پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 161 |
| تعداد شمارهها | 6,479 |
| تعداد مقالات | 70,031 |
| تعداد مشاهده مقاله | 122,982,625 |
| تعداد دریافت فایل اصل مقاله | 96,214,965 |
3D Voxelisation for Enhanced Environmental Modelling Applications | ||
| Pollution | ||
| دوره 10، شماره 1، فروردین 2024، صفحه 151-167 اصل مقاله (1.82 M) | ||
| نوع مقاله: Original Research Paper | ||
| شناسه دیجیتال (DOI): 10.22059/poll.2023.360562.1942 | ||
| نویسندگان | ||
| Nurfairunnajiha Ridzuan* 1؛ Nevil Wickramathilaka1، 2؛ Uznir Ujang1؛ Suhaibah Azri1 | ||
| 13D GIS Research Lab, Faculty of Built Environment and Surveying, Universiti Teknologi Malaysia, Malaysia | ||
| 2Southern Campus, General Sir John Kotelawala Defence University, Edison Hill, Nugegalayaya, Sewanagala, Sri Lanka | ||
| چکیده | ||
| Monitoring and managing environmental problems, particularly those impacting human health such as noise and air pollution, are essential. However, the current implementation has certain limitations that need improvement. In the case of noise pollution, accurately computing noise levels requires considering traffic noise propagating in all directions, necessitating the involvement of a 3D building model. Existing methods using raster cells and noise contours are insufficient in achieving high accuracy. To overcome this, we propose integrating a voxelisation approach and 3D kriging, enabling the depiction of traffic noise values for each voxel. In the context of air pollution, wind movement plays a significant role in the dispersion of contaminants. The current practice involves a random selection procedure for wind simulation within the model discretisation. However, we suggest replacing this randomness with a voxel-based model, which not only improves accuracy but also reduces computing time. Thus, the voxel-based model represents the building model in a wind computation environment, facilitating more realistic wind simulation results. This study demonstrates the applicability of the voxelisation technique in two different environmental modeling contexts using the building model of the city building modeling standard. The level of detail (LoD) in the represented building model differs between these approaches. For traffic noise, a low LoD (LoD1) is sufficient to depict exterior buildings accurately. However, for wind simulation, a higher LoD (LoD2) is necessary to accommodate the complexity of buildings and determine appropriate voxel sizes. In conclusion, the proposed improvements in the form of voxel-based modeling techniques offer enhanced accuracy and efficiency in environmental monitoring. The findings of this study have implications for improving the management and reduction of environmental problems, ultimately benefiting human health and well-being. | ||
| کلیدواژهها | ||
| Computational Fluid Dynamics؛ Three-Dimensional Modelling؛ Traffic Noise؛ Voxelisation؛ Wind Simulation | ||
| مراجع | ||
|
Ali, M. E., Hasan, M. F., Siddiqa, S., Molla, M. M., & Nasrin Akhter, M. (2023). FVM-RANS modeling of air pollutants dispersion and traffic emission in Dhaka City on a suburb scale. Sustainability 2023, 15(1); 673. Biljecki, F., Ledoux, H., & Stoter, J. (2016). An improved LOD specification for 3D building models. Comput. Environ. and Urban Syst., 59; 25–37. Blocken, B. (2018). LES over RANS in building simulation for outdoor and indoor applications: A foregone conclusion?’