SIMULATION OF ATMOSPHERIC POLLUTANTS DISPERSION IN AN URBAN ENVIRONMENT

International Journal of Innovative Research in Information Security (IJIRIS) ISSN: 2349-7017
Issue 02, Volume 6 (February 2019) www.ijiris.com
IJIRIS: Mendeley (Elsevier Indexed) CiteFactor Journal Citations Impact Factor 1.23
Impact Factor Value – SJIF: Innospace, Morocco (2016): 4.651| Indexcopernicus: (ICV 2016): 88.20
© 2014- 19, IJIRIS- All Rights Reserved Page -29
SIMULATION OF ATMOSPHERIC POLLUTANTS
DISPERSION IN AN URBAN ENVIRONMENT
Vinay Prabhakar
Department of Environmental Engineering,
Delhi Technological University, Delhi, India-110042
prabhakarvinay01@gmail.com
S.K. Singh
Department of Environmental Engineering,
Delhi Technological University, Delhi, India-110042
sksinghdce@gmail.com
Manuscript History
Number: IJIRIS/RS/Vol.06/Issue02/FBIS10080
DOI: 10.26562/IJIRAE.2019.FBIS10080
Received: 03, February 2019
Final Correction: 11, February 2019
Final Accepted: 18 February 2019
Published: February 2019
Citation: Prabhakar & Singh (2019). SIMULATION OF ATMOSPHERIC POLLUTANTS DISPERSION IN AN URBAN
ENVIRONMENT. IJIRIS:: International Journal of Innovative Research in Information Security, Volume VI, 29-39.
doi://10.26562/IJIRIS.2019.FBIS10080
Editor: Dr.A.Arul L.S, Chief Editor, IJIRIS, AM Publications, India
Copyright: ©2019 This is an open access article distributed under the terms of the Creative Commons Attribution
License, Which Permits unrestricted use, distribution, and reproduction in any medium, provided the original author
and source are credited
Abstract— Interest in air pollution investigation of urban environment due to existence of industrial and
commercial activities along with vehicular emission and existence of buildings and streets which setup natural
barrier for pollutant dispersion in the urban environment has increased. The air pollution modelling is a
multidisciplinary subject when the entire cities are taken under consideration where urban planning and
geometries are complex which needs a large software packages to be developed like Operational Street Pollution
Model (OSPM), California Line Source model (CALINE series) etc. On overviewing various works it can be
summarized that the air pollutant dispersion in urban street canyons and all linked phenomenon such as wind
flow, pollutant concentrations, temperature distribution etc. generally depend on wind speed and direction,
building heights and density, road width, source and intensity of air pollution, meteorological variables like
temperature, humidity etc. A unique and surprising case is observed every time on numerous combinations of
these factors. The main aim of this study is to simulate the atmospheric pollutant dispersion for given pollutant like
carbon monoxide, sulphur dioxide and nitrogen dioxide and given atmospheric conditions like wind speed and
direction. Computational Fluid Dynamics (CFD) simulation for analysing the atmospheric pollutant dispersion is
done after natural airflow analysis. Volume rendering is done for variables such as phase 2 volume fraction and
velocity with resolution as 250 pixels per inch and transparency as 20%. It can be observed that all the three
pollutant namely nitrogen dioxide, sulphur dioxide and carbon monoxide the phase 2 volume fraction changes
from 0 to 1. The wind velocity changes from 3.395×10-13 m/s to 1.692×102 m/s. The dispersion of pollutants
follow the sequence Sulphur dioxide>Carbon monoxide>Nitrogen dioxide.
Keywords— CFD; Wind Rose; Dispersion; Sustainable Development; Street Canyon; Air Pollution;
I. INTRODUCTION
Air pollution is the condition at which concentrations of certain substances in the ambient air rises more than
prescribed limit resulting into the remarkable effects on human beings, flora, fauna and materials like deterioration
of white marble due to sulphur dioxide.

