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Mitigating the urban heat island effect: thermal performance of shade-tree planting in downtown Los Angeles

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Mitigating the urban heat island effect: thermal performance of shade-tree planting in downtown Los Angeles

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Content MITIGATING THE URBAN HEAT ISLAND EFFECT:
Thermal Performance of Shade-Tree Planting in Downtown Los Angeles
by
Yuzhou Zhu
A Thesis Presented to the
FACULTY OF THE USC SCHOOL OF ARCHITECTURE
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
MASTER OF BUILDING SCIENCE
May 2024

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ACKNOWLEDGMENTS
Committee Chair:
Karen M. Kensek, LEED BD+C, DPACSA
Professor of Practice
USC, School of Architecture
Email: kensek@usc.edu
Committee Member
Esther Margulies, ASLA
Associate Director, Graduate Programs in Landscape Architecture + Urbanism
Professor of Practice
USC, School of Architecture
Email: emarguli@usc.edu
Committee Member
Anthony Brower, AIA, LEED Fellow
Global Leader of Gensler's Climate Action & Sustainability Practice
Adjunct Lecturer
USC, School of Architecture
Email: abrower@usc.edu

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TABLE OF CONTENTS
ACKNOWLEDGMENTS .............................................................................................ii
List of TABLES............................................................................................................vi
List of FIGURES .........................................................................................................vii
ABSTRACT..................................................................................................................xi
CHAPTER ONE INTRODUCTION.............................................................................1
1.1 Background Information--Urban Heat Island...........................................1
1.1.1 What is an Urban Heat Island ............................................................1
1.1.2 The effects of an urban heat island ....................................................3
1.1.3 Los Angeles as an urban heat island .....................................................5
1.2 The Benefit of Trees .................................................................................7
1.2.1 Improving thermal comfort in an urban environment...........................7
1.2.2 Other benefits......................................................................................10
1.2.3 Choosing the proper tree for Los Angeles..........................................10
1.3 Simulation software for Urban Heat Island Analysis.............................12
1.3.1 ENVI-met Description.....................................................................16
1.3.2 ENVI-met Advantages and Disadvantages......................................21
1.4 Summary.................................................................................................23
CHAPTER TWO LITERATURE REVIEW...............................................................25
2.1 Different factors that can affect the cooling effect of the trees......................25
2.1.1 Character of trees................................................................................25
2.1.2 Spatial Conditions...............................................................................28
2.1.3 Climate................................................................................................33
2.1.4 Climate in Downtown Los Angeles....................................................38
2.2 Research Findings by ENVI-met...................................................................40
2.3 Summary........................................................................................................45
CHAPTER THREE METHODOLOGIES ..................................................................47
3.1 Establish a Base Urban Model.......................................................................47
3.1.1 Input external data and build the modeling in ENVI-met...................47
3.1.2 Assign the materials in ENVI-met......................................................50
3.1.3 Input climate data into ENVI-met.......................................................50
3.1.4 Create research questions....................................................................56
3.2 Preliminary study...........................................................................................61
3.2.1 Date comparison .................................................................................61
3.2.2 Building shade analysis.......................................................................62
3.2.3 Transpiration analysis.........................................................................63
3.2.4 Tree location analysis .........................................................................63
3.2.5 Tree spatial analysis............................................................................64
3.2.6 Tree row directional analysis..............................................................65
3.3 Numeric Techniques......................................................................................66
3.3.1 Monthly average climate date method................................................66

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3.3.2 Pixel counting method. .......................................................................69
3.4 Full site study.................................................................................................71
3.5 Summary........................................................................................................72
CHAPTER FOUR PRELIMINARY STUDIES..........................................................75
4.1 Date Comparison (No tree comparing with one tree)....................................76
4.1.1 Hottest day comparison.......................................................................77
4.1.2 Coldest day comparison......................................................................79
4.1.3 Graph of hottest and coldest day comparison .....................................81
4.1.4 Graph of each month comparison.......................................................82
4.2 Building Shade Comparison ..........................................................................84
4.2.1 Hottest day comparison.......................................................................84
4.2.2 Coldest day comparison......................................................................88
4.2.3 Graph of cooling ability reduction comparison ..................................90
4.3 Transpiration..................................................................................................91
4.3.1 Hottest day comparison.......................................................................92
4.3.2 Coldest day comparison......................................................................93
4.3.3 Transpiration effect comparison .........................................................94
4.4 Tree Spatial Location Comparison. ...............................................................96
4.4.1 Hottest day comparison.......................................................................97
4.4.2 Coldest day comparison....................................................................101
4.4.3 Whole year comparison ....................................................................104
4.5 Trees’ spacing analysis................................................................................105
4.6 Proposed Tree Layout..................................................................................108
4.7 Summary......................................................................................................111
CHAPTER FIVE FULL SITE RESEARCH .............................................................115
5.1 No trees vs existing trees vs new trees (Full site hottest day) ..............117
5.1.1 Three conditions UTCI comparisons.............................................117
5.1.2 Pedestrian area average temperature analysis (12pm)...................119
5.1.3 Pedestrian area average temperature comparisons (10am, 12pm, 2pm)
122
5.2 No trees vs existing trees vs new trees (Full site Coldest day).............123
5.3 Existing trees vs new trees (Full site monthly comparison) .................124
5.3.1 January comparison .......................................................................124
5.3.2 February comparison ........................................................................125
5.3.3 March comparison ............................................................................126
5.3.4 April comparison ..............................................................................127
5.3.5 May comparison................................................................................127
5.3.6 June comparison................................................................................128
5.3.7 June comparison................................................................................128
5.3.8 August comparison ...........................................................................129
5.3.9 September comparison......................................................................130
5.3.10 October comparison........................................................................130

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5.3.11 November comparison....................................................................131
5.3.12 December comparison ....................................................................131
5.4 Another tree layout ......................................................................................133
5.5 Reduced UTCI comparisons........................................................................135
5.6 Summary......................................................................................................136
CHAPTER SIX CONCLUSION AND FUTURE WORK........................................138
6.1 Conclusion ...........................................................................................138
6.1.1 Background.......................................................................................138
6.1.2 Methodology.....................................................................................141
6.2 Analysis and Results............................................................................146
6.2.1 Preliminary studies............................................................................146
6.2.3 Site analysis ......................................................................................153
6.4 Future work..........................................................................................155
This section will cover future work that can be accomplished..................155
6.4.1 Wind analysis....................................................................................155
6.4.2 Sky view factors................................................................................156
6.4.3 Climate zones....................................................................................157
6.4.4 More simulations studying tree canopy ............................................157
6.4.5 Bigger research area..........................................................................158
6.4.6 More trees analysis ...........................................................................159
6.4.7 Automatic tree layout algorithm.......................................................159
6.4.8 Other issues beyond the tree shading component.............................159
6.4.9 Transpiration.....................................................................................160
6.4.10 Measurement of real conditions......................................................160
6.4.11 Visualization of the 3d model.........................................................160
6.4.12 The relationship between trees and building energy consumption.161
6.4.13 More types of trees..........................................................................161
6.4.14 Safety issues from planting trees ....................................................161
6.5 Summary..............................................................................................162
Appendix....................................................................................................................164
References..................................................................................................................208

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LIST OF TABLES
Table 1. 1 Seven Software for Thermal Condition Analysis...............................12
Table 3. 1 Preliminary simulation sheet ...........................................................72
Table 3. 2 Full site simulation sheet ....................................................................73
Table 4. 1 Preliminary simulation sheet ..............................................................76
Table 4. 2 Tree best cooling location ranking sheet ..........................................105
Table 4. 3 Tree best cooling location ranking sheet 2 .......................................105
Table 4. 4 Three types of trees spacing UTCI comparison................................107
Table 4. 5 Preliminary simulation result sheet 1................................................112
Table 4. 6 Preliminary simulation result sheet 2................................................113
Table 5. 1 Full site no trees average temperature at 12pm ................................120
Table 5. 2 Full site existing trees average temperature at 12pm........................121
Table 5. 3 Full site new trees average temperature at 12pm..............................121
Table 5. 4 Full site all year-round simulation result ..........................................137
Table 6. 1 Preliminary study..............................................................................147
Table 6. 2 Tree best cooling location ranking sheet 2 .......................................150
Table 6. 3 Three types of trees spacing UTCI comparison................................150
Table 6. 4 Preliminary simulation result sheet...................................................151
Table 6. 5 Full site all year simulation result.....................................................154

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LIST OF FIGURES
Figure 1. 1 [Urban Heat Island Profile], n.d., https://www.metlink.org/fieldworkresource/urban-heat-island-introduction/.......................................................2
Figure 1. 2 The Urban Heat Island Effect, http://envirometro.org/view-shareurban-heat-island-effect-infographic/ ............................................................3
Figure 1. 3 LA's Hot Spots Maps, NASA, 2018....................................................5
Figure 1. 4 Satellite map of the LA at night, NASA, 2019....................................6
Figure 1. 5 Cooling effect of trees, Zhonghua Guo, 2019. ....................................8
Figure 1. 6 Quercus ilex – Holly Oak, https://selectree.calpoly.edu/ ..................11
Figure 1. 7 Lagerstroemia indica – Crape Myrtle, https://selectree.calpoly.edu/ 11
Figure 1. 8 Evaluation of microclimate mitigation strategies in a heterogenous
street canyon, Tara Haeri, 2023 ...................................................................17
Figure 1. 9 ENVI-met simulation results, Müller, pers. Comm. .........................18
Figure 1. 10 Evaluation of different strategies of cooling, Ian McRae, 2020......19
Figure 1. 11 Maps of air temperature and wind speed, Miao C, 2022.................20
Figure 1. 12 Workflow from CFD to ENVI-met to GIS, Trane, 2023 ................21
Figure 2. 1 [Sky View Factor Calculation], n.d.,
https://www.sciencedirect.com/science/article/pii/S2212095519300604 ...29
Figure 3. 1 Methodology diagram .......................................................................47
Figure 3. 2 Research area Google 2D map ..........................................................48
Figure 3. 3 Research area location map ...............................................................49
Figure 3. 4 Research area Google 3D map ..........................................................49
Figure 3. 5 Research area 3D model in ENVI-met..............................................49
Figure 3. 6 Assign materials for building façade and road surface .....................50
Figure 3. 7 Daily Temperature Data of Downtown Los Angeles in 2022...........51
Figure 3. 8 Hottest day hourly temperature data of downtown Los Angeles in 2022
......................................................................................................................52
Figure 3. 9 Coldest day hourly temperature data of downtown Los Angeles in 2022
......................................................................................................................52
Figure 3. 10 Daily climate data of downtown Los Angeles from Jan. to Apr.....53
Figure 3. 11 Daily climate data of downtown Los Angeles from May to July....54
Figure 3. 12 Daily climate data of downtown Los Angeles from September to
December.....................................................................................................54
Figure 3. 13 Climate file setting in ENVI-met (hottest day) ...............................55
Figure 3. 14 Climate file setting in ENVI-met (coldest day)...............................55
Figure 3. 15 Hourly Air Temperature of 5 days ..................................................58
Figure 3. 16 Hourly Air Humidity of 5 days .......................................................59
Figure 3. 17 Daily Wind Speed of 5 days............................................................59
Figure 3. 18 Temperature portion of each day.....................................................60

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Figure 3. 19 With or without tree simulation diagram in date comparison .........62
Figure 3. 20 Trees under the shade of buildings simulation diagram..................62
Figure 3. 21 Trees under the shade of buildings simulation diagram..................63
Figure 3. 22 Trees eight locations simulation diagram........................................64
Figure 3. 23 Different space between trees simulation diagrams........................65
Figure 3. 24 Different locations of tree rows diagram.........................................65
Figure 3. 25 February average climate data calculation ......................................67
Figure 3. 26 February average climate data calculation (part) ............................67
Figure 3. 27 February average climate data file in ENVI-met ............................68
Figure 3. 28 Preliminary simulation UTCI diagram of Pixel counting method ..69
Figure 3. 29 Sidewalk area pixel counts image ...................................................70
Figure 3. 30 Sidewalk area average UTCI calculation ........................................71
Figure 4. 1 No tree-no site and one tree-no site modeling in ENVI-met.............77
Figure 4. 2 Hottest day UTCI comparison at 10am.............................................77
Figure 4. 3 Hottest day UTCI comparison at 12pm.............................................78
Figure 4. 4 Hottest day comparison at 2pm.........................................................78
Figure 4. 5 Coldest day UTCI comparison at 10am ............................................79
Figure 4. 6 Coldest day comparison at 12pm ......................................................80
Figure 4. 7 Coldest day comparison at 2pm ........................................................80
Figure 4. 8 Hottest and coldest day reduced UTCI comparison 1. ......................82
Figure 4. 9 Hottest and coldest day reduced UTCI comparison 2. ......................82
Figure 4. 10 Each month reduced UTCI comparison. .........................................83
Figure 4. 11 Build shade with a tree and without a tree comparison...................84
Figure 4. 12 Hottest day comparison at 10am .....................................................85
Figure 4. 13 Hottest day comparison at 12pm .....................................................86
Figure 4. 14 Hottest day comparison at 2pm .......................................................86
Figure 4. 15 Coldest day comparison at 10am.....................................................88
Figure 4. 16 Coldest day comparison at 12pm ....................................................89
Figure 4. 17 Coldest day comparison at 2pm ......................................................90
Figure 4. 18 Cooling ability reduction comparison .............................................91
Figure 4. 19 2D map of building shade with and without a tree..........................92
Figure 4. 20 Hottest day comparison at 10am .....................................................93
Figure 4. 21 Coldest day comparison at 10am.....................................................94
Figure 4. 22 Transpiration effect comparison......................................................95
Figure 4. 23 Trees in different eight locations surrounding a building. ..............97
Figure 4. 24 Hottest day sun rise time comparison (7 am)..................................98
Figure 4. 25 Hottest day noon time comparison (12 pm) ....................................98
Figure 4. 26 Hottest day sun set time comparison (17 am) .................................99
Figure 4. 27 Hottest day Building Shadow Analysis.........................................100
Figure 4. 28 Coldest day sun rise time comparison (8 am) ...............................102
Figure 4. 29 Coldest day noon comparison (12 pm)..........................................102

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Figure 4. 30 Coldest day sun set time comparison (17 am)...............................103
Figure 4. 31 Coldest day Building Shadow Analysis........................................103
Figure 4. 32 Whole year Building Shadow Analysis.........................................104
Figure 4. 33 Tree canopy with spacing..............................................................106
Figure 4. 34 Tree canopy with no spacing.........................................................106
Figure 4. 35 Tree canopy overlapping. ..............................................................107
Figure 4. 36 Tree types of list ............................................................................109
Figure 4. 37 New trees layout 2D map ..............................................................110
Figure 4. 38 New trees layout 3D map ..............................................................111
Figure 4. 39 New trees layout 3D map ..............................................................114
Figure 5. 1 Full site no tree 3D model in ENVI-met. ........................................115
Figure 5. 2 Full site existing trees 3D model in ENVI-met...............................116
Figure 5. 3 Full site new trees 3D model in ENVI-met.....................................116
Figure 5. 4 UTCI thermal comfort standard ......................................................117
Figure 5. 5 Full site no trees UTCI diagram. .....................................................118
Figure 5. 6 Full site existing trees UTCI diagram .............................................118
Figure 5. 7 Full site new trees UTCI diagram ...................................................119
Figure 5. 8 Three full site pixel comparison ......................................................120
Figure 5. 9 Full site pedestrian area UTCI comparisons at 12pm (Hottest Day)
....................................................................................................................123
Figure 5. 10 Full site pedestrian area UTCI comparisons at 12pm (Coldest Day)
....................................................................................................................124
Figure 5. 11 Full site pedestrian area UTCI comparisons at 12pm (Jan.) .........125
Figure 5. 12 Full site pedestrian area UTCI comparisons at 12pm (Feb.).........126
Figure 5. 13 Full site pedestrian area UTCI comparisons at 12pm (March) .....126
Figure 5. 14 Full site pedestrian area UTCI comparisons at 12pm (Apr.).........127
Figure 5. 15 Full site pedestrian area UTCI comparisons at 12pm (May) ........128
Figure 5. 16 Full site pedestrian area UTCI comparisons at 12pm (June) ........128
Figure 5. 17 Full site pedestrian area UTCI comparisons at 12pm (July).........129
Figure 5. 18 Full site pedestrian area UTCI comparisons at 12pm (Aug.)........129
Figure 5. 19 Full site pedestrian area UTCI comparisons at 12pm (Sep.).........130
Figure 5. 20 Full site pedestrian area UTCI comparisons at 12pm (Oct.).........131
Figure 5. 21 Full site pedestrian area UTCI comparisons at 12pm (Nov.)........131
Figure 5. 22 Full site pedestrian area UTCI comparisons at 12pm (Dec.) ........132
Figure 5. 23 Second layout ................................................................................134
Figure 5. 24 Second layout sun path. Note that the taller trees were placed on the
south where the sun’s altitude is highest....................................................134
Figure 5. 25 Two trees layout comparisons at 12pm (Hottest day.)..................135
Figure 5. 26 Reduced UTCI by using new trees scheme for each month..........136

