<table><caption>Table 5 Stakeholders contacted during the thesis. Some actors have been participating to the development of the thesis during all the three years.</caption><thead><tr><th>Actor</th><th>Actor Type</th><th>Category</th></tr></thead><tbody><tr><td>Artisanal and Small-scale mining operators</td><td></td><td></td></tr><tr><td>Medium-scale mining operators</td><td>Legal miner</td><td>Mining Operators</td></tr><tr><td>Large-scale mining operators</td><td></td><td></td></tr><tr><td>National Forestry Office (ONF)</td><td></td><td></td></tr><tr><td>Regional Health Agency (ARS)</td><td></td><td></td></tr><tr><td>French Geological Survey (BRGM)</td><td></td><td></td></tr><tr><td>French Guiana Water Office (OEG)</td><td>Public Institution</td><td></td></tr><tr><td>Research for Development Institute (IRD)</td><td></td><td></td></tr><tr><td>National Institute for Scientific Research (CNRS)</td><td></td><td></td></tr><tr><td>Amazonian Park of Guianas (PAG)</td><td></td><td></td></tr><tr><td>Urbanism and Development Agency of French Guiana (AUDeG)</td><td>Association</td><td>Public administration</td></tr><tr><td>Territorial Collectivity of French Guiana (CTG)</td><td></td><td></td></tr><tr><td>Technical and Mining Pole of French Guiana (PTMG-CTG)</td><td>Decentralized Governmental Services</td><td></td></tr><tr><td>Prefecture</td><td></td><td></td></tr><tr><td>Directorate for Environment, Development and Housing (DEAL)</td><td></td><td></td></tr><tr><td>Defence and Armed Forces</td><td></td><td></td></tr><tr><td>French Ministry for the Ecological and Inclusive Transition</td><td>Centralized Governmental Services</td><td></td></tr><tr><td>Or de Question</td><td>Association</td><td></td></tr><tr><td>Guyane Nature Environnement</td><td></td><td></td></tr><tr><td>World Wildlife Fund (WWF) France</td><td>NGO</td><td>Civil Society</td></tr><tr><td>Private Engineering consultants</td><td>Private Engineering consultants</td><td></td></tr><tr><td>Citizens participating to the Montagne d'Or Public Debate</td><td>Citizens</td><td></td></tr></tbody></table>

## 3.2. Temporal dimensions considered in this study

In this thesis, the defined temporal boundaries chosen for the assessment concern:

- the mining system and its phasing (Chapter 1): the risk assessment is applied to the mining “operational phase” (from the excavation of the ore to the recovery and waste management), since it is considered as the most critical in terms of impacts (Fig. 6)

- the temporal dimension at the territory level: the assessment is performed through the development of territorial mining scenarios (TMS) based on the information corresponding to the actual current state of FG and its gold mining sector (2017-2020).
## Conclusion

Because of its characteristics, FG seems an appropriate region where to test our methodology. This territory shows important challenges (e.g. demographic growth, human and technological development) that must be addressed in a region shaped by gold mining and with high socio-ecological vulnerability, due to its localization in the Amazon and its history. This study focuses on the river basin scale of Mana, as smaller spatial scale. Available and structured data for our purposes are limited, as it will be shown in Chapter 7 and 8 and Appendix 5. Finally, the different stakeholders considered in this study are presented along with the temporal dimension of the application, which focuses on the mining operation phase.
# Chapter 4. The mine system: overview and classification of gold mining in FG

This chapter presents and describes the specificities of gold and its exploitation across the world and then, specifically in FG. Gold mining systems in FG are detailed, described and classified in order to obtain standardized gold mine-types for the development of territorial mining scenarios (TMS) that will be compared based on their level of risk. All the georeferenced and non-spatial data used to describe both to the mining and territory systems and their sources are presented in Appendix 5.

## 1. Overview of gold mining across the world

Before describing the main features of FG, it is important to have a brief focus on gold mining across the world. Indeed, rather than a simple mineral resource like other commodities, gold is considered as a “foreign exchange reserve asset” with unique features. The exploitation of gold involves a great range of techniques and it has been the historical driver for colonizations and migrations for the pursuit of fortunes.

### 1.1. Gold demand: uses and applications of gold commodities

The exploitation of any natural resource involves a preliminary questioning of its concrete needs and, thus, the understanding of its uses and applications. Therefore, before an overview of gold mining functioning, the main uses of gold resources are briefly presented: in order to apprehend the offer through mining, it is critical to be aware of the existing demand and needs. The World Global Council (www.gold.org/goldhub) provides updated data concerning gold price trends, gold offer and supply and all the related information. As shown in Fig. 20, of a total demand of approximately 4,500 tons of gold in 2019, more than 90% are destined exclusively for jewelry (48%), investment (29%) and Central Banks (15%). Investment includes the production of physical gold bars (approximately 580 tons of gold per year), Exchange Traded Funds, medals and coins. The demand from National Central Banks is explained by the necessity of maintaining sizeable stocks of gold as part of their international reserves (Ghosh, 2016; Gopalakrishnan and Mohapatra, 2018). Finally, the rest of the total gold demand concerns the applications in the technological and industrial sectors with 262.6 tons in electronics, 13.9 in dentistry and 50 in other industrial sectors.

China and India account for more than 60% of the worldwide gold jewelry consumption, which is considerably driven by cultural factors and festivities (Schmidbauer and Rösch, 2018). However, if China assures an important internal supply, as first gold producer, India remains the biggest importer of gold (Mukherjee et al., 2017).
<figure><img src="image_22.png" /><figcaption>Figure 20 Main sectors contributing to the worldwide total gold demand in 2019 (www.gold.org/goldhub/data/demand-and-supply)</figcaption></figure>

## 1.2. The specificities of gold

It is vital to apprehend the main unique features of gold involving its exploitation and its environmental, economic and social dimensions, in order to have a preliminary view of the outcomes and concerns that gold mining might raise.

1. Gold is rather a financial speculative asset than a simple commodity. It is easily sold and not influenced by the government's instability (Hinton et al., 2003). Moreover, Because of its geological scarcity, gold has a higher market value-to-volume ratio than many other minerals, which implies that even small deposits can generate important profits (Verbrugge and Geenen, 2019). Therefore, the financial trends of world-wide gold demand have historically driven the development of new gold mines and the formal and informal rapid quests of fortune seekers to mine sites ("gold rush");

2. Gold exploitation methods can take widely varying forms and sizes (Verbrugge & Geenen, 2019). In particular, the geological accessibility of some deposits enhances the development of artisanal and small-scale mining communities (Hentschel et al., 2002), especially where gold deposits "are simply too small, scattered or remote for large-scale mining" (Verbrugge & Geenen, 2019).

3. Finally, as for other commodities, the increasing demand and the lower availability of concentrated ore, encourage miners to target low-grade deposits (Smith and Connell, 1979), generating much greater quantities of wastes. For instance, according to Lottermoser (2010), at modern gold mines, more than 99,999% of the originally mined and processed ore may finally become tailings. This must be accounted for in waste management and the related risks.
1.3. Brief overview of gold geology and mining

Worldwide gold supply accounted for approximately 4800 tons of gold in 2019, enough to cover the total demand. Around 30% of 2019 total supply are covered by recycled gold (WGC, 2020) (Fig. 21), almost entirely from jewellery. Nevertheless, currently more than 70% of gold are supplied through gold mining, including the informal and illegal sectors (Fig. 21). Informal and illegal mining can be defined respectively as “community-based mining activities that typically follows customary law” and “illegal invasive mining” occurring when “miners illegally enter the old mine workings of decommissioned mines” (Nhlengetwa, 2016).

The issues concerning gold mining have a world-wide reach and involve almost all the regions in the world. According to the S&P Global database (www.spglobal.com), around 6,000 mining projects are currently active in the world. They exploit gold as primary or secondary commodity.

<figure><img src="image_23.png" /><figcaption>Figure 21 Main sectors ensuring the worldwide total gold supply in 2019 (www.gold.org/goldhub/data/demand-and-supply)</figcaption></figure>

The geology of gold deposits and their genesis are widely discussed by scientific literature (Groves et al., 1998; Macdonald, 2007; Goldfarb et al., 2010). Gold deposit types are extremely diversified, affecting the mining techniques required for their exploitation and, hence, their risks. Detailed classifications of these deposits are mainly based on their geological settings (e.g. geometry, gold rate, geochemical behaviour, host-rocks, weathering, thermodynamic and tectonic conditions) (Robert et al., 1997).

Gold deposits are commonly and simplistically divided in primary and secondary gold deposits, based on the weathering degree and the type of accumulation of gold particles. Primary gold deposits can imply a great range of sub-types (e.g. mesothermal and orogenic, epithermal, sedimentary, intrusion-associated gold deposits, greenstone-hosted veins) (Goldfarb, 2005). Secondary gold deposits usually refer to detrital accumulations of gold particles after weathering and displacement from primary sources (placers, paleoplacers, alluvial and eluvial deposits).

Gold deposits are evenly distributed around the globe and exploited through different techniques (e.g. artisanal, industrial). However, countries production rates are highly
heterogeneous, depending not only on the gold reserves but also on public polices, technical assets and the socio-environmental factors: the availability of gold production does not rely only on the mere occurrence of a geological deposit. Therefore, the geological parameter is not the only one who needs to be accounted for in order to understand the trajectories of gold mining countries and their production rates.

Indeed, according to the WGC (consulted on 27/05/2020) more than a half of gold supply by mining is provided by ten main gold producer countries (Appendix 6), with China, Australia and Russian Federation at the top of the list. Australia and Russia have a considerably low population density (respectively 3 and 9 people per km²) which would reduce human exposure to negative mining impacts and land-use conflicts. Australia is one of the most important mining countries in the world with an important mining history which enabled the reduction of ore grade deposits, the increasing of open cast mines and the development of a regulatory framework for each inner State. Moreover, gold production has considerably increased since the choice of the government to allow the implementation of cyanide-based techniques (Mudd, 2007). If China density is relatively higher (145 people per km²), Chinese gold mining boom is due also to increasing foreign and domestic investments into mining and to 1996 Mining Law which supported a rapid expansion of demand and supply and a high diversification of mining ownership structures (Shen et al., 2009).

## 2. Gold mining in the Guiana Shield

The choice of FG gold mining sector for risk assessment at the territorial level needs a brief introduction to the wider geological and socio-ecological context in which FG is located in order to understand the specific types of gold deposits present in the region. FG gold deposits are hosted by two Greenstone Belts – a formation of metamorphized sedimentary and volcanic series – which are extended to a larger Precambrian geological area called the Guiana Shield. The Guiana Shield is located in the northern part of the Amazonian Craton (Delor et al., 2003a) and it includes Venezuela, Guyana, Suriname, FG and Brazil (Fig. 22). The Guiana Shield is considered as the “geological cousin” of the West African craton (Milesi and Picot, 1995) and its geological history and gold metallogeny are widely detailed by scientific literature (Dardenne and Schobbenhaus, 2003; Delor et al., 2003a; Daoust et al., 2011; Theveniaut et al., 2011). The main geological development of the Guiana Shield took place during the Transamazonian orogeny (2,26 to 1,95 Ga) (Milesi and Picot, 1995; Théveniaut et al., 2011) (Fig. 22) and it is mainly within these settings that most of primary gold mineralization were formed.
<figure><img src="image_24.png" /><figcaption>Figure 22 Simplified geological map of the Guiana Shield (from Daoust et al., 2011)</figcaption></figure>

Apart from geological affinities, the need for the comparison of FG with its neighboring countries is based on:

- The ecological and societal similarities, especially among the three Guianas (Guyana, Suriname and FG) where a cultural melting-pot with a common history inhabits a territory covered by the Amazon rainforest, in tropical and equatorial environments.

- The considerable development of the socio-technical phenomenon of informal and illegal artisanal and small-scale gold mining which moves without boundary restrictions in the three Guianas (Hook, 2019), largely contributing to informal migration flows from Suriname and Northern Brazil to FG;

- The transboundary dimension of the socio-ecosystems in these regions, with particular focus on the Maroni and Oyapock river water basins that separate FG respectively from Suriname and Brazil. For instance, most of the industrial gold mining activities in Suriname are located in the same basin shared with FG (Fig. 23).

Despite the common geological settings among these countries, mining projects take different shapes and involve various techniques (e.g. gold dredging performed in Suriname but not in FG) and mining regulations are extremely different from country to country. For instance, Suriname just recently ratified the Minamata Convention concerning the interdiction of mercury, including for gold mining purposes (www.mercuryconvention.org), while mercury has been banned in FG in 2006.
Fig. 23 shows the distribution of the main existing mining projects in the three Guianas. Data concern all the commodities exploited by mining activities (excluding quarrying). However, only for FG and Suriname, the distinction among primary and secondary gold mining was possible according to the available data. Despite such affinities, gold production is considerably divergent among these countries as shown by the diagrams in Fig. 24. Unlike gold production of Brazil, Suriname and Guyana shown in Fig. 24 (WGC, 2020), FG gold production in 2014 was estimated to be around 2 tons of gold per year and 1,3 tons of exported gold in 2017, for a total turnover of 48 millions of euros (Deloitte, 2018). The same figure normalizes gold production values with the surfaces (km²) of each country according to the World Bank data. As it is understandable, the ratio shows highest values for Suriname and Guyana while Brazil has the lowest rate. In comparison, according to its surface and gold production per year, FG, would present the same values as Brazil. However, the normalization cannot be limited to the surface and should integrate as well further parameters (e.g. the number of active gold mines per year, the surface or size of the geological deposits within country borders).

<figure><img src="image_25.png" /><figcaption>Figure 23 Modified satellite-derived map of the Guiana Shield and its main countries. The map shows most of the approximate gold mines located in the area in 2017, according to the governmental databases of Guyana (GGMC), Suriname (GMD), and FG (confidential collecting from CTG).</figcaption></figure>
<figure><img src="image_26.png" /><figcaption>Figure 24 Gold production in 2018 of all the countries belonging to the Guiana Shield, except FG, according to the WGC (www.gold.org/goldhub/data/historical-mine-production)</figcaption></figure>

## 3. Gold mining in French Guiana

The extraction of gold in this territory has been active only since 1855 (Moullet *et al.*, 2006; Oder, 2011). As described by Oder (2011), gold mining history in FG is composed of three periods:

1. From 1855 to 1930: the newly discovered deposits attracted more Creoles from the Caribbean and coastal areas to mining sites for personal enrichment.

2. From 1930 to 1990: due to the economic crisis of 1929, the depletion of gold deposits, the WW2 and the limited mining techniques, gold mining declines considerably. Furthermore, the 1946 departmentalization encouraged more people to choose safer and more stable jobs along the coast.

3. From 1990 to date: a second gold rush due to several phenomena:

- New and more effective gold mining techniques an equipment developed in Brazil

- The publication of the gold deposits survey in FG realized by the BRGM (1975 – 1990), an incentive for both legal and illegal gold miners.

- The rising of gold prices since the end of the Bretton Wood Agreements in 1968 (Cassard *et al.*, 2008).
However, this late period differs considerably from the first “gold rush”, since it takes place within a “sustainable-development” context where environmental protection and social enhancement are the main issues of the international political agendas, especially in FG (Oder, 2011). Therefore, gold mining activities have progressively integrated socio-environmental performances to address such issues.

This section presents a brief overview of gold mining in FG, detailing the main factors that might influence the occurrence and the intensity of specific risks. The section is directly provided by a fragment of Scammacca *et al.*, (2020).

According to the database provided by the Technical and Mining Service of the *Collectivité Territoriale Guyane (CTG)*, on December 31st 2017, around 131 valid gold mining permits and authorizations have been delivered in FG: 85 authorizations for mining exploitation (AEX), 29 mining concessions, 14 prospecting permits (PER) and 3 exploitation permits (PEX) (Appendix 4 and Table 6).

During these last decades, FG has been facing a real “gold mining paradox”: gold value increases, legal mining permits decrease, and illegal gold mining explodes (Oder, 2011). Indeed, illegal gold mining is one of the main issues related to the extractive sector in FG. It represents a way out from poverty, and it attracts hundreds of legal and thousands of illegal miners. At least 10 000 illegal gold miners (Moullet *et al.*, 2006) are active all over the territory in FG, including the PAG area. These miners, or “garimpeiros”, come mostly from the northern Brazilian State of Amapá, Suriname and Guyana where there is lack of opportunities.

However, if legal mining represents 1% of the GDP (IEDOM, 2016), illegal gold mining is the main sector of informal employment and of highest production rates, producing ten times more than the legal gold mining sector (WWF, 2018).

### 3.1. Gold mining regulatory framework in FG

In FG, mining permits are delivered according to two complementary frameworks. The Mining Code, which establish the size and the duration of the mining permit depending on the mining phase (Table 6), and the Departmental Mining Plan ("*Schéma Départemental d'Orientation Minière*" or SDOM), which states where mining is authorized or not and under which conditions it might be performed (Appendix 4). Finally, a number of European dispositions are applied in FG.

At least 10 000 illegal miners – or *garimpeiros*, when they come from Brazil – operate outside such frameworks and occupy 500 to 900 mine sites in FG (Moullet *et al.*, 2006; Tudesque *et al.*, 2012). Despite of multiple military operations, illegal mining produces ten times more than legal gold mines: the total illegal production of the last decade is estimated to be about 10 tons of gold per year, while legal production is around 1-2 (WWF, 2018).
<table><caption>Table 6 Synthetic scheme of the regulatory framework concerning mining permits and authorizations in FG (adapted from DEAL, 2015)</caption><thead><tr><th>Type of French description</th><th>ARM</th><th>AEX</th><th>PER</th><th>PEX</th><th>Concession</th></tr></thead><tbody><tr><th>Nature of</th><td>Autorisation de recherches minières</td><td>Autorisation d'exploitation minière</td><td>Permis exclusif de recherche</td><td>Permis d'exploitation</td><td>Concession</td></tr><tr><th>Max.</th><td>Exploration</td><td>Exploitation</td><td>Exploratio</td><td>Exploitation</td><td>Exploration</td></tr><tr><th>Max.</th><td>3 months</td><td>1 year</td><td>Free</td><td>Free</td><td>Free</td></tr><tr><th>Max.</th><td>4 months</td><td>4 years</td><td>5 years</td><td>5 years</td><td>50 years</td></tr><tr><th>Max.</th><td>1x4 months</td><td>1x4 years</td><td>2x5 years</td><td>2x5 years</td><td>∞ x25 years</td></tr><tr><th>Spatial scale of</th><td>Regional</td><td>Regional</td><td>National</td><td>National</td><td>National</td></tr></tbody></table>

## 3.2. Types of gold deposits in FG

The type of geological deposit determines the techniques employed for mining, and thus, the associated impacts. Two main categories of gold deposits are generally considered in FG (Thomassin et al., 2017). Primary gold is mainly found in quartz veins (type 1) or in oxidized (type 2) and residual (type 3) deposits depending on the weathering degree. On the contrary, secondary gold deposits are made of gold particles that have been subjected to an important displacement from the primary source along the lateritic profiles (type 4) or by the gravimetric concentration in streams (type 5) (Dardenne and Schobbenhaus, 2003) (Fig. 25). For instance, the phenomenon of acid mine drainage (AMD) is typical of primary gold deposits rich in sulfides and it occurs when operational and specific environmental conditions are met (e.g. oxidizing conditions, absence of carbonates, high temperature, acid pH, microbial activity) (Miramond et al., 2006; Simate and Ndlovu, 2014).

<figure><img src="image_27.png" /><figcaption>Figure 25 Conceptual image of gold mining deposits in French Guiana (adapted from BRGM, 2004)</figcaption></figure>
### 3.3. *Mining techniques for gold exploitation*

Depending on the geological deposit, gold exploitation methods can take a wide range of forms and sizes (Verbrugge and Geenen, 2019). Since primary gold exploitation requires a certain amount of logistics limited by the restrained accessibility within the rainforest, current gold exploitation in FG concerns predominantly secondary deposits (types 4 and 5) through artisanal and small-scale techniques (Moullet et al., 2006; Cassard et al., 2008; Matheus, 2018).

In FG, secondary gold mining is usually based on surface mining techniques (“placer mining”) documented by technical reports of the French Geological Survey (Laperche et al., 2008; Barras, 2010; Bourbon and Mathon, 2012; Cottard and Laperche, 2012; Aertgeerts et al., 2018). The site is prepared through excavators and mechanical diggers (Laperche et al., 2008). The exploitation of the deposit begins with the creation of a pond (called “barranque”) through the diversion of streams and the scouring of riverbanks and removal of sterile soils using hydraulic monitors to reach the secondary deposit at a depth of 2 to 8 meters (Fig. 26 and Fig. 27). Here, gold-enriched sediments from the mineralized clay layers of the “flat” (sedimentary volume of the alluvial plain) are left in suspension collected. The wastes from this first operation are released within the pond and checked again manually by panning. A second pond is opened by hydraulic monitors towards the upstream and the process is reiterated as long as it is allowed by the permit, in the case of legal mining projects. The fine-sized slurry (< 20 mm) – containing water, sediments, gold and other materials – is pumped or moved manually on terraced sluices with metal carpets or other settling tables (e.g. shaking tables, trommel machines) in order to trap gold particles by gravity. The abandoned exploitation pits are used as decantation ponds and they are interconnected through a communicating spillway system. Therefore, the mine site consists in a sequence of “barranques”, interconnected through channels which support the gradual decantation of the water from the newer ponds – where sediments settle – to the oldest – where water is decanted and re-pumped to the hydraulic monitors (Fig. 26 and Fig. 27). Once separated from the other materials through the settling tables, concentrates of gold-enriched “black sands” are transported to the laboratory area for recovery. A more accurate concentration of gold particles is then done either manually through the couy (i.e. pan) and traditional panning methods or more frequently through more sophisticated equipment such as the Knelson concentrator, shaking tables (e.g. Gemini or Goldfield types), wave tables or laboratory sluice boxes (Thomassin et al., 2017; Aertgeerts et al., 2018) (Fig. 28). Finally, they are transported to the smelter and then refined (Matheus, 2018). The revegetalization of these mine sites is addressed by a specific guide in FG (DEAL et al., 2016).

