Vulnerability assessment of groundwater pollution in the vicinity of an active dumpsite ( Olusosun ) , Lagos , Nigeria

ISSN: 2410-9649 Majolagbe et al / Chemistry International 2(4) (2016) 232-241 iscientic.org. 233 www.bosaljournals/chemint/ [email protected] in the area and /or the cost implications of digging a very deep well. Thus, groundwater can be easily susceptible to infiltration from dumpsites, septic tanks and other anthropogenic activities. Approximately, one fifth of the world total freshwater situates in the phreatic zone of the sub surface water environment (Saetsaz and Wan, 2011), therefore, the need to protect groundwater resources from contamination. Vulnerability of groundwater to pollution could be explained as the degree or tendency of groundwater in an area to be contaminated. The knowledge of groundwater vulnerability assessment has improved since it was developed in the United State of America in 1987. It helps to plan and manage groundwater resources. According to Foeazio et al., (2002), vulnerability can be specific contaminant (non intrinsic) or generality of contaminants (intrinsic). So many studies have been reported on various environmental impact of different form of pollution sources on the quality of groundwater in Lagos. Various methods of vulnerability evaluation and assessment have been developed, including overlay index method, hybrid, modeling and simulation as well as statistical methods. Ojuri and Bankole (2013) pointed out that overlay and index method resulted from the intersection of map on a regional basis and the qualitative interpretation of the data by indexing the parameters and assigning appropriate weights. Several procedures (overlay index method) of vulnerability have been reported in this category. These methods include GIS (Geography Information System) based DRASTIC, GOD, AVI, SINTACS, ISIS and EPIK (Saatas and Sulaiman, 2011). DRASTIC, undoubtedly is the most commonly used tool among the class of overlay index method for groundwater planning and decision making (Saatas and Sulaiman, 2011; Rahman 2008). Drastic model is a simple GIS based mapable method, developed by US Environmental Protection Agency (Aller et al., 1987). The Drastic system is made up of seven hydrogeological parameters which influence the fate and transport of contaminants from the soil surface to aquifer. The drastic parameter with allotted weight and rankings based on importance is summed up together to form a Drastic index. It is a flexible method which helps to systematically evaluate the potential or tendency of groundwater to be vulnerable to pollution. Drastic model can be used in both very wide and small scale. It was initially targeted at non-point source contaminant but studies have reported modification of the model, thereby evaluating groundwater vulnerability due to point source pollution (Lee, 2003). Drastic model is developed on the assumptions that (i) the pollutant is introduced on ground surface, (ii) the pollutant is further pushed into groundwater by precipitation through gravitational force and (iii) pollutant has mobility in water (Roser 1994). The Drastic model remains a very popular approach in the estimation of groundwater vulnerability among researchers, despite the criticism on the absence of specific method of validation (Ojuri and Bankole, 2013). Nitrate concentration in water (experimental data) was used to validate DRASTIC model (Alwathaf, 2011), pattern of total dissolved solids (TDS) concentration was used by Saatsaz and Wan Nor Azmin (2011), while Ojuri and Bankole (2013) employed correlation coefficients between physicochemical parameters of water and DRASTIC vulnerability indices as a validation tool. The use of various water quality indices as a tool to assess the quality status of both surface and groundwater in an area has been extensively reported (Almeida et al., 2008; Reza and Singh, 2010; Jena, 2013; Manguyika et al., 2012). Water Quality index (WQI) approach to assess quality status of water was developed by Brown et al. (1970) and various modifications and new evolution (from different countries and regions) have been witnessed since then. This include The US National Sanitation Foundation Water Quality Index (NSFWQI), Canadian Water Quality Index (Canadian Council of Ministers of the Environment (CCME) , Florida Stream Water Quality Index (FWQI), British Columbia Water Quality Index (BCWQI),WQI developed in Bascaran, modified WQI developed in India (Bhargava WQI ) and the Oregon Water Quality Index (OWQI) (Rocchini and Swain, 1995; H‘ebert,1996; Prati et al., 1971; Khan et al., 2005; Parmar and Parmar, 2010). WQI was categorized into four classes based on the type of use namely; public indices, specific consumption indices, designing and planning indices and Statistical indices (Poonam 2013). The first three are collectively referred to as expert opinion (EO). Water Quality Indices play major roles in water quality assessment of a given source as a function of time and other influencing factors (if necessary) by resolving large multiparameter water analysis data into single digit scores (Poonam, 2013). Multivariate statistical techniques have been extensively used in effective assessment and analysis of various physicochemical parameters of groundwater with respect to space and time (Liu et al., 2006; Palma et al., 2010; Oketola et al., 2013 ).The Multivariate statistical techniques include Principal component analysis (PCA),cluster analysis (CA), factor analysis (FA), discriminant analysis (DA). Cluster analysis is a powerful statistical tool that