Please use this identifier to cite or link to this item:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/64038Full metadata record
| ???org.dspace.app.webui.jsptag.ItemTag.dcfield??? | Value | Language |
|---|---|---|
| dc.contributor.advisor | 莊振義(Jehn-Yih Juang) | |
| dc.contributor.author | Min-Chun Shao | en |
| dc.contributor.author | 邵旻純 | zh_TW |
| dc.date.accessioned | 2021-06-16T17:27:30Z | - |
| dc.date.available | 2022-03-09 | |
| dc.date.copyright | 2020-03-09 | |
| dc.date.issued | 2020 | |
| dc.date.submitted | 2020-03-04 | |
| dc.identifier.citation | Abhijith, K. V., Kumar, P., Gallagher, J., McNabola, A., Baldauf, R., Pilla, F., . . . Pulvirenti, B. (2017). Air pollution abatement performances of green infrastructure in open road and built-up street canyon environments – A review. Atmospheric Environment, 162, 71-86. doi:https://doi.org/10.1016/j.atmosenv.2017.05.014
Akdağ, S. A., Ö, G., & Yağci, E. (2013, 20-23 Oct. 2013). Wind speed extrapolation methods and their effect on energy generation estimation. Paper presented at the 2013 International Conference on Renewable Energy Research and Applications (ICRERA). Arain, M. A., Blair, R., Finkelstein, N., Brook, J. R., Sahsuvaroglu, T., Beckerman, B., . . . Jerrett, M. (2007). The use of wind fields in a land use regression model to predict air pollution concentrations for health exposure studies. Atmospheric Environment, 41(16), 3453-3464. doi:https://doi.org/10.1016/j.atmosenv.2006.11.063 Balczó, M., Gromke, C., & Ruck, B. (2009). Numerical modeling of flow and pollutant dispersion in street canyons with tree planting. Meteorologische Zeitschrift, 18(2), 197-206. Barnes, M., Brade, T. K., Mackenzie, A. R., Whyatt, J., Carruthers, D., Stocker, J., . . . Hewitt, C. (2014). Spatially-varying surface roughness and ground-level air quality in an operational dispersion model. Environmental Pollution, 185, 44-51. Beauchamp, M., Malherbe, L., de Fouquet, C., Létinois, L., & Tognet, F. (2018). A polynomial approximation of the traffic contributions for kriging-based interpolation of urban air quality model. Environmental Modelling & Software, 105, 132-152. doi:https://doi.org/10.1016/j.envsoft.2018.03.033 Beckett, K. P., Freer-Smith, P. H., & Taylor, G. (2000). Particulate pollution capture by urban trees: effect of species and windspeed. Global Change Biology, 6(8), 995-1003. doi:10.1046/j.1365-2486.2000.00376.x Benoit, R. (1977). On the Integral of the Surface Layer Profile-Gradient Functions. Journal of Applied Meteorology, 16(8), 859-860. doi:10.1175/1520-0450(1977)016<0859:OTIOTS>2.0.CO;2 Blocken, B., Stathopoulos, T., & van Beeck, J. P. A. J. (2016). Pedestrian-level wind conditions around buildings: Review of wind-tunnel and CFD techniques and their accuracy for wind comfort assessment. Building and Environment, 100, 50-81. doi:https://doi.org/10.1016/j.buildenv.2016.02.004 Bottema, M., Leene, J., & Wisse, J. (1992). Towards forecasting of wind comfort. Journal of Wind Engineering and Industrial Aerodynamics, 44(1-3), 2365-2376. Cheng, F.-Y., & Hsu, C.-H. (2019). Long-term variations in PM2.5 concentrations under changing meteorological conditions in Taiwan. Scientific Reports, 9(1), 6635. doi:10.1038/s41598-019-43104-x Civil IoT Taiwan. (2017). Retrieved from https://ci.taiwan.gov.tw/dsp/en/environmental_en.aspx Counihan, J. (1975). Adiabatic atmospheric boundary layers: A review and analysis of data from the period 1880–1972. Atmospheric Environment (1967), 9(10), 871-905. doi:https://doi.org/10.1016/0004-6981(75)90088-8 Duyzer, J., van den Hout, D., Zandveld, P., & van Ratingen, S. (2015). Representativeness of air quality monitoring networks. Atmospheric Environment, 104, 88-101. doi:https://doi.org/10.1016/j.atmosenv.2014.12.067 