In the case of illegal gold mining of secondary deposits, the process is quite the same, except for the non-respect of any safety and environmental regulation. Moreover, the extension of the site is quite variable and, obviously, does not depend on any permit delimitation (Fig. 29). The exploitation process of illegal gold mining involves as well a first use
of mercury (Hg) which is either spread on the ground or on riffled sluice box (Hinton *et al.*, 2003).

Only few legal mining projects concern primary deposits (types 2 and 3), while type 1 primary gold still is mainly untouched (Massoud, 2008). Primary gold exploitation in FG is performed following two main techniques. The most current is open-cast mining, which implies the excavation of a pit (20-25 meters deep) and the transfer of waste materials on waste rock piles (Fig. 30). The ore is crushed, grinded and then pulped on sluices to separate gold particles by grain-size. A second grinding and centrifugal phases are performed to trap fine gold particles (Thomassin *et al.*, 2017; Matheus, 2018).

Recently, primary deposits, including type 1 deposits, have also started to be exploited through underground mining techniques by illegal miners (Bourbon, 2014; Bourbon, 2015). Miners reach the mineralization through the excavation of pits and/or vertical shafts whose depth varies between 10 and 40 meters (Fig. 31 and Fig. 32). The equipment is rudimentary, and the stability of the tunnels is assured by simple wooden planks on the pit walls. Nevertheless, illegal primary gold mining is one of the main responsible of the degradation of Amazon ecosystems.

<figure><img src="image_28.png" /><figcaption>Figure 26 Simplified flowsheet of secondary-gold mining (original edition after Oder, 2011)</figcaption></figure>
<figure><img src="image_29.png" /><figcaption>Figure 27 Legal secondary gold mine site in FG (photo supplied by ONF - Alexandre David)</figcaption></figure>

<figure><img src="image_30.png" /><figcaption>Figure 28 Settling-table for the first trapping of gold particles and the filtering of materials by grain-size. Location: western French Guiana</figcaption></figure>
<figure><img src="image_31.png" /><figcaption>Figure 29 Illegal secondary gold mine site in FG. As it is shown, the exploitation is much more irregular compared to legal mine sites (photo supplied by ONF - Alexandre David)</figcaption></figure>

<figure><img src="image_32.png" /><figcaption>Figure 30 Primary gold mining site in FG (photo supplied by ONF - Alexandre David)</figcaption></figure>
<figure><img src="image_33.png" /><figcaption>Figure 31 Tunnel entrance for illegal primary gold mining in FG (Bourbon, 2015)</figcaption></figure>

<figure><img src="image_34.png" /><figcaption>Figure 32 Illegal primary gold mine sites in FG. As it is shown, it is very difficult to acknowledge the notion of "mine site" when it comes to illegal mining. The site is a widely spread sequence of mining pits ((photo supplied by ONF - Alexandre David)</figcaption></figure>

3.4. Gold recovery methods

In FG, gold is recovered mainly through three techniques: gravimetric methods, cyanidation, and gold-mercury (Au-Hg) amalgamation (UNEP, 2012) (Table 7). Legal mining of primary and secondary deposits uses gravimetric methods: gold particles are mechanically separated from the matrix first, during the exploitation phase, to reduce the grain-size of recoverable ore, then, during the recovery phase, to concentrate the gold particles. Moderately sophisticated equipment (e.g. concentrators, laboratory sluice-box) are used in addition to traditional panning (Aertgeerts et al., 2018) (Fig. 33). When the wastes are badly managed, stream water turbidity is the main risk. The main risks associated with gravimetry concern mainly the increase of total suspended solids (TSS) in water streams, leading to silting. This occurs especially when water and tailings management at mine site is not properly performed.
<table><caption>Table 7 Simple and synthetic comparison of the three main gold recovery methods in FG</caption><thead><tr><th>Processing method</th><th>Gravimetry</th><th>Cyanidation</th><th>Mercury amalgamation</th></tr></thead><tbody><tr><td>Recovery rate (%)</td><td>20-40</td><td>~95</td><td>&gt; 95</td></tr><tr><td>Costs</td><td>$</td><td>$$$</td><td>$</td></tr><tr><td>Required skills</td><td>Medium skills</td><td>Very high skills</td><td>Medium skills</td></tr><tr><td>Biodegradability in the environment</td><td>-</td><td>Yes</td><td>No</td></tr><tr><td>Main risks</td><td>Silting and sedimentation<br>Land degradation<br>Erosion<br>Hg remobilization</td><td>Explosions<br>Chemical spills<br>Transport accidents<br>Health-related risks<br>Ecosystem degradation</td><td>Methylation<br>Hg emissions and pollutions<br>Health-related risks<br>Contamination of the food chain</td></tr></tbody></table>

The use of cyanide-based methods for gold recovery in FG is discussed in several reports (Moisan and Blanchard, 2012; GeoPlus Environnement, 2017; Menard, 2018; Urien, 2018). Nevertheless, they address cyanidation only at larger industrial and semi-industrial scales. Cyanidation is not used in FG at the moment, but one semi-industrial operator developed a plant and the infrastructures to perform it (Thomassin et al., 2017). The utilization of cyanidation allows high recovery rates (> 90%) and it implies specific risks that involve mainly cyanide transport (INERIS, 2018) and storage before gold recovery (e.g. explosions) or after processing (e.g. dam failures). In this case, failures might generate catastrophic consequences (WISE Uranium) and the dispersion of free cyanide ions would add severe eco-toxicological impacts (Donato et al., 2017).

The cyanidation process involves six main steps and it is furtherly detailed by GeoPlus Environnement, (2014); GeoPlus Environnement, (2015):

i) The preparation of the cyanide slurry adding sodium cyanide, solid lime and water to the excavated crushed materials and the tailings;

ii) The slurry is conveyed to a circuit where it passes through a series of leach tanks. Gold is adsorbed to an activated carbon from tank to tank and then washed of the slurry;

iii) The elution phase during which carbon is eluted to obtain a gold-enriched solution, then transferred for electrolysis. Resulting gold-enriched fluids are dried and mixed with specific substances for gold refining and ingot production.

iv) Activated carbon cleaning from inorganic pollutant;

v) The regeneration of activated carbon from organic matter contamination (e.g. grease, lubrication oils);

vi) Cyanide destruction at the exit of the last tank in the leaching circuit through a complex process. The detoxified residues are then thickened into a centrifuge to decrease their residual water content.
Since gold recovery rate by gravimetry is low and that cyanidation is not often technically and financially accessible, illegal miners use mercury (Hg) – and legal miners as well before Hg was banned by French Government in 2006. It allows a recovery up to 90% in some cases (Hinton et al., 2003). Gold particles are concentrated and amalgamated with Hg in a simple napkin to obtain a soft nugget. Since Hg melting point is lower than gold's, the amalgam is roasted using a retort or a simple spoon. Hg-related risks in FG are well discussed (Grimaldi et al., 2015) and they are at the origin of specific concerns about the health conditions of miners and local communities. Besides soils and water, air is the most impacted media from Hg-based techniques: 20% of Hg are lost during amalgamation while 70% evaporate during the roasting phase (Moullet, 2006). With an annual production of 10 tons of gold, approximately 13 tons of Hg are also supposed to be released into streams in FG (WWF, 2018). Contaminated hotspots have been localized in areas far from mining sites (Marchand et al., 2006) and the impacts on biodiversity are well documented (Fréry et al., 2001; Godard et al., 2006; Sebastiano et al., 2016; Gentès et al., 2019). The most important risks occur when Hg is transformed in methylmercury (MeHg) (Guedron et al., 2011a). Illegal or ancient legal gold mines are suitable locations for the formation of MeHg and its seasonal transfer across the watershed (Guedron et al., 2011b). MeHg contamination might affect the food chain and lead to severe endocrine disorders on local populations (Fréry et al., 2001) and on the environment. Human exposure to MeHg depends also on the receptors and their socio-cultural habits (Grandjean et al., 1999; Boudan et al., 2004; INVS, 2007; ARS, 2013).

<figure><img src="image_35.png" /><figcaption>Figure 33 One of the several machines used for gold recovery of secondary gold mines by gravimetry. Location: eastern French Guiana</figcaption></figure>
## 4. Gold mining in the Mana RB

According to the new GIS-based platform CAMINO (https://camino.beta.gouv.fr), an on-line mining registry developed by the French Government, in June 2020 approximately 110 permits and authorizations are active in FG, excluding all the expired permits and authorizations and 18 "historical concessions". Almost 20% of these 100 mining permits and authorizations concern the municipality of Mana alone which is the most important cluster of permits along with Roura municipality. Furthermore, if the permits and authorizations valid within the municipalities intersecting Mana RB are counted (e.g. Saul, Saint-Laurent-du-Maroni municipalities), this basin is one of the most important clusters of legal active mining permits and authorizations. These activities involve prospecting (Fig.34a) and exploitation (Fig.34b), including few historical concessions supposed to expire at the end of 2018. At the moment, most of the permits concern secondary gold mines while only one permit is related to a primary gold mine (Fig. 35). The remaining permits concern prospecting authorizations and permits and 9 historical concessions (Fig. 35). Finally, exchanges with local authorities pointed out the existence of approximately 2 000 illegal gold miners in Mana municipality. Indeed, besides to these legal mining activities, illegal gold mining is significant within Mana RB, as shown by the deforested areas in 2015 provided by WWF (Fig.34c). However, it is difficult to have a detailed estimation of the current number and surfaces of illegal activities, since spatial data are acquired through remote-sensing and, thus, leaving a considerable uncertainty concerning the actual land-use.

<figure><img src="image_36.png" /><figcaption>Figure 34 Map of legal mining activities within Mana RB according to the on-line mining registry consulted in June, the 15th 2020. Fig.34a and Fig.34b shows respectively current active permits and authorizations for prospecting and exploitation.
Fig.34c shows the estimated deforested areas by illegal gold mining</figcaption></figure>
<figure><img src="image_37.png" /><figcaption>Figure 35 Distribution of legal mining permits and authorizations located within Mana RB (processed from Camino, 2020)</figcaption></figure>

## 5. Classification of gold mines and development of standardized project-types

Mining risks vary considerably depending on the intrinsic features of the mine and the techniques used for exploration, ore exploitation and processing, as well as for remediation. Nevertheless, the great heterogeneity of, sometimes coexisting, mining projects is often difficult to be acknowledged during risk assessment, particularly for gold, which is exploited both by artisanal miners, medium and large companies.

Therefore, the definition of the gold mining activity in FG is achieved through the development of an operational classification of gold mines types for risk assessment purposes. The utilization of standard project types with standardized features is vital to develop and compare different TMS for decision-making support and perform risk-assessment on gold mining sector rather than a standalone gold mine.

The following sections present the reasons behind the development of a classification of gold mines types and highlight the present gaps and needs. The methodology used for such classification will be presented and each gold mine type defined for FG is detailed.

### 5.1. The need of a project-type classification for risk assessment purposes

As mentioned, risk assessment of mining projects at the territory scale implies both the holistic understanding of the territory where multiple different mines coexist and the need to foresight potential future scenarios that might come in support of decision-making. Therefore, the development of standardized gold mine types is a significant tool which would allow to consider mining projects types like “boxes” with common or different fixed parameters.

This need is more significant in FG, where gold mining takes multiple variable forms and where, at the moment, there is a lack of precise information concerning this variability. Apart from some technical reports and grey literature, no structured and systematic description of gold mines, their functioning and the induced risks, is available in scientific literature. If we
move to the existing classifications outside FG, to our knowledge no structured definition and classification of gold mines is available at the moment. Gold mine types are often defined individually, on a project-basis – and not categorized – and their detailed specificities neglected. If artisanal and small-scale gold mining are abundantly discussed (Hilson, 2002b; Seccatore et al., 2014) often without the explicit distinction between legal and illegal mines (Table 8), the definition of such activities varies from country to country (e.g. Brazil, Ethiopia, Ghana, Mexico, Suriname) (Hilson, 2002b). According to Ghanaian law, for example, small-scale gold mining is “performed by *any* method not involving substantial expenditure by an individual or group of persons not exceeding *nine* in number or by a cooperative society made up of *ten or more* persons” (Graham Sustainability Institute, 2015). However, the legal or illegal artisanal and small-scale sector is defined on a country-basis and its unanimous definition represents quite a challenge nowadays (MCSA, 2019).

Illegal gold mining is widely discussed, especially for their socio-environmental consequences and their governance impasses. However, no explicit distinction between primary and secondary gold illegal mining is usually made (Table 8), although the type of deposit implies considerably different methods. Illegal gold mining is mostly described as a social phenomenon rather than a proper industrial sector composed of individually recognizable projects, where mining is performed outside any existing regulatory framework and without formal trained skills (Bansah et al., 2018; Zvarivadza, 2018b). Such activities rely mostly on the global market variations of gold price, the abundant and mobile workforce, a free circulation of products for gold recovery and finally the laundering networks which bind the informal and formal sectors of gold production (Heemskerk, 2010).

On the opposite, gold large-scale mining activities are usually considered as any project involving large-capital multinationals and corporations. Finally, medium-scale mining is rarely discussed (Ribeiro-Duthie and Castilhos, 2016). Such “semi-industrial” activities are specific to some territories, such FG, where small and medium enterprises target primary deposits in order to evolve towards more industrial operations.

The existing reports usually oppose large-scale to artisanal and small-scale mining, and/or legal to illegal mining (MCSA, 2019) (Table 8). However, they do not represent structured classifications that cover other types of projects, especially in the gold mining sector, where they are highly variable in size, forms and techniques. Moreover, distinctions between the project-types are based on few factors, such as the annual gold production, degree of mechanization, size of the company, etc. The only exhaustive classification found in the literature is proposed by a report of the Brazilian Geological Survey, which includes artisanal, small, medium and large-scale gold mines (Ribeiro-Duthie and Castilhos, 2016). However, such categories are not structured specifically for risk assessment purposes and gold productivity is used as the only distinguishing criterion (Table 8) between the project-types. This criterion stands only for a project-centered – rather than sector-centered – approach. Indeed, the gold production of one large mine can't be compared to the production of one artisanal mine. On
the contrary, the gold production of the large-scale mining sector could be compared to the
production of the entire artisanal sector.

Risk assessment at the territory level and the development of TMS must involve a first step
of structured classification that accounts for the range of various coexisting gold mines. This
proposed classification aims to fulfill the gaps of the described existing classifications of mining
projects.
<table><caption>Table 8 Examples of existing definitions of gold mines in literature</caption><thead><tr><th>Used Name</th><th>Definition</th><th>Reference</th></tr></thead><tbody><tr><td>ASM</td><td>Individuals, groups, families or cooperatives mining with minimal or no mechanization, often in the informal (illegal) sector of the market</td><td>Hentschel et al., 2002</td></tr><tr><td>ASM</td><td>Any single mining operation having an annual production of unprocessed materials of 50,000 metric tons or less at the entrance of the mine</td><td>UN, 1972</td></tr><tr><td>SSM</td><td>Any operation producing between 10,000 t/a and 100,000 t/a of ore</td><td>Vale, 2000</td></tr><tr><td></td><td>Mining operations both labour intensive and low-tech</td><td>Barry, 1997</td></tr><tr><td>ASM</td><td>Activities involving the exploitation of marginal ore deposits unprofitable to mine on a large-scale., using rudimentary tools (e.g. picks, shovels, wheelbarrows, panning dishes)</td><td>Zvarivadza, 2018</td></tr><tr><td>Small-scale mining</td><td>Any operation with intense labor activity located in remote and isolated sites using rudimentary techniques and low technological knowledge, low degree of mechanization, and low levels of environmental, health and safety awareness</td><td>Hilson, 2002b</td></tr><tr><td></td><td>Micro-miners who pan the riverbanks to produce 0,1 to 0,5 g of Au per day</td><td>Veiga, 2013</td></tr><tr><td>ASM</td><td>ASM is a subset of SSM (mining activity producing less than 100,000 t/d ROM* for profit) where operation does not follow the conventional ecological and engineering principles of mining and uses rudimentary or basic simple techniques</td><td>Seccatore et al., 2014</td></tr><tr><td>SSGM</td><td>Mining projects with rudimentary methods and limited mine planning, by a workforce that is not formally trained in mining engineering or geology and operates entirely or partly in the informal economy</td><td>UNDP, 2011</td></tr><tr><td>ASM</td><td>Mining operations with a production <100 000 oz/year</td><td>Ribeiro-Duthie and Castilhos, 2016</td></tr><tr><td>ASGM</td><td>Legal or illegal gold extractions conducted by individuals or groups with limited capital investment, manual labor, simple and basic machinery</td><td>Graham Sustainability Institute, 2015</td></tr><tr><td>MSM</td><td>Projects which may assure long or short-term operations. They might also associate with other MSM or be merged to or acquired by other major corporation groups. They produce >100 000 < 1 000 000 oz/year</td><td>Ribeiro-Duthie and Castilhos, 2016</td></tr><tr><td>LSM</td><td>Projects usually involving a company "with many employees which exploits the deposit until the ore is completely excavated"</td><td>Kricher, (1997)</td></tr><tr><td>LSM</td><td>Mining operations performed by multinationals companies well-known in the market and generally on the long term. They usually report corporate social responsibility goals and certifications. LSM operations require trainings, investments and expertise in environmental management. Their production is >1 000 000 oz/year</td><td>Ribeiro-Duthie and Castilhos, (2016)</td></tr><tr><td>LSM</td><td>Projects that can influence the development trajectory of nations, alter the social fabric of local communities and disrupt the environment on which livelihoods depend</td><td>Kemp et al., (2016)</td></tr><tr><td>LSM</td><td>Complex and highly demanding endeavors carried by multinationals which require large investments, systematic management structures, in-depth geological and economic analyses</td><td>Vergara, (2009)</td></tr></tbody></table>

<footnote>*Ton per day in the Run-of-Mine production</footnote>
## 5.2. Methodology used to classify gold mines types in FG

As shown in Fig. 36, the methodology used for the current classification of gold mine types in FG followed different steps:

i) In a first step, the foreground data concerning the technical and operational parameters of the existing and potential gold mines were collected based on a thorough bibliographic review (scientific literature, technical reports), on free interviews of local experts between December 2017 and November 2018 and on mine field visits. Further data have been gathered through the participation to the public debate on the *Montagne d'Or* project (CNDP, 2018) and the information found in several technical reports regarding industrial projects within the Guiana Shield (Allain et al., 2008; Theveniaut et al., 2011; SRK, 2016; SRK, 2017).

ii) Gold mines in FG were classified based on different criteria (e.g. geological, juridical, operational). For each typology, standard flowsheets of the mines have been developed to show the mine functioning. These conceptual models are based on the following questions: what is the function of a gold mine? What are the objectives and performances which lead to the achievement of this function? What are the components of a mining project that support such objectives? The conceptual models and their detailed features have been discussed with local experts for validation.

iii) For each project type, a series of standard qualitative and quantitative features (e.g. number of workers, tailings dam's height, pit depth, etc.) have been specified based on mine site visits, interviews and FG-related literature (Table 9).

iv) In a fourth final step, both positive and negative potential consequences of each project type were identified based on the same data sources presented above. The results of this last step are presented in Chapter 6.

<figure><img src="image_38.png" /><figcaption>Figure 36 Conceptual flowsheet of the methodology used for the classification of gold mine types in FG and their correspondent impacts</figcaption></figure>
5.3. Result of the classification: types of gold mines in FG

As it is shown in Fig. 37, four main gold mine types (i-ASM, ASM, MSM, LSM) have been distinguished according to 5 selected criteria intrinsic to the mining projects themselves (legal status, type of gold deposit, etc.). The five main criteria are chosen based on the fact that they can be generalized to the current gold mines in FG and on the technical and operational features they integrate (e.g. juridical, technical, environmental). For instance, the type of geological deposit is a useful common denominator which allows to categorize current gold mining activities in FG. It would not be the case if currently, gold mining was performed only on secondary gold deposits and a potential future primary gold exploitation was not considered at all by public authorities. On the contrary, project localization can't be a common criterion for the classification since it obviously changes on a project-basis. Therefore, the five criteria chosen are the following and they have different natures:

i) Geological: the type of geological deposit predominantly exploited;

ii) Legal: the possession or not of a mining permit or authorization;

iii) Operational: the techniques used for ore exploitation;

iv) Operational: the gold recovery methods;

v) Geographical: the surface occupied by the mine site.

Illegal artisanal and small-scale gold mining (i-ASM) in FG can involve underground or surface (placer) mining respectively for the exploitation of primary (subtype i-ASMp) or secondary (subtype i-ASMs) gold deposits. The main feature of i-ASM is the utilization of Hg-gold amalgam for gold recovery.

