[MUSIC] [MUSIC] [MUSIC] [MUSIC] Hello and welcome to in which we will investigate water pollution in the Ébrié lagoon system. we will focus particularly on the affected regions surrounding the Abidjan agglomeration, which has, in recent years, been subject to unregulated and growing release of domestic and industrial waste. Over the course of this video, we will follow along with a case study and see how GIS can be used to map heavy metal contamination in a lagoon system. For this specific case study, we will be using results obtained from geochemical analyses conducted on sediments that were sampled in 2001 by Professor Afian and his Marine Geosciences team at C.U.R.A.T. Since problems were first identified with these waters in the 1970’s, their restoration has stood to be one of the largest challenges facing the Ivorian authorities. This video is structured into 4 different sections. In the first of which we will introduce the study context and explain the problem in more detail. Then, we will present the lagoon’s geographic situation, then outline the GIS methodology used to map pollution indices, and we will finish by reviewing the results obtained by the GIS analysis and explain how they could be incorporated into decision-making processes. [MUSIC] [MUSIC] This study was conducted to evaluate the pollution levels throughout the lagoon. We will explore the problematic by first introducing the the pollution problem in the lagoon, then the causes and threats, and finally, the advantages of using GIS for studying pollution. Pollution is a sign of the degradation and deterioration of the aquatic environment. This is an important issue in Abidjan, where the Ébrié lagoon system is threatened by multiple different types of pollution. Once called the “Pearl of the lagoon”, this vast water body borders the city of Abidjan, and for decades has been undergoing a slow degradation. What pollution sources are problematic in the water body? They can be classified into four different categories. Physical pollution, characterized by a modification of the water temperature and turbidity. Chemical pollutants – namely, nitrates, heavy metals and other micropollutants that are also continuously being released into the water. Organic pollution becomes problematic when there is an overconsumption of oxygen, and finally, the waters are also subject to microbiological pollution through bacteria and parasites. As the city continues to expand, it is becoming increasingly important that the lagoon’s pollution problems are dealt with. The most significant contributor to the pollution comes from domestic waste, of which [INAUDIBLE] is approximately 37,000 tonnes per year. Some measures have already been taken, but in order to restore the lagoon to its former glory, the situation necessitates concrete and immediate intervention in order to ensure that the damage done so far does not become irreversible. This photo of the lagoon taken from the M’pouto neighbourhood illustrates the extent of the garbage and the proliferation of aquatic vegetation and provides a striking indication of the state of the lagoon’s pollution levels. It is essential that this situation is reversed in order to restore this important water body that bathes the city of Abidjan. In this case study, we are focusing specifically on the chemical pollution in the Ébrié lagoon. This pollution has resulted from industrial waste and is laden with heavy metals such as cadmium, lead, mercury, magnesium, and copper; further, wastes from oil spills, untreated wastewaters, household waste, pesticides and agricultural pollutions sourced from eroding agricultural lands are also significant. This pollution threatens the integrity of the environment, and also affects biological diversity, health, the food chain, tourism and fishing, etc... It poses a very serious problem for the integrated management of water resources, and the minister in charge of water resource management and the environment has made resolving this situation a priority. GIS tools can be used to create a complete database containing information from chemical analyses and cartographic or satellite data. They can be used to perform analyses that combine these data and images in order to understand the extent of the pollution, to identify at-risk locations, to raise stakeholder awareness and to encourage citizens to adopt behaviours that will promote the restoration of the Ébrié lagoon system. [MUSIC] [MUSIC] [MUSIC] Let’s now look at the lagoon and the environment surrounding Abidjan. The area chosen for the study here cuts across the agglomeration from the west to the east. Over the last 60 years, the Ivory Coast has undergone significant urbanization, and nowhere has this been more apparent than in the country’s economic capital of Abidjan, which is located on the banks of the Ébrié lagoon. This rapid urbanization that concentrated the city’s population also drove the government to create development plans for the city that eventually led to the occupation of lands outside Branco national park. As a consequence, all borders of the lagoon are now subject to some degree of anthropogenic land use: In particular agricultural lands, pastures, shallow crops, [INAUDIBLE], urban development, road developments, factories and other built-up land uses. This study focused predominantly on the three bays in the lagoon network. In the east: Biétry Bay; in the centre: Koumassi Bay, and in the east: Abou Abou Bay. These three sites were chosen because they are particularly sensitive to the pressures induced by surrounding population and the various industrial activities that have developed along their coasts. Heavy industries such as refineries, soap factories and breweries are located along the shores of Biétry Bay. Smaller industries devoted to making PVC or wood products are located in the areas surrounding Koumassi Bay. Abou Abou has the least industrial activity, but is very densely populated. It is also surrounded by mangroves and palm plantations. [MUSIC] [MUSIC] [MUSIC] The methodology used in this study is comprised of three steps: Firstly, data acquisition. Second, the creation of pollution maps. Finally, decision support. We will elaborate on these three steps in the coming sections. In 2001, 0 to 2 centimeters of sediment samples were collected in the three bays near domestic and industrial waste