Research Journal of Chemical Sciences ______________________________________________ ISSN 2231-606X Vol. 2(8), 1-8, August (2012) Res.J.Chem.Sci. International Science Congress Association 1 Index Model Analysis Approach to Heavy Metal Pollution Assessment in Sediments of Nworie and Otamiri Rivers in Imo State of Nigeria Iwuoha G.N. *, Osuji L.C. and Horsfall M. Jnr. Department of pure and industrial chemistry, University of Port Harcourt, Rivers State, NIGERIAAvailable online at: www.isca.in Received 30th August 2011, revised 14th September 2011, accepted 17th May 2012Abstract The distribution, controlling geochemical factors and contamination status of heavy metals in sediments of Otamiri and Nworie rivers in Owerri, Imo State Nigeria, were investigated. Two groups of bed sediments samples were collected from five sample stations during February 2008 and June 2008. The sample were analyzed to determine their heavy metals (Cd, Pb, Ni, Zn, Cu, Fe and Cr), and the results showed that the heavy metal concentrations were slightly higher in February than those sampled in June. The results also showed that metal content directly correlate to flow condition of the rivers as station 2 recorded higher values than others.. Environmental assessment of sediments pollution by heavy metal was carried out using geo-accumulation index (Igeo), pollution load index (PLI), and enrichment factors (EF). EF not only show the class of pollution status of the river bed sediments but could be an exact indicator (i.e. showing the percentage contribution) from the sources (lithogenic or anthropogenic) of the contaminant and is very simple to use. Based on these indicators for predicting the level of heavy metal of Otamiri/Nworie river sediments, the ENRICHMENT FACTOR (EFmodel was chosen as the best over other models. Keywords: Heavy metal, sediment, contamination status, geoaccumulation index, pollution load index, enrichment factor. Introduction Heavy metals are among the most common environmental pollutants and their occurrence in waters and biota indicate the presence of natural or anthropogenic sources. The existence of trace metals in aquatic environments has led to serious concerns about their influence on plant and animal life1,2. Study on the geochemistry of the River sediments in the present study area has not been undertaken by previous workers so far. However, the surface water chemistry of the river have received wide attention in the recent past. Commendable work in this line was done in 1998. River borne sediments, especially the suspended matter, act as a major carrier and source of heavy metals in the aquatic system. Geochemical study of sediments to evaluate the concentration of heavy metals is necessary as it helps to assess the ecotoxic potential of the river sediments. According to study, the concentration of contaminants can be classified into three types which are: i. contamination indices – which compare the contaminants with the clean or polluted stations measured elsewhere ii. background enrichments indices – which compare the results for the contaminants with the baseline or background levels and iii. Ecological risk indices which compare the results for the contaminants with quality guidelines. Environmental quality indices are a powerful tool for development, evaluation and converging raw environmental information to decision makers, managers or for the public. In recent decades, different assessment indices applied to estuarine environment here been developed4, 5. Sediment quality values are a useful tool to screen the potential for contaminants within sediment to induce biological effects and compare sediment contaminant concentration with corresponding quality