Exploration of the spatial-Composite Risk Index (CRI) for the characterization of toxicokinetics in petrochemical active areas.

The spatial modeling of the petrochemical active regions in the Niger Delta (ND), Nigeria was carried out through the analysis exploration and extraction of geospatial data and resultant risk maps were generated. The pollutants assessed include; heavy metals, polychlorinated aromatic hydrocarbons (PAHs), benzene-toluene-ethylene-xylene (BTEX), and total petroleum hydrocarbons (TPHs) and properties of the pollutants such as bioaccumulation, persistence and toxicity were used to calculate the Hazard Index (HI) and thus created a ranking system. The Composite Risk Index (CRI) was developed successively considering the concentrations of all pollutants and the computed HI using the samples collected in ND areas of Nigeria. The carcinogenic PAHs showed spatial abundance in the areas sampled and elevated levels of soil heavy metals were also observed. In this study, mathematical tool such as the artificial neural network (ANN) self-organizing map (SOM) and geostatistical analysis such as kriging were applied to develop the risk map of the areas which represent the spatial spread of the CRI. The results show that the application of spatially developed integral risk map for pollutant assessment is effective and facilitates with decision making with regards the environment and humans exposed in this region.

[1]  W. Shiu,et al.  Illustrated handbook of physical-chemical properties and environmental fate for organic chemicals. Volume 5: pesticide chemicals. , 1992 .

[2]  Anne Lohrli.  Chapman and Hall , 1985 .

[3]  J. Q. Shang,et al.  Detecting heavy metal contamination in soil using complex permittivity and artificial neural networks , 2004 .

[4]  Young-Seuk Park,et al.  Hierarchical community classification and assessment of aquatic ecosystems using artificial neural networks. , 2004, The Science of the total environment.

[5]  P L Andersson,et al.  An overview of OSPAR priority compounds and selection of a representative training set. , 2004, Chemosphere.

[6]  Bogusław Buszewski,et al.  A New Model of Heavy Metal Transport in the Soil Using Nonlinear Artificial Neural Networks , 2006 .

[7]  Inmaculada Ortiz,et al.  Validated analytical strategy for the determination of polycyclic aromatic compounds in marine sediments by liquid chromatography coupled with diode-array detection and mass spectrometry. , 2006, Journal of chromatography. A.

[8]  Nickolette Roney,et al.  Six interaction profiles for simple mixtures. , 2003, Chemosphere.

[9]  B. Goldstein.  Advances in risk assessment and communication. , 2005, Annual review of public health.

[10]  Hugh Hansen,et al.  Using the ATSDR Guidance Manual for the Assessment of Joint Toxic Action of Chemical Mixtures. , 2004, Environmental toxicology and pharmacology.

[11]  Thomas Kemper,et al.  Estimate of heavy metal contamination in soils after a mining accident using reflectance spectroscopy. , 2002, Environmental science & technology.

[12]  Y. Heyden,et al.  Chemometric analysis of soil pollution data using the Tucker N-way method , 2006, Analytical and bioanalytical chemistry.

[13]  Sovan Lek,et al.  Utilisation of non-supervised neural networks and principal component analysis to study fish assemblages , 2001 .

[14]  Gabriela Espinosa,et al.  An Integrated SOM-Fuzzy ARTMAP Neural System for the Evaluation of Toxicity. , 2002 .

[15]  K. Bundy,et al.  A mathematical algorithm to identify toxicity and prioritize pollutants in field sediments. , 2002, Chemosphere.

[16]  J. Andrade,et al.  3-Way characterization of soils by Procrustes rotation, matrix-augmented principal components analysis and parallel factor analysis. , 2007, Analytica chimica acta.

[17]  M. Schuhmacher,et al.  Metal pollution of soils and vegetation in an area with petrochemical industry. , 2003, The Science of the total environment.

[18]  R. Stierum,et al.  Safety Evaluation of Chemical Mixtures and Combinations of Chemical and Non-Chemical Stressors , 2004, Reviews on environmental health.

[19]  Richard Olawoyin,et al.  Application of artificial neural network (ANN)-self-organizing map (SOM) for the categorization of water, soil and sediment quality in petrochemical regions , 2013, Expert Syst. Appl..

[20]  Romà Tauler,et al.  Use of chemometric and geostatistical methods to evaluate pesticide pollution in the irrigation and drainage channels of the Ebro river delta during the rice-growing season , 2007, Analytical and Bioanalytical Chemistry.

[21]  J. Havel,et al.  Content of aliphatic hydrocarbons in limpets as a new way for classification of species using artificial neural networks. , 2004, Chemosphere.

[22]  Luis E Olcese,et al.  A method to estimate emission rates from industrial stacks based on neural networks. , 2004, Chemosphere.

[23]  I. Nisbet,et al.  Toxic equivalency factors (TEFs) for polycyclic aromatic hydrocarbons (PAHs). , 1992, Regulatory toxicology and pharmacology : RTP.

[24]  M. L. Land,et al.  The risk assessment information system , 1998 .

[25]  J. L. Pérez Pavón,et al.  Headspace mass spectrometry methodology: application to oil spill identification in soils , 2008, Analytical and Bioanalytical Chemistry.

[26]  Emily Monosson,et al.  Chemical Mixtures: Considering the Evolution of Toxicology and Chemical Assessment , 2004, Environmental health perspectives.

[27]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .

[28]  Kazem Rangzan,et al.  Remote Sensing and GIS Approach for Water-Well Site Selection, Southwest Iran , 2008 .

[29]  Richard Olawoyin,et al.  Potential risk effect from elevated levels of soil heavy metals on human health in the Niger delta. , 2012, Ecotoxicology and environmental safety.