Fick, S. E., & Hijmans, R. J. (2017). WorldClim 2: new 1‐km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37(12), 4302-4315. Friedland, C. J., Joyner, T. A., Massarra, C., Rohli, R. V., Treviño, A. M., Ghosh, S., . . . Weatherhead, M. (2017). Isotropic and anisotropic kriging approaches for interpolating surface-level wind speeds across large, geographically diverse regions. Geomatics, Natural Hazards and Risk, 8(2), 207-224. doi:10.1080/19475705.2016.1185749 Gallagher, J., Baldauf, R., Fuller, C. H., Kumar, P., Gill, L. W., & McNabola, A. (2015). Passive methods for improving air quality in the built environment: A review of porous and solid barriers. Atmospheric Environment, 120, 61-70. doi:https://doi.org/10.1016/j.atmosenv.2015.08.075 Gao, J., Tian, H., Cheng, K., Lu, L., Zheng, M., Wang, S., . . . Wang, Y. (2015). The variation of chemical characteristics of PM2.5 and PM10 and formation causes during two haze pollution events in urban Beijing, China. Atmospheric Environment, 107, 1-8. doi:https://doi.org/10.1016/j.atmosenv.2015.02.022 Golder, D. (1972). Relations among stability parameters in the surface layer. Boundary-Layer Meteorology, 3(1), 47-58. doi:10.1007/BF00769106 Gromke, C. (2011). A vegetation modeling concept for building and environmental aerodynamics wind tunnel tests and its application in pollutant dispersion studies. Environmental Pollution, 159(8-9), 2094-2099. Gromke, C., Buccolieri, R., Di Sabatino, S., & Ruck, B. (2008). Dispersion study in a street canyon with tree planting by means of wind tunnel and numerical investigations–evaluation of CFD data with experimental data. Atmospheric Environment, 42(37), 8640-8650. Gromke, C., Jamarkattel, N., & Ruck, B. (2016). Influence of roadside hedgerows on air quality in urban street canyons. Atmospheric Environment, 139, 75-86. Gromke, C., & Ruck, B. (2009). On the impact of trees on dispersion processes of traffic emissions in street canyons. Boundary-Layer Meteorology, 131(1), 19-34. Han, X., Liu, D., Xu, C., & Shen, W. Z. (2018). Atmospheric stability and topography effects on wind turbine performance and wake properties in complex terrain. Renewable energy, 126, 640-651. Hang, J., Li, Y., Sandberg, M., Buccolieri, R., & Di Sabatino, S. (2012). The influence of building height variability on pollutant dispersion and pedestrian ventilation in idealized high-rise urban areas. Building and Environment, 56, 346-360. Hansen, K. S., Barthelmie, R. J., Jensen, L. E., & Sommer, A. (2012). The impact of turbulence intensity and atmospheric stability on power deficits due to wind turbine wakes at Horns Rev wind farm. Wind Energy, 15(1), 183-196. Hellmann, G. (1917). Über die Bewegung der Luft in den untersten Schichten der Atmosphäre: Königlich Preussischen Akademie der Wissenschaften. Irwin, J. S. (1979). A theoretical variation of the wind profile power-law exponent as a function of surface roughness and stability. Atmospheric Environment (1967), 13(1), 191-194. doi:https://doi.org/10.1016/0004-6981(79)90260-9 Janhäll, S. (2015). Review on urban vegetation and particle air pollution–Deposition and dispersion. Atmospheric Environment, 105, 130-137. Jeanjean, A. P., Hinchliffe, G., McMullan, W., Monks, P. S., & Leigh, R. J. (2015). A CFD study on the effectiveness of trees to disperse road traffic emissions at a city scale. Atmospheric Environment, 120, 1-14. Jerrett, M., Burnett, R. T., Beckerman, B. S., Turner, M. C., Krewski, D., Thurston, G., . . . Iii, C. A. P. (2013). Spatial Analysis of Air Pollution and Mortality in California. 