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Geostatistical Analysis of Contamination of Soils in an Urban Area in Ghana

Authors: S. K. Appiah, E. N. Aidoo, D. Asamoah Owusu, M. W. Nuonabuor


Urbanization remains one of the unique predominant factors which is linked to the destruction of urban environment and its associated cases of soil contamination by heavy metals through the natural and anthropogenic activities. These activities are important sources of toxic heavy metals such as arsenic (As), cadmium (Cd), chromium (Cr), copper (Cu), iron (Fe), manganese (Mn), and lead (Pb), nickel (Ni) and zinc (Zn). Often, these heavy metals lead to increased levels in some areas due to the impact of atmospheric deposition caused by their proximity to industrial plants or the indiscriminately burning of substances. Information gathered on potentially hazardous levels of these heavy metals in soils leads to establish serious health and urban agriculture implications. However, characterization of spatial variations of soil contamination by heavy metals in Ghana is limited. Kumasi is a Metropolitan city in Ghana, West Africa and is challenged with the recent spate of deteriorating soil quality due to rapid economic development and other human activities such as “Galamsey”, illegal mining operations within the metropolis. The paper seeks to use both univariate and multivariate geostatistical techniques to assess the spatial distribution of heavy metals in soils and the potential risk associated with ingestion of sources of soil contamination in the Metropolis. Geostatistical tools have the ability to detect changes in correlation structure and how a good knowledge of the study area can help to explain the different scales of variation detected. To achieve this task, point referenced data on heavy metals measured from topsoil samples in a previous study, were collected at various locations. Linear models of regionalisation and coregionalisation were fitted to all experimental semivariograms to describe the spatial dependence between the topsoil heavy metals at different spatial scales, which led to ordinary kriging and cokriging at unsampled locations and production of risk maps of soil contamination by these heavy metals. Results obtained from both the univariate and multivariate semivariogram models showed strong spatial dependence with range of autocorrelations ranging from 100 to 300 meters. The risk maps produced show strong spatial heterogeneity for almost all the soil heavy metals with extremely risk of contamination found close to areas with commercial and industrial activities. Hence, ongoing pollution interventions should be geared towards these highly risk areas for efficient management of soil contamination to avert further pollution in the metropolis.

Keywords: Coregionalization, ordinary cokriging, multivariate geostatistical analysis, soil contamination, soil heavy metals, risk maps, spatial distribution.

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[1] M. R. Mehr, B. Keshavarzi, F. Moore, E. Sacchi, A. R. Lahijanzadeh, et al., “Contamination level and human hazard assessment of heavy metals and polycyclic aromatic hydrocarbons (PATHs) in street dust deposited in Mahshahr, southwest of Iran”, Human and Ecological Risk Assessment: An International Journal, vol. 22, no. 8, pp. 1726-1748, Aug. 2016.
[2] C. Liu, L. Lu, T. Huang, Y. Huang, L. Ding, W. Zhao, “The distribution and health risk assessment of metals in soils in the vicinity of industrial sites in Dongguan, China”, International Journal of Environmental Research and Public Health, vol. 13, no. 832, Aug. 2016.
[3] S. K. Reza, U. Baruah, S. K. Singh, T. H. Das, “Geostatistical and multivariate analysis of soil heavy metal contamination near coal mining area, northeastern India”, Environmental Earth Sciences, vol. 73, no. 9, pp. 5425-5433, May. 2015.
[4] S. K. Reza, U. Baruah, D. Sarkar, “Hazard assessment of heavy metal contamination by the paper industry, northeastern India”, International Journal of Environmental Studies, vol. 70, no. 1, pp. 23-32, Nov. 2013.
[5] G. Darko, M. Dodd, M. A. Nkansah, E. Ansah, Y. Aduse-Poku, “Distribution and bioaccessibility of metals in urban soils of Kumasi, Ghana”, Environmental Monitoring and Assessment, vol. 189, no. 260, May 2017.
[6] G. Darko, M. Dodd, M. A. Nkansah, Y. Aduse-Poku, E. Ansah, D. D. Wemegah, L. S. Borquaye, “Distribution and ecological risks of toxic metals in the top soils in the in Kumasi metropolis, Ghana”, Cogent Environmental Science, vol. 3, no. 1354965, July 2017.
