1 Introduction

Abattoirs are known over the world to pollute the environment either directly or indirectly from the various processes. Waste water from the abattoir is usually concentrated sources of oxygen consuming waste (Girards, 2005). Macroinvetebrate in the other hand in a water body are of value as long term indicator of water quality and can provide signs of impending water pollution and habitat fragmentation. Benthic macroinvetebrates show high variability and are able to integrate the effects of short term environmental variations which have been used in characterizing rivers and streams in many part of the world. Adakole and Anunne (2003) reported that organic pollution of Bindare stream caused a decrease in the benthic macroinvetebrate species richness and a decrease in density species richness.

Environmental modification or pollution can alter macroinvetebrate communities (Chirhart, 2003). Since macroinvetebrate have limited mobility and can stay in an area without moving away easily, the type of the macroinvetebrate obtain may be used as indicator of the status of the water quality of the system at that location. Similarly, the life cycle of macroinvetebrate fauna may last for a year or more and this reflect the type of benthic fauna in aquatic system due to the effect of the pollutant rather than chemical analysis of the water. The long term effect of pollutant on aquatic ecosystem may be shown in the type of macroinvetebrate found in that system. This may be used in the classification of the ecological integrity of that aquatic environment (Chirhart, 2003). The sensitivity of macroinvetebrate to deterioration in physiochemical conditions makes them a good bioindicator condition in pollution studies (Sikoki and Zabbey, 2006).

Thus, a change in the physicochemical aspect of a water body brings about a corresponding change in the relative composition and abundance of the organisms in that water (Adeyemi et al., 2009). All the same, chemical and physical measurements used in evaluating water quality provide data that primary reflect conditions that exist when the water sample was taken (Muralidharan et al., 2010).

There are several research works on the pollution status of the Wurukum Abattoir. Akaahan et al. (2014) have reported on the macroinvertebrates fauna group and their relationship with environmental variables in river Benue where the Wurukum abattoir was a sampling station. This study approaches the research from the concept of Abundance-Biomass Comparison (Warwick, 1986) under the assumption that in pollution conditions, numbers of individual of species (abundance) will be more evenly distributed than biomass distributions as induced disturbance by the pollutants.

2 Experimental Details

Study Area

River Benue with its source from the Cameroonian mountains flows eastwards into Nigeria. It is the second largest river in Nigeria and measures approximately about 1.488 km² with alluvia fertile flood plains in on either bank. The River flows through Makurdi and confluence in with River Niger in Kogi state, Nigeria. Makurdi the capital city of Benue is located on latitude 7°41'N and longitude 8°28'E.the size of the River Benue within Makurdi and major settlement runs through is approximately 671 meters (Udo, 1981). Wurukum Abattoir is located at the bank of River Benue and close to New Bridge that links the north and southern parts of Makurdi Township. The Benue River at this point receives effluent from the abattoir waste which are washed directory into the River.

Sampling was carried out weekly for a period of four months (November, 2015 - February, 2016) at four stations selected along River Benue near the Wurukum Abattoir as follows (Figure 1):

Station A: was located at the point above the abattoir from the direction of flow i.e. close to the New Bridge

Station B: was located at a point where waste from the abattoir is discharged directly into the channel adjacent to the river. This station receives a wash down of the abattoir effluent.

Station C: was located at the point where the channel joined the main river.

Station D: was located at a point on the main river near the Old Bridge and 50 meters from station C.

Sample Collection

Water samples were for physicochemical analyses were collected and analysed for temperature (in situ using mercury in glass thermometer) while total dissolved solids (TDS), electrical conductivity (EC), and pH were analysed in the laboratory using HANA1893 digital metal. Dissolved oxygen (DO), biochemical oxygen demand (BOD) and Alkalinity were examined in the laboratory using standard methods (APHA, 1992).

