Water Quality and Aquatic Ecosystem Assessment Using Water Quality Indices in West Africa: Challenge and Perspectives

*Lallébila Tampo, Idrissa Kaboré, Seyf-Laye Alfa-Sika Mande and Limam Moctar Bawa*

## **Abstract**

"Water quality is health" as it is said, "water is life." The water quality and aquatic ecosystem health assessment is essential for early detection of water habitat degradation and overall aquatic ecosystem disturbances. All water management programs/ agencies need simple and cheaper tools for water quality assessment. However, in West Africa there is an urgent need of water quality assessment tools, as far they are very limited. This chapter presents water quality indices as simple and cost-effective tool to monitor water quality. We explore the categories of water quality indices (WQIs), their use/application, and their scope in West African countries. We found that some multimetric indices (MMIs) are developed in West Africa, but they are not well known/used by local water managers and decision makers. There are not yet biotic indices (BIs) and physicochemical water quality indices (PCQWIs) typical to Western African countries areas, but most of them are applied/adapted to meet the needs of West African water quality and ecosystem health assessment. In this chapter, we present the results of some studies led in some West African countries regarding water quality indices (MMIs, BIs, PCWQIs), and address challenges and perspectives for long-term management of water and biological resources in developing countries.

**Keywords:** water quality, indices, West Africa, water management, aquatic ecosystem, pollution

## **1. Introduction**

It is often said "water is life" but it must also be said, "water quality is health." Water is one of the most important resources for life to sustain our planet earth. It requires proper attention in terms of quantity as well as quality. Until the late 1960, the interest in water has been the available amount for consumption, except when poor water quality conditions persist, the available water was considered acceptable for consumption. Only during this last century, water quality has been deteriorated by human pressures to the point where this is now considered as a big concern for all

nations [1]. In our modern society, the population and industrial growth with the establishment of the populations nearby water catchments and sources are exacerbating the deterioration of water quality [2]. The increasing demand of water resources has also contributed to the change in ecosystems functioning directly through human activities and indirectly by the non-point source pollution. The quality of water is threatened by a large number of pathogens [3], as well as anthropogenic chemical release from municipalities that enter into the water cycle [4]. In addition, discharges from municipal and industrial waste treatment plants have been identified as major sources of aquatic ecosystem pollution in industrialized countries [5]. Other pollution sources of water, including agricultural activities (e.g., using chemical fertilizers and pesticides), atmospheric deposition, industrial and mining wastes/activities, marine dumping, may heavily affect water resources and health of all aquatic ecosystems. Therefore, various pollutants may end up into the water column and degrade its quality [6]. Another problem that we observe recently in coastal areas such as southern Togo (coastal sedimentary basin) is the increase of salinity of groundwater due to seawater intrusion in coastal aquifers [7]. In the context of fast population growth and increasing water sources pollution, the measurements/assessment of water quality and aquatic ecosystem health is essential for early detection of water quality and habitat degradation, and may help to reinforce the preservation of water and biological resources. Furthermore, water quality measurement is essential for the comparison of data worldwide and can help to solve issues in decision making in terms of water resources management policies. Even if in the developed countries, the quality management tools are developed, and there is a strong need for education and training of water managers and users. These gaps have been recognized by European Commission that fund a series of training courses covering several topics such as monitoring and measurements of lake ecological status, heavy metals and organic compounds in waters, and the use of biological indices. These approaches contribute to supply good surface water and groundwater over the Europe for many uses. In general, despite improvements of water management laws in recent decades, access to good quality water remains a critical issue, and water quality is paramount for public health. Water and aquatic ecosystems pollution is a societal concern around the globe [6]. According to Nobel Laureate Richard Smalley (1996 Nobel in Chemistry), good water is the second challenge next to energy, among the humanity's top 10 problems in the next 50 years. Of all the global struggles around environmental protection and restoration, the quality of water and aquatic ecosystem health may be the most significant challenge and opportunity in the anthropocene. Water quantity and quality (access and management) are interlinked to global biohealth for the maintaining the well-being of a sustainable environment for plants, animals, and humans. Therefore, information about water quality is crucial for guiding efforts, and to reduce waterborne illnesses, identifying risks of disturbances and improving sanitation programs [8]. However in developing countries such as West Africa, many constraints have been identified in water monitoring and management including poor regulatory enforcement and insufficient resources, equipment and logistical, as well as the weakness of traditional analysis based on physicochemical, which is also very expensive. However, the detection of water quality and establishment of monitoring programs require not only creation of water agencies but also simple and low-cost evaluation tools. Also, the urgent need of local water agencies to manage and establish a restoration plan for polluted water and impaired aquatic ecosystems remains a challenge. This chapter aims at giving a background in terms of water quality measurements such as water quality indices (WQIs) in West Africa. Therefore, the chapter presents the adaptation or development of WQIs (methodology, study area/region/country and context of use) in some countries/regions of West Africa, and we raised challenges and perspectives for sustainable water resource management in West Africa.

## **2. Concept of water quality**

Water quality is commonly and globally defined by its physical, chemical, and biological characteristics. Water may be used for drinking, irrigation, industrial processes, production of fish, shellfish and crustaceans, as well as habitats for wildlife including animals and plants [9]. The water quality depends on its uses. Indeed, the quality may be good enough for drinking but not suitable for other use. It may be good for irrigation of some crops but not suitable for irrigation of other crops. It may be well for livestock but not suitable for fish culture, whereas water quantity is determined by a single parameter (e.g., water mass or water volume), water quality depends on its component, as well as everything the water might have picked up during its runoff [1]. It is closely linked to the surrounding environment and land use. The modification of natural stream flows and the temperature can also have a major impact on water quality. Groundwater is a major source of water and should be away from contamination sources (e.g., urban or industrial, wastes dumping). The waters including streams/ rivers and lakes waters are habitats for many aquatic ecosystem organisms. An ecosystem is a community of organisms including plants, animals, fungi, and bacteria, interacting with one another, and with their environment [9]. Protecting aquatic ecosystems is therefore important to maintain water integrity. The aquatic components are an integral part of our environment and play an important role in maintaining water quality, and are often use as valuable indicator of water quality. The physical characteristics (temperature, turbidity, Secchi disk depth, color, salinity, suspended solids, dissolved solids, etc.), chemical characteristics (dissolved oxygen, biological oxygen demand, chemical oxygen demand, pH, nutrients, heavy metals, hardness, alkalinity, etc.), and biological characteristics (algae/total chlorophyll, total biomass, macrophytes, bacteria, macroinvertebrates, fish, etc.) of water are worldwide used in the bioassessment, and were considered as water quality indicators.

Thus, there are many approaches to assess/describe the quality of given water (e.g., using its physical, chemical, or biological characteristics). One way to describe the quality of a water sample is to list out the concentrations of everything that the sample contains following the water quality standard guideline. The second way is a simplification of water quality data by aggregating the measurements of water quality parameters/indicators in a single number (water quality index (WQI)) to expresses overall water quality. Between the two ways, the WQIs are more beneficial and adapted for water quality control. Therefore, the formulation and use of indices have been strongly advocated by agencies [1]. Furthermore, once theWQIs are developed and applied, they serve as a convenient tool to examine trends, and to highlight specific environmental conditions [10, 11], and to help governmental decision makers in monitoring the effectiveness of regulatory programs.

## **3. Concepts of indices and use of water quality indices (WQIs)**

The indices are representations of a condition/situation derived from a combination of several relevant parameters/measurements. The combination leads to a single ordinal number (e.g., score) that helps to understand and interpret water status [1]. The

concept of using an index to represent a single value is not a novel idea. For example, these approaches have been used in others domains, such as economics and commerce [12]. Indices have also been used in ecology to represent species richness, evenness, diversity, etc. Also, in numerous others fields such as medicine, sociology, process safety, indices are extensively used. The environmental indices have also been used in life cycle assessment [13] and to characterize different types of environmental damages, including global warming. However, the major component of environmental indices including water quality indices (WQIs) is used as communication tool by regulatory agencies to describe the "quality" or "health" of water/aquatic ecosystems [10, 11], as well as to assess the impact of regulatory policies on various environmental management practices [14, 15]. The water quality indices provide a simple method for expressing the quality of water or the health of aquatic ecosystem. The significance of the WQIs can be appreciated as environmental performance indicators or holistic environmental performance index [16]. Water quality indices can be constructed in two ways: (i) increasing scale indices in which the index scores/numbers increase with the degree of pollution and (ii) decreasing scale indices in which the index scores/numbers decrease with the degree of pollution. The first one can be considered as "water pollution indices" and the second one as "water quality indices." However, the difference between the two ways/types can be seen as essentially cosmetic. Indeed, "water quality" is a general term of which "water pollution" indicates "undesirable water quality," is a specific case. Based on water quality parameters/indicators that are incorporated in an index to judge water quality, the WQIs can be vaguely classified into two categories: i) indices predominantly based on physical and chemical characteristics/features and ii) indices based on bioassessment or biological characteristics/features. In this chapter, the first category of indices will be named physicochemical water quality indices (PCWQIs). They incorporate one or more microbiological parameters (e.g., fecal coliforms, total coliforms, *Escherichia coli*) and predominantly based on physicochemical parameters/indicators. The second category of WQIs named biological water quality indices (BWQIs) is based on sampling, identification, enumeration of biological organisms including macroinvertebrates, fish, algae, macrophytes, etc.

## **3.1 WQIs based on bioassessment/biological water quality indices (BWQIs)**

The assessment of water quality and aquatic ecosystem health using biological approaches is based on investigation of biota. However, it is necessary to identify indicator organisms that reflect water quality or aquatic ecosystem health, but not only due to the vagaries of chance. These organisms must be easily observed and counted. Therefore, the organisms or group of organisms used can include fish, amphibians, bacteria, protozoans, diatoms, algae, macrophytes, macroinvertebrates, etc. Only BWQIs based on macroinvertebrate features are discussed in this chapter. There are three types of BWQIs as follows:

*Water Quality and Aquatic Ecosystem Assessment Using Water Quality Indices in West Africa… DOI: http://dx.doi.org/10.5772/intechopen.109137*

iii. *Multivariate approaches for bioassessment of water quality:* are methods, which use statistical tools to develop relationships between fauna and environmental characteristics for an "ideal" or high-quality reference site.

