Pollution Index
Estuaries receive significant anthropogenic inputs from both point and non-point upstream sources and from metropolitan areas, tourism and industries located along the estuarine edges. Estuarine sediment contamination is receiving increasing attention from the scientific community, since it is recognized as a major source of ecosystem health stress (Chapman and Wang, 2001; Riba et al., 2002b). Thus, the proper assessment of sediment contamination in estuaries and its biological and ecological significance is crucial.
For better management of estuarine ecosystems their contamination assessment should be easily communicated to local managers and decision makers. Environmental quality indicators and indices are a powerful tool for processing, analyzing and conveying raw environmental information to decision makers, managers, technicians or the public (Ramos et al., 2002). Their spatial visualization through maps using a Geographical Information System makes their transmission even easier and more successful.
In recent decades different metal assessment indices applied to estuarine environments have been developed. Each one of them aggregates the concentration of metal contaminants and can be classified in three types—(i) contamination indices: which compare the contaminants with clean and/or polluted stations measured in the study area or simply aggregate the metal concentrations; (ii) background enrichment indices: which compare the results for the contaminants with different baseline or background levels, available in literature, that can be used for any study area; and (iii) ecological risk indices: which compare the results for the contaminants with Sediment Quality Guidelines or Values—SQG. They also differ in the aggregation methods used. Table A.1 (see Appendix A) presents an overview of indices to assess contaminants on the basis of their chronological evolution, their description and some comments and/ or drawbacks.
When using summary indices, normalized for example to a reference value, substantial loss of information can occur during the conversion of multivariate data into single proportional indices, including spatial information. However, such indices have provided useful information in the past and continue to do so. They also provide a single and highly visual data presentation, which can be explained to and understood by non-scientists (Chapman, 1996).
SQGs are very useful to screen sediment contamination by comparing sediment contaminant concentration with the corresponding quality guideline. These guidelines evaluate the degree to which the sediment-associated chemical status might adversely affect aquatic organisms and are designed to assist sediment assessors and managers responsible for the interpretation of sediment quality (Wenning and Ingersoll, 2002). They have been largely developed for marine waters (e.g. Long et al., 1995) but a few have been specifically developed for estuarine waters (Chapman and Wang, 2001). The work by Wilson and Jeffrey (1987) is a rare example of SQGs developed specifically for estuaries. Donze et al. (1990) listed background concentration for several estuaries in Europe and the USA.
The Sado Estuary in Portugal is a good example of a site where human pressures and natural values compete with each other and where the degree of metal contamination has not been subject to overall assessment, for the outer estuary, in a way that managers can understand. The Sado Estuary is the second largest in Portugal, with an area of approximately 24,000 ha. It is located on the west coast of Portugal. Most of the estuary is classified as a natural reserve but it also plays an important role in the local and national economy. There are many industries, mainly on the northern margin of the estuary. The most polluting industries are those involving pulp and paper, pesticides, fertilizers, yeast, food and shipyards (Catarino et al., 1987). Furthermore, harbor-associated activities and the city of Setu´bal, along with the copper mines on the Sado watershed, use the estuary for waste disposal purposes without suitable treatment. In other areas around the estuary intensive farming, mostly of rice, represents the main use for the land, together with traditional salt pans and increasingly intensive fish farms. The Sado Estuary is characterized by a North Channel with weaker residual currents and shear stress. This enhances the accumulation of sediment allowing locally introduced pollutants to settle rather than be transported away. The southern channel, separated from the North Channel by sand banks, is highly dynamic, with tides being the main cause of water circulation. Geomorphological characteristics distinguish the outer estuary (our study area) from the inner one, which corresponds to a narrow channel (Alcacel Channel). The inner part of the outer estuary (entrances toA ´ guas de Moura and Alcacer Channels) is quite shallow, with tidal flats (Neves, 1985).