guideline. These indexes evaluate the degree to which the sediment associated chemical status might adversely affect aquatic organisms and are designed to assist sediment assessors and manager responsible for the interpretation of sediment quality. It is also to rank and prioritize the contaminated areas or the chemicals for further investigation. Material and MethodsSampling and Sample Storage: For the purpose of this study, five sample stations 1,2,3,4 and 5 were established on the Rivers, (72E, 527N). Station 1 at reservoir base area, and it serves as control. Station 4 at Aba Road, about 2km downstream of Station 1, were there is massive solid waste dump. Station 2 at Nekede, about 3km downstream of station 4, where massive sand excavation is going on. Station 3 at FUTO, about 3km downstream of station 2, where fishing is going on and station 5 at Nworie, a tributary of Otamiri River about 4-5km close to the point of confluence. Sediments were collected periodically. On each occasion, samples were taken from triplicate spots at each station to form one composite sample and the mean values noted for that season. Sediment samples were collected using Eckman grab into plastic bags previously cleaned with detergents and preserved with 5ml of 11.3m HCl (aq). These samples were wrapped in Aluminium foils stored in an ice chest before being taken to the laboratory for analysis. Treatment and Sample Analysis: Sediment samples were dried at about 105C in an oven to constant weight and ground Research Journal of Chemical Sciences __________________________________________________________ ISSN 2231-606X Vol. 2(8), 1-8, August (2012) Res.J.Chem.SciInternational Science Congress Association 2 to powder and then sieved through 0.5mm sieve to remove coarse materials. Digestion of all powdered sediment were according7,8. One gram of each sample were digested using 1.5.1 mixture of perchloric acid, conc. HNO and conc. HSOin a fume chamber at 80C until a colourless liquid was obtained. Each digested sediment were analysed for the listed heavy metals (Cd, Pb, Ni, Zn, Cu, Fe and Cr) at their respective resonance line using Atomic Absorption spectrophotometer. The sediment pH was determined by mixing dry sediment with distilled water. Total organic carbon (TOC %) was determined by Walkey and Black method10. Statistical Analysis using student’s t-test sample were applied, to study the seasonal variation and the extend of the metal contents and their correlation in the bed sediments using Ms-Excel 2007 and Data analysis softwares. Igeo, EF and PLI for the sediment were also applied in order to classify the pollution intensity of the sediment. Another sediment index model is the Sediment Quality Triad. It’s more complex which encompasses sediment chemistry, biological community and toxicity data. The formula below has been applied to the study of heavy metal, to assess the anthropogenic and ithogenic contribution11. By re-arranging the formula for Enrichment Factor (EF), we have; [M]lithogenic= [Al]sample × ([M]/[Al])lithogenicWhere ([M]/[Al])lithogenic corresponds to the average ratio of the earth crust. The anthropogenic heavy metals can be estimated as formula: [M]anthropogenic = [M]total - [M]lithogenic. Results and DiscussionThe results show very low concentration of Cd, Cu and Cr accross the sample stations and values are below the detection limit of the machine and background values of the upper earth crust. The concentration of Pb and Fe were highest at station 2, above the mean values of 0.3 and 147.64 respectfully for all the stations in February and similar trend was also observed in October. The concentration of Nickel (Ni) was highest at station 1 above the mean values of 1.4 for