188(5), 593-599. doi:10.1164/rccm.201303-0609OC Jung, C., & Schindler, D. (2015). Statistical modeling of near-surface wind speed: a case study from Baden-Wuerttemberg (Southwest Germany). Austin Journal of Earth Science, 2(1), 1006. Justus, C. G., Hargraves, W. R., & Yalcin, A. (1976). Nationwide Assessment of Potential Output from Wind-Powered Generators. Journal of Applied Meteorology, 15(7), 673-678. doi:10.1175/1520-0450(1976)015<0673:NAOPOF>2.0.CO;2 Karra, S., Malki-Epshtein, L., & Neophytou, M. K. A. (2017). Air flow and pollution in a real, heterogeneous urban street canyon: A field and laboratory study. Atmospheric Environment, 165, 370-384. doi:https://doi.org/10.1016/j.atmosenv.2017.06.035 Kent, C. W., Grimmond, S., & Gatey, D. (2017). Aerodynamic roughness parameters in cities: Inclusion of vegetation. Journal of Wind Engineering and Industrial Aerodynamics, 169, 168-176. doi:https://doi.org/10.1016/j.jweia.2017.07.016 Kent, C. W., Grimmond, S., Gatey, D., & Hirano, K. (2019). Urban morphology parameters from global digital elevation models: Implications for aerodynamic roughness and for wind-speed estimation. Remote Sensing of Environment, 221, 316-339. doi:https://doi.org/10.1016/j.rse.2018.09.024 Kent, C. W., Lee, K., Ward, H. C., Hong, J.-W., Hong, J., Gatey, D., & Grimmond, S. (2018). Aerodynamic roughness variation with vegetation: analysis in a suburban neighbourhood and a city park. Urban Ecosystems, 21(2), 227-243. doi:10.1007/s11252-017-0710-1 Ketterer, C., Gangwisch, M., Fröhlich, D., & Matzarakis, A. (2017). Comparison of selected approaches for urban roughness determination based on voronoi cells. International Journal of Biometeorology, 61(1), 189-198. doi:10.1007/s00484-016-1203-2 Kondo, J., & Yamazawa, H. (1983). Surface wind speed and aerodynamic roughness over complex ground surface. Tenki, 30(55), 3-561. Kondo, J., & Yamazawa, H. (1986). Aerodynamic roughness over an inhomogeneous ground surface. Boundary-Layer Meteorology, 35(4), 331-348. doi:10.1007/bf00118563 Lane, S. E., Barlow, J. F., & Wood, C. R. (2013). An assessment of a three-beam Doppler lidar wind profiling method for use in urban areas. Journal of Wind Engineering and Industrial Aerodynamics, 119, 53-59. doi:https://doi.org/10.1016/j.jweia.2013.05.010 Lee, P.-C., Roberts, J. M., Catov, J. M., Talbott, E. O., & Ritz, B. (2013). First Trimester Exposure to Ambient Air Pollution, Pregnancy Complications and Adverse Birth Outcomes in Allegheny County, PA. Maternal and Child Health Journal, 17(3), 545-555. doi:10.1007/s10995-012-1028-5 Lettau, H. (1969). Note on Aerodynamic Roughness-Parameter Estimation on the Basis of Roughness-Element Description. Journal of Applied Meteorology, 8(5), 828-832. doi:10.1175/1520-0450(1969)008<0828:NOARPE>2.0.CO;2 Li, X.-B., Lu, Q.-C., Lu, S.-J., He, H.-D., Peng, Z.-R., Gao, Y., & Wang, Z.-Y. (2016). The impacts of roadside vegetation barriers on the dispersion of gaseous traffic pollution in urban street canyons. Urban forestry & urban greening, 17, 80-91. Manwell, J. F., McGowan, J. G., & Rogers, A. L. (2003). Wind energy explained: Theory, design and application. Chichester: Wiley. Masters, G. M. (2013). Renewable and efficient electric power systems: John Wiley & Sons. McPherson, G. E., Nowak, D. J., & Rowntree, R. A. (1994). Chicago's urban forest ecosystem: results of the Chicago Urban Forest Climate Project. Gen. Tech. Rep. NE-186. Radnor, PA: US Department of Agriculture, Forest Service, Northeastern Forest Experiment Station. 201 p., 186. Montero, G., Rodríguez, E., Oliver, A., Calvo, J., Escobar, J. M., & Montenegro, R. (2018). Optimisation technique for improving wind downscaling results by estimating roughness parameters. Journal of Wind Engineering and Industrial Aerodynamics, 174, 411-423. Ng, E., Yuan, C., Chen, L., Ren, C., & Fung, J. C. (2011). Improving the wind environment in high-density cities by understanding urban morphology and surface roughness: a study in Hong Kong. Landscape and Urban planning, 101(1), 59-74. Ng, W.