Macroinvertebrates were collected using a van Veen grab (0.1 m²). Each of the sediment sample collected wash three times in the laboratory through three sets of sieves. First 2 mm, then 1 mm and finally 0.5 mm sieves to collect the macro invertebrates (Esenowo and Ugwumba, 2010). The washed and preserved sediments with macro benthos were poured into enamel white tray and sorted out. The sorting was made effective by adding moderate volume of water into container to improve visibility (George et al., 2009). Large benthic fauna were picked out with facets while the smaller once were pipette out. The preserved animal were identified under light and stereo microscope and counted. The identification was carried out using keys (Needham, 1962). After identification, each of the macroinvetebrate was weighed.

Statistical Analysis

Physicochemical properties data obtained from water samples were subjected to One Way Analysis of variance (ANOVA) to test for differences among station using Minitab 17. Pollution status at the various stations over the period was determined using Abundance Biomass Comparison curve according to Warwick (1986) on Primer 6 software. Biodiversity indices such as Shannon-Weiner H & J, Simpson 1-D & 1/D, Berger-Parker D, McIntosh D & E, Evar, Brillouin Index, Margalef and Specie Richness were computed using Genstat 12^th Edition. Prism 6 software was used in plotting trend graphs of water quality parameters. Multivariate analysis (cluster analysis) was used to categories the macro invertebrates into tolerance threshold to the abattoir effluent on Minitab 17 software.

3 Results

Macro invertebrates abundance and distribution

The relative abundance of the various macro invertebrates taxa encountered at the different stations during the study period is presented in Table 1.

Fourteen species of macro invertebrates were encountered during the study period, with arthropods recording the highest number (9 species) followed by mollusc (3 species) and annelids (2 species). Chironomus larvae recorded 6.05% in station B and 3.5% in station in station C while station A and D recorded 0%. Tubifex tubifex were recorded the mean abundance of 52.45% and 44.19% and in stations A and C respectively while stations A and D recorded 0%. Eristalis tennax was 17.93% and 9.80% in stations B and C respectively, whole stations A and D recorded 0% each. Libellula sp were 13.43%, 4.80%, 6.05% and 7.57% in station A, B, C, and D respectively. Gienurus gratus were recorded 29.11%, 3.28%, 3.75% and 16.42% respectively. Ranatra sp were 2.02% and 4.32% in stations B and C respectively. Pisticus marginalis were 2.02%, 4.90%, 7.46% and 7.59% in stations A, B, C, and D respectively. Ephimera dentica were 25.37%, 1.52%, 0.86%, and 24.05% is stations A, B, C, and D respectively. Baetis rhodani were 24.37%, 1.52%, 0.86%, and 25.05% in stations A, B, C, and D respectively. Valvata piscinalis were 3.54% and 6.05% in stations A and D respectively, while Magaritifera sp was recorded only in station A throughout the study period; it recorded 1.49%. Stations B, C, and D recorded 0% throughout the study period. Lymnae sp were 0.86% and 4.80% in station B and C respectively, while stations A and D recorded 0% throughout the study period. Macrobdella decora were 2.31% and 3.54% in stations C and B, while stations A and D recorded 0% during the study period.

Species diversity indices (Table 2) calculated using different indices showed that station B recorded highest in species richness (44.00%) and lowest in station A (22.00%). Shannon-Weiner H was 3.13% and 2.90% in station B and D respectively. Shannon-Weiner J recorded highest in station A (0.96%) and lowest in station B (0.80%). Simpson 1-D recorded highest in station A (0.96%) and lowest in station B (0.80%). Simpson 1/D was highest in station (23.77%) and lowest in station C (11.81%). Berger-Parker D recorded in station recorded the highest values in station B (0.19%) and lowest in station C (0.10%). McIntosh D & E recorded highest in station A (0.74% and 0.96% respectively), and lowest almost similar values in station B and C (0.22% and 0.21% respectively). Brillouin Index recorded highest in station B (0.94%) and lowest in station D (0.53%). Margalef recorded the highest value in station B (7.19%) and lowest in station D (0.81%).

Figure 2 shows the dendrogram of macro invertebrates based on their degree of tolerance threshold to Wurukum Abattoir Effluent. It showed that Chironomus larvae, Eristalis tennax, Tubifex tubifex, Valvata piscinalis, Macrobdella decora were highly tolerant to the effluent Chironomus larvae, Libellula sp, Pisticus marginalis, Gienurus gratus, Libellula sp were moderately tolerant to the abattoir effluent, while Ephimera dentica, Baeti srhodani, Gerris remigis and Valvata piscinalis were least tolerant to the abattoir effluent.