all the stations in February and similar trend was also observed in October. The concentration of Zn was highest at stations 2 during February but its value was highest at station 3 during October. This shows that the heavy metal’s concentration in the sediment follows almost similar pattern in the two seasons. The results also shows that the heavy metal concentration in the sediment across the five stations is in the order SS2 � SS1 � SS3 � SS4 � SS5. Station 2 is just located after the confluence point downstream of station 4, hence, the combined influx of accumulated sediment from station 4(a dumpsite), Nworie, and land use system must have led to the highest values for station 2 and further dispersion downstream to station 3. The high concentration of Fe over other metals is due to the reported high level of Fe in the upper earth crust of Southern Nigeria. The average Igeo of sediments across the sample stations show that both River sediments are in unpolluted and excellent condition, for both seasons. There was observed no enrichment of the heavy metals across the sample stations in both seasons except for Zn and Ni at stations 1, 2, and 3 during both seasons, where Enrichment factor’s grade were moderately severe enrichment. This was the same observation for Pb in the two seasons at station 2. The PLI of the stations for both seasons show that the sediments of the Rivers are in perfect conditions. Table-1 Pollution Intensities of selected indexes’ range Range of indexes Pollution intensity geo =0 0 geo 1 1 geo 2 2 geo 3 3 geo 4 4 geo 5 geo 5 Unpolluted (UNP) UNP to moderately polluted Moderately (MDTLY) polluted MDTLY to strongly polluted Strongly polluted Strongly to very strongly polluted Very strongly polluted 0 1 � 1 Perfection Baseline pollution Increasing pollution level EF 1 1 EF 3 3 EF 5 5 EF 10 10 EF 25 25 EF 50 EF &#x-3.3;夀 50 No enrichment Minor enrichment Moderate enrichment Moderately severe enrh. Severe enrichment Very severe enrichment Extremely severe enrichment Research Journal of Chemical Sciences __________________________________________________________ ISSN 2231-606X Vol. 2(8), 1-8, August (2012) Res.J.Chem.SciInternational Science Congress Association 3 Table-2 Index models, mathematical expression, range and class of indexes Index models Mathematical equations Class of index Range 0f indexes GEOACCUMUL-ATION INDEX. (Igeo) geo = log (Cn/1.5B) Where Cn = measured concentration of heavy metals in mangrove sediments = Geochemical background values in average shale. 0 1 2 3 4 5 6 geo =0 0 geo 1 1 geo 2 2 geo 3 3 geo 4 4 geo 5 geo 5 POLLUTION LOAD INDEX. (PLI) P L I = n CF1xCF2xCF3 x …XCFn CF=Cmetal/C background Value 0 1 � 1 ENRICHMENT FACTOR. (EF) EF = (C/CAl) sample / (C/CAl) earth crust. Where (C/CAlsample is the ratio of concentration of trace metal (C) to that of Al(CAl) in the sediment sample and (C/CAlearth’s crust is the same reference ratio in the earth crust. 1 2 3 4 5 6 7 EF 1 1 EF 3 3 EF 5 5 EF 10 10 EF 25 25 EF 50 EF &#x-3.3;女 50 MARINE SEDIMENT POLLUTION INDEX. MSPI MSPI = (ni = 1 qiwi) 2 / 100 Where qi is the sediment quality rating of the I contaminant, wi is the weight attributed to the I variable (proportion of eigenvalues obtained from results of a principal component analysis, PCA). Table-3 Pre- dredging data for some heavy metals in sediments dry session in mg/kg Cd Pb Ni Zn Cu Fe Cr SS1 0.001 0.52 2.79 7.51 0.001 261.17 0.001 SS2 0.001 0.83 2.17 13.4 0.001 284.64 0.001 SS3 0.001 0.07 2.1 12.89 0.001 157.32 0.001 SS4 0.001 0.09 0.06 0.57 0.001 32.14 0.001 SS5 0.001 0.002 0.001 0.062 0.001 2.9 0.001 mean 0.001 0.303 1.4 6.89 0.001 147.64 0.001 Table-4 Pre-Dredging data for some heavy metals in sediments rainy session in mg/kg