-Y., & Chau, C.-K. (2014). A modeling investigation of the impact of street and building configurations on personal air pollutant exposure in isolated deep urban canyons. Science of the Total Environment, 468, 429-448. Nikolova, I., Janssen, S., Vos, P., Vrancken, K., Mishra, V., & Berghmans, P. (2011). Dispersion modelling of traffic induced ultrafine particles in a street canyon in Antwerp, Belgium and comparison with observations. Science of the Total Environment, 412, 336-343. Oke, T. R. (Ed.) (1987). Boundary layer climates (2nd ed.). London: Routledge, Taylor & Francis Group. Ozelkan, E., Chen, G., & Ustundag, B. B. (2016). Spatial estimation of wind speed: a new integrative model using inverse distance weighting and power law. International Journal of Digital Earth, 9(8), 733-747. doi:10.1080/17538947.2015.1127437 Panofsky, H. A., Blackadar, A. K., & McVehil, G. E. (1960). The diabatic wind profile. Quarterly Journal of the Royal Meteorological Society, 86(369), 390-398. doi:10.1002/qj.49708636911 Rahman, M. A., Moser, A., Rötzer, T., & Pauleit, S. (2017). Microclimatic differences and their influence on transpirational cooling of Tilia cordata in two contrasting street canyons in Munich, Germany. Agricultural and Forest Meteorology, 232, 443-456. doi:https://doi.org/10.1016/j.agrformet.2016.10.006 Rakowska, A., Wong, K. C., Townsend, T., Chan, K. L., Westerdahl, D., Ng, S., . . . Ning, Z. (2014). Impact of traffic volume and composition on the air quality and pedestrian exposure in urban street canyon. Atmospheric Environment, 98, 260-270. doi:https://doi.org/10.1016/j.atmosenv.2014.08.073 Ramos, Y., St-Onge, B., Blanchet, J.-P., & Smargiassi, A. (2016). Spatio-temporal models to estimate daily concentrations of fine particulate matter in Montreal: Kriging with external drift and inverse distance-weighted approaches. Journal of Exposure Science & Environmental Epidemiology, 26(4), 405-414. doi:10.1038/jes.2015.79 Rohde, R. A., & Muller, R. A. (2015). Air Pollution in China: Mapping of Concentrations and Sources. PLOS ONE, 10(8), e0135749. doi:10.1371/journal.pone.0135749 Santiago, J. L., Martín, F., & Martilli, A. (2013). A computational fluid dynamic modelling approach to assess the representativeness of urban monitoring stations. Science of the Total Environment, 454, 61-72. 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 and Applied Geophysics, 175(8), 3051-3068. doi:10.1007/s00024-018-1846-5 Seinfeld, J. H., & Pandis, S. N. (2016). Atmospheric chemistry and physics: from air pollution to climate change: John Wiley & Sons. Seo, E. S., & Choi, S. H. (2019). Classification of surface roughness using spatial autocorrelation in the economical wind resistant design of buildings. Spatial Information Research, 27(3), 267-274. doi:10.1007/s41324-018-00233-1 Shi, Y., Lau, K. K.-L., & Ng, E. (2017). Incorporating wind availability into land use regression modelling of air quality in mountainous high-density urban environment. Environmental research, 157, 17-29. Shi, Y., Xie, X., Fung, J. C.-H., & Ng, E. (2018). Identifying critical building morphological design factors of street-level air pollution dispersion in high-density built environment using mobile monitoring. Building and Environment, 128, 248-259. Shrestha, D. P., Saepuloh, A., & van der Meer, F. (2019). Land cover classification in the tropics, solving the problem of cloud covered areas using topographic parameters. International Journal of Applied Earth Observation and Geoinformation, 77, 84-93. doi:https://doi.org/10.1016/j.jag.2018.12.010 Sigal, A., Cioccale, M., Rodríguez, C. R., & Leiva, E. P. M. (2015). Study of the natural resource