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Figure 6. 1 The Urban Heat Island Effect http://envirometro.org/view-shareurban-heat-island-effect-infographic/ ........................................................139
Figure 6. 2 Cooling effect of trees, Zhonghua Guo, 2019. ................................140
Figure 6. 3 Methodology diagram .....................................................................142
Figure 6. 4 Research area...................................................................................142
Figure 6. 5 Species of trees................................................................................143
Figure 6. 6 Sidewalk area pixel counts image. ..................................................144
Figure 6. 7 Sidewalk area average UTCI calculation ........................................144
Figure 6. 8 February average climate data calculation ......................................145
Figure 6. 9 Each month reduced UTCI comparison. .........................................148
Figure 6. 10 Cooling ability reduction because of building shadows................149
Figure 6. 11 Transpiration effect comparison....................................................150
Figure 6. 12 New trees layout 3D map ..............................................................153
Figure 6. 13 Reduced UTCI after using new trees scheme................................154
Figure 6. 14 ENVI-met wind analysis. ..............................................................156
Figure 6. 15 ENVI-met SVF analysis................................................................157
Figure 6. 16 ENVI-met big area analysis...........................................................158
Figure 6. 17 LA's Hot Spots Maps, NASA, 2018..............................................159

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ABSTRACT
With the continuous development of cities, the scale of cities continues to expand, and
the rapid increase of urban populations are followed by many urban environmental
problems, such as urban heat island effects, urban air pollution, urban light pollution,
and so on. Especially the urban heat island effect, increasing extreme heat in urban
areas will create longer periods of unhealthy and dangerous conditions for humans and
other species. Focusing on studying the thermal environment problems related to the
urban heat island effect and the use of trees to mitigate the problem. Using ENVI-met
to simulate different factors that can affect the cooling ability of trees which include the
different time of the year, the building shade, transpiration, the location of the trees,
and the spacing of trees are the main variables studied. Based on tree-planting strategies
that can improve the cooling ability of trees obtained from the above tests, a new treeplanting scheme is designed, and the thermal comfort condition inside the new scheme
can also be analyzed. Finally, the influence of the new planting scheme on the thermal
environment in the sidewalk area of the study area compared with the existing tree
planting scheme in different months of the year is compared, so as to evaluate whether
the new tree planting scheme can alleviate the heat island effect in the study area. This
study found that, for the area under consideration in Los Angeles, the new tree planting
scheme can decrease the UTCI temperature by 2.2 degrees Celsius on the sidewalks on

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the hottest day of the year, 0.97 degrees Celsius on the coldest day of the year, and 1.52
degrees Celsius for the entire year comparing with the existing tree planting scheme.
HYPOTHESIS
Planting of trees can mitigate the urban heat island effect by changing the thermal
environment outside buildings, on sidewalks in Los Angeles. Optimizing the planting
method of trees can further improve the ability of trees to improve the thermal
environment of sidewalks, it can help reduce about 2 degrees Celsius for the
surrounding area.
KEYWORDS
Urban Heat Island (UHI), thermal environment, ENVI-met, UTCI, tree cooling effect,
tree shading, tree planting strategy.
RESEARCH OBJECTIVES
⚫ To study the different factors that affect the cooling ability of trees, and to
summarize the tree planting strategies that are conducive to improving tree cooling
effect.

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⚫ To calculate the mitigation ability of the trees to reduce the urban heat island effect
by comparing the thermal environment data before and after planting trees,
improvements in the external thermal environment of the trees are obtained.
⚫ To analyze the year-round improvement of thermal environment in sidewalk areas
in the downtown Los Angeles study area by a new tree planting scheme.

1
CHAPTER ONE INTRODUCTION
Trees have a thermal regulation effect in urban heat islands in hot dry climate.
Improved tree-planting strategies can enhance the cooling ability of trees. Designing a
new tree planting scheme using a more reasonable tree planting strategy can more
effectively reduce outdoor temperatures and alleviate the urban heat island effect in a
city like downtown Los Angeles. This chapter makes a brief introduction to the relevant
background information of what is urban heat island, the benefits of trees, and
simulation software available for urban heat island analysis.
1.1 Background Information--Urban Heat Island
Key elements that define urban heat island (UHI) are described.
1.1.1 What is an Urban Heat Island
An urban heat island (UHI) refers to the localized increase in temperature within urban
areas compared to their surrounding rural areas (Figure 1.1). This phenomenon occurs
due to human activities and the built environment, which result in higher heat
absorption, reduced vegetation, increased energy consumption, and altered land cover
(Vujović, et al., 2021). The UHI effect is most prominent during the night when urban

2
areas tend to retain heat due to the heat-absorbing materials they are constructed from
and the reduced cooling effects of vegetation (Abrar, et al., 2022).
Figure 1. 1 [Urban Heat Island Profile], n.d., https://www.metlink.org/fieldwork-resource/urban-heatisland-introduction/
The primary factors contributing to the UHI effect include heat absorption, reduced
vegetation, altered surfaces, and human activities (Lee, et al., 2014).
Urban areas consist of buildings, roads, and other structures that absorb the heat from
the sun during the day and release it during the night, which contributes to an increase
in overall temperature in urban areas. Vegetation can provide shade and cool the
surrounding area through evapotranspiration. Urbanization leads to the removal of
vegetation, which can lead to UHI. The replacement of natural surfaces with concrete

3
and asphalt leads to reduced reflectivity, causing more sunlight to be absorbed and
raising temperatures, which also lead to UHI. Human activities such as transportation,
industry, and energy consumption release heat into the environment, contributing to
elevated temperatures and UHI.
1.1.2 The effects of an urban heat island
The influence of urban heat island effect is mainly concentrated in five aspects:
increased energy consumption, negative health impacts, poor air and water quality,
changes to the microclimate, economic stress (Figure 1.2).
Figure 1. 2 The Urban Heat Island Effect, http://envirometro.org/view-share-urban-heatisland-effect-infographic/

4
Urban heat island can increase energy consumption. Higher temperatures in urban areas
lead to increased use of air conditioning and cooling systems, thereby raising energy
demand and costs (Azevedo, J.A., 2016).
Urban heat island can have negative health impacts. Elevated temperatures can pose
health risks, such as heat-related illnesses and stress, particularly affecting vulnerable
populations like the elderly and children (Chan, et al., 2011).
Urban heat island can undermine the air and water quality. UHI can worsen air quality
as higher temperatures can enhance the formation of ground-level ozone and other
pollutants (Kang, H., et al., 2022).
Also, warmer urban runoff can negatively impact water bodies by affecting aquatic
ecosystems and water quality (Li, L., et al., 2021).
Urban heat island can change the microclimate. UHI effects can interact with broader
climate patterns, potentially exacerbating local warming and influencing weather
patterns.
The urban heat island effect can also cause economic stress. Increased energy
consumption, health costs, and infrastructure strain due to the UHI effect can have
economic implications for municipal governments and individuals.

5
1.1.3 Los Angeles as an urban heat island
Los Angeles has a significant urban heat island (UHI) problem. The UHI effect in Los
Angeles is particularly noticeable during heatwaves and hot summer months (Figure
1.3). On a typical warm summer day, the temperature difference between downtown
Los Angeles and the suburbs might be in the range of 5 to 10 degrees Fahrenheit or
even higher, and this temperature difference has been on the rise in recent years. The
high-lighted areas of the Los Angeles night satellite map show where the city's street
lights, vehicles and building lighting systems are located, and the brighter the areas are,
the more urbanized and the more densely built (Figure 1.4). Areas with a high degree
of urbanization roughly coincide with the areas of urban hot spots.
Figure 1. 3 LA's Hot Spots Maps, NASA, 2018.

6
Figure 1. 4 Satellite map of the LA at night, NASA, 2019.
The urban heat island effect in Los Angeles is primarily attributed to five factors, higher
temperatures in urban areas, heat buildup, air pollution, reduced shading and cooling of
green spaces, and higher energy consumption for cooling.
Los Angeles is one of the most densely populated and heavily urbanized cities in the
United States (Garcia, B., 2020). The proliferation of buildings, roads, and other
infrastructure has replaced natural vegetation and open spaces with heat-absorbing
materials like concrete and asphalt. These materials absorb and retain heat, contributing
to higher temperatures in urban areas. Los Angeles has a Mediterranean climate
characterized by warm, dry summers. This climate, combined with the region's
topography, can lead to temperature inversions and heat buildup, exacerbating the UHI
effect. Los Angeles experiences high levels of air pollution, which can trap heat in the

7
atmosphere and further increase temperatures (Ko, J., 2022). The city's green spaces,
trees, and vegetation are limited in some areas of Los Angeles, further reducing
opportunities for shade and cooling. The demand for energy to power air conditioning
and other cooling systems during hot periods in Los Angeles is huge, which contributes
to the UHI effect.
1.2 The Benefit of Trees
Planting trees in cities can help the urban environment by providing cooling through
shade and other benefits.
1.2.1 Improving thermal comfort in an urban environment
Trees play a significant role in cooling down the urban environment. Five ways in
which trees contribute to reducing heat and improving the thermal comfort of cities are
providing shade, evapotranspiration cooling, reducing irradiance, increasing air
circulation, psychological cooling (Figure 1.5) (Bosch, M., et al., 2021).

8
Figure 1. 5 Cooling effect of trees, Zhonghua Guo, 2019.
Trees provide shade, which helps reduce the direct exposure of buildings and surfaces
to the sun's radiation. This shading effect can lower temperatures in the immediate
vicinity and decrease the heat absorbed by structures, streets, and sidewalks. The
shading provided by trees can reduce energy consumption such as the need for air
conditioning in buildings during the hot summer months. This, in turn, lowers energy
consumption and reduces the urban heat island effect, where cities are typically warmer
than their surrounding rural areas.
Trees release water vapor through a process called evapotranspiration. This cooling
mechanism has a two-fold effect: it cools the air around the tree, and it adds moisture
to the atmosphere, which can help alleviate dry and hot conditions in urban areas.

9
Trees can reduce irradiance, the amount of solar radiation and sunlight that reaches the
ground by absorption, reflection, and diffusion. Trees absorb some of the sunlight that
hits their leaves so the absorbed energy doesn't reach the ground as direct sunlight,
reducing irradiance. Some of the sunlight that hits a tree is reflected, especially off the
surface of its leaves. This reflected light may not reach the ground beneath the tree,
further reducing the amount of irradiance. Trees can also scatter and diffuse sunlight.
When light passes through the leaves and branches, it is redirected in various directions,
reducing the intensity of direct sunlight on the ground.
Trees can alter the movement of air near the ground, which help dissipate heat by
increasing air circulation. Because trees can create turbulence and eddies in the air,
which, in turn, promotes better mixing of warm and cool air masses. This can help
dissipate heat, especially in urban areas with concrete and asphalt that tend to trap heat.
This can be especially beneficial in reducing localized hotspots in cities and improving
overall thermal comfort.
The presence of trees and green spaces in urban areas can have a psychological cooling
effect on people. The visual and psychological comfort provided by greenery can make
urban environments feel cooler, even when the actual air temperature remains the same.
Also, urban forests and green spaces created by trees provide areas where people can

10
gather and socialize. These spaces can encourage outdoor activities and reduce the need
for indoor cooling.
1.2.2 Other benefits
In addition to thermal cooling effects, trees have other benefits. Trees can absorb and
use sunlight as "food" to grow, thereby transferring solar energy into biomass. Urban
trees contribute to biodiversity by providing habitats for birds and insects. Healthy
ecosystems within cities can contribute to a more balanced and resilient urban
environment. Trees help improve air quality by absorbing pollutants and trapping
particulate matter on their leaves. Cleaner air contributes to a healthier and more
comfortable urban environment.
1.2.3 Choosing the proper tree for Los Angeles
Ficus Microcarpa is very prevalent in downtown Los Angeles, but it is not an approved
tree for street trees in the City of LA because its root growth can cause damage to the
adjacent sidewalk area. It is a great shade tree, but it drops fruit and damages paving if
it is too close to the tree. It also needs a planting area that is 10 feet minimum in any
direction to try to accommodate the roots of this tree. However, other species that are
encouraged by the City of LA and allowed as street shade trees: Afrocarpus falcatus –
African Fern Pine, Quercus ilex – Holly Oak (Figure 1.6). Many people choose a tree

11
for their yard, or a street tree based on aesthetic qualities. Popular choices are
Lagerstroemia indica – Crape Myrtle (Figure 1.7) or Handroanthus heptaphyllus Pink
Trumpet Tree. These trees are smaller and provide less shade (Margulies, 2023).
Figure 1. 6 Quercus ilex – Holly Oak, https://selectree.calpoly.edu/
Figure 1. 7 Lagerstroemia indica – Crape Myrtle, https://selectree.calpoly.edu/

12
In choosing the tree species, Quercus ilex – Holly Oak and Lagerstroemia indica –
Crape Myrtle are chosen for later simulation because they can be modeled in ENVImet, and they are both good at cooling the surroundings area.
To maximize the cooling benefits of trees in urban areas, it is important for city planners
and policymakers to consider factors such as tree species selection, proper maintenance,
and strategic placement of trees in areas with high heat exposure. Urban forestry
programs and initiatives can help cities harness the cooling potential of trees to create
more livable and sustainable urban environments (De Guzman, et al., 2022).
1.3 Simulation software for Urban Heat Island Analysis
At least seven software programs are currently being used in urban heat island effect
analysis that includes thermal environment and wind environment. The selection of
these seven software programs was obtained by visiting multiple Internet forums and
reviewing many relevant literatures; they include the following: Urban Weather
Generator, CitySim, ArchGIS, Umi Urban Microclimate tool, Uheat, ENVI-met, and
SimScale (Table 1).
Table 1. 1 Seven Software for Thermal Condition Analysis

13
Name of the Software and Website Short Description Input Data and Output Data
Urban Weather Generator
https://urbanmicroclimate.scripts.
mit.edu/uwg.php
UWG is part of the Weather Research and
Forecasting (WRF) model and simulates urban
weather and climate, considering urban heat
island effects and thermal interactions.
Temperature, Humidity, Wind Speed
and Direction, Solar Radiation,
Precipitation, Cloud Cover, Air
Pollution, Urban Canopy Effect,
Seasonal Variations, Extreme Weather
Events, Climate Change Scenarios.
CitySim
www.citysim.pro
UWG is part of the Weather Research and
Forecasting (WRF) model and simulates urban
weather and climate, considering urban heat
island effects and thermal interactions.
Energy Consumption, Solar Radiatio,
Thermal Behavio, Daylighting, Urban
Microclimate, Carbon Emissions,
Cooling and Heating Demand, Urban
Heat Island Effect, Renewable Energy
Potential, Building Energy Simulation,
Occupant Comfor, Urban Design
Scenario.
ArchGIS
https://www.arcgis.com/index.html
ArcGIS, as a geographic information system
(GIS) software, is not primarily designed for
simulating urban external thermal conditions
or conducting complex thermal analysis.
However, ArcGIS can play a supportive role in
Different kinds of Spacial Data and
Image Data.

14
understanding and visualizing thermal data
within the context of urban environments.
Umi Urban Microclimate tool
https://urbanmicroclimate.scripts.
mit.edu/umc.php
Developed by Autodesk, this tool is part of
Autodesk's Building Performance Analysis
(BPA) suite and focuses on simulating the
urban microclimate at a city scale.
Temperature Profiles, Heat Fluxes,
Wind Speed and Direction, Solar
Radiation, Humidity, Air Quality,
Urban Canyons, Shade Analysis,
Cooling Potential, Urban Microclimate
Mapping, Energy Consumption,
Pedestrian Comfort
Uheat
https://www.arup.com/services/tools/uh
eat
UHeat is a software tool used for simulating
and analyzing urban thermal environments. It's
specifically designed for modeling heat
distribution, thermal comfort, and
microclimate interactions within urban areas.
UHeat allows users to assess the impact of
various urban features, including buildings,
vegetation, and land use, on local thermal
conditions.
Temperature Data, Heat Fluxes,
Radiation Data, Urban Canyons, Green
Spaces, Mitigation Strategies, Thermal
Comfort, Visualization, Simulation
Scenarios

15
ENVI-met
https://www.envi-met.com/
ENVI-met is a microclimate simulation
software designed to model complex urban
thermal environments, including interactions
between buildings, vegetation, and other urban
features.
Temperature Distribution, Airflow and
Wind Patterns, Radiation and Solar
Irradiance, Humidity and Relative
Humidity, Heat Fluxes, Precipitation
and Rainfall, Thermal Comfort Indices,
Urban Feature, Pollution and Air
Quality, Rainwater Runoff and
Evaporation, Vegetation Growth
SimScale
https://www.simscale.com/
SimScale is a cloud-based simulation platform
that can model urban microclimates, including
heat transfer and fluid flow in complex
geometries.
Wind Comfort Analysis, Thermal
analysis.
ENVI-met is especially useful because ENVI-met is very good at thermal environment
analysis of plants, especially trees, it can provide a variety of tree species selection and
detailed tree shading and transpiration simulation. ENVI-met is also good at CFD based
wind environment simulation, and it can simulate a wider range of wind environment
in a shorter time than other software, which is very suitable for urban scale wind
environment simulation.

