A Classification Urban Precinct Ventilation Zones using Key Indicators of Spatial Form: Case Study in Bangkok
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This document discusses the classification of precinct ventilation zones (PVZs) in Bangkok to understand the impact of urban morphology on air quality and ventilation performance. The study identifies key urban form indicators and employs geospatial clustering methods to analyze and categorize areas based on their ventilation potential. The findings suggest that urban planning strategies could enhance air quality by optimizing urban form to improve air circulation and reduce pollution exposure.
A Classification Urban Precinct Ventilation Zones using Key Indicators of Spatial Form: Case Study in Bangkok
- 1. Paper ID: A031 1 Center of Excellent in Urban Mobility Research and Innovation (CoE-UMRI) 2 Thammasat University Research Unit in Architecture for Sustainable Living and Environment 3 Faculty of Architecture and Planning, Thammasat University, Pathumthani, Thailand 4 School of Thermal Engineering, Shandong Jianzhu University, Jinan, China * Corresponding author, E-mail address: s.manat@gmail.com Manat Srivanit1,3 Daranee Jareemit2,3 and Jiying Liu4 Session 6-Urban Planning and Development 2021 4th International Conference on Civil Engineering and Architecture Virtual Conference: July 10-12, 2021; Seoul, South Korea
- 2. Presentation outline 1. Introduction Conceptual of urban precinct ventilation zone 2. Research objectives and method 3. Results and discussion Spatial patterns of urban form indicators with respect to ventilation potential Precinct ventilation zones (PVZs) classification 4. Conclusions 5. Further research is needed 2
- 3. 1. Introduction 3 According to the US AQI report, Bangkok is ranked in the 12th list of the cities with the worst outdoor air quality in the world (The Nation Thailand, 2021). Policy-makers have made an effort to controls urban traffic congestion and reduce vehicle-induced pollutant dispersion. Besides traffic control, “Urban Ventilation” is another key parameter that could dilute air pollution in the city. The effect of urban morphology on air ventilation and pollutant dispersion has been investigated in different scales (Wen et al., 2018; Chen et al., 2017; Yazid et al., 2014; Vardoulakis et al, 2003). Source emission shares in Bangkok (Narita et al., 2019) Urban air pollution monitoring at different scales (Srivastava and Rao, 2010)
- 4. 4 Table 1. Urban air pollution monitoring at different scales Scale Influence factor Approach Important contribution Micro-scale (block and building scales) Built form indicators of street aspect ratio, building height and canyon width to track the variations of airflow patterns Aerodynamic characteristics of urban structures by using urban wind modeling and wind tunnel testing (Edussuriya et al.,2011; Merlier et al.,2018; Neophytou et al, 2014; Oke, 1987) Support cooperation between urban designers and urban physicists to instruct reasonable wind- sensitive design Local Scale (pedestrian- level wind environment at neighborhood scale) Two main factors are affected by local conditions: • Ventilation potential (summarized in the air circulation classes), and • Human exposure to air pollution (summarized in potential increased exposure zones). Precinct Ventilation Zone (PVZ) system (He et al.,2019; Guo et al.,2020; Jin et al.,2019) Air Quality Management Zones (AQMZs) (Alcoforado et al., 2009; Houet&Pigeon, 2011; Stewart&Oke, 2012) Development of ventilation- performance-based urban planning and air quality management. Mesoscale (city and regional scale) Classify areas with a type that characterizes the land use structure, which can be complemented with a ventilation class Determination of ventilation corridors (Suder & Szymanowski, 2014; Wong, Nichol, To, & Wang, 2010) Climate maps and the climatope approach (Acero et al., 2013; Renet al., 2011; Scherer, Fehrenbach, Beha, & Parlow, 1999) Understand the urban ventilation of the city for decisions related to air paths, urban permeability and site porosity.
