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Topic Review
Oil Spill Modeling
Several oil spill simulation models exist in the literature, which are used worldwide to simulate the evolution of an oil slick created from marine traffic, petroleum production, or other sources. These models may range from simple parametric calculations to advanced, new-generation, operational, three-dimensional numerical models, coupled to meteorological, hydrodynamic, and wave models, forecasting in high-resolution and with high precision the transport and fate of oil. This study presents a review of the transport and oil weathering processes and their parameterization and critically examines eighteen state-of-the-art oil spill models in terms of their capacity (a) to simulate these processes, (b) to consider oil released from surface or submerged sources, (c) to assimilate real-time field data for model initiation and forcing, and (d) to assess uncertainty in the produced predictions. Based on our review, the most common oil weathering processes involved are spreading, advection, diffusion, evaporation, emulsification, and dispersion. The majority of existing oil spill models do not consider significant physical processes, such as oil dissolution, photo-oxidation, biodegradation, and vertical mixing. Moreover, timely response to oil spills is lacking in the new generation of oil spill models. Further improvements in oil spill modeling should emphasize more comprehensive parametrization of oil dissolution, biodegradation, entrainment, and prediction of oil particles size distribution following wave action and well blow outs.
  • 1.8K
  • 02 Mar 2021
Topic Review
Phosphogypsum
Phosphogypsum is an almost unused by-product of phosphate fertilizer production, which includes several valuable components—calcium sulphates and rare-earth elements such as silicon, iron, titanium, magnesium, aluminum, and manganese, as well as toxic elements such as heavy metals and others.
  • 971
  • 25 Feb 2021
Topic Review
Endocrine Disrupting Compounds
Endocrine disrupting compounds (EDCs) are contaminants with estrogenic or andro-genic activity that negatively impact human and animal communities.
  • 371
  • 24 Feb 2021
Topic Review
Polycyclic Aromatic Hydrocarbons
Polycyclic aromatic hydrocarbons (PAHs) is an organic pollutant with persistence and carcinogenicity. They are universally present in the environment and food processing. Biological approaches toward remediating PAHs-contaminated sites are a viable, economical, and environmentally friendly alternative compared to conventional physical and/or chemical remediation methods. Recently, various strategies relating to low molecular weight organic acids (LMWOAs) have been developed to enhance the microbial degradation of PAHs. However, the remaining challenge is to reveal the role of LMWOAs in the PAHs biodegradation process, and the latter limits researchers from expanding the application scope of biodegradation.
  • 1.1K
  • 24 Feb 2021
Topic Review
Soil Protection in Floodplains
Soils in floodplains and riparian zones provide important ecosystem functions and services. These ecosystems belong to the most threatened ecosystems worldwide. Therefore, the management of floodplains has changed from river control to the restoration of rivers and floodplains. However, restoration activities can also negatively impact soils in these areas. Thus, a detailed knowledge of the soils is needed to prevent detrimental soil changes. The aim of this study is therefore to assess the kind and extent of soil information used in research on floodplains and riparian zones.
  • 450
  • 23 Feb 2021
Topic Review
Radar-based Rainfall Information
Radar-based rainfall information has been widely used in hydrological and meteorological applications, as it provides data with a high spatial and temporal resolution that improve rainfall representation. However, the broad diversity of studies makes it difficult to gather a condensed overview of the usefulness and limitations of radar technology and its application in particular situations. 
  • 1.5K
  • 22 Feb 2021
Topic Review
Homothetic Behavior of Betweenness Centralities
       In mathematics, a homothetic behavior is characterized by a transformation of an affine space by a factor λ and results in an invariance of this space form or configuration, albeit its overall scale changes. In this sense, if two objects or parts of those objects have distinct sizes, but conserve the same appearance, they can be considered homothetic. In networks, the occurrence of homothetic behaviors would imply that a section of the network, when modelled independently, ought to retain a certain regularity in their distribution of centrality hierarchies (visual similitude) when compared to a larger section, independently modelled as well, that contains it. Hence, the smaller network maintains its overall proportions (configuration, hierarchies and values) across scales. This visual similitude was perceived while apposing several Normalized Angular Choice (NACH) models, a Space Syntax’ derivative from mathematical betweenness. Network homotheties, due to their invariability in form and value, can be used as an alternative to extensive network generalization for the construction of large spatial networks. Hence, data maps can be constructed sooner and more accurately as “pieces of a puzzle”, since each individual lesser scale graph possesses a faster processing time.
  • 718
  • 21 Feb 2021
Topic Review
Air Quality and Shipping Emissions
Maritime transport has been recognized as an essential driver for economic and social development, especially for coastal regions. However, shipping and in-port activities pose public health issues and environmental pressures, exposing coastal population to associated emissions (i.e., particulate matter and gaseous pollutants). In the last decades, several policies have been implemented at local/regional and international level, reducing the content of sulphur in marine fuels. This work provides a brief comment of some recent results regarding the impacts of maritime emissions on air quality, health effects and future projections, taking into account the current implementation of the IMO-2020 legislation. Finally, future perspectives and potential mitigation strategies are discussed.
  • 667
  • 18 Feb 2021
Topic Review
Non-Invasive Indoor Thermal Discomfort Detection
Since 1997, scientists have been trying to utilize new non-invasive approaches for thermal discomfort detection, which promise to be more effective for comparing frameworks that need direct responses from users. Due to rapid technological development in the bio-metrical field, a systematic literature review to investigate the possibility of thermal discomfort detection at the work place by non-invasive means using bio-sensing technology was performed. Firstly, the problem intervention comparison outcome context (PICOC) framework was introduced in the study to identify the main points for meta-analysis and, in turn, to provide relevant keywords for the literature search. In total, 2776 studies were found and processed using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology. After filtering by defined criterion, 35 articles were obtained for detailed investigation with respect to facility types used in the experiment, amount of people for data collection and algorithms used for prediction of the thermal discomfort event. The given study concludes that there is potential for the creation of non-invasive thermal discomfort detection models via utilization of bio-sensing technologies, which will provide a better user interaction with the built environment, potentially decrease energy use and enable better productivity. There is definitely room for improvement within the field of non-invasive thermal discomfort detection, especially with respect to data collection, algorithm implementation and sample size, in order to have opportunities for the deployment of developed solutions in real life. Based on the literature review, the potential of novel technology is seen to utilize a more intelligent approach for performing non-invasive thermal discomfort prediction. The architecture of deep neural networks should be studied more due to the specifics of its hidden layers and its ability of hierarchical data extraction. This machine learning algorithm can provide a better model for thermal discomfort detection based on a data set with different types of bio-metrical variables.
  • 992
  • 11 Feb 2021
Topic Review
NASA Micropulse Lidar Rain Algorithm
Precipitation modifies atmospheric column thermodynamics through the process of evaporation and serves as a proxy for latent heat modulation. For this reason, a correct precipitation parameterization (especially for low-intensity precipitation) within global scale models is crucial. In addition to improving our modeling of the hydrological cycle, this will reduce the associated uncertainty of global climate models in correctly forecasting future scenarios, and will enable the application of mitigation strategies. In this study we present a proof of concept algorithm to automatically detect precipitation from lidar measurements obtained from the National Aeronautics and Space Administration Micropulse lidar network (MPLNET). 
  • 890
  • 09 Feb 2021
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