Legal mining is divided in three main categories: ASM, MSM and LSM. Legal artisanal and small-scale mining (ASM) involve placer mining activities with gravity-based recovery methods. Medium-scale mining (MSM) and large-scale mining (LSM) involve the predominant exploitation of primary deposits, but secondary gold is exploited as well depending on the morphology of the deposits within the site of the mining permit (Fig. 37). MSM is divided in two subtypes, depending if the project involves gravimetric (MSMg) or cyanide-based (MSMc) methods for gold recovery. Thus, the main distinction here between MSMg and LSM is in terms of surface footprint (Fig. 37) and, therefore, in workforce, infrastructures and mining engines needed.

The specific features of each type of gold mines in FG is presented in Table 9. These specificities are furtherly detailed in Appendix 7; they concern the representative characteristics for each gold mine type. All the detailed features can be grouped into:

- The targeted geological deposit and its features;

- Regulatory and socio-environmental constraints;

- Company information;
- Mine related infrastructures, related to: excavation and exploitation, mineral processing, waste-rocks and tailings management, waste and process-water management, embankment structures, additional infrastructures;

- Total mine production;

- Direct and indirect employment;

- Capital expenditures (CAPEX);

- Operational expenditures (OPEX);

- Taxation and royalties.

Despite a considerable effort, the unavailability of data still left some gaps to be filled in the future. However, the presented characteristics confirm that the horizontal variability between each typology ("inter-class" variability) is higher than the vertical variability between the characteristics of each project type ("intra-class" variability). Thus, these criteria suffice to clearly distinguish the types of gold mines in FG and validate the proposed clear-cut classification. The presented data represent the inputs of the mining systems that will be used to develop the different TMS and assess the corresponding global risk.

<figure><img src="image_39.png" /><figcaption>Figure 37 Conceptual tree showing the main criteria used for the classification of the main gold mine types in FG</figcaption></figure>
<table><caption>Table 9 Synthesis of the main specificities of each legal gold mine type in French Guiana. In grey, the classification criteria. The complete table is available in the Appendix 7</caption><thead><tr><th rowspan="2">Main characteristics of each mine-type</th><th rowspan="2">ASM</th><th colspan="2">MSM</th><th rowspan="2">LSM</th></tr><tr><th>MSMg</th><th>MSMc</th></tr></thead><tbody><tr><td>Predominantly exploited gold deposit<fnref n="1" /></td><td>Secondary (type 4-5)</td><td>Primary (type 2-3)</td><td>Primary (type 1-2-3)</td><td>Primary (type 1-2-3)</td></tr><tr><td>Mining permit or authorization</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Soil footprint of the mine site (ha)</td><td>&lt;100</td><td>&gt;100</td><td>&gt;500</td><td>&gt;500</td></tr><tr><td>Main exploitation technique</td><td>Placer mining ponds (barranques)</td><td>Pit</td><td>Pit</td><td>Pit</td></tr><tr><td>Gold recovery methods</td><td>Gravimetry</td><td>Gravimetry</td><td>CN-</td><td>CN- (+ gravimetry)</td></tr><tr><td>Total OPEX (k€)</td><td>&gt; 100</td><td>&gt; 1,000</td><td>&gt; 3,000</td><td>&gt; 1,000,000</td></tr><tr><td>Average revenues per year (k€/yr)</td><td>&lt; 1,000</td><td>&gt; 1,000</td><td>&gt; 1,000</td><td>&gt; 200,000</td></tr><tr><td>Maximal produced gold per year (kg/yr)</td><td>&lt; 150</td><td>150 - 300</td><td>&gt; 400</td><td>&gt; 5,000</td></tr><tr><td>Number of direct employees</td><td>10-15</td><td>&gt;50</td><td>50 - 350</td><td>&gt; 350</td></tr><tr><td>Gold resources (t)</td><td>&lt; 10</td><td>10</td><td>10</td><td>&gt; 50</td></tr><tr><td>Extracted materials (waste-rock and ore) (kt/yr)</td><td>5 - 35</td><td>&gt;150</td><td>&gt;150</td><td>5,000 - 25,000</td></tr><tr><td>Exploitation pit/pond depth (m)</td><td>2 - 8</td><td>&gt; 10 &lt; 100</td><td>&gt; 10 &lt; 100</td><td>&gt; 100</td></tr><tr><td>Processing plant type</td><td>Gravimetric</td><td>Gravimetric</td><td>Gravimetric + Cyanidation</td><td>Gravimetric + Cyanidation</td></tr><tr><td>Cyanidation plant surface (ha)</td><td>-<fnref n="2" /></td><td>-</td><td>&lt; 1</td><td>&gt; 10</td></tr><tr><td>Processed materials by cyanidation (t/day)</td><td>-</td><td>-</td><td>100</td><td>&gt; 5,000</td></tr><tr><td>Gold recovery rate (%)</td><td>20 - 30</td><td>20-40</td><td>&gt; 90</td><td>&gt; 90</td></tr><tr><td>Decantation and storage system (wastewater and tailings)</td><td>Barranque (TSF and waterpond)</td><td>Gravitary TSF, water pond</td><td>Gravitary TSF, Cyanide TSF, Decantation ponds</td><td>Cyanide TSF</td></tr><tr><td>Total surface of gravitary tailings pond (ha)</td><td>0.5 - 2</td><td>10 - 20</td><td>&gt; 20</td><td>n/a</td></tr><tr><td>Total capacity of gravitary TSF (m3)</td><td>&lt; 500,000</td><td>250,000 - 600,000</td><td>250,000 - 600,000</td><td>-</td></tr><tr><td>Total surface of cyanide tailings storage facility (TSF) pond (ha)</td><td>-</td><td>-</td><td>5 - 15</td><td>&gt; 150</td></tr><tr><td>Cyanide tailings per year (m3/yr)</td><td>-</td><td>-</td><td>&gt; 60,000</td><td>&gt; 10,000,000</td></tr><tr><td>Total capacity of cyanide TSF (m3)</td><td>-</td><td>-</td><td>250,000 - 600,000</td><td>&gt; 150,000,000</td></tr><tr><td>Maximal height of TSF dam (m)</td><td>2 - 3</td><td>15 - 20</td><td>15 - 20</td><td>&gt; 50</td></tr><tr><td>TSF dam elevation method</td><td>-</td><td>Upstream</td><td>Upstream</td><td>Downstream</td></tr><tr><td>Life-camp surface (ha)</td><td>&lt; 1</td><td>~ 1</td><td>&gt; 2</td><td>&gt; 10</td></tr><tr><td>Equipment (e.g. hydraulic monitors, bulldozer)</td><td>&lt; 10</td><td>10 - 50</td><td>&gt; 50</td><td>&gt; 100</td></tr><tr><td>Total electrical power</td><td>~ 40 KVA</td><td>&gt; 250 KVA</td><td>1178.4 (kW)</td><td>~ 20,000 (kW)</td></tr><tr><td>Fuel stockage capacity (l)</td><td>5,000 - 10,000</td><td>15,000 - 30,000</td><td>15,000 - 30,000</td><td>&gt; 100,000</td></tr></tbody></table>

### 5.3.1. Legal gold mine types: ASM, MSM and LSM

The classification of the defined gold mine types is illustrated through the development of functioning flowsheets which give a conceptual representation of the different functioning of each mine type and to be furtherly developed and detailed to be used as quantifiable model (for example, to perform LCA). In our study, the presented flowsheets have only the purpose to give a conceptual overview of the functioning of legal gold mines in FG, from the secondary

<footnote n="1">1. Refer to section 3.2 for the detailed definition of gold deposit types</footnote>

<footnote n="2">2. The empty cases (“-”) concerns the features “not applicable” to the corresponding project-type</footnote>
gold mining techniques in ASM (Fig. 38) to the exploitation of primary gold deposits with MSM (Fig. 39) and LSM (Fig. 40). The components of a mine are here grouped into three different categories whether they are related to:

a) ore excavation, exploitation and related waste management infrastructures (e.g. mining pit, waste rock dump);

b) mineral processing and related waste management infrastructures (e.g. processing plant, settling ponds);

c) mine basic infrastructures (e.g. life camp, electric equipment).

Fig. 39 and Fig. 40 are specifically related respectively to MSMg and MSMc. For LSM-types, the same flowsheet of a MSMc can be suggested, but with different parameters values, as described in Table 9 (e.g. height of the TSF, pit capacity, number of workers).

<figure><img src="image_40.png" /><figcaption>Figure 38 Conceptual flowsheet of ASM project-types in FG. The conceptual model includes different steps during the operational mining phase. Edited and designed by © Charlotte Travaillé.</figcaption></figure>

<figure><img src="image_41.png" /><figcaption>Figure 39 Conceptual flowsheet of MSMg project-types in FG. The conceptual model includes different steps during the operational mining phase. Edited and designed by © Charlotte Travaillé.</figcaption></figure>
<figure><img src="image_42.png" /><figcaption>Figure 40 Conceptual flowsheet of MSMc project-types in FG. The conceptual model includes different steps during the operational mining phase. Besides the utilization of cyanidation, the functioning differs from the MSMg presented in Fig. 39.</figcaption></figure>

Edited and designed by © Charlotte Travaillé.

### 5.3.2. A special mention for i-ASM and the complexity of a project-centered analysis

If the present study does not focus specifically on the assessment of illegal gold mines in FG, a special mention should be given to i-ASM types since the availability of data presented for legal gold mines (Table 9) is significantly different for illegal activities. Seccatore et al., (2014), try to estimate worldwide gold production of i-ASM, reporting some information from literature concerning the technical and operational parameters of i-ASM (Table 10). As mentioned, illegal gold mining should be considered as a social phenomenon – rather than a proper mining project – which is peculiar to gold and other few commodities (Hentschel et al., 2002). The main characteristics of both i-ASMp and i-ASMs in FG are related to:

- The non-respect of legal constraints. i-ASM project types lack of standards with important consequences for the safety, health and security of i-ASM miners and local communities and the environment (Hentschel et al., 2003; Smith et al., 2016).

- The highly variable number of miners on site. The French National Forestry Service (ONF) discovered a primary i-ASM life-camp with approximatively 1800 inhabitants. Personnel can be low or medium-skilled (Bansah et al., 2018; Zvarivadza, 2018), lacking specific trainings (Ledwaba and Mutemeri, 2017).

- A wide variety of mining methods ranging from simple extraction methods (e.g. digging and panning) to chemical treatments (recovery through gold-Hg amalgam) (Pokorny et al., 2019). Since most of the gold is not recovered through gravimetry, mercury (Hg) – banned by French Government since 2006 – allows a recovery rate up to 90% (Hinton et al., 2003). Gold flakes are concentrated thanks to gold density and then amalgamated with Hg in a simple napkin to obtain a soft nugget. Since Hg melting point is lower than gold's (Brooks et al., 2011), the amalgam is then roasted using a retort or a simple spoon. The typical value in the amalgamation process varies from 1 to 5 g of Hg per gram of gold (Kahhat et al., 2019). Recent gold tailings of ancient legal and illegal mine sites can be re-processed as well.
- I-ASM communities are supported by an important supply chain for Hg and fuel, electrical generators, steel rods (IEDOM, 2016; WWF, 2018).

- A wide variety of means of transport and access to the mine site. These may involve quads, off-road vehicles and canoes, allowing illegal miners to easily move across different areas of the forest through the hydrographic network (WWF, 2018).

<table><caption>Table 10 Summary of production and investment features that could define i-ASM (Seccatore et al., 2014)</caption><thead><tr><th>Source</th><th>ROM<sup>a</sup> (t/y)</th><th>Daily ROM (t/d)</th><th>CAPEX ($)</th><th>Revenues ($/y)</th><th>Human resources</th></tr></thead><tbody><tr><td>ONU</td><td>&lt; 50 x 10<sup>3</sup></td><td>&lt; 200</td><td>&lt; 1 x 10<sup>6</sup></td><td>&lt; 1.5 x 10<sup>6</sup></td><td>40</td></tr><tr><td>DNPM (Brazil)</td><td>10 x 10<sup>3</sup> &lt; ROM &lt; 100 x 10<sup>3</sup></td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Ley de Minería (Ecuador)</td><td></td><td>&lt; 300 (metallic ore)</td><td></td><td></td><td></td></tr><tr><td></td><td></td><td>&lt; 800 (nonmetallic ore)</td><td></td><td></td><td></td></tr></tbody></table>

The difficulty in defining these kinds of projects relies on the fact that they are extremely heterogeneous, and that data are usually not available. Annual production rates highly differ from country to country (Seccatore et al., 2014) and from site to site. The notion it-self of “site” is complex to apply to the illegal sector since it is often impossible to determine where one ends and the next one starts. Moreover, there is a critical permeability between legal projects and i-ASM in FG which might question the proposed classification and, generally speaking, local strategies for mining governance in FG. Since it is a social phenomenon, i-ASM heterogeneity relies on technical, operational and socio-environmental parameters. It may be more appropriate addressed them as “I-ASM communities” (Van Bockstael, 2019) which represent a particular challenge for the regional development of gold mining territories of the African continent, Central and South America.

For the mentioned reasons, it has not been possible to detail the specific features of i-ASM on a project-basis as for the legal mining sector in Table 9. The only acknowledgment on this issue is based on the assessed spatial data through satellite imagery and remote-sensing provided by WWF in 2015 which report a total of 1783 “sites”. According to these data, more than 98% of such “sites” perform secondary gold mining while the rest (27 sites) concern i-ASMp. In 2008, 3396 gold sites have been reported, and more than 75% represented illegal workings for which minimal information is gathered, generally limited to their location (Cassard et al., 2008).

Table 11 shows some specificities concerning the existing and current illegal gold mining sector in FG, without any distinction on a project basis. Indeed, as mentioned, the notion of mining *site* is questionable in the illegal sector, and thus, it is not possible to have the same level of specificities such as for legal mining projects (Table 9). The right column presents some assumptions on a project-basis analysis for the illegal sector based on the ratio between the

<footnote><sup>a</sup> Run-of-Mine means the raw unprocessed or uncrushed material in its natural state obtained after blasting or digging, from the mineralised zone of a lease area.</footnote>
real values estimated and presented by existing bibliographic and cartographic sources and number of reported sites in 2015 by WWF. Spatialized data are provided by WWF, estimated through remote-sensing processing (Fig.34c). The number of “sites” and the surfaces estimated have been used to estimate “standardized” per-project features for the illegal sector (Table 11, right column). However, unfortunately no distinction between primary and secondary gold illegal mining projects can be quantitatively expressed at this stage.

<table><caption>Table 11 Estimated current features for the entire illegal gold mining sector.</caption><thead><tr><th>Illegal gold mining sector (i-ASM) (i-ASMp + i-ASMs)</th><th>Total</th><th>Source</th><th>Illegal gold mine (no distinction between i-ASMp and i-ASMs)</th></tr></thead><tbody><tr><td>Soil footprint (ha per year)</td><td>1,000 – 2,000</td><td>Rahm, 2017</td><td>20.0</td></tr><tr><td>Maximal produced gold per yera (kg/year)</td><td>~ 9,071</td><td>WWF, 2018</td><td>6.6</td></tr><tr><td>Number of employees (direct and indirect)</td><td>~10,000</td><td></td><td>10.0</td></tr><tr><td>Hg consumption for the recovery of 1 g of gold (g/g of gold)</td><td>1-5</td><td>Kahhat et al., 2019</td><td>1-5</td></tr><tr><td>Water consumption (l/g of gold)</td><td>n/a</td><td></td><td>n/a</td></tr><tr><td>Total capacity of the water pond (m3)</td><td>n/a</td><td></td><td>n/a</td></tr><tr><td>Maximal height of dams</td><td>n/a</td><td></td><td>n/a</td></tr><tr><td>Total number of illegal gold mining sites estimated in 2015</td><td>1,379</td><td>WWF, 2015</td><td>n/a</td></tr></tbody></table>
## 6. Data used in this study for each gold mine-type

The brief fragment of the available data shown in Table 9 and 11 presents a wide range of values for each project-type. In the current study, the main specific project criteria and parameters that will be used are chosen among these ranges of values and shown in the Table 12. We present them here in a synthetized table in order to facilitate the understanding of the following chapters. These data are for the assessment of both the RSa (Chapter 7) and RSo (Chapter 8) scores. It is important to consider the potential uncertainty concerning data related to i-ASM since the notion of “mining site” is fuzzy, unlike in legal mining. This would imply the collection of further data related to the illegal gold mining sector in FG.

<table><caption>Table 12 Selected values for the parameters used in the current study based on Table 9 and 11 per project type</caption><thead><tr><th>Project types<br>Base Parameter</th><th>i-ASM<br>(i-ASMp + i-ASMs)</th><th>ASM</th><th>MSMg</th><th>MSMc</th><th>LSM</th></tr></thead><tbody><tr><td>Annual gold production (kg/year)</td><td>6.6<sup>a</sup></td><td>6.6<sup>a</sup></td><td>150</td><td>300</td><td>800</td><td>5000</td></tr><tr><td>Direct jobs</td><td>10<sup>b</sup></td><td>10<sup>b</sup></td><td>15</td><td>75</td><td>200</td><td>500</td></tr><tr><td>Soil footprint (ha)</td><td>20<sup>c</sup></td><td>20<sup>c</sup></td><td>100</td><td>300</td><td>300</td><td>550</td></tr><tr><td>TSF height (m)</td><td>n/a</td><td>4</td><td>4<sup>d</sup></td><td>15</td><td>20</td><td>54</td></tr><tr><td>TSF storage capacity (Mm3)</td><td>n/a</td><td>0.5</td><td>0.5<sup>d</sup></td><td>1.5</td><td>2</td><td>100</td></tr><tr><td>Total royalties (k€)<sup>e</sup></td><td>-</td><td>-</td><td>125,611</td><td>251,223</td><td>669,928</td><td>4,187,050</td></tr></tbody></table>

<footnote><sup>a</sup> Approximate ratio between 10 tons of gold produced per year by a total of 10 000 reported illegal miners (WWF, 2018)</footnote>

<footnote><sup>b</sup> Approximate ratio between 10 000 reported illegal miners in total and the 1379 mining sites they are distributed (I (WWF, 2015; WWF, 2018)</footnote>

<footnote><sup>c</sup> Approximate ratio between 1379 illegal “sites” reported and a total surface of 25386 ha consumed (WWF, 2015).</footnote>

<footnote><sup>d</sup> Value extrapolated from ASM and applied to i-ASMs.</footnote>

<footnote><sup>e</sup> Royalties are calculated based on the amount of annually produced gold per-project. They represent the sum of Municipal (137,9 €/kg Au), Departmental (27,5 €/kg Au) and Regional (672,01 €/kg Au) taxes (SRK, 2017).</footnote>
Conclusion

This chapter presented gold mining from the worldwide scale to the specificities of the example chosen for this study, where gold is the main extracted metal. Despite gold deposits are still underexploited, gold mining plays a critical role in the historical and current socio-ecological dynamics of FG. In some cases, human settlements have been developed on ancient mining sites, shaping the territory as it is nowadays. Despite gold is a speculative commodity, the presentation of FG gold mining sector allowed to acknowledge:

* The coexistence in FG of multiple coexisting gold mines of different types and techniques, distributed on the whole territory;

* Public concerns related to the development of gold mining sector in FG, discussed by mining operators, public administration and civil society. The public debate in 2018 over the industrial gold mine *Montagne d'Or* highlighted the panel of views and the potential conflicts around gold mining activities. More recently, the potential development of another large-scale project called *Esperance* has been presented by two important mining companies. Land-planning strategies need to be discussed in order to address socio-economic and environmental issues related to mining on a wider scale.

Based on these observations, gold mining in FG confirms to be a pertinent case-study to test our approach. Gold deposits, mining techniques and legislation in FG have been presented in order to develop a multi-criteria classification of gold mine-types in FG. In the next chapter, these gold mine-types, whose features are detailed, will be combined to the territory system for the development of territorial mining scenarios (TMS). The TMS are assessed and compared in Part III of this thesis.
# Chapter 5. Development of the Territorial Mining scenarios (TMS)

The previous chapters described the territory and the mining systems, showing the pertinence of our case-study and its features. The approach is applied at the river basin scale and a classification of gold mines in FG has been developed to standardized mine-types. In this chapter, the territory and mine systems – represented by the gold mine-types – are combined to develop territorial mining scenarios to support land-planning strategies at the territory level. Only for the demonstration of the approach in this thesis, the gold mine-types are located in fictional spots, instead of real existing mining projects.

## 1. Territorial objectives and Territorial Mining Scenarios (TMS)

Our study aims to foresight and compare potential future scenarios of mining development, based on risk assessment at a territorial level. The territorial mining scenarios (TMS) we will consider are defined to fulfill specific pre-defined objectives. For the current demonstrative application, three main objectives are selected at the scale of Mana river basin (Table 13). Each potential TMS may satisfy totally or partially one or more of these objectives. At this stage, the territorial objectives are arbitrary and have not been decided with local stakeholders. They concern:

i. A total gold mining production approximately of 5,000 kg per year;

ii. A total direct employment related to gold mining sector of 3,000 jobs;

iii. A total surface consumed that should not exceed 2,000 ha.

