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black carbon; EF: emission factor). Table 10. Datasets and calculations used for airborne emission comparisons (APG: associated petroleum gas; BC:

Table 10 black carbon; EF: emission factor). Table 10. Datasets and calculations used for airborne emission comparisons (APG: associated petroleum gas; BC:

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Figure 1. Multiple dimension scales of the vulnerability concept (Birkmann, 2011). potentially reorganizing the territory’s land use.
Figure 1. Multiple dimension scales of the vulnerability concept (Birkmann, 2011). potentially reorganizing the territory’s land use.
vulnerability. Figure 2. Flowchart of the general methodological approach. Modified from the CHARIM project (2005). The spatial approach, risk is absent in areas where there is no overlap between hazard anc exposure component, since not explicitly considered in this thesis, has been displayed with dotted lines, to
vulnerability. Figure 2. Flowchart of the general methodological approach. Modified from the CHARIM project (2005). The spatial approach, risk is absent in areas where there is no overlap between hazard anc exposure component, since not explicitly considered in this thesis, has been displayed with dotted lines, to
Boden et al., 2012; Dlugokencky, 2003; Robalino-Lopez et al., 2014; Simpson et al., 2012). Figure 3. Upstream and midstream crude oil production chain. Oil is produced by extracting techniques at oil wasted off by venting or flaring because of security and safety reasons (Anenberg et al., 2012;
Boden et al., 2012; Dlugokencky, 2003; Robalino-Lopez et al., 2014; Simpson et al., 2012). Figure 3. Upstream and midstream crude oil production chain. Oil is produced by extracting techniques at oil wasted off by venting or flaring because of security and safety reasons (Anenberg et al., 2012;
Table 2. Environmental and health effects of main non-metallic crude oil substances, mostly comprised within TPH (GF = gas flaring; OS = oil spills; OP = unlined oil pits).
Table 2. Environmental and health effects of main non-metallic crude oil substances, mostly comprised within TPH (GF = gas flaring; OS = oil spills; OP = unlined oil pits).
Table 4. Metals found in crude oil, mostly disseminated via oil spills and unlined pits, and their carcinogenic and ecotoxicological effects.
Table 4. Metals found in crude oil, mostly disseminated via oil spills and unlined pits, and their carcinogenic and ecotoxicological effects.
Table 4. (continued) Metals found in crude oil, mostly disseminated via oil spills and unlined pits, and their carcinogenic and ecotoxicological effects (continued). 15 NORMs = Naturally Occurring Radioactive Material.
Table 4. (continued) Metals found in crude oil, mostly disseminated via oil spills and unlined pits, and their carcinogenic and ecotoxicological effects (continued). 15 NORMs = Naturally Occurring Radioactive Material.
Figure 4. Location of the study area. (A) The North-eastern Ecuadorian Amazon (NEA) in Ecuador, bordering Colombia in the North and Peru in the South. (B) The study area within the two NEA provinces of Sucumbios and
Figure 4. Location of the study area. (A) The North-eastern Ecuadorian Amazon (NEA) in Ecuador, bordering Colombia in the North and Peru in the South. (B) The study area within the two NEA provinces of Sucumbios and
‘igure 5. Temporal evolution of land use by main protection status in the province of Sucumbios (part of NEA). Percentage of primary forest (PF) vs. agriculture (AG) as proxy of deforestation. A) Primary forest percentage
‘igure 5. Temporal evolution of land use by main protection status in the province of Sucumbios (part of NEA). Percentage of primary forest (PF) vs. agriculture (AG) as proxy of deforestation. A) Primary forest percentage
2005; SENPLADES, 2013). This resulted in the infamous long-term (1993-2018) Texaco trial
2005; SENPLADES, 2013). This resulted in the infamous long-term (1993-2018) Texaco trial
legal events and conservation measures. Table is adapted from Juteau et al., (2014) and Saqalli et al. (2018). able 5. The three main different periods recognized during the crude oil production in Ecuador, compared with main
