Assessment of Pollution Load Indices of Heavy Metals in Cassava Mill Effluents Contaminated Soil: a Case Study of Small-scale Processors in a Rural Community in the Niger Delta, Nigeria

Cassava mill effluents are discharged into the environment by smallholder cassava processor in rural communities in the Niger Delta region of Nigeria. Cassava mill effluents are known to induce toxicity in some biodiversity such as livestock (sheep, goat), vegetation, microorganisms and fisheries. This study evaluated the pollution load indices of heavy metals in cassava mill effluents contaminated soil in rural community in the Niger Delta region of Nigeria. Secondary data from cassava mill effluents soil were used for the study. The data were classified based on seasons. The pollution load was calculated following standard protocol. Nine pollution indices were considered including Contamination factor (CF), Degree of contamination (CD), Pollution load index (PLI), Pollution index (PI), Sum of pollution index (SPI), Pollution index/ Contamination Index (PI/CI), Metal pollution Index (MPI), Average Pollution Index (API) and Nemerow integrated pollution index (NIPI). In few instance that some heavy metals was not detected, 50% of mean detected individual metals were considered for the location that the metals were not detected. Geometric (BGM) and median mean (BMM) were considered for the background scenarios except for API and PI/CI in which median mean was used. The pollution load resulting from these heavy metals viz: Fe, Cr, Zn, Cu, Co, Ni, Mn, Pb and Cd revealed that CF and CD had low to moderate contamination level in both seasons apart from Pb that had considerable pollution in one of the locations for wet season, PLI were within no pollution to moderate pollution, PI were also within no pollution to low pollution level and NIPI were within warning line of pollution to low level of pollution for dry season, and warning line of pollution to high pollution in wet season. MPI, PI/CI and API showed slight pollution. The findings of this study also showed that cassava processing by smallholder in rural communities in the Niger Delta is slightly contributing to heavy metals pollution is receiving soil which varies according to seasons. Furthermore, age and heavy metal content in the cassava tuber and quantity of cassava processed in each mill and other anthropogenic activities could account for difference in pollution among the various locations, while runoff resulting from rainfall could account for the seasonal influence.

Heavy metals enter into the soil through natural or anthropogenic sources (Hernandez et al., 2003;Wang et al., 2012;Rivera et al., 2015;Mazurek et al., 2017). Natural source of heavy metals in the environment is related to lithogenic and pedogenic processes (Kabata-Pendias, 2011;Mazurek et al., 2017). Anthropogenic (human) activities also contribute to heavy metals concentration in the environment.
Most industrial and agricultural activities lead to the release of toxic substances into the receiving environment including soil, air and water. One of the major pollutant releases into the environment from most industrial and processing outfit is heavy metals. According to Idris et al. (2013), Izah et al. (2016b;2017b,c), heavy metals are metalloid with density higher than 5 cm 2 or 5 times denser than the density of water. Wang et al. (2010) also described heavy metals as one of the major substance that causes global environmental pollution. The toxicity of heavy metals on the environment may be due to their ability to persistent and bioaccumulate (Ghazaryan et al., 2015;Hassaan et al., 2016;Izah and Angaye, 2016). Heavy metals in the environment (soil and water) are up-taken by some living things in the environment and stored faster than they can metabolize (Hassaan et al., 2016). For instance, in water/sediment, fisheries tend to biaccumulate heavy metals in their body parts including muscle, bone, liver, kidney, blood etc (Izah and Angaye, 2016). As such, heavy metals could pose a significant threat to human health irrespective of the environment (water and soil) (Ghazaryan et al., 2015).
Heavy metals are typically classified into two major forms including essential and non-essential metals. Essential heavy metals have beneficial role in living things at certain concentration. Some of these important heavy metals include iron, manganese, copper, zinc, chromium among other. High concentration of essential metals in biological system could lead to toxicity on the exposed organisms. While other, such as lead, cadmium, mercury and arsenic have no known role on living organisms. As such they are highly lethal even at low concentration.
In recent time, an elevated concentration of heavy metals in soils in many regions of the world is a major source of concern especially in developing nations (Zhou et al., 2016). The worry of heavy metals in soil could be due to their ability to resist biodegradation, toxicity and accumulative characteristics (Mohseni-Bandpei et al., 2016). Studies on soil heavy metals are mainly focused on heavily urbanized areas including industrial areas and city agglomerations, as well as on the areas of constant and linear emitters, which include industrial plants, waste landfills and roads (Al-Anbari et al., 2015).
Several pollution indices are available in literature for the assessment of environmental quality (Hakanson et al., 1980;Tomlinson et al., 1980;Liu et al., 2004;Cheng et al., 2007;Qingjie et al., 2008;Yang et al. 2011Yang et al. , 2013Sarala and Sabitha, 2012;Guan et al., 2014;El-Metwally et al., 2017;Gasiorek et al., 2017) with regard to some environmental components (soil, water and sediment). According to Sarala and Sabitha (2012), the use of varying algorithms could lead to discrepancy on pollution evaluation in an environment (such as sediment and soil). As such, its essential to use appropriate and/ or best fit method to evaluate environmental components such as soil and sediment for effective decision making and spatial planning (Sarala and Sabitha, 2012). Specifically, pollution index and or/ contamination indices is an important tool for processing, analyzing, and conveying raw environmental information to decision makers, managers, technicians, environmentalist and the general public at large (Caeiro et al., 2005;Sarala andSabitha, 2012). 1980;Sutherland, 2000;Tijani et al., 2004;Yu et al., 2004;Qingjie et al., 2008;Wang et al., 2010Wang et al., , 2016Liang et al., 2011;Suresh et al., 2011;Yang et al. 2011;Sarala and Sabitha, 2012;Zhu et al., 2012;Fiori et al., 2013;Swarnalatha et al., 2013;Elias et al., 2014;Jiang et al., 2014;Singovszka et al., 2014;Tang et al., 2014;Uriah and Shehu, 2014;Vowotor et al, 2014;Al-Anbari et al., 2015;Ghaleno et al., 2015;Ghazaryan et al., 2015;Karydas et al., 2015;Soliman et al., 2015;Hassaan et al., 2016;Mohseni-Bandpei et al., 2016;Todorova et al., 2016;Bhutiani et al., 2017). But information on pollution load of heavy metals resulting from the discharge of cassava mill effluents into the soil is scanty in literature. Therefore, this study is aimed at investigating the pollution load of heavy metals in cassava mill effluents contaminated soil in a rural community in the Niger Delta region of Nigeria. The study applied several pollution indices viz: contamination factor (CF), contamination degree (CD), pollution load index (PLI), pollution index (PI), Pollution index/ Contamination Index (PI/CI), Metal pollution Index (MPI), Sum of pollution index (SPI), Average Pollution Index (API) and Newmerow integrated pollution index (NIPI). The findings of this study may be useful to environmentalist and policy makers in Nigeria and other cassava processing countries of the world.