[7] Y. G. Gu, Q. S. Li, J. H. Fan, B. Y. He, H. B. Fu, “Identification of heavy metal sources in the reclaimed farm soils of the pearl river estuary in China using multivariate geostatistical approach”, Ecotoxicology and Environmental Safety, vol. 105, no. 1, pp. 7-12, April. 2014.
[8] A. Colgan, P. Hankard, D. J. Spurgeon, C. Svendsen, R. A. Wadsworth, J. M. Weeks, “Closing the loop: A spatial analysis to link observed environmental damage to predicted heavy metal emissions”, Environmental Toxicology and Chemistry, vol. 22, pp. 970-976.
[9] J. A. Rodríguez Martín, M. L. Arias, J. M. Grau Corbí “Heavy metals contents in agricultural topsoils in the Ebo basin (Spain). Application of the multivariate geostatistical methods to study spatial variations”, Environmental Pollution, vol. 144, pp. 1001-1012, Jan 2006.
[10] S.K. Appiah, J. Apau, A. Andoh, D. Armah, G. Yeboah, “Estimation of risk levels of water-quality parameters in groundwater in a local community of Ghana”, Journal of Scientific and Engineering research, vol. 4, no. 9, pp. 528-539, 2017.
[11] A. Mahmood, R. N. Malik, “Human risk assessment of heavy metals via consumption of contaminated vegetables collected from different irrigation sources in Lhore, Pakistan”, Arabian Journal of Chemistry, vol. 7, pp. 91-99, July, 2014.
[12] M. Kumar, S. C. Subhash, m. Kumar Jha, “Heavy metals concentration Assessment in ground water and general public health aspects around Granite mining sites of Laxman pura, U.P., Jhansi”, International Research Journal of Environment Sciences, vol. 5, no. 1, pp. 1-6, 2016.
[13] L. Järup, “Hazards of heavy metal contamination”, British Medical Bulletin, vol. 68, no. 68, pp. 167–182, 2003.
[14] P. Goovaerts, “Geostatistics for soil science: State–of--the art and perspective”, Geoderma, vol. 89, pp. 1-45, 1999.
[15] P. Goovaerts, Geostatistics for natural resources evaluation, New York: Oxford University Press, 1997.
[16] R. Webster, O. Atteia, J. P. Dubios, J. P. (1994). “Coregionalisation of trace metals in the soil in the Swiss Jura”, European Journal of Soil Science, vol. 45, pp. 205-218, 1994.
[17] S. K. Appiah, U. Mueller, J. Cross, “Spatio-temporal modelling of malaria incidence for evaluation of public health policy interventions in Ghana, West Africa”, in Proc. 19th International Congress on Modelling and Simulation (MODSIM 2011), 2011 Perth, Australia, Dec. 2011, pp. 676-682.
[18] A. Ersoy, T. Y. Yunsel, M. Cetin, “Characterization of land contaminated by heavy metal mining using geostatistical methods”, Archives of Environmental Contamination and Toxicology, vol. 46, pp. 162-175, 2004.
[19] Y. Sun, Q. Zhou, X. Xie, R. Liu, “Spatial, sources and risk assessment of heavy metal contamination of urban soils in typical regions of Shenyang, China”, Journal of Hazardous Materials, vol. 174. pp. 455-462, Sep., 2010.
[20] Y.-M. Zheng, T.-B. Chen, J.-Z. He “Multivariate geostatistical analysis of heavy metals in topsoils from Beijing, China”, Journal of Soil Sediments, vol. 8, no. 1, pp. 51-58, 2008.
[21] J. Zou, W. Dai, S. Gong, Z. Ma, “Analysis of spatial variations and sources of heavy metals in farmland soils of Beijing suburbs”, PLoS ONE, vol. 10. No. 2, pp. 1-13, Feb 2015.
[22] Y. Yang, J. Wu, G. Christakos, “Prediction of soil heavy metal distribution using spatiotemporal kriging with trend model”, Ecological Indicators”, vol. 56, pp. 125-133, March, 2015.
[23] M. Sh. Yeh, Y. P. Lin, L. Chang, “Designing an optimal multivariate geostatistical groundwater quality monitoring network using factorial kriging and genetic algorithms”, Environmental Geology, vol. 50: pp. 101-121, 2006.