legal events and conservation measures. Table is adapted from Juteau et al., (2014) and Saqalli et al. (2018). able 5. The three main different periods recognized during the crude oil production in Ecuador, compared with main
compared with main legal events and conservation measures. Table is adapted from Juteau et al., (2014) and Saqalli et Table 5. (continued) The three main different periods recognized during the crude oil production in Ecuador. of hydrocarbon regulations”; and documentation that indicate the retrieved data is of improved
compared with main legal events and conservation measures. Table is adapted from Juteau et al., (2014) and Saqalli et Table 5. (continued) The three main different periods recognized during the crude oil production in Ecuador. of hydrocarbon regulations”; and documentation that indicate the retrieved data is of improved
Figure 7. Oil blocks in the North-eastern Ecuadorian Amazon. Programme of Socio-environmental Remediation of
Figure 7. Oil blocks in the North-eastern Ecuadorian Amazon. Programme of Socio-environmental Remediation of
Left image: an active oil well. Right image: a well being drilled (Auca oilfield)
Left image: an active oil well. Right image: a well being drilled (Auca oilfield)
Figure 8. (A) The NEA in Ecuador; (B) study area within the two NEA provinces of Sucumbios and Orellana including Figure 8. (A) The NEA in Ecuador; (B) study area within the two NEA provinces of Sucumbios and Orellana including oil fields and oil separation batteries. The names of the human settlements represented are abbreviated: DAY= Dayuma; NL = Nueva Loja (aka. Lago Agrio); SH = Shushufindi; TP = Tarapoa; PUT = Putumayo; COCA = Puerto Francisco de Orellana; JS = Joya de Los Sachas; YUT=Yuturi; DI=Dicaro; TP=Tiputini. Yasuni National Park (Bass et al., 2010; Finer et al., 2008). NIE EO ag OS ee ee ee ee ee ee eee ee eee ee ee eS eee a 0 m.a.s.1., Amazon lowlands), representing a surface area of 35,051 km? (Figure 8A). Upstream | & gas production infrastructures are found in this zone, and associated potentially polluting tivities take place there (Figure 8B). The North-eastern Ecuadorian Amazon (NEA) is a sensitive mazon forest, a biodiversity hotspot. For an illustration purpose, at least 210 mammals, 131 aphibians, 558 birds and 3,213 vascular plant total species richness, have been reported only in yaqq1Nn1 National Park (Race et al ONIN: Finer et al DNNR)
Figure 8. (A) The NEA in Ecuador; (B) study area within the two NEA provinces of Sucumbios and Orellana including Figure 8. (A) The NEA in Ecuador; (B) study area within the two NEA provinces of Sucumbios and Orellana including oil fields and oil separation batteries. The names of the human settlements represented are abbreviated: DAY= Dayuma; NL = Nueva Loja (aka. Lago Agrio); SH = Shushufindi; TP = Tarapoa; PUT = Putumayo; COCA = Puerto Francisco de Orellana; JS = Joya de Los Sachas; YUT=Yuturi; DI=Dicaro; TP=Tiputini. Yasuni National Park (Bass et al., 2010; Finer et al., 2008). NIE EO ag OS ee ee ee ee ee ee eee ee eee ee ee eS eee a 0 m.a.s.1., Amazon lowlands), representing a surface area of 35,051 km? (Figure 8A). Upstream | & gas production infrastructures are found in this zone, and associated potentially polluting tivities take place there (Figure 8B). The North-eastern Ecuadorian Amazon (NEA) is a sensitive mazon forest, a biodiversity hotspot. For an illustration purpose, at least 210 mammals, 131 aphibians, 558 birds and 3,213 vascular plant total species richness, have been reported only in yaqq1Nn1 National Park (Race et al ONIN: Finer et al DNNR)
Table 6. Databases used for spatial estimation of oil spills. were available).
Table 6. Databases used for spatial estimation of oil spills. were available).
Figure 9. Historical number and volumes of oil spills. Number of accidental oil spills per year (individual value: whole period. No datum was recorded from 1991 to 1993 (departure of Texaco).
Figure 9. Historical number and volumes of oil spills. Number of accidental oil spills per year (individual value: whole period. No datum was recorded from 1991 to 1993 (departure of Texaco).
