Predicting self-pollution inside school buses using a CFD and multi-zone coupled model

Li, Fēi; Lee, Eon S.; Liu, Junjie; Zhu, Yifang

doi:10.1016/j.atmosenv.2015.02.024

Cited by 33 publications

(11 citation statements)

References 22 publications

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“…This is a more detailed microscale approach which can be implemented for future research. CFD technology due to its features can be applied to this complex not steady fluid flow phenomena, and quantitatively describe the receptors exposition in each particular location considering transport phenomena such as turbulence, mass, and heat transfer [35][36][37][38][39][40].…”

Section: Discussionmentioning

confidence: 99%

GHG diffuse emissions estimation, and energy security to ENSO using MERRA-2 for largely hydroelectricity-based system

Cuellar

Salomón

Guevara-Luna

et al. 2019

Rev.Fac.Ing.Univ.Antioquia

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In Colombia, hydropower share was 70 % of the total installed capacity and more than 50 % of the monthly generation share in 2015, which coincided with the strongest El Niño Southern Oscillation (ENSO) phenomenon reported in the region. ENSO has been recognized as an influential climate pattern on meteorological variables. The generation via hydropower implies the construction of dams and water reservoirs; these flooded areas generate an important amount of Green House Gases (GHG). In this regard, the main aim of this research was to quantify the diffusing emissions of CO2 and CH4 in the flooded areas of the main hydroelectric power generation facilities in Colombia. GHG emissions were calculated by the implementation of an IPCC methodology. The obtained results show that more than 1,042,500 t CO2-Eq (i.e. CO2 and CH4) are emitted in Colombia per year from this source, representing 4.4 % of the total GHG emissions in the country. As the second aim, the vulnerability of Colombia’s energy independence, in terms of power supply to ENSO and climate change was analyzed using the MERRA-2 dataset from NASA, for the years between 2010 and 2017.

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Section: Discussionmentioning

confidence: 99%

GHG diffuse emissions estimation, and energy security to ENSO using MERRA-2 for largely hydroelectricity-based system

Cuellar

Salomón

Guevara-Luna

et al. 2019

Rev.Fac.Ing.Univ.Antioquia

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“…17 Jiang et al (2018). Note that Zhang and Zhu (2011) find that retrofits significantly decreased tailpipe emissions but had no significant effect on on-bus ambient air quality, while Li et al (2015) show that tailpipe emissions do in fact enter the cabin. Borak and Sirianni (2007) conduct a meta-analysis and concludes that control technologies like retrofits can in fact eliminate "self-pollution" from diesel exhaust into bus cabins.…”

Section: Emission Reduction Programsmentioning

confidence: 99%

School bus emissions, student health and academic performance

Austin

Heutel

Kreisman

2019

Economics of Education Review

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“…In this study, the unsteady Reynolds-averaged Navier-Stokes (URANS) approach with the Renormalization Group (RNG) k-ε model was employed to simulate airflow and particle distributions of the rooms. For indoor environment simulations, the RNG k-ε model has been widely used with considerable success [43][44][45]. The governing equations can be generally written as:…”

Section: Methodsmentioning

confidence: 99%

An improved particle swarm optimization method for locating time-varying indoor particle sources

Feng

Cai

et al. 2019

Building and Environment

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A B S T R A C TThe indoor transmission of airborne particles can spread disease and have health-related and even life-threatening effects on occupants, thus necessitating effective ways to locate indoor particle sources. The identification of particle sources from concentration distributions is a difficult task because particles are often released at a time-varying rate, and particle transport mechanisms are more complex than those of gas. This study proposes an improved multi-robot olfactory search method for locating two types of time-varying indoor particle sources: 1) periodic sources such as occupants' respiratory activities and 2) decaying sources such as laboratory leaky containers with hazardous chemicals. The method considers both particle concentrations and indoor air velocities by including an upwind term in the standard particle swarm optimization (PSO) algorithm, preventing robots from becoming trapped into a local optimum, which occurs when using other algorithms. We also considered two ventilation types (mixing ventilation and displacement ventilation) when particles are emitted from different source types, comprising four scenarios. For each scenario, particle concentration and air velocity were simulated using computational fluid dynamics (CFD) and then fed to the PSO algorithm for source localization. In addition, we validated the CFD approach for one scenario by comparing experimental data (e.g., velocities and particle concentrations) under laboratory settings. The results showed that the proposed method can locate the two types of particle sources within approximately 55 s, and the success rates of source localization exceeding 96%, which is a much higher level than levels achieved from the standard PSO and wind utilization II algorithms.

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Predicting self-pollution inside school buses using a CFD and multi-zone coupled model

Cited by 33 publications

References 22 publications

GHG diffuse emissions estimation, and energy security to ENSO using MERRA-2 for largely hydroelectricity-based system

GHG diffuse emissions estimation, and energy security to ENSO using MERRA-2 for largely hydroelectricity-based system

School bus emissions, student health and academic performance

An improved particle swarm optimization method for locating time-varying indoor particle sources

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