Hereafter, it is assumed that each scenario meets only one of the mentioned objectives. As shown in Table 13, a set of 11 TMS is proposed:

- 5 TMS for the first objective (annual gold production at 5 000 kg Au/year), one of which relies only on illegal mining;

- 3 TMS for the second objective (3000 direct jobs);

- 3TMS for the third objective (soil footprint of 2000 ha).
<table><caption>Table 13 Development of TMS for each objective. On the right part of the table, the number and types of gold mine are specified in order to meet each considered objective (left columns, in green). The remaining two objectives not underlined in green represents the corresponding values calculated based on the number and types of projects of the considered TMS and the parameters presented in Table 9 and 11. For this demonstrative application, only the TMS related to the 1st objective are furtherly assessed.</caption><thead><tr><th rowspan="2"></th><th colspan="3">Territory objectives</th><th colspan="7">Territorial Mining Scenario (TMS)</th></tr><tr><th>Annual gold production (kg/year)</th><th>Direct jobs</th><th>Soil footprint (ha)</th><th>Scenario Code</th><th>i-ASMp</th><th>i-ASMs</th><th>ASM</th><th>MSMg</th><th>MSMc</th><th>LSM</th></tr></thead><tbody><tr><td rowspan="5">Objective 1</td><td>5,000</td><td>500</td><td>550</td><td>TMS-A</td><td></td><td></td><td></td><td></td><td></td><td>1</td></tr><tr><td>5,000</td><td>1,250</td><td>1,875</td><td>TMS-B</td><td></td><td></td><td></td><td></td><td>6</td><td></td></tr><tr><td>5,000</td><td>500</td><td>3,333.3</td><td>TMS-C</td><td></td><td></td><td>33</td><td></td><td></td><td></td></tr><tr><td>5,000</td><td>800</td><td>3,500</td><td>TMS-D</td><td></td><td></td><td>20</td><td>4</td><td>1</td><td></td></tr><tr><td>5,000</td><td>-</td><td>-</td><td>TMS-illegal</td><td colspan="5">758</td><td></td></tr><tr><td rowspan="3">Objective 2 (example)</td><td>30,000</td><td>3,000</td><td>20,000</td><td>TMS-E</td><td></td><td></td><td>200</td><td></td><td></td><td></td></tr><tr><td>12,000</td><td>3,000</td><td>4,500</td><td>TMS-F</td><td></td><td></td><td></td><td></td><td>15</td><td></td></tr><tr><td>24,000</td><td>3,000</td><td>9,266.7</td><td>TMS-G</td><td></td><td></td><td>67</td><td></td><td>5</td><td>2</td></tr><tr><td rowspan="3">Objective 3 (example)</td><td>3,000.0</td><td>300</td><td>2,000</td><td>TMS-H</td><td></td><td></td><td>20</td><td></td><td></td><td></td></tr><tr><td>18,181.8</td><td>1,818.2</td><td>2,000</td><td>TMS-I</td><td></td><td></td><td></td><td></td><td></td><td>4</td></tr><tr><td>4,166.7</td><td>2,000</td><td>2,000</td><td>TMS-L</td><td></td><td></td><td>10</td><td></td><td>3</td><td></td></tr></tbody></table>

For each TMS, a specific number of gold mine-types are selected based on the characteristics presented and discussed in Tables 9 and 11. The TMS can be simple or complex, depending on whether they involve the combination of one or multiple mine-types.

In this study, the "Objective 1" of gold production of 5 000 kg Au/year can be met by one LSM (TMS-A), by 6 MSMc (TMS-B), or by 33 ASM (TMS-C) but also a mixed scenario involving different types of gold mining project types, such as 20 ASM, 4MSMg and 1 MSMc (TMS-D). If we assume a scenario where legal gold mining is absent and i-ASM is the only gold producer, the same objective might be met by 758 i-ASM (TMS-illegal).

For each set of TMS, the considered objective is underlined in green (Table 13), while the other two columns represent the corresponding secondary values based on the number of project-types of the TMS. For instance, in TMS-C, the objective 1 is met by 33 ASM-type. In the same TMS, these 33 ASM projects could also provide 500 direct jobs for a total consumed surface of 3,333.3 ha. These values are calculated based on the number of project-types per TMS and the corresponding values chosen in Table 9 and 11.
Above the mentioned 3 territorial objectives, only the TMS related to the first objective (i.e. annual gold production of 5 000 kg per year) are assessed hereafter. This choice is arbitrary, but it is justified by the possibility to compare different scenarios based on the main business objective of a mining project, which is mining production. Therefore, among the 11 TMS, this study focuses on five TMS: TMS-A, TMS-B, TMS-C, TMS-D and TMS-illegal (Fig. 41). The proposed TMS will then be compared and ranked based on their level of risks, their coherence with all the defined objectives, and by considering different perspectives and expectations of the involved actors.

<figure><img src="image_43.png" /><figcaption>Figure 41 Selected objectives and related Territorial Mining Scenarios (TMS) for each objective. Once the TMS are developed and characterized, the corresponding number and types of gold mine are fictionally located within Mana river basin.</figcaption></figure>

## 2. Fictional localization of the gold mining projects

For each TMS, the corresponding gold mines of each type, constituting a “mining system” will now be localized within the selected territory system (i.e. Mana river basin) (Fig. 41).

The fictional localization is based on multiple criteria. If the existence of the geological deposit is the first criterion for project localization, other factors play an important role in mining siting. For instance, regulatory frameworks state where mining is or isn't authorized. As mentioned, the Departmental Mining Plan (SDOM, *Schéma Départemental d'Orientation Minière*) divides the FG region in four zones where mining is fully or partially authorized or forbidden.

We use three simple criteria for the fictional siting of mines in each proposed TMS according to the geological, juridical and geotechnical suitability within the Mana river basin (Fig. 42). By definition, in the case of i-ASM, only the geological and geotechnical suitability are considered.
<figure><img src="image_44.png" /><figcaption>Figure 42 Methodology, criteria and indicators used for the identification of the suitable areas for the fictional localization of the mining project types in FG</figcaption></figure>

The first criterion concerns the **geological suitability** of the area, indicated by the map of the primary and secondary gold deposits in FG. Since the dataset acquired from the GIS service of French National Geological Survey (BRGM) is point-shaped, two buffer zones (of variable radius) have been created on QGis respectively for primary and secondary gold deposits. Primary gold deposits will be used for i-ASMp, MSM and LSM while secondary gold deposits will be used for i-ASMs and ASM.

The second criterion is the **juridical suitability**. The SDOM zoning for mining activities (DEAL, 2015) is used as indicator. In order to simplify the cartographic rendering, the SDOM has been divided through a binary approach in: zone 1 (including the original zones 1 and 2 where mining is allowed) and zone 2 (including zones 3 and 4 where mining is forbidden).

Finally, we added a **geotechnical suitability** criterion, assuming that not all terrains would be adapted for the development of the mining site and the operation of human workforce and machinery. Some authors estimated that waste dumps and tailings facilities could not be located on areas with a slope superior to 15% (Oosthuizen, 1999; Suleman, 2017). Hence, this indicator has been used to map the geotechnical suitability of the area.

Fig. 43 presents the intersection of the three criteria (Fig.43a, b and c). As a result, the suitable areas for the fictional localization of the mines are presented in Fig.43d. Once the criteria used to determine mining suitability are defined and mapped, for each TMS, the corresponding mines of each type are fictionally located (Fig. 44). The localization within the suitable areas is concretely performed in this study using the geometrical centroid of the polygon or randomly, using aleatory GIS-based algorithms.

However, for operational applications, the aleatory localization of the mine within the suitability area should be retrieved in order to analyze the influence of the localization on the results. This could be a helpful tool to support decision-making, for instance testing multiple localizations and assessing which ones represent the lowest risk level. Furthermore, it is important to underline that other criteria should be considered for a more reliable siting of the
mining projects. For instance, physical accessibility is a key parameter for the feasibility of a mining project, depending on the existence of road access in order to develop the mine site.

<figure><img src="image_45.png" /></figure>

Figure 43 Cartographic application of the proposed methodology for the identification of the suitable areas for the fictional localization of gold mine types based on the geological (a), juridical (b) and geotechnical (c) suitability. On the right, the final result with the suitable areas in Mana river basin (d).

<figure><img src="image_46.png" /></figure>

Figure 44 Final fictional localization of the gold mine types in Mana river basin according to the selected TMS. Therefore, 3 TMS based on project-type (TMS-A, B, C), one mixed TMS (TMS-D) and one illegal-TMS
## Conclusion

The development of the TMS represents a key-step in the approach proposed in this study since they represent potential strategies for mining development and land-planning. TMS are developed based on pre-defined objectives that stakeholders set at the territory level. Each TMS will be compared according to its level of risk which will be assessed at the territory-level and not only at the mine-site.
# Chapter 6. Risk identification and risk scenarios development

## 1. Risk identification for each gold mine-type in French Guiana

Risk identification aims to develop a reliable database of risk events, on which multiple risk scenarios will be developed for each TMS. Risk are identified according to different sources. In this chapter, the methods and sources used for risk identification are presented. The main positive and negative risks related to each gold mine-type are overviewed. Finally, two distinguished risk scenarios are developed for each TMS. These risk scenarios will be the object of the assessment and the final scoring of the TMS, presented in Part III of this thesis.

### 1.1. Methods and sources of identification

In this step, two complementary databases of risks have been developed: a risk database specific to FG gold mining sector, and a database of worldwide historical accidents in the mining sector (all commodities included). Both databases aim to structure existing positive and negative risks related to FG gold mining and worldwide mining at the same time, in order to consequently select the risk events and build the risk scenarios to assess for each TMS (Fig. 45).

The first database provides several information concerning the date, type, causes and consequences of accidents (see the Glossary) of different natures occurred in mining projects worldwide, and gives complementary data for each case such as: the exploited commodity, the name and location of the project, the type of mining techniques used, etc.. This database is not exhaustive, and it is being filled with new cases and complementary information gathered from different sources (e.g. newspaper, online articles and media, institutional reports), including existing databases. The general structure of the database is presented in Appendix 8.

The second risk database (Appendix 9) has been developed after collecting the foreground data through a bibliographic review (scientific literature, technical reports), mine site visits, free interviews directed to public authorities, mining operators and civil society. An important amount of data has been gathered through the participation to the public debate on the Montagne d'Or gold mining project, (CNDP, 2018).
<figure><img src="image_47.png" /><figcaption>Figure 45 Materials and methods used for risk identification. Once the identification is sufficiently reliable, the risk scenarios (RS) are developed based on the identified risks. For the Rsa, the central event of tailings dam failure has been selected</figcaption></figure>

## 1.2. Worldwide historical accidents in the mining sector

Despite not peculiar to the FG case, since it includes the occurred accidents related to mining worldwide, a historical database can be an important tool in risk assessment to support the identification of risk events and inform project management. The historical database developed in this study remains still unfinished (415 accidents listed) and under development, mainly because of the great variety of specific existing databases that need to be integrated within it (Table 14) and because not all the accidents are publicly reported. Appendix 8 gives a conceptual view of the type of information contained within the database and its general structure.

However, this database is biased, since 80% of the 415 reported accidents is related to existing databases of tailings dam failures and cyanide spills that concerns accidents occurred during the operational phase, particularly related to waste and tailings management. This is due to the fact that, unlike more current but less catastrophic events (e.g. spills, landslides, transport accidents, other malfunctions), major dam tailings failures are the most reported and mediatized accidents in the mining sector and thus, their identification is more accessible. For instance, Figure 46 shows the evolution of the reported accidents between 1850 and 2020. The figure shows a general significant increase of accidents from 1960s, which can be due to the increasing of mining activities, especially through the exploitation of low-graded ores, leading to wider exploited surfaces and a considerably higher amount of wastes to be stored (Martin and Davies, 2000; Strachan and Goodwin, 2015). However, this tendency can be explained as well by the higher mediatization of such accidental events starting from the 1960s, the increase of mainstream communication (e.g. radios, televisions) but also to the strengthening of institutional monitoring, reporting and controls from public authorities. Indeed, during this period, the raising of "sustainable development" issues exacerbated national and international regulatory frameworks (Martin and Davies, 2000; Rico et al., 2008a), especially in relation to specific industrial activities such as the mining sector. The highest peak is due to a series of
earthquakes that hit Chili in 1965, triggering the failure of several tailings' dams of mining projects across the country.

<table><caption>Table 14 Example of existing databases of historical accidents in the mining sectors. These databases, most of which are related to tailings dam failure, need to be integrated within the unfinished database of historical accidents in the worldwide mining sector</caption><thead><tr><th>Type of accident</th><th>Database reference</th><th>Number of accidents</th><th>Period</th><th>Location</th><th>Sector</th></tr></thead><tbody><tr><td>Tailings dam failure</td><td>WISE, as of June 6, 2020</td><td>141</td><td>1960 - 2020</td><td>Worldwide</td><td>Mining</td></tr><tr><td>Tailings dam failure</td><td>Bowker and Chambers, 2017</td><td>289</td><td>1915 - 2016</td><td>Worldwide</td><td>Mining</td></tr><tr><td>Tailings dam failure</td><td>ICOLD and UNEP, 2001</td><td>221</td><td>1917-2000</td><td>Worldwide and US</td><td>Mining</td></tr><tr><td>Tailings dam failure</td><td>do Carmo et al., 2017</td><td>308</td><td>1915 - 2016</td><td>Worldwide</td><td>Mining</td></tr><tr><td>Tailings dam failure</td><td>https://worldminetailingsfailures.org as of March 1, 2019</td><td>357</td><td>1915 - 2019</td><td>Worldwide</td><td>Mining</td></tr><tr><td>Tailings dam failure</td><td>Bowker and Chambers, 2015</td><td>226</td><td>1917 - 2009</td><td>Worldwide</td><td>Mining</td></tr><tr><td>Tailings dam failure</td><td>Rico et al., 2008a</td><td>147</td><td>n.a.</td><td>Worldwide</td><td>Mining</td></tr><tr><td>Dam failure</td><td>Zhang et al., 2016</td><td>1,443</td><td>1799-2006</td><td>Worldwide</td><td>All</td></tr><tr><td>Cyanide spill</td><td>Rainforestinfo.org as of February 28, 2018</td><td>36</td><td>1994 - 2006</td><td>Worldwide</td><td>All</td></tr><tr><td>Various</td><td>http://mineaccidents.com.au as of February 28, 2018</td><td>77</td><td>1882 - 2000 and 2013 - 2014</td><td>Australia</td><td>Mining</td></tr><tr><td>Various</td><td>https://www.cdc.gov</td><td>726</td><td>1839 - 2010</td><td>US</td><td>Mining</td></tr><tr><td>Various</td><td>https://www.msha.gov</td><td>445</td><td>2007 - 2020</td><td>US</td><td>Mining</td></tr></tbody></table>

<figure><img src="image_48.png" /><figcaption>Figure 46 Evolution of the number of reported historical accidents in the mining sector worldwide according to the events currently reported by our database between 1850 and 2020</figcaption></figure>

Even though these types of databases give some significant information concerning historical mining accidents, statistical results might still considerably be influenced by the partial data used. Indeed, some bias should be pointed out for the fact that:

- Each country has a different number of tailings dams (Martin and Davies, 2000).
- Not all the types of accidents are reported worldwide. Existing databases concern essentially tailings dam failures and, less frequently, explosion or subsidence accidents. Therefore, only the mediatized accidents with greater consequences are currently easy-to-find, leaving also minor accidents, with "less" significant consequences but higher probability of occurrence, still underreported.

- Not all the countries offer the same level of transparency and communication, both through media, reports and scientific papers. In some cases, institutional databases are provided (e.g. US, Australia) while in other cases no reliable information is found. Also, some failures are not reported due to risks of bad publicity and legal ramification, depending on the country and on the company (Rico et al., 2008a; Kossoff et al., 2014).

- Not all the types of mining projects are listed. More specifically, existing databases usually focus on industrial large-scale projects leaving small-scale and artisanal mining accidents still underreported because they might be more difficult to monitor. In some cases, the same type of "catastrophic" accidents (i.e. dam failure) in large-scale mines might have much less significant "catastrophic" consequences – even if occurring more often – classifying it as a minor unreported accident.

Despite these biases, this first database will be particularly used in this study for the selection of the central risk event for the development of the accidental risk scenario (RSa) (see Section 3). The RSa is based on tailings dam failure, which, if not the most common accident in the mining sector, is surely one of the most reported.

## 1.3. *The identified risks per project-type*

Fig. 47 shows the distribution of actors according to whom the risks have been identified in the second risk database, related to FG gold mining sector. The chart underlines the number and percentage of risks mainly mentioned by each actor category, not individually, which means that some risks have been considered by multiple actors at the same time. For instance, the risks related to the use of cyanidation are mentioned by all the three considered groups of stakeholders. A considerable fraction is represented by civil society since the public debate for the LSM project *Montagne d'Or*, provided the identification of a great range of risks. Among them, the main concerns of local communities against LSM-type projects, were related especially to: the utilization of cyanidation for gold recovery and to the management of wastewater and tailings, the potential formation of acid mine drainage, the environmental consequences and the potential security issues related to deep exploitation pits, the impacts of the mine site on deforestation, the impacts of the project on local economy and job opportunities. A detailed synthesis of the public debate and their results is available in a report (CNDP, 2018). It is important to point out that the three groups of stakeholders considered in this study are not equally represented during risk identification.
The main identified negative and positive consequences related to gold mining in FG have been listed in Table 15 for each mine type. Whether these events are positive or negative depends on their effect on the objectives and performances of the mine and territory systems, which can be respectively enhanced or worsened. The table shows the type of source used for their identification of each consequence. The color code aims only to help reading the table in order to understand how many sources are used for the identification.

<figure><img src="image_49.png" /><figcaption>Figure 47 Distribution of the identified risks related to FG gold mining sector according to the different stakeholders</figcaption></figure>

<table><caption>Table 15 Typical potential positive and negative consequences of the gold mining sector in FG, according to each mining project type. As explained in the legend below, the symbols in each case represent the source of identification for each consequence per gold mine-type. The colors instead aim to help the reading of the table and they indicate how many sources that have been used mentioned each consequence per project-type,</caption><thead><tr><th></th><th>i-ASMp</th><th>i-ASMs</th><th>ASM</th><th>MSMg</th><th>MSMc</th><th>LSM</th><th>Source</th></tr></thead><tbody><tr><td>Negative</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Destruction of ecological habitats and biodiversity</td><td>o ●</td><td>o ●</td><td>o ●</td><td>o ●</td><td>o ●</td><td>o ●</td><td>Laperche et al., 2007</td></tr><tr><td>Deforestation</td><td>o ●</td><td>o ●</td><td>o ●</td><td>o ●</td><td>o ●</td><td>o ●</td><td>Galochet and Morel, 2015</td></tr><tr><td>Surface water turbidity</td><td>o ●</td><td>o ●</td><td>o ●</td><td>o ●</td><td>o ●</td><td>o ●</td><td>ONF, 2017a; ONF, 2017b; Rahm et al., 2017</td></tr><tr><td>Heavy metals contamination in water and soils</td><td>o ●</td><td>o ●</td><td>o ●</td><td>o ●</td><td>o ●</td><td>o ●</td><td>Tudesque et al., 2012</td></tr><tr><td>Direct Hg pollution and contamination (e.g. gold recovery techniques)</td><td>o ●</td><td>o ●</td><td>o ●</td><td>o ●</td><td>o ●</td><td>o ●</td><td>Gallay et al., 2018</td></tr><tr><td>Indirect Hg contamination due to the remobilization of ancient Hg</td><td>o ●</td><td>o ●</td><td>o ●</td><td>o ●</td><td>o ●</td><td>o ●</td><td>Laperche et al., 2008</td></tr><tr><td>Dam failure related flood, contamination and turbidity</td><td>o</td><td>o</td><td>o</td><td>o ●</td><td>o ●</td><td>o ●</td><td>Marchand et al., 2006</td></tr><tr><td>Cyanide spills</td><td>o</td><td>o</td><td>o</td><td>o</td><td>o ●</td><td>o ●</td><td>Guedron et al., 2011b</td></tr><tr><td>Soil erosion</td><td>o</td><td>o ●</td><td>o ●</td><td>o ●</td><td>o ●</td><td>o ●</td><td>Kahhat et al., 2019</td></tr><tr><td>Physical alteration of riverbeds (hydromorphological impacts)</td><td>o</td><td>o ●</td><td>o ●</td><td>o ●</td><td>o ●</td><td>o ●</td><td>Guedron et al., 2011a</td></tr><tr><td>Acid mine drainage (AMD)</td><td>o</td><td>o</td><td>o</td><td>o ●</td><td>o ●</td><td>o ●</td><td>Gentès et al., 2019</td></tr><tr><td>Atmospheric pollution</td><td>o ●</td><td>o ●</td><td>o ●</td><td>o ●</td><td>o ●</td><td>o ●</td><td>Bourbon and Mathon, 2012</td></tr><tr><td>Landscape degradation</td><td>o ●</td><td>o ●</td><td>o ●</td><td>o ●</td><td>o ●</td><td>o ●</td><td>Ineris, 2018</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>Moisan and Blanchard, 2012</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>Urien, 2018</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>Laperche et al., 2008</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>Labriere et al., 2015</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>Laperche et al., 2008</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>Bourbon and Mathon, 2012</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>Miramond et al., 2006</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>Simate and Ndlovu, 2014</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>Melières et al., 2003</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>Laperche et al., 2007</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>Laperche et al., 2008</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>WWF, 2018</td></tr></tbody></table>
<table><tbody><tr><td>Mine subsidence</td><td>○ ●</td><td></td><td></td><td></td><td></td><td>Bourbon, 2014</td></tr><tr><td>Pit instability</td><td>○ ●</td><td></td><td></td><td>○ ●</td><td>○ ●</td><td>Bourbon, 2015</td></tr><tr><td>Landslides</td><td>○ ●</td><td>○</td><td>○ ●</td><td>○ ●</td><td>○ ●</td><td>Laperche et al., 2008</td></tr><tr><td>Health problems and serious epidemiological diseases (e.g. malaria, HIV)</td><td>○ ●</td><td>○ ●</td><td>○ ●</td><td>○ ●</td><td>○ ●</td><td>Bourbon and Mathon, 2012</td></tr><tr><td>Migration of legal and illegal miners from bordering countries ("gold rushes")</td><td>○ ●</td><td>○ ●</td><td>○ ●</td><td>○ ●</td><td>○ ●</td><td>de Santi et al., 2016</td></tr><tr><td>Work injuries</td><td>○ ●</td><td>○ ●</td><td>○ ●</td><td>○ ●</td><td>○ ●</td><td>Douine et al., 2018</td></tr><tr><td>Crimes and insecurity for local communities (e.g. drug, arm dealing, prostitution, child labor)</td><td>○ ●</td><td>○ ●</td><td>○ ●</td><td>○ ●</td><td>○ ●</td><td>Moullet et al., 2006</td></tr><tr><td>Social disruption (e.g. opposition, conflicts)</td><td>○ ●</td><td>○ ●</td><td>○ ●</td><td>○ ●</td><td>○ ●</td><td>Hook, 2019</td></tr><tr><td>Resource dispossession (e.g. land, water, crops)</td><td>○ ●</td><td>○ ●</td><td>○ ●</td><td>○ ●</td><td>○ ●</td><td>Hentschel et al., 2002</td></tr><tr><td>Direct economic outcomes (e.g. taxes, royalties)</td><td></td><td></td><td>○ ●</td><td>○ ●</td><td>○ ●</td><td>Amirshenava and Osanloo, 2018</td></tr><tr><td>Direct jobs opportunities</td><td>○ ●</td><td>○ ●</td><td>○ ●</td><td>○ ●</td><td>○ ●</td><td>Hentschel et al., 2002</td></tr><tr><td>Raw material supply</td><td>○ ●</td><td>○ ●</td><td>○ ●</td><td>○ ●</td><td>○ ●</td><td>WWF, 2018</td></tr><tr><td>Development of local business as services providers</td><td>○</td><td>○</td><td>○</td><td>○ ●</td><td>○ ●</td><td>Hentschel et al., 2002</td></tr><tr><td>Expansion of social infrastructures (e.g. roads, energy, services)</td><td>○</td><td>○</td><td>○</td><td>○ ●</td><td>○ ●</td><td>Amirshenava and Osanloo, 2019</td></tr><tr><td>Support of local education, research and skill development</td><td></td><td></td><td></td><td>○ ●</td><td>○ ●</td><td>Amirshenava and Osanloo, 2019</td></tr><tr><td>Support to ethnic minorities</td><td>○</td><td>○</td><td>○</td><td>○ ●</td><td>○ ●</td><td>MDO, 2018</td></tr><tr><td>Fight against illegal mining</td><td></td><td></td><td></td><td>○ ●</td><td>○ ●</td><td>WWF, 2018</td></tr></tbody></table>

