Blue water turns black: economic impact of oil spill on tourism and fishing in Brazilian Northeast

Ribeiro, Luiz Carlos de Santana; Souza, Kênia Barreiro de; Domingues, Édson Paulo; Magalhães, Aline Souza

doi:10.1080/13683500.2020.1760222

Cited by 29 publications

(13 citation statements)

References 21 publications

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“…The socioeconomic vulnerability did not correspond to the areas/states most affected by oil (Table 1), and this pattern indicates that the place where oil arrives and the communities and economic development existing in the affected area are more relevant than the quantity of oil encountered (Soares et al 2020b). To this effect, Ribeiro et al (2020) described that the coastal areas of Piauí, Rio Grande do Norte, and Ceará were severely affected in terms of gross domestic product and employment due to activities directly affected by this oil spill, such as fishing and tourism.…”

Section: Socioeconomic Impactsmentioning

confidence: 98%

“…In this context, the COVID-19 pandemic aggravated the negative effects of the oil spill since impact analysis and monitoring activities of research groups were halted due to the lockdown decrees (Magalhães et al 2021). In addition, COVID-19 generated negative cumulative and synergistic impacts on important economic sectors such as hotels, services, and tourism that were already losing revenues because of the oil spill (Câmara et al 2021;Ribeiro et al 2020). Another group affected by the combination of the oil spill and COVID-19 were traditional human communities engaged in fishing and shellfish collection that had already faced a decline in seafood consumption during the oil spill (Araújo et al 2020;Estevo et al 2021), as they were isolated from the capture and sale of seafood due to COVID-19 (Magalhães et al 2021).…”

Section: Socioeconomic Impactsmentioning

confidence: 99%

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The most extensive oil spill registered in tropical oceans (Brazil): the balance sheet of a disaster

Soares

Teixeira

Bezerra

et al. 2022

Environ Sci Pollut Res

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This article presents a synthesis of information about the massive oil spill in Brazil (2019/2020). The event affected 11 states; however, the majority of the oil residue was collected (~ 5380 tons) near nine states (99.8%) in northeastern Brazil. This spill was not the largest in volume (between 5000 m 3 and 12,000 m 3 ) recorded in tropical oceans, but it was the most extensive (2890 km). This spill develops an overwashed tar that remains mostly in the undersurface drift (non-floating oil plume) below 17 m of depth while on the continental shelf. Ten ecosystems were impacted, with potentially more severe effects in mangroves and seagrasses. Certain negative effects are still understudied, such as effects on tropical reefs and rhodolith beds. A total of 57 protected areas in seven management categories were affected, most of which (60%) were characterized as multiple-use regions. The spill affected at least 34 threatened species, with impacts detected on plankton and benthic communities. Acute impacts were reported on echinoderms, coral symbionts, polychaetes, and sponges with evidence of oil ingestion. Socioeconomic impacts were detected in food security, public health, lodging, gender equality, tourism, and fishing, with reduced sales, prices, tourist attractiveness, gross domestic product, and employment. Moreover, chemical contamination was detected in some states by toxic metals (Hg, As, Cd, Pb, and Zn) and polycyclic aromatic hydrocarbons (acenaphthalene, fluoranthene, fluorene, naphthalene, and phenanthrene). This summary aims to aid in the design of science-based strategies to understand the impacts and develop strategies for the most extensive spill observed in tropical oceans.

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Section: Socioeconomic Impactsmentioning

confidence: 98%

Section: Socioeconomic Impactsmentioning

confidence: 99%

The most extensive oil spill registered in tropical oceans (Brazil): the balance sheet of a disaster

Soares

Teixeira

Bezerra

et al. 2022

Environ Sci Pollut Res

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show abstract

“…Indeed, as an ecological concern, the disasters lead to consequences affecting much of the natural marine environment (Krata and Jachowski 2020), (Krestenitis et al 2019a) and even human health (Webler and Lord 2010; D'Andrea and Reddy 2018). Thus, early detection and immediate warning of the oil spill become crucial to attenuate the environmental consequences, control oil dispersion, and ensure human lives are not in danger (Ribeiro et al 2020).…”