Abundance biomass comparison curve (ABC) of macroinvertebrates

Station A showed no disturbance (unpolluted) condition; hence the curve for biomass lied above the abundance curve with a wide margin between the abundance and biomass curve (Figure 3).

Station B showed disturbance (polluted condition), hence the curve for abundance lied above biomass with a wide margin (Figure 4). Station C showed disturbance, hence the curve for abundance lied above the biomass curve. The margin indicating the extent of pollution was however not as wide as that in station B (Figure 5). Station D showed relatively polluted condition. Hence, the curve for biomass overlapped the abundance curve with a narrow range (Figure 6).

Physicochemical Parameters

The mean variation of water quality by station is shown in Table 3. The monthly fluctuations in the physicochemical parameters are presented in figures 7-13.

The temperature of river Benue during the study period dropped from November, 2015 to February, 2016 in all the stations except station B whose temperature rise in December to January but ultimately dropped in February, 2016 (Figure 7). The mean temperature was highest in station A (23.51 ± 0.72°C) and was lowest in station B (24.28 ± 0.80°C). There was no significant difference (P > 0.05) among the station throughout the study period.

The pH of river Benue was uniform from, November, 2015 to January, 2016 and rise from January, 2015 to February, 2016 (Figure 8). Mean value of pH was highest in station D (10.11 ± 0.36) and was lowest in station C (9.64 ± 0.36). There was no significant difference (P > 0.05) among the stations throughout the study period (Table 3).

Electrical Conductivity was almost uniform in station A and D and was high in stations B and C across the study period (Figure 9). The mean Electrical Conductivity was highest in station B (496.5 ± 636 µs/cm) and lowest in station A (43.40 ± 4.67 mg/l). There was significant difference (P < 0.05) among the stations during the study period (Table 3).

The Total Dissolved Solids was low in station A and D and was high in stations B and C throughout the study period (Figure 10). The mean value of total dissolved solids was highest in station B and (347.70 ± 3.17 mg/l) and was lowest in stations D (31.30 ± 2.11 mg/l). There was significant difference (P < 0.05) among the stations during the study period (Table 3).

The dissolved oxygen was high in station A, D, B, and C in descending order and was almost uniform across the stations throughout the study period (Figure 11). The mean value of dissolved oxygen was highest in station A (5.64 ± 0.09 mg/l) and lowest in station B (4.23 ± 0.06 mg/l).There was significant difference among the stations during the study period (Table 3).

The Biochemical Oxygen Demand was similar in stations B, C and D except in station A which recorded highest oxygen demand in the month of November, 2015 and the BOD dropped in December and became uniform throughout the study period (Figure 12). The highest value of Biochemical Oxygen Demand was recorded in station B (0.91 ± 0.08 mg/l) and the lowest value was recorded in station A (0.63 ± 0.04 mg/l). There was significant difference (P < 0.05) in Biochemical Oxygen Demand among the stations (Table 3).

Alkalinity was low and almost low in stations A and D and was high in station B and C throughout the study period (Figure 13). Highest value of alkalinity was recorded in station B (180.40 ± 1.40 mg/l) and lowest in station A (59.10 ± 1.16 mg/l) there was significant` difference (P < 0.05) among the stations during the study period (Table 3).

The correlation matrix of the water quality variables is shown in Table 4. Apart from BOD (0.076) and alkalinity (0.088), all the other parameters showed a negative weak correlation with temperature (pH -0.102, EC -0.019, TDS -0.029, DO -0.089). pH also showed a weak negative correlation  with most of the other parameters apart from DO (0.106). EC (-0.136), TDS (-0.153), BOD (-0.062) and Alkalinity (-0.105), all had weak negative correlation with pH. Apart from DO which showed strong negative correlation (-0.792), TDS (0.0555), BOD (0.272), Alkalinity (0.577) showed positive correlation with Electrical Conductivity. The relationship was statistically significant (P < 0.01) with all the parameters except BOD. Also, DO (-0.778) showed negative correlation, BOD (0 266), alkalinity (0.555) showed a strong negative correlation with TDS. The relationship was statistically significant (0.01). BOD (-0.400), alkalinity (-0.767) all showed a negative correlation with DO. The relationship was statistically significant (P < 0.01) with BOD and statistically significant (P < 0.05) with alkalinity. There was a positive correlation between (0.124) BOD and alkalinity while alkalinity showed no correlation with other parameters.