Los Sachas. Auca, LA = Lago Agrio which are located in Pacayacu parish, LT = Libertador in Shushufindi, SC=Sacha in Joya d
Los Sachas. Auca, LA = Lago Agrio which are located in Pacayacu parish, LT = Libertador in Shushufindi, SC=Sacha in Joya d
Figure 11. Variations of the Steinhaus index assessing the similarity between observed and estimated oil spill volume
Figure 11. Variations of the Steinhaus index assessing the similarity between observed and estimated oil spill volume
Images of black carbon released at flare stacks in Shushufindi
Images of black carbon released at flare stacks in Shushufindi
‘igure 12. Location of flaring infrastructures in the NEA. (A) The North-eastern Ecuadorian Amazon (NEA) in
‘igure 12. Location of flaring infrastructures in the NEA. (A) The North-eastern Ecuadorian Amazon (NEA) in
Table 9. Database compilation used for airborne emission estimates and spatial allocation. tudy. Comparison analysis considered the materials, methods and type of analysis implemented O estimate airborne emissions. Other estimates of APG flared volumes were retrieved from night- ime satellite observations of gas flares provided by the National Geophysical Data Centre (NOAA) ind from historical energy statistics compiled by the United Nations (UNDATA). Thus, the mission factors (EFs) used in this study were applied to BC calculations. COz estimates were lirectly retrieved from the Carbon Dioxide Information Analysis Center (CDIAC). The methods en rr : i a ne i. i ey en ei. ie |
Table 9. Database compilation used for airborne emission estimates and spatial allocation. tudy. Comparison analysis considered the materials, methods and type of analysis implemented O estimate airborne emissions. Other estimates of APG flared volumes were retrieved from night- ime satellite observations of gas flares provided by the National Geophysical Data Centre (NOAA) ind from historical energy statistics compiled by the United Nations (UNDATA). Thus, the mission factors (EFs) used in this study were applied to BC calculations. COz estimates were lirectly retrieved from the Carbon Dioxide Information Analysis Center (CDIAC). The methods en rr : i a ne i. i ey en ei. ie |
Figure 13. Temporal patterns of flared gas volumes and corresponding BC emissions in tons. Total oil production Annual oil production seemed to be globally stable during T3, with year-to-year fluctuations Bulletins of the National Board of Hydrocarbons’ website, by oil field, restricted to the study area.
Figure 13. Temporal patterns of flared gas volumes and corresponding BC emissions in tons. Total oil production Annual oil production seemed to be globally stable during T3, with year-to-year fluctuations Bulletins of the National Board of Hydrocarbons’ website, by oil field, restricted to the study area.
Table 11 Total and annual median gas flared and BC emissions in the T3 managerial period. BC is expressed in tons with base (EF=0.5) and upper bound (EF=0.75) emission factors. Average (+SD) values for GHG estimates. Only the four highest oilfields are shown (gas flare and BC). Flare stack values exclude oilfields with no declared flare stacks. oilfields with no declared flare stacks.
Table 11 Total and annual median gas flared and BC emissions in the T3 managerial period. BC is expressed in tons with base (EF=0.5) and upper bound (EF=0.75) emission factors. Average (+SD) values for GHG estimates. Only the four highest oilfields are shown (gas flare and BC). Flare stack values exclude oilfields with no declared flare stacks. oilfields with no declared flare stacks.
Figure 14. Spatial variations of airborne emissions within the North-eastern Ecuadorian Amazon (NEA) from 2003 to 2012. A) Air pollution related Carbon emissions reported at upper and lower bound average values. The values scale show lower and upper EF emissions.
Figure 14. Spatial variations of airborne emissions within the North-eastern Ecuadorian Amazon (NEA) from 2003 to 2012. A) Air pollution related Carbon emissions reported at upper and lower bound average values. The values scale show lower and upper EF emissions.
fable 12. Total and average annual GHG emissions for four oilfields from sampled data collected in 2003, 2007 and 1012 (n =22 oil wells).
fable 12. Total and average annual GHG emissions for four oilfields from sampled data collected in 2003, 2007 and 1012 (n =22 oil wells).
Figure 15. Coloured bars illustrate CHi and CO> estimates with percentiles of CE and Xflared at 0.5. Error bars *+SD calculated using pooled estimators. Rounded data.
Figure 15. Coloured bars illustrate CHi and CO> estimates with percentiles of CE and Xflared at 0.5. Error bars *+SD calculated using pooled estimators. Rounded data.