Cd Pb Ni Zn Cu Fe Cr SS1 0.001 0.42 2.66 6.3 0.001 251.52 0.001 SS2 0.001 0.63 1.83 12.01 0.001 268.3 0.001 SS3 0.001 0.05 1.95 13.1 0.001 143.81 0.001 SS4 0.001 0.06 0.053 0.48 0.001 19.61 0.001 SS5 0.0023 0.002 0.001 0.043 0.001 1.44 0.001 mean 0.001 0.232 1.29 6.387 0.001 136.94 0.001 Research Journal of Chemical Sciences __________________________________________________________ ISSN 2231-606X Vol. 2(8), 1-8, August (2012) Res.J.Chem.SciInternational Science Congress Association 4 Table-5 Geoaccumulation Index for Pre-Dredging Sediment (DRY) Cd Pb Ni Zn Cu Cr Fe MEAN SS1 -8.82 -5.18 -5.2 -3.41 -16.04 -17.04 5.33 -7.19429 SS2 -8.82 -5.85 -5.19 -4.25 -16.04 -17.04 5.2 -7.42714 SS3 -8.82 -8.74 -5.6 -3.47 -16.04 -17.04 4.47 -7.89143 SS4 -8.82 -8.38 -10.73 -7.97 -16.04 -17.04 2.18 -9.54286 SS5 -8.82 -13.87 -16.64 -11.17 -16.04 -17.04 -1.29 -12.1243 MEAN -8.82 -8.404 -8.672 -6.054 -16.04 -17.04 3.178 -8.836 Table-6 Enrichment Factor for Pre-Dredged Sediment (Dry) Cd Pb Ni Zn Cu Cr Fe SS1 1.02 2.505 5.37 7.61 0.0021 0.0021 5347 SS2 1.04 4.07 4.24 13.81 0.0022 0.0022 5930 SS3 1.03 0.338 4.05 13.1 0.002 0.002 3233.04 SS4 0.94 0.4 0.1 0.53 0.002 0.002 601.87 SS5 0.97 0.009 0.0018 0.06 0.002 0.002 56.2 Table-7 Geoaccumulation Index for Pre-Dredging Sediment (Rain) Cd Pb Ni Zn Cu Cr Fe Average SS1 -8.82 -5.57347 -5.79586 -3.57347 -15.3466 -17.04 5.24489 -7.2721 SS2 -8.82 -6.15843 -5.26534 -4.50635 -16.02 -17.04 5.15097 -7.5227 SS3 -8.82 -9.22594 -5.71786 -3.44222 -16.02 -17.04 4.34412 -7.9888 SS4 -8.82 -8.96578 -10.9092 -8.21304 -16.02 -17.04 1.46989 -9.7854 SS5 -8.82 -13.8655 -16.6388 -11.7027 -14.84 -17.04 -2.3004 -12.172 MEAN -8.82 -8.75782 -8.86541 -6.28757 -15.6493 -17.04 2.78188 -8.9483 Table-8 Enrichment Factor for Pre-Dredged Sediment (Rain) Cd Pb Ni Zn Cu Cr Fe SS1 1.02 2 5.08 6.33 0.0021 0.0021 5112.2 SS2 1.03 3.04 3.53 12.21 0.0043 0.0022 5513.76 SS3 1.03 0.24 3.76 13.33 0.002 0.002 2951.77 SS4 0.95 0.27 0.09 0.45 0.002 0.002 371.4 SS5 0.99 0.0093 0.0019 0.042 0.0041 0.002 28.44 Figure-1 Map of Owerri. From Wikipedia Research Journal of Chemical Sciences ______ Vol. 2(8), 1-8, August (2012) International Science Congress Association Map of Owerri showing sampling points and Land usage Geoaccumulation index of heavy metals at various sampling station sediments    ______ _________________________________ ______________ International Science Congress Association Figure-2 Map of Owerri showing sampling points and Land usage Figure-3 index of heavy metals at various sampling station sediments     ______________ _____ ISSN 2231-606X Res.J.Chem.Sci 5 index of heavy metals at various sampling station sediments  \n \r   \n \n  Research Journal of Chemical Sciences __________________________________________________________ ISSN 2231-606X Vol. 2(8), 1-8, August (2012) Res.J.Chem.SciInternational Science Congress Association 6 Figure-4 Geoaccumulation index of heavy metals at various sampling station sediments Figure-5 Enrichment factors (EF) of heavy metals at various sampling station sediments Figure-6 Enrichment factors (EF) of heavy metals at various sampling station sediments   \n \r   \n \n     \n \r   \n \n  \n \r\n\n         \n \r   \n \n  \n \r\n\n      Research Journal of Chemical Sciences __________________________________________________________ ISSN 2231-606X Vol. 2(8), 1-8, August (2012) Res.J.Chem.SciInternational Science Congress Association 7 Pollution Load Index (Dry) Figure-7 Pollution load index of heavy metals at various sampling station sediments Pollution Load Index (Dry) Figure-8 Pollution load index of heavy metals at various sampling station sediments Conclusion More organic compounds (PAHs, PCBs, and pesticides) should be integrated into the