Use of disinfection by-products (DBPs) generation simulation models in the risk analysis of secondary water contamination

2020 ◽  
Vol 199 ◽  
pp. 486-492
Author(s):  
Izabela Zimoch ◽  
Ewelina Bartkiewicz
Keyword(s):  
Risk Analysis ◽  
Water Contamination ◽  
Simulation Models ◽  
By Products

Assessment of air and water contamination by disinfection by-products at 41 indoor swimming pools

2016 ◽  
Vol 148 ◽  
pp. 411-420 ◽  
Author(s):  
Robert Tardif ◽  
Cyril Catto ◽  
Sami Haddad ◽  
Sabrina Simard ◽  
Manuel Rodriguez
Keyword(s):  
Water Contamination ◽  
Swimming Pools ◽  
By Products

scholarly journals Utilization of Waste-Based Sorbents for Removal of Pharmaceuticals from Water: A Review

2021 ◽  
Vol 29 (48) ◽  
pp. 22-36
Author(s):  
Lenka Blinová ◽  
Maroš Sirotiak
Keyword(s):  
Water Contamination ◽  
Human Life ◽  
Low Cost ◽  
Industrial Wastes ◽  
Contaminated Water ◽  
Spent Coffee Grounds ◽  
Coffee Grounds ◽  
By Products

Abstract Nowadays, the water contamination which is due to pharmaceuticals is increasing and alarming. The pharmaceuticals in water are very hazardous and toxic not only for the human life but also for environment. One of the promising methods of removing pharmaceuticals from the contaminated water is adsorption. Agricultural and industrial wastes or by-products can be used as low-cost adsorbents for pharmaceuticals removal. Low-cost adsorbents provide particular economic and environmental advantages. This paper presents an overview of utilizing of the waste-based adsorbents (mainly spent coffee grounds) for the removal of pharmaceuticals from water.

scholarly journals Open landfill site and threat to the proximity resident’s: Addressing perceived consequences of unscientific solid waste dumping using GIS techniques

2022 ◽  
Author(s):  
Subham Roy ◽  
Arghadeep Bose ◽  
Debanjan Basak ◽  
Indrajit Roy Chowdhury
Keyword(s):  
Solid Waste ◽  
Water Contamination ◽  
Landfill Site ◽  
Negative Consequences ◽  
Satisfaction Index ◽  
The People ◽  
Health Related ◽  
Dump Yard ◽  
The Impact ◽  
By Products

Abstract Municipal solid waste (MSW) disposal is a rapidly expanding sector that caters to the rising demand for disposal facilities; as a result, MSW treatment is becoming a significant challenge in concern to environmental and public health. The by-products of solid waste dumped in landfills have negative consequences on the environment and people living near disposal sites. This research aimed to assess whether the disposal of garbage in landfills affected the people who live near them. Consequently, residents living near MSW disposal facilities are exposed to various risks. A systematic questionnaire was devised and disseminated in this research to examine the adjacent people's concerns and attitudes around the landfill of the rapidly expanding Siliguri city of West Bengal. It was accomplished by assessing the opinions of persons living within the 1000m of the landfill site and how it impacts their life. The novelty of the research includes geographical analysis of physical impressions, including odour nuisance, breathing problems, impacts of flies and mosquitoes, water contamination, issues due to illegal burning, and health-related problems, which was conducted using geographic information system methodologies. Besides, the Landfill satisfaction index (LSI) tool was used to assess the perception of the people residing near the landfill. Also, to statistically validate the perception of the people related to the impact of landfills on their life, gamma coefficient (y) test of Goodman-Kruskal was applied. The findings reveal that the majority of the respondent within 600m are more susceptible to various risks due to unscientific landfill. Subsequently, a large portion of the responding sample was aware of the detrimental effects of landfills on the health and environment, therefore, individuals living near the dump yard preferred to live farther away. The findings also reveal that geographical proximity has a clear relationship between respondent perception and distance to the landfill. The study concludes with a discussion on how the research results may be beneficial for designing landfill sites and can be utilized by urban planners, environmentalists, and engineers.