Effects mainly cause due to significant increase in concentration of pollutants are unpleasant odours, irritation to
the senses, respiratory illness, severe diseases, smoke visibility and obscuration, weather and climate changes and
destructions to build-up areas due to corrosion. As there is rapid increase in urbanization and industrialization the
air pollution became a serious problem in the last 200 years with considerable use of fossil fuels. Around 5 lakhs of
the world’s population mainly children and aged people die prematurely every year due to air pollution. There is a
huge requirement of funds for mitigation of air pollution impacts. Air pollution may either due to urban and
industrial activities or from natural phenomenon. Researchers show interest in air pollution investigation of urban
environment due to existence of industrial and commercial activities along with vehicular emission and existence
of buildings and streets which setup natural barrier for pollutant dispersion in the urban environment. Now-a-days
the idea of sustainable development widespread across the world so architecture prefer natural ventilation in the
urban areas for best thermal and wind comfort by using big windows in place of small ones which reduce the cost
of energy. Wind direction and wind flow pattern affect directly the air pollution dispersion as find out by study of
natural ventilation. An example of urban site can be any residential complex in Delhi. To provide pleasant
atmosphere for resident the pollutant dispersion caused by vehicular exhaust shall be reduced and intensification
of the ventilation across the buildings is the key parameters. A study on vehicular pollution in Delhi reveals that
around 20 to 25% is contributed by PM10 and PM2.5 during winter season. Also, IIT Kanpur finds that around 9%
and 20% of emission loads are contributed by PM10 and PM2.5 respectively and out of which 10% contributed by
passenger cars [1]. According to Ministry of Environment, Forest and Climate Change and Shekhar and SK S[2]
apart from transport sector, domestic and power sectors also contribute to Air Pollution of Delhi with nearly
421.84 tons of CO, 110.45 tons NO2, 184.37 tons HC and 12.77 tons PM is released in capital atmosphere per day[2].
Pollutants like ozone, peroxyacytyl nitrate, oxides of sulphur and nitrogen dispersed easily over large areas by
wind. Thus, morphology and meteorology play an important role in air pollution dispersion. Tiwary and Colls [2]
is also among the researcher and scientist who are working on the problem of air quality and their risk involved
during long exposure of high concentration on human as well as flora, fauna and buildings [3]. Street canyons are
the pollutant emitting source surrounded by the different elevation of buildings and different widths of roads.
There is essential need of understanding and predicting the air pollutant dispersion in the street canyons and
reducing the emission of detrimental pollutants [4]. To study how the wind flow and pollutant disperse in the
street canyons many of the techniques are available like using field measurements, physical modelling at
laboratory scale by constructing a pilot plant and computational fluid dynamics (CFD) [5]. In reality the air
pollution modelling is a multidisciplinary subject when the entire cities are taken under consideration where urban
planning and geometries are complex which needs a large software packages to be developed like Operational
Street Pollution Model (OSPM) used for European cities [6]. Other packages which are used for air modeling are
Highway air pollution model (HIWAY-2), California Line Source Model (CALINE-4), General Line Model (GM),
General Finite Line Source Model (GFLSM), Osaka Municipal Government volume source model (OMG), ROADWAY
and Main Geophysical Observatory (MGO) [7]. Based on the various research works it can be summarized that the
air pollutant dispersion in urban street canyons and all linked phenomenon such as wind flow, pollutant
concentrations, temperature distribution etc. generally depend on wind speed and direction, building heights and
density, road width, source and intensity of air pollution, meteorological variables like temperature, humidity etc. A
unique and surprising case is observed every time on numerous combinations of these factors. The main aim of this
study is to simulate the atmospheric pollutant dispersion for given pollutant like carbon monoxide, sulphur dioxide
and nitrogen dioxide and given atmospheric conditions like wind speed and direction. ANSYS FLUENT for
modelling the natural air ventilation and pollutant dispersion has been used in the residential complex with
consideration of existing meteorological conditions for 3D numerical simulations were carried out by means of CFD.
Other aim is to build up the confidence in using CFD for other future works.
II. ATMOSPHERIC POLLUTANT DISPERSION SCENARIO IN THE WORLD
Researchers study on the pollutant dispersion based on the different techniques available like using either field
measurement, physical modelling at laboratory scale by constructing a pilot plant or statistical study or using
computational fluid dynamics throughout the world and some of the research findings are presented in table I.
Table I - Atmospheric Pollutant Dispersion Scenario in Different Parts of World
S. No. Study Area Description Findings References
1. Bagdad, Iraq Statistical tools like ANOVA
and t-test for analysing the
carbon dioxide and ozone
variation with temperature
Significant variations in monthly
observations in two ways
whereas t-test results shows that
only CO2 give significant
difference than O3
Aenab et al (2015)[8]