. Build. Simul., 11(5); 821–870. Brenguier, F., Boué, P., Ben-Zion, Y., Vernon, F., Johnson, C. W., Mordret, A., Coutant, O., Share, P. E., Beaucé, E., Hollis, D., & Lecocq, T. (2019). Train traffic as a powerful noise source for monitoring active faults with seismic interferometry. Geophys. Res. Lett., 46(16); 9529–9536. Chatzimichailidis, A. E., Argyropoulos, C. D., Assael, M. J., & Kakosimos, K. E. (2019). Implicit definition of flow patterns in street canyons-recirculation zone-using exploratory quantitative and qualitative methods. Atmos., 10(12); 794. Che, D., & Jia, Q. (2019). Three-dimensional geological modeling of coal seams using weighted Kriging method and Multi-Source Data. IEEE Access, 7; 118037–118045. Chen, C., Hu, K., Li H., Yun A., & Li, B. (2015). Three-dimensional mapping of soil organic carbon by combining kriging method with profile depth function. PLoS ONE, 10(6), e0129038. Dubey, R., Bharadwaj, S., Sharma, V. B., Bhatt, A., & Biswas, S. (2022). Smartphone-based traffic noise mapping system. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2022; 613–620. Franke, J., Hellsten, A., Schlünzen, K. H., & Carissimo, B. (2007). Best practice guideline for the CFD simulation of flows in the urban environment-a summary. In 11th Conf. on Harmonisation within Atmospheric Dispersion Modelling for Regulatory Purposes, Cambridge, UK, July 2007 Cambridge Environmental Research Consultants. García-Sánchez, C., Vitalis, S., Paden, I., & Stoter, J. (2021). The impact of level of detail in 3D city models for CFD-based wind flow simulations. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-4/W4; 67–72. Gaur, N., & Raj, R. (2022). Aerodynamic mitigation by corner modification on square model under wind loads employing CFD and wind tunnel. Ain Shams Eng. J., 13(1); 101521. Gilani, T. A., & Mir, M. S. (2021). Modelling road traffic noise under heterogeneous traffic conditions using the graph-theoretic approach. Environ. Sci. Pollut. Res., 28(27); 36651–36668. Gimenez, J. M., & Bre, F. (2019). Optimization of RANS turbulence models using genetic algorithms to improve the prediction of wind pressure coefficients on low-rise buildings. J. Wind Eng. Ind. Aerodyn., 193; 103978. Grunwald, S., & Barak, P. (2003). 3D geographic reconstruction and visualization techniques applied to land resource management. Trans. in GIS, 7(2); 231–241. Grunwald, S., Barak, P., & Rooney, D. (2001). Web-based virtual models for the earth science community. Am. Soc. Agric. Eng., 2001; 1-9. Haron, Z., Ming Han, L., Darus, N., Ling Lee, Y., Jahya, Z., Abdul Hamid, M. F., Yahya, K., & Ngian Shek, P. (2015). A preliminary study of environmental noise in public university. J. Teknol., 77(16); 145–151. Hood, R. A. (1987). Accuracy of calculation of road traffic noise. Appl. Acoust., 21; 139–146. Huang, Y., Wang, L., Hou, Y., Zhang, W., & Zhang, Y. (2018). A prototype IOT based wireless sensor network for traffic information monitoring. Int. J. Pavement R. Technol., 11(2); 146–152. ISO 9613-2. (1996). Acoustics – Attenuation of sound during propagation outdoors – Part 2: General method of calculation Jasim, S. A., Rudiansyah, M., Ongdashkyzy, O. A., Taban, T. Z., Chupradit, S., Iswanto, A. H., Suhayb, M. K., Falih, K. T., Alshahrani, N. Z., & Mustafa, Y. F. (2022). Determining the parameters of noise pollution in the central area of the Almaty city in Kazakhstan. Noise Mapp., 9(1); 120–127. Jiang, Q., Peng, J., Biswas, A., Hu, J., Zhao, R., He, K., & Shi, Z. (2019). Characterising dryland salinity in three dimensions. Sci. Total Enviro., 682; 190–199. Juan, Y. H., Wen, C. Y., Li, Z., & Yang, A. S. (2021). Impacts of urban morphology on improving urban wind energy