scholarly journals Risk analysis of ground water contamination detection networks, using stochastic modeling and decision theory

2013 ◽  
Author(s):  
Κωνσταντίνος Παπαπετρίδης
Keyword(s):  
Monte Carlo ◽  
Risk Analysis ◽  
Decision Theory ◽  
Ground Water ◽  
Stochastic Modeling ◽  
Water Contamination ◽  
Ground Water Contamination

Ο κύριος στόχος της διδακτορικής διατριβής είναι η μελέτη και η εκτίμηση ρίσκου της πιθανότητας ανίχνευσης ρύπανσης των υπόγειων νερών, η οποία δημιουργείται σε πολύ μικρό χρονικό διάστημα και προέρχεται από μια σημειακή πηγή εντός μιας περιοχής ελέγχου. Η μελέτη έγινε με τη δημιουργία ενός δισδιάστατου στοχαστικού μοντέλου μεταφοράς και διασποράς ρύπανσης υπόγειων υδάτων βασισμένο στην τεχνική Monte Carlo. Η εκτίμηση ρίσκου γίνεται μέσα στο πλαίσιο λήψης απόφασης για την αποκατάσταση της περιβαλλοντικής ζημιάς και του τρόπου που επηρεάζεται η απόδοση, με όρους πιθανότητας, ενός συστήματος πηγαδιών παρακολούθησης υπόγειας ρύπανσης από την καθυστέρηση ελέγχου της επέκτασης της ρύπανσης. Στη διατριβή διερευνήθηκε η επίδραση του υδρογεωλογικού περιβάλλοντος, της βροχόπτωσης, της συχνότητας δειγματοληψίας καθώς και της καθυστερημένης επέμβασης, μετά την επιτυχή ανίχνευση υπόγειας ρύπανσης, στην απόδοση ενός γραμμικού συστήματος πηγαδιών επιτήρησης υπόγειας ρύπανσης. Οι βασικοί στόχοι της διδακτορικής διατριβής ήταν:•Η αξιολόγηση της επίδρασης των υδρογεωλογικών χαρακτηριστικών του πεδίου (ετερογένεια, συντελεστής διασποράς), της περιοχής ελέγχου και του αριθμού των πηγών ρύπανσης στην πιθανότητα επιτυχούς ανίχνευσης υπόγειας ρύπανσης από μια γραμμική διάταξη πηγαδιών παρακολούθησης, όπου φάνηκε ότι ο κύριος παράγοντας επίδρασης στην ανίχνευση της ρύπανσης είναι η διασπορά του πεδίου.•Η αξιολόγηση της επίδρασης της συχνότητας δειγματοληψίας στην πιθανότητα επιτυχούς ανίχνευσης υπόγειας ρύπανσης, όπου ανάλογα με τη διασπορά του πεδίου πρέπει να πραγματοποιείται τουλάχιστον δύο φορές το χρόνο.•Ο προσδιορισμός της πιθανότητας ανίχνευσης υπόγειας ρύπανσης από μια γραμμική διάταξη πηγαδιών παρακολούθησης, η οποία προέρχεται από επαναλαμβανόμενα περιστατικά ρύπανσης τα οποία συσχετίζονται με την απορροή κατακρημνισμάτων, σε βάθος χρόνου 30 ετών. •Η αξιολόγηση της καθυστέρησης του ελέγχου και της αποκατάστασης της υπόγειας ρύπανσης σε σχέση με την απόδοση του δικτύου πηγαδιών παρακολούθησης, όπου δείχτηκε ότι καθυστέρηση στον έλεγχο της ρύπανσης μετά από επιτυχή ανίχνευση ισοδυναμεί με υποβάθμιση της απόδοσης των διατάξεων πηγαδιών παρακολούθησης ρύπανσης. •Η μελέτη της επίδρασης των παραπάνω παραγόντων κατά τη διαδικασία λήψεως απόφασης για τον έλεγχο και την αποκατάσταση υπόγειας ρύπανσης που προέρχεται από μια επιτηρούμενη περιοχή (ΧΥΤΑ, υπόγεια δεξαμενή, υπόγειοι αγωγοί μεταφοράς).