Figure 16. Location of the study area with rivers and human settlements. (A) The study area within the two provinces Singue; TP = Tiputini.
Figure 16. Location of the study area with rivers and human settlements. (A) The study area within the two provinces Singue; TP = Tiputini.
Table 15. Definition of land use classes in the NEA. biodiversity is found per unit of surface in a given LU. The method for assigning BDv to LU was
Table 15. Definition of land use classes in the NEA. biodiversity is found per unit of surface in a given LU. The method for assigning BDv to LU was
pantropical regions (based on a literature review of 8-40 studies depending on the LU).
pantropical regions (based on a literature review of 8-40 studies depending on the LU).
the biodiversity value (BDv) in the neo-tropical region for the ten LU categories. Figure 17. Classification of land use according to MAGAP-MAE-IEE (2014). Color hues selected from combination 12.4% medium high, 6.7% medium, 8.2% medium low and 0.3% low (Figure 18). Table 17 shows
the biodiversity value (BDv) in the neo-tropical region for the ten LU categories. Figure 17. Classification of land use according to MAGAP-MAE-IEE (2014). Color hues selected from combination 12.4% medium high, 6.7% medium, 8.2% medium low and 0.3% low (Figure 18). Table 17 shows
Figure 18. Ecological vulnerability according to potential biodiversity derived from land use in 2015.
Figure 18. Ecological vulnerability according to potential biodiversity derived from land use in 2015.
Biodiversity Vulnerability Index (BVI).
Biodiversity Vulnerability Index (BVI).
Figure 19. Distribution of protected areas in the NEA. White colored areas indicate no protection status. been declared except for the Biosphere Reserve of Sumaco.
Figure 19. Distribution of protected areas in the NEA. White colored areas indicate no protection status. been declared except for the Biosphere Reserve of Sumaco.
several sites are protected by more than one legal status.
several sites are protected by more than one legal status.
biodiversity and protection status. Figure 21. Difference between ecological integrity and biodiversity vulnerabilities. Positive values indicate high
biodiversity and protection status. Figure 21. Difference between ecological integrity and biodiversity vulnerabilities. Positive values indicate high
Table 19. Spatial congruence analysis of overlapped zones between Protection status and Land Use.
Table 19. Spatial congruence analysis of overlapped zones between Protection status and Land Use.
Figure 22. Addition of ecological integrity and biodiversity indices representing the overall vulnerability of natural
Figure 22. Addition of ecological integrity and biodiversity indices representing the overall vulnerability of natural
Unlined oil pit Shushufindi #61 dubbed “the Presidential pit” Amazon: A modified GIS-DRASTIC approach “Not taking risks one doesn't understand is often the best form of risk management. ’ Chapter IV - Risk assessment of unlined oil pits to groundwater quality in the Ecuadorian
Unlined oil pit Shushufindi #61 dubbed “the Presidential pit” Amazon: A modified GIS-DRASTIC approach “Not taking risks one doesn't understand is often the best form of risk management. ’ Chapter IV - Risk assessment of unlined oil pits to groundwater quality in the Ecuadorian
Figure 23. Location of unlined oil pits in the study area. A: NEA region; B: study area within the two NEA provinces of Sucumbios and Orellana including oilfields and oil stations. Represented human settlements are abbreviated: DAY= Dayuma; NL = Nueva Loja (aka Lago Agrio); SH = Shushufindi; TP = Tarapoa; POM = Pompeya; PUT = Putumayo; COCA = Puerto Francisco de Orellana; JS = Joya de Los Sachas; YUT=Yuturi; DI=Dicaro; TIP=Tiputini. ‘igure 23. Location of unlined oil pits in the study area. A: NEA region; B: study area within the two NEA provinces
Figure 23. Location of unlined oil pits in the study area. A: NEA region; B: study area within the two NEA provinces of Sucumbios and Orellana including oilfields and oil stations. Represented human settlements are abbreviated: DAY= Dayuma; NL = Nueva Loja (aka Lago Agrio); SH = Shushufindi; TP = Tarapoa; POM = Pompeya; PUT = Putumayo; COCA = Puerto Francisco de Orellana; JS = Joya de Los Sachas; YUT=Yuturi; DI=Dicaro; TIP=Tiputini. ‘igure 23. Location of unlined oil pits in the study area. A: NEA region; B: study area within the two NEA provinces
Table 20. Summary of the data used for the different components of groundwater vulnerability and hazard.