16
1.3.1 ENVI-met Description
ENVI-met is a specialized software tool used for the simulation and analysis of
microclimates and urban environments. It stands for "Environmental Memory -
Microclimatology and Thermal Environment." ENVI-met is particularly useful for
urban planners, architects, and researchers who want to understand and improve the
thermal comfort, air quality, and energy efficiency of urban spaces (Mounayar, R., &
Florentin, D.B., 2022).
There are seven key features and applications of ENVI-met (Calzada, J.R., & Vidmar,
J., 2013): conducting microclimate simulation, helping with urban planning and design,
calculating thermal comfort indices, analyzing the effects of green infrastructure,
helping assess the impact of climate change, simulating air pollutant dispersion,
evaluating pedestrian comfort.
ENVI-met can conduct microclimate simulation. ENVI-met is designed to simulate the
complex interactions between various environmental factors in urban areas, such as
temperature, humidity, wind patterns, and solar radiation. It models the microclimates
within a city, including how these conditions change over time and in response to
different urban design and land use scenarios (Figure 1.8).

17
Figure 1. 8 Evaluation of microclimate mitigation strategies in a heterogenous street canyon, Tara
Haeri, 2023
ENVI-met can help with urban planning and design. Urban planners and architects use
ENVI-met to assess the impact of different urban designs, building configurations, and
vegetation on local microclimates. This helps in creating more sustainable and
comfortable urban environments.
ENVI-met can calculate thermal comfort indices, such as the Physiological Equivalent
Temperature (PET) and Universal Thermal Climate Index (UTCI), to evaluate how
people would perceive and experience the thermal conditions in specific areas of a city
(Figure 1.9).

18
Figure 1. 9 ENVI-met simulation results, Müller, pers. Comm.
ENVI-met allows users to analyze the effects of green infrastructure elements like parks,
green roofs, and vegetation on the urban heat island effect, air quality, and overall
environmental quality (Figure 1.10).

19
Figure 1. 10 Evaluation of different strategies of cooling, Ian McRae, 2020
ENVI-met can help assess the potential impact of climate change on urban
environments and assist in developing strategies to mitigate its effects, such as
designing heat-resilient urban spaces.
ENVI-met helps in evaluating pedestrian comfort by considering factors like shade,
wind speed, and temperature variations, which are crucial for walkability and outdoor
activities (Figure 1.11).

20
Figure 1. 11 Maps of air temperature and wind speed, Miao C, 2022
ENVI-met is a valuable tool for understanding the complexities of urban microclimates
and is used to inform urban planning and design decisions that promote sustainability,
comfort, and environmental quality in cities. It is often used in conjunction with
geographic information systems (GIS) to integrate real-world geographical data into
the simulations (Trane, M., et al., 2023) (Figure 1.12).

21
Figure 1. 12 Workflow from CFD to ENVI-met to GIS, Trane, 2023
1.3.2 ENVI-met Advantages and Disadvantages
ENVIR-met has many advantages and disadvantages.
⚫ Advantages:
ENVI-met excels at simulating microscale phenomena, providing high-resolution
insights into the local climate, temperature variations, and airflow within a specific area.
The software is particularly useful for urban planners and designers, helping them
assess the impact of different design elements (buildings, green spaces, etc.) on the
microclimate, aiding in the development of more sustainable and comfortable urban
environments.

22
ENVI-met allows users to assess the thermal comfort of outdoor spaces, helping design
areas that are more comfortable for pedestrians and residents, especially in the context
of mitigating the urban heat island effect.
The model can assess the impact of vegetation on the local microclimate, helping
planners decide where to place green spaces to maximize cooling effects. Users can
customize various parameters, such as land use, materials, and weather conditions,
providing flexibility in simulating diverse scenarios and conditions.
ENVI-met is used as an educational tool for teaching students about microclimatology,
urban planning, and the interactions between the built environment and climate
（https://www.envi-met.com/microclimate-simulation-software/, 2024/01/22).
⚫ Disadvantages:
ENVI-met has a relatively steep learning curve. Users need a solid understanding of
meteorology, climatology, and the software itself, making it less accessible to those
without a background in these fields (https://www.envi-met.com/microclimatesimulation-software/, 2024/01/22).

23
The accuracy of ENVI-met simulations depends on the availability and quality of input
data. Collecting accurate and comprehensive data for a specific location can be
challenging.
Simulating microscale environments with high resolution can be computationally
demanding. Running detailed simulations may require powerful computing resources.
The model's accuracy depends on the availability of local meteorological data for
validation. In some cases, obtaining such data for validation purposes can be
challenging (Hayder Alsaad, 2022).
ENVI-met is specifically designed for microscale simulations, and its application is
more focused on local environments. It may not be the best choice for larger-scale
meteorological or climate studies.
ENVI-met is a commercial software, and obtaining licenses can be costly. This may
limit its accessibility for some researchers and smaller organizations.
1.4 Summary
This chapter made a brief introduction to the relevant background information of what
is urban heat island, the benefits of trees, and simulation software available for urban

24
heat island analysis. The urban heat island effect has become a major problem in highdensity cities, and this problem will become worse with global warming and extreme
temperature events. Urban heat island effect also brings negative effects on the
economy, society, environment, and health of urban dweller. Among the factors leading
to the urban heat island effect is the amount of solar radiation in the urban area, the
building density and materials within the city, and human activity in the urban area.
Trees can be one part of mitigating urban heat island effects as they can block part of
the solar radiation and can change the urban microclimate by improving the ambient
humidity, air quality, and wind environment. Both effects can change the thermal
environment of the city, thus alleviating the urban heat island effect. At the same time,
in order to quantify the cooling ability of trees and analyze the factors that affect the
cooling ability of trees, a variety of software can be used, among which ENVI-met is a
good match to analyze the impact of trees on urban thermal environment due to its
detailed simulation and analysis.

25
CHAPTER TWO LITERATURE REVIEW
This chapter describes the different factors that can affect the cooling effect of the trees
and contains a literature review of trees’ cooling effects, which can mitigate the urban
heat island effect.
2.1 Different factors that can affect the cooling effect of the trees.
According to the relevant literature, the cooling ability of trees is mainly affected by
three factors. The first is the characteristics of the tree itself, the second is the spatial
characteristics of the location of the tree, and the third is the climate characteristics of
the location of the tree.
2.1.1 Character of trees
The characteristics of the tree itself are the key factors affecting the cooling ability of
the tree. The characteristics of trees affect the cooling ability of trees are mainly in two
ways: the shading ability of trees and transpiration from the leaves (Ngao, J., et al.,
2021). The leaf density of trees, the size and shape of the canopy, and the height of trees
are the key factors affecting the transpiration and shading effect of trees (Yun, S.H., et
al., 2020).
⚫ Leaf Density

26
The number and density of leaves on a tree affect the overall transpiration rate. More
leaves generally result in higher transpiration, as there are more stomata available for
water vapor release. Increased transpiration contributes to a greater cooling effect, as
the process involves the absorption of heat from the surrounding environment (Gupta,
S.K., et al., 2018).
Leaf density can affect shade creation. Leaf density also influences the amount of shade
provided by a tree. A denser canopy blocks more sunlight, reducing the amount of direct
sunlight that reaches the ground. This shading effect helps to lower temperatures in the
immediate vicinity of the tree, contributing to a cooler microclimate.
Leaf density can affect evaporative cooling. As water is released through stomata
during transpiration, it undergoes a phase change from liquid to vapor. This process
requires energy, and this energy is drawn from the surrounding environment, leading
to a cooling effect. A higher leaf density allows for more water to be transpired,
enhancing the overall evaporative cooling potential of the tree (Gupta, S.K., et al., 2018).
⚫ Trees Canopy Size and Shape
Larger canopies provide more extensive shading, covering a larger area beneath the tree.
This results in a greater reduction of direct sunlight reaching the ground, leading to
more substantial cooling in the shaded area. The shape of the canopy can also influence

27
the distribution of shade. A well-rounded, symmetrical canopy may provide more
uniform shading, while other canopy shapes like columnar trees have lower shading
potential.
The overall transpiration rate of a tree is influenced by the total leaf area, which
correlates with the size of the canopy. Larger canopies generally have more leaves and,
consequently, a higher transpiration rate, contributing to increased evaporative cooling.
The arrangement of leaves within the canopy, influenced by its shape, can affect airflow
and moisture exchange, influencing the efficiency of transpiration and evaporative
cooling (Norby., et al., 2022).
Larger canopies may alter local wind patterns and create more complex airflow around
the tree. This can influence the dispersion of heat and contribute to a more effective
cooling effect in the surrounding area. The shape of the canopy can affect how air
moves through and around the tree (Xu, T., et al., 2022). Compact, rounded canopies
have different effects that irregularly shaped canopies (Salin, S. M., 2011.). However,
there is a counter point to this where some studies have shown that trees hold heat
longer in some conditions because the canopy is trapping air.
⚫ Trees Height

28
Taller trees with higher canopies cast larger and more extensive shadows. This results
in a greater reduction of direct sunlight reaching the ground, leading to more substantial
shading and a larger area experiencing cooling effects (Zhang R, Zhao Z., 2022).
Taller trees often have more extensive and numerous branches and leaves. This larger
leaf surface area contributes to a higher rate of transpiration, where water is released
from the leaves into the atmosphere. The increased transpiration enhances the overall
evaporative cooling effect of the tree (Zhang R, Zhao Z., 2022).
Taller trees have the potential to modify the microclimate over a larger area due to their
increased height. They can influence temperature, humidity, and wind patterns, creating
a more significant cooling effect in the landscape (Feng Xianhui, et al., 2023).
Taller trees can act as windbreaks, reducing wind speed in their immediate vicinity.
This reduction in wind speed can contribute to a more stable microclimate and enhance
the effectiveness of other cooling mechanisms such as shading and transpiration (Gao
K, 2020).
2.1.2 Spatial Conditions
The spatial environment characteristics of trees can affect the cooling effect of trees.
The following four spatial characteristics have the most severe impact on the cooling

29
ability of trees: sky view factor, shadows from the neighboring buildings, tree location
and arrangement of the trees.
⚫ Sky View Factor
The Sky View Factor (SVF) is a quantitative measure used in urban climatology and
environmental studies to assess the exposure of a location to the sky. It represents the
ratio of the visible sky hemisphere as observed from a specific point on the Earth's
surface to the total possible visible sky hemisphere. In simpler terms, it describes the
proportion of the sky that is visible from a particular location (Dirksen, et al., 2019). In
2D perspective, the SVF in the street can be calculated by the following formula
(Dirksen, et al., 2019):
Figure 2. 1 [Sky View Factor Calculation], n.d.,
https://www.sciencedirect.com/science/article/pii/S2212095519300604
The Sky View Factor is typically expressed as a percentage, with values ranging from
0% to 100%. A breakdown of the key points related to SVF are the following: 0%

30
SVF indicates complete obstruction of the sky, with no visible sky from the observed
point. This could occur in deep canyons or densely built urban environments where
buildings block the entire view of the sky. 100% SVF represents an unobstructed view
of the entire sky hemisphere from the observed location. This occurs in open areas with
no vertical obstacles, such as a flat, open field (Dirksen, et al., 2019).
⚫ Shadows from the Neighboring Buildings
Negative Impact: If a building casts a shadow directly over a tree, it can reduce the
amount of direct sunlight reaching the tree's canopy. Since direct sunlight is crucial for
photosynthesis, the process by which trees produce food and energy, a significant
reduction in sunlight can potentially limit the tree's growth and overall health (Vo, T.T.,
Hu, L., 2021).
Positive Impact: While the direct sunlight may be blocked, the shadow cast by a
building can create shaded areas around the tree. This shading effect contributes to a
localized cooling of the immediate surroundings, making the area more comfortable,
especially in hot climates. People and other vegetation in the shaded area may benefit
from the cooler microclimate. In this way, shadows from neighboring buildings can be
an asset like shading from trees.
⚫ Tree Location

31
The orientation of trees around a building can have a significant impact on the cooling
ability of trees and the overall microclimate. Key considerations are related to
orientation and prevailing wind direction (Yan S, et al., 2023).
The orientation of trees relative to a building can determine how effectively they
provide shading. Placing trees on the west and east sides of a building can help mitigate
the impact of the morning and afternoon sun, reducing solar heat gain and improving
indoor comfort.
Depending on the prevailing wind direction in a region, the orientation of trees can be
strategically planned to act as windbreaks. This can be particularly important in colder
climates where trees can help reduce heat loss from buildings by blocking cold winds.
The orientation of trees in outdoor spaces, such as courtyards and patios, can influence
the cooling effect in those areas. Properly oriented trees can provide shade during the
hottest parts of the day, making these outdoor spaces more comfortable
(https://www.holstonelectric.com/trees-and-energy-conservation, accessed
2024/01/21).
The choice between deciduous and evergreen trees can also play a role in the seasonal
cooling effect. Deciduous trees, which shed their leaves in the fall, allow more sunlight
to reach a building during the winter months when solar heating may be beneficial.

32
When planning tree placement around buildings in urban areas, considerations for
mitigating the urban heat island effect should be taken into account. Strategic planting
of trees on the south and west sides of buildings can help reduce the impact of direct
sunlight and contribute to cooler surroundings (https://www.epa.gov/heatislands/usingtrees-and-vegetation-reduce-heat-islands, accessed 2024/01/21).
Properly oriented trees can contribute to the modification of the local microclimate,
reducing air temperatures around a building. This cooling effect is especially valuable
in urban environments where high temperatures can be exacerbated by the heatretaining properties of buildings and pavement. Trees planted on the south and west
sides of buildings can provide effective solar control, reducing the need for air
conditioning and contributing to energy efficiency in warm climates.
⚫ Arrangement of Trees
Tree canopies provide shade, and if the shadows of adjacent tree canopies do not block
each other throughout the day, the amount of shade provided by a certain number of
canopies will be maximized, as will the cooling effect. Therefore, when the spacing
between adjacent trees is precisely such that the canopy shade meets the above
conditions, the shading and cooling capacity of the tree can be maximized while saving
space (Zhao, et al., 2017).

33
2.1.3 Climate
The cooling ability of trees is also affected by local climatic conditions, such as air
temperature, humidity, wind speed and direction, and cloud cover. There are four key
climate conditions that most affect the cooling ability of trees: climate type, relative
humidity, air temperature, and wind speed and direction.
⚫ Climate Type
The climate type has a significant impact on how trees contribute to cooling effects due
to variations in temperature, precipitation, and other environmental factors. Different
climate types present distinct challenges and opportunities for using trees to mitigate
heat and enhance local comfort. The climate type influences a tree's cooling effect. In
hot and arid climates, the primary cooling mechanism of trees is through shade and
evaporative cooling. Trees provide shade to reduce direct exposure to intense sunlight,
and their transpiration process contributes to cooling the surrounding air through the
release of water vapor. In arid climates some trees have lower transpiration rates to
conserve the more limited amount of water available. As a result, trees in arid climates
may need careful water management to thrive. Drought-resistant species are often
preferred, and efficient irrigation systems may be necessary to maintain their health and
cooling benefits (Li, Y., et al., 2015).