<table><tbody><tr><td>○ General literature</td><td>▲ Expert interviews and site visits</td><td>No impact or no data</td><td>2 sources (other than "○")</td></tr><tr><td>●FG-related literature</td><td>■ Public Debate of MDO</td><td>1 source (other than "○")</td><td>3 sources (other than "○")</td></tr></tbody></table>

### 1.3.1. The positive risks

The typical positive risk (i.e. opportunity) associated to mining concerns the **increase of employment rates** and the **creation of local wealth**. Indeed, this is the most important argument when it comes for gold mining operators to counterbalance the negative outcomes that may be generated. However, some authors emphasize that the perception of inhabitants might be concerned as well not only on how many people might be directly or indirectly employed by the mining project but also, who these people are. In some cases, especially LSM projects, tend to import short-term technical expertise which consequently limit the most qualified job offers often for non-local staff (Pokorny et al., 2019). For instance, in FG, MSM and LSM often employ foreign miners, mainly Brazilians, because of their wider and greater mining experience compared to local workers. In several exchanges with local stakeholders, it often came up that
FG does not have a "mining culture". Moreover, in i-ASM and legal ASM, work conditions are often volatile (Pokorny et al., 2019).

The potential **economic outcomes** of mining activities are also among the most important opportunities often proposed to underline the importance of gold mining for territorial development (Betancur-Corredor et al., 2018). For instance, Hilson and Laing, (2016) discuss the case of gold mining in Guyana showing how even a modification of gold prices "is capable of drawing valuable financial and human capital from other sectors of the economy". These aspects are considerably related to the priorities of local population and public authorities, but also mining operators, concerning the enhancement of the territorial development on the short and long run. Economic consequences of gold mining can result in the form of **taxes and royalties** that mining companies pay to local administrations. For instance, in FG the Fiscal Code states that, among others, mining companies should pay a royalty of 137,9 €, 27,00 € and 672,01 € per kilogram of gold produced, respectively to municipalities, departments and the region (SRK, 2017). The reallocation and reutilization of these incomes for local administration might support the **enhancement of local infrastructures and services**, (Oyarzo and Paredes, 2019). However, this depends on who is the actual beneficiary of these incomes and on how trustful is the relationship between citizens and the government for the reallocation of economic resources. Moreover, some stakeholders criticized the fact that such services (e.g. infrastructures, education) should be supplied by the State and not by potential substitutes incarnated by private mining corporations. Indeed, in the case of LSM, a potential risk often claimed especially by local communities is the imposition of "export-led models" that leads public authorities to "privilege foreign companies and enrich host governments" (Hilson and Laing, 2016). In other cases, the development of road infrastructures due to new gold mining project is seen in FG as a potential facilitation for illegal gold miners to access new mine sites.

The "natural" outcome of gold mining is also the **supply of gold**. Hence, where there is a need for raw material supply for the uses of gold mentioned in Chapter 4, gold mining fills such needs. The questionability of gold needs and applications is not a matter of discussion in this thesis. Legal gold mining sector production in FG is currently overthrown by illegal operations which produce a considerably higher amount of gold annually and export it through laundering networks which bind the informal and formal sectors of gold production (Heemskerk, 2010). However, a part of local community states that illegal gold mining would be reduced by the increasing of legal mining activities, since the latter would "dispossess" the first ones of profitable lands. On the other hand, some stakeholders state the opposite, suggesting that legal mining might also – willingly or unwillingly – "attract" illegal mining activities that would exploit and re-process legal tailings and residues with more viable but destructive methods (i.e. gold-mercury amalgam). If this may be questionable for MSM and LSM-types, it can be particularly true for legal ASM, which do not seem to play a key role in reducing the illegal mining phenomenon. This is because, in the current framework of FG, small-
scale activities do not have the means to fight illegal mining and risk to be overwhelmed by the phenomenon (e.g. land claims, robberies and hold-ups).

### 1.3.2. The negative risks

The potential negative social and environmental consequences of gold mining should be discussed taking into account their considerable interactions. Indeed, the degradation of environmental components inexorably leads to the degradation of ecosystem services supplied by these components and that satisfy the needs of local communities. For instance, Keovilignavong (2019) discusses that the negative environmental consequences of gold mining in a region of Laos degraded considerably the quality of life of inhabitants, resulting in "declining their ability access to and use of these resources for their livelihood benefits".

In FG, the main typical identified negative risks related to gold mining concern the consequences of such activities on the different environmental components (e.g. soil, water, biodiversity) of the region and the health and well-being of its inhabitants (Table 15). This might be explained by three main reasons:

- because of the intrinsic vulnerability of FG local ecosystem, due to a dense hydrographic network, the large surfaces occupied by Amazon rainforest, its biodiversity and the sensitivity of its water, soil and forest ecosystems;

- The contamination of soils with anthropogenic mercury (Hg), due to gold processing techniques in legal – until 2006 – and illegal gold mines;

- The remobilization of the anthropic and natural Hg presents in soils, water and sediments.

**Hg-related risks** in FG's gold-mining sector have been widely discussed (Grimaldi et al., 2015), and are linked to specific concerns about the health of miners and welfare of local communities. Soil, air and water are all affected: 20% of Hg is lost during amalgamation, while 70% evaporates during the roasting phase (Moullet et al., 2006). Such emissions depend on the technological level of the operator and would be significantly lower if the use of retort were common among i-ASM (Seccatore et al., 2014; Kahhat et al., 2019). The remobilization of natural or anthropogenic Hg due to soil removal during mining is considered as relevant as direct Hg emissions from recovery process in i-ASM (Kahhat et al., 2019). With an annual gold production of 10 tons, approximately 13 tons of Hg are also released into streams in FG (WWF, 2018). Contaminated hotspots due to runoff have emerged in areas located kilometers far from mining sites (Marchand et al., 2006) and the impacts on biodiversity are well documented (Fréry et al., 2001; Godard et al., 2006; Sebastiano et al., 2016; Gentès et al., 2019). However, since Hg in water is bioavailable for only few organisms (Laperche et al., 2007), the most significant risks for health occur when Hg is transformed in methylmercury (MeHg) (Guedron et al., 2011a). Since methylation depends on anoxic conditions, organic matter rate and other
physical and biological factors, illegal and ancient legal gold mines are the locations of high concentrations and seasonal cross-watershed transfers of MeHg (Guedron et al., 2011b). Contamination might affect the food chain by biomagnification and lead to severe endocrine disorders among local populations (Budnik and Casteleyn, 2019). Human exposure to MeHg depends also on the receptors and their socio-cultural habits (Grandjean et al., 1999; Boudan et al., 2004; ARS, 2013). For instance, Frery et al. (2001) suggest that Wayana population in FG has a high exposure to Hg related to fish consumption and that 90% of the population in the Haut-Maroni in 2015 had a rate beyond the standard thresholds (WWF, 2018).

**Deforestation** is another potential consequence often associated to gold mining, particularly in rainforest regions (Polidori et al., 2001). Rather than a risk, deforestation is almost the inexorable consequence of any activity leading to land anthropization. In gold mining, the deforested surfaces increase during the development and exploitation phases, when mining operators clear the mine site, develop and build mining roads and infrastructures. This phenomenon is particularly worsened in the case of i-ASM, where it occurs without control or monitoring from local authorities (Rahm, 2017). Among the measures to compensate and re-enhance local vegetation and soil quality, land rehabilitation practices and revegetalization are often performed during and after mining (DEAL et al., 2016).

Because of deforestation and of the peculiar structure of local soils (Roose and Sarrailh, 1990), **erosion and sedimentation** are natural phenomena in FG, exacerbated by gold mining activities. This level of perturbation increases the release of total suspended solids (TSS) (Gallay et al., 2018), and thus, silting, water turbidity, heavy metal release (Tudesque et al., 2012; Labriere et al., 2015; Venturieri et al., 2017), consequentially affecting biodiversity, soils and freshwater resources (Laperche et al., 2008; Asconit, 2013; Da Silva, 2014; Galochet and Morel, 2015; ONF, 2017a). Indeed, TSS release is a major driver of soil and water pollution and it is exacerbated during mining development and operations, even in ASM types.

As shown also by the final report of the public debate concerning the *Montagne d'Or* project (CNDP, 2018), the utilization of cyanidation for gold recovery is a major concern among local communities and associations, but also public authorities. Indeed, the dispersion of free **cyanide** ions on the human and natural environments would cause severe toxicological impacts (INERIS, 2012; Donato et al.; 2017). However, unlike Hg, cyanides are biodegradable in the environment and they are not bioaccumulated by humans (Hylander et al., 2007; Laitos, 2013). Moreover, the occurrence and intensity of the potential health disorders generated vary according to the type of cyanide compound used (INERIS, 2011). Cyanide-related risks depend as well on the technical performances of the mining operators. For instance, industrial cyanidation relies nowadays on the international Code for Cyanide Management (ICMI, 2016). Nevertheless, cyanide should be stored on the long-term in appropriate conditions to avoid its oxidation and liberation of toxic acid elements (Mudder and Botz, 2004). At the moment, only one MSM-type in FG developed a cyanide plant for gold recovery. Information of gold
cyanidation in FG is available in several published reports (Moisan and Blanchard, 2012; INERIS, 2018; Urien, 2018).

Another risk often identified concerns the potential consequences of **acide mine drainage (AMD)**. By our knowledge, the occurrence of this phenomenon in FG is specifically addressed by only one report in 2006 (Miramond *et al.*, 2006). AMD depends on the geological deposit and the physico-chemical nature of the hosting rocks. Usually, AMD is specific to primary gold deposits (metamorphic or magmatic) – rich in sulfides such as FeS₂ – and it occurs when operational and specific environmental conditions are met (e.g. oxidizing conditions, altered mineralogy of the substratum, absence of carbonates, high temperature, acid pH, microbial activity) (Price, 2005; Miramond *et al.*, 2006; Lottermoser, 2010). Despite in FG, AMD risk might be relatively low in some superficial rocks, it becomes higher when deeper materials are excavated and exposed, for instance, in the non-oxydized area of the substratum. AMD contains high concentrations of acid and dissolved metals that may be released into soils, groundwater, streams, leading to high toxicity for aquatic organisms and ecosystems, infrastructures corrosion and freshwater contamination (Simate and Ndlovu, 2014). Finally, in some deposits, other metalloids might be exposed leading to specific toxic risks. For instance, in the case of *Montagne d'Or* project, the potential presence of arsenic-enriched hosting rocks was at the origin of concerns for local population.

A great range of **risks concern the i-ASM sector** in FG. Indeed, the noncompliance with the obligations legally required by mining permits, results in the amplification of the common consequences generally related to mining. According to WWF, (2018), almost 1 800 km of water streams were integrally degraded in 2015 by i-ASM and approximatively 12 000 hectares were deforested – of which nearly 3 000 are located within protected areas (Rahm, 2017). Also, the hostile living conditions at mining sites lead to poor health caused by infectious and non-infectious diseases, which spread beyond mining borders (Douine *et al.*, 2018). Malaria but also digestive and cutaneous disorders, anemia, HIV transmission might affect local miners, communities, undocumented immigrants and even French soldiers deployed on the territory (Douine *et al.*, 2016; de Santi *et al.*, 2017; Douine *et al.*, 2018; Jones *et al.*, 2018). Moreover, i-ASM leads to the development of "segregated economic enclaves" (Pokorny *et al.*, 2019) which in FG might find a good example in the village of Saint-Elie.

## 2. Accidental Risk scenario (RSa) development

The two databases resulting from risk identification are used to develop two different risk scenarios for each TMS. The first considered risk scenario evaluates gold mines operating in accidental conditions, i.e. outside the normal functioning of the project. A central event is
selected and integrated within a Bow-Tie to identify its causes and consequences. The
assessment of this RSa has been one of the main focus of this thesis.

## 2.1. *Selection of an initial central event: dam failure induced by overtopping*

In the current study, only one accidental risk event could be considered to develop the RSa. Based on the two risk databases previously exposed, the failure of the tailings dams has been chosen as the initial central event for the development of the RSa. This event will be the core of the Bow-Tie diagram. The choice of tailings dam failure is due to the fact that, as shown by the historical database, it is the most reported historical accident in the mining sector. Furthermore, this risk event has been repeatedly reported during the public debate concerning the gold mining project of Montagne d'Or.

Indeed, mining wastes and tailings management is today a major concern for mining
operators because of their quantities and short and long-term impacts on human health and
on the environment (Martin and Davies, 2000; Kossoff *et al*., 2014). In order to characterize the
risk of tailings dam failure, a thorough bibliographic review, along with discussions with experts
have been carried out. In the following sub-sections, a short synthesis of this review is
presented in order to detail tailings dams and their utilization in FG, so to apprehend the
triggering factors of tailings dam failures and their consequences. More details can be found in
Appendix 10. The acknowledgment of the root causes and the consequences of the dam failure
will support the development of the Bow-Tie diagram.
### 2.1.1. Overview of mining dams: definition and application in the mining sector

Mining dams are retaining and protection embankments that are used for mine sites and/or metallurgical plants (Bourbon and Mathon, 2012) (Fig. 48). They have two main functions:

- to create sedimentation impoundments for the storage and settlement of fine waste materials coming from mining operations or mineral processing (called “tailings”). In this latter case, they are referred to as tailings store facility (TSF) (Lottermoser, 2010; Kossoff et al., 2014; Ponce-Zambrano et al., 2014).

- to create decantation ponds to hold and clarify water that may be returned for the mine to operate in a closed-circuit, after settlement in dam via penstocks and piping (Babbitt and Graham, 1996).

<figure><img src="image_50.png" /><figcaption>Figure 48 Simplified figure of a mining dam and its main components. On the higher right side of the figure, the three main methods used to raise the dam are shown</figcaption></figure>

In order to fulfill the mentioned functions of storage and decantation of mine tailings and operation waters, three main performances are defined for a TSF (Bourbon and Mathon, 2012; Li et al., 2018; USSD, 2018):

i) Physical stability, which is the capacity to support the applied stress for a long time without suffering any significant deformation or movement;

ii) Impermeability, which is the capacity of the mining dam not to allow the transfer of fluids under specific pressure conditions, except when needed;

iii) Sufficient storage capacity which represents the aptitude of the embankment to contain the required volume of materials and fluids, without reducing stability and impermeability.

### 2.1.2. Overview of tailings management and gold mining dams in French Guiana

Several technical reports give an insight about the specificities and the utilization of TSF in FG gold mining sector (Laperche et al., 2008; Barras, 2010; Bourbon and Mathon, 2012; Cottard and Leperche, 2012; INERIS, 2014; SRK, 2017; GéoplusEnvironnement, 2017;
Aesteergts, 2018; Urien, 2018). These TSF differ considerably depending on whether the exploitation concerns primary or secondary gold deposits. In both cases, they are usually built from local soils and materials, because of the difficulties of access and transport of heavy materials. If here they are only overviewed, some technical features of mining dams in gold mining exploitations in FG are detailed in Appendix 10.

In **primary gold exploitations**, the arrangement of mining dams consists in one or several ponds designed to stock mine tailings in a slurry (10 to 30% of solid fractions), having a sandy to clayey texture (Bourbon and Mathon, 2012) (Fig. 49-50). These ponds are embanked by two or more dams, usually built with the anciently or recently excavated materials available on site (Cottard and Laperche, 2012). In FG, the most common elevation technique is the upstream method because it is easier to preform and economically more viable. Nevertheless, because of the increasing world-wide accidents also due to this construction technique, the upstream raising method is currently being debated to avoid further uses.

<figure><img src="image_51.png" /><figcaption>Figure 49 Simplified scheme of the tailings and water ponds in a MSMg-type in FG based on a satellite image. Source: Modified from Google Map, 2020 Maxar Technologies</figcaption></figure>

<figure><img src="image_52.png" /><figcaption>Figure 50 A mining dam in a MSM-type in FG</figcaption></figure>
In secondary gold exploitations, the arrangement of mining dams consists in a sequence of ponds used both for tailings storage and water settlement as long as the exploitation progresses (Fig. 51; Appendix 10). As it was explained in Chapter 4, these structures embank the *barranques* and, as the exploitation progresses along the riverbed, the previous extracting ponds are used to stock turbid water and the tailings of the “active” upstream *barranque* (Barras, 2010; Bourbon and Mathon, 2012). Once extracted, the minerals are processed by grain-size, a first time through gravimetry directly in the current exploitation pond, and then sent into the downstream ponds to settle (Fig. 51; Appendix 10). No chemical additives are used.

Therefore, the main differences between the dams in primary and secondary gold mining are:

1) A greater storage capacity and height of primary gold mining dams, due to the difference of surface exploited, the financial and technical equipment available and the production rate.

2) The systematic distinction, for primary gold mining projects, between tailings dams and water settling ponds.

3) The localization of the ponds which depends on a progressive downstream-to-upstream sequencing for secondary gold mining and are essentially topography-driven for primary gold exploitations.

4) The potential storage of decyanated tailings outcoming from processing plants and the utilization of dry storage for some primary gold exploitations.

<figure><img src="image_53.png" /><figcaption>Figure 51 Example of a mining dam system in a secondary gold mining site (ASM-type) in FG (Barras, 2010)</figcaption></figure>
### 2.1.3. Mechanisms of failures of mining dams, causes and consequences

In May 2020, approximately 23 000 tons of oil products were spilled into the environment in a nickel mine in Russia due to the possible collapse of a fuel reservoir (Kim, 2020 for NPR News). In March 2020, in Heilongjiang Province (China), more than 2.5 million cube meters of tailings contaminated the environment, threatening drinking water provisioning of almost 70 000 people. In January 2019, more than 12 million cubic meters were released after the collapse of a tailings dam in an iron mine near Brumadinho (Brazil), killing more than 250 people. These were only the major among the 21 big tailings dam failures that have been reported by WISE, 2020 after the 2015 Samarco failure. Indeed, the greatest challenge for mining is the treatment and storage of mine waste, estimated to 5 – 14 000 Mt/year (Kossoff et al., 2014; Schoenberger, 2016). Tailings ponds represent the most significant environmental liability associated with mining operations (Martin and Davies, 2000), being more vulnerable than conventional dams (Rico et al., 2008a).

Several definitions of failures are given by Smith and Connell (1979); FEMA (2004); Caldwell et al., (2015), but none applies to all the contexts. In this study, a **mining dam failure** is referred to as the effect of the potential occurrence of single or multiple events which reduce one or more performances of a mining dam (e.g. stability, impermeability, storage capacity), leading to the alteration of its functions (i.e. tailings and water storage and settling).

Multiple failure mechanisms are described by authors, such as mine subsidence, slope instability, foundation failure, overtopping or seepage (Kossoff et al., 2014; INERIS, 2014; Ponce-Zambrano et al., 2014; Strachan and Goodwin, 2015; UNEP, 2017) (Fig. 52). Among them, overtopping is estimated to be the currently leading mechanism of failure (Bowker and Chambers, 2017; UNEP, 2017) (Fig. 52). Moreover, it seems that overtopping events are increasing, which may show deficiencies in design and water management (Concha Larrauri, 2017). For this reason, overtopping is chosen for this study as failure mechanism for the development of the RSa here presented.