2. Blantyre,
Malawi
Statistical tools like t-test,
ANOVA, hierarchical cluster
analysis (HCA) and factor
analysis (FA) for carbon
monoxide, sulphur dioxide
and nitrogen dioxide
variation with temperature
Carbon monoxide concentration
crosses the standard. Significant
difference in values of hourly and
diurnal values with change in
local weather based on t-test.
Significantly variation in monthly
values based on ANOVA.
Significant difference in pollutant
concentration with temperature
based on FA.
Mapoma et al
(2014)[9]
3. Hamburg,
Germany
Wind tunnel experiments at
Meteorological Institute,
University of Hamburg
With increasing street widths the
concentration of air pollutant
decreases everywhere on the
wall of buildings
Meroney et al
(1996)[10]
4. Hamburg,
Germany
Correlation of air pollutants
with roof shapes and ratios
of building’s height with
street width (H/B) at
Meteorological Institute
Stable and symmetric vortices are
formed in case of flat roof instead
of inclined roof. In case of narrow
streets the pollution level is
remarkable high in comparison to
wider street. Influence of building
density and roof shape on the
wind characteristics over a town
as upto three building height.
Rafailidis (1997)[11]
5. Japan Wind tunnel experiment at
Japanese National Institute
for Environmental Sciences
Cavity eddies that are appearing
from the street canyons are weak
during stable atmospheric
condition (Rb=0.79) while
stronger during unstable
atmospheric condition (Rb=-
0.21).Positive feedback effect is
observed during stable
atmospheric conditions. Wind
speed approaches to zero when
the stability exceed the
prescribed limit (Rb=-0.4 to 0.8)
Uehara et al
(2000)[12]
6. Tokyo, Japan Wind tunnel experiment at
Tokyo Polytechnic
University
For each case examined shows
significance for the pollutant
sources. Vertical profiles of the
longitudinal mean velocity are
very thick around the obstacle
emerging region and increases
with increase in obstacle altitude
as near top of the obstacle the
longitudinal mean velocity has
maximum value. Concentration
fluctuation intensity decreases
with increase in distance from
the source.
Yassin et al (2008)[13]
7. Glasgow, UK A field measurements in
order to validate the result
obtained from CFD package
i.e. Phoenix for the fluid
flow and dispersion of
pollutant in street canyon.
At lower concentration of the
pollutant converges satisfactory
at leeward face of upwind
building and for CO
concentrations above 8 ppm the
deviations are observed during
the comparison of both field
measurement and computational
result interpretation.
Chan et al (2002)[14]

8. Katraj,
Maharashtra,
India
A box model is applied in
the street canyon
NOx concentration in urban
canyon majorly due to vehicular
pollution is 19.77µg/m3.
Kanakiya et al
(2015)[15]
9. Different
parts of
World
CFD packages such as
MIMO, FLUENT, PHOENIX
and others which model
mainly on the Reynolds
Average Navier-Stokes
equations (RANS) for
carrying out the study.
Surrounding built environment,
street’s width, the height of
constructed buildings and the
strength of air pollutant sources
greatly affected the flows of
pollutants along with the
progressing time.
Lower pollution is observed in
wider streets but vary with
height of building for constant
width roads
Assimakopoulos et al
(1996-2007)
[16,17,18,19,20]
10. Anywhere in
the World
Simulating the transport
and deposition of particle
near a small isolated
building
Dramatically decrease in the
exhaust emission with increase in
height of chimney of the building.
Significant variation in deposition
patterns of particle on different
surfaces of the building with
different size of particles.
Ahmadi et al
(2000-2002)[21]
11. Czech
Republic
A 3-D CFD analysis using
FLUENT as the software
The real situation was very
satisfactory satisfied by the
standard k-Ɛ turbulence model.
At centre mean horizontal
velocity near the lower region
street canyon have very small
values.
Janour et al
(2008-2010)[22,13]
12. Anywhere in
the World
A CFD analysis in an urban
street canyon
Decrease in concentration of
pollutant with increase in inflow
turbulent intensity. At wind
incident angle of 45˚ the flow is
diagonally symmetrical behind
the upwind building but increase
in angle decrease the pollutant
concentration escaping out from
street canyon.
Kim and Baik
(2004)[23]
13. Central
London and
downtown
Montreal
Large-Eddy Simulations
(LES)
RANS simulation is heavily
reliant on the turbulent Schmidt
number whose optimum value is
case dependent. Using grid
resolution of 1 meter in LES is
ample for exact prediction of the
flow and average dispersion
characteristics
Gousseau et al (2008-
2011)[24,25,26]
III.METHODOLOGY
Meteorological data of Delhi for the year 2017 was collected from the website of accuweather and tabulate into
excel for using as an input for Lakes WRPlot View to plot wind rose diagram of the study area i.e. Delhi. Followed
by analysing the natural air flow by simulating the wind direction which represents the highest and the lowest
average wind speeds obtained from the meteorological data. Wind speed variation along with directions to find out
optimal wind flow pattern in order to establish the highest ventilation across the residential building as it will
enhance the sustainable built environment.
CFD simulation is done for analysing the atmospheric pollutant dispersion using Ansys 18.2. The series of steps
followed in Fluid Flow (Fluent) is shown in Fig. 1. In this study the geometry was created in AutoCAD and imported
in Ansys Fluent for further process.

