scholarly journals Multimedia environmental fate and speciation of engineered nanoparticles: a probabilistic modeling approach

2016 ◽  
Vol 3 (4) ◽  
pp. 715-727 ◽  
Author(s):  
J. A. J. Meesters ◽  
J. T. K. Quik ◽  
A. A. Koelmans ◽  
A. J. Hendriks ◽  
D. van de Meent
Keyword(s):  
Physicochemical Properties ◽  
Environmental Exposure ◽  
Environmental Fate ◽  
Probabilistic Modeling ◽  
Natural Variability ◽  
Engineered Nanoparticles ◽  
Modeling Approach ◽  
Exposure Estimation ◽  
Environmental Systems

The robustness of novel multimedia fate models in environmental exposure estimation of engineered nanoparticles (ENPs) is clarified by evaluating uncertainties in the emission, physicochemical properties and natural variability in environmental systems.

scholarly journals A model sensitivity analysis to determine the most important physicochemical properties driving environmental fate and exposure of engineered nanoparticles

2019 ◽  
Vol 6 (7) ◽  
pp. 2049-2060 ◽  
Author(s):  
J. A. J. Meesters ◽  
W. J. G. M. Peijnenburg ◽  
A. J. Hendriks ◽  
D. Van de Meent ◽  
J. T. K. Quik
Keyword(s):  
Sensitivity Analysis ◽  
Physicochemical Properties ◽  
Rate Constant ◽  
Environmental Fate ◽  
Engineered Nanoparticles ◽  
Sensitivity Analyses ◽  
Transformation Rate ◽  
Model Sensitivity ◽  
Attachment Efficiency ◽  
Transformation Rate Constant

Sensitivity analyses indicate attachment efficiency and transformation rate constant are most important in modeling environmental fate of engineered nanoparticles.

Kernel Distributions in Main Spikes of Salt‐Stressed Wheat: A Probabilistic Modeling Approach

Crop Science ◽  
1992 ◽  
Vol 32 (3) ◽  
pp. 704-712 ◽  
Author(s):  
Scott M. Lesch ◽  
Catherine M. Grieve ◽  
Eugene V. Maas ◽  
Leland E. Francois
Keyword(s):  
Probabilistic Modeling ◽  
Modeling Approach

scholarly journals OPERA models for predicting physicochemical properties and environmental fate endpoints

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Kamel Mansouri ◽  
Chris M. Grulke ◽  
Richard S. Judson ◽  
Antony J. Williams
Keyword(s):  
Physicochemical Properties ◽  
Environmental Fate

scholarly journals A Probabilistic Approach for Economic Evaluation of Occupational Health and Safety Interventions: A Case Study of Silica Exposure Reduction Interventions in the Construction Sector

2020 ◽  
Author(s):  
Amirabbas Mofidi ◽  
Emile Tompa ◽  
SeyedBagher Mortazavi ◽  
Akbar Esfahanipour ◽  
Paul A. Demers
Keyword(s):  
Lung Cancer ◽  
Economic Evaluation ◽  
Probabilistic Modeling ◽  
Hazardous Substances ◽  
Construction Sector ◽  
Net Benefit ◽  
Expected Cost ◽  
Silica Exposure ◽  
Exposure Reduction ◽  
Modeling Approach

Abstract Background: Construction workers are at a high risk of exposure to various types of hazardous substances such as crystalline silica. Though multiple studies indicate the evidence regarding the effectiveness of different silica exposure reduction interventions in the construction sector, the decisions for selecting a specific silica exposure reduction intervention are best informed by an economic evaluation. Economic evaluation of interventions is subjected to uncertainties in practice, mostly due to the lack of precise data on important variables. In this study, we aim to identify the most cost-beneficial silica exposure reduction intervention for the construction sector under uncertain situation. Methods: We apply a probabilistic modeling approach that covers a large number of variables relevant to the cost of lung cancer, as well as the costs of silica exposure reduction interventions. To estimate the societal lifetime cost of lung cancer, we use an incidence cost approach. To estimate the net benefit of each intervention, we compare the expected cost of lung cancer cases averted, with expected cost of implementation of the intervention in one calendar year. Sensitivity analysis is used to quantify how different variables effects interventions net benefit.Results: A positive net benefit is expected for all considered interventions. The highest number of lung cancer cases are averted by combined use of wet method, local exhaust ventilation and personal protective equipment, about 107 cases, with expected net benefit of $45.9 million. Results also suggest that the level of exposure is an important determinant for the selection of the most cost-beneficial intervention.Conclusions: This study provides important insights for decision makers about silica exposure reduction interventions in the construction sector. It also provides an overview of the potential advantages of using probabilistic modeling approach to undertake economic evaluations, particularly when researchers are confronted with a large number of uncertain variables.