<figure><img src="image_54.png" /><figcaption>Figure 52 Most common mechanisms of tailings dam failures occurred worldwide between 1915 and 2017 according to UNEP (2017)</figcaption></figure>
**Overtopping** is defined as the event during which water flows over the top of the dam, sometimes causing the erosion of the embankment (UNEP, 2017). The probability of occurrence of such events is influenced by a very wide range of interacting causes related both to the internal management of the mining project and its infrastructures (e.g. construction methods, water balance monitoring), and the external environment of the mining project itself (e.g. meteorological conditions, topography but also socio-political and economic causes). Despite rare in some cases, dam failures generated by overtopping do happen, and their consequences are extremely variegated, from limited overflows at mine site to catastrophic large floods. These consequences might affect the surrounding environment (e.g. silting and erosion, water pollution, deforestation), local population (e.g. deaths and injuries, flooded houses) and infrastructures (e.g. reduction of drinking water provision, limited road access). These consequences have always a negative effect that rebounds on mining companies, who may consequently face high financial and reputational costs (Franks et al., 2011; Concha-Larrauri and Lall, 2018). Moreover, they may generate a widespread controversy and projects' opposition (Caldwell et al., 2015), leading to clean-up, compensation and litigation costs, the non-willingness of governments and communities to support new mining operations and, thus, lost future opportunities for mining operators (Franks et al., 2011). The causes and consequences of the dam failure generated by overtopping are detailed in Appendix 10.

## 2.2. Conceptual structure of the scenario: the Bow-Tie diagram

The RSa chosen for this study is developed based on a mining dam failure due to overtopping. The RSa is presented through the Bow-Tie diagram, detailed in Chapter 1. Fig. 53 shows the simplified diagram with the main causes (on the left) and consequences (on the right) selected for a further assessment. It is important to point out that, at the moment, the safety measures – that the mining operator can implement to reduce the probability of occurrence and/or the consequences of a dam failure – have not been included within the Bow-Tie. This implies a bias that need to be accounted for in results interpretation and that must be addressed in further studies. This aspect will be discussed at the end of the thesis. Nevertheless, because of the demonstrative purposes of the current application, the diagram is used simply as a conceptual scheme to present the selected scenario and to understand the interactions among the different causes and consequences related to a dam failure event due to overtopping.

Among the causes, two main factors are considered: the occurrence of a landslide within the pond causing the formation of a wave, and the increasing of water level. The consequences, as it will be furtherly detailed, concern the events triggered by the overtopping at the mine site (e.g. destruction of the dam, degradation of mining infrastructures, direct impacts on workforce) and socio-environmental consequences at the territory level, both within and outside the potential flooded area (e.g. degradation of drinking water, impacts on soils and surface waters, social struggle due to the loss of revenues for local households dependent on the mine site). The consequences end up to the “financial loss of the operator”
to show the feedback loop where the consequences affect the mine site, the territory and,
finally, they strike back the mining operator.
<figure><img src="image_55.png" /><figcaption>Figure 53 Selected causes and consequences of the tailings dam failure due to overtopping. The whole bow-tie diagram represents the selected accidental risk scenario (RSa) for this first application of the proposed methodology.</figcaption></figure>
### 3. Ordinary Risk scenario (RSo) development

The second risk scenario evaluates gold mining projects under normal operating conditions (see the Glossary), representing as closely as possible the level of performance that can be reasonably expected by the project. In this case, the operation of the mine coincides with the occurrence of ordinary risks. Unlike the RSa, the occurrence of these risks is almost certain when the projects are in operation. Thus, during the assessment, the probability of occurrence of these risks will be considered equal to one.

The assessment of the RSo has not been the main object of this thesis and hence, it has not been significantly detailed (e.g. no Bow-Tie and limited assessment as shown in Chapter 8). Nevertheless, we decided to include it at this stage, since it is fundamental to obtain the final TMS scores and compare them.

The ordinary risk scenario (RSo) is composed both of positive and negative consequences (Table 16) selected among the identified risks in Table 15. Hereafter, only four positive socio-economic and one negative consequences (i.e. landscape degradation and the surfaces deforested by the development of the mine site(s)) have been selected.

In this scenario only the triggered consequences are considered, neglecting the consequences suffered by the mine(s). This aspect will not be considered neither for the second risk scenario, which means that the final scores of the TMS will both result from the assessment of risks generated – not suffered – by the mining project. The integration of both suffered and triggered risks related to mining projects based on their level of socio-environmental performances will be discussed in the fourth Part of this manuscript (Chapter 11).

<table><caption>Table 16 Selected positive and negative consequences for the ordinary risk scenario (RSo)</caption><thead><tr><th></th><th></th></tr></thead><tbody><tr><td><strong>Positive consequences:</strong></td><td>Raw material supply (Gold)</td></tr><tr><td></td><td>Direct jobs opportunities</td></tr><tr><td></td><td>Expansion of social infrastructures (e.g. roads, energy, services)</td></tr><tr><td></td><td>Support of local education, research and skill development</td></tr><tr><td><strong>Negative consequences:</strong></td><td>Landscape degradation</td></tr></tbody></table>
Conclusion

The current chapter presents the development of two risk scenarios for each TMS based on two databases of identified risks. ). Tailings dam failure has been selected as the main central event of during accidental operating conditions of the mining project and will be detailed in Chapter 7. The risk scenario in normal operating conditions has only been introduced in our study and will be briefly assessed in Chapter 8. This risk scenario must be the object of further studies (for instance, through a bow-tie approach). The assessment and combination of these RSo and RSa will allow the scoring and comparison of the TMS.

As shown in Chapter 4, for each mine type, a range of values is given to each technical and operational parameter. Table 10 presented the parameters used according to each mine-type for the assessment of the risk scenarios.
Part III – Application on gold mining in French Guiana:
assessment of the risk scenarios and TMS scoring
# Chapter 7. Assessment of the accidental Risk Scenario (RSa)

This chapter presents the assessment of the accidental risk scenario (RSa) developed in Chapter 6. The scenario we selected is based on the failure of a mining dam caused by overtopping, considered as the initial central event. In this case, a“failure” is defined as the loss of the storing capacity of the dam.

The next sections (1 to 4) detail the vulnerability indicators and then expose the estimation of the impacted area, as well as the different consequences at the scale of the territory. Consequences at the project scale will only be shortly discussed, since we intend to focus on the territorial level. Fig. 54 illustrates the steps followed for the assessment of the RSa in this study. The assessment focuses on the estimation of the area impacted by the dam failure (Fig. 55b) and the identification of the socio-ecological components directly or indirectly impacted by the flood (e.g. local population, infrastructures, water, soil) and the assessment of their vulnerability (Fig. 55c). This allows to obtain a consequence map (Fig.55e) that is then combined to probability scores to assign a final score to each TMS. Probability scores depend on the financial and technical means of the mines and their aptitude in implementing safety barriers, which only qualitatively assumed in this study.

Finally, an alternative way to estimate the RSa score is just proposed in order to integrate the probability scores from the beginning of the assessment.

<figure><img src="image_56.png" /><figcaption>Figure 54 Simplified conceptual flowsheet of the methodology used for the assessment of the RSa. As it is shown and furtherly detailed, the "mining project scale" consequences are not considered in the overall score</figcaption></figure>
<figure><img src="image_57.png" /><figcaption>Figure 55 The assessment of the RSa is the result of the combination of different components. After the localization of the mine types on the territory (a), the estimation of the impacted area (b) and the assessment of the socio-ecological vulnerability of the territorial system (c) to obtain a consequences map (d). Finally, the integration of probability assumptions (e) to obtain a risk score for each TMS.</figcaption></figure>

# 1. Assessment of dam failure consequences through vulnerability indicators

## 1.1. *The spatial variability of the dam failure consequences: from the project to the territory*

Direct and indirect consequences of dam failure (see the Glossary) can be both physically tangible and intangible. As mentioned, a consequence is the combination of the existence of an impacted areas and the vulnerability of the socio-ecological assets within this area.

Mining dam failure represents a good example of how an accidental event generated at the mine might physically overcome the mine site limits and impact the surrounding socio-ecological system. As shown by the Bow-Tie diagram previously presented in Chapter 6, the consequences of the failure affect the mine site, including the potential destruction of its infrastructures and the potential injuries of mine workers. These consequences affect mining operators, who might have to reduce or stop their activity. However, they might affect as well the local communities that rely on the normal functioning of the mine site for instance, as service providers, and on the revenues provided by local miners. If and when the flood exceeds the mine perimeters, it may have severe consequences on local population, public
infrastructures, soil and water resources, biodiversity and thus, the ecosystem services
provided by them and the derived socio-economic services covering the needs of local
population.

Concerning this territory level, two areas are distinguished, based on the type of consequences generated by dam failure. One impacted area is related to the direct consequences of the risk events and it is spatially represented by the flooded area due to the dam failure. The second impacted area is related to the indirect consequences that are caused by the occurrence of the flood and that might affect areas outside the flooded area. Finally, these negative consequences rebound on mining operators which, at last, can be charged of reparatory, compensation and reputational costs, legal proceedings, popular contestations that might jeopardize the mining activity, the success of the mining company and its shareholders and finally potential future business opportunities.

Because of the territorial dimension of our approach, the assessment of the RSa focuses on the consequences at the territory level. The estimation of project-scale risks is an important point of discussion that will be pointed out in the last part of this thesis.

1.2. *Flood consequences and the corresponding indicators*

The consequences considered for the current RSa are listed in Table 17. Six consequences are at the mine scale (c1, c2, c3, c16, c17 and c18). These six consequences are not georeferenced because either they occur at the mine site (c1, c2, c3) – in which case no spatialized description at the scale of the mine site is available in this approach at this stage – either they could not be spatially assessed in this study because of unavailable data (c16, c17, c18)

The remaining consequences are at the territory level, i.e. outside the mine site. As mentioned, and they can be direct or indirect and might overcome the flooded area. As shown in Table 17, consequences are structured in a chain of events and they have different natures (e.g. social, environmental). In this study, consequences are assessed through the estimation of the flooded area and the level of socio-ecological vulnerability of Mana river basin, defined as its susceptibility, based on its social and biophysical conditions, to experience damage from a given event, in this case, the failure of the dam (Muller *et al.*, 2011; Behanzin *et al.*, 2016).

Territory vulnerability to flood can be assessed based on different methods, as reviewed
by (Nasiri et al., 2013) and existing frameworks (Graham, 1999; FEMA, 2012). In this study, an
indicator-based approach has been adopted. Table 17 presents vulnerability indicators that
have been identified and selected for each consequence, based on scientific literature
(Appendix 3) and on the currently available data for the Mana river basin. These indicators can
be quantified through different methods, such as numerical modeling, direct measure surveys,
remote observations, etc.. For instance, population vulnerability to floods has been assessed
and discussed by Muller et al., (2011)-based on empirical methods or throughout surveys.
Kumar, (2002) pointed out that floods due to mining dam failures might contribute to the
outbreaks of typhoid – and other water-borne diseases as cholera, jaundice and dysentery – especially due to pollution of domestic water by domestic and industrial waste. Thus, health-related consequences of a tailings dam failure can be indicated by the number of typhoid cases in a territory. Vulnerability of public infrastructures and roads has been estimated based on buildings characteristics (Kreibich et al., 2009; Muller et al., 2011) or roads serviceability (Benedetto and Chiavari, 2010; Balijepalli and Oppong, 2014; Singh et al., 2018).

Socio-ecological vulnerability related to water quality can be assessed analyzing the amount of specific heavy metals that can be remobilized in sediments and surface water resources (Gallay et al., 2018; Goix et al., 2019; Niane et al., 2019), water turbidity Rudorff et al., (2018) or the impacts on aquatic biodiversity (Brosse et al., 2011). Soil erosion can be commonly estimated based on the revised universal soil loss equation (RUSLE) (Renard et al., 1997; Ganasri and Ramesh, 2016; Almagro et al., 2017; Hermann et al., 2018; Karan et al., 2019) or through parameters such as infiltration/ runoff balance as indicator of soil erosion control (Scammacca, 2017).

These were just few examples to show the great variability of indicators that can be used to estimate the selected consequences. The indicators selected in this study are given in Table 17, along with the already available data used for their estimation. No supplementary field surveys or particular numerical investigations have been realized.
<table><caption>Table 17 Consequences for the RSa in this study, the source and target actor, their spatial dimension and the indicators selected for their assessment. In italic, the consequences not assessed in the demonstrative application. In particular, RSa_nc1 to 3 and 16 to 18 are at the scale of the mining project and it has been chosen to do not include them</caption><thead><tr><th>Code</th><th>Consequence description</th><th>Source actor</th><th>Target</th><th>Spatial scale</th><th>Vulnerability indicator</th><th>Source of the data used for the assessment</th></tr></thead><tbody><tr><td>c1</td><td>Destruction of the dam</td><td>Operator → Operator</td><td></td><td rowspan="3">Project</td><td>Dam cost</td><td>Appendix 5</td></tr><tr><td>c2</td><td>Direct death and injuries of workforce</td><td>Operator → Operator</td><td></td><td>Maximal n° of workers at mine site</td><td>Appendix 5</td></tr><tr><td>c3</td><td>Destruction of mining infrastructures</td><td>Operator → Operator</td><td></td><td>Total CAPEX</td><td>Appendix 5</td></tr><tr><td>c4</td><td>Direct impact on local population</td><td>Operator → Civil Society</td><td></td><td>within FA</td><td>Population density per land-use type</td><td>Insee, 2015; Audeg, 2016; ONF 2017; Audeg, 2018; 2013, private data; https://data.gouv.fr consulted the 11.04.2019</td></tr><tr><td>c5</td><td>Degradation of public infrastructures (buildings, roads)</td><td>Operator → Civil Society</td><td></td><td>within FA</td><td>N° and type of buildings and roads</td><td>Adapted from https://eauguyane.fr</td></tr><tr><td>c6</td><td>Reduction of surface water ecosystem services</td><td>Operator → Ecosystem</td><td></td><td>within FA</td><td>Expert-based ecological and chemical status of surface water</td><td>Georeferenced from Blancaneaux, 2001; Marius, 1966; Marius, 1969; Blancaneaux et al., 1973; Turenne, 1973 Balland et al., 2005; Parizot, 2011; WWF, 2013; SISE and ARS, 2015 (private)</td></tr><tr><td>c7</td><td>Run-off and soil degradation</td><td>Operator → Ecosystem</td><td></td><td>within FA</td><td>Soil permeability</td><td>ARS, 2013; ARS, 2016</td></tr><tr><td>c8</td><td>Reduction of drinking water public provisioning service</td><td>Operator → Civil Society</td><td></td><td rowspan="3">Territory</td><td>outside FA</td><td>N° of people dependent from the public water points</td></tr><tr><td>c9</td><td>Reduction of drinking water provisioning service (other than public)</td><td>Operator → Civil Society</td><td></td><td>outside FA</td><td>Number of households without available public drinking water</td></tr><tr><td>c10</td><td>Reduction of biodiversity-related ecosystem services</td><td>Operator → Ecosystem</td><td></td><td>within FA</td><td>Biodiversity potential (ONF, 2015)</td><td>Adapted from ONF, 2015</td></tr><tr><td>c11</td><td>Reduction of forest-related ecosystem services (wood production)</td><td>Operator → Ecosystem</td><td></td><td>within FA</td><td>Wood quality (ONF, 2015)</td><td>Adapted from ONF, 2015</td></tr><tr><td>c12</td><td>Reduction of esthetic ecosystem services (landscape degradation)</td><td>Operator → Ecosystem</td><td></td><td>within FA</td><td>Landscape quality (ONF, 2015)</td><td>Adapted from ONF, 2015</td></tr></tbody></table>
<table><tr><td>c13</td><td>Reduction of crop production service</td><td>Operator → Civil Society</td><td>within FA</td><td>Soil agronomic potential</td><td>Georeferenced from Blancaneaux, 2001; Marius and Arthur, 1966; Marius, 1969; Blancaneaux et al., 1973; Turenne, 1973</td></tr><tr><td>c14</td><td>Health Impacts</td><td>Operator → Civil Society</td><td>outsid e FA</td><td>Typhoide vulberability (ARS, 2012)</td><td>Georeferenced from ARS, 2013</td></tr><tr><td>c15</td><td>Social struggle</td><td>Operator → Population</td><td>outsid e FA</td><td>Avg worker salary (= potential loss of incomes for local families)</td><td>Not available</td></tr><tr><td>c16</td><td>Social opposition</td><td>Civil Society → Operator</td><td>outsid e FA</td><td>N° of participants in opposition groups</td><td>Not available</td></tr><tr><td>c17</td><td>Public sanctions</td><td>Public authority → Operator</td><td>outsid e FA</td><td>Maximal sanctions stated by law</td><td>Not available</td></tr><tr><td>c18</td><td>Financial loss for the operator</td><td>Operator → Operator</td><td>Project</td><td>outsid e FA i12+ refused permits + compensation measures</td><td>Not available</td></tr></table>
## 2. Calculation of the flooded area

A consequence is the combination of two elements: an impacted areas (i.e. a flooded area) and the vulnerability of the socio-ecological components of the territory system within this area. This section presents the assessment of the estimated impacted areas. First, a general presentation of existing current methods is presented. Then the methods selected for this current application and the results are presented and discussed.

### 2.1. Literature review of existing methods to estimate the flooded area

Dam failure is a complex phenomenon to analyze and the methods to assess and map flooded areas are widely diversified (Razack, 2014). From a general point of view, these methods are usually based tools that are not specific to mining and involve the coupling of hydrological (e.g. rainfall-runoff balance analysis) and hydraulic modeling (e.g. water level, flood velocity and propagation, dam break outflow assessment), based on fluid mechanics (Pilotti et al., 2014; Liu, 2018) and uncertainty analysis. These methods might involve the assessment of floods generated by different types of dams. Sometimes they are inspired from natural flood management practices. For instance, specific national guidelines have been realized to address dam flood management in U.S. (Wahl, 1998; FEMA, 2012; FEMA, 2016), India (CWC, 2018), Australia (DNRME, 2008) and they could be applied to mining dam failures.

Most of the methods used to estimate the area flooded by a dam failure are synthetized in the bibliographic review presented in Appendix 11. One of the most used models is HEC-RAS, developed by the U.S. Army Corps of Engineers, that allows detailed computation to identify the flooded area (Duressa and Jubil, 2018). Also, DMBRK, FLDWAV or MIKEFLOOD (Razack, 2014) allow precise assessment based on the characteristics of the study area and on the geometry of the dam. A simplified and performant version of these models is SMPDBK, which requires less input data (e.g. stream centerline, cross sections, and information regarding the storage and failure of the dam being modeled). Globally, such methods might demand a considerable amount of data such as dam characteristics, dam break parameters and its geometry, land use and coefficients for soil roughness (i.e. Manning values), and boundary conditions (e.g., discharge, water level) ((Zhu, 2004; Dorn et al., 2014).

Besides computational models, even more detailed analysis might involve experimental modeling for fluid-dynamics and rheological studies, in order to understand the movement of the flow on the terrain, especially in the case of tailings dam failure, where the outflow is not only composed with water (Liu, 2018). Blight et al., (1981) suggest a mathematical model to describe flow of tailings slurry from a breached dam. In other cases, specific physical and experimental modeling are developed to address failures caused specifically by overtopping (Wu and Qin, 2018; Larese et al., 2019).
All these methods might be very complex, time-consuming, based on precise information of the dam and they are often performed at the scale of the dam itself. New broader-scale models and modules have been developed over the years directly in GIS softwares (Schauble et al., 2008). Tools such as FloodGIS (Albano et al., 2019), FloodRisk (Mancusi et al., 2015), Geomorphic Flood Area Tool (Samela, 2018) or the specific module for GRASS GIS called r.damflood (Marzocchi et al., 2013) can be used in substitution to the computational models mentioned before, especially for assessment at larger scales. These GIS-based tools are often open-source and in some cases, they might allow as well the assessment of the consequences of the flood on population and the environment. Other modules might be used to obtain information in order to assess the flood-prone areas, such as the module Maximum Flow Path Length (Lindsay, 2016) based on direction equations. Among GIS-based models, for instance, El Morjani et al., (2007) suggest an interesting and easy-to-apply framework to estimate an index at the country level based on a statistical approach requiring broad-scale data concerning land use and cover, DEM, soil type and texture, lithology, flow accumulation volume and distance from the accumulation path and rainfall data.

Concerning the specific application to mining dams, Concha Larrauri, (2017) and Concha Larrauri and Lall, (2018) propose a method for estimating the reach of a mining dam failure using only terrain data, the capacity of the pond and the height of the dam. This method uses algorithms based on the updating of the empirical equations by Rico et al., (2008b) using historical dam failure accident data. To our knowledge, it is the only method specifically developed for tailings dam failures that have been found so far in the literature and that allows the large-scale assessment of flooded areas. Furthermore, the method can be applied on multiple coexistent mines.

Since the purpose of this study is to test the feasibility of our approach, no detailed information is needed. Therefore, considering the limitations of the mentioned models requiring important input data, we decided to use the *method proposed by Concha Larrauri* (2017) for the estimation of the flooded area.