#### *3.1.1 Biotic indices (BIs)*

#### *<IP_ADDRESS> Background*

The Saprobien system of Kolkwitz and Marsson used in Germany rivers in the early 1900s has been generally considered as the first biological scoring system so the first biotic indices for water quality assessment [17, 18]. Indices based on the Saprobien System are determined by the presence and absence of specific species that are sensitive to organic pollution. Thus, this concept used in Germany was expanded to other countries making it the first-water quality index. The development of that index is welcome since human activities produced an unprecedented pollution harmful to water and the biota [19, 20]. In 1964, the Trent Biotic Index (TBI) and other several modern biotic indices were developed in USA [21]. In the subsequent years, we have seen a slowly increasing reliance on biotic indices as a water quality management tool, especially in the developed countries. The biological indices are increasingly becoming a key element of environmental management policies and water resource in most developed countries [22, 23]. However, the use of biotic indices is not yet well known in developing countries. In many African countries, particularly in West African countries, there is no standardized or accredited biotic index used at regional scale. In recent years, most modern biotic indices are based on benthic macroinvertebrates [24]. To provide cost-effective tool accessible locally for ecological assessments, there is an increasing emphasis on the use of BIs [25, 26]. BIs based on macroinvertebrates are developed or applied in many countries including some developing countries such as South Africa, whereas in West Africa, there is a deficiency of information on the use of macroinvertebrates and biotic indices for water quality and ecosystem health assessment [27–33].

#### *<IP_ADDRESS> Approaches to BIs formulation*

Like the physicochemical quality-based indices, the biotic indices can be used to communicate in an understandable way to natural resource managers, decision makers, politicians, and the general public [34]. The biotic indices are "response based" approaches for environmental monitoring wherein the strategy is to assess the overall aquatic environmental health. It involves the monitoring of biological or ecological indicators to characterize the response of the environmental disturbances. The disturbance of an aquatic ecosystem or a water body can be monitored *via* some factors that alter the biotic integrity, such as chemical variables, physical features, hydrology, energy source [35, 36]. The scope of response can result from the environmental modification. Then, the value of the response of each taxon is estimated as a tolerant/sensitive (score value), and each particular group of taxon is assigned to a sensitivity weighting or a score to particular pollutants or pollution. The score generally varies from 0 to 10. Like all WQIs, the score of a taxon increases with the increase of the sensitivity. To determine the value of biotic index in sampling site, the scores of all the individual taxa sampled at this site are summed and/or averaged to provide a valuable value of ecological health of the community; thus, the health of the water

body, can be gauged. Even if the scoring system varies, most of modern and accredited biotic indices were adapted from [37] formula (1):

$$BI = \frac{\sum n\_i s\_i}{N} \tag{1}$$

where *ni* is the number of specimens in each taxonomic group, *si* is the score for that taxonomic group, and *N* is the total number of organisms in the sample.

#### *3.1.2 Indices of biological integrity (IBIs)*

#### *<IP_ADDRESS> Background*

The first IBI was introduced by [38] and was based on fish before being extended to other organisms. The IBI used only attributes of fish assemblage to assess the condition of rivers and its catchment. This index of biotic integrity reflects water-land linkages (e.g., water quality and land use), physical habitat quality, hydrological regime, and biological interactions [39, 40]. The IBI was designed to integrate information from individual, population, assemblage, and ecosystem levels into a single numerical indicator. Initially proposed by [38] and later improved by [41, 42], the IBI combines 12 fish assemblage attributes/metrics in three categories: (i) species richness and composition, (ii) trophic composition, and (iii) fish status and abundance. These data are used to assess sites condition by comparing reference sites "undisturbed" and "disturbed sites" within the same ecoregion [43] or in a similar ecoregion [44]. When there are no appropriate undisturbed sites, the least-disturbed regional sites can be considered [44] or the reference condition can be modeled from knowledge of historical data and fish habitat preferences [44]. This index was first developed for streams in the Midwestern of United States and has been proven useful in many other regions of North America [45]. It has also been applied to estuaries [46], lakes [47], and rivers outside the United States and Canada [48]. However, even if an IBI follows an ecoregional approach, it was often adapted to fulfill the requirement at regional scale. But these adapted IBIs are not always successful. For example, a modified IBI in semiarid southeastern Colorado, USA, was not able to reflect habitat degradation [49]. Nevertheless, over the years, a number of shortcomings of Karr's IBI have been identified and attempts have been made to overcome those infirmities. In the process, new IBIs have been developed based not only on fish, but also on macroinvertebrates and diatoms. Interestingly, despite the variety of adaptations throughout the world, the fundamentals of the original IBI still stand strong. In West Africa, some IBIs based on macroinvertebrates are developed for water quality and ecosystem health assessment during these last years [10, 11, 50–52].

#### *<IP_ADDRESS> Fundamentals and principles to (IBIs) formulation*

The IBIs, also called indices of biotic integrity, incorporate a suitable metrics in a single index called multimetric index (MMI). IBIs use a combination of indices in an endeavor to assess multiple anthropogenic pressures on aquatic ecosystems. The undisturbed ecosystems support an unbalanced biological condition over time. The organisms that inhabit a natural system, both individually and community level, can be used as potential indicators of ecosystem conditions because their presence as well as their well-being can be influenced by the human-induced perturbation [53].

#### *Water Quality and Aquatic Ecosystem Assessment Using Water Quality Indices in West Africa… DOI: http://dx.doi.org/10.5772/intechopen.109137*

Whereas biotic indices seek the representation of a natural water body through certain species or group of species, IBIs metrics are chosen to reflect the taxonomic composition, trophic relationships, abundance, and condition of organisms within an aquatic community [54, 55]. Thus, IBIs aim to convey a more integrated picture of ecosystem health than BIs. Due to the multiple metric combinations in IBIs, we can suppose that IBIs are more sophisticated than BIs. IBIs can reflect important components of ecology: taxonomic richness, habitat, and trophic guild composition, besides individual health and abundance. Differences in expected species richness and composition associated with different regions or basins, water body sizes and location in drainage are factored into metric selection and scoring. The main focus of the IBIs is based on the assemblage structure (indicator organisms or combinations of organisms) such as fish, plankton, benthos, and macrophytes rather than ecosystem processes, and yet both structural and functional metrics are often included in the IBIs [56]. IBIs provide a perception of ecological assemblage integrity to common people. Assessment of biological integrity using the IBIs requires "reference habitat" to compare the successful of index to discriminate human perturbation.

#### *<IP_ADDRESS> Approaches and steps to the development of IBI*

Building a robust and effective IBI is based on proper selection of measurable attributes that provide reliable and relevant signals about the effects of human pressures on biota. The biological attributes (metrics) ultimately incorporated into an IBI should reflect specific predictable responses of organisms to landscape condition modification. These metrics have to be sensitive to a range of physical, chemical, and biological features. They should be relatively easy to measure and interpret. A typical IBI include several attributes of biota, including taxa richness, indicator groups, health of individual organisms, and ecological processes. The most important criterion for choosing a biological attribute as a metric is whether the attribute responds predictably along a gradient of human influence. An effective IBI comprising well-chosen metrics should integrate information from ecosystem, community, and individual levels and clearly discriminate the biological "signal" including the effects of human activities from natural variation. The main steps to the development of IBI are described as follows:

Selection of candidate metrics.

This selection is based on the consideration of ecological relevance as well as feasibility of measurement. Candidate metrics should include measures of species diversity, productivity (abundance and biomass), and tolerance to anthropogenic stress. Barbour et al. [57] recommended to group macroinvertebrate metrics by categories, such as taxonomic richness, taxonomic composition, tolerance/intolerance, feeding group (e.g., predators, scrapers and filter feeders) and habit type (e.g., clingers and burrowers), as well as life history. When the ecological relevance is based on very specific ecological concepts, candidate metrics are basically a priori selected. However, indices developed with a utilitarian approach typically begin with a large list of candidate metrics, which is then pruned.

Selection of core and relevant metrics.

This step consists in retaining among ecologically relevant candidate metrics, those that are more sensitive (responsive to anthropogenic action, both degradative and restorative), and more representative (able to measure status and trends relative to policy decisions and management actions). The core metric nomination/selection is based on community level and characters that represent key community aspects.

This selection protocols include the following: (i) *a priori* selection based on a specific ecological foundation and/or best professional judgment; (ii) selection based on univariate statistical tests comparing undergraded and degraded samples from calibration data [58]; and (iii) utilitarian selection based on multivariate tests using a calibration dataset [59, 60]. Otherwise, many steps can be considered for relevant metrics selection.

*Metric range:* The first hurdle, which a candidate metric must overcome, is the range test. The "range" is the distribution of metric values across all available data, and the goal of the range test is to identify metrics that provide a large range of dataset. Metrics that have very small ranges (e.g., richness metrics based on only few taxa) or the ones that have similar values (e.g., most sites have values = 0) should be excluded.

*Reproducibility:* A metric providing fairly reproducible values at individual sites is more useful than a metric, which is less precise due to its variation within sites. Low sampling variation is necessary if a metric is to have a high probability of discriminating good and poor sites [61].

*Adjusting for natural gradients:* Metric values can vary with both stressor and natural gradients (e.g., elevation, slope, and stream size). Thus, knowledge of how to allocate the variability in metric values between natural and anthropogenic gradients is important. Selection of metrics that seem to respond strongly to stressor but, in fact, are merely correlated with the same natural gradient should be avoided [61]. One of the techniques to normalize metrics for natural gradients is to remove the stressor gradient from the data by focusing solely on reference sites data and to quantify the remaining correspondence between the metric value and the natural gradient.

*Responsiveness:* The efficiency of a metric is directly linked with its ability to distinguish degraded from relatively undisturbed sites. This can be tested in many ways. For example, metrics can be chosen on the basis of their correlation with specific stressors (e.g., nutrients, organic pollution, human pressures, and sedimentation). Some of the original metrics used by Karr [38] were chosen on the basis of their theory responses to specific aquatic stressors. However, several difficulties occur when metrics are evaluated in terms of relationships with specific stressors. First, many stressors are strongly correlated with one another, and attributing metric response to any particular stressor could inflate the role of this stressor. Second, not all stressors are well quantified (e.g., short-lived pesticides or herbicides), or even known, at all sites. In the absence of pristine sites that may serve as frames of reference, the evaluation of the responsiveness of the metric is based on the metric ability to distinguish leastdisturbed (reference) from the most-disturbed sites. Metric scoring and typical threshold selection are based on a set of least-disturbed sites.