and economic feasibility of the production and delivery of wind hydrogen in the province of Córdoba, Argentina. International Journal of Hydrogen Energy, 40(13), 4413-4425. doi:https://doi.org/10.1016/j.ijhydene.2015.01.149 Song, W., Jia, H., Li, Z., & Tang, D. (2018). Using geographical semi-variogram method to quantify the difference between NO2 and PM2.5 spatial distribution characteristics in urban areas. Science of the Total Environment, 631-632, 688-694. doi:https://doi.org/10.1016/j.scitotenv.2018.03.040 Stathopoulos, T., & Storms, R. (1986). Wind environmental conditions in passages between buildings. Journal of Wind Engineering and Industrial Aerodynamics, 24(1), 19-31. doi:https://doi.org/10.1016/0167-6105(86)90070-X Stepek, A., & Wijnant, I. L. (2011). Interpolating wind speed normals from the sparse Dutch network to a high resolution grid using local roughness from land use maps: KNMI. Taipei digital observatory of schools. (2013). Taiwan Air Quality Monitoring Network. (2009). Tominaga, Y., Mochida, A., Yoshie, R., Kataoka, H., Nozu, T., Yoshikawa, M., & Shirasawa, T. (2008). AIJ guidelines for practical applications of CFD to pedestrian wind environment around buildings. Journal of Wind Engineering and Industrial Aerodynamics, 96(10), 1749-1761. doi:https://doi.org/10.1016/j.jweia.2008.02.058 Tong, Z., Baldauf, R. W., Isakov, V., Deshmukh, P., & Zhang, K. M. (2016). Roadside vegetation barrier designs to mitigate near-road air pollution impacts. Science of the Total Environment, 541, 920-927. Turner, D. (1969). Workbook of Atmospheric Diffusion Estimates, USEPA 999-AP-26. Van Ackere, S., Van Eetvelde, G., Schillebeeckx, D., Papa, E., Van Wyngene, K., & Vandevelde, L. (2015). Wind resource mapping using landscape roughness and spatial interpolation methods. Energies, 8(8), 8682-8703. van Hooff, T., & Blocken, B. (2010). Coupled urban wind flow and indoor natural ventilation modelling on a high-resolution grid: A case study for the Amsterdam ArenA stadium. Environmental Modelling & Software, 25(1), 51-65. doi:https://doi.org/10.1016/j.envsoft.2009.07.008 Vos, P. E., Maiheu, B., Vankerkom, J., & Janssen, S. (2013). Improving local air quality in cities: to tree or not to tree? Environmental Pollution, 183, 113-122. Wang, A.-Q., Lin, Z.-P., Liang, W.-Y., Kuo, C.-Y., Tseng, S.-L., Chen, Y.-C., . . . Lan, S.-T. (2018). Across Different Land Use Regions: The Analysis of Wind Corridors Establishment and Assessment of Urban Ventilation Environment. Wania, A., Bruse, M., Blond, N., & Weber, C. (2012). Analysing the influence of different street vegetation on traffic-induced particle dispersion using microscale simulations. Journal of environmental management, 94(1), 91-101. Wieringa, J. (1986). Roughness‐dependent geographical interpolation of surface wind speed averages. Quarterly Journal of the Royal Meteorological Society, 112(473), 867-889. Wong, D. W., Yuan, L., & Perlin, S. A. (2004). Comparison of spatial interpolation methods for the estimation of air quality data. Journal of Exposure Science & Environmental Epidemiology, 14(5), 404-415. doi:10.1038/sj.jea.7500338 Ye, W. (2013). Spatial variation and interpolation of wind speed statistics and its implication in design wind load. Yoshie, R., Mochida, A., Tominaga, Y., Kataoka, H., Harimoto, K., Nozu, T., & Shirasawa, T. (2007). Cooperative project for CFD prediction of pedestrian wind environment in the Architectural Institute of Japan. Journal of Wind Engineering and Industrial Aerodynamics, 95(9), 1551-1578. doi:https://doi.org/10.1016/j.jweia.2007.02.023 Yu, H., Russell, A., Mulholland, J., Odman, T., Hu, Y., Chang, H. H., & Kumar, N. (2018). Cross-comparison and evaluation of air pollution field estimation methods. Atmospheric Environment, 179, 49-60. doi:https://doi.org/10.1016/j.atmosenv.2018.01.045 Yuan, C. (2018). Urban Wind Environment: Integrated Climate-Sensitive Planning and Design: Springer. Zaki, S. A., Hagishima, A., Tanimoto, J., & Ikegaya, N. (2011). Aerodynamic Parameters of Urban Building Arrays with Random Geometries. Boundary-Layer Meteorology, 138(1), 99-120. doi:10.1007/s10546-010-9551-7 Zhang, H., Wang, Y., Hu, J., Ying, Q., & Hu, X.