34
In hot and humid climates, where high temperatures are often accompanied by high
humidity, transpiration remains a crucial cooling mechanism. The release of water
vapor during transpiration is less effective in cooling the air when humidity levels are
already high. Hot and humid climates can also foster the growth of diseases and pests
that may affect tree health. Choosing resilient tree species and proper maintenance
practices become essential (https://www.usgs.gov/special-topics/water-scienceschool/science/evapotranspiration-and-water-cycle, accessed 2024/01/29).
In temperate climates with distinct seasons, trees can provide cooling effects during hot
summers and act as windbreaks in cold winters. Deciduous trees, which shed their
leaves in the winter, allow sunlight to reach surfaces when needed for warmth. In cold
climates, trees can serve as effective windbreaks, reducing wind chill and providing
insulation against cold temperatures. Evergreen trees, which retain their leaves yearround, offer continuous protection. Trees in cold climates may accumulate snow,
adding an insulating layer and contributing to temperature regulation.
In regions with a Mediterranean climate characterized by hot, dry summers and mild,
wet winters, trees that are adapted to drought conditions can be valuable for cooling
effects during the hot season. Some regions with a Mediterranean climate are prone to
wildfires, and careful tree selection and maintenance are crucial to reduce fire risks

35
(https://gaucinpropertyrenovation.com/plants%20for%20a%20mediterranean%20gard
en, accessed 2024/01/29).
⚫ Relative Humidity
Relative humidity plays a crucial role in determining the effectiveness of trees in
providing cooling effects, particularly through the process of transpiration.
Transpiration is the release of water vapor from the leaves of trees and plants into the
atmosphere. The relationship between trees and relative humidity influences how much
cooling effect can be achieved.
In areas with high relative humidity, the air is already saturated with moisture, making
it more difficult for additional water vapor to evaporate. As a result, the cooling effect
of transpiration is less pronounced in high-humidity environments. In contrast, in
regions with lower relative humidity, the air has greater capacity to absorb moisture. In
such conditions, transpiration is more efficient at cooling the air because there is a larger
gradient for the movement of water vapor from the tree to the atmosphere
(https://www.noaa.gov/jetstream/ll-leaf, accessed 2024/01/21).Transpiration helps in
moderating temperatures by absorbing heat from the environment during the process of
water vaporization. In areas with lower humidity, this cooling effect is more effective
in offsetting high temperatures. The ability of trees to transpire effectively may also be
influenced by the availability of water in the soil. In arid regions with low humidity,

36
trees that are adapted to drought conditions may be more efficient at conserving water
and maintaining cooling effects (Abrams. 1990).
Trees not only influence the overall humidity but also create a localized microclimate.
In areas with lower humidity, the cooling effect provided by tree shading and
transpiration can make outdoor environments more comfortable. While the direct
impact of relative humidity is on the efficiency of transpiration, the combination of
shading and local cooling effects can contribute to a more comfortable environment for
humans, especially in dry or moderately humid conditions (Feng Xianhui, et al., 2023).
⚫ Air Temperature
Transpiration, the process by which trees release water vapor through their leaves, is
influenced by air temperature. Generally, higher temperatures increase the rate of
transpiration. This is because warmer air can hold more moisture, creating a steeper
gradient for water vapor to move from the tree into the atmosphere
(https://www.noaa.gov/jetstream/ll-leaf, accessed 2024/01/21).
Cooling Effect: The increased transpiration rate in warmer temperatures contributes to
a greater cooling effect. As water evaporates from the leaves, it absorbs heat from the
surrounding environment, helping to cool both the tree and its immediate surroundings.

37
Trees provide shade, reducing the direct exposure of surfaces to sunlight. This shading
effect is especially important in mitigating the impact of high temperatures. Shaded
areas experience lower temperatures compared to areas exposed to direct sunlight. In
urban areas, where structures and pavement can absorb and radiate heat, the presence
of trees can help reduce the urban heat island effect by providing shade and moderating
temperatures.
Local Cooling: Trees influence the microclimate around them. The cooling effect is
more pronounced in warmer temperatures, as the contrast between shaded and
unshaded areas becomes more significant (Tjoelker, et al., 2023).
Winter and Summer Effects: In colder climates, deciduous trees lose their leaves in
the winter, allowing sunlight to reach surfaces and contribute to warmth. In the
summer, the leaves provide shade and cooling. Evergreen trees, on the other hand,
retain their leaves year-round, providing a constant shading effect
(https://www.purdue.edu/fnr/extension/plant-for-the-sun/, accessed 2024/01/29).
.
⚫ Wind Speed and Direction
Wind facilitates the process of transpiration by carrying away the water vapor released
by trees. Higher wind speeds result in increased evaporation, enhancing the cooling

38
effect of transpiration (https://van.physics.illinois.edu/ask/listing/1471, accessed
2024/01/22).
Wind can cause stomatal opening on the undersides of leaves, promoting increased
water vapor release. This enhances the tree's ability to cool its immediate surroundings.
Wind contributes to convective cooling by carrying away heat from the surfaces of
leaves. This is particularly important during hot periods when convective heat transfer
helps maintain the tree's temperature within a range conducive to transpiration
(https://en.wikipedia.org/wiki/Transpiration, accessed 2024/01/22).
2.1.4 Climate in Downtown Los Angeles
⚫ Climate Type
Los Angeles has a Mediterranean climate, which is characterized by mild, wet winters
and hot, dry summers. This climate type is often referred to as a "dry-summer
subtropical" climate. In downtown Los Angeles, people can expect warm to hot
temperatures during the summer months, with average highs reaching the 80s and 90s
Fahrenheit (around 27-37 degrees Celsius). Winters are mild, with average highs in the
60s and 70s Fahrenheit (around 15-25 degrees Celsius). Rainfall is relatively low, and
the majority of precipitation occurs during the winter months. The Mediterranean

39
climate is common in coastal areas of Southern California
(https://en.wikipedia.org/wiki/Climate_of_Los_Angeles, accessed 2024/01/27).
⚫ Relative Humidity
The relative humidity in downtown Los Angeles can vary throughout the year. In
general, Los Angeles tends to have lower humidity levels, especially during the summer
when it experiences dry and warm conditions. During the summer months, relative
humidity in downtown Los Angeles often drops to relatively low levels, sometimes
below 30%. In contrast, the relative humidity can be somewhat higher during the winter
months when the region may experience occasional rainfall. Winter storms and rain can
temporarily increase humidity levels, but they are still generally lower compared to
more humid climates (https://www.laalmanac.com/weather/we19.php, accessed
2024/01/27).
⚫ Air Temperature
The air temperature in the downtown Los Angeles area can be obtained through the Los
Angeles Local Weather Bureau and can provide accurate data for the average air
temperature for every hour of any day throughout the year. Detailed data will be
presented in Chapter 3.
⚫ Wind Speed and Direction

40
The wind speed and direction in the downtown Los Angeles area can be obtained
through the Los Angeles Local Weather Bureau and can be accurate fot the average
wind speed and direction for every hour of any day throughout the year. Detailed data
will be presented in Chapter 3.
2.2 Research Findings by ENVI-met
This section introduces 9 articles that utilize ENVI-met for analysis and simulations.
The following important conclusions of these articles are valueable for the simulation
and research in chapter 3.
2.2.1 Trees' Cooling Effect on Surrounding Air Temperature Monitoring System:
Implementation and Observation
The type of tree, the movement of a building's shadow, the orientation of the building,
and the type of canopy all affect the cooling ability of the tree (Junid, S.A., et al., 2020).
The temperature reduction of different trees in different conditions in one day was 9.2,
7.6, 6.1, 5.7, 5.1 degrees Celsius from the highest to the lowest. In the tropics, trees
have an average cooling capacity of 8 degrees Celsius per day.
2.2.2 Cooling Effect of Urban Trees on the Built Environment in Contiguous
United States

41
The results show that the cooling effect of trees can reduce the average near-surface air
temperature in American cities, including Cascadia, Great Lakes Northeast, California
Arizona Sun Corridor, Texas Triangle, and Florida by 3.06 °C. The shading effect of
trees is higher at night than the daytime when only considering the radiation shading of
urban trees (Wang, Chenghao, et al., 2018). Winter is higher than summer, autumn is
best, spring is worst, the denser the leaves of the tree, the better the cooling effect.
2.2.3 Optimizing the spatial arrangement of trees in residential neighborhoods for
better cooling effects: Integrating modeling with in-situ measurements
Trees located in different spatial environments have different effects on the interception
of solar radiation, resulting in different temperatures around them (Wu, et al., 2017).
For example, trees that are fully exposed to solar radiation have a stronger cooling effect
(0.22°C lower air temperature) than trees that are in the shadow of surrounding
buildings. The amount of tree cover on a street has a significant effect on a tree's ability
to cool down. For every 10% increase in tree coverage, pedestrian perceived
temperature decreased by 0.22℃. However, when the increase of vegetation cover is
less than 5%, the effect of tree cover on temperature will be small, and the increase of
tree cover has no effect on the cooling rate.
2.2.4 Parametric study of the influence of environmental factors and tree
properties on the transportive cooling effect of trees

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The fourth article gives eight important conclusions of trees’ cooling effect which can
be useful for the later research and simulations (Manickathan, et al., 2018):
1. Single-row trees have the best transpiration cooling effect under low wind speed
conditions.
2. Pedestrians will feel the cooling effect of tree transpiration only when the wind
speed is low.
3. Under humid and low temperature conditions, the transpiration and cooling
effect of trees is weakened, because the air pressure is close to saturation, and
the transpiration effect of vegetation is weakened. Cities in this climate should
develop mitigation strategies that focus on cooling through shading rather than
maximizing transpiration cooling.
4. Solar radiation has a great influence on thermal comfort, and in all cases comfort
levels in the shade of trees are substantially higher than around trees due to the
shading effect of trees. Since solar radiation is the main factor affecting thermal
comfort, tree transpiration cooling has benefits but contributes little.
5. Compared with environmental factors such as wind speed, temperature and
relative humidity, tree characteristics, leaf size and minimum stomatal
resistance have less influence on vegetation transpiration and cooling effect.
6. Due to the coupling effect of wind speed and air temperature, the transpiration
cooling effect of vegetation depends on its leaf area density.

43
7. The increase in the height of vegetation is beneficial for its cooling effect,
because the top leaves of trees are warmer and farther away from pedestrians.
This ensures better transpiration cooling at pedestrian level.
8. In general, cities should use a combination of tall broad-canopy trees, which
provide shade for urban surfaces, and trees at pedestrian height, which provide
transpiration cooling near the ground. This combination maximizes cooling
through shading and transpiration.
2.2.5 Comparative and combinative cooling effects of different spatial
arrangements of buildings and trees on microclimate
High-rise buildings can provide more green land than low-rise buildings by
concentrating density into a smaller footprint, resulting in lower near-surface
temperatures. (Wu, et al., 2019). The larger the green area, the more significant the
cooling effect (but the cooling effect of the green area is non-linear with the area).
Strong solar irradiation inhibited the cooling effect of green space.
2.2.6 Cooling effect of urban trees and its spatiotemporal characteristics: A
comparative study
The cooling rate of urban trees is nonlinear correlated with the average land surface
temperature, and the slope of the cooling rate increases rapidly with the increase of the
average land surface temperature (He Cheng, et al., 2021). The study further confirmed

44
that different outdoor warming degrees in the same city would not only affect the total
cooling rate of urban trees, but also affect the spatial allocation effect of urban trees. At
the same time, the optimal spatial configuration of trees is different in different cities.
In some cities, more closely spaced trees can provide a stronger cooling effect for the
city, while some cities need trees scattered in various locations to expand the benefits
of tree cooling.
2.2.7 Right tree, right place (urban canyon): Tree species selection approach for
optimum urban heat mitigation - development and evaluation
The results show that in different urban forms, tree form (tree species) has different
degrees of temperature regulation. In subtropical climate, the effect of daytime and
nighttime temperature regulation is between 0.3°C - 1.0°C and 0.0°C - 2.0°C,
depending on tree morphology and sky view factor values (Morakinyo, et al., 2020).
2.2.8 Maximizing the pedestrian radiative cooling benefit per street tree
Planting a shade tree on a street that currently has no shade trees provides
approximately 1.5-2 times the radiative cooling effect for pedestrians than planting
the same tree on a street where most of the sidewalks have shade (Lachapelle, et al.,
2023). Thus, within a block or neighborhood, the relatively even distribution of trees
on sun-exposed pedestrian routes and sidewalks avoids shading each other, thereby
optimizing the outdoor radiant heat reduction of each tree in warm conditions.

45
2.2.9 Temperature and human thermal comfort effects of street trees across three
contrasting street canyon environments
In the summer heat wave environment, in shallow street canyons without many highrise buildings around, street trees have an average daytime cooling capacity of
0.20.6 °C and a maximum daytime cooling capacity of 1.5 °C (Coutts, A., et all.,
2015). However, in the deep street canyons with more high-rise buildings, the cooling
ability of trees is significantly reduced, because the shade of trees is blocked by the
shadow of high-rise buildings. Street trees have excellent cooling ability and can
make the UTCI ( spell out Uban Tree Cooling Index? ) value from very strong (UTCI
< 38 °C) reduced to strong (UTCI < 32 °C). The cooling capacity of trees increases as
streets become shallower and wider.
2.3 Summary
This chapter first analyzes the three important factors affecting the cooling ability of
trees, then gives the brief conclusions of trees cooling ability, the most important
conclusion includes in hot climate a tree can give about 3-6 °C cooling effect of the
surrounding area.

46
Previous research has shown the positive impact of trees on the thermal environment
of sidewalks are of great help to the subsequent full site analysis of the thermal
environment of sidewalks in Los Angeles downtown research area. Many of the
research studies simulated the effect of trees in ENVI-met on a site at a specific time.
It would be useful to study the average monthly climate conditions of an entire year,
so as to analyze the comprehensive impact of trees on the thermal environment of the
site for the whole year to check both the advantages and disadvantages of the addition
of trees.

47
CHAPTER THREE METHODOLOGIES
This chapter describes the four main parts of the research methodology: establishes a
base urban model, creates a preliminary study for learning the software, determines
numeric techniques for the simulations, and does a full site study (Figure 3.1).
Figure 3. 1 Methodology diagram
3.1 Establish a Base Urban Model
This section divides the 3D modeling part of ENVI-met into three main parts: input
data and build the mode, assign the materials, and input the climate data. This section
explains each of these.
3.1.1 Input external data and build the modeling in ENVI-met.
Under study is a block in downtown Los Angeles. By finding the location of the block

48
in Google Earth, two-dimensional satellite images of the study area of the block are
imported into ENVI-met to determine the location of buildings and plants (Figure 3.2).
The research area is bounded by South Hope St. to the east, West Olympic Blvd to the
south, Flower St. to the west, and West 9th St to the north (Figure 3.3).
Figure 3. 2 Research area Google 2D map

49
Figure 3. 3 Research area location map
After the location is determined, the height of the buildings and trees is determined
through Google Earth 3D map images (Figure 3.4) and modeled in ENVI-met (Figure
3.5).
Figure 3. 4 Research area Google 3D map
Figure 3. 5 Research area 3D model in ENVI-met.

50
3.1.2 Assign the materials in ENVI-met.
Entering the data for the materials in the completed EVNI-met model makes subsequent
simulation results more accurate. Based on satellite photography and field
investigations the materials of the buildings have been assigned the default
concrete wall and stone brick wall in the software respectively, and the pavement
material in the site is set as concrete (Figure 3.6).
Figure 3. 6 Assign materials for building façade and road surface
3.1.3 Input climate data into ENVI-met.
Before ENVI-met conducts a simulation, five major climate parameters including
hourly air temperature, hourly air humidity, average wind speed of the whole day,

51
average wind direction of the day before yesterday and cloud coefficient are required
to be input. To facilitate the comparison of subsequent results, cloud coefficient is
uniformly input with a value of 0 to minimize the influence of clouds on solar radiation.
Therefore, when comparing the effects of tree cooling on different dates, only hourly
air temperature, hourly air humidity, average wind speed of the whole day, and average
wind direction of the day these four parameters are needed.
On its website, the National Weather Service provides a detailed history of climate data
in downtown Los Angeles. The climate data used in the simulations are all from
downtown Los Angeles in 2022 (Figure 3.7).
Figure 3. 7 Daily Temperature Data of Downtown Los Angeles in 2022
The highest annual temperature in downtown Los Angeles in 2022 occurred on

52
September 4, and the lowest annual temperature occurred on February 24. In the
northern hemisphere where Los Angeles is located, the spring equinox is March 21, the
autumn equinox is September 23, the summer solstice is June 21, and the winter solstice
is December 22. To reduce the number of simulations, the preliminary test will select
climate data of hottest day and coldest day of the whole year (Figure 3.8, Figure 3.9),
and the average climate day of each month (Figure 3.10 - 3.12).
Figure 3. 8 Hottest day hourly temperature data of downtown Los Angeles in 2022
Figure 3. 9 Coldest day hourly temperature data of downtown Los Angeles in 2022

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Figure 3. 10 Daily climate data of downtown Los Angeles from Jan. to Apr.