*Final metric selection and check for metric redundancy:* All metrics that are successful with discriminatory power may be included in the IBIs. Candidate metric that is the most discriminating is chosen first and then preceded iteratively by adding the most responsive metric from each metric category until all categories are represented. This iterative process is based on the assumption that choosing the most responsive individual metrics will provide the most robust IBI such as each metric provides unique information (i.e., that the metrics are not redundant).

Metric redundancy can be defined at least in two different ways: (i) Metrics provide very similar biological information or (ii) metrics are highly correlated with other metrics. The first of these definitions may be important, because one would be disinclined to include two metrics that are based on identical (or broadly overlapping) biological or taxonomic information [61]. However, apparent redundant metrics might be, in fact, relatively uncorrelated. Thus, one might avoid including metrics

*Water Quality and Aquatic Ecosystem Assessment Using Water Quality Indices in West Africa… DOI: http://dx.doi.org/10.5772/intechopen.109137*

with values that are strongly correlated. If two metrics co-vary because the same taxa are changing in abundance as levels of disturbance rise and fall, then the metrics are effectively correlated. However, if metrics co-vary because they respond to similar stressors, then the correlated metrics are not necessarily redundant.

Metric combination.

The most difficult challenge in index development is not only the selection of metrics, but also the combination to capture the dynamics of essential ecological processes, and metric easy use by water managers. Without a strong and obvious ecological foundation, an index will not be relevant and therefore difficult to be used. The first step in metrics combination is to normalize selected metrics. In this chapter, we present two methods for metrics scoring: (i) discrete scoring (with values such as 1, 3, or 5, based on a subjective assessment of the range of each metric) and (ii) continuous scoring (with calculated values using some formula). Discrete scoring can mask subtle differences since and forces the scores to be in one or the other discrete interval. This could dampen the ability of the index to differentiate ecological condition classes [62, 63]. The continuous scoring can avoid the subjective nature of discrete scoring. The use of continuous scoring requires two considerations: how to decide what values of a metric indicate "ideal" biological condition (e.g., a scores 1, 10, or 100) and what values indicate unacceptably bad condition (e.g., a score of 0). The 95th percentile of the reference site distribution of values for each metric is generally used as the scoring ceiling and the 5th percentile of the distribution of values at all sites as the scoring floor. This approach produces an IBI with the highest responsiveness and lowest variability. Metric values between the ceiling and floor are interpolated linearly to yield intermediate values, and the final MMI for a site is calculated as the sum of its scored metrics. When interpreting final scores, the IBI is generally rescaled to range, for example, from 0 to 100, 0 to 10, or 0 to 1. The final MMIs, which are generally the sum or the mean of normalized values of selected metrics, follow or adapt the general formula below:

$$\text{MMI} = \sum \text{Selected metric scores} \tag{2}$$

MMI validation.

The robustness of any IBI depends on the effectiveness of metric (e.g., widely and repeatedly). Index validation should ideally include the following: (i) testing of the index with different from the data set used in index development; (ii) setting a correct *a priori* classification criteria, and (iii) *a posteriori* criterion based on best professional judgment. More commonly after index development, new data are used as validation data. Independent validation, by scientists other than those proposing the index, is highly appreciated. Some degree of intercalibration or validation can also be achieved by determining the level of agreement provided by an index with the best professional judgment [64] or by comparing the level of agreement between indices of different geographical origin, such as when comparing results of indices from Europe and USA.

#### *3.1.3 Multivariate approach for bioassessment*

The multivariate approaches for bioassessment are methods that use statistical tools to develop relationships between biota and environmental characteristics of "ideal" or reference site. Thus, the relationships are used to predict the pattern of fauna distribution. The observed fauna at the test site is then compared with

predicted fauna. The usual elements of multivariate analyses often include the cluster analyses, ordination techniques, and discriminant analyses [1, 65]. In contrast, IBIs follow inductive approach that relies on a posteriori assumption. A number of multivariate approaches have been standardized and consequently adopted for widespread use at international level. For example, the first of widely used multivariate approach is the River Invertebrate Prediction And Classification System (RIVPACS) developed in the UK. The RIVPACS approach has been adopted in many countries up to now [65, 66]. However, no multivariate approach is developed and even applied/adapted in West African countries. Therefore, this approach is not discussed in this chapter.

#### **3.2 WQI based predominantly on physicochemical characteristics (PCWQIs)**

The water quality indices based predominantly on physicochemical characteristics are often called in the most of literature "water quality indices" (WQIs). This leads to confusion with other indices such as BWQIs. In order to avoid this confusion, in this chapter, WQIs based predominantly on physicochemical characteristics will called "physicochemical water quality indices"(PCWQIs).

#### *3.2.1 Background*

PCQWIs using a numerical scale began with Horton's index in 1965. Horton used three main criteria to select variables/parameters for its combination in index: (i) The number of variables to be handled by the index should be limited to avoid making the index unwieldy; (ii) the variables should be of significance in most areas; and (iii) only variables of which reliable data are available, or obtainable, should be included. Based on these criteria, Horton selected 10 most commonly measured water quality variables for index formulation. Horton's index did not include any toxic chemicals and was highly subjective as they are based on the judgment of the author and associates [67]. Thus, several authors have built less and less subjective PCWQIs based on pioneering works. The useful PCWQIs incorporate up to 14 variables including toxic chemicals, physical parameters, microbiological parameters, etc. Nowadays, there is increasing use of ordination (e.g., factor analysis, principal component analysis), and other concepts such as entropy and genetic algorithms in making "hybrid" indices or in enhancing the applicability of PCWQIs.

#### *3.2.2 Steps and approaches of PCWQIs formulation*

Four steps are often used in the development of a PCWQI: (i) parameters selection; (ii) transformation of the parameters of different units and dimensions to a common scale; (iii) assignment of weightages to all parameters; and (iv) aggregation of subindices to produce a final index score. However, additional steps may also be taken to improve the index efficiency.

#### *<IP_ADDRESS> Parameter selection*

A PCWQI would become unwieldy if each and every possible physical and chemical parameter is included in the index. As possible constituents of water are paramount, parameter selection is as fraught with uncertainty and subjectivity, and this is *Water Quality and Aquatic Ecosystem Assessment Using Water Quality Indices in West Africa… DOI: http://dx.doi.org/10.5772/intechopen.109137*

crucial for the usefulness of any index. Enormous efforts, attention, experiences, and consensus-gathering skills are needed to ensure the most relevant parameters included in a PCWQI. Therefore, studies have suggested to involve large number of experts opinion, WHO standards/criteria, and statistical approaches in order to attempt the reduction of the subjectivity in the index building.

## *<IP_ADDRESS> Transformation of the parameters of different units and dimensions to a common scale: Making subindices*

Most of water are expressed in different units and, therefore, have different behavior in terms of concentration-impact relationship. Thus, before index formulation, all parameter units have to be transformed into a single scale varying usually from 0 to 1. Some index scales might have the range of 0–100. This step is a standardization or normalization of water quality parameters using functions called subindices.

Subindices development.

As described in [1], for each selected parameter to be integrated in the final index their units and range of concentrations (from highly acceptable to highly unacceptable) are transformed in a single scale. For example, if one considers a set of *n* pollutant variables denoted as (*x1*, *x2*, *x3,* xi, *xn*), then for each pollutant variable *xi*, a subindex *Ii* is computed using subindex function fi(xi). To develop the final index, based on a sound available data, some expert consensus and mathematical formula, authors suggest to use the subindex function as follows:

$$I\_i = F\_i \mathfrak{x}\_i \tag{3}$$

Once the subindices are defined/calculated, they usually are aggregated together in a second mathematical step to form the final index as follow:

$$I = \mathbf{g}(I\_1 + I\_2 + \dots \mathbf{I}\_n) \tag{4}$$

The aggregation function Eq. (4) usually consists of either a summation operation, in which individual subindices are added together or aggregated to form the final index as illustrated in **Figure 1** following [1].

Different types of subindices.

**Figure 1.** *The index development process [1].*

Subindices can be classified as one of four general types: linear; nonlinear; segmented linear; segmented nonlinear. In this chapter, only the linear subindices are considered/applied following the model of Eq. (5):

$$I = \alpha \mathfrak{x} + \beta \tag{5}$$

where *I* is the subindex, *x* the pollutant variable, and *α* and *β* the constants. With this function, a direct proportion exists between the subindex and the pollutant variable. The linear indices are simple to compute and easy to understand.

#### *<IP_ADDRESS> Assignment of weightages*

Even if all short-listed parameters are deemed to be important as water quality indicators, but they do not have the same weights. Within the selected parameters, some would have a high importance than others. There are some indices, which assume equal weightage for all the parameters. However, in many cases, different weightage is given to the different parameters. The assignment of weightage is, like selection of parameters, therefore, subjective and so, need well-formulated techniques of opinion and expert's consensus.

#### *<IP_ADDRESS> Aggregation of subindices to produce a final index*

Several methods are often used for final index formulation. For example, the two most basic ones are:

*Additive:* The subindices are combined through summation (e.g., arithmetic mean). This is the commonly used method.

*Multiplicative:* In multiplicative aggregation, the subindices are combined through product operation (e.g., geometric mean).

In this chapter, only additive aggregation is addressed including linear sum index or a weighted linear sum index.

Linear sum index.

A linear sum index is often computed by the addition of unweighted subindices following the model of Eq. (6):

$$I = \sum\_{i=1}^{n} I\_i \tag{6}$$

where *Ii* is the subindex for pollutant variable *i* and *n* is the number of pollutant variables.

Weighted linear sum index.

A weighted linear sum index can be given by Eq. (7):

$$I = \sum\_{i=1}^{n} W\_i I\_i \tag{7}$$

where *Ii* is the subindex for *i th* variable and *Wi* the weight for *i th* variable with:

$$\sum\_{i=1}^{n} W\_i = \mathbf{1} \tag{8}$$

## **4. Illustrative examples of BWQIs in West African countries**

#### **4.1 Multimetric indices (MMIs)**

In this section, some illustrative examples of MMI developed in West Africa are featured. They cover different biogeographic regions in terms of climate and water bodies' types. The summaries of these illustrative examples are focused on the methodologies, and brief discussion of their validation and application.

