Session 6-Urban Planning and Development 2021 4th International Conference on Civil Engineering and Architecture (Virtual Conference): July 10-12, 2021; Seoul, South Korea

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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14

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?