Section: Introductionmentioning

confidence: 99%

“…In 2019, a huge oil spill on the Brazilian coast (Araújo et al 2020) emerged, impacting the environment, tourism business, and shermen. In total, 1009 affected locations were identi ed across 130 municipalities in all nine northeastern states and two southeastern states only ve months after this tragic event (Ribeiro et al 2020).…”

Section: Introductionmentioning

confidence: 99%

Automated detection of oil spills in images: combining a novel feature extraction technique based on the q- Exponential distribution with machine learning models

Negreiros

Lins

Maior

et al. 2022

Preprint

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Oil spills are harmful, with negative environmental, social, and economic consequences. Generally, a risk-based framework involves preventing, detecting, and mitigating these undesirable events. Regarding detection, rapid oil spill identification is essential for mitigation, which fosters the use of automated procedures. Usually, automated oil spill detection involves radar images, computer vision, and machine learning techniques for classification. In this work, we propose a novel feature extraction method based on the q-Exponential probability distribution, named q-EFE. Such a model is suitable to account for atypical extreme pixel values, as it can have the power-law behavior. The q-EFE is combined with machine learning (ML) models, comprising a computer vision methodology to automatically classify images as “with oil spill” or “without oil spill”. We used a public dataset with 1112 Synthetic Aperture Radar (SAR) images to validate our methodology. Considering the proposed q-Exponential-based feature extraction, the SVM and XGB models outperformed deep learning models, including a ResNet50 one, and LBP and GLCM techniques for the biggest dataset size. The obtained results suggest that the proposed q-EFE can extract complex features from SAR images. Combined with ML models, it can perform image classification with satisfactory balanced accuracy.

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“…The effects to wildlife are broad, ranging from exposure of birds (Jenssen, 1994;Stephenson, 1997;Fox et al, 2016) and mammals (Engelhardt, 1987;Bodkin et al, 2002;Ridoux et al, 2004) to oil to toxic, mutagenic and/or carcinogenic effects of polycyclic aromatic hydrocarbons (PAHs) present in crude oil and products based on fossil oil (Hylland, 2006;Abdel-Shafy and Mansour, 2016;Hayakawa, 2018;Honda and Suzuki, 2020). Besides harming the natural environment, oil spills can impair the economy in the affected region (Cohen, 1993;Taleghani and Tyagi, 2017;Ribeiro et al, 2021) and have adverse effects on human health and psychology (D'Andrea and Reddy, 2014;Shultz et al, 2015;Sandifer et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Oil Spill Detection Using Fluorometric Sensors: Laboratory Validation and Implementation to a FerryBox and a Moored SmartBuoy

et al. 2021

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A large part of oil spills happen near busy marine fairways. Presently, oil spill detection and monitoring are mostly done with satellite remote sensing algorithms, or with remote sensors or visual surveillance from aerial vehicles or ships. These techniques have their drawbacks and limitations. We evaluated the feasibility of using fluorometric sensors in flow-through systems for real-time detection of oil spills. The sensors were capable of detecting diesel oil for at least 20 days in laboratory conditions, but the presence of CDOM, turbidity and algae-derived substances substantially affected the detection capabilities. Algae extract was observed to have the strongest effect on the fluorescence signal, enhancing the signal in all combinations of sensors and solutions. The sensors were then integrated to a FerryBox system and a moored SmartBuoy. The field tests support the results of the laboratory experiments, namely that the primary source of the measured variation was the presence of interference compounds. The 2 month experiments data did not reveal peaks indicative of oil spills. Both autonomous systems worked well, providing real-time data. The main uncertainty is how the sensors' calibration and specificity to oil, and the measurement depth, affects oil detection. We recommend exploring mathematical approaches and more advanced sensors to correct for natural interferences.

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Blue water turns black: economic impact of oil spill on tourism and fishing in Brazilian Northeast

Cited by 29 publications

References 21 publications

The most extensive oil spill registered in tropical oceans (Brazil): the balance sheet of a disaster

The most extensive oil spill registered in tropical oceans (Brazil): the balance sheet of a disaster

Automated detection of oil spills in images: combining a novel feature extraction technique based on the q- Exponential distribution with machine learning models

Oil Spill Detection Using Fluorometric Sensors: Laboratory Validation and Implementation to a FerryBox and a Moored SmartBuoy

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