#### *4.1.1 A multimetric index of Zio river basin in Togo (MMIZB)*

#### *<IP_ADDRESS> Methodology*

This study focuses on Zio River basin, one of the basins with many permanent flow streams and drained by several streams coming mainly from Togo Mountains. The basin is characterized by a tropical climate including the main ecoregions of Togo (moist forests, gallery forests and a mosaic of agricultural land, degraded forest, and savanna).

The study followed human pressures gradient ranging from least impaired sites (references) to impaired sites. The impaired sites are characterized by intense agriculture and other human activities described in [68]. Forty-two sites were sampled from downstream to upstream according to the following criteria: (i) minimally impaired sites (reference sites); (ii) fairly or slightly impaired sites (intermediate sites); and (iii) severely impaired sites. Environmental variables were used to compare differences between reference and impaired sites using Mann-Whitney comparison test, and seasonal aspects on environmental variables were assessed using the Kruskal-Wallis comparison test (p < 0.05). The analysis procedures including candidate metrics examination, core metrics selection, and final multimetric index formulation is detailed in [10].

Lumb et al. [10] have demonstrated that high correlation coefficients (r > 0.80, p < 0.05) were interpreted as indicating redundant metrics and in these cases, only one metric (the most usual metric in tropical climate and strongly correlated with environmental variables) was retained for multimetric index development.

To fulfill all requirements described previously, the continuous scoring was used to score/standardize the metric values. The scoring procedure of MMIZB index was achieved in four steps with two formula following Eqs. (9) and (10): (i) computing all six core metrics; (ii) scoring the metrics Eq. (9) for metrics that decrease with increasing impairment, and Eq. (10) for metrics that increase with increasing impairment; (iii) applying a simple interpolation to adjust values to a range from 0 to 100; (iv) final multimetric index value is obtained by summing scores of selected metrics, and classes boundaries were set to promote local use of index by managers.

$$\text{Standard metric} = \frac{\text{Metric result} - \text{25th percentile of impairazed sites}}{\text{75th percentile of reference sites} - \text{25th percentile of impairazed sites}} \times 10 \tag{9}$$

$$\text{Standard metric} = \frac{\text{Metric result} - \text{75th percentile of impained sites}}{\text{25th percentile of reference sites} - \text{75th percentile of impained sites}} \times 100$$

$$\text{(10)}$$

#### *<IP_ADDRESS> MMIZB index validation and application*

The validation test of the MMIZB index sensitivity was conducted with 11 new sites of Zio River basin following steps described in [10]. These sites were chosen following some reference conditions approach described in [57] with local experts' consensus and were not used in the building process of MMIZB index. The test was performed using a principal component analysis (PCA) of 13 key environmental variables. The assessment of the index sensitivity was done using pressures scores of first axis of the PCA, and organic pollution of Zio river basin was assessed using Prati's Index [69]. The multimetric macroinvertebrates index of Vietnam (MMI\_Vietnam) [70] was also used to determine the robustness of MMIZB index. This sensitivity of the index was also tested by assessing whether there was a clear discrimination among reference sites and impaired sites using whisker box plots.

MMIZB was developed in a watershed/basin (the main hydrological unit) covering three of the five ecoregions of Togo. Furthermore, it was validated by the data from the three ecoregions, MMI of Vietnam (MMI of a tropical basin), and water quality index of organic pollution. MMIZB is then considered as one of the first relevant multimetric index developed at a watershed level in the tropical regions of West Africa. Therefore, MMIZB may be used/applied for watersheds/basins ecosystem health and water quality assessment in Togo, West Africa, and other tropical countries.

### *4.1.2 The Sahel River multimetric index (SRMI) of West African Sahel Rivers, Burkina Faso*

#### *<IP_ADDRESS> Methodology*

Burkina Faso is a sub-Saharan, landlocked country in the central part of West Africa. Most of Burkina Faso lies within the West Sudanian Savannah. The climate is tropical and semi-arid and characterized by a north-south gradient in rainfall distribution, with high variability in time and space. The study was undertaken in the three main basins in Burkina Faso: Nakanbé (formerly White Volta) in the central part of the country, Mouhoun (formerly Black Volta) in the west, and Comoé in the southwest part of Burkina Faso. The Volta river is composed of Nakanbé and Mouhoun at country scale. The Mouhoun river has permanent run-off, and the Nakanbé downstream has two hydropower dams, which have almost perennial flows see [11]. According to Refs. [11, 29], the catchment of these rivers is under pressure from various human activities that lower the ecological integrity of rivers.

Sampling sites selected within the three basins covered a gradient from protected areas "slightly impaired" to highly impaired sites [11, 36]. Six reference sites were defined as "natural or near-natural sites" following a priori criteria: no disturbance or near-to-natural hydro-morphological features, preserved natural habitats, no human activity within 100 m of the riparian zone, dissolved oxygen (DO) > 6 mg/L, conductivity <75 μs/cm, no sand or gravel excavation, no visible sign of sensory features, natural vegetation cover typical to area > 80%, and the presence of wild birds and mammals possibly. The selected impaired sites were identified as those in agricultural or urbanized riparian zones with collapsed and eroded riverbanks. The impaired sites were also exposed to point and non-point sources of pollution. For environmental variables measured at reference and impaired sites, seasonal and temporal variability was assessed using the Mann-Whitney comparison test [11].

*Water Quality and Aquatic Ecosystem Assessment Using Water Quality Indices in West Africa… DOI: http://dx.doi.org/10.5772/intechopen.109137*

The authors followed the same procedures: candidate metrics examination, core metrics selection as described in Ref. [10], except the final multimetric index formulation. The selected core metrics were normalized using Eq. (11) for metrics that decrease with impairment and Eq. (12) for metrics that increase with impairment. The continuous scoring was used to avoid distortion of scores by potentially extreme maximum values and sample size noise. Therefore, 95th and 5th percentile thresholds were applied. The scores of each normalized core metric were scaled from 0 to 1. Finally, the individual metrics were summed by aggregating the scores of each normalized core metric to form the SRMI. A value close to 1 represents high ecological status, and a value close to 0 represents bad ecological status. The practical relevance for water management was considered when the numerical range of the SRMI (0–1) was grouped into five ecological quality classes: (I) high ecological quality class, (II) good ecological quality class, (III) fair ecological quality class, (IV) poor ecological quality, and (V) bad ecological quality class based on literature and experts consensus.

$$\text{Normalised value } (V^{\prime}) = \frac{V\_i}{V\_{95^{\prime}}} \tag{11}$$

$$\text{Normalised value } (V\text{i}') = \frac{V\_{\text{max}} - V\_i}{V\_{\text{max}} - V\_{\text{596}}} \tag{12}$$

where *Vi*<sup>0</sup> is the normalized value of the metric, *Vi* is the metric value, *V*95% is the 95th percentile, *V*5% is the 5th percentile, *V*max is the maximum value of the metric.

#### *<IP_ADDRESS> SRMI validation and application*

Kaboré et al. [11] have proven sensitivity of their index with data covering most of ecoregions of West Africa. The same authors have shown that the SRMI responded to a set of environmental parameters associated with a gradient of human pressures affecting the ecological integrity of water bodies (R2 ≥ |0.50|; p < 0.05; p < 0.001). This confirms the usefulness of an unprecedented and promising tool for biological monitoring and decision making in Sahelian regions' water management [11]. Therefore, SRMI may be used/applied for water quality and aquatic ecosystem health assessment in other sahelian countries or regions of Africa.

#### *4.1.3 Others MMIs of West Africa countries*

In North Central Nigeria, Edegbene et al. [50] have formulated a macroinvertebrates multimetric index named "River Chanchaga multimetric index" (MMIchanchaga) to assess the ecological status of River Chanchaga. The authors used several number of core metrics (13) for final index formulation compared to those of [10, 11]. At the same time, Edegbene et al. [51] have produced macroinvertebrates tool for urban river systems assessment in Delta region of Nigeria using five core metrics for the final index formulation. Additionally, a macroinvertebrate-based multimetric index has been also formulated by [52] for assessing ecological condition of forested stream sites draining Nigerian urbanizing landscapes. The mutimetric is a relevant sophisticated tool for ecosystems health assessement in West Africa, because it is acknowledged advantage of combining the sensitivity of many metrics to different aspects. However, Kaboré et al. [11] argued that develop a less sophisticated method; for example, a biotic score remains a challenge for West African limnologists. In regard of all these advantages proved by macroinvertebrate, we encourage West Africa limnologists to use macroinvertebrates at family taxonomy resolution for bioassessment and biomonitoring program implementation due to their cost-benefit (e.g., ease of sampling and identification of specimens) and limited taxonomic knowledge of the local water managers.

### **4.2 Promising tools using macroinvertebrates for ecosystem health assessment in West Africa**

Freshwater biodiversity is threatened by climate and land use change. Freshwater ecosystems depend strongly on physical and chemical features such as water quantity, water quality, water flow, and surrounding vegetation. Freshwater ecosystems throughout the world are threatened by human activities that directly alter hydrology system, such as construction of physical barriers (e.g., for dams' and against floods), water extraction, and filling or draining of shallow habitats. Pollution of waterways with toxic substances and excessive nutrients, as well as destructive land use practices in surrounding freshwater ecosystems, leads to alterations of water quality. In West Africa, the impacts on water bodies are expected to increase due to high levels of economic and population growth in this region. Effects of those anthropic multiples pressures on water ecosystems are rendering benthic macroinvertebrate assemblages among others aquatic organisms more vulnerable including change of community composition, increase of opportunistic species number, and decline of sensitive taxa and general biodiversity. In West Africa, several authors such as Tampo et al. [28], Kaboré et al. [29], Kaboré et al. [30], Tampo et al. [31], Agblonon Houelome et al. [32], and Edegbene et al. [33] among others have demonstrated the sensitivity of macroinvertebrates to water quality and human disturbances. From these authors, the macroinvertebrates community including functional surrogates, taxonomic composition, and diversity reflect the environmental condition, and any modification from physical, chemical, and biological integrity of freshwater ecosystems can dramatically affect water and biological resources, and as human health.