helps in grouping similar pairs of correlation in a large symmetric matrix. It can reduce large data set into groups with similar features, systematically compare various chemical constituents. Cluster analysis can present its result in a two-dimensional hierarchical diagram called dendogram. An observation can be refereed at any point or level of similarity or dissimilarity. This paper therefore aimed at estimating the potential groundwater intrinsic vulnerability to pollution from Olusosun dumpsite in Lagos, Nigeria, using a modified DRASTIC model (DRALTC) and assessment of groundwater quality around the dumpsite using water quality indices. This will help the policy makers in better understanding of groundwater vulnerability and the adequate measures towards sustainability of the environment. MATERIAL AND METHODS Description of Study Area Olusosun refuse dumpsite is a government located within the longitude 3°372' East to 3°374' East and latitude 6°588' North to 6°595' North in Ojota, Lagos State. It is the largest dumpsite in Nigeria. It is about 18 meters deep and covers close to 42 hectares of land. Olusosun refuse dump was established in 1988 with a life span of 35 years. The dumpsite is surrounded by Oregun industrial layout, Olusosun residential compound, Shangisha residential areas and commercial neighborhood (Fig.1). It receives an average of 1.2 million tons of wastes ISSN: 2410-9649 Majolagbe et al / Chemistry International 2(4) (2016) 232-241 iscientic.org. 234 www.bosaljournals/chemint/ [email protected] annually and is presently serving as a pilot project site for biogas production in Nigeria (Aboyade, 2004). Sampling and Chemical Analyses Forty (40) water samples were collected from twenty different hand dug wells around Olusosun dumpsite bimonthly, for two consecutive years and analysed for various physicochemical parameters using standard procedures. The pH (pH meter, pHep HANNA HI 98107), electrical conductivity (Mettler Toledo) and temperature (thermometer, 0 100 o C) of the water samples were determined in-situ. Alkalinity, acidity, Total hardness, Total suspended solids (TSS), total dissolved solids (TDS), total solids (TS), chloride, sulphate, phosphate and nitrate were determined using American Public Health Association methods (APHA, 2005). Na and K were analysed using h flame photometer and other trace metals byFlame Atomic Absorption Spectrophotometer (Buck scientific 210VGP model). The use of Borehole Exploitation Logs and Soil Survey Reports Various sources of data were used for this study as captured by Ojuri and Bankole (2013). Data on geology, topography and soil features of Lagos state was obtained from the reconnaissance soil survey of Nigeria (FDALR, 1995)as well as the climatic data (BBC, 2011). DRASTIC Model and Estimation of Vulnerability Index DRASTIC is a groundwater quality model for evaluating the pollution potential of large areas using the hydrogeologic settings of the region. A hydrogeological setting is defined as a mapable unit with common hydrogeologic characteristics. There are seven hydrogeological parameters or factors that make up the acronym DRASTIC. DRASTIC parameters influence the fate and transport of water from soil surface to aquifer. In this study, modification was made on the DRASTIC model so as to reflect and accommodate some peculiarity of the dumpsite. This result in the formation of six hydrogeologic parameter based model, DRALTC. Each factor was then assigned a weight (w) based on its relative significance in affecting the pollution potential. The weight was further allotted a rating (r) for different ranges of values. The typical ratings range from 1 10 and weights are from 1 – 5 as shown in Table 1.0. The DRALTC vulnerability Index was computed through the summation of products of ratings and weights for each factor as follows: DRALTC Index = DrDw + RrRw + ArAw + LrLw + TrTw + CrCw Where Dr = Rating to Depth to water, Dw = Weights assigned to Depth to water, Rr = Ratings for ranges of aquifer recharge, Rw = Weights for ranges the aquifer recharge, Ar = Ratings assigned to aquifer media, Aw = Weights assigned to aquifer media, Lr = Ratings to the distance from well to the dumpsite, Lw = Weights assigned to distance from well to the dumpsite, Tr = Ratings for topography (slope), Tw = Weights for topography, Cr = Ratings Fig 1: Sampling locations around Olusosun dumpsite in Kosofe local Government Area ISSN: 2410-9649 Majolagbe et al / Chemistry International 2(4) (2016) 232-241 iscientic.org. 235 www.bosaljournals/chemint/ [email protected] for rates clay content, Cw = Weights given to clay content. The vulnerability index of the study area can be classified into four groups : >190, Very High groundwater pollution potential; 160 – 190, High groundwater pollution potential; 101 – 159, Moderate groundwater pollution potential; < 100, Low groundwater pollution potential. Water Quality Indices Two international water quality indices were applied in this study so as to have wider interpretations of the field data used. These are Water Quality Index (WQI) and Contamination Index (CI). I) Water Quality Index (WQI) Three steps are involved in the calculation of WQI as described by Srinivas and Nageswararao (2013). In the first step, each of the parameters was assigned a weight (wi) according to its relative importance in the overall quality of the water for drinking purpose. A maximum weight of 5 has been assigned to nitrate due to its major importance in water quality assessment. In the second step, the relative weight was calculated from the following equation