One of the aims of this work is to select different types of indices to aggregate and assess the heavy metal contamination of the Sado Estuary sediment. The different types of indices are compared and discussed. Another aimis to evaluate the contamination permetal, also using interpolation surfaces to compare and gauge the results of the indices on the basis of a qualitative sensitivity analysis. The sediment metal assessment will be represented and evaluated in management units (spatially contiguous and homogeneous regions of sediment structure), which are to be part of a broad environmental data management framework applied to the Sado Estuary, though that is beyond the scope of this work. The support infrastructure of this framework is a set of management units delineated using multivariate geostatistical tools and sediment parameters like total organic matter (TOM), fine fraction (FF) and redox potential (Eh). These tools and this data allowed the computing of 19 management units, classified into four groups according to the increase in organic load (Fig. 1) (Caeiro et al., 2003).
Uses and Limitation
Pollution Index is based on the individual metal calculations to assess the metal pollution in any water body. The measured metals show a different degree of population for different utilizations.
Categorization Table
Pollution Index is categorized into 5 water quality classes:
Table 1. Categories of Pollution index.
Standards Required
Any national or international water quality standards could be used in this method (Pollution Index).
Variables Selection
It is used to measure the impact of any heavy metal on water quality for various uses.
Calculation of Pollution Index:
According to Caerio et al., (2005) the equation used for the calculation of Pollution Index is:

Ci: the concentration of each element; Si: metal level according to national water quality criteria.
Case Studies based on Pollution Index
Good water quality is fundamental to human health and sustenance of aquatic ecosystems. The Lisikili river in Zambezi region, Namibia is a major perennial river which serves diverse economic purposes in the host community. However, it has been receiving pollution threat from effluents discharge and surface run-off from intensive agricultural lands, as well as cottage and hospitality industries and no research has been carried out on the pollution status of the river. Thus, the main aim of this study is to conduct preliminary assessment of some heavy metals pollution status of the river water. Eight (8) water samples were collected at 8 random points within a stretch of approximately 2km on each extremity and median parts of the river. Two major economic fish from the river, tilapia fish (Oreochromis niloticus) and cat fish (Siluriformes) (8 samples of each) were collected using fish net at the points of water sampling. The samples were transported to analytical laboratory in ice boxes for processing and analyses for the levels of Pb, As, Cr, Cd, Cu, Zn, Mn and Fe using Inductively Coupled Plasma-Optical Emission Spectrophotometer (ICP: Perkin Elmer Optima 7000 DV). The results obtained showed wide mean concentrations of the heavy metals in the river water; iron recorded the highest level of 2.375 mg/l and arsenic (0.047 mg/l) recorded the lowest level. Apart from Zn (0.259 mg/l) and Cu (0.073 mg/l) with the present concentrations lower than their guideline permissible limits, the mean concentrations of the other heavy metals exceeded their maximum permissible guideline values for the protection of human and aquatic health. Based on the classification of metal pollution index (PI) for water, apart from Cu (PI = 0.03) and Zn (PI = 0.04); all the other heavy metals recorded pollution indices which suggest moderate to strong effect on the river water quality. In both the catfish and tilapia fish (wet weight whole sample), iron (4.926 mg/kg and 3.323 mg/kg) recorded the highest mean concentration while Cd (0.136 mg/kg and 0.078mg/kg) recorded the lowest level respectively. Generally, the present levels of the heavy metals were below their regulatory limits for the protection of human health. However, the fish’s bio-accumulation factors of the metals suggest that they have high potentials to bio-accumulate some of the heavy metals to high levels and this has adverse implication for human consumption. Because heavy metals are non-biodegradable and bio-accumulative in nature which therefore, make their presence in human foods even at very minute levels potential toxins, it is important to monitor their accumulations in the river and fish and advice precautionary measures to limit excessive human exposures to the heavy metals content.
References
Abah, J., Mashebe, P. and Onjefu, S.A. (2016). Preliminary assessment of some heavy metals pollution status of Lisikili river water in Zambezi region, Namibia. International Journal of Environment and Pollution Research, 4(2), 13-30.