-M. (2015). Relationships between meteorological parameters and criteria air pollutants in three megacities in China. Environmental research, 140, 242-254. doi:https://doi.org/10.1016/j.envres.2015.04.004 Zhang, Q., Quan, J., Tie, X., Li, X., Liu, Q., Gao, Y., & Zhao, D. (2015). Effects of meteorology and secondary particle formation on visibility during heavy haze events in Beijing, China. Science of the Total Environment, 502, 578-584. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/64038 | - |
| dc.description.abstract | 在都會區中行人高度(離地面兩公尺高範圍內)的空氣污染物對於人體健康會產生直接危害，而風場為影響空氣污染物空間分布的主要因素之一。過去研究多使用環境流體力學模式，利用推估或量測的風場，於單一街道或小範圍街廓模擬污染物濃度空間分布。然而，此方法受計算資源限制，無法及時呈現大範圍區域之模擬結果。而針對研究範圍大於街道尺度的文獻，大多在不考慮地表特性的情況下使用空間內插方法進行風場推估，並未考慮地表大氣動力特性與大氣穩定度的影響。本研究在考量平均地表粗糙度以及都市中的大氣穩定度下，使用改良的空間內插方法估算行人高度範圍的風速，並用來檢視與行人高度空氣污染物濃度的特徵。在都市當中，植被與建物會影響地表粗糙度並進而影響風場，本研究以風速剖面指數(wind profile power law) ，在中性大氣穩定度條件下，針對風速比較反距離權重法（均方根誤差為1.51 ms-1）以及普通克利金（均方根誤差為1.52 ms-1）兩種內插方法，發現兩者皆適合本研究使用，採取計算速度較快的前者。此外，本研究比較三種不同大氣穩定條件，包括極度不穩定(extremely unstable）、中性(neutral）以及中度穩定(moderately stable），在風速推估的結果發現極度不穩定的大氣狀況設定(冬季均方根誤差為1.11 ms-1；夏季均方根誤差為0.85 ms-1)在台北地區的風速估算較符合實際量測值。而對於行人高度的污染物，藉由設置於同棟建築物不同樓層的空氣盒子數據當中，發現擺放於行人高度的污染濃度平均高於高樓層的量測值（≈ 0~5 μgm-3），因此本研究僅採用現有位於2公尺高的空氣盒子與推估出的行人高度風速進行時間序列分析。取得風速與污染物之間的回歸關係，利用回歸係數計算的污染物估計值與實際量測值平均誤差在2 μgm-3以內。本研究另外透過網際網路地理資訊系統，提供使用者即時環境資料空間資訊。 | zh_TW |
| dc.description.abstract | Wind field is a key factor to alter the spatial distribution of air pollutants in the urban area because the complex compositions of different land-use types and buildings strongly affect the physical properties and air flow in the built-up area. Street-level wind velocity is recognized to have significant impacts on air pollutant concentrations at the pedestrian level (~2m) which is becoming one of the major environmental issues for human health in many metropolitan areas around the world. Previous studies have discussed the distribution of air pollutants for different wind conditions with Computational Fluid Dynamics modelling in the small area of street canyon, and spatial interpolation approaches have been used to estimate the distribution of wind fields and air pollutant concentration in the relatively large areas as well. However, the former technique requires considerable computational resources, and the latter methods usually ignore the heterogeneity of surface characteristics and the role of atmospheric stability on wind speed estimation. In this study, surface roughness and atmospheric stability in urban area are taken into consideration, and a modified interpolation method is used to obtain wind velocity for the range of the pedestrian level. Besides, the patterns of air pollutant concentrations are inspected through the results of estimated wind speed. In urban area, vegetation and buildings strongly affect surface roughness and wind filed. The results show that under neutral stability condition, inverse distance weighting (IDW) (root mean square error (RMSE = 1.51 ms-1) performs almost the same accuracy as ordinary kriging OK (RMSE = 1.52 ms-1) for the wind speed estimation with wind profile power law. Because of low time cost, IDW is selected in this study. Furthermore, three kinds of atmospheric stability conditions, including extremely unstable, neutral, and moderately stable stability, are