outlets. So, in each Biétry, Koumassi, and the Abou Abou Bays. Sampling locations were identified using a GPS. At the Ivorian Antipollution Centre’s chemistry lab (CIAPOL) the samples were processed, and the concentrations of different heavy metals were evaluated using ICP mass spectrometry. Zinc, iron, copper, cadmium and manganese were measured using ICP-MS. hydrocarbon content was evaluated using spectrofluorimetry. Multiple additional steps were required to map the pollution indices using GIS. First, we used the WS 84 zone 30 north projection system to georeference a map of the Ébrié lagoon and the X, Y coordinates of the sampled points. This step is essential as it ensures that all data are accurately superpositioned on top of one another. Next we digitized the boundaries of the study area, that is the borders of the Ébrié lagoon in Abidjan. This is necessary to ensure that the interpolations don’t extend beyond the study area. Another important step was the exportation of the chemical analyses. By using the points’ X,Y coordinates and their associated heavy metal concentrations, values can be interpolated for the rest of the map. Why might we want to interpolate? In this case, data interpolation provides us with a good indication of the spatial tendency of the lagoon’s geochemical properties. Correspondingly, by taking into account the distribution and density of the sampled points, an inverse distance weighting interpolation based on a 3 nearest neighbours weighting scheme was deemed to be the interpolation method that was best suited to this analysis. Now why would we want to reclassify the results? This is done to define pollution classes that are related to the heavy metal concentrations recorded in the lagoon. Four classes were defined. Level 1 indicated a very low concentration, while levels 2, 3, and 4 corresponded to, respectively, low, high and very high heavy metal concentrations. The thresholds used to establish this classification corresponded to significant levels of natural variation observed within each measured parameter. The values as defined in this study don't correspond to any international norms. Instead, they characterize the purely local behaviour to quantify the variation across the study site. In QGIS, we can open the attribute tables that indicate the heavy metal concentrations, as we can see here the values of cadmium, copper and iron are given for each point. We will use this table to perform our inverse distance weighting interpolation. We select cadmium as the attribute used for interpolation. We also specify an appropriate cell size, assign a name to the interpolation file and run the function. We then redo the same exercise, again using the same tool, but this time with iron. It’s important to ensure that you have always specified the cell size that will be used for the interpolation – here we use 50 meters. And then we run the same interpolation for iron. We end up with our two maps. We can reset the map style so that the different concentrations are illustrated in greyscale or in color. Let’s set the iron map to be displayed in color. We’ll also display the cadmium map in color. As you may have noticed, we always inverse the color palettes so that high concentrations are shown in red. And now we have generated our interpolation maps from the attribute tables that we imported into our GIS program. The next step reclassifies the interpolated data. For this - for the reclassification - we will use the reclassification tool. But before we can do that, we have to import the file and convert it into a GRID file. Let’s take the cadmium file and convert it. But first, for the reclassification, we need another file – which we must name – a text file. And, now we have successfully converted the cadmium file. We will follow the same process to convert the iron file, and we assign it a name in order to convert it to a GRID file. This is a matrix file. After this, we have the two maps: one for iron and one for cadmium. We will now use this table, which is a reclassification table. Here you can see the iron and cadmium concentrations, as well as for the other heavy metal concentrations, and we have our 4 levels; levels 1, 2, and 3. In order to reclassify these values, as I said earlier, we will use a file that defines the decision rules. Correspondingly, we choose iron, and for the decision rules, we need a text file. We will create this text file. This file includes all possible values that exist in the table that I showed you earlier; meaning for iron, values of 0 to 1 are reclassified to level 1; values from 1 to 2 are reclassified to level 2; 2 to 2 are assigned level 3; and 3 to 4 are assigned level 4. Now we have these different levels that we are going to use to reclassify our map. We specify an appropriate name and save the folder: here fer (Iron in French). and we specify that we want the fer file to be used for the reclassification, so that we have reclassified the iron data, and we run the function. We have now finished the reclassification, which consisted of using different predefined intervals to assign a level of pollution to each class: level 1 being a very low level of pollution, and levels 2, 3, and 4 being, respectively, low, high and very high. And we can repeat the same exercise for cadmium. But first, let’s open the map that we just created and display it in color with the table pseudo [INAUDIBLE], and as always, we will inverse the palette so that the highest level of pollution is shown in red. We must also ensure that the color palette is classified using equal intervals. We divide these into the 4 classes that correspond to the pollutant concentrations levels And now our new map has been built. We apply the new changes so that the map shows the reclassified iron values 1, 2, 3 et 4. Now we can do the same for cadmium. So we choose cadmium, and assign the new file a name for the reclassification. For cadmium we have the values that are given and those that we want to use for the reclassification. Correspondingly, our intervals will be 0 to 100, from 100 to 170, and so on… We continue filling in the file so that the threshold values correctly correspond to our desired levels. Again, these are: level 1, level 2, level 3, and level 4, and they correspond to the different thresholds established for the cadmium reclassification. These are used to