Study area
Ndemili Umusadege, Utagba-Uno is one of the communities in Ndokwa-West local government area of Delta state. Ndemili lies between latitude N06º01' and longitude E006º17'. Like other regions of the Delta state, the average annual precipitation of the area is about 1900 mm (Orji and Egboka, 2015). The atmospheric temperature and relative humidity of the area is approximately 28±6º C and 50 -95% respectively all year round. Major economic activities in the area include farming. Some of the major crops farmed in the area are food crops such as cassava, yam, maize, oil palm etc (Izah et al., 2017d). The cassava cultivated in the study area are typically processed into gari (cassava flakes) and Akpu (a food made from slurry of fermented cassava tuber).

Data source
Secondary data was used for the determination of pollution load indices of heavy metals in cassava mill effluents contaminated soil. The background mean values (geometric and median mean) and concentration of heavy metals based on two seasons data from five locations previously reported by Izah et al. (2017d) (Table 1). The values were used to calculate the pollution load indices based on seasons (viz dry and wet) at the different locations.

Pollution load assessment model
Pollution by heavy metals has been widely studied using several indices including CF, CD, PLI, PI, PI/CI, MPI, API, SPI and NIPI. The basis of determining the pollution load is to quantify the extent of heavy metals pollution by cassava mills effluents in receiving soil in comparison to its natural background. Several mean data have been recommended/ suggested to be used as natural background reference value for the assessment of pollution load and ecological risk assessment. Some of these means include geometric mean (BGM) (Thambavani and Uma Mageswari, 2013;Bhutiani et al., 2017) and median mean (BMM) (Sarala and Sabitha, 2012;Monakhov et al., 2015;Bhutiani et al., 2017). According to Sarala and Sabitha (2012), the use measures of the central tendency such as median instead of an arithmetic mean shows the main trend in the index values for management purpose. Furthermore, BGM and BMM have been applied in determining pollution load in environmental components. Based on the values presented in Table 1, CF, CD, PLI, PI, PI/CI, MPI, API, SPI and NIPI were calculated and the resultant values was compared to the criteria presented in Table 2  Note: Hakanson (1980)   This provides the ease of calculation and interpretation.
Note: Sarala and Sabitha (2012), Caeiro et al. (2005) 1.3.1 Contamination factor Contamination factor (CF) is used to assess contamination level in relative to average concentration of the respective heavy metals in the environment i.e. soil to the measured background values from previous study with similar geological origin or uncontaminated soil (Sutherland, 2000;Tijani et al., 2004;Uriah and Shehu, 2014). CF is often expressed based on the formula previously described by Hakanson (1980) (CD) is sometimes known as degree of contamination. CD is the sum of all contamination factors, which provides information about total contamination in a particular sampling location (Singovszka et al., 2014;Bhutiani et al., 2017). Contamination degree is often expressed based on the formula previously described by Hakanson (1980) and have been applied by Bhutiani et al. (2017), Uriah and Shehu (2014) CF is the contamination factor for the respective metals and n is the number of elements (n = 9).