These results revealed that macroinvertebrate assemblages have high potential use as ecological indicators making them particularly beneficial for bioassessment because: i) they are the most popular indicators and their use dates back to the late 1840; ii) they are the major group of organisms in terms of species richness and individual abundance in most water bodies; iii) their life cycles are sufficiently long that they will likely be exposed to pollution and environmental stress; iv) sampling the benthic macroinvertebrates assemblage is relatively simple and does not require complicated devices or great effort; v) although they are mobile, they have mostly sedentary habits so they are likely to be exposed to local pollution or environmental stress; vi) the benthic macroinvertebrates biology are well known, and thus, sufficient identification keys, ecological data bases, and methodological standards are existent.

### **5. Examples of PCWQIs application in West African countries**

As mentioned for BIs, there is not yet a standard PCWQI developed for West Africa countries. However, water quality is assessed in West Africa countries by using/adapting PCWQIs developed at abroad far from African reality/context. We present here some examples of studies applying/adapting PCWQIs in West Africa. *Water Quality and Aquatic Ecosystem Assessment Using Water Quality Indices in West Africa… DOI: http://dx.doi.org/10.5772/intechopen.109137*

#### **5.1 Assessment of water quality using PCWQIs in Nigeria**

Many studies reported the application/adaptation of PCWQIs for the assessment of water quality in several water sources across Nigeria [71, 72]. The application/ adaptation of PCWQIs commonly based on the following: (i) the most common measured water quality variables (temperature, electrical conductivity, biochemical oxygen demand, fecal coliform, pH, dissolved oxygen, total phosphates, turbidity, nitrates and total solids, total hardness, etc.); (ii) water quality rating scale; (iii) relative weight, and (iv) overall PCWQI calculation. Some PCWQIs are often calculated by averaging the individual index values of some or all of the parameters within five water quality indicator categories (water clarity, dissolved oxygen, oxygen demand, nutrients, and bacteria) that depicts the pollution level or status of the water. Other PCQWI commonly applied/adapted in Nigeria is based on Canadian Council of Ministers of Environment (CCME). These methods combined three factors into a single index [73]. Several authors have applied theses PCWQIs to evaluate the water quality from different water sources especially surface and groundwater across the different zones of Nigeria. According to Adelagun et al. [71], PCWQIs can be applied to evaluate the water quality from different water sources across different zones. Etim et al. [74] have carried out a study to compute a PCWQI in order to assess the suitability of water from different sources collected from different areas in the Niger Delta region of Nigeria. From Ref. [74], the PCWQI has been calculated using standard drinking water quality recommended by the World Health Organization (WHO) and Indian Council for Medical Research (ICMR).

#### **5.2 Assessment of water quality using PCWQIs in Ghana**

According to Ref. [75], using PCWQI as a tool for water quality assessment has become one of the new ways to disseminate scientific information to the general public and policy makers in Ghana. The concept of WQI to describe the state of water quality in Ghana has not been popular until 2003, when the water resource commission (WRC) produced an adapted WQI document and proposed the WQI concept for assessing surface water quality in Ghana [75]. Indeed, in 2003 the WRC produced a document (Ghana Raw Water Quality Guidelines and Criteria: Adapted Water Quality Index) based on Solway Water Quality Index, like an index to characterize the overall raw water quality in Ghana. The Adapted Water Quality Index is a classification system that uses an index from selected water quality parameters. The index classifies water quality into one of the four categories: good, fairly good, poor, and grossly polluted. Each category describes the state of water quality compared to objectives that usually represent the natural state. Thus, the index indicates the degree to which the natural water quality is affected by human activities. From Ref. [75], the concept of a water quality index for Ghana arose from two needs. The first is to share and communicate with the public, in a consistent way. The second is to provide a general means of comparing and ranking various water bodies throughout Ghana. In the same country, Miyittah et al. [76] have used the Canadian Council of Ministers of the Environment WQI (CCMEWQI) approach to assess the pollution status of Aby Lagoon System.

Banoeng-Yakubo [77] and Boateng et al. [78] have also conducted studies using PCWQI to analysis groundwater quality of the northern section of the Volta region. Authors defined steps for computing the PCWQI. For example, in the first step, each of the chemical parameters was assigned a weight (wi) based on their perceived

effects on primary health. The highest weight of five was assigned to parameters, which have the major effects on water quality in the study area [78].

#### **5.3 Assessment of water quality using PCWQIs in Togo**

The assessment of water quality using PCWQIs is not well known or is almost at its beginning in Togo. However, water quality indices have been applied to assess water quality or suitability for drinking and irrigation purposes [7], and for the validation of other water quality indices [10]. We present here two examples of study cases applying PCWQIs. One study for water quality assessment in a peri-urban area conducted by [7] and another conducted by [79] on groundwater in the Southeastern Togo. Indeed, based on some monitoring data, Tampo et al. [7] conducted a study in a periurban area, where PCWQIs have been used to compare the suitability of three types of water (treated wastewater, and ground and surface water) for domestic and irrigation purposes. The authors adapted three types of PCWQIs such as Prati's index of pollution (PIP), national sanitation foundation's water quality index (NSFWQI), and overall index of pollution (OIP) for the comparison and classification of waters. This study used physicochemical and microbiological parameters as indicators to incorporate in the PCWQIs. Another study conducted by Napo et al. [79] in the Southeastern Togo adapted PCWQIs methods to assess the groundwater water quality. The PCWQI was calculated by aggregation subindices of 13 physicochemical parameters in linear sum index.

## **6. Water quality assessment: challenges and perspectives in West Africa**

There are a few multimetric indices developed in West Africa, but they are not well known by local population, and are less used or not at all applied for water quality and aquatic ecosystem health assessment. In this context of exacerbating human pressures on freshwater ecosystems, there is an urgent needed to share these findings with water users and decision makers, because the effectiveness of any index-driven management policies will increase with better knowledge of conservation status. But, how to link resilience of climate change and environmental conservation with ensuring livelihoods of a growing population remains a great challenge. All the funds are not using efficiently and mostly directed to the primary needs (e.g. food, security, heath) leading to conceal the environment problems. Others hands, there is a need for an applied education program, on several scales "bottom up and top down" on environmental issues, as well as, for academic, water agencies, and NGOs to use scientific knowledge in theory and practice. Often, the lack of clear mainstreams cooperation between university and ministries on water quality assessment methods leads to waste investments and lower researches capacity in terms of development and policy basis to sustainable management of aquatic ecosystems in West Africa. Therefore, we encourage all researches that provide interdisciplinary national data incorporating social and political components on water quality assessment. This may significantly strengthen national capacities in the improvement of water management. For less sophisticated such as scoring biotic indices, out of perspectives argued by Kaboré et al. [11] there are no biotic indices developed for Western Africa region. The findings from authors such as Tampo [10], Kaboré et al. [11], Tampo et al. [31], Agblonon Houelome et al. [32], Edegbene et al. [50–52], among others, have confirmed the advantages of using macroinvertebrates features because they are effective tools and

*Water Quality and Aquatic Ecosystem Assessment Using Water Quality Indices in West Africa… DOI: http://dx.doi.org/10.5772/intechopen.109137*

respond to a gradient of multiple human pressures affecting the ecological integrity of freshwater ecosystems. Other authors such as Tampo et al. [7], Adelagun et al. [71], Iwar et al. [72], Banoeng-Yakubo et al. [77], and Napo et al. [79] found that PCWQI score based on the main/important physicochemical parameters can be used to classify water from excellent and good or bad for consumption and crop irrigation. All these studies reveal the importance of PCWQIs application for water quality monitoring in West Africa, but some standards/guidelines about water quality are needed to be intensively explored and determined at each country level in order to refine PCWQIs application in West African countries.

With ongoing severe freshwater pollution and multiple human pressures, the need of tools accessible locally is crucial for prioritizing conservation efforts and efficient management of freshwater ecosystems in Western Africa region. Funds to develop methods for aquatic ecosystems health assessment in West Africa remain weak. Thus, more cooperations between NGOs, local government, stakeholders, and international partners are still need to reinforce the capacity building of developing countries for an integrative water management and governance when society meets ecology.

### **7. Conclusion**

In West Africa, there still is a lack of both water management tools and water agency. The creation of water management agency may be encouraged by the development of cheaper tools for water quality assessment. Water quality indices seem to be an alternative in Africa where traditional analysis is not only expensive but also inaccessible. The water quality indices can be used as effective tools for ecological awareness in West Africa. There are an urgently call for new co-creation of water management tools including water managers and agency, decision makers, as well as water users. This may help to internalize findings and their use for long-term management of water resources, the overall biota, ecosystems functioning, and services for human well-being.

*Water Quality – New Perspectives*

## **Author details**

Lallébila Tampo1,2\*, Idrissa Kaboré3 , Seyf-Laye Alfa-Sika Mande1,2,4 and Limam Moctar Bawa<sup>2</sup>

1 Faculty of Science and Technology, University of Kara, BP, Togo

2 Laboratory of Applied Hydrology and Environment, Faculty of Sciences, University of Lomé, BP, Togo

3 Laboratory of Animals Biology and Ecology (LBEA), 03 BP 7021, University Joseph KI-ZERBO, Ouagadougou, Burkina Faso

4 Beijing Key Laboratory of Water Resources and Environmental Engineering, China University of Geosciences (Beijing), Beijing, P.R. China

\*Address all correspondence to<EMAIL_ADDRESS>
© 2023 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*Water Quality and Aquatic Ecosystem Assessment Using Water Quality Indices in West Africa… DOI: http://dx.doi.org/10.5772/intechopen.109137*

## **References**

[1] Abbasi T, Abbasi SA. Water Quality Indices. Amsterdam: Elsevier; 2012. p. 362. DOI: 10.1016/B978-0-444-54304- 2.00016-6

[2] Vorosmarty CJ, McIntyre PB, Gessner MO, Dudgeon D, Prusevich A, Green P, et al. Global threats to human water security and river biodiversity. Nature. 2010;**467**:555-561. DOI: 10.1038/ nature09440

[3] Rizzo L, Manaia C, Merlin C, Schwartz T, Dagot C, Ploy MC, et al. Urban wastewater treatment plants as hotspots for antibiotic resistant bacteria and genes spread into the environment: A review. Science of the Total Environment. 2013;**447**:345-360. DOI: 10.1016/j.scitotenv.2013.01.032

