SIMULATION OF ATMOSPHERIC POLLUTANTS DISPERSION IN … · International Journal of Innovative...

International Journal of Innovative Research in Information Security (IJIRIS) ISSN: 2349-7017 Issue 02, Volume 6 (February 2019) www.ijiris.com

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SIMULATION OF ATMOSPHERIC POLLUTANTS

DISPERSION IN AN URBAN ENVIRONMENT

Vinay Prabhakar Department of Environmental Engineering,

Delhi Technological University, Delhi, India-110042 [email protected]

S.K. Singh Department of Environmental Engineering,

Delhi Technological University, Delhi, India-110042 [email protected]

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.

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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


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