Bioscience Methods 2017, Vol.8, No.1, 1-18
8
Where W= weight of median value for pollution variable; C = maximum concentration of pollution variable per
location.
1.3.6 Average pollution index
Average pollution index (API) is one of the algorithm integrated indices used to assess pollution (Sarala and
Sabitha, 2012). API has been defined by Qingjie et al. (2008), Sarala and Sabitha (2012), Yang et al. (2013) as
summation of all single pollution index divided by the number of heavy metals under consideration.
API =
1
n
∑ PI (CI)
(Equal 7)
Where PI (CI) = single pollution index of heavy metal; and n = number of heavy metals under consideration.
Contamination based on API for median mean was determined by comparing the values to the contamination
classes provide for integrated indices by Sarala and Sabitha (2012). This include class 1- unpolluted, class 2 –
lowly polluted, class 3 – moderately polluted, class 4 – strongly polluted and class 5 – extremely polluted. Value
of API > 1.0 is an indication of low contamination level of the soil (Qingjie et al., 2008).
1.3.7. Metal pollution index
Metal pollution index (MPI) is a simple approach used to describe the integrated effect of heavy metals
contamination (El-Metwally et al., 2017). MPI was calculated based on the method previously described by
El-Metwally et al. (2017), AMA (1992) and have been applied by Usero et al. (1996), Sarala and Sabitha (2012).
Furthermore, Qingjie et al. (2008) have applied this equation in environmental risk assessment and called it root
of the product of pollution index.
MPI =
(
x
)
1⁄
(Equal 8)
Where MC= Metal concentration; n= number of number of metals considered.
The resultant values were compared with index comparison for MPI previously described by Sarala and Sabitha
(2012), Caeiro et al. (2005) (Table 3b).
1.3.8 Sum of pollution index
Sum of Pollution index (SPI) previously described by Qingjie et al. (2008) was used for the applied.
RPPI =
Pi + Pi + Pi + Pi + Pi + Pi + Pi + Pi + Pi
(Equal 9)
Where
Pi
= single pollution index of heavy metals
2 Results and Discussion
Table 4 presents CF of heavy metals in cassava mill effluent contaminated soil in a rural community in Delta state,
Nigeria. The results showed that heavy metals contamination ranged from low contamination (CF<1) to
considerable contamination (3 ≤ CF < 6). Contamination due to copper was moderate at LA and LB and Low at
LC for both seasons. It also showed moderate contamination at dry and wet season for LD and LE respectively at
BMM scenario. Furthermore, it was moderate and low for LB and LC respectively. It was also moderate in dry
season of LA and LD and wet season of LE at BGM scenario.
For zinc, there was moderate contamination for LD and LE for both seasons. Also, there was moderate
contamination in wet and dry season for LB and LC respectively (BMM scenario) and all were moderately
contaminated apart from LA in both seasons and wet season for LC at BGM scenario. In BMM and BGM scenario,
manganese was only moderately contaminated in wet and dry season for LB and LC respectively. However, in LD
and LE moderate contamination exit for both seasons. Iron under BMM scenario showed moderate contamination
at LC to LE at both seasons and LB in only wet season. While in BGM scenario, there was low contamination for
LA and LB in both seasons and also low for LE in wet season. In both BMM and BGM scenario, lead
contamination was considerably high in wet season for LB. Furthermore, it was moderate at 60% of the entire
location (with both seasons of study inclusive) in BMM scenario and 40% moderate contamination across both
