2 edition of Predicting risk of exposure to peak concentrations in fluctuating plumes found in the catalog.
Predicting risk of exposure to peak concentrations in fluctuating plumes
D. J. Wilson
|Statement||prepared for Pollution Control Division, Alberta Environment by D.J. Wilson.|
|Contributions||Alberta. Pollution Control Division.|
|LC Classifications||TD884 .W73 1982|
|The Physical Object|
|Pagination||ii, 90 p. ;|
|Number of Pages||90|
|LC Control Number||83162408|
For some toxic chemicals such as hydrogen sulphide, a short-term exposure at high (peak) concentrations is much worse than exposure to low concentration for a longer time. Figure 1 shows an example where sheltering provides protection from the peak outdoor air exposure and is very effective in preventing serious injuries or fatalities for those. Finally, to convert to peak concentrations, the mean ash concentration fields output by NAME were multiplied by a factor of This is known as the “peak‐to‐mean” factor and has been adopted by the LVAAC to account for peak concentrations that cannot be resolved by the NAME model (Webster et al., ).Cited by: 1.
The cost and technical limitations of monitoring can be mitigated using a validated air dispersion model to simulate concentrations of volatile organic chemicals in ambient air. The SOil Fumigant Exposure Assessment (SOFEA) model was developed to explore volatile pesticide exposure and bystander by: 1. The simulations predict ranges of exposure in a population; identify critical pathways, factors, and uncertainties; and enhance dose model estimates. Thus, SHEDS can be used to provide detailed source to exposure estimates for a particular chemical (or group of closely related chemicals) and for a selected route (e.g., inhalation of volatile organics in indoor air or dietary ingestion of .
An existing Lagrangian dispersion model (Weil, , ) was modified to: (1) predict dispersion by the motion of buoyant particles rather than by a "meandering" plume used earlier, (2) account for environmental turbulence effects on plumes through detrainment of plume material by the ambient turbulence, and (3) incorporate the new gravity. Health risk assessment is a multifaceted process that relies on an assortment of methods, data, and models. The overall accuracy of a risk assessment hinges on the validity of the various methods and models chosen, which in turn are governed by the scope and quality of data. The degree of confidence that one can place in a risk assessment depends on the reliability of the .
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The Gaussian model (PLUMES) with modified stability classes gave relative mean absolute errors of 42–64% in predicting transect maxima. Dilution rates were found to be much larger than would be experienced under similar conditions over flat : S.
Sakiyama, R. Angle. Predicting risk of exposure to peak concentrations in fluctuating plumes. En- vironmental Protection Services report, Alberta Environment, Edmonton.
Recommended articles Citing articles (0)Cited by: 4. Abstract. Dispersion in the atmospheric boundary layer (ABL) is a random process caused by the stochastic nature of turbulence. As a result, the fluctuating concentration observed downwind of a source is typically of the same order as the ensemble-mean concentration 〈c〉; the fluctuation is characterized by its root-mean-square value σ c.This paper discusses the nature of the Cited by: 1.
Conditional concentration statistics for surface plumes in the atmospheric boundary layer ‘Peak to Average Concentration Ratios According to a Fluctuating Plume Dispersion Model’, Int. Air Pollut. 3, Wilson, D. J.:Predicting Risk of Exposure to Peak Concentrations in Fluctuating Plumes, Alberta Environment, 90 pp Cited by: Concentration fluctuations in plumes.
Author(s) / Creator(s) Netterville, D.D.J. The random fluctuations of concentration levels in a plume is studied using a wind tunnel simulation of dispersion in the neutrally stable atmospheric boundary by: Modelling the concentration fluctuation and individual exposure in complex urban environments model to predict peak concentrations within a factor such as reactive plumes, risk.
Powers of timescale ratios are given to scale the peak concentrations in odour plumes from given means, for different respective averaging times ranging from tenths of a.
The toxicity of pollutants under pulse exposure may be considerably high at peak concentrations and gradually decline at low concentrations, while toxicity will accumulate in vivo under constant exposure. Thus, the toxicity of pollutants under constant or pulse exposure is ultimately different and difficult to be quantitatively : Min Chen, Yongfei Gao, Xiaoxue Bian, Jianfeng Feng, Weiqi Ma, Lin Zhu.