Pollution index and Nemerow integrated pollution index
Pollution index (PI) and Nemerow integrated pollution index (NIPI) is another type of indices used to assess extent of pollution in an industrial area (Cheng et al., 2007;Sarala and Sabitha, 2012). NIPI considers the overall level of soil pollution, taking into account the concentration of the various heavy metals under consideration (Guan et al., 2014;Kowalska et al., 2016;Mazurek et al., 2017).
PI has the same formula with CF. But unlike CF, PI consider the mean concentration of heavy metals from at least five locations/stations. PI formula has been previously described by Yu et al. (2004) . NIPI can be used to assess the quality of soil (Liang et al., 2011). NIPI have been widely employed by authors in assessing risk pollution potentials of heavy metals in the environmental especially soil (Liu et al., 2004;Yu et al., 2004;Cheng et al., 2007;Yang et al., 2011Yang et al., , 2013Sarala and Sabitha, 2012;Jiang et al., 2014;Al-Anbari et al., 2015).
Nemerow integrated pollution index (NIPI) = √ PI mean 2 + PI Maximum 2 2 (Equal 5) Where PI mean 2 is the mean value of PI of individual heavy metals and PI Maximum 2 is the maximum PI value of individual heavy metals.

Pollution index (contamination index)
Pollution index (contamination index) (PI/CI) is often used in identifying pollution in priority areas (locations) (Sarala and Sabitha, 2012). According to Sarala and Sabitha (2012), PI/CI requires several measurements in the same sampling site. PI/CI was developed by Johansson and Johnsson (1976) and Ott (1978) and has been applied by Sarala and Sabitha (2012). The resultant values were compared with index comparison for MPI previously described by Sarala and Sabitha (2012), Caeiro et al. (2005) (Table 3b).

Sum of pollution index
Sum of Pollution index (SPI) previously described by Qingjie et al. (2008) was used for the applied.