54
Figure 3. 11 Daily climate data of downtown Los Angeles from May to July
Figure 3. 12 Daily climate data of downtown Los Angeles from September to December
After obtaining the above data, the data will be input into the simulation setting of
ENVI-met to generate the corresponding climate file, and then the subsequent
experimental simulation can be carried out. These values were used for the coldest and

55
hottest days (Figure 3.13) (Figure 3.14).
Figure 3. 13 Climate file setting in ENVI-met (hottest day)
Figure 3. 14 Climate file setting in ENVI-met (coldest day)

56
3.1.4 Create research questions
Questions were developed to study the urban sidewalk conditions.
Question 1:
The givens are the following: specific representative dates (the coldest and hottest
days of the year), hour studies from sunrise to sunset, within the scope of the study
(the block of downtown Los Angeles), under three different conditions (no trees,
existing trees, and the proposed tree scheme), and measuring at the level of 1.4
meters in the sidewalk area.
What is the improvement of UTCI for these three studies? How much improvement,
if any, in degrees C did the trees make?
The proposed tree scheme will use the same number and size of the existing trees
on the site. The locations of the trees will be based on the preliminary studies
discussed later in this chapter.
Method 1:
① Conduct full site no tree simulation (Coldest day, hottest day).
Conduct full site existing tree simulation (coldest day, hottest day).
Conduct full site proposed tree simulation (coldest day, hottest day).

57
Excel tables were made based on UTCI data of different points simulated by
ENVI-met on the 1.4-meter-high horizontal plane in the sidewalk area. By
comparing the UTCI data of the no tree, existing tree, and proposed tree
conditions at the same location, the thermal comfort improvement of the
proposed tree design scheme to the UTCI at each location is presented
respectively. Excel will be used to calculate the temperature difference for the
three case studies, and the analysis will be made of the results.
Question 2:
Based on the proposed tree study, the cooling effect of trees is studied for average
monthly temperatures. In which months do trees have a greater benefit, in which
months do trees have a smaller benefit, in which months do trees have a positive
effect and in which months do trees have a negative effect?
Method 2:
The first step is to verify the feasibility of averaging 30 days of climate data into
one day per month.
The method is as follows:
First, a simple simulation scene is selected for five consecutive days. After the

58
simulation, the proportion of each temperature interval is calculated by using the
color recognition function of Photoshop from the simulation result diagram. Then
the proportion of the color temperature for each of the five days is determined and
the average is calculated (Figure 3.15-3.17).
Figure 3. 15 Hourly Air Temperature of 5 days

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Figure 3. 16 Hourly Air Humidity of 5 days
Figure 3. 17 Daily Wind Speed of 5 days
next, in the exact same simulation scenario, only the number of ENVI-met inputs is
changed (the data is the average of five days of temperature data into one day of
data), the result is simulated, and again the ratio of different color temperatures is
identified in Photoshop.

Finally, the color temperature ratio between the first method and the second method
is compared. If the difference is not too much, it indicates that the method of using

60
the average temperature is feasible; if the difference is large, it is negated (Figure
3.18).

Figure 3. 18 Temperature portion of each day
If the method of using the average temperature is feasible, continue the experiment
in this step. Input the average climate data of each month into ENVI-met
respectively and get the monthly full site no tree, full site existing tree, and full site
proposed tree simulations. For details on this step, please refer Section 3.3.

Using the same method as in Question 1, assess the improvement of thermal
comfort in UTCI by proposed tree design scheme at different points within the
sidewalk area.

61
3.2 Preliminary study
This section will describe the six variables that affect the cooling capacity of trees
studied in the preliminary study: date, building shade, transpiration, tree location, tree
spacing between each other, tree row direction. Except for the date comparison, the
other five factors will be conducted for the hottest day and coldest day simulations in
the whole year respectively, to study the influence of different variables on the
cooling ability of trees during the hot and cold weather and to evaluate the influence
of different variables on the positive and negative cooling effects of trees. At the same
time, the date variable needs to simulate the average climate data of each month to
judge the cooling ability of trees in each month, and to judge which months have
more obvious positive effect on tree cooling and which months have more obvious
negative effect.
3.2.1 Date comparison
Date comparison will simulate a blank field and a single tree to determine the effect of
a tree’s cooling ability on its surrounding when only climate data changes (Figure
3.19).

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Figure 3. 19 With or without tree simulation diagram in date comparison
3.2.2 Building shade analysis
Building shade simulation will compare the influence of trees on the surrounding
temperature in an area completely covered by building shadow, to study the proportion
of the influence of the shading effect of a tree on the cooling ability of a tree (Figure
3.20).
Figure 3. 20 Trees under the shade of buildings simulation diagram
N
N
N
N

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3.2.3 Transpiration analysis
Transpiration analysis will use the same simulation scenario as building shade analysis
to analyze whether the transpiration of trees has obvious influence on cooling ability of
a tree after excluding the influence of the shade effect of the tree (Figure 3.21).
Figure 3. 21 Trees under the shade of buildings simulation diagram
3.2.4 Tree location analysis
Tree location analysis simulates the cooling effect of trees located on the east, south,
west, north, southeast, northeast, southwest, northwest and northeast of the building.
Because it is time consuming to set up eight simulation scenes, the simulation scenes
of each of the four positions that do not affect each other are gathered into one
simulation scene, which saves time and does not affect the accuracy of the simulation
results (Figure 3.22).
N N

64
Figure 3. 22 Trees eight locations simulation diagram
3.2.5 Tree spatial analysis
Three scenarios will be simulated for the space analysis: tree canopy overlaps with each
other, canopy edges that connect with each other, and canopies that are separated by 15
feet (Figure 3.23). This will be used to analyze the influence of different spacing
between trees on the shading ability of tree canopy to provide cooling.
N
N
N

65
Figure 3. 23 Different space between trees simulation diagrams
3.2.6 Tree row directional analysis
For the directional analysis the tree rows are located on the south, east, north, west sides
of the building, to analyze the tree rows’ cooling ability (Figure 3.24).
Figure 3. 24 Different locations of tree rows diagram
N
N

66
3.3 Numeric Techniques
This section will introduce two research methods that will be used in the subsequent
full site analysis to analyze the average sidewalk UTCI value of each month in the site
throughout the year.
3.3.1 Monthly average climate date method
The purpose of this method is to save a lot of software simulation time. It takes a lot
of time and effort to run simulations throughout the year with ENVI-met on every
day of every month throughout the year. Therefore, by adding up the climate data of
every hour of every day in a month and averaging it, one can get the average climate
data of every hour of a day in that month, which is called average climate day of the
month. This method can not only save a lot of time, but also result in a small error
of the data. This section uses climate data for February as an example.
First, the air temperature data, humidity data and wind speed data found on the
Internet from February 1 to February 28 are entered into Excel, and then the average
temperature, humidity, and wind speed of each hour in these 28 days are calculated
as the climate data of the February Average Day (Figure 3.25) (Figure 3.26).

67
Figure 3. 25 February average climate data calculation
Figure 3. 26 February average climate data calculation (part)
Then, the obtained average climate data for February was input into ENVI-met to
facilitate the subsequent simulation of the thermal environment of the study scene
(Figure 27).

68
Figure 3. 27 February average climate data file in ENVI-met

69
3.3.2 Pixel counting method.
This method is used to study the average temperature of sidewalk in the full site
analysis. Since full site simulation takes a lot of time, a smaller preliminary
simulation is used here to introduce the method (Figure 3.28).
Figure 3. 28 Preliminary simulation UTCI diagram of Pixel counting method
After the UTCI distribution map is obtained under the no trees, existing trees, and
new trees conditions of full site analysis, the pixels of different colors in the
sidewalk area are counted. Pixels of different colors represent different UTCI values,
so the number and proportion of pixels of various colors can be counted in excel, and
the corresponding UTCI values of different pixels can be brought in to calculate the
average UTCI value within the scope of the sidewalk, and the thermal environment

70
of the site can be evaluated at the same time.
First, the simulated UTCI image is imported into Photoshop, then the non-sidewalk
area is removed in Photoshop, leaving the sidewalk area. At the same time, since
there are many miscellaneous colors in the original pattern, it is necessary to use the
Index color function in Photoshop to reduce the color types of the pattern to 15, to
facilitate the subsequent statistics of the proportion of different colors. Finally, the
UTCI pixel image of the sidewalk area is captured.
Then, the obtained image is imported into pixel counter software (2024/04/05,
https://townsean.github.io/canvas-pixel-color-counter/) to analyze the number of
pixels in each color, and pixels in different colors represent different temperatures
(Figure 3.29).
Figure 3. 29 Sidewalk area pixel counts image

71
By importing the UTCI value and proportion represented by different color pixels into
excel, the average UTCI value within the scope of the sidewalk can be calculated (Figure
3.30).
Figure 3. 30 Sidewalk area average UTCI calculation
3.4 Full site study
The full site study refers to using the entire block for simulation. There are three
scenarios of tree planting schemes: no trees, existing trees, and proposedtrees
respectively, in the coldest, hottest, and monthly UTCI diagrams. They will be used
to calculate the average temperature of the sidewalk area. The monthly cooling
performance of trees under the two schemes of existing trees and proposed trees will
be studied to determine in which months the proposed trees scheme performs better,
to judge whether the proposed tree planting scheme can provide better thermal
comfort performance for the surrounding environment throughout the year.
One additional simulation will be done just for the hottest day of the year with an

72
alternative tree layout.
3.5 Summary
This section introduced how to establish base research model in ENVI-met, the two
research questions of this paper, the preliminary study of six factors affecting the
cooling ability of trees, the two critical numeric techniques, and the full site study of
three different scenarios. All the methods mentioned in this chapter will play a very
important role in the subsequent preliminary study and full site analysis. Chapter 4
will have the results of the preliminary simulations, and Chapter 5 with have the
results of the full site simulations.
Chapter 4 preliminary simulations to be conducted are shown in the sheet below
(Table 3.1):
Table 3. 1 Preliminary simulation sheet

73
Chapter 5 full site simulations to be conducted is shown in the sheet below (Table
3.2):
Table 3. 2 Full site simulation sheet

74

75
CHAPTER FOUR PRELIMINARY STUDIES
The newly designed tree scheme will use the same number and size of the existing trees
on the site. The locations of the trees will be based on the preliminary studies discussed
later in this chapter. UTCI is the metric used. The universal thermal climate index
(UTCI) is a human biometeorology parameter that is used to assess the linkages
between outdoor environment and human well‐being. Thermal comfort indices describe
how the human body experiences atmospheric conditions, specifically air temperature,
humidity, wind, and radiation. (https://climateadapt.eea.europa.eu/en/metadata/indicators/thermal-comfort-indices-universalthermal-climate-index-1979-2019, 2024/03/01)
This chapter will discuss the results and analysis of preliminary studies using ENVImet to investigate the cooling capacity of trees affecting the surrounding air and will
demonstrate this by comparing UTCI indices at the height of 1.4m surface. It includes
the analysis of the planting strategies conducive to improving tree cooling ability from
five perspectives: date, location, building shade, transpiration, and spacing. Many
simulations were done to learn more about the relationship of the trees to the UTCI
(Table 4.1).
Chapter 4 has the results of the preliminary simulations, and Chapter 5 has the results
of the full site simulations.

76
Table 4. 1 Preliminary simulation sheet
4.1 Date Comparison (No tree comparing with one tree).
This section will compare the relevant UTCI data in the simulated area under two
climate conditions: the hottest day of the year and the coldest day of the year (for 10
am, 12 pm, and 2 pm) in a completely simulated environment with only one tree (Figure
4.1). Because the cooling capacity of trees only provides benefits in hot weather and
has a negative effect in cold weather, this simulation is to find out whether the tree can
really cool the surrounding area and whether or not the trees’ ability to cool the
surrounding area is more beneficial in hot weather than harmful in cold weather.

77
Figure 4. 1 No tree-no site and one tree-no site modeling in ENVI-met.
4.1.1 Hottest day comparison
The cooling ability of a tree is obvious at 10am on the hottest day (Figure 4.2). The
UTCI drops in the center of the tree reached 8.88 degrees Celsius, and the UTCI drops
in the area covered by the shadow area is 2.66 degrees Celsius.
Figure 4. 2 Hottest day UTCI comparison at 10am
The cooling ability of a tree is obvious at 12pm on the hottest day (Figure 4.3). The
UTCI drops in the center of the tree reached 9.64 degrees Celsius, and the UTCI drops
in the area covered by the shadow area is 2.89 degrees Celsius.
N N

78

Figure 4. 3 Hottest day UTCI comparison at 12pm
The cooling ability of a tree is significant at 2pm on the hottest day (Figure 4.4). The
UTCI drops in the center of the tree reached 9.95 degrees Celsius, and the UTCI drops
in the area covered by the shadow area is 2.98 degrees Celsius.

Figure 4. 4 Hottest day comparison at 2pm
On the hottest days, the UTCI in the center of the tree decreased by about 9-10 degrees
Celsius, while the UTCI in the shadow area of the tree decreased by about 2.5-3 degrees
Celsius. It is worth noting that the shade of the tree on the hottest day has a cooling

79
effect on the surrounding area of about 15 feet.
4.1.2 Coldest day comparison
The cooling ability of a tree is more significant at 10am on the coldest day comparing
with 10am on the hottest day (Figure 4.5). The UTCI drops in the center of the tree
reached 11.06 degrees Celsius, and the UTCI drops in the area covered by the shadow
area is 2.21 degrees Celsius. However, the shading area provided by the tree on the
coldest day is significantly smaller than that provided by the tree on the hottest day.
Because it can be found that the orange area around the trees on the hottest day is
significantly larger than the orange area around the trees on the coldest day, this may
be related to the Angle of the sun and the amount of radiation (Figure 4.4, Figure 4.5).
Figure 4. 5 Coldest day UTCI comparison at 10am
The cooling ability of a tree is more significant at 12pm on the coldest day comparing
with 12pm on the hottest day (Figure 4.6). The UTCI drops in the center of the tree
reached 9.89 degrees Celsius, and the UTCI drops in the area covered by the shadow

80
area is 1.98 degrees Celsius. However, the shading area provided by the tree on the
coldest day is significantly smaller than that provided by the tree on the hottest day.
Figure 4. 6 Coldest day comparison at 12pm
The cooling ability of a tree is almost the same at 2pm on the coldest day comparing
with 2pm on the hottest day (Figure 4.7). The UTCI drops in the center of the tree
reached 9.99 degrees Celsius, and the UTCI drops in the area covered by the shadow
area is 2 degrees Celsius. However, the shading area provided by the tree on the coldest
day is significantly smaller than that provided by the tree on the hottest day.
Figure 4. 7 Coldest day comparison at 2pm

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On the coldest days of the year, the UTCI in the central area of the trees decreased by
10-11 degrees Celsius, which is slightly higher than the value on the hottest days. The
UTCI in the shadow area of trees decreased by about 2 degrees Celsius, which was
slightly lower than the value on the hottest day, but it is worth noting that the influence
area of the shadow area of trees on the surrounding UTCI in the coldest day is
significantly smaller than that on the hottest day.
4.1.3 Graph of hottest and coldest day comparison
During the hottest day, when the maximum air temperature is higher, the cooling ability
of trees is also stronger (Figure 4.8, Figure 4.9). Although the cooling value of the
central area of the tree is stronger on the coldest day than on the hottest day, because
the area affected by the cooling ability of the tree on the hottest day is significantly
larger than that on the coldest day, the tree reduces higher average UTCI of the
surrounding area on the hottest day than on the coldest day, indicating that the positive
benefits of tree cooling in hot days are greater than the side effects in the cold days. As
a result, the tree’s benefit is bigger than its harm.

82
Figure 4. 8 Hottest and coldest day reduced UTCI comparison 1.
Figure 4. 9 Hottest and coldest day reduced UTCI comparison 2.
4.1.4 Graph of each month comparison
The cooling ability of the tree center did not change significantly with month, but the
cooling ability of the surrounding area changed significantly. The higher the average
8.88 9.64 9.95
11.06
9.89 9.99
0
2
4
6
8
10
12
10am 12pm 2pm
UTCI (°C )
time
Reduced UTCI in the
Center of the Tree
Hottest Day Coldest Day
2.66 2.89 2.98
2.21 1.98 2
0
0.5
1
1.5
2
2.5
3
3.5
10am 12pm 2pm
UTCI (°C )
time
Reduced UTCI in the Shadow
Area of the Tree
Hottest Day Coldest Day

83
temperature of the month, the greater the cooling of the trees to the surrounding ten feet
(Figure 4.10).
Figure 4. 10 Each month reduced UTCI comparison.
8.89 9.22 9.13 9.09 9.11 9.14 9.03 8.98 8.91 8.99 9.02 9.12
0.5 0.4 0.7 0.9 1.1 1.2 1.3 1.5 1.1 0.9 0.7 0.5
0
1
2
3
4
5
6
7
8
9
10
Jan. Feb. Mar. Apr. May. Jun. Jul. Aug. Sep. Oct. Nov. Dec.
Tree's cooling effect in different area
Max Differentiation Surrounding Area Differentiation (10feet)

84
4.2 Building Shade Comparison
This section will analyze the relationship between a tree's cooling capacity and building
shade, simulating how the tree's cooling capacity will be affected at 10am, 12pm and
2pm on the hottest and coldest days of 2022, when the tree is completely within the
building shade. The simulation results are based on the air UTCI value of 1.4m height.
The building shade comparison is also useful in learning how the software analyzes the
effects of a shadow (tree) in a shadow (building) (Figure 4.11).
Figure 4. 11 Build shade with a tree and without a tree comparison.
4.2.1 Hottest day comparison
It can be found that after the tree being completely blocked by building shadow, the
ability of the tree to reduce the UTCI of the surrounding environment becomes
significantly weaker. The UTCI difference between the central area of the tree and the
surrounding area is about 0.8 to 1.5 degrees Celsius, which is significantly lower than
N N

85
the UTCI difference of 8.88 degrees Celsius between the central area of the tree and the
surrounding area when the tree is fully exposed to the sun in the first section, indicating
that the cooling ability of the tree decreases significantly after shielded by the building
shadow because its shading ability disappeared (Figure 4.12).
Figure 4. 12 Hottest day comparison at 10am
The same result is evident at 12 noon. The UTCI difference between the central area of
the tree and the surrounding air is about 0.8 to 1.7 degrees Celsius, which is
significantly lower than the 9.64 degrees Celsius obtained at the same time point in
section 1, which also indicates that the cooling ability of the tree in the shadow of
buildings is significantly reduced compared with exposing to the sun (Figure 4.13).

