Contextual Thresholds Research Articles

Risk assessment of sediment PAH, BTEX, and emerging contaminants in Chanomi Creek Niger Delta, Nigeria.

This study assessed the levels of polycyclic aromatic hydrocarbons (PAHs), benzene, toluene, ethylbenzene, and xylene (BTEX), and emerging contaminants in Chanomi Creek. Sediment samples were collected between March 2019 and July 2020 to evaluate the concentrations of PAH, BTEX, and emerging contaminants using GC-MS and GC-FID with Headspace extraction. Results indicated mean PAH concentrations were 22.691 ± 15.09µg/kg. The highest individual PAH concentrations were fluorene (7.085µg/kg), naphthalene (4.517µg/kg), and phenanthrene (3.081µg/kg). Carbazole (0.828µg/kg) was discovered as a novel environmental toxin with dioxin-like toxicity and widespread prevalence in sediments. The most common congener (25%) was ethylbenzene, followed by toluene and ortho- and meta-xylene (21%) and benzene (13%). The analysis of diagnostic ratios revealed that the main factors responsible for the presence of PAHs in the study area are the residential use of firewood, emissions from industrial activities, bush burning, and petroleum slicks. The risk assessment indicated that most PAHs exceeded the permissible risk quotient values, suggesting a moderate to high ecological risk. However, cutaneous exposure to PAHs and BTEX was found to have minimal impact on human health, with no significant hazards identified in adults and children. Nevertheless, the study revealed low cancer risks associated with PAH and BTEX compounds for both age groups. The continued discharge of PAHs and BTEX compounds into Chanomi Creek could have significant long-term negative effects on human and aquatic health. Thus, contamination risk awareness programs and the development of stringent contextual thresholds for identified contaminants could enhance environmental and public health protection.

Good people are not like good knives

AbstractIs anything good simpliciter? And can things count as ‘good’ independent of the context in which ‘good’ is used? Traditionally, a number of meta‐ethicists have given positive answers. But more recently, some philosophers have used observations based on natural language to argue that things can only count as ‘good’ relative to ends and contextual thresholds. I will use work from contemporary linguistics to argue that ‘good’ is ambiguous, and that it has a moral disambiguation that attributes a fixed degree of goodness. This implies that things can count as ‘good’ simpliciter, independent of context. Not only does this result provide support for the traditional view, but it also vindicates some aspects of the more recent view.

Pragmatic reasoning and semantic convention: A case study on gradable adjectives

Gradable adjectives denote properties that are relativized to contextual thresholds of application: how long an object must be in order to count as long in a context of utterance depends on what the threshold is in that context. But thresholds are variable across contexts and adjectives, and are in general uncertain. This leads to two questions about the meanings of gradable adjectives in particular contexts of utterance: what truth conditions are they understood to introduce, and what information are they taken to communicate? In this paper, we consider two kinds of answers to these questions, one from semantic theory, and one from Bayesian pragmatics, and assess them relative to human judgments about truth and communicated information. Our findings indicate that Bayesian accounts can model human judgments about what is communicated better than they can model human judgments about truth conditions, and their weakness on the latter is mainly because they fall short in accurately predicting the relevant threshold distributions. We explore the possibility that the overall performance of the Bayesian accounts can be potentially improved when they are supplemented with the threshold conventions postulated by semantic theory. EARLY ACCESS

Pollution, Waste, and the New CE-CBE Integrative Recycling Paradigm in the Knowledge Based Society/Economy

The paper is focused on the anthropic and natural (mattergic and bioeconomic) recycling process of the pollutant products with the possibility to reintroduce in the technological flow a major quantity of the matter with a very low level of energy used with a top level of the informaction (actional information). Is introduced the contextual (2+2)Rs paradigm (reduce, recycle & reuse, recombine-recover) connected to the ENSEC, envinronmental-sociopolitic-economic paradigm of the knowledge based society/economy (KBS/E). The perspective of such a pattern is an optimistic one; so, preserving mattergic resources, even energetic, with a larger contribution of the smart components and technics of the recycling process, we could overcome the contextual thresholds of a future bioeconomy. This represents a new perspective of the recycling process and is considered an important oportunity for the global economy facing the current crisis. In order to have a comprehensive vision of the pollution and waste issue, we are puting together in this paper, in a synergistic-generative significant way (synergy 1+1>2, and significance 1-1≠0), three conceptual and analytical knowledge-spaces: transdisciplinarity as a methodology, globalization vs glocalization as a reality, and sustainable development as a necessity. The linear thermo economic recycling model is compared with the nonlinear, simply circular economic one, in the bio economic context, giving a comprehensive description of what does circular bio economic recycling pattern really means.