Results and Discussion
Among the 9 heavy metals studied under both seasons in the 5 locations, 59 (representing 65.56%), 2 (representing 2.22%) and 29 (representing 32.22%) showed moderate contamination, considerably contamination and low contamination respectively under BMM scenario. While in BGM 49 (representing 54.45%), 1 (representing 1.11%) and 40 (representing 44.44%) showed moderate contamination, considerably contamination and low contamination respectively. This study showed that contamination level differs depending on heavy metals. This could be due to variation in anthropogenic activities leading to heavy metal generation, difference source of cassava tuber processed as well as age of the cassava tuber. Quantity of cassava mill effluents discharged into the soil in the various locations could also account for variation among the contamination level in each of the location. Runoff resulting from rainfall during the dry season could also be potential source of variation in the contamination factor.
Based on seasons, wet season has higher contamination (moderate and considerably) level compared to dry season under BMM scenario. Furthermore, in BGM scenario, dry season has higher contamination (moderate and considerably) compared to wet season. Comparing the two different background scenarios, fluctuations in the values could be associated to variation in the mean data. The trend in this study has been reported by Bhutiani et al. (2017).
The degree of contamination of heavy metals concentration in cassava mill effluent contaminated soil is presented in Figure 1. Among all the locations and season, there was moderate risk level (8 ≤ CD<16). Though, there was slight variation between both background levels. This suggests that the soil is being contaminated by the prevailing activities in each location.  Vol.8, No.1, 1-18 http://bm.biopublisher.ca Figure 2 presents the pollution load index of heavy metals concentration in cassava mill effluents contaminated soil. Pollution load index showed that LC in both seasons is moderately polluted, while wet season in LB and CE and dry season in LD showed moderate pollution under BMM consideration. While in BGM scenario, wet season in LB and LE and dry season of LC and LD also showed moderate pollution as well. The trend in both background level of this study is similar to findings of Bhutiani et al. (2017). This is also an indication that the level of pollution is affected by seasons as well as spatial distribution within the cassava mill effluents contaminated soil. The statistical analysis of Pollution index of heavy metals concentration in cassava mill effluents contaminated soil is presented in Table 5 The mean value of all the heavy metals in both seasons under both background scenarios ranged from no pollution (P1≤1) to low pollution (1<PI≤2). Under BMM scenario, copper, iron, lead, cadmium and chromium in both seasons, and nickel and cobalt in wet season showed low pollution, while the other heavy metals indicate no pollution. While in BGM consideration scenario, there was low pollution in all the metals under study across both seasons of study. This is an indication that pollution resulting from cassava mill effluent in small scale processing in rural community in the Niger Delta is low.  Table 6 presents the Nemerow integrated pollution index (NIPI) of heavy metals concentration in cassava mill effluents contaminated soil. NIPI ranged from warning line of pollution (NIPI≤0.7) to high level of pollution (NIPI>3). Under BMM consideration, there was low level of pollution (1<NIPI≤2) apart from cobalt in dry season. Variation exist under both scenarios in wet season, thus copper, zinc, manganese, iron, cadmium and cobalt showed low pollution, chromium and nickel showed moderate level of pollution while lead showed high level of pollution. Like dry season BMM consideration, dry season of BGM indicate low level of pollution in all the metals. While in wet season of BGM consideration, copper, zinc, manganese, iron, chromium and cobalt showed low pollution, lead and cadmium nickel showed moderate level of pollution while lead showed high level of pollution. The moderate/high pollution in lead is an evident of considerable contamination. While the moderate pollution in nickel and cadmium is an evident of moderate contamination. The slight variations that exist between both backgrounds suggest the differences in the mean value used in the study. In NIPI pollution categorization, it appears that dry season has lower pollution compared to wet season from the cassava mill effluents contaminated environment.  Table 7 presents the pollution index (contamination index) (PI/CI) of heavy metals concentration in cassava mill effluent contaminated soil in a rural community in Delta state, Nigeria. The PI/CI showed that the soil were between unpolluted to low polluted except for few instance viz: copper in dry season for LB, lead of wet and dry season for LC and CE, and wet season for LA and LE which were within low pollution to moderately polluted.  (Figure 3). This is an indication of low level of pollution in soil associated with the discharge of cassava mill effluent into the soil.  sum of pollution index showed wide range of disparity. This is an indication of seasonal influence. The variation among the different locations could be due to deviation in topography, making some of the areas more prone to runoff after rainfall. Furthermore, other anthropogenic activities could also account for variation in the various locations with regard to sum of pollution index. From all pollution load indices consider, the study showed that cassava mill effluents in receiving soil are contributing to slight heavy metals pollution. According to Qiu (2010), heavy metals pollution from industrial setting typically originates from three sources including exhaust, human activities and secondary pollution. Based on the various pollution indices under study, the heavy metals resulting from cassava mill effluent is leading to low/slightly polluted to moderate pollution. This trend has been reported in soil near sugar mill when several integrated and contamination factors were applied in the assessment of pollution load (Sarala and Sabitha, 2012).
The pollution level based on the different indices used showed variation among the different mills in the study area. According to Mazurek et al. (2017), Hernandez et al. (2003), heavy metals pollution in soil varies according to its chemical and physical characteristics including texture, buffering ability and the capacity to neutralize contaminants. Mazurek et al. (2017), Pajak et al. (2015) also reported that the distribution/arrangement of soil heavy metals depends on landscape and or/ topography. This could account for minor variation among the various locations of study.

Conclusions
Nigeria is the world leading producer of cassava accounting for over 20% of global output. Cassava processing is majorly carried out by small-scale processors in the Niger Delta region of Nigeria. During cassava processing, effluent is produced from the dewatering zone which accounts for about 16% of total cassava tuber weight. This effluent is toxic to some living things. This study evaluated the pollution load of heavy metals in cassava mill effluents contaminated soil in rural community in the Niger Delta region of Nigeria. Secondary data from cassava mill effluents soil were used in this study. Pollution load were considered based on two background scenarios viz: BGM and BMM. The results revealed low to considerable contamination (CF, API, MPI), low to moderate contamination (CD, PI/CI), no pollution to moderate pollution (PLI), no pollution to low pollution (PI) and warning line of pollution to high pollution (NIPI). Therefore, cassava mill effluents from small-scale cassava processing in the Niger Delta are contributing to heavy metals pollution in the soil which tends to vary according to seasons.