potential for generic high-rise building arrays. Appl. Energy, 299; 117304. Kaseb, Z., Hafezi, M., Tahbaz, M., & Delfani, S. (2020). A framework for pedestrian-level wind conditions improvement in urban areas: CFD simulation and optimization. Build. Environ., 184; 107191. Konde, A., & Saran, S. (2017). Web-enabled spatiotemporal semantic analysis of traffic noise using CityGML. J. Geomat., 11(2); 248–259. Kumar, K., Ledoux, H., Commandeur, T. J. F., & Stoter, J. E. (2017). Modelling urban noise in City GML ADE: case of the Netherlands. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci., 4(4W5); 73–81. Kummitha, O. R., Kumar, R. V., & Krishna, V. M. (2021). CFD analysis for airflow distribution of a conventional building plan for different wind directions. J. Comput. Des. Eng., 8(2); 559–569. Kurakula, V. K., & Kuffer, M. (2008). 3D noise modeling for urban environmental planning and management. CORP 008: Mobility Nodes as Innovation Hubs: Proc. 13th Int. Conf. Urban Planning Reg. Dev. Inf. Society, Vienna, May 19–21 2008, Competence Center of Urban and Regional Planning, Schwechat 2008, 517–523 Kutzner, T., Chaturvedi, K., & Kolbe, T. H. (2020). CityGML 3.0: New functions open up new applications. J. Photogramm. Remote Sens. Geoinf. Sci., 88(1); 43–61. Laxmi, V., Dey, J., Kalawapudi, K., Vijay, R., & Kumar, R. (2019). An innovative approach of urban noise monitoring using cycle in Nagpur, India. Environ. Sci. Pollut. Res., 26(36); 36812–36819. Lee, H. M., Luo, W., Xie, J., & Lee, H. P. (2022). Traffic noise reduction strategy in a large city and an analysis of its effect. Appl. Sci.,12(12); 6027. Lee, K. Y., & Mak, C. M. (2021). Effects of wind direction and building array arrangement on airflow and contaminant distributions in the central space of buildings. Build. Environ., 205; 108234. Lee, M., Park, G., Park, C., & Kim, C. (2020). Improvement of grid independence test for computational fluid dynamics model of building based on grid resolution. Adv. Civ. Eng., 2020; 8827936. Li, J., Liu, P., Wang, X., Cui, H., & Ma, Y. (2022). 3D geological implicit modeling method of regular voxel splitting based on layered interpolation data. Sci. Rep., 12(1); 1–14. Li, J., & Heap, A. D. (2014). Spatial interpolation methods applied in the environmental sciences: A review. Environ. Model. Softw., 53, 173–89. Lv, T., Fu, J., & Li, B. (2022). Design and application of multi-dimensional visualization system for large-scale ocean data. ISPRS Int. J. Geo-Information, 11(9); 491. Madsen, R. B., Kim, H., Kallesøe, A. J., Sandersen, P. B. E., Vilhelmsen, T. N., Hansen, T. M., Christiansen, A. V., Møller, I., & Hansen, B. (2021). 3D multiple-point geostatistical simulation of joint subsurface redox and geological architectures. Hydrol. Earth Syst. Sci., 25(5); 2759–2787. Masum, M. H., Pal, S. K., Akhie, A. A., Ruva, I. J., Akter, N., & Nath, S. (2021). Spatiotemporal monitoring and assessment of noise pollution in an urban setting. Environ. Chall., 5; 100218. Mileff, P., & Dudra, J. (2019). Simplified voxel based visualization. Prod. Syst. Inf. Eng., 8; 5–18. Miltiadou, M., Campbell, N. D. F., Cosker, D., & Grant, M. G. (2021). A comparative study about data structures used for efficient management of voxelised full-waveform airborne LiDAR data during 3D polygonal model creation. Remote Sens. 2021, 13(4); 559. Mishra, R. K., Mishra, A. R., & Singh, A. (2018). Traffic noise analysis using RLS-90 model in urban city. INTER-NOISE and NOISE-CON Congr. Conf. Proc., 259(3); 6490-6502. Moonen, P., Defraeye, T., Dorer, V., Blocken, B., & Carmeliet, J. (2012). Urban Physics: Effect of the micro-climate on comfort, health and energy demand. Front. Archit. Res., 1(3); 197–228. Rajasekarababu, K. B., Vinayagamurthy, G., & Selvi Rajan, S. (2019). Experimental and computational investigation of outdoor wind flow around a setback building. Build. Simul., 12(5); 891–904. Ranjbar, H. R., Gharagozlou, A. R., & Vafaei Nejad, A. R. (2012). 