Potential Application of Hough Transform to Extract Hot Spot Delineated Boundaries Using Landsat-8 Satellite Images

Coal fires, also known as subsurface fires or hot spots are all-inclusive issues in coal mines everywhere throughout the globe. Aimless mining over a period of past 100 years has prompted large scale damages to the ecosystem of the earth. For example, debasement in nature of water, soil, air, vegetation dissemination and variations in land topography have caused degradation. Research is needed to be more attentive on developing the prospective use of the satellite image analysis for hot spot detection because ground-based hot spots monitoring is time-taking, complex, cumbrous and very expensive. In this paper, a two-stage model has been developed to extract the hot spot delineated boundaries in Jharia coal field (JCF) region. In the first stage, contextual thresholding (CT) technique has been used to classify the hot spot and non-hot spot regions. After thorough processing, hot spots regions have been retrieved and for performance evaluation sensitivity and specificity are calculated, which suggest that hot spots were detected accurately in successful and efficient way. In second stage, the Canny edge detection algorithm is applied to detect the edges of the hot spot regions and then the binary image is generated, which is later converted into a vector image. Finally Hough transform is implemented on the obtained vector images for delineating hot spot boundaries. In future, delineated hot spot boundaries may be used to obtain the expansion or shrinking information of hot spot regions and it can be used for area estimation also.

The Demandingness of Virtue

How demanding is the virtuous life? Can virtue exist alongside hints of vice? Is it possible to be virtuous within a vicious society? A line of thinking running through Diogenes and the Stoics is that even a hint of corruption is inimical to virtue, that participating in a vicious society makes it impossible for a person to be virtuous. One response to this difficulty is to claim that virtue is a threshold concept, that context sets a threshold for what is considered virtuous. On this way of thinking, what counts as virtuous in one society may be more demanding than what passes for virtuous in another. This response seems plausible when considering that virtue-theoretic terms like `honest' are gradable adjectives. Many gradable adjectives, like `tall' and `expensive,' have contextual thresholds that shift depending on the situation, and so is tenable that virtue-theoretic adjectives might function with contextual thresholds as well. A major difficulty for this response, however, is that virtue terms are absolute gradable adjectives, a variety of gradable adjectives that do not require a contextual threshold. Absolute gradable adjectives instead draw their truth conditions from their maximal degree, suggesting that Diogenes and the Stoics were correct to think that virtue is incompatible with even a small degree of vice.

Persistent Hot Spot Detection and Characterisation Using SLSTR

Gas flaring is a disposal process widely used in the oil extraction and processing industry. It consists in the burning of unwanted gas at the tip of a stack and due to its thermal characteristic and the thermal emission it is possible to observe and to quantify it from space. Spaceborne observations allows us to collect information across regions and hence to provide a base for estimation of emissions on global scale. We have successfully adapted the Visible Infrared Imaging Radiometer Suite (VIIRS) Nightfire algorithm for the detection and characterisation of persistent hot spots, including gas flares, to the Sea and Land Surface Temperature Radiometer (SLSTR) observations on-board the Sentinel-3 satellites. A hot event at temperatures typical of a gas flare will produce a local maximum in the night-time readings of the shortwave and mid-infrared (SWIR and MIR) channels of SLSTR. The SWIR band centered at 1.61 μm is closest to the expected spectral radiance maximum and serves as the primary detection band. The hot source is characterised in terms of temperature and area by fitting the sum of two Planck curves, one for the hot source and another for the background, to the radiances from all the available SWIR, MIR and thermal infra-red channels of SLSTR. The flaring radiative power is calculated from the gas flare temperature and area. Our algorithm differs from the original VIIRS Nightfire algorithm in three key aspects: (1) It uses a granule-based contextual thresholding to detect hot pixels, being independent of the number of hot sources present and their intensity. (2) It analyses entire clusters of hot source detections instead of individual pixels. This is arguably a more comprehensive use of the available information. (3) The co-registration errors between hot source clusters in the different spectral bands are calculated and corrected. This also contributes to the SLSTR instrument validation. Cross-comparisons of the new gas flare characterisation with temporally close observations by the higher resolution German FireBIRD TET-1 small satellite and with the Nightfire product based on VIIRS on-board the Suomi-NPP satellite show general agreement for an individual flaring site in Siberia and for several flaring regions around the world. Small systematic differences to VIIRS Nightfire are nevertheless apparent. Based on the hot spot characterisation, gas flares can be identified and flared gas volumes and pollutant emissions can be calculated with previously published methods.