86
Figure 4. 13 Hottest day comparison at 12pm
The same result is evident at 2pm. The UTCI difference between the central area of the
tree and the surrounding air is about 0.8 to 1.7 degrees Celsius, which is significantly
lower than the 9.95 degrees Celsius obtained at the same time point in section 1, which
also indicates that the cooling ability of the tree in the shadow of buildings is
significantly reduced compared with exposing to the sun (Figure 4.14).
Figure 4. 14 Hottest day comparison at 2pm
Through these three sets of simulations, it can be found that on the hottest day of the
year, when the tree is completely in the shadow of the building, the reduction of UTCI

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is reduced from about 9.75 degrees Celsius to about 1.3 degrees Celsius, the decrease
in the cooling capacity of the tree is about 87%.

88
4.2.2 Coldest day comparison
It can be found that after the tree being completely blocked by building shadow, the
ability of the tree to reduce the UTCI of the surrounding environment becomes
significantly weaker. On the coldest days, the UTCI in the center of the tree and the
surrounding temperature barely changed, which is significantly lower than the UTCI
difference of 11.06 degrees Celsius between the central area of the tree and the
surrounding area when the tree is fully exposed to the sun in the first section, indicating
that the cooling ability of the tree decreases significantly after shielded by the building
shadow because its shading ability disappeared (Figure 4.15). And the decrease in
cooling ability of the tree is significantly greater than the hottest day.
Figure 4. 15 Coldest day comparison at 10am
The same result is evident at 12 noon. The UTCI in the center of the tree and the
surrounding temperature barely changed, which is significantly lower than the 9.89

89
degrees Celsius obtained at the same time point in section 1, which also indicates that
the cooling ability of the tree in the shadow of buildings is significantly reduced
compared with exposing to the sun (Figure 4.16).
Figure 4. 16 Coldest day comparison at 12pm
The same result is evident at 2pm. The UTCI in the center of the tree and the
surrounding temperature barely changed, which is significantly lower than the 9.99
degrees Celsius obtained at the same time point in section 1, which also indicates that
the cooling ability of the tree in the shadow of buildings is significantly reduced
compared with exposing to the sun (Figure 4.17).

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Figure 4. 17 Coldest day comparison at 2pm
Through these three sets of simulations, it can be found that on the coldest day of the
year, when the tree is completely in the shadow of the building, the reduction of UTCI
is reduced from about 10.5 degrees Celsius to about 0.7 degrees Celsius, the decrease
in the cooling capacity of the tree is about 94%.
4.2.3 Graph of cooling ability reduction comparison
There is a huge difference in the cooling ability of the tree when it is fully exposed to
the sun and when it is completely in the shadow of a building. Building shadows
significantly reduce the cooling effect of the tree, suggesting that a tree reduces
surrounding temperature mainly by providing shade. At the same time, it can be found
that in cold weather, the reduction degree because of building shade on the cooling
ability of a tree is larger, indicating that a tree relies more on the shade provided by
itself to reduce temperature in cold weather, and the proportion of a tree cooling by
other means is lower than that in hot weather (Figure 4.18).

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Figure 4. 18 Cooling ability reduction comparison
4.3 Transpiration
This section mainly studies the influence of transpiration effect on cooling ability of a
tree. As for how to study the transpiration effect of a tree, there are many methods.
Because the cooling capacity of trees is mainly provided by two ways, the first is
canopy shading of trees, and the second is transpiration of trees. If the control variable
method is used to exclude the influence of tree shading on the surrounding air
temperature, the influence of tree transpiration on the surrounding environment can be
obtained. In the previous section, when studying the influence of building shadow on
the cooling ability of trees, in the simulations conducted, the shading effect of trees is
excluded. Therefore, considering that the previous section conducted a temperature
difference experiment with or without a tree under the shadow of buildings, the results
obtained in the previous section can be directly used to analyze the transpiration effect
87.10% 87.00% 87.40%
95.00%
93.60% 93.30%
82.00%
84.00%
86.00%
88.00%
90.00%
92.00%
94.00%
96.00%
10am 12pm 2pm
percentage
time
Cooling ability reduction because of building shade
Hottest day Coldest day

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of a tree (Figure 4.19).

Figure 4. 19 2D map of building shade with and without a tree
4.3.1 Hottest day comparison
Because the temperature at the center of the tree in the figure below is 1.1 degrees
Celsius lower than the same UTCI without the tree, and because the tree is completely
in the shadow of the building, the cooling effect of the tree is not provided by the
shading effect of the tree (Figure 4.20). So, this 1.1-degree Celsius reduction in UTCI
is provided by other cooling effects of the trees. Since a tree does not have the ability
to affect the wind environment and cause the surrounding temperature to drop, the
cooling effect here is mainly provided by the transpiration effect of trees.
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Figure 4. 20 Hottest day comparison at 10am
According to the same reason, through the analysis in the previous section, it can be
found that the cooling capacity provided by a tree at 10am, 12pm, and 2pm on the
hottest day of the year is 1.1 degrees Celsius, 1.16 degrees Celsius, and 0.99 degrees
Celsius, respectively, in the central area of the tree.
4.3.2 Coldest day comparison
The method also works on the coldest days. However, through comparative analysis, it
is found that under the temperature conditions in winter, the presence or absence of aa
tree in the shadow of buildings has almost no influence on the temperature change of
the surrounding environment (Figure 4.21).
By comparison, the cooling effect of tree transpiration in winter is very weak, about 0.5
degrees Celsius. This indicates that the transpiration effect of trees is significantly
stronger in hot weather than in cold weather, and this conclusion is consistent with the

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background information about the change of tree transpiration intensity in Chapter 1.
Figure 4. 21 Coldest day comparison at 10am
4.3.3 Transpiration effect comparison
It is not difficult to find that transpiration in hot weather can significantly reduce the
surrounding temperature, while transpiration in cold weather has no obvious cooling
effect (Figure 4.22). At the same time, because trees reduce the surrounding
temperature in cold weather is a negative effect of their cooling ability, the simulation
results indicate that the positive effect of transpiration is significantly stronger than its
negative effect.
For most people, the typical threshold for detecting a temperature change is around 0.5
to 1 degree Celsius. Trees transpiration cooling effect on surrounding UTCI during
hottest day and coldest day just near 0.5 to 1 degree Celsius, which means the body can
just feel the influence of tree transpiration of surrounding temperature, but the effect is

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not obvious and might not change the person’s assessment of thermal comfort on the
surrounding environment.
Figure 4. 22 Transpiration effect comparison
1.10 1.16
0.99
0.41 0.46 0.43
0.00
0.20
0.40
0.60
0.80
1.00
1.20
1.40
10am 12pm 2pm
UTCI (°C)
Time
Transpiration cooling effect on UTCI
Hottest day Coldest day

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4.4 Tree Spatial Location Comparison.
This section will simulate the cooling effect of trees on the UTCI of the surrounding
area in eight locations around the building (including east, south, west, north, northeast,
southeast, northwest, and southwest) at three time points of sunrise time, 12pm, and
sun set time during the hottest and coldest days of the year. By comparing the UTCI
decline of trees at different locations on the hottest and coldest days, the locations with
the greatest positive benefit of trees to the surrounding thermal environment on the
hottest and coldest days will be analyzed respectively. At the same time, by analyzing
the location of the shadow of the building, the location of the tree cooling benefit can
also be analyzed throughout the year.
Because trees provide shade is the most important way for trees to cool surrounding air,
analyzing whether trees at different locations can provide more shade during daylight
hours will be the main method for determining whether trees at that location can more
effectively reduce the UTCI value of the surrounding environment.
In this preliminary study, the height of the building in the model is the same as that of
the highest building in the research site, which is 148 feet (45 meters), so that the
simulation results of the preliminary study are more consistent with the actual situation

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of the subsequent full site simulation (Figure 4.23).
Figure 4. 23 Trees in different eight locations surrounding a building.
4.4.1 Hottest day comparison
At sunrise, the trees on the west, north, and northwest sides of the building are
completely within the shadow of the building, so the trees in these three locations do
not provide more shade currently than the trees in other locations, and the cooling effect
is less. Trees located on the southwest and northeast sides provide more shade, so trees
in these two locations cool down better (Figure 4.24).

N N

98
Figure 4. 24 Hottest day sun rise time comparison (7 am)
At noon, it can be found that the trees at the north, east and northeast sides of the
building are under the shadow of the building, so their cooling effect is poor. While the
trees at the other five positions are not affected by the shadow of the building, the
shadow area provided by themselves is not very different, so the cooling ability of the
trees at the other five positions is not very different, which is about 4-4.5 degrees
Celsius in UTCI (Figure 4.25).
Figure 4. 25 Hottest day noon time comparison (12 pm)
At sunset, the trees on the south, east, and southeast sides of the building are in the
shadow of the building and have less cooling capacity, while the trees on the
southwest and northeast sides of the building provide more shading area, so the trees
in these two locations have a better cooling capacity of the surrounding environment
(Figure 4.26).

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Figure 4. 26 Hottest day sun set time comparison (17 am)
After analyzing the building shadow throughout the day on the hottest day, the E tree
located on the southwest side of the building was not affected by the building shadow
throughout the day, so the tree in this position has the best cooling effect on the day,
while the tree located on the northeast side of the building spent the longest time under
the building shadow throughout the day and has the worst cooling effect (Figure 4.27).
At the same time, point A and point D near point E are not affected by the building
shadow during the period of maximum sunshine, and their cooling capacity is second
only to point E. However, because point A has less influence on the building shadow
when the average temperature is higher, its cooling capacity is slightly stronger than
point D. By analogy, points F and H are better than points B and C, while point F is
slightly better than point H, and point B is slightly better than point C (Figure 4.27).

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Figure 4. 27 Hottest day Building Shadow Analysis
One could study all the hours from sunrise to sunset to get a more precise numeric value.
Also, for future study, one could try calculating the best overall tree location based on
the height of the building. The technique used gives a rough indication of what locations
make good sense but is not optimized.
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4.4.2 Coldest day comparison
On the coldest day of the year, the cooling effect provided by the shadow of trees may
be negative. Therefore, it is not only necessary to analyze the relationship between the
trees at eight locations and the building shadow on that day, but also to compare the
UTCI reduction value of the trees at each location with the value on the hottest day, so
as to comprehensively analyze the improvement of the surrounding thermal
environment of the trees under different temperature conditions.
At sunrise, the trees on the west, north, and northwest sides of the building are
completely within the shadow of the building, so the trees in these three locations do
not provide more shade currently than the trees in other locations, and the cooling effect
is less. Trees located on the southwest and northeast sides provide more shade, so trees
in these two locations cool down better (Figure 4.28). The result is the same as when
the sun rises on the hottest day.

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Figure 4. 28 Coldest day sun rise time comparison (8 am)
At noon, it can be found that the trees at the north, east and northeast sides of the
building are under the shadow of the building, so their cooling effect is poor. While the
trees at the other five positions are not affected by the shadow of the building, the
shadow area provided by themselves is not very different, so the cooling ability of the
trees at the other five positions is not very different, which is about 5-5.5 degrees
Celsius in UTCI (Figure 4.29). Although the cooling capacity of the central area of the
tree is slightly stronger on the coldest day than on the hottest day, the shadow area
provided by the tree is much smaller than that on the hottest day, so the negative effect
of the tree on the cold day is weaker than the positive effect on the hot day.
Figure 4. 29 Coldest day noon comparison (12 pm)
At sunset, the trees on the south, east, and southeast sides of the building are in the
shadow of the building and have less cooling capacity, while the trees on the
southwest side of the building provide more shading area, so the tree in this location

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has a better cooling capacity of the surrounding environment (Figure 4.30).
Figure 4. 30 Coldest day sun set time comparison (17 am)
After analyzing the building shadow throughout the day on the coldest day, the result
of the tree’s cooling effect is similar with the result on the hottest day. The cooling
capacity of trees is ranked from strong to weak as E, A, D, F, H, B, C, and G (Figure
4.31).
Figure 4. 31 Coldest day Building Shadow Analysis
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However, according to the Date comparison in the last section, it can be found that
although the temperature difference between the central area of trees and the
surrounding area is bigger in the coldest day than in the hottest day, the trees in the
hottest day can provide more shading area, so they have a cooling effect on the larger
surrounding area. Therefore, the positive cooling effect of trees in Los Angeles in hot
weather is greater than the negative cooling effect in cold weather.
4.4.3 Whole year comparison
Analysis of the relationship between building shade and tree positions throughout the
year yielded roughly the same results as on the coldest and hottest days. The cooling
capacity of trees is ranked from strong to weak as E, A, D, F, H, B, C, and G (Figure
4.32).
Figure 4. 32 Whole year Building Shadow Analysis
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105
Given that cooling trees has more positive benefits than harm, and that Los Angeles
spends more time in hot weather than cold weather throughout the year, the best
locations for trees around buildings in downtown Los Angeles are ranked from best to
worst as E, A, D, F, H, B, C, and G (Table 4.2, Table 4.3).
Table 4. 2 Tree best cooling location ranking sheet
Locatio
n (in
project)
Southwest West South Northwest Southeast North East Northeast
Ranking 1 2 3 4 5 6 7 8
Table 4. 3 Tree best cooling location ranking sheet 2
Location
(True)
South Southwest Southeast West East Northwest Northeast North
Ranking 1 2 3 4 5 6 7 8
4.5 Trees’ spacing analysis
In this section, the UTCI size comparison of tree species with medium trunk thickness,

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medium tree height, medium crown size, and dense crown and leaf density was studied
under three conditions of proper crown spacing, crown close to each other, and large
overlap of crowns, to analyze how to maximize the cooling ability of trees by tree
density arrangement (Figure 4.33, Figure 4.34, Figure 4.35).
Figure 4. 33 Tree canopy with spacing
Figure 4. 34 Tree canopy with no spacing
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107
Figure 4. 35 Tree canopy overlapping.
The simulations found that within the same study range, the mean temperature around
trees was lowest when their canopies did not overlap at all, the second lowest when
their canopies were close to each other, and the highest when their canopies
overlapped. This is due to the fact that when the trees are properly spaced, the shade
provided by the trees in the sun does not overlap with each other, resulting in a larger
area of shade for cooling. When the tree canopy overlaps over a large area, the
shadow area of the overlap is wasted, thus reducing the shadow area that can be
cooled (Table 4.4).
Table 4. 4 Three types of trees spacing UTCI comparison
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108
However, this study only simulated the effect of tree spacing on the cooling effect of
planting trees with dense canopies. However, when the tree canopy is medium or
sparse, overlapping the trees has a benefit. In this case, with a set number of trees, it is
best to space the trees so that they do not overlap.
4.6 Proposed Tree Layout
Based on the above analysis of the different factors affecting the cooling capacity of
trees, the original tree distribution can be redesigned to improve the cooling capacity of
trees.
After being counted, the species and number of trees are shown in the table below
(Figure 4.36). The new tree planting scheme mainly studies the thermal comfort
34.34
34.88
35.56
33.6
33.8
34
34.2
34.4
34.6
34.8
35
35.2
35.4
35.6
35.8
With spacing No spacing Overlaping
UTCI (°C)
Spacing Condition
Three types of trees spacing comparison

109
environment of the sidewalk area, so the new tree planting scheme only moves the trees
originally located around the building and the sidewalk area in the site, and the trees
located in the middle of the block retain their original positions because they will not
have a significant impact on the thermal environment of the sidewalk area.
Figure 4. 36 Tree types of list
The first is to place large shade trees with large canopies and high tree height in the
lower left corner of the building to provide the most shade area according to the priority
order of tree location derived from Section 4.4. The trees with medium crowns and
medium shade capacity are then distributed to the left and lower sides of the site, and
finally the landscape trees with the smallest crowns and least shade capacity are placed
on the upper and right sides, where they will be easily completely covered by the
building's shade. Second, the spacing between the trees should be moderate, so that the