classify different heavy metals that we analyzed at both the basin level and at the Abidjan regional level. And now, we have reclassified cadmium. We can display the reclassification in grey, as it is now, or we can display it with a specified color palette- with a pseudo-color. We are going to use this color palette. We will use equal intervals, with 4 classes. Don’t forget to inverse the palette so that the highest class is displayed in red. Now we can click OK and we have finished the cadmium reclassification. As you can see, the interpolation extends beyond our zone of interest that is beyond the lagoon borders. We want to fix this by clipping the interpolation results to the banks of the Ébrié lagoon. We use the reclassified images to do this, but the images must first be converted to image files. To do this we save these new files, which were converted to GRID images, which will enable us to then clip them to our desired spatial extent. So now we specify the format that we want to save them in, here that can be either TIFF or GeoTiff, and we save them. After we have finished this for cadmium, we must repeat the same process for the iron GRID. [NO_AUDIO] We save the iron file as a TIFF in an appropriate storage location. We will also save these data that we just created and add the maps - that is the cadmium and iron measurements converted into image files - to the QGIS project. We can now see how the different concentrations are distributed by clicking on the iron and then on the cadmium layer. We can now clip either the iron or the cadmium maps. We'll use Cadmium as an example. we give it a name and storage location, and use the digitized layer of the study area as the mask layer so that we only have pollution concentrations within the lagoon. We will redo the same with the other element. Now we have clipped both image files. This procedure would have to be replicated for all of the contaminants that were analyzed. So this process that we followed for the cadmium and iron concentrations would have to be repeated for magnesium, zinc, copper and hydrocarbon concentrations. And we have our study area and the different concentrations that vary depending on the element. We looked at two examples, iron and cadmium in order to illustrate how their concentrations are differently distributed. [MUSIC] The objective of this process is to help support the decision-making process. In our case, this means dividing the lagoon into zones of high, moderate or low contamination. In order to do this we qualify areas as being heavily polluted if they exhibit high concentrations of the analyzed heavy metals. If there are many areas with heavy metal contamination, areas that register as having lower levels of heavy metals will be classified as being less polluted. Correspondingly, the polluted zones are categorized through a combination of the raster images; this could be done through adding the different maps together to create indices to represent the level of heavy metal and hydrocarbon contamination. This is how we come up a summary map that is used for decision making. The spatial distribution of iron and copper concentrations highlight the following points of concern in the Biétry Bay where we have a very high concentration of both copper and iron. In Koumassi Bay there is a very high concentration of iron and a moderate copper concentration. In Abou Abou Bay, copper and iron are both present in very low concentrations. But what do we see if we look at the spatial distribution of cadmium and manganese? Indeed, both elements are present in high concentrations in Biétry Bay. Whereas in Koumassi Bay, Manganese has a low concentration and cadmium is highly concentrated. and in Abou Abou Bay, Manganese is present in strong concentrations and there is a low concentration of cadmium. Looking at the hydrocarbons that were measured in the sediment, they are highly concentrated in Biétry Bay, while they are present in low to very low concentrations in Koumassi and Abou Abou Bays. The map that was generated by adding all of the pollution indices together clearly shows that Biétry and Koumassi Bays have high levels of heavy metal contamination. This can be explained by the presence of heavy industries and comparatively dense urbanization that is observed in these zones. Matters are exasperated by the low level of sanitation equipment. Abou Abou Bay is comparatively less polluted. This can be largely attributed to the lower concentration of anthropogenic activities. The different levels of pollution that are observable throughout the lagoon highlight the impact that industrial activities have had on Abidjan’s water quality. As we can see from this example, GIS tools can help to identify the risks that riverain populations are exposed to. The maps that were obtained at the end of this study could be used to help guide the Ivorian authorities to establish effective protection measures. Reducing pollution, implementing a pollute-and-pay scheme for those that pollute the environment or the water could be enforced through legislatorial channels. Other measures such as ecosystem restoration would directly target the ecosystem. Before we can begin calling the Ébrié system the Pearl of lagoons again, action needs to be taken and the tendency towards polluting needs to be reversed. Finally, in what concerns the government-led project that is working to remediate the Cocody Bay, a new attitude needs to be adopted that advocates for more respect for the environment. In order to achieve this, awareness of the issue needs to be heightened. [MUSIC] So, what can we take away from this lesson? We have seen that we can use data for chemical analyses collected in the field and analyse their heavy metal concentrations – here we considered: cadmium, copper, iron, manganese, zinc and hydrocarbons. Through this study we have also seen that these values can be combined in order to identify areas that are particularly vulnerable to pollution. As we saw here, Biétry Bay was heavily polluted, while Abou Abou Bay was comparatively less polluted. There is a strong correlation between certain elements – notably iron, zinc and copper. This example of GIS for mapping pollution indices could equally be applied to other continuous variables. And that is it for this lesson, goodbye for now and I will see you in the next lesson. [MUSIC] [MUSIC]