Development of an Efficient Contextual Algorithm for Discrimination of Tall Vegetation and Urban for PALSAR Data

Fully polarimetric synthetic aperture radar based land cover classification has been intensively investigated for past several decades, but it is still a challenging task to segregate tall vegetation and urban because scattering mechanism involved for both the classes is not sufficient to get the proper threshold in order to differentiate them. Therefore, there is a need to develop such a technique that has the capability to classify these classes with significantly better accuracy. Textural information of an image is known to be an alternate source of extracting useful information of targets. While dealing with natural targets, such as tall vegetation, characteristic of textural feature, i.e., roughness, may be an important parameter which could identify these targets, since both the classes possess different types of roughness. Henceforth, commonly used texture features, i.e., fractal dimension, lacunarity, Moran’s I, entropy, and correlation were critically analyzed and realized that these features are still lacking in the concerned segregation, because generally they are pixel-based. Consequently, neighboring pixels are taken into account and an approach has been developed by considering the randomness response (or manner of distribution of scatterers) based on relative similarity of total backscattering power of neighboring pixels by proposing a similarity entropy feature. An optimized threshold method is also developed by means of the contextual thresholding in order to provide a proper decision boundary between the two classes. The proposed approach is successfully tested and validated on different Phased Array type L-band Synthetic Aperture Radar data with sensitivity of tall vegetation and urban as 0.93 and 0.932, respectively.

An efficient application of fusion approach for hot spot detection with MODIS and PALSAR-1 data

Hot spot detection with satellite images, especially with synthetic aperture radar (SAR) images is still a challenging task. Several researchers have used TM/optical data for identification of hot spot but the use of SAR data is very limited for this type of application. The fusion of SAR data with TM/optical data may add additional information which in turn will lead for enhancement of detection capability of the hot spot. Therefore, this study explores the possibility of fusion of Moderate Resolution Imaging Spectroradiometer (MODIS) and Phased Array L-band Synthetic Aperture Radar (PALSAR) satellite images for the hot spot detection. Image fusion is emerging as a powerful tool where information of various sensors can be used for obtaining better results. For this purpose, vegetation greenness and roughness information which is obtained from MODIS and PALSAR satellite images, respectively, are used for fusion, and then, a contextual-based thresholding algorithm is applied to the fused image for hot spot detection. The proposed approach comprises of two steps: (1) application of genetic algorithm-based scheme for image fusion of MODIS and PALSAR satellite images, and (2) classification of the fused image as either hot spot or non-hot spot pixels by employing a contextual thresholding technique. The algorithm is tested over the Jharia Coal Field region of India, where hot spot is one of the major problems and it is observed that the proposed thresholding technique classifies the each pixel of the fused image into two categories: hot spot and non-hot spot and the proposed approach detects the hot spot with better accuracy and less false alarm.

Enhancement of a fire-detection algorithm by eliminating solar contamination effects and atmospheric path radiance: application to MODIS data

Traditional fire-detection algorithms with either fixed or contextual thresholds mainly rely on the temperature contrast between a fire pixel and its surrounding pixels in the mid-infrared (MIR) and thermal-infrared (TIR) bands. Solar contamination and thermal atmospheric path radiance in the MIR band can weaken the contrast between the high- and low-temperature objects and undermine the capability of detecting fires during daytime. The degree of solar contamination in the MIR band depends on variable surface albedo, solar zenith angle and atmospheric conditions. A method is proposed to eliminate the solar radiation and thermal path radiance received by the MODerate Resolution Imaging Spectroradiometer (MODIS) sensor in the MIR band. The modified MIR brightness temperature is incorporated into the existing fire-detection algorithm (referred to as ‘MOD14’) after re-tuning the daytime thresholds. The performance of the revised algorithm (referred to as ‘MOD-MOD’) was tested using coincident data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) onboard the Terra satellite and visual inspection of large quantities of MODIS imageries. Moderate improvements are achieved in the detection rate while retaining low commission errors. Improvement of the detection by MOD-MOD depends on land-cover type. The majority of the false detections occur over deforested area.

An improved algorithm for small and cool fire detection using MODIS data: A preliminary study in the southeastern United States

Traditional fire detection algorithms mainly rely on hot spot detection using thermal infrared (TIR) channels with fixed or contextual thresholds. Three solar reflectance channels (0.65 μm, 0.86 μm, and 2.1 μm) were recently adopted into the MODIS version 4 contextual algorithm to improve the active fire detection. In the southeastern United States, where most fires are small and relatively cool, the MODIS version 4 contextual algorithm can be adjusted and improved for more accurate regional fire detection. Based on the MODIS version 4 contextual algorithm and a smoke detection algorithm, an improved algorithm using four TIR channels and seven solar reflectance channels is described. This approach is presented with fire events in the southeastern United States. The study reveals that the T 22 of most small, cool fires undetected by the MODIS version 4 contextual algorithm is lower than 310 K. The improved algorithm is more sensitive to small, cool fires in the southeast especially for fires detected at large scan angles.