110
trees are not too closely arranged so that their shadows overlap too much and reduce
the shade area of the trees, and at the same time, the trees are not too loose to plant more
trees in the location of the best cooling capacity and waste favorable space (Figure 4.37,
Figure 4.38).
Figure 4. 37 New trees layout 2D map
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Figure 4. 38 New trees layout 3D map
4.7 Summary
This chapter contained the analysis of five important factors affecting the cooling
ability of trees and draws five conclusions that can improve the cooling ability of trees
through several preliminary simulations (Table 4.5, Table 4.6).
1. The time factor affects the cooling ability of trees; trees can provide more
shade in summer, and their cooling ability is significantly greater than in
winter.
2. The cooling of trees is mainly provided by their shading ability, and the
shading of building shadows will significantly reduce the cooling ability of
trees, so it is necessary to make tree shadows and building shadows as far as

112
possible.
3. The transpiration of trees can also provide cooling ability, which is not
affected by the shading of buildings, but the cooling ability of trees'
transpiration is weaker than that of trees' shading, and the transpiration effect
of a small number of trees has little effect on the change of thermal
environment and the thermal comfort of human body.
4. The location of trees affects the cooling ability of trees. In the study site, trees
have the best cooling ability when they are located on the south side of the
building, while trees have worse cooling ability when they are closer to the
north side of the building.
5. Trees provide the best cooling capacity when their canopies are spaced, and
their canopies do not coincide.
Table 4. 5 Preliminary simulation result sheet 1

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Table 4. 6 Preliminary simulation result sheet 2

114
Chapter 4 has the results of the preliminary simulations, and Chapter 5 has the results
of the full site simulations (Figure 4.39)
Figure 4. 39 New trees layout 3D map

115
CHAPTER FIVE FULL SITE RESEARCH
This chapter will show the results of the simulation and analysis of three different
situations for the full site, including different times of the hottest and coldest days for
the entire 2022 year and the average climate conditions of each month in 12 months.
The three different situations include no trees planted on the site, the existing trees on
the site, and the new tree planting plan proposed after the conclusion of the research in
Chapter 4 (Figure 5.1, Figure 5.2, Figure 5.3). Most of the calculations are shown in
Appendix A. The UTCI value at 1.4m height in the sidewalk area of the site is compared
to study the improvement of the thermal comfort of the new tree planting scheme in the
sidewalk environment, as well as the improvement of the thermal comfort compared
with the existing tree planting scheme, and the improvement of the thermal comfort of
the trees in each month of the year (Figure 5.4).
Figure 5. 1 Full site no tree 3D model in ENVI-met.
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Figure 5. 2 Full site existing trees 3D model in ENVI-met.
Figure 5. 3 Full site new trees 3D model in ENVI-met.
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Figure 5. 4 UTCI thermal comfort standard
5.1 No trees vs existing trees vs new trees (Full site hottest day)
This section will introduce three conditions’ full site UTCI diagram comparisons and
the pedestrian area average UTCI analysis under these three conditions to study
whether the new tree planting scheme can improve the UTCI more effectively at the
full site level and reduce the pedestrian area average UTCI more effectively than the
existing tree planting scheme or the scheme without trees.
5.1.1 Three conditions UTCI comparisons
After the ENVI-met simulation, the UTCI human thermal comfort diagram was
obtained at 12pm on September 4, the hottest day in Los Angeles in 2022, under
the three conditions of no trees, existing trees, and new trees (Figure 5.5, Figure
5.6, Figure 5.7).

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Figure 5. 5 Full site no trees UTCI diagram.
Figure 5. 6 Full site existing trees UTCI diagram

119
Figure 5. 7 Full site new trees UTCI diagram
5.1.2 Pedestrian area average temperature analysis (12pm)
By using pixel counting website to calculate the number of pixels of different
colors in the pedestrian area, the average air temperature in the pedestrian area was
calculated according to different temperature conditions represented by different
colors, to compare the thermal comfort of UTCI (Figure 5.8).

120
Figure 5. 8 Three full site pixel comparison
The average temperature data in the Excel table is shown in the figure below (Table
5.1, Table 5.2, Table 5.3):
Table 5. 1 Full site no trees average temperature at 12pm

121
Table 5. 2 Full site existing trees average temperature at 12pm
Table 5. 3 Full site new trees average temperature at 12pm

122
At 12 noon on the hottest day of the year in Los Angeles, the UTCI value was 43.22
degrees Celsius, 40.71 degrees Celsius, and 38.51 degrees Celsius in the 1.4-meter level
within the sidewalk area under three conditions: no trees, existing trees, and new trees.
It not only shows that trees have a cooling effect on the surrounding environment, but
also that when the location of trees is changed without changing the number and species
of trees, the UTCI value in the sidewalk area decreases by 2.2 degrees Celsius, which
is consistent with the conclusion obtained in Chapter 4. All three conditions are in very
strong heat stress level.
5.1.3 Pedestrian area average temperature comparisons (10am, 12pm, 2pm)
It can be seen that in the hottest days of the whole year, the new tree planting scheme
has a better ability to improve the thermal environment in different time periods than
the existing tree planting scheme, and the higher the average temperature, the stronger
the cooling ability of the new tree planting scheme (Figure 5.9).

123
Figure 5. 9 Full site pedestrian area UTCI comparisons at 12pm (Hottest Day)
5.2 No trees vs existing trees vs new trees (Full site Coldest day)
In the coldest days of the whole year, the full site sidewalk area average UTCI is the
following: 23.42 degrees Celsius for no trees, 20.51 degrees Celsius for existing trees,
and 19.54 degrees Celsius for new trees. The new tree planting scheme can cool down
the surrounding area better than the existing tree planting scheme for 0.97 degree
Celsius. The trees’ shade on cool days is not beneficial, whereas it is on the hot days.
By comparing the hottest and coldest days’ results, the simulations show that the new
trees positive effect in summer is bigger than its negative effect in winter (Figure 5.10).
All three conditions are in no thermal stress level.
43.22
40.71
38.51
36
37
38
39
40
41
42
43
44
no trees existing trees new trees
UTCI (°C)
Three tree conditions
The average UTCI in Sidewalk area at 12pm (Hottest
Day)

124
Figure 5. 10 Full site pedestrian area UTCI comparisons at 12pm (Coldest Day)
5.3 Existing trees vs new trees (Full site monthly comparison)
This section will compare the average UTCI value of sidewalk area at 12 noon each
month under existing trees and new trees, and judge whether new trees can effectively
improve the thermal comfort of sidewalk area by comparing the two sizes, in which
months the new trees have a significant effect on the thermal environment, and in
which months the effect is not obvious. Only the final results are compared here. See
Appendix A for the detailed UTCI figure and temperature calculation process.
5.3.1 January comparison
The UTCI in the sidewalk area dropped from 24.81 degrees Celsius to 23.23 degrees
Celsius in January by 1.58 degrees Celsius. Both conditions are at no thermal stress
level (Figure 5.11).
23.42
20.51
19.54
17
18
19
20
21
22
23
24
no trees existing trees new trees
UTCI (°C)
Three tree conditions
The average UTCI in Sidewalk area at 12pm (Coldest
Day)

125
Figure 5. 11 Full site pedestrian area UTCI comparisons at 12pm (Jan.)
5.3.2 February comparison
The UTCI in the sidewalk area dropped from 25.92 degrees Celsius to 24.38 degrees
Celsius in January by 1.54 degrees Celsius. Both conditions are at no thermal stress
level (Figure 5.12).
24.81
23.23
22
22.5
23
23.5
24
24.5
25
existing trees new trees
UTCI (°C)
Trees condition
The average UTCI in Sidewalk area at 12pm (Jan.)

126
Figure 5. 12 Full site pedestrian area UTCI comparisons at 12pm (Feb.)
5.3.3 March comparison
The UTCI in the sidewalk area dropped from 28.36 degrees Celsius to 27.17 degrees
Celsius in January by 1.19 degrees Celsius. Both conditions are at moderate heat
stress level (Figure 5.13).
Figure 5. 13 Full site pedestrian area UTCI comparisons at 12pm (March)
25.92
24.38
23.5
24
24.5
25
25.5
26
26.5
existing trees new trees
UTCI (°C)
Trees condition
The average UTCI in Sidewalk area at 12pm (Feb.)
28.36
27.17
26.5
27
27.5
28
28.5
existing trees new trees
UTCI (°C)
Trees condition
The average UTCI in Sidewalk area at 12pm (March.)

127
5.3.4 April comparison
The UTCI in the sidewalk area dropped from 30.36 degrees Celsius to 28.87 degrees
Celsius in January by 1.49 degrees Celsius. Both conditions are at moderate heat
stress level (Figure 5.14).
Figure 5. 14 Full site pedestrian area UTCI comparisons at 12pm (Apr.)
5.3.5 May comparison
The UTCI in the sidewalk area dropped from 31.68 degrees Celsius to 30.32 degrees
Celsius in January by 1.36 degrees Celsius. Both conditions are at moderate heat
stress level (Figure 5.15).
30.36
28.87
28
28.5
29
29.5
30
30.5
existing trees new trees
UTCI (°C)
Trees condition
The average UTCI in Sidewalk area at 12pm (Apr.)

128
Figure 5. 15 Full site pedestrian area UTCI comparisons at 12pm (May)
5.3.6 June comparison
The UTCI in the sidewalk area dropped from 34.86 degrees Celsius to 33.55 degrees
Celsius in January by 1.31 degrees Celsius. Both conditions are at strong heat stress
level (Figure 5.16).
Figure 5. 16 Full site pedestrian area UTCI comparisons at 12pm (June)
5.3.7 June comparison
The UTCI in the sidewalk area dropped from 35.28 degrees Celsius to 33.87 degrees
31.68
30.32
29.5
30
30.5
31
31.5
32
existing trees new trees
UTCI (°C)
Trees condition
The average UTCI in Sidewalk area at 12pm (May)
34.86
33.55
32.5
33
33.5
34
34.5
35
existing trees new trees
UTCI (°C)
Trees condition
The average UTCI in Sidewalk area at 12pm (June)

129
Celsius in January by 1.41 degrees Celsius. Both conditions are at strong heat stress
level (Figure 5.17).
Figure 5. 17 Full site pedestrian area UTCI comparisons at 12pm (July)
5.3.8 August comparison
The UTCI in the sidewalk area dropped from 36.8 degrees Celsius to 34.81 degrees
Celsius in January by 2 degrees Celsius. Both conditions are at strong heat stress level
(Figure 5.18).
Figure 5. 18 Full site pedestrian area UTCI comparisons at 12pm (Aug.)
35.28
33.87
33
33.5
34
34.5
35
35.5
existing trees new trees
UTCI (°C)
Trees condition
The average UTCI in Sidewalk area at 12pm
(July)
36.8
34.81
33
34
35
36
37
existing trees new trees
UTCI (°C)
Trees conditon
The average UTCI in Sidewalk area at 12pm
(Aug.)

130
5.3.9 September comparison
The UTCI in the sidewalk area dropped from 36.66 degrees Celsius to 34.7 degrees
Celsius in January by 1.96 degrees Celsius. Both conditions are at strong heat stress
level (Figure 5.19).
Figure 5. 19 Full site pedestrian area UTCI comparisons at 12pm (Sep.)
5.3.10 October comparison
The UTCI in the sidewalk area dropped from 31.37 degrees Celsius to 29.47 degrees
Celsius in January by 1.96 degrees Celsius. Both conditions are at moderate heat
stress level (Figure 5.20).
36.66
34.7
33.5
34
34.5
35
35.5
36
36.5
37
existing trees new trees
UTCI (°C)
Trees condition
The average UTCI in Sidewalk area at 12pm
(Sep.)

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Figure 5. 20 Full site pedestrian area UTCI comparisons at 12pm (Oct.)
5.3.11 November comparison
The UTCI in the sidewalk area dropped from 27.44 degrees Celsius to 26.05 degrees
Celsius in January by 1.39 degrees Celsius. Both conditions are at moderate heat
stress level (Figure 5.21).
Figure 5. 21 Full site pedestrian area UTCI comparisons at 12pm (Nov.)
5.3.12 December comparison
The UTCI in the sidewalk area dropped from 25.64 degrees Celsius to 24.55 degrees
31.37
29.47
28.5
29
29.5
30
30.5
31
31.5
32
existing trees new trees
UTCI (°C)
Trees condition
The average UTCI in Sidewalk area at 12pm (Oct.)
27.44
26.05
25
25.5
26
26.5
27
27.5
28
existing trees new trees
UTCI (°C)
Trees condtion
The average UTCI in Sidewalk area at 12pm
(Nov.)

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Celsius in January by 1.09 degrees Celsius. Both conditions are at moderate heat
stress level (Figure 5.22).
Figure 5. 22 Full site pedestrian area UTCI comparisons at 12pm (Dec.)
It was found that although the new trees scheme can reduce the average temperature of
the sidewalk area to a certain extent, it cannot completely change the thermal stress
level of the side area. At the same time, it can be found that in downtown Los Angeles,
except for the absence of thermal stress in February, other months are affected by
thermal stress to varying degrees (Table 3). Therefore, the cooling ability of trees is
particularly important for the climate condition here.
Table 3 Full site all year-round thermal comfort stress level
25.64
24.55
24
24.5
25
25.5
26
existing trees new trees
UTCI (°C)
Trees condition
The average UTCI in Sidewalk area at 12pm
(Dec.)

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5.4 Another tree layout
This section simulates a personal scheme proposed by Professor Karen Kensek to
concentrate trees in areas exposed to sunlight as much as possible. She used the solar
path on June 21 to help her roughly decide where the tallest trees should go (on the
south for high azimuth angle), shorter trees on east and west, and more trees on west
than east side as the afternoons are hotter than the morning (Figure 5.23, Figure 5.24).

134
Figure 5. 23 Second layout
Figure 5. 24 Second layout sun path. Note that the taller trees were placed on the south where the sun’s
altitude is highest.
N

135
The result shows that this second layout has a better cooling effect than new trees layout
(Figure 5.25). More study in different climate zones and latitudes could be done to see
if an automatic tree layout algorithm could be determined for a site based on the solar
path and the daily temperatures.
Figure 5. 25 Two trees layout comparisons at 12pm (Hottest day.)
5.5 Reduced UTCI comparisons
According to the previous analysis, the UTCI value of the new trees decreased each
month compared to the sidewalk area with existing trees was obtained (Figure 5.26).
It can be found that the new trees scheme has obvious cooling effect from August to
October and considering that the average temperature in the study area is higher in
this March, the positive benefits of new trees are obvious. However, it can also be
found that the new trees scheme still has a relatively obvious negative cooling effect
38.51
37.41
36.8
37
37.2
37.4
37.6
37.8
38
38.2
38.4
38.6
new trees Second trees lay out
UTCI (°C)
Trees condition
Two trees layout comparisons at 12pm (Hottest
Day)

136
when the average temperature is the lowest in January and January. However,
combined with the annual data analysis, the positive return of new trees is still greater
than the negative return, which is in line with the previous experimental expectation.
Figure 5. 26 Reduced UTCI by using new trees scheme for each month.
5.6 Summary
This chapter compares the effect of the new tree planting scheme and the original
scheme on the UTCI value of 1.4m height of the sidewalk area in the coldest and
hottest days of the year and in each month of the year within the study site. The new
tree planting scheme obtained by applying a reasonable tree planting strategy can
effectively reduce the annual UTCI value and reduce the average air temperature by
1.5 degrees Celsius. At the same time, the cooling range of trees in summer is higher
than that in winter, indicating that the positive benefit of tree cooling is greater than
its negative benefit. A small number of trees are not enough to have a huge impact on
the thermal comfort level of the surrounding environment, but their impact on
1.58 1.54
1.19
1.49 1.36 1.31 1.41
1.99 1.96 1.9
1.39
1.09
0
0.5
1
1.5
2
2.5
UTCI (°C)
Time
Reduced UTCI

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temperature can be fully perceived by pedestrians (Table 5.4).
Table 5. 4 Full site all year-round simulation result
Reasonable tree planting strategies include avoiding the building shadow, planting
trees on the south side of the building as much as possible, maintaining proper tree
spacing, and choosing trees with dense canopies. The first two of these four
recommendations have a more pronounced impact on the cooling capacity of trees.

