Practical modelling of gas dispersion in low wind speed conditions, for application in risk assessment

Lines, I.G.; Deaves, D.M.; Atkins, W S

doi:10.1016/s0304-3894(96)01873-0

Cited by 17 publications

(8 citation statements)

References 13 publications

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“…In fact, most of the existing regulatory dispersion models become unreliable as u approaches zero, so that their application is generally limited to u > 2:0 m s À1 (Wilson et al, 1976;Lines et al, 1997). In these conditions dispersion is mainly governed by meandering (low frequency horizontal wind oscillations).…”

Section: Introductionmentioning

confidence: 99%

An Analysis Of Sonic Anemometer Observations In Low Wind Speed Conditions

Anfossi

Oettl

Degrazia

et al. 2005

Boundary-Layer Meteorology

169

184

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When the wind speed decreases below a certain value (1-2 m s À1 ) meandering (low frequency horizontal wind oscillations) starts to prevail. In these conditions it becomes difficult to define a precise mean wind direction and to estimate the airborne dispersion. To study the wind and turbulence characteristics during meandering, two sonic anemometer datasets, containing hourly wind observations, were analysed: the first one, lasting 1 year, was recorded in complex terrain (Graz, Austria) and the second one, lasting about 1 month, was recorded in a rather flat area (Tisby, Sweden). It was found that meandering seems to exist under all meteorological conditions regardless of the stability or wind speed and it was confirmed that meandering sets a lower limit for the horizontal wind component variances. Further, it was found that the autocorrelation functions of the horizontal wind components, computed for the low wind cases, show an oscillating behaviour with the presence of large negative lobes. Two different relationships from the literature, and relevant to these oscillatory aspects, were fitted to the data. They contain two parameters: one associated and relevant to the classical integral time scale and the second with meandering occurrence. Based on these relationships, expressions for the mean square displacement of particles r 2 y ðtÞ were also derived.

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Section: Introductionmentioning

confidence: 99%

An Analysis Of Sonic Anemometer Observations In Low Wind Speed Conditions

Anfossi

Oettl

Degrazia

et al. 2005

Boundary-Layer Meteorology

169

184

View full text Add to dashboard Cite

show abstract

“…As shown in Figure 10a, by choosing a relevant area and an approximate time range of the emergency, the corresponding city emergencies information including the name, occurred time, location and emergency state can be shown on the Tianditu [51] which is used for providing elementary geographic information services in China. The decision result of the gas concentrations monitoring, including the gas diffusion region and the gas concentration at each point in the diffusion region simulated through the Gaussian diffusion model [52], as shown in Figure 10b, is visually displayed for decision makers, for example, the color of the observation point in the diffusion region is deepened with the increase of the gas concentration, and decision makers can through the specific observation values to dispatch vehicles for the dilution of gas concentration. In addition, users can also refer to more announcement messages of the related departments for the gas leak emergency responses (see Figure 10c), to improve the intelligence level of the smart city emergency responses.…”

Section: Data Application and Visualizationmentioning

confidence: 99%

A Spatio-Temporal Enhanced Metadata Model for Interdisciplinary Instant Point Observations in Smart Cities

Chen

Liu

et al. 2017

IJGI

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Due to the incomprehensive and inconsistent description of spatial and temporal information for city data observed by sensors in various fields, it is a great challenge to share the massive, multi-source and heterogeneous interdisciplinary instant point observation data resources. In this paper, a spatio-temporal enhanced metadata model for point observation data sharing was proposed. The proposed Data Meta-Model (DMM) focused on the spatio-temporal characteristics and formulated a ten-tuple information description structure to provide a unified and spatio-temporal enhanced description of the point observation data. To verify the feasibility of the point observation data sharing based on DMM, a prototype system was established, and the performance improvement of Sensor Observation Service (SOS) for the instant access and insertion of point observation data was realized through the proposed MongoSOS, which is a Not Only SQL (NoSQL) SOS based on the MongoDB database and has the capability of distributed storage. For example, the response time of the access and insertion for navigation and positioning data can be realized at the millisecond level. Case studies were conducted, including the gas concentrations monitoring for the gas leak emergency response and the smart city public vehicle monitoring based on BeiDou Navigation Satellite System (BDS) used for recording the dynamic observation information. The results demonstrated the versatility and extensibility of the DMM, and the spatio-temporal enhanced sharing for interdisciplinary instant point observations in smart cities.

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“…The model component in the stage involves a Gaussian diffusion model, a fire model, and an explosion model. Additionally, real-time meteorological observation data are provided for input to the Gaussian diffusion model [34] to identify the diffusion direction and intensity. The model types are described below.…”

Section: Risk Recognition For Gas Leakagementioning

confidence: 99%

Spatio-Temporal Risk Assessment Process Modeling for Urban Hazard Events in Sensor Web Environment

Wang

Chuanbo

Chen

et al. 2016

IJGI

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Abstract:Immediate risk assessment and analysis are crucial in managing urban hazard events (UHEs). However, it is a challenge to develop an immediate risk assessment process (RAP) that can integrate distributed sensors and data to determine the uncertain model parameters of facilities, environments, and populations. To solve this problem, this paper proposes a RAP modeling method within a unified spatio-temporal framework and forms a 10-tuple process information description structure based on a Meta-Object Facility (MOF). A RAP is designed as an abstract RAP chain that collects urban information resources and performs immediate risk assessments. In addition, we propose a prototype system known as Risk Assessment Process Management (RAPM) to achieve the functions of RAP modeling, management, execution and visualization. An urban gas leakage event is simulated as an example in which individual risk and social risk are used to illustrate the applicability of the RAP modeling method based on the 10-tuple metadata framework. The experimental results show that the proposed RAP immediately assesses risk by the aggregation of urban sensors, data, and model resources. Moreover, an extension mechanism is introduced in the spatio-temporal RAP modeling method to assess risk and to provide decision-making support for different UHEs.

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Practical modelling of gas dispersion in low wind speed conditions, for application in risk assessment

Cited by 17 publications

References 13 publications

An Analysis Of Sonic Anemometer Observations In Low Wind Speed Conditions

An Analysis Of Sonic Anemometer Observations In Low Wind Speed Conditions

A Spatio-Temporal Enhanced Metadata Model for Interdisciplinary Instant Point Observations in Smart Cities

Spatio-Temporal Risk Assessment Process Modeling for Urban Hazard Events in Sensor Web Environment

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