Spatial Analysis of Accidental Oil Spills Using Heterogeneous Data: A Case Study from the North-Eastern Ecuadorian Amazon

Juan Durango-Cordero, Mehdi Saqalli, Christophe Laplanche, Marine Locquet and Arnaud Elger
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Juan Durango-Cordero: ECOLAB, Université de Toulouse, CNRS, INPT, UPS, 31400 Toulouse, France
Mehdi Saqalli: GEODE, Université de Toulouse, CNRS, UT2J, 31058 Toulouse, France
Christophe Laplanche: ECOLAB, Université de Toulouse, CNRS, INPT, UPS, 31400 Toulouse, France
Marine Locquet: ECOLAB, Université de Toulouse, CNRS, INPT, UPS, 31400 Toulouse, France
Arnaud Elger: ECOLAB, Université de Toulouse, CNRS, INPT, UPS, 31400 Toulouse, France

Sustainability, 2018, vol. 10, issue 12, 1-13

Abstract: Accidental oil spills were assessed in the north-eastern Ecuadorian Amazon, a rich biodiversity and cultural heritage area. Institutional reports were used to estimate oil spill volumes over the period 2001–2011. However, we had to make with heterogeneous and incomplete data. After statistically discriminating well- and poorly-documented oil blocks, some spill factors were derived from the former to spatially allocate oil spills where fragmentary data were available. Spatial prediction accuracy was assessed using similarity metrics in a cross-validation approach. Results showed 464 spill events (42.2/year), accounting for 10,000.2 t of crude oil, equivalent to annual discharges of 909.1 (±SD = 1219.5) t. Total spill volumes increased by 54.8% when spill factors were used to perform allocation to poorly-documented blocks. Resulting maps displayed pollution ‘hotspots’ in Dayuma and Joya de Los Sachas, with the highest inputs averaging 13.8 t km −2 year −1 . The accuracy of spatial prediction ranged from 32 to 97%, depending on the metric and the weight given to double-zeros. Simulated situations showed that estimation accuracy depends on variabilities in incident occurrences and in spill volumes per incident. Our method is suitable for mapping hazards and risks in sensitive ecosystems, particularly in areas where incomplete data hinder this process.

Keywords: spatial prediction; hydrocarbons; spill estimates; the Amazon; pollution hotspot (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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