## 2.2. *The method used to estimate the flooded area*

In this study, two methods are used, based on empirical equations derived from historical tailings dam failure databases, relief, and expert-based GIS processing of flow accumulation areas. Both of the methods represent easy-to-use and GIS-based tools relying on few input data such as dam height and storage capacity (available in Table 10) and the Digital Elevation Model (DEM), available by NASA's SRTM. They are not to be considered as substitutive of more precise and detailed hydrological and hydraulic methods for flood-prone areas identification.
Fig. 56 presents a simplified conceptual flowsheet of the two methods used in this study to estimate the flooded areas due to a mining dam failure. The *first selected terrain-based method* is proposed by Concha Larrauri, (2017) and Concha Larrauri and Lall (2018). It involves the estimation of the volume released by the failure and the maximal run-out distance of the tailings per quartiles (Q50, Q25, Q75) (Fig. 57). The required input data concern both the technical features of the mine and, thus, of the mining dam (i.e. stored volume and dam height) and the characteristics of the surrounding environment (i.e. DEM). The mining dam parameters (e.g. dam height and storage capacity, as from Table 18) have been directly input on the web application https://columbiawater.shinyapps.io/ShinyappRicoRedo/ in order to obtain the released volume and the run-out distance. The quartile used for this study is the Q50, which represents the median, which is considered as moresolid than the simple average in the case of asymmetric distributions. The spatialization of the resulting outputs consists in a buffer area whose centroid is the mining project and whose radius is the maximal run-out distance of the tailings. Finally, the zones within the buffer with higher elevation than the total elevation of the mining dam are erased. The total elevation of the dam is the sum of the dam height and the elevation value of its localization. Since the selected dam failure mode is overtopping, dam freeboard has not been subtracted by the total elevation of the dam.

However, since elevation is the only external factor influencing the surface of the flooded area, this method allows only to obtain a very large area potentially flooded. Furthermore, no distinction between upstream and downstream is done, which makes difficult to understand the flowpath followed by the flood and, hence, the areas actually impacted. Therefore, the large flood area obtained from this first method is coupled to a second method in order to furtherly precise the flooded area.

<figure><img src="image_58.png" /><figcaption>Figure 56 Simplified conceptual flowsheet of the coupled methodologies used in this demonstrative application to estimate the flood-prone areas due to a mining dam failure by overtopping.</figcaption></figure>
<figure><img src="image_59.png" /><figcaption>Figure 57 Screenshot of the results obtained for the estimation of the flooded area for the TMS-A, based on the first method (Concha Larrauri et al., 2017) on the website: https://columbiawater.shinyapps.io/ShinyappRicoRedo/. The predicted values are estimated in quartiles (Q50, Q25, Q75). We use the average value of the Q50.</figcaption></figure>

<table><caption>Table 18 Input and Output per gold mine-type for the estimation of the released volumes and maximal run-out distance of the tailings based on Concha Larrauri, (2017)</caption><thead><tr><th rowspan="2">Mining dam features</th><th rowspan="2">Code</th><th rowspan="2">Unit</th><th colspan="5">Mining project types</th></tr><tr><th>i-ASM</th><th>ASM</th><th>MSMg</th><th>MSMc</th><th>LSM</th></tr></thead><tbody><tr><td colspan="8"><strong>Input</strong></td></tr><tr><td>Dam height</td><td>H</td><td>m</td><td>4</td><td>4</td><td>15</td><td>20</td><td>54</td></tr><tr><td>Storage capacity</td><td>Vt</td><td>Mm<sup>3</sup></td><td>500,000</td><td>500,000</td><td>1.5</td><td>2</td><td>100</td></tr><tr><td colspan="8"><strong>Output</strong></td></tr><tr><td>Released volume</td><td>Vf (Q50)</td><td>Mm<sup>3</sup></td><td>0.2</td><td>0.2</td><td>0.5</td><td>0.6</td><td>27</td></tr><tr><td>Maximal run-out distance</td><td>Dmax (Q50)</td><td>km</td><td>1.4</td><td>1.4</td><td>4.9</td><td>6.6</td><td>79</td></tr></tbody></table>

We developed a second method through GIS processing using open access GIS modules from Qgis and Whitebox Geospatial Analysis (WGA) tools. This method is relief-based as well, but it allows to identify the downstream path of the flood and the areas of flow accumulation, based on the DEM. The only input data used, except the localization of the mining dam, consist in the DEM. The main steps of GIS processing performed are the following:

1. The *Maximum Flow Path Length* (MFPL) SAGA module is used in QGis to calculate, based on the DEM, the maximum downstream/upstream distance or weighted distance along the flow path for each map cell based on “Deterministic 8” (D8) flow directions (O’Callaghan and Mark, 1984) (Fig. 58a). The module’s output is a raster whose values describe the upstream/downstream flow path length from a cell to the outflow.
However, because of the initial data used, the module does not give any information concerning water level. In the current application, repeated tests and observations of elevation profiles (through the module *Profile Tool*) suggest fixing a specific value to the output values of the MFPL module in order to obtain flow accumulation areas. These accumulation areas represent the flood-prone zones. Since the MFPL module is only based on the DEM, these accumulation zones are not dependent on the localization of the mining dam, dam height or the run-out distance of the tailings.

2. The output of the MFPL is then coupled to the maximal flood perimeter obtained from the first method (Concha Larrauri et al., 2017) (Fig. 58b). This allows to integrate the data related to the mine-type used for the first method, which concern technical features that vary according to each gold mine. Thereby, the extension of the flood areas can be differentiated according to each mine-type and, thus, to each TMS.

3. The third step involves the estimation of the preferential flow direction of the flood using the WGA tool or the GRASS r.drain function already available on QGis (Fig. 58c). The estimation is based on the localization of the mine site (as a point layer) and it results in a flow direction polyline.

4. Finally, all the accumulation zones which are not adjacent, crossing, intersecting and touching the polyline of the preferential flow direction (fig. 58c) are erased. This step is monitored through the *Profile Tool* module and it allows to filter and select only the accumulation areas along the estimated direction of the flood (Fig. 58d).

<figure><img src="image_60.png" /><figcaption>Figure 58 Example of the steps followed for the estimation of the flooded area according to the second method here presented</figcaption></figure>
## 2.3. Validation by comparison with the Brumadinho tailings dam failure

The combination of the two methods used in this study have been tested before on the tailings dam failure of the Córrego do Feijão iron ore mine, near Brumadinho (Brazil). It has been mentioned that at this stage, our purpose is to show the feasibility of the approach proposed in this thesis with the data currently available. This means that the level of precision of the assessment and of this comparison with a real tailings dam failure event is subjected to uncertainties that must be quantified in further studies.

The tailings dam concerned by the failure is the N°1 with a height of 86 meters and a total capacity of 11,7 million cubic meters of tailings. The dam was not active at the moment of the failure. The failure occurred on January 25th, 2019, releasing almost its complete holdings and killing more than 250 people. The slurry wave traveled approximately 7 to 10 km downhill until Rio Paraopeba (Du et al., 2020; Rotta et al., 2020; Thompson et al., 2020). Among recent studies concerning the failure, Raman and Liu, (2019) simulated the failure with the HEC-RAS model, obtaining a great similarity between observed and modelled inundation patterns.

Table 19 shows the outputs estimated from the first method used (i.e. Concha Larrauri, 2017) compared to the real failure event characteristics (Fig. 59a). The estimated values concerning the run-out distance traveled by the tailings are much greater than the actual ones. For instance, based on the real parameters of the dam, the released volume (Q50) is underestimated at 3.5 Mm³, instead of 11,7 Mm³. On the contrary, the run-out distance is highly overestimated (131.6 km against 7-10 km). Even if we assume, as suggested by the model, that 3.5 Mm³ of slurry are released, the estimated run-out distance appears much higher than the real one (35.2 against 7-10 km). However, despite the immediate run-out distance is about 7-10 km, considering the physical accumulation of tailings, the impacts have been recorded over longer distances by recent studies (Thompson et al., 2020)

<table><caption>Table 19 Input and outputs based on the method proposed by Concha Larrauri, (2017) on the case of Brumadinho tailings dam failure and comparison with the real values of the failure.</caption><thead><tr><th>Mining dam features</th><th>Code</th><th>Unit</th><th>No.1 Corrego de Feijao tailings dam (ESTIMATED)</th><th>No.1 Corrego de Feijao tailings dam (REAL)</th></tr></thead><tbody><tr><td>Input</td><td></td><td></td><td></td><td></td></tr><tr><td>Dam height</td><td>H</td><td>m</td><td>86</td><td>86</td></tr><tr><td>Storage capacity</td><td>Vt</td><td>Mm³</td><td>11.7</td><td>11.7</td></tr><tr><td>Output</td><td></td><td></td><td></td><td></td></tr><tr><td>Released volume</td><td>Vf (Q50)</td><td>Mm³</td><td>3.5</td><td>11.7</td></tr><tr><td>Maximal run-out distance of 3.5 Mm³ assumed released</td><td>Dmax (Q50)</td><td>km</td><td>35.2</td><td>7-10</td></tr><tr><td>Maximal run-out distance of 11.7 Mm³ assumed released</td><td>Dmax2 (Q50)</td><td>km</td><td>131.6</td><td>7-10</td></tr></tbody></table>
Concerning the second method, Fig. 59 shows the cartographic results of the estimated accumulation zones (Fig. 59b) overlapping the actual flooded area (Fig. 59c). DEM has been downloaded by NASA's SRTM.

The validation of the proposed method is object to discussion. Indeed, first of all, the comparison between the estimated and the real flooded areas should be based on preliminary criteria (e.g. surface of the flood area, direction and flow geometry). Also, in terms of surface, the difference between the real (~2 500 km²) and the estimated flooded areas (~1400 km²) corresponds to more than 50% of the real flooded area. The direction of the flow is obviously driven by the terrain and it is well considered by the proposed method.

The surface difference can be partly justified by the fact that the proposed method does not account for the topographic depressions due to the alterations induced by the development of the mine site which represent an immediate sink of flood accumulation (Fig.59c). Moreover, the underestimation of the real flooded areas is due as well to the fact that the proposed method does not include flow speed and flood water level, which might be greater than the aptitude of the topography to contain the flood. Finally, although the flooded area is underestimated, its length is overestimated since initial water level is not used as input data.

Despite such considerations, it has been said that the purpose of this study is to show the feasibility of our approach. The methods combined in the thesis to obtain the flooded area do not substitute more solid existing methods. Yet, they allow to obtain the values for the estimated area of a flood that is sufficiently reliable for the objectives of our current study.

<figure><img src="image_61.png" /><figcaption>Figure 59 Comparison between the real flooded area from the Brumadinho tailings dam failure (a) and (b) the area estimated based on the second simple method proposed in the current demonstrative application of the methodology. Finally, the overlapping of the real and estimated areas allows a full comparison, emphasizing the limits of the method here used (c).</figcaption></figure>
## 2.4. Practical estimation of the flood area

Table 18 shows the input and the outputs of the first method (Concha Larrauri, 2017) for each mine-type. Clearly, the outputs of the first method – used to determine the maximal potential perimeter of the flood – increase proportionally to the mine-type and its size. However, it must be pointed out that this method has been developed based on data of historical failures of LSM mines. Therefore, its suitability to smaller mining projects (e.g. ASM, MSM) must be quantified with its uncertainty. Furthermore, the increase of the results for each mine-type is not proportional to the increase of the values of the inputs. This means that, despite it is not the purpose of this study, the interactions between the parameters and their influence on the results must be quantified, for instance, through a sensitivity analysis. The values of the maximal run-out distance represent the radius of the maximal perimeter impacted by the flood for each gold mine type.

The maps of Figure 60 represent the application of the two methods used for the estimation of the flooded areas for each TMS. The methods for the estimation of the flooded area are applied to each mine in each TMS independently, and then the identified areas are merged on Qgis. Thereby, the flooded area considered for each TMS is represented by the total area flooded by the dam failures generated by the corresponding gold mine-types. Finally, for each TMS, the flooded area is classified (1 to 5) based on its surface (Table 20). This latter step is important because it will be integrated for the assessment of the final score of the RSa.

Among all the TMS, the TMS-A presents the largest flooded surfaces, while the artisanal scenario (TMS-C) has the smallest. In terms of potential consequences, there is no comparison between LSM and ASM mines, even when the ratio of their distribution is 1:33, such as in this study. TMS-B, TMS-D and TMS-illegal present approximatively an equivalent total flooded area, despite the great difference in the number and the distribution of the mine-types.

Despite, LSM (TSM-A) have the greatest flood area, it must be pointed out that, because of their often significant financial and technical resources compared to the other gold mine-types, they are susceptible to implement better and more efficient safety measures. This has not been integrated in the application of our approach at this stage and it will be discussed in Chapter 11 of this thesis. Indeed, safety measures may reduce the probability of occurrence of the failure, reducing the global RSa score.
<figure><img src="image_62.png" /><figcaption>Figure 60 Assessment and cartography of the flooded area per each TMS based on the mentioned methodology here used</figcaption></figure>

Table 20 On the left, the thresholds and ranges used to classify the level of consequences for each TMS according to the surface of the flooded areas. This parameter will be integrated at the end of the RSa assessment to obtain the global RSa score per TMS

<table><thead><tr><th colspan="2">Classification thresholds</th></tr><tr><th>Class</th><th>Range (km²)</th></tr></thead><tbody><tr><td>1</td><td>&lt; 1.00</td></tr><tr><td>2</td><td>1.00 – 5.00</td></tr><tr><td>3</td><td>5.01 – 50.00</td></tr><tr><td>4</td><td>50.01 – 100.00</td></tr><tr><td>5</td><td>&gt; 100.00</td></tr></tbody></table>

<table><thead><tr><th>Scenarios</th><th>Flooded area (km²)</th><th>Class</th></tr></thead><tbody><tr><td>TMS-A</td><td>227.76</td><td>5</td></tr><tr><td>TMS-B</td><td>32.89</td><td>3</td></tr><tr><td>TMS-C</td><td>4.35</td><td>2</td></tr><tr><td>TMS-D</td><td>22.71</td><td>3</td></tr><tr><td>TMS-illegal</td><td>35.76</td><td>3</td></tr></tbody></table>
### 3. Mapping socio-ecological vulnerability to flooding

The second component of consequence assessment is the estimation of the socio-ecological vulnerability of the territory where the gold mines are located. Socio-ecological vulnerability is here the susceptibility of the territory to experience damage from a given event (Muller et al., 2011; Behanzin et al., 2016). It is assessed through the quantification of specific indicators for each consequence, as presented in Table 17.

The following sub-sections present the general frameworks, with few examples, developed for the quantification of the vulnerability indicators both within and outside the flooded area. The assessment is based on the semantic and spatialized data concerning soil and water quality, population density etc., provided by local stakeholders or collected through bibliographic reviews and field visits, as presented in Appendix 5.

#### 3.1. Assessment of each vulnerability indicator

Because of the territorial scale of the assessment and the limited available data, already existing thresholds adapted to mining activities in FG have not been found. Furthermore, the few existing thresholds currently available (e.g. water and soil quality) to estimate the potential socio-ecological consequences of mining activities are in some cases qualitative, expert-based and not suited for our purposes. Therefore, specific methods have been purposely built for the assessment of each vulnerability indicator, in adaptation to the FG context. Fig. 61 shows the general framework used for each assessment method. The set of data (which is presented in Appendix 5) involves both semantic and spatialized information in order to allow a GIS-based representation. The assessment of each indicator is performed both quantitatively or qualitatively, depending on the initial data. In this chapter, just few examples of the developed methods to estimate of each vulnerability indicators are given (Fig.62, 63, 64, Appendix 12).

The results of the assessment are normalized and then, the vulnerabilities are converted to a set of scores from 1 to 5, where 1 refers to low vulnerability, and thus, lower potential consequence and 5 refers to higher vulnerability and thus, higher potential consequence. In some cases, few assumptions have been made. For instance, in the case of surface water quality (c6), it has been assumed that the hydrological zones with a global good quality are susceptible to be more vulnerable: hence, in this application, surface water vulnerability is assumed as directly proportional to his quality. The conversion from the estimated values for each indicator to vulnerability scores is based on a matrix with thresholds specifically adapted to the FG case for each indicator.
<figure><img src="image_63.png" /><figcaption>Figure 61 Simplified conceptual flowsheet of the methodology used for the assessment of socio-ecological vulnerability</figcaption></figure>

For instance, the consequences of a dam failure-induced flood on local population might are here assessed based on population density and land-use, divided in three main categories: urban, agricultural, natural (Fig. 63). In other cases, when more data are available, this might be assessed knowing the population at risk but also warning time and evacuation measures different methods (Brown and Graham, 1988). However, no procedure is currently available to accurately predict the consequences of a dam failure on population. According to (Ge et al., 2020), “such estimates are often no better than guesses”.

Another example may concern the consequences on soil-related ecosystem services (Fig. 64 and Appendix 12). These are estimated based on a method specifically developed in this study based on both georeferenced and semantic data from scientific literature. Concerning the first type of data, soil surveys and the related soil maps are limited in FG and concern mainly the coastal areas. Soil resources of the region are less documented also because of the difficulty of access for field surveys and sampling. The only map found on a regional level is a simplified soil map realized by Blancaneaux, (2001) that has been specifically georeferenced on Qgis. Synthetized and adapted semantic data have been associated to each corresponding soil map unit. The original semantic data (e.g. soil depth, texture, C/N ratio, pH) were collected through a throughout bibliographic review of the studies realized over the past 60 years through direct measurements, field surveys, numerical modeling (Marius and Arthur, 1966; Léveque, 1967; Marius, 1969; Blancaneaux et al. 1973; Turenne, 1973; Veillon, 1986; Bravard et al., 1990; Veillon, 1990; Betsch, 1998; Leprun et al., 2001; Ferry et al., 2003; Guedron et al., 2009; Grimaldi et al., 2015; ONF, 2015).

In other cases, original data were already quantified and converted into semi-quantitative classes. For instance, Fig. 62 shows the semi-quantitative and spatialized data concerning the households without direct public drinking water provisioning (c9), according to ARS, (2013) and then adapted according to ARS, (2016). As it is shown, these data are directly converted into vulnerability classes. As another example, the original data used for the assessment of the socio-ecological vulnerability related to biodiversity, landscape quality and
wood production (c10, c11, c12) are based on the Catalog of forest habitats developed by ONF,
(2015) which is briefly presented in Appendix 13.

<figure><img src="image_64.png" /></figure>

Figure 62 Example of extrapolation of data from already existing information. In this case, ARS, (2016) provides a cartography of the households without current drinking water on a municipality basis. The map has been georeferenced and adapted for the current demonstrative application of the proposed methodology
<figure><img src="image_65.png" /><figcaption>Figure 63 Flowsheet of the methodology specifically developed for the assessment of the “population density indicator” used for the estimation of social vulnerability related to the consequence “Impact on local population”</figcaption></figure>

<figure><img src="image_66.png" /><figcaption>Figure 64 Flowsheet of the methodology specifically developed for the assessment of the “soil permeability” and “soil agronomic potential” used respectively for the estimation of socio-ecological vulnerability related to the consequences “Run-off and soil degradation” and “reduction of crop production service”.</figcaption></figure>
3.2. Mapping of the territorial vulnerability within the flooded area

Once each indicator has been assessed and converted into vulnerability classes spatially explicit, 11 maps are obtained (Fig. 65). As shown by the Figure, each map represents the corresponding vulnerability at the scale of Mana river basin, based on each indicator presented in the previous Table 17. All the maps are normalized on a scale from 1 to 5, where 1 refers to a lower vulnerability, and thus, lower potential consequences, while 5 refers to higher vulnerability and potential consequences.

The limits between each polygon depend on the spatial scale chosen for each indicator assessment – or the scale at which the original data were already set – and thus, its resolution. For instance, c4 is estimation is based on a simplified land-use map while c8-9-14 are mapped based on the administrative boundaries of the municipal areas in FG. c10-11-12, on the contrary, have a higher resolution since they are based on a precise mapping realized by the ONF, (2015) through remote observations and local surveys.

<figure><img src="image_67.png" /><figcaption>Figure 65 Assessment and mapping of each socio-ecological vulnerability indicators based on 5 vulnerability classes. These maps are independent from the mining system and on the TMS. Therefore, their applicability is extended to all TMS.</figcaption></figure>
## 4. Estimation of the consequence scores of the RSa

The consequences of the RSa are assessed through the combination of a flooded area, which strongly depends on the mining system, and socio-ecological vulnerability of the territory system (Fig. 66). This process is achieved through geoprocessing methods in GIS software (e.g. intersection, vector masking).

The vulnerability scores are intrinsic to the territory system and, thus, do not change depending on the TMS. This means that the same vulnerability maps are applied to each TMS. On the contrary, the impacted area (i.e. the flooded area in this case) varies according to technical and operational parameters related to the gold mine-type and thus, for each TMS. This section presents the steps followed to obtain a global RSa consequence score from the simple individual vulnerability layers as presented in Fig. 65.

<figure><img src="image_68.png" /><figcaption>Figure 66 Aggregation methods used for the estimation of the global RSa score</figcaption></figure>

### 4.1 Aggregation of the vulnerability scores to obtain total vulnerability map of the territory and combination with the flooded area maps

After their assessment and mapping (Fig. 65), the vulnerability maps are merged on Ggis to obtain an overall vulnerability map of the territory. The result is one map where each polygon represents the intersection of all the merged vulnerability layers (Fig. 67). Then, the geometric mean is calculated. This means that each polygon of the global vulnerability map contains the geometric mean of the vulnerability scores of all the used layer (Fig. 67a). Hence, at this stage, the vulnerability territory is given multiple different scores for each polygon of the map.

