Polycyclic aromatic hydrocarbon concentrations, mutagenicity, and Microtox® acute toxicity testing of Peruvian crude oil and oil-contaminated water and sediment

Reátegui‐Zirena, Evelyn G.; Stewart, Paul M.; Whatley, Alicia; Chu-Koo, Fred William; Solí­s, Víctor Sotero; Merino-Zegarra, Claudia; Vela-Paima, Elías

doi:10.1007/s10661-013-3527-2

Cited by 13 publications

(6 citation statements)

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“…The results presented in the third row of Table 10 are parallel to the results reached by [87], which also correspond to detection of oil spills in optical satellite images. In the case of our study, instead of detecting oil spills on the surface of the water such as in [87], the detection was performed in lanes built in the vicinity of the Marañon River in order to support oil pipelines. A significant oil spill turns the soil impermeable causing accumulation of water around the pipelines, whereas those lanes should normally present either dry or just humid soil around the pipelines.…”

Section: Discussionsupporting

confidence: 78%

“…Therefore, the present achieved results suggest that the incongruence occurs because, contrary to the non-contextual classifier (decision tree), the contextual one (boost) is able to detect the water, even for cases in which there is high turbidity in the water of the studied river or reservoirs, because of the high concentration of brown mud. The results presented in the third row of Table 10 are parallel to the results reached by [87], which also correspond to detection of oil spills in optical satellite images. In the case of our study, instead of detecting oil spills on the surface of the water such as in [87], the detection was performed in lanes built in the vicinity of the Marañon River in order to support oil pipelines.…”

Section: Discussionsupporting

confidence: 78%

“…Additionally, the results presented in Table 8, which are related to the Arava Valley, have demonstrated that neither the non-contextual nor the contextual classifier classify crude oil as water in a dry environment, which corroborates the causes of the incongruence previously mentioned. [87]. The image shows the crude oil around the pipeline.…”

Section: Discussionmentioning

confidence: 99%

“…The image of the Marañon River in Peru, acquired by the Landsat 8 satellite on 27 August 2016, was chosen because its water received high quantity of crude oil on 22 August 2016. This oil spill was the consequence of a Colombian environmental disaster caused by a pipeline disruption (4 • 48 45.6"S 75 • 23 56.8"W) [87]. The crude oil is present around almost all extensions of the same pipeline since it was the source of other similar environmental disasters in the preceding three years.…”

Section: Methodsmentioning

confidence: 99%

“…The pipeline, in the middle-bottom of the image, suffered a severe disruption on 22 August 2016. As a consequence, the lane and the Marañon River received high quantity of crude oil causing an environmental disaster (4 • 48 45.6"S 75 • 23 56.8"W)[87]. The image shows the crude oil around the pipeline.…”

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confidence: 99%

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An Incongruence-Based Anomaly Detection Strategy for Analyzing Water Pollution in Images from Remote Sensing

et al. 2019

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The potential applications of computational tools, such as anomaly detection and incongruence, for analyzing data attract much attention from the scientific research community. However, there remains a need for more studies to determine how anomaly detection and incongruence applied to analyze data of static images from remote sensing will assist in detecting water pollution. In this study, an incongruence-based anomaly detection strategy for analyzing water pollution in images from remote sensing is presented. Our strategy semi-automatically detects occurrences of one type of anomaly based on the divergence between two image classifications (contextual and non-contextual). The results indicate that our strategy accurately analyzes the majority of images. Incongruence as a strategy for detecting anomalies in real-application (non-synthetic) data found in images from remote sensing is relevant for recognizing crude oil close to open water bodies or water pollution caused by the presence of brown mud in large rivers. It can also assist surveillance systems by detecting environmental disasters or performing mappings. Kittler et al. in [13] proposed a taxonomy of anomalies which expanded the concept of anomaly beyond the conventional meaning of outlier. They used sensory data quality assessment [26], contextual [9] and non-contextual [27][28][29][30] classifiers [5,[15][16][17][18], and an incongruence indicator [16][17][18] to identify each type of anomaly [13]. According to this taxonomy [13] anomalies can be, for example, of the types unknown object, measurement model drift, unknown structure, unexpected structural component, component model drift, and unexpected structure and structural components. This taxonomy [13] is well-known and widely accepted by the scientific community because it has the potential to be applied for solving problems in many different research areas [13]. Therefore, studies related to the application of the taxonomy [13] on synthetic data are common, such as in [16][17][18]. However, to the knowledge of the authors, studies have not addressed the practical application of the taxonomy [13] to solve real-world problems [2,[31][32][33], because it remains a challenge for all research areas.Anomaly detection [13] and incongruence [15][16][17][18] are two powerful computational tools from pattern recognition (PR) [3,[11][12][13][14][15][16][17][18]34,35] and computer vision (CV) [3,36]. PR is a scientific area of study dedicated to analyze patterns and regularities in data [3]. PR provides powerful tools [34] for many different applications and research areas [35] such as scientific research, private and public industries, military activities, etc., [14,[21][22][23][24]32,[37][38][39][40][41][42][43][44][45][46][47][48]. For example, PR is important for geosciences as its tools are used to analyze geographical features of environments in digital images from remote sensing, i.e., scenes [11,12,[49][50][51][52][53][54]. Additionally, PR also provides powerful tools to help machine perception...

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Section: Discussionsupporting

confidence: 78%

Section: Discussionsupporting

confidence: 78%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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confidence: 99%

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