The plume inlet is assigned to the northern part of the geometry, wind inlet to the western part of the geometry
based on the wind rose diagram and outlet is the remaining part of the geometry. Multiphase volume of fluid model
with two eulerian phases and implicit formulation and realizable k-epsilon viscous model was used in the study.
Fig. 1 Flowchart of Sequence of Step to be followed in Fluent
IV. RESULTS AND DISCUSSIONS
A. Analysis of Natural Air flow
Analysis of natural air flow can be done by simulating the wind direction representing the lowest and highest
average wind velocity can be obtained from wind rose diagram shown in fig. 2 for Delhi of year 2017. It can also see
from wind rose diagram that wind blowing from west is around 50% in number. It can be found that both the
lowest and highest wind velocity blows from west with velocity of 1.286 m/s and 4.63 m/s respectively. It also be
concluded from the analyses of wind that the wind blowing from west is optimal to establish the highest ventilation
across the residential complex i.e. Pocket- G/6, Sector-11, Rohini, Delhi as study area.
Fig. 2 Wind Rose Diagram for Delhi of Year 2017
B. Analysis of Nitrogen Dioxide Dispersion
The plume inlet is taken in the north direction as mentioned above in the previous section. The fig. 3 shows how
the nitrogen dioxide disperses in different side views. The fig. 3 shows that in the north direction the volume
fraction of nitrogen dioxide ranges from 0.75 to 1. Also, the inlet of the plume shown by red colour in the north
direction have volume fraction of 1 except few building block as seen from north side views. In east side view it can
be seen that there is dispersion of nitrogen dioxide mainly at the top and bottom of the central building blocks. In
southern view also there is significant dispersion of nitrogen dioxide as volume fraction changes from 0 to 1. Also,
in southern view the volume fraction of 1 showing maximum nitrogen dioxide concentration after two building
blocks from left shown by red colour and due to wind blowing from west it disperse the nitrogen dioxide and
volume fraction changes to zero. The west side view observe that the volume fraction of nitrogen dioxide varies
from 0 to 0.4 and 0.75 to 1 as the nitrogen dioxide concentration is negligible initially but increases may be of
deposition of nitrogen dioxide. It can be conclude that the wind blowing from west favours the dispersion of
nitrogen dioxide.

