Road risk analysis due to the transportation of chlorine in Rosario city

Scenna, Nicolás J.; Cruz, Alejandro S. M. Santa

doi:10.1016/j.ress.2004.11.004

Cited by 35 publications

(12 citation statements)

References 9 publications

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“…These features make the box models and other integral models valuable engineering tools. They are used for environmental impact assessment, risk assessment, emergency response planning and efficient land use planning [47][48][49]. Examples of box models are presented in Table 1.…”

Section: Intermediate/integral and Shallow Layer Modelsmentioning

confidence: 99%

A Review of Mathematical Models for the Atmospheric Dispersion of Heavy Gases. Part I. A Classification of Models

Markiewicz¹

2012

Ecological Chemistry and Engineering S

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Abstract:In this two part article in its first part models of heavy gas dispersion in the atmosphere are classified and the distinguished groups of models are characterised. In the second part the procedures for the model quality evaluation are described and the main results of model evaluation projects are summarised. Substances released to the atmosphere which have a density greater than the density of the atmospheric air are called heavy gases or dense gases. The dispersion of heavy gases is different from that encountered in the case of neutrally or positively buoyant gases. Specific models have been developed to describe it. The heavy gas dispersion models differ in the complexity and mathematical description. Based on these criteria four main groups of models are distinguished: simple/empirical models, intermediate/integral and shallow layer models, advanced/Lagrangian particle trajectory and Lagrangian puff dispersion models and sophisticated/Computer Fluid Dynamics (CFD) models. This classification is an extension of the classification proposed earlier in the literature.

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Section: Intermediate/integral and Shallow Layer Modelsmentioning

confidence: 99%

A Review of Mathematical Models for the Atmospheric Dispersion of Heavy Gases. Part I. A Classification of Models

Markiewicz¹

2012

Ecological Chemistry and Engineering S

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“…For fungi dispersion, the settling velocity is c a l c u l a t e d a s f u n c t i o n o f t h e s c e n a r i o characteristics (particle diameter ranges, Reynolds number, spreading coefficients, etc.) The proposed methodology is based on a simple representation of emitters and receptors (a squared grid) according to Godoy et al, 2007, 2010and Scenna & Santa Cruz, 2005. Every potential receptor is supposed to be placed in the geometrical centre of each grid square represented by the (X j ,Y k ) coordinates.…”

Section: Stochastic Concentration Levels and Impact Distances Calculamentioning

confidence: 99%

“…1 shows the general implemented computational strategy which can be concise in the flowchart and presents the three basic modules the computational package STRRAP (Godoy et al, 2007(Godoy et al, , 2010Scenna & Santa Cruz, 2005) has: 1. The pre-processor 2.…”

Section: Stochastic Concentration Levels and Impact Distances Calculamentioning

confidence: 99%

Bioaerosol Emissions: A Stochastic Approach

Godoy¹,

Cruz²,

Scenna³

2011

Air Quality-Models and Applications

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“…Logistics risk assessment is usually emphasised on risk analysis of hazardous material or dangerous goods transportation (Spadoni et al 1995;Zhang, Hodgson, and Erkut 2000;Scenna and Santa Cruz 2005;Verma 2011;Samvedi, Jain, and Chan 2013). In general, publications are considered to apply separately on road, rail, air, or sea (Spadoni et al 1995;Scenna and Santa Cruz 2005;Tsai and Su 2005;Verma 2011); however, a very limited number apply to multimodal logistics, particularly green logistics. According to the European Conference of Ministers of Transport, multimodal transport is the integration of two or more modes of transport to move passengers or goods from one source to a destination in a single contract (Kengpol, Meethom, and Tuominen 2012).…”

Section: Introductionmentioning

confidence: 99%

The development of a decision support framework for a quantitative risk assessment in multimodal green logistics: an empirical study

Kengpol

Tuammee

2015

International Journal of Production Research

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The objective of this research was to develop a decision support framework (DSF) to assess quantitative risk in multimodal green logistics. This risk assessment is the combination of a number of models, the failure mode and effects analysis, the risk contour plot, the quantitative risk assessment, the analytic hierarchy process and the data envelopment analysis which can support a user to perform risk assessment in various decisions. The contribution of this research is that the risk assessment model can generate an optimal green logistics route in accordance with weight from the user. The highlight of this DSF is that the quantitative assessment model can reduce bias on risk assessment of logistics route. An in-depth case study, recommendations, limitations and further research are also provided.

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Road risk analysis due to the transportation of chlorine in Rosario city

Cited by 35 publications

References 9 publications

A Review of Mathematical Models for the Atmospheric Dispersion of Heavy Gases. Part I. A Classification of Models

A Review of Mathematical Models for the Atmospheric Dispersion of Heavy Gases. Part I. A Classification of Models

Bioaerosol Emissions: A Stochastic Approach

The development of a decision support framework for a quantitative risk assessment in multimodal green logistics: an empirical study

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