compared in this study. The results under unstable stability condition for wind speed estimation (RMSE is 1.11 ms-1 in winter; RMSE is 0.85 ms-1 in summer.) are more consistent with field measurements than the others in the study area. In this study, real-time spatial distribution of wind speed at pedestrian height are achieved with surface roughness, atmospheric stability, and the interpolation method. This model is verified by selecting several wind stations to ensure the performance of the proposed approach, and difference between estimated results and actual wind speed is almost within -1~1 ms-1. Additionally, by installing AirBox devices at different heights outside the wall of buildings, it is found that the mean concentrations at pedestrian height is greater than the highest floor (≈ 0~5 μgm-3). Therefore, this study only uses the AirBox devices at 2-meter-hight, and the relationship between these particulate matters and estimated wind speed for pedestrian height is carried out the time-series analysis. With the correlation coefficient of relationship, the average errors between actual and estimated values are within 2 μgm-3 in the study area. In this study, the real time environment spatial data is also provided for users through Web Geographic Information System (WebGIS). | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T17:27:30Z (GMT). No. of bitstreams: 1 ntu-109-R06228015-1.pdf: 11411602 bytes, checksum: aec49aa8a6572b5c363683bb5a71d930 (MD5) Previous issue date: 2020 | en |
| dc.description.tableofcontents | 摘要 i
Abstract ii List of Figures 2 List of Tables 4 1. Introduction 5 1.1 Background and motivation 5 1.2 Objectives 9 2. Literature review 10 2.1 Air pollutants estimated at pedestrian level 10 2.2 Estimation for air pollutants in unknown area 13 2.3 Wind effects for air pollutants’ distribution 17 2.4 Wind field estimated at pedestrian height level 23 3. Data and methodology 27 3.1 Study area 27 3.2 Data 29 3.2.1 Digitized building data 29 3.2.2 Green area data 29 3.2.3 Wind speed data 30 3.2.4 Air pollutants 33 3.3 Wind speed at pedestrian level 36 3.3.1 Calculate surface roughness 36 3.3.2 Classify atmospheric stability 39 3.3.3 Interpolate wind velocity data 41 3.3.4 Validation for results of wind estimation 42 3.4 Air pollutants at pedestrian level 43 3.4.1 Field measurements from experiment 43 3.5 Real time wind speed system 45 4. Results and discussion 48 4.1 Estimation of surface roughness distribution 48 4.2 Classification of atmospheric stability for winter and summer 68 4.3 Wind speed distribution at pedestrian level 70 4.4 Field measurements of particulate matter at varying height 73 4.5 The relationship between particulate matters and wind speed 77 5. Conclusion and suggestion 83 6. Reference 86 | |
| dc.language.iso | en | |
| dc.title | 不同大氣穩定度下街道尺度風速與空氣污染物時空分布分析 | zh_TW |
| dc.title | Spatial-temporal Patterns of Street-level Wind Fields and Air Pollutants Under Different Atmospheric Stability | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 108-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 林子平(Tzu-Ping Lin),朱佳仁(Chia-Ren Chu),郭巧玲(Chiao-Ling Kuo) | |
| dc.subject.keyword | 都市建成環境,地理資訊系統,地表粗糙長度,懸浮微粒, | zh_TW |
| dc.subject.keyword | built environment,GIS,PM2.5,PM10,surface roughness length, | en |
| dc.relation.page | 91 | |
| dc.identifier.doi | 10.6342/NTU202000570 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2020-03-04 | |
| dc.contributor.author-college | 理學院 | zh_TW |
| dc.contributor.author-dept | 地理環境資源學研究所 | zh_TW |
| Appears in Collections: | 地理環境資源學系 | |
Files in This Item:
| File | Size | Format | |
|---|---|---|---|
| ntu-109-1.pdf Restricted Access | 11.14 MB | Adobe PDF |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.