3D analysis and investigation of traffic noise impact from Hemmat Highway located in Tehran on buildings and surrounding areas. J. Geogr. Inf. Sys., 4(4); 322-334. Ridzuan, N., Ujang, U., & Azri, S. (2023). 3D vectorization and rasterization of CityGML standard in wind simulation. Earth Sci. Informatics, 16(3); 2635-2647. Saran, S., Oberai, K., Wate, P., Konde, A., Dutta, A., Kumar, K., & Senthil Kumar, A. (2018). Utilities of virtual 3D city models based on CityGML: various use cases. J. Indian Soc. Remote Sens., 46(6); 957–972. Sattar, A. M. A., Elhakeem, M., Gerges, B. N., Gharabaghi, B., & Gultepe, I. (2018). Wind-induced air-flow patterns in an urban setting: Observations and numerical modeling. Pure Appl. Geophys., 175(8); 3051–3068. Shirzadi, M., Mirzaei, P. A., & Tominaga, Y. (2020). RANS model calibration using stochastic optimization for accuracy improvement of urban airflow CFD modeling. J. Build. Eng., 32; 101756. Sukkuea, A., & Heednacram, A. (2022). Prediction on spatial elevation using improved kriging algorithms:An application in environmental management. Expert Syst. Appl., 207; 117971. Sun, D., Shi, X., Zhang, Y., & Zhang, L. (2021). Spatiotemporal distribution of traffic emission based on wind tunnel experiment and computational fluid dynamics (CFD) simulation. J. Clean. Prod., 282; 124495. Wickramathilaka, N., Ujang, U., Azri, S., & Choon, T. L. (2023a). Calculation of road traffic noise, development of data, and spatial interpolations for traffic noise visualization in three-dimensional space. Geomat. Environ. Eng., 17(5), 61–85. Wickramathilaka, N., Ujang, U., Azri, S., & Choon, T. L. (2023b). Three-dimensional visualisation of traffic noise based on the Henk de-Klujijver Model. Noise Mapp., 10(1). Wu, H., Zhu, Q., Guo, Y., Zheng, W., Zhang, L., Wang, Q., Zhou, R., Ding, Y., Wang, W., Pirasteh, S., & Liu, M. (2022). Multi-level voxel representations for digital twin models of tunnel geological environment. Int. J. Appl. Earth Obs. Geoinf., 112; 102887. Yang, J., Shi, B., Shi, Y., Marvin, S., Zheng, Y., & Xia, G. (2020). Air pollution dispersal in high density urban areas: Research on the triadic relation of wind, air pollution, and urban form. Sustain. Cities Soc., 54; 101941. Zhang, M., Bae, W., & Kim, J. (2019). The effects of the layouts of vegetation and wind flow in an apartment housing complex to mitigate outdoor microclimate air temperature. Sustainability 2019, 11(11); 3081. Zhang, X., Weerasuriya, A. U., & Tse, K. T. (2020). CFD simulation of natural ventilation of a generic building in various incident wind directions: Comparison of turbulence modelling, evaluation methods, and ventilation mechanisms. Energy Build., 229, 110516. Zheng, X., Montazeri, H., & Blocken, B. (2020). CFD simulations of wind flow and mean surface pressure for buildings with balconies: Comparison of RANS and LES. Build. Environ., 173; 106747. Zheng, X., & Yang, J. (2021). CFD simulations of wind flow and pollutant dispersion in a street canyon with traffic flow: Comparison between RANS and LES. Sustain. Cities Soc., 75; 103307. | ||
|
آمار تعداد مشاهده مقاله: 307 تعداد دریافت فایل اصل مقاله: 488 |
||