North East
South West
Fig. 3 Nitrogen Dioxide Dispersion in Side Views
C. Analysis of Sulphur Dioxide Dispersion
The plume inlet is taken in the north direction as mentioned above in the previous section. The fig. 4 shows how
the sulphur dioxide disperses in different side views. The figure 4 shows that in the north direction the volume
fraction of sulphur dioxide ranges from 0.75 to 1. Also, the inlet of the plume shown by red colour in the north
direction have volume fraction of 1 except few building block as seen from north side views.
In east side view it can be seen that there is dispersion of sulphur dioxide after two building from the left and the
volume fraction varies from 0 to 1. In southern view also there is significant dispersion of sulphur dioxide as
volume fraction changes from 0 to 1 but on comparison it with nitrogen dioxide dispersion it has less dispersion as
shown by yellow colour after two building from the right. Also, in southern view the volume fraction of 1 showing
maximum sulphur dioxide concentration after two building blocks from left shown by red colour and due to wind
blowing from west to east it disperse the sulphur dioxide and volume fraction changes to zero.
The west side view observe that the volume fraction of sulphur dioxide varies from 0 to 0.4 and 0.75 to 1 as the
sulphur dioxide concentration is negligible initially but increases may be of deposition of sulphur dioxide. It can be
conclude that the wind blowing from west to east favours the dispersion of sulphur dioxide.

North East
South West
Fig. 4 Sulphur Dioxide Dispersion in Side Views
D. Analysis of Carbon Monoxide Dispersion
The plume inlet is taken in the north direction as mentioned above in the previous section. The figure 5 shows how
the carbon monoxide disperses in different side views. The fig. 5 shows that in the north direction the volume
fraction of carbon monoxide ranges from 0.75 to 1. Also, the inlet of the plume shown by red colour in the north
direction have volume fraction of 1 except few building block as seen from north side views.
In east side view it can be seen that there is dispersion of carbon monoxide mainly at the top and bottom of the
central building blocks In southern view also there is significant dispersion of carbon monoxide as volume fraction
changes from 0 to 1 but on comparison it with nitrogen dioxide dispersion it has less dispersion as shown by
yellow colour after two building from the right but shown similar trend like sulphur dioxide. Also, in southern view
the volume fraction of 1 showing maximum carbon monoxide concentration after two building blocks from left
shown by red colour and due to wind blowing from west to east it disperse the carbon monoxide and volume
fraction changes to zero.
The west side view observe that the volume fraction of carbon monoxide varies from 0 to 0.4 and 0.75 to 1 as the
carbon monoxide concentration is negligible initially but increases may be of accumulation of carbon monoxide. It
can be conclude that the wind blowing from west to east favours the dispersion of carbon monoxide.

North East
South West
Fig. 5 Carbon Monoxide Dispersion in Side Views
E. Analysis of Wind Velocity Profile
The velocity changes from 3.395×10-13 m/s to 8.462×101 m/s in the east and west side views while it varies
3.395×10-13 m/s to 1.692×102 m/s in the north and south side views. It can also be seen from the fig. 6 that the
pattern observe in the north side view from left to right is nearly opposite in south side view and similarly, the
pattern observe in east side view from left to right is almost opposite in west side view.
North East

South West
Fig. 6 Wind Velocity Profile in Side Views
F. Analysis of Atmospheric Pollutant Dispersion along with Wind Velocity Profile
The fig. 7 shows the variation in dispersion of atmospheric pollutant along with wind velocity profile in the
residential complex from the top view. It can be observed that all the three pollutant namely nitrogen dioxide,
sulphur dioxide and carbon monoxide the phase 2 volume fraction changes from 0 to 1.
Nitrogen Dioxide (NO2) Carbon Monoxide (CO)
Sulphur Dioxide (SO2) Wind Velocity
Fig. 7 Variations in Dispersion of Pollutants and Wind Velocity from Top View