[24] Md. Bodrud-Doza, A. R. M. Towfiqul, F. Ahmed, S. Das Islam, F. Ahmed, “Characterisation of groundwater quality using evaluation indices, multivariate statistics and geostatistics in central Bangladesh”, Water Science, vol. 30, pp. 19-40, 2016.
[25] R. M. Lark, E. L. Ander, M. R. Cave, K.V. Knights, M. M. Glennon, R. P. Scanlon, “Mapping trace element deficiency by cokriging from regional geochemical soil data: A case study on cobalt for grazing sheep in Ireland”, Geoderma, vol. 226-227, pp. 64-78, 2014. March, 2014.
[26] O. Asghari, O., S. Sheikhmohammadi, M. Abedi, G. H. Norouzi, “Multivariate geostatistics based on a model of geo-electrical properties for copper grade estimation: A case study in Seridune, Iran”, Bollettino di Geofisica Teorica ed Applicata, vol. 57, no. 1, pp. 43-58, Mar, 2016.
[27] P. Goovaerts, “Ordinary cokriging revisited”, Mathematical Geology, vol. 30, no. 1, pp. 21-41, 1998a.
[28] P. Goovaerts, “Geostatistical tools for characterizing the spatial variability of microbiological and physio-chemical soil properties”, Biology and Fertility of Soils, vol. 27, pp., no. 4, pp. 315-334, 1998b.
[29] Y.-P. Lin, “Multivariate geostatistical methods to identify and map spatial variations of soil heavy metals”, Environmental Geology, vol. 42, pp. 1-10, February 2002.
[30] M. A. Nkansah, M. Korankye, G. Darko, M. Dodd, “Heavy metal content and potential health risk of geophagic white clay from the Kumasi metropolis in Ghana”, Toxicology Reports, vol. 3, pp. 644-651, Aug. 2016.
[31] PSS/GSS, National population projection by sex, 2010-2020. Accra: Population Statistics Section (PSS), Ghana Statistical Service (GSS), 2018.
[32] A. Iddrisu, Y. Mano, T. Sanabe, “Enterpreneurial skills and industrial development: The case of a car repair and metalworking cluster in Ghana”, Journal of the Knowdlege Economy, vol. 3, pp. 302-326, 2012.
[33] J. E. Marcovecchio, S.E. Botte, R. H. Freije, “Heavy Metals, Major Metals, Trace Elements”, in Handbook of Water Analysis”, 2nd ed., L. M. Nollet, Ed. London: CRC Press, 2007, pp. 483.
[34] E. H. Isaaks, R. M. Srivastava, RM, 1989, An Introduction to applied geostatistics. New York: Oxford University Press, 1989.
[35] G. Matheron, “Principles of geostatistics”, Economic Geology, vol. 59, pp. 1246-1266, 1963.
[36] R. Webster, R., M. A. Oliver, Geostatistics for environmental scientists, 2nd ed., Chichester, UK: John Wiley and Son, 2007.
[37] M. C. Ribeiro, P. Pinho, E. L. Llop, C. Branquinho, A. J. Sousa, M. J. Perira, “Multivariate geostatistical methods for analysis of relationships between ecological indicators and environmental factors at multiple spatial scales”, Ecological Indicators, vol. 29, pp. 339-347, Jan. 2013.
[38] H. Wackernagel, Multivariate geostatistics: An Introduction with Applications, 3rd, ed., New York: Springer-Verlag, 2003.
[39] C.V. Deutsch, A. G. Journel, GSLIB: Geostatistical software library and user's guide, 2nd ed., New York: Oxford University Press, 1998.
[40] M.-K. Qu, W.-D. Li, C.-R. Zhang, S.-Q. Wang, Y. Yang, L.-Y. He, “Source apportionment of heavy metals in soils using multivariate statistics and geostatistics”, Pedosphere, vol. 23, pp. 437-444, 2013.
[41] CCME. Canadian soil quality guidelines for the protection of environmental and human health, CCME soil quality index 1.0. Technical Report, pp. 1-10, 2007.
[42] UNECA. Economic report on Africa 2017: Urbanization and industrialization for African’ transformation. United Nations Economic Commission for Africa (ECA), Addis Ababa, 2017.