- 5. Urban form (Building density, height, aspect ratio, geometry etc.) Urban Air Pollution (PM2.5, PM10, SO2, NO2, O3, CO etc.) Local Wind Environment (Velocity and direction) Direct influence Urban form impact on wind speed and ventilation condition Optimize urban form through urban planning/design can reduce air pollution situation Wind speed and ventilation condition impact on air pollutants dispersion Types of Precinct Ventilation Zones (Mapping main problem areas for air quality management) Fig. 1. A conceptual of urban precinct ventilation zone and interrelationship of urban form, wind environment, and air pollution. 5
- 6. 6 The Area of Study Bangkok Metropolitan Area (BMA) has a morphological identity characteristic with complex urban form and variations in building height, leading to a poor wind environment. This effect relates to the urban heat island effect and air quality problem. Previous works have been classified the urban morphology using local climate zone classification and its impact on urban heat effect and outdoor thermal comfort (Khamchiangta et al., 2019; Srivanit et al., 2014). Lack of study uses urban ventilation indicators to classify the zones in Bangkok. Main Transportation Routes in BMA (Khamchiangta and Dhakal, 2020) The Bangkok and Vicinity Development Structure Plan (City Planning Department of BMA, 2013)
- 7. Context A systematic review on the impact of urban morphology on ventilation performance Input Developing a spatial set of urban form indicators with respect to ventilation potential (1) Building Coverage Ratio (BCR) (2) Floor Area Ratio (FAR) (3) Sky View Factor (SVF) (4) Average of Building Height (AvgH) (5) Standard Deviation of Building Height Process Using GIS-based methods to map the key indicators Geospatial clustering method to grouping a set of spatial indicators into groups Output Precinct ventilation zone (PVZ) classification system 2. Research objectives and method Fig. 2. The methodological approach was used in this study. 7 2km The location of the study area This study focuses on developing a geospatial clustering-based methodology using the k-means method to classify key urban form indicators concerning the ventilation environment in the inner core of Bangkok city (as the high land-use intensity and the most densely populated area). (an area of 125 km2, includes 8 districts)
- 8. Table 2. A spatial set of urban form indicators with respect to ventilation potential. Factors Equation reference Building coverage ratio (BCR) Floor area ratio (FAR) Sky view factor (SVF) Lindberg, F., & Grimmond, C. S. B. (2010). Continuous sky view factor maps from high resolution urban digital elevation models. Climate Research, 42(3), 177–183. Average of building height Standard deviation of building height Zhou, X., Okaze, T., Ren, C., Cai, M., Ishida, Y., & Mochida, A. (2020). Mapping local climate zones for a Japanese large city by an extended workflow of WUDAPT Level 0 method. Urban Climate, 33. Zheng, Y., Ren, C., Xu, Y., Wang, R., Ho, J., Lau, K., & Ng, E. (2018). GIS-based mapping of Local Climate Zone in the high- density city of Hong Kong. Urban Climate, 24, 419–448. https://doi.org/10.1016/j.uclim.2017.05.008 SVF 8 This study selected the most five-key urban-form, which were commonly used to assess the urban ventilation performance and air quality studies for the local scale. The geospatial datasets were retrieved from the Department of City Planning, Bangkok Metropolitan Administration, and Open-source QGIS V.3.12.1 software was used in this study.
