 mL digestion tubes. The mixture was then allowed to stand overnight at 15°C. The following day, the digestion tubes were placed on a heating block and the temperature was gradually raised to 100°C for 8 h. The samples were allowed to digest for 3 h, after which the volume was reduced to 3–4 mL. The digest was cooled at room temperature, and diluted to 10 mL with double deionised water in graduated tubes. Total concentrations of Pb, Cu, Zn, Fe, Ni, Cd, and Cr were determined using atomic absorption spectrometry (Perkin Elmer Analyst 100).

3.6.2 Biological Oxygen Demand (BOD)/Dissolved Oxygen (DO) Determination

BOD and DO were determined using standard protocols (APHA 1965). Samples of water were incubated at 20°C for 5 days in a dark water bath. Every day for 5 days, DO was determined. The difference between initial value and the value at each time ( t ) period (i.e. Oxygen demand) was plotted as the BOD t (mg L−1).

Samples for the determination of BOD and DO were tested within 10 days of ­collection to avoid degradation. The protocol included adding 1.0 mL of ­manganous sulphate reagent followed immediately by 1.0 mL of alkaline-iodide-azide solution to the BOD bottle (300 mL). The bottle was restoppered immediately and the contents mixed by shaking vigourously for at least 20 s or until the precipitated manganous and manganic hydroxide is evenly dispersed. After 2–3 min of shaking again, the precipitate in the sample was allowed to settle for 1 h. By means of a two-way pipette and vacuum system, 100 mL of solution was transferred from the BOD bottle to a specially-painted Erlenmeyer flask containing a magnetic stirring bar. Titration was carried out immediately with thiosulphate solution until the solution turned to pale straw colour. Four drops of starch solution was added. Titration was continued until the blue colour disappeared. The dissolved oxygen was calculated using the normality and and volume of soduim thiosulphate with BOD values obtained as explained earlier. The results of the parameters mentioned are as shown in Table 3.7.
Table 3.7

Characterisation of the tannery effluent showing identified parameters and levels in three main phases (raw effluent, treated effluent and final effluent) (n = 5)

Parameters

Raw effluent

Treated effluent (General sedimentation)

Final effluent (Anaerobic lagoons)

LSD (5%)

pHz

7.72 (0.19)

7.1 (0.1)

7.66 (0.24)

0.58

COD

2437.84 (660.3)

5978.16 (4626.1)

1307.4 (291.4)

8329

BOD

1255 (309.9)

5738.1 (4688.7)

438.5 (194.9)

8366

Cl

1725 (495.5)

483.9 (216.4)

1693.7 (757.4)

1719

Sulphide

62.4 (14.7)

57.2 (15.1)

89.96 (26)

60

Susp. Solids

562 (121.6)

448.2 (153)

330.67 (43.3)

394

Total Cr

23.02 (18.3)

1.71 (0.4)

0.93 (0.2)

33

Oil/grease

332.3 (108.2)

273.9 (101)

94.38 (31)

267

Figures in parenthes are SEM’s (Standard errors of means)

3.6.2.1 Effect of BOD/COD

Both BOD and COD levels were highest at the general sedimentation phase (BOD 5,978 mg L−1, COD 5,978 mg L−1) of the tannery effluent treatment pits, with the levels drastically reducing in the final effluents (BOD 438 mg L−1, COD 1,307 mg L−1) after the anaerobic lagoons. Beam-house operations involving soaking, liming and deliming processes generate large quantities of waste such as wastewater (up to 400% during liming and reliming process) consumed in proportion to the weight of the treated hides (Thanikaivelan et al. 2003). The discharged water is full of dissolved substances, which affect its quality. The Beam-house mainly affects the following parameters of water effluent; COD, suspended solids, chlorides, sulphides and organic nitrogen. Conventional ­liming-­reliming ­processes lead to 35–45 kg of biological oxygen demand (BOD), 100–125 kg of chemical oxygen demand (COD) and 140–160 kg of total solids (TS) for every ton of raw skins/hides processed (Aloy et al. 1976).

3.6.3 Sulphate and Chloride Determination

Appropriate standards for the determination of sulphate and chloride using ion exchange chromatography (Dionex, series 4500i – Autosampler AS40) were prepared in various concentration ranges (0, 2, 5 & 10 mg L−1) and a calibration curve obtained. For sulphate determination, a stock solution of 1,000 mg L−1 SO4 was prepared by dissolving 1.818 g of potassium sulphate (K2SO4) in 1 L of deionised water. A chloride stock standard solution of 1,000 mg L−1 Cl was prepared by dissolving 1.648 g of sodium chloride (NaCl) in 1 L of deionised water. The eluent was prepared by weighing out 0.95 g of Na2CO3 and 0.71 g of NaHCO3. The preparation was then dissolved and made up to 5 L with deionised water. The regenerant was prepared by adding 3.5 mL of concentrated H2SO4 to approximately 300 mL of deionised water and making up to 5 L with deionised water. The following instrumentation conditions were maintained:
  1. 1.
    Eluent
    • Eluent flow rate: 1.2 mL min−1

    • Suppressor: H2 SO4

    • Background conductivity: 17.3 μS

     
  2. 2.
    Analyte
    • Analyte flow rate: 1.2 mL min−1

    • Temperature compensation: Selectable compensation between 0.0 and 3.0% per 1.7°C

    • Pressure: 2,900 psi

    • Limit: 5,000 psi

     

3.6.4 Total Phenols

A spectrophotometric determination of phenols based on a multicommuted flow system with a 100 cm optical path flow cell was used to determine total phenols (Fig. 3.10). All solutions were prepared with distilled and deionized water; and analytical grade chemicals. Phenol reference solutions within 10.0 and 100 g L−1 were prepared by appropriate dilutions of a 1.00 g L−1 stock solution. Reagent R1 was prepared by dissolving 50.0 mg of 4-aminoantipyrine (4-AAP) in 50 mL of a buffer containing 5.2 g L−1 NaHCO3, 5.8 g L−1 H3BO3 and 6.2 g L−1 KOH (pH 10.0). Reagent R2 was a 0.20% m/v K3[Fe(CN)6] solution prepared in water. Water was used as the carrier.
Fig. 3.10

Flow diagram of the system for determination of phenols

Where; Vi, three-way solenoid valves; B, reaction coil (80 cm); D, long pathlength flow cell (100 cm optical path); C, water carrier (5.4 mL min−1); S, sample (6.0 mL min−1); R1, 0.10% (m/v) 4-AAP buffered at pH 10.0 (0.8 mL min−1); R2, 0.2% (m/v) K3[Fe(CN)6] (0.6 mL min−1); W, waste. Dashed lines represent the flow paths when the valves are switched on.

Solutions containing 7.0 mol L−1 phenol, m-cresol, p-cresol, p-chlorophenol, catechol, hydroquinone, p-aminophenol and p-nitrophenol were employed for evaluation of the relative response for different phenols.

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Copyright information

© Springer New York 2010

Authors and Affiliations

  1. 1.Leather Development CouncilNairobiKenya

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