[4] Schwarzenbach RP, Escher BI, Fenner K, Hofstetter TB, Johnson CA, von Gunten U, et al. The challenge of micropollutants in aquatic systems. Science. 2006;**313**(5790):1072-1077. DOI: 10.1126/science.1127291

[5] Reemtsma T, Weiss T, Mueller J, Petrovic M, Gonzalez S, Barcelo D, et al. Polar pollutants entry into the water cycle by municipal wastewater: A European perspective. Environmental Science & Technology. 2006;**40**(17): 5451-5458. DOI: 10.1021/es060908a

[6] Voudouris K, Voutsa D, editors. Water Quality Monitoring and Assessment. London, UK, Rijeka, Croatia: InTech; 2012. p. 602. DOI: 10.5772/2411

[7] Tampo L, Alfa-Sika Mande SL, Adekanmbi AO, Boguido G, Akpataku KV, Ayah M, et al. Treated wastewater suitability for reuse in comparison to groundwater and surface water in a peri-urban area: Implications

for water quality management. Science of the Total Environment. 2022;**815**: 152780. DOI: 10.1016/j.scitotenv.2021. 152780

[8] Peletz R, Kisiangani J, Bonham M, Ronoh P, Delaire C, Kumpel E, et al. Why do water quality monitoring programs succeed or fail? A qualitative comparative analysis of regulated testing systems in sub-Saharan Africa. International Journal of Hygiene and Environmental Health. 2018;**221**:907-920. DOI: 10.1016/j.ijheh. 2018.05.010

[9] Postolache O, Girão PS, Pereira JMD. Water quality monitoring and associated distributed measurement systems: An overview. In: Voudouris K, Voutsa D, editors. Water Quality Monitoring and Assessment. Rijeka, Croatia: InTech; 2012. pp. 25-64. DOI: 10.5772/32159

[10] Tampo L, Lazar IM, Koboré I, Oueda A, Akpataku VK, Djaneye-Boundjou G, et al. A multimetric index for assessment of aquatic ecosystem health based on macroinvertebrates for the Zio river basin in Togo. Limnologica. 2020;**83**:125783. DOI: /10.1016/j.limno. 2020.125783

[11] Kaboré I, Ouèda A, Moog O, Meulenbroek P, Tampo L, Bancé V, et al. A benthic invertebrates-based biotic index to assess the ecological status of West African Sahel Rivers, Burkina Faso. Journal of Environmental Management. 2022;**307**:114503. DOI: 10.1016/j. jenvman.2022.114503

[12] Diewert WE, Nakamura AO. Indices. In: Essays in Index Number Theory. Vol. 1. Amsterdam: North Holland Press; 1993. pp. 71-104

[13] Khan AA, Paterson R, Khan H. Modification and application of the Canadian Council of Ministers of the Environment Water Quality Index (CCME WQI) for the communication of drinking water quality data in Newfoundland and Labrador. Water Quality Research Journal of Canada. 2004;**39**(3):285-293. DOI: 10.2166/ WQRJ.2004.039

[14] Pusatli OT, Camur MZ, Yazicigil ZH. Susceptibility indexing method for irrigation water management planning: Applications to K. Menderes river basin Turkey. Journal of Environmental Management. 2009;**90**(1):341-347. DOI: 10.1016/j.jenvman.2007.10.002

[15] Sadiq R, Haji SA, Cool G, Rodriguez MJ. Using penalty functions to evaluate aggregation models for environmental indices. Journal of Environmental Management. 2010; **91**(3):706-716. DOI: 10.1016/j. jenvman.2009.09.034

[16] Lumb A, Sharma TC, Bibeault JF. A review of genesis and evolution of water quality index (WQI) and some future directions. Waters Quality, Exposure and Health. 2011;**3**:11-24. DOI: 10.1007/ s12403-011-0040-0

[17] Washington HG. Diversity, biotic and similarity indices. A review with special relevance to aquatic ecosystems. Water Research. 1984;**18**:653-694. DOI: 10.1016/0043-1354(84)90164-7

[18] Rico E, Rallo A, Sevillano MA, Arretxe ML. Comparison of several biological indices based on river macroinvertebrate benthic community for assessment of running water quality. Annals Limnology. 1992;**28**:147-156. DOI: 10.1051/limn/1992013

[19] Davis WS, Simon TP, editors. Biological Assessment and Criteria: Tools for Water Resource Planning and Decision Making. London: Lewis

Publishers; 1995. p. 432. DOI: 10.13140/ RG.2.1.4916.2726

[20] Perry J, Vanderklein E. Water Quality: Management of a Natural Resource. USA: Wiley-Blackwell; 1996. p. 656. DOI: 10.2307/2405269

[21] Woodiwiss FS. The biological system of stream classification used by the Trent River board. Chemistry and Industry. 1964;**83**:443-447

[22] Norris RH, Norris KR. The need for biological assessment of water quality: Australian perspective. Australian Journal of Ecology. 1995;**20**:1-6. DOI: 10.1111/j.1442-9993.1995.tb00516.x

[23] Moog O, Chovanec A. Assessing the ecological integrity of rivers: Walking the line among ecological, political and administrative interests. Hydrobiologia. 2000;**422**:99-109. DOI: 10.1023/A:<PHONE_NUMBER>050

[24] Ollis DJ, Dalls HF, Esler K, Boucher C. Bioassessment of the ecological integrity of river ecosystems. African Journal of Aquatic Science. 2006;**31**:205-227. DOI: 10.2989/<PHONE_NUMBER>9503892

[25] Brown CA. A comparison of several methods of assessing river condition using benthic macroinvertebrate assemblages. African Journal of Aquatic Science. 2001;**26**:135-147. DOI: 10.2989/<PHONE_NUMBER>9503735

[26] Metzeling L, Chessman B, Hardwick R, Wong V. Rapid assessment of rivers using macroinvertebrates: The role of experience, and comparisons with quantitative methods. Hydrobiologia. 2003;**510**:39-52. DOI: 10.1023/B: HYDR.0000008500.34301.a0

[27] Arimoro FO, Muller WJ. Mayfly (Insecta: Ephemeroptera) community *Water Quality and Aquatic Ecosystem Assessment Using Water Quality Indices in West Africa… DOI: http://dx.doi.org/10.5772/intechopen.109137*

structure as an indicator of the ecological status of a stream in the Niger Delta area of Nigeria. Environmental Monitoring and Assessment. 2010;**166**: 581-594. DOI: 10.1007/s10661-009- 1025-3

[28] Tampo L, Oueda A, Nuto Y, Kabore I, Bawa L, Djaneye-boundjou G, et al. Using physicochemical variables and benthic macroinvertebrates for ecosystem health assessment of inland rivers of Togo. International Journal of Innovation and Applied Studies. 2015;**12**: 961-976

[29] Kaboré I, Moog O, Alp M, Guenda W, Koblinger T, Mano K, et al. Using macroinvertebrates for ecosystem health assessment in semi-arid streams of Burkina Faso. Hydrobiologia. 2016; **766**:57-74. DOI: 10.1007/s10750-015- 2443-6

[30] Kaboré I, Ouédraogo I, Tampo L, Ouéda A, Moog O, Guenda W, et al. Composition and dynamic of benthic macroinvertebrates community in semiarid area rivers of Burkina Faso (West Africa). International Journal of Biological and Chemical Sciences. 2016; **10**:1542-1561. DOI: 10.4314/ijbcs.v10i4.8

[31] Tampo L, Kaboré I, Alhassan EH, Ouéda A, Bawa LM, Djaneye-Boundjou G. Benthic macroinvertebrates as ecological indicators: Their sensitivity to the water quality and human disturbances in a Tropical River. Frontiers in Water. 2021;**3**:662765. DOI: 10.3389/frwa.2021.662765

[32] Agblonon Houelome TM, Agbohessi TP, Adandedjan D, Nechifor R, Chikou A, Lazar IM, et al. Ecological quality of the Alibori River, northern Benin, using macroinvertebrate indicators. African Journal of Aquatic Science. 2022;**2**(47):178-184. DOI: 10.2989/16085914.2022.2044749

[33] Edegbene AO, Odume ON, Arimoro FO, Keke UN. Identifying and classifying macroinvertebrate indicator signature traits and ecological preferences along urban pollution gradient in the Niger Delta. Environmental Pollution. 2021;**281**: 117076. DOI: 10.1016/j.envpol.2021. 117076

[34] Stark JD. SQMCI: A biotic index for freshwater macroinvertebrate codedabundance data. New Zealand Journal of Marine and Freshwater Research. 1998; **32**:55-66. DOI: 10.1080/00288330.1998. 9516805

[35] Abbasi T, Abbasi SA. Water quality indices base on bioassessmentethe biotic indices. Journal of Water and Health (IWA Publishing). 2011;**9**(2):330-348. DOI: 10.2166/wh.2011.133

[36] Kaboré I, Moog O, Ouéda A, Sendzimir J, Ouédraogo R, Guenda W, et al. Developing reference criteria for the ecological status of West African rivers. Environmental Monitoring and Assessment. 2018;**190**:2. DOI: 10.1007/ s10661-017-6360-1

[37] Hilsenhoff WL. Use of arthropods to evaluate water quality of streams. Wisconsin Dept. of Natural Resources Technical Bulletin. 1977;**100**:15

[38] Karr JR. Assessment of biotic integrity using fish communities. Fisheries. 1981;**6**:21-27. DOI: 10.1577/ 1548-8446(1981)006<0021: AOBIUF>2.0.CO;2

[39] Steedman RJ. Modification and assessment of an index of biotic integrity to quantify stream quality in Southern Ontario. Canadian Journal of Fisheries and Aquatic Sciences. 1988;**45**:492-500. DOI: 10.1139/f88-059

[40] Allan JD, Erickson DL, Fay J. The influence of catchment land use on stream integrity across multiple spatial scales. Freshwater Biology. 1997;**37**: 149-161. DOI: 10.1046/j.1365-2427.1997. d01-546.x

[41] Fausch KD, Karr JR, Yant PR. Regional application of an index of biotic integrity based on stream fish communities. Transactions of the American Fisheries Society. 1984;**113**: 39-55. DOI: 10.1577/1548-8659(1984)113 <39:RAOAIO>2.0.CO;2

[42] Karr JR, Fausch KD, Angermeier PL, Yant PR, Schlosser IJ. Assessing biological integrity in running waters. A method and its rationale. Illinois Natural History Survey Special Publication 5. 1986. p.28

[43] Omernik JM. Ecoregions of the conterminous United States. Annals of the Association of American Geographers. 1987;**77**:118-125. DOI: 10.1111/j.1467-8306.1987. tb00149.x

[44] Hughes RM. Defining acceptable biological status by comparing with reference conditions. In: Davis WS, Simon TP, editors. Biological Assessment and Criteria: Tools for Water Resource Planning and Decision Making. Boca Raton: Lewis Publishers; 1995. pp. 31-47

[45] Simon TP, Lyons J. Application of the index of biotic integrity to evaluate water resource integrity in freshwater ecosystems. In: Davis WS, Simon TP, editors. Biological Assessment and Criteria: Tools for Water Resource Planning and Decision. Boca Raton: Lewis Publishers; 1995. pp. 245-262

[46] Thompson BA, Fitzhugh GR. A Use Attainability Study: An Evaluation of Fish and Macroinvertebrate Assemblages of the Lower Calcasieu River, Louisiana.