Table 20. Summary of the data used for the different components of groundwater vulnerability and hazard.
Figure 24. Hydrogeological settings and aquifer media depicting categories by permeability and corresponding given rating.
Figure 24. Hydrogeological settings and aquifer media depicting categories by permeability and corresponding given rating.
pits with missing data are in bold. Table 21. Descriptive statistics for pit volumetric dimensions and TPH contents. Statistic used for extrapolation to oil
pits with missing data are in bold. Table 21. Descriptive statistics for pit volumetric dimensions and TPH contents. Statistic used for extrapolation to oil
Figure 25. Vulnerability ratings scores obtained with the seven parameters for the modified DRASTIC model
Figure 25. Vulnerability ratings scores obtained with the seven parameters for the modified DRASTIC model
surfaces (Figure 26.). Figure 26. Intrinsic vulnerability index values and corresponding categories for the entire study area. Surface areas corresponding to high, medium high, medium, medium low and low vulnerabilities represent respectively 0.25% (90 km’), 15.3% (4,650.4 km’), 31% (11,110 km), 54% (19,048 km?) and 0.4% (141.4 km’). Nueva Loja, Shushufindi, Coca, and Pompeya are in the highest vulnerable zones, followed by Tarapoa and Tiputini. Levels are classified using equal intervals in vulnerability indices. Figure 26. Intrinsic vulnerability index values and corresponding categories for the entire study area. Surface areas are of similar surface area while low and high vulnerability classes correspond to equal smaller
surfaces (Figure 26.). Figure 26. Intrinsic vulnerability index values and corresponding categories for the entire study area. Surface areas corresponding to high, medium high, medium, medium low and low vulnerabilities represent respectively 0.25% (90 km’), 15.3% (4,650.4 km’), 31% (11,110 km), 54% (19,048 km?) and 0.4% (141.4 km’). Nueva Loja, Shushufindi, Coca, and Pompeya are in the highest vulnerable zones, followed by Tarapoa and Tiputini. Levels are classified using equal intervals in vulnerability indices. Figure 26. Intrinsic vulnerability index values and corresponding categories for the entire study area. Surface areas are of similar surface area while low and high vulnerability classes correspond to equal smaller
DRASTIC index variations according to the single map removal sensitivity analysis (SD: standard deviation). Table 22. Single map removal sensitivity analysis of the DRASTIC index. Influence of the various parameters on
DRASTIC index variations according to the single map removal sensitivity analysis (SD: standard deviation). Table 22. Single map removal sensitivity analysis of the DRASTIC index. Influence of the various parameters on
Table 23. Statistics of the single parameter sensitivity analysis.
Table 23. Statistics of the single parameter sensitivity analysis.
figures illustrating each risk MDR are indicated. A = change of the surface at risk based on potential MDRs. Table 24. Surface areas (in %) corresponding to each risk level, depending on the maximum distance of plume. The
figures illustrating each risk MDR are indicated. A = change of the surface at risk based on potential MDRs. Table 24. Surface areas (in %) corresponding to each risk level, depending on the maximum distance of plume. The
A2. A) Average weights given to the different contamination sources according to survey responses including all
A2. A) Average weights given to the different contamination sources according to survey responses including all
A3. Average weights given to the various contaminant sources, disaggregated by stakeholder.
A3. Average weights given to the various contaminant sources, disaggregated by stakeholder.
Fig C1. Depth to water showing values measured and given ratings. Section C. Risk of groundwater contamination by unlined oil pits.
Fig C1. Depth to water showing values measured and given ratings. Section C. Risk of groundwater contamination by unlined oil pits.
Fig D1. Oil infrastructure density: platforms, stations, flare stacks and mud pits in number per square kilometre, Section D. Density of oil infrastructures by watershed.
Fig D1. Oil infrastructure density: platforms, stations, flare stacks and mud pits in number per square kilometre, Section D. Density of oil infrastructures by watershed.
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