- 9. (a) Building coverage ratio: BCR (b) Floor area ratio: FAR (c) Sky view factor: SVF (d) Average of building height: AvgH (e) Standard deviation of building height: StdH 3. Results and discussion Fig. 3. In order to delimit different PVZs with standard features, a grid size of 100×100 m which was overlaid with 12,519 grid cells, was applied to calculate each urban form indicator. 9
- 10. BCR FAR SVF AvgH StdH BCR Pearson Correlation P -value 1 0.538** 0.000 0.545** 0.000 0.568** 0.000 0.339** 0.000 FAR Pearson Correlation P -value 0.538** 0.000 1 0.238** 0.000 0.613** 0.000 0.698** 0.000 SVF Pearson Correlation P -value 0.545** 0.000 0.238** 0.000 1 0.491** 0.000 0.244** 0.000 AvgH Pearson Correlation P -value 0.568** 0.000 0.613** 0.000 0.491** 0.000 1 0.703** 0.000 StdH Pearson Correlation P -value 0.339** 0.000 0.698** 0.000 0.244** 0.000 0.703** 0.000 1 Note. N=12519, (**)=Correlation is significant at the 0.01 level Table 3. (left) Correlation matrix of Pearson correlation coefficients of five key indicators, and (right) Final cluster centers of k-means cluster analysis. Cluster 1 Cluster 2 Cluster 3 BCR Low (3.24) High (40.24) Moderate (31.70) FAR Low (0.07) Moderate (1.02) High (5.50) SVF High (0.83) Moderate (0.74) Low (0.41) AvgH Low (1.73) Moderate (6.70) High (25.03) StdH Low (0.60) Moderate (2.88) High (27.26) 10 Pearson's correlation analysis was used to assess the effective urban form indicators. In this study, classifying PVZs was performed by Cluster Analysis following the Ward method by running the k-means in statistics software, and the Elbow method was used for determining the optimal number of clusters (Thorndike, 1953).
- 11. (b) (c) (c) (d) (d) (b) (a) Mapping the distribution of precinct ventilation zones (PVZs) in the study area Box-plot representations of the median values (thick black line) for each indicator (a)-(e). 1.7 6.8 50.3 23.2 16.1 11.5 10.5 13.2 25.0 57.2 59.3 64.4 43.1 41.4 43.4 86.8 3.0 1.1 3.3 4.5 0% 20% 40% 60% 80% 100% Bang Rak Din Daeng Huai Khwang Khlong Toei Pathum Wan Ratchathewi Sathon Watthana PVZ 1 PVZ 2 PVZ 3 11 The PVZs district-level distribution of proportion (a) Building coverage ratio: BCR (b) Floor area ratio: FAR (c) Sky view factor: SVF (d) Average of building height: AvgH (e) Standard deviation of building height: StdH Watthana Khlong Toei Huai Khwang Din Daeng Ratchathewi Sathon Bang Rak Pathum Wan
- 12. 4. Conclusions 12 1) This study classified a precinct ventilation zone by applying a geospatial approach using five indicators. The data were clustered using the k-means method to get an three clusters (low, moderate, and high values) of the precinct ventilation zone (PVZ) map in the inner core of Bangkok city. 2) It was found that most of the inner areas in Bangkok are in the standard PVZ 2, rep-resenting mid-rise buildings, whereas the PVZ 3 (densely high-rise buildings) accounts for 2.4% of the total studied area. This zone covers four central business districts (Bang Rak, Khlong Toei, Huai Khwang, and Din Daeng) with the highest precinct surface structure, it cloud block the wind and increase wind turbulence. Bang Rak district Huai Khwang district
- 13. 5. Further research is needed 13 The precinct ventilation classification method could constitute a preliminary step to assess further the risk of poor ventilation in near-surface pollutant concentrations in different areas. Then, future studies should use the local-scale climate modeling for an in- depth evaluation of the local effect. Urban planners and architects could identify the methods and enforcements that promote better urban ventilation and pollutant dispersion for specific ventilation zone. For applying in Thailand's urban context, the accuracy of PVZ classifications needs to be validated with the field data of airflow ventilation and pollutant concentration. Although the low-density areas (PVZ 1) have low surface roughness, urban green structures (e.g. tall vegetation area density, urban tree cover (Nowak&Greenfield, 2012)) might have a significant impacts on local air ventilation and air pollutant dispersion for low-density areas.
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- 15. END Acknowledgments: This project is supported by a corporate research fund from the National Science and Technology Development Agency and Thammasat University (research grant num-ber: JRA-CO-2563-13105-TH). We especially want to thanks Danaipat Prasitreak and Titi Chuaytong for data collection and GIS analysis in this research project. Thank You For Your Attention ! Any Questions?