Baton Rouge, USA: Center for Wetland Resources. Coastal Fisheries Institute, Louisiana State University; 1986. p. 143

[47] Minns CK, Cairns VW, Randall RG, Moore JE. An index of biotic integrity (IBI) for fish assemblages in the littoral zone of Great Lakes areas of concern. Canadian Journal of Fisheries and Aquatic Sciences. 1994;**51**:1804-1822. DOI: 10.1139/f94-18

[48] Hughes RM, Oberdorff T. Applications of IBI concepts and metrics to waters outside the United States and Canada. In: Simon TP, editor. Assessment Approaches for Estimating Biological Integrity Using Fish Assemblages. Lewis Press: Boca Raton; 1998. pp. 79-83

[49] Bramblett RG, Fausch KD. Variable fish communities and the index of biotic integrity in a western Great Plains river. Transactions of the American Fisheries Society. 1991;**120**:752-769. DOI: 10.1577/ 1548-8659(1991)120<0752:VFCATI>2.3. CO;2

[50] Edegbene AO, Elakhame LA, Arimoro FO, Osimen EC, Odume ON. Development of macroinvertebrates multimetric index for ecological evaluation of a river in north Central Nigeria. Environmental Monitoring and Assessment. 2019;**191**:274. DOI: 10.1007/ s10661-019-7438-8

[51] Edegbene AO, Arimoro FO, Odume ON. Developing and applying a macroinvertebrate based multimetric index for urban rivers in the Niger Delta, Nigeria. Ecology and Evolution. 2019;**9**: 12869-12885. DOI: 10.1002/ece3.5769

[52] Edegbene AO, Akamagwuna FC, Odume ON, Arimoro FO, Ovie TTE, Akumabor EC, et al. A macroinvertebrate-based multimetric index for assessing ecological condition *Water Quality and Aquatic Ecosystem Assessment Using Water Quality Indices in West Africa… DOI: http://dx.doi.org/10.5772/intechopen.109137*

of forested stream sites draining Nigerian urbanizing landscapes. Sustainability. 2022;**14**:11289. DOI: 10.3390/su141811289

[53] Mack J. Developing a wetland IBI with statewide application after multiple testing iterations. Ecological Indicators. 2007;**7**:864-881. DOI: 10.1016/j. ecolind.2006.11.002

[54] Karr JR, Yoder CO. Biological assessment and criteria improve total maximum daily load decision making. Journal of Environmental Engineering. 2004;**130**:594-604. DOI: 10.1061/ (ASCE)0733-9372(2004)130:6(594)

[55] Dolph CL, Sheshukov AY, Chizinski CJ, Vondracek B, Wilson B. The index of biological integrity and the bootstrap: Can random sampling error affect stream impairment decisions? Ecological Indicators. 2010;**10**:527-537. DOI: 10.1016/j.ecolind.2009.10.001

[56] Schindler DW. Experimental perturbations of whole lakes as tests of hypotheses concerning ecosystem structure and function. Oikos. 1990;**57**: 25-41. DOI: 10.2307/3565733

[57] Barbour MT, Gerritsen J, Snyder BD, Stribling JB. Rapid Bioassessment Protocols for Use in Streams and Wadeable Rivers: Periphyton, Benthic Macroinvertebrates and Fish. 2nd ed. Washington, DC: Report EPA 841-B-99-002, US Environmental Protection Agency, Office of Water; 1999. p. 337

[58] Weisberg SB, Ranasinghe JA, Dauer DM, Schaffner LC, Diaz RJ, Frithsen JB. An estuarine benthic index of biotic integrity (B-IBI) for Chesapeake Bay. Estuaries. 1997;**20**(1): 149-158. DOI: 10.2307/1352728

[59] Engle VD, Summers JK, Gaston GR. A benthic index of environmental

condition of Gulf of Mexico estuaries. Estuaries. 1994;**17**:372-384. DOI: 10.2307/1352670

[60] Paul JF, Scott KJ, Campbell DE, Gentile JH, Strobel CS, Valente RM, et al. Developing and applying a benthic index of estuarine condition for the Virginian biogeographic province. Ecological Indicators. 2001;**1**:83-99. DOI: 10.1016/ S1470-160X(01)00010-3

[61] Stoddard JL, Herlihy AT, Peck DV, Hughes RM, Whittier TR, Tarquinio E. A process for creating multimetric indices for large-scale aquatic surveys. Journal of the North American Benthological Society. 2008;**27**(4):878-891. DOI: 10.1899/08-053.1

[62] Ganasan V, Hughes RM. Application of an index of biological integrity (IBI) to fish assemblages of the rivers Khan and Kshipra (Madhya Pradesh), India. Freshwater Biology. 1998;**40**:367-383. DOI: 10.1046/j.1365-2427.1998.00347.x

[63] Blocksom KA, Kurtenbach JP, Klemm DJ, Fulk FA, Cormier SM. Development and evaluation of the Lake macroinvertebrate integrity Inde (LMII) for New Jersey lakes and reservoirs. Environment Monitoring and Assessment. 2002;**77**:311-333. DOI: 10.1023/A:1016096925401

[64] Weisberg SB, Thompson B, Ranasinghe JA, Montagne DE, Cadien DB, Dauer DM, et al. The level of agreement among experts applying best professional judgment to assess the condition of benthic in faunal communities. Ecology Indicators. 2008; **8**:389-394. DOI: 10.1016/j.ecolind. 2007.04.001

[65] Clarke RT, Wright JF, Furse MT. RIVPACS models for predicting the expected macroinvertebrate fauna and assessing the ecological quality of rivers. Ecological Modelling. 2003;**160**(3): 219-233. DOI: 10.1016/S0304-3800(02) 00255-7

[66] Davy-Bowker J, Clarke RT, Johnson RK, Kokes J, Murphy JF, Zahradkova S. A comparison of the European Water Framework Directive physical typology and RIVPACS-type models as alternative methods of establishing reference conditions for benthic macroinvertebrates. Hydrobiologia. 2006;**566**:91-105. DOI: 10.1007/s10750-006-0068-5

[67] Horton RK. An index number system for rating water quality. Journal of Water Pollution Control Federation. 1965;**37**(3):300-306. DOI: 10.4236/ jep.2013.48099

[68] Tampo L. Hydrochimie et hydrobiologie du bassin du Zio (Togo). Editions Universitaires Européenne. 2018. p. 268

[69] Prati L, Pavanello R, Pesarin F. Assessment of surface water quality by a single index of pollution. Water Research. 1971;**5**:741-751. DOI: 10.1016/ 0043-1354(71)90097-2

[70] Nguyen HH, Everaert G, Gabriels W, Hoang TH, Goethals PLM. A multimetric macroinvertebrate index for assessing the water quality of the Cau river basin in Vietnam. Limnologica. 2014;**45**:16-23. DOI: 10.1016/j.limno. 2013.10.001

[71] Adelagun ROA, Etim EE, Godwin OE. Application of water quality index for the assessment of water from different sources in Nigeria. In: Moujdin IA, Summers JK, editors. Promising Techniques for Wastewater Treatment and Water Quality Assessment. London, UK: IntechOpen; 2021. pp. 1-19. DOI: 10.5772/ intechopen.87732

[72] Iwar RT, Utsev JT, Hassan M. Assessment of heavy metal and physicochemical pollution loadings of river Benue water at Makurdi using water quality index (WQI) and multivariate statistics. Applied Water Science. 2021; **11**:124. DOI: 10.1007/s13201-021- 01456-8

[73] Canadian Council of Ministers of Environment (CCME). Water Quality Index User's Manual. Canadian Water Quality Guidelines for the Protection of Aquatic Life; 2001. pp. 1-5

[74] Etim EE, Odoh R, Itodo AU, Umoh SD, Lawal U. Water quality index for the assessment of water quality from different sources in the Niger Delta region of Nigeria. Frontiers in Science. 2013;**3**:89-95. DOI: 10.5923/j. fs.20130303.02

[75] Darko HF, Ansa-Asare O, Paintsil A. A number description of Ghanaian water quality—A case study of the southwestern and coastal rivers systems of Ghana. Journal of Environmental Protection. 2013;**4**:1318-1327. DOI: 10.4236/jep.2013.411153

[76] Miyittah MK, Tulashie SK, Tsyawo FW, Sarfo JK, Darko AA. Assessment of surface water quality status of the Aby Lagoon System in the Western region of Ghana. Heliyon. 2020; **6**:e04466. DOI: 10.1016/j.heliyon.2020. e04466

[77] Banoeng-Yakubo B, Yidana SM, Nti ET, Akabzaa Asiedu D. Analysis of groundwater quality using water quality index and conventional graphical methods: The Volta region, Ghana. Environ Earth Science. 2009;**59**:867. DOI: 10.1007/s12665-009-0082-9

[78] Boateng TW, Opoku F, Osafo Acquaah S, Akoto O. Groundwater quality assessment using statistical