4 Discussions

Macroinvertebrates Abundance

The number of recorded macro invertebrate population was generally low in station A and D because of some ecological imbalance arising from interaction of some important factors governing the abundance and distribution of benthic macro invertebrate communities. Such factors include water, immediate substrates for occupation and food availability. This agrees with the work of Dance and Hynes (1980). According to Brinkhurst (1970) the bigger the size of water body the poorer the macro invertebrate’s richness. In addition, high human activities around the sampling stations which release waste into the river could also be the possible explanation. The presence of pollution tolerant macroinvertebrates such as Chiromonus larvae, Eristalis tennax, Tubifex tubifex and Macrobdella decora in stations B and C could be attributed to the effect of the abattoir waste discharged into River Benue. This agrees with the work of Reish (1973). The high Biochemical Oxygen Demand and low Dissolves Oxygen recorded in this present study may have favoured the presence of these pollution indicator species as also confirmed by Sharma et al., (2013). The adaptation of pollution tolerant organisms such as Chiromonus larvae includes the possession of haemoglobin which gives it the ability to withstand low Dissolved Oxygen induced mostly by organic pollutant. Hence their presence in water is an indication of the degree of change due to anthropogenic activities in the river.  Their high presence in stations B and C is a common feature of originally polluted water bodies as confirmed by Arimoro and Osawke (2006). The relatively high abundance of intolerant species Ephemera dentica, Baetis rhodani, Libellula sp in stations A and D and their low presence or complete absence in station A and D could be attributed to their low tolerance to polluted conditions.

Station A and C showed no pollution since the curve for biomass lies above that for abundance. This is because the species were characterised by the competitive dominants in macro invertebrate communities which were k-selected or conservative species with the attributes of large body size and long-life span: they were rarely dominant numerically, but were dominant in terms of biomass. This agrees with the work of Warwick and Clarke (1994), Agard et al., (1993). In station B and C and the abundance curve lied above that for biomass with a wide margin indicating the place was polluted. This because the stations were characterised by communities that were smaller r-selected or opportunistic species with a short life-span like Chironomus larvae which were  usually numerically dominant but do not represent a large proportion of the community biomass hence when comparing their weight and number, their number was more than their weight. This agrees with the works of Agard et al., (1993).

The temperature in this study was high in the month of November, and decreased to the month of February, 2016. The drop in temperature could be attributed to change in weather from raining season to harmattan from November, 2015 to January, 2016 and a rise in temperature from the month of January to February, 2016 could be attributed to decrease in harmattan during the month. Sharma et al. (2013), Yogesh and Pendse (2001) reported similar fluctuation in temperature in various water bodies with season. The mean temperature in this study (23.51°C) disagrees with the findings of Akaahan et al., (2015); Agouru and Audu (2012) who reported 28.74°C and 27.45°C respectively on their various studies on same river Benue. The highest temperature was recorded in station B which could be as a result of biodegradation activities by microbes on waste release from the abattoir.

The pH in this present study ranged between 10-10.2. This disagrees with the findings of Akaahan et al., (2015) who reported that the pH of river Benue was slightly alkaline (6.63), Agouru and Audu (2012) reported 7.77, and increase in temperature over the month was associated with decrease. The monthly fluctuation could be attributed to fluctuation in water volume of River Benue during the study period. The high pH during period of high water volume (i.e. raining season) disagrees with Ebere (2002) and Nweke (2000) who confirmed higher pH in the dry season than in the raining seasons. Electrical Conductivity, Total Dissolved Solids, Biochemical Oxygen Demand and Alkalinity correlated negatively with pH while DO correlated positively with pH.