contamination evaluation and correlated with other parameters. Biological testing and ecological analysis of benthic structure of the river sediment should be carried out for more complete decision of the sediment quality assessment. geo and PLI show mainly the class of pollution status of the heavy metal in the river bed sediment only and not reliable in predicting their exact sources PCA and Multi-variate data analysis shows more accurate value and significant correlation from which sediment pollution from anthropogenic sources could be traced, yet, it does not rank pollutants , it also require large sample size of at least 200 cases in order to improve the PCA loading value for the formulation of the MPSL index12. EF not only show the class of pollution status of the river bed sediments but could be an exact indicator (i.e. showing the percentage contribution) from the sources (lithogenic or anthropogenic) of the contaminant and is very simple to use. This can be achieved by using equation 1 and 2 respectively. Based on these model indicators for predicting the level of heavy metal of Otamiri/Nworie river sediments, the enrichment factor (EF) model was chosen as the best over other models. AcknowledgementThe authors are grateful to the management of Roefnel Energy Services for permission to use their laboratory for the analyses during the research.             Research Journal of Chemical Sciences __________________________________________________________ ISSN 2231-606X Vol. 2(8), 1-8, August (2012) Res.J.Chem.SciInternational Science Congress Association 8 References1.Sheikh M.A., Noah N.M., Tsuha K., Oomoti T., Occurrence of tributyltin compounds and characteristics of heavy metals, Int. J. Environ, Sci Tech., 4(1), 49-60 (2007)2.Zvinowanda C.M., Okonkwo J.O., Shabalala P.N. and Agyei N.M., A Novel adsorbent for heavy metal remediation in aqueous environments, Int. J. Environs Sci. Tech., 6(3) 425-435 (2009)3.Ibe K.M. and Njemanze G.N., The impact of urbanization and protection of water resources, Owerri, Nigeria, J of Environmental hydrology, , 9 (1998) 4.Caeciro S., Costa M.H, Ramos T.B, Ternades F, Silveira N., Coimbra A., Medeiros G. and Painho M., Assessing heavy metal contamination in Sado Estuary sediment, An index analysis approach, Ecological indicators, 5, 151-169 (2005)5.Spencer K.L and Macleod C.L., Distribution and portioning of Heavy Metals in Estuarine sediment Quality standard, Hydrology and earth system science, 6, 989-998 (2002)6.Farkas A., Erratico C. and Vigano L., Assessment of the environmental significance of heavy metal pollution in surficial sediment of the river b. Chemosphere, 68, 761-768 (2007)7.Sreedevi P. Suresh A., Sivaramakanisgma B., Prabharathi B. and Radha Krishaiah K., Bioaccumulation of Nickel in the Organs of the fresh water fish cyriono Carpio and the fresh water mussel Lamelliokens Marginalsi, under lethal and sublethal Nickel stress chemisphere, 24(1), 29–36 (1992)8.Oguzie F.A., Heavy metals in fish, water and effluents of lower Ikpoba river in Benin, Nigeria Pak, J. Sci ind. Res., 46(3), 156–160 (2003)9.Hendershot W.H., Lalande H. and Duquette M., Soil pH; soil sampling and method of analysis, Lewis publishers, U.S.A. (1993)10.Schnitzer M., Soil organic matter, in page A.L. Eds., methods of soil analysis part II. Chemical and microbiological properties, 2nd Ed. ASA inc. and SSSA inc. publishers; Madison, WI, US (1982)11.Hernandez L., Probst A., Probst J.I. and Ulrich E., Heavy metal distribution in French forest soil, evidence for atmosphere contamination, The science of total Enviroment, 312, 195-210 (2003)12.Shin P.K.S. and Lam W.K.C., Development of a marine sediment pollution index, Environmental pollution, 113, 281-29 (2001)