scholarly journals Tutorial: Parallel Computing of Simulation Models for Risk Analysis

Risk Analysis ◽  
2016 ◽  
Vol 36 (10) ◽  
pp. 1844-1854 ◽  
Author(s):  
Allison C. Reilly ◽  
Andrea Staid ◽  
Michael Gao ◽  
Seth D. Guikema
Keyword(s):  
Parallel Computing ◽  
Risk Analysis ◽  
Simulation Models

scholarly journals Adversarial Risk Analysis for Enhancing Combat Simulation Models

2015 ◽  
Vol 6 (2) ◽  
pp. 82-103 ◽  
Author(s):  
Juho Roponen ◽  
Ahti Salo
Keyword(s):  
Game Theory ◽  
Decision Making ◽  
Risk Analysis ◽  
Simulation Models ◽  
The Past ◽  
Combat Simulation ◽  
Decision Making Processes ◽  
Game Theoretic ◽  
Statistical Risk ◽  
Military Combat

Abstract Adversarial Risk Analysis (ARA) builds on statistical risk analysis and game theory to analyze decision situations involving two or more intelligent opponents who make decisions under uncertainty. During the past few years, the ARA approach-which is based on the explicit modelling of the decision making processes of a rational opponent-has been applied extensively in areas such as counterterrorism and corporate competition. In the context of military combat modelling, however, ARA has not been used systematically, even if there have been attempts to predict the opponent’s decisions based on wargaming, application of game theoretic equilibria, and the use of expert judgements. Against this backdrop, we argue that combining ARA with military combat modelling holds promise for enhancing the capabilities of combat modelling tools. We identify ways of combining ARA with combat modelling and give an illustrative example of how ARA can provide insights into a problem where the defender needs to estimate the utility gained from hiding its troop movements from the attacker. Even if the ARA approach can be challenging to apply, it can be instructive in that relevant assumptions about the resources, expectations and goals that guide the adversary’s decisions must be explicated.

scholarly journals ARTIFICIAL NEURAL NETWORK WITH EXPERIMENTAL DESIGN: A FAST TOOL FOR RISK ANALYSIS AND FORECASTING IN AN OIL RESERVOIR, A CASE STUDY

2020 ◽  
Vol 4 (5) ◽  
pp. 40-50
Author(s):  
Nasser Alizadeh Tabrizi
Keyword(s):  
Risk Analysis ◽  
Oil Recovery ◽  
Computing Time ◽  
Simulation Models ◽  
Production Optimization ◽  
Oil Reservoir ◽  
Total Production ◽  
Proxy Model ◽  
Artificial Neural ◽  
Screening Sensitivity

Running simulation models is CPU intensive. In computing expensive tasks such as parameter screening, sensitivity and risk analysis (uncertainty analysis) and production optimization, it can be useful to establish a simple surrogate model (proxy model) that mimics the simulation model with regard to a specific target value (for example, total production) in order to reduce the computing time and to study the available uncertainties in the reservoir and their impacts on production. Artificial Neural Networks (ANN) are one of the main tools used in machine learning. The quality of the ANN as a proxy model is dependent on how the experiments that were used to make and train it are designed. In particular, it is crucial to understand the input parameters such that their respective dependencies, correlations, and ranges are incorporated in the modelling. A combination of simulation runs should be set up that can be used to train the ANN. This task is usually referred to as the design of experiments (DOE) which gives the most informative data sets to train ANN. In this study DOE was used to train the ANN in an oil reservoir under gas injection scenario and the trained ANN, in turn, was applied to create the production profiles which were further used for risk analysis. The accuracy of the results obtained in this study indicates that ANN as a proxy model combined with DOE as a sampling method for training purpose is a fast and reliable tool that can replace the simulator. This dynamic proxy model can be used for risk analysis, production optimization and production forecasting of oil reservoirs under Enhanced Oil Recovery (EOR) methods.

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