The phase 2 volume fraction value equal to one means the atmospheric concentration has maximum value and
decreases with volume fraction decreases. A trend of atmospheric pollutant dispersion is found out from the figure
7 that maximum dispersion occur in case of sulphur dioxide followed by carbon monoxide and at last the nitrogen
dioxide. The wind velocity changes from 3.395×10-13 m/s to 1.692×102 m/s. It can also be seen from figure that
atmospheric pollutants disperse before the first road due to low wind velocity. The researchers like Jiang et. al.
[12,13], Tzempelikos et. al.[28] and Barmpas et. al.[5] also find that the lower the wind velocity the lower the
dispersion of atmospheric pollutants and vice-versa[27–30].
V. CONCLUSIONS
The wind velocity blowing from west favours the atmospheric pollutants like carbon monoxide, nitrogen dioxide
and sulphur dioxide but due to relatively low velocity the dispersion of the air pollutants restricted to the buildings
before the road as seen from figure 7. It can also be concluded that the velocity during our study varies from
3.395×10-13 m/s to 1.692×102 m/s. It can also be concluded that the dispersion of pollutants follow the sequence
Sulphur dioxide>Carbon monoxide>Nitrogen dioxide. Apart from the study be considered it can also conclude that
with increasing growth of digital India mission, the dependency on computers or electronic gadgets increases. The
computational fluid dynamics which is used as a tool in our study also increasing day by day as serve as a much
cheaper method than experimental especially in case of atmospheric pollutant dispersion at place or in or around a
full scale buildings. It can also be concluded that atmospheric pollutant dispersion and natural ventilation is a
complex procedure in real urban environment but CFD make it is easier and fast for analysis.
REFERENCES
1. Katiyar, A., Singh, S. K. & Haritash, A. K. Effect of Odd Even Scheme To Combat Air Pollution in Nct of Delhi. Int.
J. Adv. Res 5, 2320–5407 (2017).
2. Shekhar Singh, S. & SK, S. Environmental Concerns in National Capital Territory of Delhi, India. J. Climatol.
Weather Forecast. 03, 3–7 (2015).
3. Tiwary, A. & Colls, J. Air Pollution: Measurement, modelling and mitigation. Atmospheric Environment (2010).
doi:doi:10.4324/9780203476024.fmatt
4. Paliatsos, A. G., Kaldellis, J. K., Koronakis, P. S. & Garofalakis, J. E. Fifteen year air quality trends associated with
the vehicle traffic in Athens, Greece. Fresenius Environ. Bull. 11, 1119–1126 (2002).
5. LI, X., LIU, C., LEUNG, D. & LAM, K. Recent progress in CFD modelling of wind field and pollutant transport in
street canyons. Atmos. Environ. 40, 5640–5658 (2006).
6. Assael, M. J., Delaki, M. & Kakosimos, K. E. Applying the OSPM model to the calculation of PM10concentration
levels in the historical centre of the city of Thessaloniki. Atmos. Environ. 42, 65–77 (2008).
7. Taseiko, O. V., Mikhailuta, S. V., Pitt, A., Lezhenin, A. A. & Zakharov, Y. V. Air pollution dispersion within urban
street canyons. Atmos. Environ. 43, 245–252 (2009).
8. Aenab, A. M., Needs, C., Foundation, S. & Singh, S. K. Air Quality Assessment : A Statistical Approach to
Stationary Air Monitoring Stations Air Quality Assessment : A Statistical Approach to Stationary Air
Monitoring Stations. (2015).
9. Mapoma, H. W. T., Tenthani, C., Tsakama, M. & Kosamu, I. B. M. Air quality assessment of carbon monoxide,
nitrogen dioxide and sulfur dioxide levels in Blantyre, Malawi: A statistical approach to a stationary
environmental monitoring station. African J. Environ. Sci. Technol. 8, 330–343 (2014).
10. Meroney, R. N., Pavageau, M., Rafailidis, S. & Schatzmann, M. Study of line source characteristics for 2-D
physical modelling of pollutant dispersion in street canyons. J. Wind Eng. Ind. Aerodyn. 62, 37–56 (1996).
11. Rafailidis, S. Influence of Building Areal Density and Roof Shape on the Wind Characteristics Above a Town.
Boundary-Layer Meteorol. 85, 255–271 (1997).
12. Uehara, K., Murakami, S., Oikawa, S. & Wakamatsu, S. Wind tunnel experiments on how thermal stratification
affects flow in and above urban street canyons. Atmos. Environ. (2000). doi:10.1016/S1352-2310(99)00410-
0
13. Yassin, M. F., Kellnerová, R. & Jaňour, Z. Impact of street intersections on air quality in an urban environment.
Atmos. Environ. 42, 4948–4963 (2008).
14. Chan, T. L., Dong, G., Leung, C. W., Cheung, C. S. & Hung, W. T. Validation of a two-dimensional pollutant
dispersion model in an isolated street canyon. Atmos. Environ. 36, 861–872 (2002).
15. Kanakiya, R. S., Singh, S. K. & Mehta, P. M. Urban Canyon Modelling : A Need for the Design of Future Indian
Cities. 4, 86–95 (2015).
16. Assimakopoulos, V. D., ApSimon, H. M. & Moussiopoulos, N. A numerical study of atmospheric pollutant
dispersion in different two-dimensional street canyon configurations. Atmos. Environ. 37, 4037–4049 (2003).