*Water Quality and Aquatic Ecosystem Assessment Using Water Quality Indices in West Africa… DOI: http://dx.doi.org/10.5772/intechopen.109137*

approach and water quality index in Ejisu-Juaben municipality, Ghana. Environment and Earth Science. 2016;**75**: 489. DOI: 10.1007/s12665-015-5105-0

[79] Napo G, Akpataku KV, Alfa-Sika Mande SL, Gnazou MDT, Bawa LM, Djaneye – Boundjou G. Assessment of shallow groundwater quality using water quality index and human risk assessment in the Vogan-Attitogon plateau, Southeastern (Togo). Journal of Environment Pollution and Human Health. 2021;**9**(2):50-63. DOI: 10.12691/ jephh-9-2-4

## **Chapter 5**


*4.1. High Occurrence of Children with Difficulties with Speech Fluency*

The high occurrence of difficulties with speech fluency in children with Down syndrome compared to typically developing children at the same non-verbal mental age level was expected based on inferences drawn from existing research. No previous studies have exactly investigated the occurrence of difficulties with speech fluency in children with Down syndrome compared to typically developing children at the same non-verbal mental age level. However, our results pattern align with the results from a survey study that used a group comparison design to investigate the occurrence of fluency disorders. Schieve et al. [38] included a sample of 27 individuals with Down syndrome and 1393 typically developing individuals and found occurrences of 15.6% and 1.5%, respectively. The results also align with results from an audio sample study comparing separate estimates of the occurrence in individuals with Down syndrome to estimates in previous research on the occurrence in typically developing individuals [27]. They found an occurrence of 31% in children with Down syndrome, which stands in stark contrast to the commonly cited values of a 1% prevalence of stuttering in typically developing individuals [35] and a lifespan incidence of more than 5% [71].

In addition to aspects of speech and language skills, various other developmental aspects of physical abilities and psychological state have been suggested to explain unique variance in the development of stuttering in otherwise typically developing children [2,72]. Nevertheless, such multifactorial models were not developed to explain difficulties with

speech fluency in general, and they were not designed for unique populations such as children with Down syndrome. It is apparent from prior research that children with Down syndrome have challenges with all of these aspects of development, including language [26,73], speech motor skills [16,30], and emotionality (e.g., [74,75]). The complex developmental profile found in children with Down syndrome, including a range of different challenges, may also make these children vulnerable to developing difficulties with speech fluency.


*3.2. Diversity, Taxonomic Composition, and Distribution of Coral Species*

The taxonomic study across the eight reef areas surveyed revealed a total of 113 scleractinian coral species, from 39 genera and 16 families, together with seven soft coral genera (Table 1). Among these, 36 species of hard corals appear to be new records for Sri Lanka (Table 2), including species of the genera *Oulastrea, Coeloeris,* and *Siderastrea*, although all have been recorded in other parts of the Central Indian Ocean. The full species list (Table 1) includes 39 members of the family Merulinidae (44% of the total), 28 species of Acroporidae (32%), and 13 species of Poritidae (15%). The predominant genera present were *Acropora* (17 spp.), *Dipsastraea* (10), *Porites* (7), *Favites* (6), *Montipora* (6), *Goniopora* (6), *Turbinaria* (5), and *Platygyra* (5) (Supplementary Table S2).

**Table 2.** Numbers of families, genera, and species of scleractinian corals recorded in each of the eight reef areas studied around the Jaffna Peninsula, Sri Lanka.

The numbers of genera and species recorded differed between the study sites (Table 2). On the northern coast, massive coral growth forms were predominant and the principal genera present were *Dipsastraea* (6 spp.), *Porites* (6), *Montipora* (6), *Favites* (5), *Platygyra* (4), *Goniastrea* (3), and *Symphyllia* (3). Encrusting growth forms of the genera *Symphyllia, Lobophyllia* and *Acanthastrea* were also more common on the northern coast. In contrast, around the island sites, tabulate and branching *Acropora*, foliose *Montipora*, massive *Porites,* and smaller *Goniastrea* predominated, with branching and tabulate *Acropora* and foliose growth forms occupying large areas. The Pungudutivu and Kayts island sites, in particular, were dominated by branching and tabulate *Acropora,* followed in substrate cover by *Porites*. The genus *Echinopora* was observed at Point Pedro, Thondaimanaru, Pungudutivu, and Kayts Islands. Only a single colony of *Pocillopora verrucosa* was documented, being at Point Pedro, while scattered *Pocillopora damicornis* were recorded on the Inabarsitty and Thondaimanru reefs. The newly recorded species *Acropora gemmifera* was found at all the study sites, but *Acropora aspera*, *Acropora digitifera, Montipora flabellata,* and *Echinopora gemmacea* were uncommon, with only one colony recorded at any site.

Soft coral colonies representing the genera *Capnella, Cladiella, Clavalaria, Lobophyton, Subergorgia,* and *Sarcophyton* (Supplementary Table S1) were uncommon and made only a minor contribution (2–4%) to substrate cover, but they were found on the northern coast and on the islands of Karainagar and Delft.

A comparison of the number of species present at different sites showed that the northern coastal sites had higher richness and diversity than the island sites (Tables 3 and 4). Calculation of Shannon's diversity and evenness indices suggested that all the reefs, excluding Pungudutivu, had comparable diversity and evenness values (H' ≥ 2.5; E ≥ 0.9), with a low dominance index (D ≤ 0.1). The Point Pedro reef front had nearly 50 hard coral species, followed by Thondaimanaru with 44 hard coral species (Table 3), but the Shannon–Weiner diversity index at Thondaimanaru was higher (H' = 3.020) than that at Point Pedro (H' = 2.927) (Table 4).

The relationships between mean values of live coral cover and the different diversity indices (Shannon's Diversity Index—H', Simpson's Dominance Index—D, and Evenness index—E) were investigated separately for each of the two parts of the study area, i.e., the island sites (Karainagar, Pungudutivu, Kayts, and Delft) and the northern coastline (Point Pedro, Inbarsitty, Thondaimanaru, Valithoondal). The relationship between these indices on the northern coast and on the island sites was further analysed with a Pearson correlation. On the northern coast, there was a moderate positive relationship between diversity and live coral cover (Pearson correlation: r = 0.64, *p* > 0.05). In contrast, among the island sites, there was a strong negative correlation between both diversity index (Pearson correlation: r = −0.96, *p* < 0.05) and evenness (Pearson correlation: r = −0.98, *p* < 0.05) on the one hand and live coral cover on the other. Similarly, there was a negative correlation (Pearson correlation: r = −0.51, *p* > 0.05) between the dominance index and mean live coral cover on the northern coast, but a strong positive correlation (Pearson correlation: r = 0.96, *p* < 0.05) between these variables among the island sites (Supplementary Figure S2).

**Table 4.** The richness and values of diversity and evenness indices for hard corals at each of the study sites of the Jaffna Peninsula.


3.2.8. In Vitro Model of Human Intestinal Epithelium

Permeability and absorption rate of SRO (i.e., fatty acids), PAC and LYC were determined using an in vitro model of human intestinal epithelium based on Caco-2 cells. Briefly, Caco-2 cells are seeded on Transwell® polytetrafluoroethylene inserts (1 μm pore size) at an initial density of 1.5 × 105 cells/cm2 and allowed to mature and differentiate for 21 days. Indeed, thanks to the compartmentalized nature of the Transwell® system (apical (or lumen) and basolateral (or serosal) compartment), Caco-2 cells differentiate, acquiring morphological and functional feature typical of enterocytes, as the presence of microvilli, tight junctions and P-glycoprotein. Absorption experiment were performed between 21 and 28 days post seeding.


**Author details**

recommend patients sun protective clothing such as sunglasses, hats or long sleeve clothing. Patients should be avoided direct exposure to the sunlight between 9 AM - 3 PM and tanning beds [174]. There is great concern in regard to the total amount of sun exposure during infancy and early childhood [175]. The importance of sun protection in childhood should be empha‐

Self-examination of skin by informed patients in terms of suspicious nevi is an important

Skin self examination has the potential to significantly reduce melanoma mortality. One retrospective study concluded that skin self examination has the potential to reduce melanoma mortality by 63% [179]. One study found that 44% of diagnosed recurrent melanoma was initially detected by patients based on symptoms that raised suspicion of metastasis [180]. For this reason, patients shoul be educated about the skin self examination. Patient education in skin self examination includes information on the warning signs of melanoma. Also it includes directions on how to perform a thorough whole-body skin examination [176]. Patients at higher risk should carefully examine their own skin monthly and also be frequently examined by

Older individuals are both more likely to acquire and to die from cutaneous melanoma; thus, elderly people should be a primary target for secondary melanoma prevention. We must be careful for the early detection and patient routine screening. Also secondary melanoma prevention should be focused on targeted education to older men and their spouses for early

Following the diagnosis of cutaneous melanoma, all patients should be educated on the risks of developing a second primary melanoma. In addition, counselling on the common clinical characteristics of cutaneous melanomas and instruction on how to perform a skin self exami‐ nation should be provided. In the event of the development of new pigmented lesions or changes in preexisting pigmented lesions, patients should be advised to seek medical attention. In addition, appropriate lifelong follow-up surveillance is critical for the detection of thinner,

Most of the studies suggested that many cancer patients want to get detailed information about their disease, treatment options and prognosis of the disease. The most common complaint of these patients is not to told what is wrong with them, during the treatment. Cancer care professionals are beginning to recognize that patients' information needs and

With growing evidence that well-informed patients are more satisfied with their care and do better clinically. Efforts are needed to improve the information provision to melanoma patients. Exploration of the patients' personal information needs must lead to a more patienttailored approach of informing melanoma patients. A good opportunity would be the implementation of a survivorship care plan, which aims at providing a cancer survivor with a summary of their course of treatment, management of late effects, and strategies for health

detection and reduction of mortality in this extremely high-risk group [181].

contributory factor in the early diagnosis of melanoma [178].

dermatologists professionally [174].

more curable melanomas [182].

preferences [183].

promotion [184].

sized [176].

112 Highlights in Skin Cancer

Zekayi Kutlubay\* , Burhan Engin, Server Serdaroğlu and Yalçın Tüzün

\*Address all correspondence to<EMAIL_ADDRESS>
İstanbul University, Cerrahpaşa Medical Faculty, Department of Dermatology, İstanbul, Turkey