Basic Concepts of Modeling Pesticide Fate in the Crop Root Zone

Weed Science ◽  
1985 ◽  
Vol 33 (S2) ◽  
pp. 25-32 ◽  
Author(s):  
R. J. Wagenet ◽  
P.S.C. Rao
Keyword(s):  
Environmental Fate ◽  
Root Zone ◽  
Effective Means ◽  
Simulation Models ◽  
Cost Effective ◽  
Natural Processes ◽  
Modeling Approach ◽  
Efficacy Trials ◽  
Sorption Leaching ◽  
Crop Root

Modeling is increasingly being used as a tool for the evaluation of the environmental fate of pesticides. Sorption, leaching, degradation, and volatilization are some of the processes being integrated through the use of simulation modeling techniques. Several research programs are focusing their attention on such issues (16, 17, 18, 32, 35), with regulatory agencies involved in management of pesticides also taking a modeling approach (3, 7). Because of the extreme complexity of agroecosystems, it is obvious that the use of simulation models will continue to be the most expeditious, reliable, and cost-effective means of integrating the various processes acting upon a pesticide to determine its fate. For example, modeling will help to summarize and interpret efficacy trials and will provide the vehicle for transferring experimental results to unstudied situations, such as the potential environmental fate of an applied herbicide. However, proper development, testing, and responsible use of a modeling approach must be based upon a thorough, comprehensive understanding of interdependent and dynamic natural processes.

scholarly journals Exploring the Environmental Exposure to Methoxychlor, α-HCH and Endosulfan–sulfate Residues in Lake Naivasha (Kenya) Using a Multimedia Fate Modeling Approach

2020 ◽  
Vol 17 (8) ◽  
pp. 2727
Author(s):  
Yasser Abbasi ◽  
Chris M. Mannaerts
Keyword(s):  
Sensitivity Analysis ◽  
Water Column ◽  
Environmental Exposure ◽  
Pesticide Residues ◽  
Half Life ◽  
Mass Fraction ◽  
Passive Sampling ◽  
Lake Naivasha ◽  
Endosulfan Sulfate ◽  
Modeling Approach

Distribution of pesticide residues in the environment and their transport to surface water bodies is one of the most important environmental challenges. Fate of pesticides in the complex environments, especially in aquatic phases such as lakes and rivers, is governed by the main properties of the contaminants and the environmental properties. In this study, a multimedia mass modeling approach using the Quantitative Water Air Sediment Interaction (QWASI) model was applied to explore the fate of organochlorine pesticide residues of methoxychlor, α-HCH and endosulfan–sulfate in the lake Naivasha (Kenya). The required physicochemical data of the pesticides such as molar mass, vapor pressure, air–water partitioning coefficient (KAW), solubility, and the Henry’s law constant were provided as the inputs of the model. The environment data also were collected using field measurements and taken from the literature. The sensitivity analysis of the model was applied using One At a Time (OAT) approach and calibrated using measured pesticide residues by passive sampling method. Finally, the calibrated model was used to estimate the fate and distribution of the pesticide residues in different media of the lake. The result of sensitivity analysis showed that the five most sensitive parameters were KOC, logKow, half-life of the pollutants in water, half-life of the pollutants in sediment, and KAW. The variations of outputs for the three studied pesticide residues against inputs were noticeably different. For example, the range of changes in the concentration of α-HCH residue was between 96% to 102%, while for methoxychlor and endosulfan-sulfate it was between 65% to 125%. The results of calibration demonstrated that the model was calibrated reasonably with the R2 of 0.65 and RMSE of 16.4. It was found that methoxychlor had a mass fraction of almost 70% in water column and almost 30% of mass fraction in the sediment. In contrast, endosulfan–sulfate had highest most fraction in the water column (>99%) and just a negligible percentage in the sediment compartment. α-HCH also had the same situation like endosulfan–sulfate (e.g., 99% and 1% in water and sediment, respectively). Finally, it was concluded that the application of QWASI in combination with passive sampling technique allowed an insight to the fate process of the studied OCPs and helped actual concentration predictions. Therefore, the results of this study can also be used to perform risk assessment and investigate the environmental exposure of pesticide residues.

Addressing the complexity of water chemistry in environmental fate modeling for engineered nanoparticles

2015 ◽  
Vol 535 ◽  
pp. 150-159 ◽  
Author(s):  
Nicole Sani-Kast ◽  
Martin Scheringer ◽  
Danielle Slomberg ◽  
Jérôme Labille ◽  
Antonia Praetorius ◽  
...  
Keyword(s):  
Water Chemistry ◽  
Environmental Fate ◽  
Engineered Nanoparticles ◽  
Environmental Fate Modeling

scholarly journals Machine learning provides predictive analysis into silver nanoparticle protein corona formation from physicochemical properties

2018 ◽  
Vol 5 (1) ◽  
pp. 64-71 ◽  
Author(s):  
Matthew R. Findlay ◽  
Daniel N. Freitas ◽  
Maryam Mobed-Miremadi ◽  
Korin E. Wheeler
Keyword(s):  
Machine Learning ◽  
Surface Chemistry ◽  
Physicochemical Properties ◽  
Silver Nanoparticle ◽  
Protein Corona ◽  
Engineered Nanomaterials ◽  
Predictive Analysis ◽  
Environmental Systems ◽  
Corona Formation ◽  
P Proteins

Proteins encountered in biological and environmental systems bind to engineered nanomaterials (ENMs) to form a protein corona (PC) that alters the surface chemistry, reactivity, and fate of the ENMs.

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