Fig. 67b shows the difference in results if the maximal value of each layers would have been calculated instead of the geometric mean.
The total vulnerability map (Fig. 67a) is combined on Qgis with the map of the flooded areas each TMS, as presented in Fig. 60. These represent consequence maps that contains a series of values according to the corresponding polygons. In order to be able to compare the TMS between them, these results must be reduced to a single score for each TMS.

<figure><img src="image_69.png" /><figcaption>Figure 67 Aggregation of all the socio-ecological vulnerability indicators based on the simple methods of the « geometric mean » (a) and the «maximal value" (b)</figcaption></figure>

4.3. Estimation of a final consequence score for the RSa

As shown in Fig. 68, the global RSa consequence score is obtained by combining the
total impacted surface score and the total negative consequences score. The first one is given
in Table 20. The second, on the contrary, is obtained reducing all the values of the geometric
means contained in the maps that combine territorial vulnerability with the flooded areas for
each TMS. Indeed, as mentioned, the fact that each consequence map is represented by
multiple value makes it difficult to compare the TMS. Therefore, these are reduced to a single
score by extracting the maximal value of all the geometric means (indicating the worst of the
mean scores).

Since there is no need to have a spatial representation of this unique score, the final
consequence score is calculated in sheets (Table 21). The total RSa consequences score is
combined with the total impacted surface score – shown in the previous Table 20 – in order to
obtain the global RSa consequence score for each TMS (Table 21). This score is then converted
according to the following equation in order to compare the TMS based on a final score where
5 represent the best territorial scenario and 0 the worst.
$$ \textit{Converted score} = (\textit{max} - \nu) + \textit{min} $$

Equation 1

Where *max* is the maximum value of the negative score (i.e. 5), *ν* is the corresponding value to convert and *min* is the minimum value of the negative score (i.e. 1 or 0 depending if the conversion is on the vulnerability or risk scores) (Table 22).

<figure><img src="image_70.png" /><figcaption>Figure 68 Assessment and mapping of the consequences generated by the dam failure caused by overtopping for each TMS. It is the result of the combination of the flooded area maps and the socio-ecological vulnerability map</figcaption></figure>
<table><caption>Table 21 All the consequence scores combined to obtain the converted global RSa score for each TMS.</caption><thead><tr><th rowspan="2"></th><th colspan="5">Scores per Territorial Mining Scenarios (TMS)</th></tr><tr><th>TMS-A</th><th>TMS-B</th><th>TMS-C</th><th>TMS-D</th><th>TMS-illegal</th></tr></thead><tbody><tr><th>Total RSa impacted surface score</th><td>5</td><td>3</td><td>2</td><td>3</td><td>3</td></tr><tr><th>Total RSa consequences score</th><td>3.3</td><td>2.5</td><td>2.5</td><td>2.5</td><td>2.7</td></tr><tr><th>Global RSa score</th><td>3.3</td><td>1.5</td><td>1</td><td>1.5</td><td>1.6</td></tr><tr><th>Converted global RSa scores</th><td>1.7</td><td>3.5</td><td>4</td><td>3.5</td><td>3.4</td></tr></tbody></table>

<table><caption>Table 22 Conversion rules of the negative consequences</caption><thead><tr><th></th><th>Original negative classes</th><th>Converted negative classes</th></tr></thead><tbody><tr><th>Less negative</th><td>1</td><td>5</td></tr><tr><th></th><td>2</td><td>4</td></tr><tr><th></th><td>3</td><td>3</td></tr><tr><th></th><td>4</td><td>2</td></tr><tr><th>Very negative</th><td>5</td><td>1</td></tr></tbody></table>

## 5. Probability assessment and risk estimation

As mentioned, a risk is considered as the combination of the consequences of a risk event and its probability to occur. The evaluation of the RSa level now implies the estimation of the dam failure probability. In the case of the RSa, the integration of the probability is more complex compared to the RSo. Since it is an accidental scenario, a fundamental step is to identify the factors triggering the dam failure by overtopping, to analyze their interaction and assess the actual probability of occurrence. As mentioned in Chapter 6, the failure rate of tailings dams (1.2%) is much higher than water dams (0.01%) and it is estimated that approximately “three of the world’s 3,500 tailings dams fail every year” (Lyu et al., 2019). However, these data refer often to large-scale mines, because of their potential catastrophic consequences and their mediatization, but also because of the easier traceability compared to medium and small-scale mining projects.

Probability assessment of dam failure can be performed through multiple models, as detailed in Chapter 1. During this thesis, a parallel project has been carried with two students in order to develop a methodology to assess the probability of failure by overtopping based on the combination of multiple triggering factors (e.g. landslide within the pond, wave formation within the pond, impact of the wind on the wave) (Sauer et al., 2019). The methodology is presented but it is not readily applicable, and it needs a further optimization (Appendix 14).

In this study, the probability is assessed based on a simple scale of five classes shown in Table 23, ranging from a value of 0, corresponding to the impossibility for the consequence to occur, to 1, meaning that the consequence is certain (Populaire et al., 2002). For the RSa, we decided to estimate the probability of occurrence of the tailings dam failure based on different
assumptions. It is assumed that the potential efficacy of safety measures and means to reduce the probability of dam failure event to occur depend on gold-mine types and their size in terms of potential technical and financial resources. The larger the gold mine – in size, productivity and financial resources – the better are the safety measures potentially implemented to prevent dam failures. Therefore, one scenario can be imagined where an overall higher probability of occurrence of the dam failure event is assumed (RSa1) (Table 24).

<table><caption>Table 23 Probability classes between 0 (i.e. "impossible") and 1 (i.e. "certain")</caption><thead><tr><td>Probability class</td><td>Value</td></tr></thead><tbody><tr><td>Impossible</td><td>0</td></tr><tr><td>Uncertain</td><td>0.25</td></tr><tr><td>Even chance</td><td>0.5</td></tr><tr><td>Expected</td><td>0.75</td></tr><tr><td>Certain</td><td>1</td></tr></tbody></table>

<table><caption>Table 24 Probability assumptions used for this study for the assessment of RSo and RSa scores.</caption><caption>Probability scores</caption><thead><tr><th>RS</th><th>Assumptions</th><th>i-ASM</th><th>ASM</th><th>MSMg</th><th>MSMc</th><th>LSM</th></tr></thead><tbody><tr><td>RSo</td><td>Certain occurrence of the risk events</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>RSa_1</td><td>Overall higher probability of occurrence of the event</td><td>1</td><td>0.75</td><td>0.5</td><td>0.5</td><td>0.5</td></tr></tbody></table>

This probability assumption has been integrated to the geometric means contained within the "consequence map" (Fig. 68). The probability scores have been multiplied to the consequence score of each layer and then, the maximum value has been extracted. This means that the process followed is the same as the one presented in the previous sub-sections, with the only difference that the geometric means have been weighted with the probability scores.

As example, Fig. 69 shows the comparison between the consequence map of TMS-A without the probability (Fig. 69a) and with the assumed probability scores (Fig. 69b). The overall risk is much lower since the dam failure occurrence is assumed to be uncertain (i.e. probability score equal to 0.75).
<figure><img src="image_71.png" /><figcaption>Figure 69 Comparison of the cartographic results with and without probability (RSa_1) for the TMS-A</figcaption></figure>

## 6. Calculation of the RSa risk score for each TMS

At this stage, the potential flooded area by the dam failure and the vulnerability of the territory system have been estimated. These two components have been combined in order to obtain a global RSa consequence score. Finally, since the probability assessment has not been detailed during this thesis, a probability assumption has been given in Table 24.

As shown in the following Table 25, the probability assumption has been integrated in the assessment in order to obtain a risk score for each TMS. Table 25 shows, first, the total impacted area scores and the total consequences scores. The total consequences scores are presented with and without the integration of the probability assumption presented in Table 25a. Then, the global scores of each TMS are obtained combining the total impacted surface and total consequences scores. These are shown with and without the probability assumption (Table 25b). Finally, since the scores represent negative consequences scores, they are converted to allow their further combination with the RSo scores (which represent positive values) (Table 25c) (Chapter 8) and obtain the final global TMS scores (Chapter 9). As mentioned, the conversion rule is given by Eq.1 and Table 22. The lower the final score, the higher is the risk level.
<table><caption>Table 25 Final scores of the current RSa. The table shows the scores of the impacted area based on the surface and of the mapped consequences combined with the probability assumption. Then, the global scores are obtained combining the impacted surface scores and the total consequences scores. Finally, the converted scores are shown in order to combine them with the RSo score and obtain an overall TMS score (Chapter 9).</caption><thead><tr><th rowspan="3">Number and types of mines</th><th colspan="5">Scores per Territorial Mining Scenarios (TMS)</th></tr><tr><th>TMS-A</th><th>TMS-B</th><th>TMS-C</th><th>TMS-D</th><th>TMS-illegal</th></tr><tr><th>(1 LSM)</th><th>(6 MSMc)</th><th>(33 ASM)</th><th>(1 MSMc + 4 MSMg + 20 ASM)</th><th>(758 i-ASM)</th></tr></thead><tbody><tr><td>Total impacted surface score</td><td>5</td><td>3</td><td>2</td><td>3</td><td>3</td></tr><tr><td>Total consequences score:</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>a) Without probability</td><td>3.3</td><td>2.5</td><td>2.5</td><td>2.5</td><td>2.7</td></tr><tr><td>Risk score RSa_1)</td><td>1.6</td><td>1.3</td><td>1.9</td><td>1.9</td><td>2.7</td></tr><tr><td>Global scores:</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>b) Global RSa score (no probability)</td><td>3.3</td><td>1.5</td><td>1</td><td>1.5</td><td>1.6</td></tr><tr><td>Global RSa score (RSa_1)</td><td>1.6</td><td>0.8</td><td>0.7</td><td>1.1</td><td>1.6</td></tr><tr><td>Converted global scores:</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>c) Converted global scores (no probability)</td><td>1.7</td><td>3.5</td><td>4</td><td>3.5</td><td>3.4</td></tr><tr><td>Converted global scores (RSa_1)</td><td>3.4</td><td>4.2</td><td>4.3</td><td>3.9</td><td>3.4</td></tr></tbody></table>

## 7. Alternative estimation of the RSa score for each TMS

The method proposed until here can be twisted and complex to perform. Nevertheless, it allows to synthetize all the information directly into one score, especially when initial data might not be always spatialized. Furthermore, at this stage, the surface of the impacted area is integrated within the final score.

Another easier way to proceed to the estimation of the RSa score would be to assign the probability scores directly to the flooded areas before consequences assessment following the approach shown in Fig 70. In this case, the flooded area of each gold mine-type incorporates the scores of probabilities of occurrence given in Table 24. Once the flood map is combined with the vulnerability map for each TMS-containing, as mentioned, the geometric means of the vulnerability scores of each polygon – only the maximal values of these means within the flooded area are considered. Then, the maximal scores are multiplied with the vulnerability scores to obtain a final risk score, which is not spatialized. In the case of the mixed scenario (TMS-D), since multiple gold mine-types are present, three maximal values are obtained. In this case, the geometric mean of these values is extracted and then converted into positive scores according to Table 22 and Eq.1.
<figure><img src="image_72.png" /><figcaption>Figure 70 Conceptual flowsheet of the second alternative way to integrate probability for the estimation of the RSa scores</figcaption></figure>

The obtained results are given in Table 26. As it is shown, compared to the method previously used (Table 25), the scenarios presenting the “best” and “worst” scores do not change, then being respectively the TMS-B and the TMS-illegal. However, the ranking of the TMS changes between the remaining scenarios. TMS-C, which in the first method is the second “best” scenario, is now the second “worst”. TMS-D obtains the second place in the second method and the TMS-A, which was one of the worst in the first method has now a score superior to 4.

In this case, the changes in the results are given essentially by the fact that in this second method, more importance is given to the probability scores – which are assumed based on the potential safety measures implemented in each gold mine-type. Here, the scores related to the impacted area are not considered. Hence, TMS involving LSM or MSM types present a better score while the artisanal types the worst ones. This was not the case in the previous method where the surface of the impacted area outweighed the probability of failure. This explains why, for instance, the TMS-A, which has the biggest surface area despite its lower probability of failure, is ranked 4th on 5 in the first method.

<table><caption>Table 26 RSa scores of each TMS based on a second alternative way to proceed to the assessment</caption><thead><tr><th>TMS</th><th>TMS-A</th><th>TMS-B</th><th>TMS-C</th><th colspan="3">TMS-D</th><th>TMS-illegal</th></tr><tr><th>Number and types of mines</th><th>1 LSM</th><th>6 MSMc</th><th>33 ASM</th><th>1 MSMc</th><th>4 MSMg</th><th>20 ASM</th><th>758 -iASM</th></tr></thead><tbody><tr><td>Maximal value of the geometric means of the consequence scores</td><td>3.26</td><td>2.52</td><td>2.49</td><td></td><td>2.52</td><td></td><td>2.71</td></tr><tr><td>Probability scores</td><td>0.5</td><td>0.5</td><td>0.75</td><td>0.5</td><td>0.5</td><td>0.75</td><td>1</td></tr><tr><td>RSa scores</td><td>1.63</td><td>1.26</td><td>1.86</td><td colspan="3">1.54 (= geometric mean)</td><td>2.71</td></tr><tr><td>Converted RSa scores</td><td><strong>4.37</strong></td><td><strong>4.74</strong></td><td><strong>4.13</strong></td><td></td><td><strong>4.46</strong></td><td></td><td><strong>3.29</strong></td></tr></tbody></table>
Conclusion

Chapter 7 aims to assess the RSa score. The RSa is based on the central risk event of a tailings dam failure caused by overtopping. The assessment has been based on the combination of multiple components: a flooded area and the socio-ecological vulnerability of Mana river basin. The flooded area has been estimated through the combination of two basic methods according to the available data. Specific indicator-based methods have been developed and adapted to FG for the estimation of territorial vulnerability (Table 17). After the aggregation of all the components and the integration of assumed probability scores, the final RSa scores for each TMS scores have been obtained.

The estimation of the RSa has been the main focus of this study. The assessment has been adapted to the lack of data and the wide resolution of our approach. Despite a significant level of uncertainty that must be quantified in the future studies, the estimation of the RSa scores has been rapid, simple and adapted to the existing information. It allowed the obtention of final scores that, combined with the following RSo score – that has been only introduced in this study – will enable to compare the TMS based on a global score.
# Chapter 8. Assessment of the ordinary risk scenario (RSo)

In this thesis, the main focus was given to the RSa assessment. Nevertheless, it has been explained that our approach aims to compare and score multiple territorial mining scenarios (TMS) based on the assessment of the risks related to two different operating conditions. Since this wouldn't be possible based on the assessment of the RSa only, this chapter has the purpose to present the assessment of the risk level of the ordinary risk scenario (RSo) for each TMS developed in this study. Only an example will be exposed hereafter, following the steps presented in Fig. 71.

The RSo includes both positive and negative risks, that are evaluated through the selection of specific indicators and the definition of thresholds, in order to obtain consequences classes. The assessment of the level of this RSo focuses mainly on the consequences presented in Table 16 and the following Table 27. Indeed, the probability of occurrence of the risks in the RSo is considered to be certain (section 2.2.), because such events occur inexorably when a mine operates. As mentioned in Chapter 2, despite their occurrence is certain, these events are here considered as “risks”, to distinguish between the “normal” and “abnormal” operating conditions of a mining project.

Once the selected consequences are assessed individually, they are combined all together and then integrated with the probability, to obtain a global RSo score for each TMS. Since no spatialized data are used for the RSo in this study, its assessment does not have a cartographic rendering, unlike the assessment presented in Chapter 7.

<figure><img src="image_73.png" /><figcaption>Figure 71 Conceptual simplified framework of the methodology used for the assessment of the RSo</figcaption></figure>

## 1. Evaluation of the positive and negative consequences for each TMS

### 1.1. *Selection of consequences indicators*

Table 27 presents 4 positive and 1 negative consequences chosen for the current RSo based on the risk databases presented in Table 16, Chapter 6. The only negative consequence selected is landscape degradation, considered as a overall negative environmental footprint of
the mining project. As it will be discussed, this dissymmetry has a significant impact on the final scores.

The first step consists in the selection of specific indicators representing each consequence. The selected indicators are the result of a thorough bibliographic review both. Most of the indicators have been chosen based on:

- the current frameworks developed for social impact assessment in the mining sector reviewed by Mancini and Sala, (2018),

- the indicators proposed by the EO-Miners project (Falck and Spangenberg, 2014),

- the Global Report Initiative (GRI, 2013, and by Azapagic, 2004).

The proposed indicators are specific to the mining sector and cover a great range of consequences, both positive and negative, sometimes accounting for the different sizes and types of mining projects (Falck and Spangenberg, 2014).

<table><caption>Table 27 Consequences chosen for the current RSo and corresponding consequence indicators that have been chosen</caption><thead><tr><th>Type of consequences</th><th>Code</th><th>Consequences</th><th>Consequence Indicator</th></tr></thead><tbody><tr><td rowspan="4">Positive</td><td>RSo_pc1</td><td>Gold production (Raw material supply)</td><td>Annual gold production (kg/yr)</td></tr><tr><td>RSo_pc2</td><td>Direct jobs opportunities</td><td>Number of miners or Total direct jobs/Annual gold production ratio</td></tr><tr><td>RSo_pc3</td><td>Expansion of social infrastructures (e.g. roads, energy, services)</td><td>Total royalties (k€)</td></tr><tr><td>RSo_pc4</td><td>Support of local education, research and skill development</td><td>Total royalties (k€)</td></tr><tr><td>Negative</td><td>RSo_nc1</td><td>Landscape degradation / deforestation</td><td>Topographic footprint of the mine site (ha)</td></tr></tbody></table>

## 1.2. Definition of consequences thresholds

The choice of the consequence's indicators is followed by the definition of thresholds, that help classifying the level of each consequence. This means to divide the range of possible values for the TMS – and not for the project-types individually – in different classes according to the intensity of the consequence. For instance, according to our parameters, one ASM mine employs approximately 15 miners (see Table 12). This means that the TMS-C, implying 33 ASM mines, can create direct jobs for 495 people. At the same time, for the same amount of gold produced – which is the territorial objective on which the TMS were created in Chapter 5 – the TMS-B (six MSMc) can create direct jobs for 1,200 people. Therefore, for each consequence, the range of possible levels cumulated in the TMS has been divided into 5 classes (Table 28). Nevertheless, this classification has been realized just for the demonstrative purposes of the study, since no references have been found for FG gold mining sector.
Since the consequence's indicators are evaluated five classes have been defined based on the consequence level considered within the threshold, where "1" is equal to the minimum value (i.e. least positive for the positive consequences and least negative for the negative ones) and "5" to the maximum one (very positive for the positive consequences and very negative for the negative ones) (Table 28). Therefore, for positive risks, class "1" means that the positive consequence is very low, or that the output is "not positive at all" while for class "5", the consequence is highly positive. On the other hand, intensity of negative risks is inversed: a negative risk assessed as class "1" is considered very low, while a class "5" is considered highly negative.

<table><caption>Table 28 Class thresholds defined for each range of values of each considered consequences in the RSo.</caption><thead><tr><th rowspan="2">Consequence</th><th rowspan="2">Unit</th><th colspan="5">Class thresholds</th></tr><tr><th>1</th><th>2</th><th>3</th><th>4</th><th>5</th></tr></thead><tbody><tr><td>Gold production</td><td>Kg/year</td><td>&lt; 10</td><td>10 – 100</td><td>101 – 1,000</td><td>1,001 – 10,000</td><td>&gt; 10,000</td></tr><tr><td>Direct jobs opportunities</td><td>n</td><td>&lt; 50</td><td>50 – 200</td><td>201 – 500</td><td>501 – 1,000</td><td>&gt; 1,000</td></tr><tr><td>Expansion of social infrastructures</td><td>k€</td><td>&lt; 500</td><td>500 – 1,000</td><td>1,001 – 10,000</td><td>10,001 – 25,000</td><td>&gt; 25,000</td></tr><tr><td>Support of local education, research and skill development</td><td>k€</td><td>&lt; 500</td><td>500 – 1,000</td><td>1,001 – 10,000</td><td>10,001 – 25,000</td><td>&gt; 25,000</td></tr><tr><td>Landscape degradation</td><td>ha</td><td>&lt; 100</td><td>100 – 500</td><td>501 – 1,500</td><td>1,501 – 8,000</td><td>&gt; 8,000</td></tr></tbody></table>

## 1.3. Final assessment of each consequence

As mentioned, the risk assessment does not focus on each single project but on the given TMS. For instance, the RSo assessment of TMS-A, which includes one LSM, is performed based on the values resulting from this one project-type alone. For the TMS-C (i.e. 33 ASM), the assessment corresponds to the cumulative level of consequences of 33 ASM types. In this study, the cumulative level of consequences is given by the sum of the values per gold-mine types in each TMS, as shown in Table 29. This table presents the evaluated consequences based on the assessment of their indicators for each TMS. The levels of consequences are then classified (Table 30) based on the classes of thresholds presented in Table 29.

As shown by Table 29 and Table 30, the values and scores of the first positive consequence (i.e. gold supply) do not differ for each TMS. This is actually normal since the indicator chosen to assess the gold supply is annual gold production, which is the same parameter used to define the "territorial objectives" and to build the TMS in Chapter 5. The indicators used to evaluate the level of expansion of local infrastructures and of support to local education and research by the TMS are represented by mining royalties. These, are calculated based on the rates defined by the French Fiscal Code per kilogram of gold produced by a mine,