17. Hassan, A. A. & Crowther, J. M. Modelling of fluid flow and pollutant dispersion in a street canyon. in
Environmental Monitoring and Assessment (1998). doi:10.1023/A:1005928630000
18. Huang, H., Akutsu, Y., Arai, M. & Tamura, M. A two-dimensional air quality model in an urban street canyon:
Evaluation and sensitivity analysis. Atmos. Environ. 34, 689–698 (2000).
19. Di Sabatino, S., Buccolieri, R., Pulvirenti, B. & Britter, R. Simulations of pollutant dispersion within idealised
urban-type geometries with CFD and integral models. Atmos. Environ. 41, 8316–8329 (2007).
20. Sini, J. F., Anquetin, S. & Mestayer, P. G. Pollutant dispersion and thermal effects in urban street canyons.
Atmos. Environ. 30, 2659–2677 (1996).
21. Ahmadi, G. & Li, A. Computer simulation of particle transport and deposition near a small isolated building. J.
Wind Eng. Ind. Aerodyn. 84, 23–46 (2000).
22. Janour, Z., Jurcakova, K., Brych, K., Dittrt, F. & Dittrich, F. Potential risks at an industrial site: A wind tunnel
study. Process Saf. Environ. Prot. 88, 185–190 (2010).
23. Kim, J. J. & Baik, J. J. A numerical study of the effects of ambient wind direction on flow and dispersion in urban
street canyons using the RNG k-ε turbulence model. Atmos. Environ. 38, 3039–3048 (2004).
24. Gousseau, P., Blocken, B., Stathopoulos, T. & van Heijst, G. J. F. CFD simulation of near-field pollutant
dispersion on a high-resolution grid: A case study by LES and RANS for a building group in downtown
Montreal. Atmos. Environ. (2011). doi:10.1016/j.atmosenv.2010.09.065
25. Li, X. X., Liu, C. H. & Leung, D. Y. C. Large-eddy simulation of flow and pollutant dispersion in high-aspect-ratio
urban street canyons with wall model. Boundary-Layer Meteorol. (2008). doi:10.1007/s10546-008-9313-y
26. Xie, Z. T. & Castro, I. P. Large-eddy simulation for flow and dispersion in urban streets. Atmos. Environ. (2009).
doi:10.1016/j.atmosenv.2009.01.016
27. Barmpas, F., Bouris, D. & Moussiopoulos, N. 3D Numerical Simulation of the Transient Thermal Behavior of a
Simplified Building Envelope Under External Flow. J. Sol. Energy Eng. (2009). doi:10.1115/1.3139137
28. Jiang, Y. & Chen, Q. Effect of fluctuating wind direction on cross natural ventilation in buildings from large
eddy simulation. Build. Environ. (2002). doi:10.1016/S0360-1323(01)00036-1
29. Tzempelikos, A., Athienitis, A. K. & Karava, P. Simulation of facade and envelope design options for a new
institutional building. Sol. Energy (2007). doi:10.1016/j.solener.2007.02.006
30. Jiang, Y., Alexander, D., Jenkins, H., Arthur, R. & Chen, Q. Natural ventilation in buildings: Measurement in a
wind tunnel and numerical simulation with large-eddy simulation. J. Wind Eng. Ind. Aerodyn. (2003).
doi:10.1016/S0167-6105(02)00380-X

SIMULATION OF ATMOSPHERIC POLLUTANTS DISPERSION IN AN URBAN ENVIRONMENT

Recomendados

Recomendados

Más contenido relacionado

La actualidad más candente

La actualidad más candente (20)

Similar a SIMULATION OF ATMOSPHERIC POLLUTANTS DISPERSION IN AN URBAN ENVIRONMENT

Similar a SIMULATION OF ATMOSPHERIC POLLUTANTS DISPERSION IN AN URBAN ENVIRONMENT (20)

Más de AM Publications

Más de AM Publications (20)

Último

Último (20)

SIMULATION OF ATMOSPHERIC POLLUTANTS DISPERSION IN AN URBAN ENVIRONMENT