Electrical conductivity indicates the presence of ions in the water which is usually due to the saline intrusion and leaching of organic waste from the abattoir. The conductivity is an indispensable water quality parameter for indicating risk associated with salinity. The mean result of electrical conductivity obtained in this study showed lowest in station A (43.40 ± 46.7 µs/cm) and highest in station B (496.50 ± 6.38 µs/cm). These values were contrary to that of Akaahan (2014), Ishaq S et al., (2012) who reported 86.85 µs/cm. The result of stations A and D was however below the maximum limit of 100.00 µs/cm while station B and was above the limit set by WHO and Nigerian standard for drinking water quality (WHO, 2004). The mean high electrical conductivity in station A and D could be attributed to decaying of organic matter which increases the salinity of the water, while low salinity in stations A and D could be attributed to lack of saline intrusion in the river Benue at Makurdi. Electrical conductivity was almost uniform across the station throughout the study period showing high values in stations B and C and low values in station A and D. Electrical conductivity correlated.

Total Dissolved Solids is an index of the amount of dissolved substances from anthropogenic sources in a water body (Davies, 2013). The high total dissolved solids in stations B and C is an indication of organic pollution from the abattoir. It is also an indication of high dissolved solids at the abattoir effluent than the river agreeing with the studies of Emeka et al., (2011). The result of the present study is below the WHO permissible limited limit of 1 500 mg/l.

Biochemical Oxygen Demand is the amount of oxygen necessary for oxidative decomposition of organic matter by microorganism. In the present study, Biochemical Oxygen ranged highest in station B and C indicating high level of biological activities going on by microbes by micro organism on the waste discharged from the abattoir resulting in the use of oxygen, hence high values of BOD. This high value of Biochemical Oxygen Demand in this study agrees with Sharma et al., (2013) in his study of macro invertebrates’ community and diversity in relation to water quality status of Kuda River in India. Idowu and Ugwumba (2005) also confirm higher values in polluted areas.

The concentration of Dissolved Oxygen measures the degree of pollution by organic matter, the destruction of organic substances as well as the self purification capacity of the water body. The depletion of dissolves oxygen at station B may be attributed to huge amount of organic waste from the abattoir which requires oxygen for chemical oxidation and breakdown resulting in the deterioration in oxygen. This agrees with the works of Taiwo et al., (2014). Dissolved oxygen values obtained in this study were below WHO (2004) permissible limit of 8 mg/L for discharged into water and standard for sustaining life of 5 mg/l. A concentration below this value will adversely affect aquatic biological life, while concentration below 2 mg/L may lead to death for most fishes (Chapman, 1992). This implies that the dissolves oxygen in station A can adversely sustain life while stations B, C, and D were slightly below the permissible limit. Dissolved oxygen fluctuate within the months showed almost uniform values across the stations except in station C where there slight increase in dissolved oxygen in the month of January.

Alkalinity was lowest in station A (59.10 mg/l) and highest in station B (189.40 mg/l) which is within the permissible limit of WHO (2004). The high in alkalinity in station B and C could be due to decomposition of organic of the water at this station. Alkalinity was similar across the study period which disagrees with the work of The seasonality in total alkalinity in this study is in agreement with the observation of  Hall et al. (1977) in the middle and lower Zambezi estuary.

This study showed that Wurukum abattoir impairs the health of River Benue as evidenced in that macroinvertebrates found in stations B and C such as Chironomus larvae, Eristali tennax, Macrobdella decora are pollution tolerant organisms. Also the presence of high values of Biochemical Oxygen Demand, Total Dissolved Solids, Electrical Conductivity and low Dissolved Oxygen in stations B and C is was an indication of presence of organic waste discharge into the river from the abattoir. The Abundance Biomass Comparison curve also indicated that site Band C was polluted as evidenced by the curve for abundance overlapping that for biomass.

It is therefore recommended the activities of Wurukum Abattoir should be monitored closely by relevant agencies (Benue State Ministry of Environment) in order to prevent full blown environment al problem and attendance health hazard in the near future. The abattoir effluent could be effectively recycled into arable crop usage due to the high nutrient value. This might rid the environmental of high organic load discharge v the abattoir.

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