As a result, plumes from short- or moderately tall stacks disperse more rapidly and can produce high ground-level concentrations (GLCs). Approach: The aim of the proposed research is to improve our knowledge and predictive capability of buoyant plume dispersion in the urban environment with focus on the nocturnal case.
There are three key. According to Mortarini et al () and Franzese (), Gifford's () fluctuating plume model proved to be a simple and effective tool for predicting concentration moments of. In an atmospheric exposure to a point source plume with concentrations fluctuating between 0 and more than 20 times the mean concentration, this calculated non-linear effect on toxicity is very large.
There have been attempts to deal with the toxicity of fluctuating concentrations by simplifying the fluctuating time by: Exposure of aquatic nontarget organisms to pesticides almost always occurs as pulses or fluctuating concentrations.
Extrapolation from laboratory to field thus depends on an understanding and ability to simulate effects resulting from these types of exposure.
For steady sources the occasional occurrence of pollutant concentrations well in excess of the long-term mean may be, and often are, the main cause of resulting harm or nuisance.
Accidental releases of toxic substances over relatively short periods can produce locally serious threats to Author: P. Barry. The resulting closed form predictions are presented in a form suitable for estimating risk of exposure to peak concentrations.
/85 $ + Printed in Great Britain. Pergamon Press Ltd. INTERMITTENCY AND CONDITIONALLY-AVERAGED CONCENTRATION FLUCTUATION STATISTICS IN PLUMES D. WILSON*, A. Cited by: Plume meandering and averaging time effects were measured directly using a high spatial resolution, high frequency, linescan laser-induced fluorescence (LIF) technique for measuring scalar concentrations in a plume dispersing in a water channel.
Concentration Fluctuations and Averaging Time in Vapor Clouds DAVID J. WILSON Department of Mechanical Engineering University of Alberta Edmonton, Alberta, Canada T6G 2G8 CENTER FOR CHEMICAL PROCESS SAFETY of the American Institute of Chemical Engineers East 47th Street 0 New York, NY MODELLING OF FLUCTUATING CONCENTRATION FIELDS IN COMPLEX INDUSTRIAL AREAS Giovanni Leuzzi 1, Paolo Monti, Armando Pelliccioni2, Claudio Gariazzo2 1DICEA, Università di Roma “La Sapienza”, Roma, Italy 2INAIL-DIPIA, Monteporzio Catone, Roma, Italy Abstract: In the framework of the new legislation related to accidental scenarios, the forecast.
come about after repeated or prolonged exposure. Inhalation of the peak concentrations of fume is thought to be particularly hazardous because it may be one important factor in initiating sensitisation. AU persons are at risk of becoming sensitised. However sensitisation is unpredictable.
Only some individuals at risk will become sensitised. The shape of the probability distribution of a set of high-resolution concentration fluctuation measurements from an ion plume is studied using order statistics and certain selected quantiles derived from them.
A number of graphical techniques based on the order statistics are shown to be useful for the assessment of the symmetry and tailweight of the underlying Cited by: Operational Readiness (Air Toxins) predicting where potential high-risk exposure zones may be.
Results indicate that within 50 metres of a burning house, exposures are on average constantly exceeding peak and short-term exposure limits and there is no safe approach without protection. At metres, peak concentrations can still be. Aquatic nontarget organisms are exposed to fluctuating concentrations or sequential pulses of contaminants, so we need to predict effects resulting from such patterns of exposure.
We present a process-based model, the Threshold Damage Model (TDM), that links exposure with effects and demonstrate how to simulate the survival of the aquatic invertebrate Gammarus Cited by: ISPRS International Journal of Geo-Information is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is CHF (Swiss Francs). Submitted papers should be well.EMSOFT is used to explore how chemicals in the soil can vaporize and become an inhalation exposure risk in humans.
The model can predict concentrations of soil contaminants, rates of transfer into the atmosphere, and potential ingestion and dermal contact risks.