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CHAPTER SIX CONCLUSION AND FUTURE WORK
This chapter includes two parts: conclusion and future work. The conclusion includes
a review of the background information, methodology, preliminary simulations, and
full site simulation. The future work section contains the parts that can be improved and
the directions that can be extended if time and equipment conditions permit.
6.1 Conclusion
The conclusion part includes the summary content from chapter 1- 5. about background
information on the urban heat island and the use of trees to mitigate it, methodology of
the research, preliminary simulations about trees in ENVI-met, and a full site simulation
of a block in Los Angeles with existing and new tree patterns.
6.1.1 Background
The urban heat island effect has become a major problem in high-density cities, and
this problem will become worse with global warming and extreme temperature events.
Urban heat island effect also brings negative effects on the economy, society,
environment, and health of urban dweller. Among the factors leading to the urban heat
island effect is the amount of solar radiation in the urban area, the building density and
materials within the city, and human activity in the urban area (Figure 6.1).

139
Figure 6. 1 The Urban Heat Island Effect http://envirometro.org/view-share-urban-heat-island-effectinfographic/
Trees can be one part of mitigating urban heat island effects as they can block part of
the solar radiation and can change the urban microclimate by improving the ambient
humidity, air quality, and wind environment. Both effects can change the thermal
environment of the city, thus alleviating the urban heat island effect. At the same time,
in order to quantify the cooling ability of trees and analyze the factors that affect the
cooling ability of trees, a variety of software can be used, among which ENVI-met is a
good match to analyze the impact of trees on urban thermal environment due to its
detailed simulation and analysis (Figure 6.2).

140
Figure 6. 2 Cooling effect of trees, Zhonghua Guo, 2019.
There are three important factors affecting the cooling ability of trees: the
characteristics of the tree, the special conditions of the tree, and the climate type of the
area. And the most important conclusion of trees cooling ability is in hot climate a tree
can give about 3-6 °C cooling effect of the surrounding area.
Previous research has shown a positive impact of trees on the thermal environment of
sidewalks by providing shade, which is of great help to the subsequent full site analysis
of the thermal environment of sidewalks in Los Angeles downtown research area,
because it can provide a reference UTCI number for this study on the cooling amplitude
of trees on the sidewalk environment, it can be used to judge whether the results
obtained in the follow-up study are correct and reasonable.

141
Many of the research studies simulated the effect of trees in ENVI-met on a site at a
specific time. It would be useful to study the average monthly climate conditions of an
entire year. Then one can analyze the comprehensive impact of trees on the thermal
environment of the site for the whole year to check both the advantages and
disadvantages of the addition of trees. Unfortunately, the slow speed of simulation in
ENVI-met limited which time periods were evenly studied.
6.1.2 Methodology
A methodology was established to study a specific site in Los Angeles that included
establishing the base model, studying simulated trees in the software ENVIR-met,
creating two techniques to analyze the results from the simulation, and conducting a
full site study. (Figure 6.3).

142
Figure 6. 3 Methodology diagram
The research area is bounded by South Hope St. to the east, West Olympic Blvd to the
south, Flower St. to the west, and West 9th St to the north (Figure 6.4).
Figure 6. 4 Research area
One research object was to determine how to reduce the average UTCI of sidewalk area
in the study area by redistributing the position of trees while keeping the number and
species of trees in the study area unchanged (Figure 6.5).

143
Figure 6. 5 Species of trees
The two research questions are analyzing how many degrees the new tree scheme cools
down the average UTCI of a research sidewalk area compared to existing conditions,
and which months do the new trees scheme work better and which months do the new
trees scheme has a negative effect on research area.
The preliminary study of five factors affecting the cooling ability of trees which include
the date, the building shade, the transpiration of the tree, the location of the tree, and
spacing between the trees.
The two critical numeric techniques which are pixel counting method and average
climate date method to calculate the average UTCI of the sidewalk area. The pixel
counting method is obtain the UTCI image of the research area and then import the

144
image into pixel counter software (2024/04/05, https://townsean.github.io/canvaspixel-color-counter/) to analyze the number of pixels in each color, and pixels in
different colors represent different temperatures. By importing the UTCI value and
proportion represented by different color pixels into excel, the average UTCI value
within the scope of the sidewalk can be calculated (Figure 6.6, Figure 6.7).
Figure 6. 6 Sidewalk area pixel counts image.
Figure 6. 7 Sidewalk area average UTCI calculation

145
The average climate date method is to get the air temperature data, humidity data and
wind speed data found on the Internet from February 1 to February 28, and enter the
data into Excel, and then the average temperature, humidity, and wind speed of each
hour in these 28 days are calculated as the climate data of the February Average Day
(Figure 6.8).
Figure 6. 8 February average climate data calculation
The full site study of three different scenarios includes moving away all of the trees of
the research area, keeping the trees in existing condition, and replanting the trees in the
research area to analyze the difference of the average UTCI in sidewalk area. In the
future, it can be considered to screen out the UTCI data of corresponding coordinate
points in ENVI-met by writing Python programs, so as to obtain more accurate sidewalk
area average UTCI data than pixel counting. In addition, more tree schemes can be
designed to analyze the changes of different distribution modes of trees on the average
UTCI of sidewalk area in a more detailed manner, so as to achieve the purpose of
changing the thermal stress level of sidewalk area.

146
6.2 Analysis and Results
This section includes all the simulations of preliminary studies and the full site studies
and also includes the process of the UTCI calculations.
6.2.1 Preliminary studies
The preliminary analysis of five important factors affecting the cooling ability of trees
include the time, the shade of building, the transpiration of trees, the location of the
tree, and the spacing between the trees (Table 6.1).

147
Table 6. 1 Preliminary study

148
The time factor affects the cooling ability of trees; trees can provide more shade in
summer, and their cooling ability is significantly greater than in winter (Figure 6.9).
Figure 6. 9 Each month reduced UTCI comparison.
The cooling of trees is mainly provided by their shading ability, and the shading of
building shadows will significantly reduce the cooling ability of trees, so it is
necessary to make tree shadows and building shadows as far as possible (Figure 6.10).
8.89 9.22 9.13 9.09 9.11 9.14 9.03 8.98 8.91 8.99 9.02 9.12
0.5 0.4 0.7 0.9 1.1 1.2 1.3 1.5 1.1 0.9 0.7 0.5
0
1
2
3
4
5
6
7
8
9
10
Jan. Feb. Mar. Apr. May. Jun. Jul. Aug. Sep. Oct. Nov. Dec.
Tree's cooling effect in different area
Max Differentiation Surrounding Area Differentiation (10feet)

149
Figure 6. 10 Cooling ability reduction because of building shadows
The transpiration of trees can also provide cooling ability, which is not affected by the
shading of buildings, but the cooling ability of trees' transpiration is weaker than that
of trees' shading, and the transpiration effect of a small number of trees has little
effect on the change of thermal environment and the thermal comfort of human body
(Figure 6.11).
87.10% 87.00% 87.40%
95.00%
93.60% 93.30%
82.00%
84.00%
86.00%
88.00%
90.00%
92.00%
94.00%
96.00%
10am 12pm 2pm
percentage
time
Cooling ability reduction because of building shade
Hottest day Coldest day
1.10 1.16
0.99
0.41 0.46 0.43
0.00
0.20
0.40
0.60
0.80
1.00
1.20
1.40
10am 12pm 2pm
UTCI (°C)
Time
Transpiration cooling effect on UTCI
Hottest day Coldest day

150
Figure 6. 11 Transpiration effect comparison
The location of trees affects the cooling ability of trees. In the study site, trees have
the best cooling ability when they are located on the south side of the building, while
trees have worse cooling ability when they are closer to the north side of the building.
The rank of location can be seen in the table (Table 6.2)
Table 6. 2 Tree best cooling location ranking sheet 2
Location
(True)
South Southwest Southeast West East Northwest Northeast North
Ranking 1 2 3 4 5 6 7 8
Trees provide the best cooling capacity when their canopies are spaced, and their
canopies do not coincide when the number of trees is fixed. (Table 6.3).
Table 6. 3 Three types of trees spacing UTCI comparison

151
After all the preliminary simulations, the results are showed in the table (Table 6.4).
Table 6. 4 Preliminary simulation result sheet
34.34
34.88
35.56
33.6
33.8
34
34.2
34.4
34.6
34.8
35
35.2
35.4
35.6
35.8
With spacing No spacing Overlaping
UTCI (°C)
Spacing Condition
Three types of trees spacing comparison

152
And the new trees scheme is based on the strategies got from the preliminary
simulations (Figure 6.12). There are 92 trees to work with, the new trees scheme
first puts the high and big trees at the south corner of the building and put the small
trees at the north corner of the building to provide more shade area based on the
result of building shade simulation, then keeps a reasonable space between the trees
to avoid the overlap of the shadow of trees’ canopy based on the trees spacing
simulation.

153
Figure 6. 12 New trees layout 3D map
6.2.3 Site analysis
The effect of the new tree planting scheme and the original scheme on the UTCI value
of 1.4m height of the sidewalk area in the coldest and hottest days of the year and in
each month of the year within the study site was studied. A new tree planting scheme
obtained by applying a reasonable tree planting strategy can effectively reduce the
annual UTCI value and reduce the average air temperature by 1.5 degrees Celsius. At
the same time, the cooling range of trees in summer is higher than that in winter,
indicating that the positive benefit of tree cooling is greater than its negative benefit.
The study also found that a small number of trees are not enough to have a huge
impact on the thermal comfort level of the surrounding environment, but their impact
on temperature can be perceived by pedestrians (Figure 6.13).

154
Figure 6. 13 Reduced UTCI after using new trees scheme.
The results of the full site simulations show that the reduced UTCI ranges from about
1 to 2 degrees Celsius (Table 6.5).
Table 6. 5 Full site all year simulation result
1.58 1.54
1.19
1.49 1.36 1.31 1.41
1.99 1.96 1.9
1.39
1.09
0
0.5
1
1.5
2
2.5
UTCI (°C)
Time
Reduced UTCI

155
6.4 Future work
This section will cover future work that can be accomplished.
6.4.1 Wind analysis
Depending on the climate wind can be an important factor for urban heat island
mitigation. Subsequent studies can incorporate the effect of prevailing wind direction
and wind speed in the study area on the cooling ability of trees. Wind analysis after
full site analysis can be added to further fine-tune the position of trees to optimize the
wind environment of the study area (Figure 6.14).

156
Figure 6. 14 ENVI-met wind analysis.
6.4.2 Sky view factors
The Sky View Factor (SVF) is a quantitative measure used in urban climatology and
environmental studies to assess the exposure of a location to the sky. It represents the
ratio of the visible sky hemisphere as observed from a specific point on the Earth's
surface to the total possible visible sky hemisphere. In simpler terms, it describes the
proportion of the sky that is visible from a particular location (Dirksen, et al., 2019)
(Figure 6.15). The effects of changes in SVF factors on tree cooling could be
quantized.

157
Figure 6. 15 ENVI-met SVF analysis.
6.4.3 Climate zones
Different climate zones will affect the conclusions about the placement and benefits
of trees. This is because the different climate conditions can have different
combinations of air temperature, air humidity, and wind analysis, which can
significantly affect the cooling effect of trees.
6.4.4 More simulations studying tree canopy
Simulation of many scenarios were not done, because ENVI-met simulation takes a
lot of time. A full site simulation for 24 hours needs about 2 days to simulate on one
computer. For example, in location simulation, four positions that do not interfere
with each other are divided into a group, and eight groups of simulation will be
carried out separately if time permits. For example, in the tree canopy spacing
analysis, a variety of tree crowns with different thinning densities were simulated and

158
divided into dense, medium and sparse groups to draw more comprehensive
conclusions about tree planting spacing.
6.4.5 Bigger research area
One could study all the hours from sunrise to sunset to get a more precise numeric
value. Also, for future study, one could try calculating the best overall tree location
based on the height of the building. The technique used gives a rough indication of
what locations make good sense but is not optimized. The impact of the cooling
effects of trees on the heat island effect in downtown Los Angeles on a macro scale
could have been considered (Figure 6.16).
Figure 6. 16 ENVI-met big area analysis.

159
6.4.6 More trees analysis
More trees will be simulated to further study the effect of transpiration on the thermal
environment in the case of large trees. It can also study how many additional trees can
improve thermal comfort throughout Los Angeles, thereby mitigating the heat island
effect in Los Angeles (Figure 6.17).
Figure 6. 17 LA's Hot Spots Maps, NASA, 2018.
6.4.7 Automatic tree layout algorithm
An automatic tree layout algorithm could be determined for a site based on the solar
path, daily air temperatures, and daily air humidity. The use of AI has potential here.
6.4.8 Other issues beyond the tree shading component
• Is time more relevant to temperature or radiation absorption and storage.?
• Is there something else one can learn about placement that is not as well

160
known? For example, synergy between tree species?
• Does ground vegetation require less sunlight and garners benefits from being
in the shade of trees such that the combined ecosystem provides greater
benefit than shade alone?
6.4.9 Transpiration
At present, the research on transpiration is too shallow. A more detailed method to
analyze the factors that influence the transpiration of trees, as well as the specific
influence of transpiration on the surrounding humidity and temperature is needed.
6.4.10 Measurement of real conditions
Computer simulations might not capture nuances with real trees. It is necessary to use
temperature measuring instruments to detect the temperature of the real trees in the
site and the temperature of the sidewalk area. The obtained results can be compared
with the experimental results to analyze whether the experimental results are within a
reasonable range, and whether the simulation results of the software are accurate can
be judged at the same time.
6.4.11 Visualization of the 3d model
Visual simulations could also have been done. It would be useful to study the
pedestrian and occupants view by the new position of trees. ENVI-met is not
professional and prominent in the visualization of building models. Unreal Engine can
be used to render the visual rendering model of the thermal environment changes in

161
the sidewalk area after using different tree schemes, so that the display of
experimental results can be more vivid and specific.
6.4.12 The relationship between trees and building energy consumption
Since the improvement of the thermal comfort of trees to the external environment of
the building will change the cooling load inside the building to some extent, it can be
studied that the new tree scheme will reduce the cooling load of the internal building
throughout the year, so as to calculate the annual energy consumption savings.
Essentially new temperature values in ENVIR-met can be used to create custom
weather files that could be used in energy software. Climate change issues could also
be studies in this context and overall with the cooling impacts of trees.
6.4.13 More types of trees
At present, the simulation studies are all the results of ever green trees. In the future,
we can study the results of deciduous trees and ever green trees, so as to study
whether the condition of trees losing their leaves in winter helps to improve the side
effect of trees cooling down in cold weather. At a more nuanced level, specific
species of trees could be studied.
6.4.14 Safety issues from planting trees
Other characteristics of trees will also affect pedestrians, such as the impact of tree
roots on road surface, the threat of falling branches on pedestrian safety, the impact of

162
trees on surrounding fire conditions and other impacts, which are potential research
values of trees.
6.5 Summary
Trees can have an impact on the surrounding thermal environment and can reduce the
ambient air temperature. At the same time, time factors, the location of trees, the
spacing of trees planted, and the shading relationship between trees and buildings all
affect the cooling ability of trees. Trees cooled best when average temperatures were
higher, canopy shadows did not block each other, and trees were located on the
southern side of the building and less obscured by building shadows. By changing the
way the trees are planted, the new tree planting scheme for a specific block in
downtown Los Angeles can reduce the temperature 1.4m about the sidewalks by 1.75
degrees Celsius throughout the year compared to the existing tree layout. If every
block of the city can optimize the tree planting scheme, the urban heat island effect
problem will be improved to some extent.
The study suggests strategies to improve trees' cooling capabilities, including
changing their orientation and arrangement. At the same time, the impact of evergreen
trees on the thermal environmental comfort of the surrounding environment,
especially the sidewalk area, at different times of the year under the specific climatic
conditions of Los Angeles was analyzed. This can provide help for the planting of

163
street trees in Los Angeles Street areas and at the serve as a reference methodology
for other cities.

164
APPENDIX
The Appendix includes UTCI maps of existing and new trees for each month of the full
site from January through December. It also includes the average UTCI value of
existing trees and new trees sidewalk area in each month and its calculation process.
Table 1 All items of Appendix
Full site hottest day:

165
Figure A1 Full site no trees UTCI diagram
Figure A2 Full site existing trees UTCI diagram

166
Figure A3 Full site new trees UTCI diagram

167
Figure A4 Three full site pixel comparison

168
Table 2: Full site existing trees average temperature at 12pm
Table 3: Full site new trees average temperature at 12pm

169
Full site coldest day:
Figure A5 No trees full site coldest day 12pm

170
Figure A6 Existing trees full site coldest day 12pm
Figure A7 New trees full site coldest day 12pm

171
Table 1 No trees average temperature (Coldest day).
Table 2 Existing trees average temperature (Coldest day).

172
Table 3 New trees average temperature (Coldest day).

173
Full site January:
Figure A8 Existing trees full site (Jan at 12pm)
Figure A9 Existing trees sidewalk pixel (Jan at 12pm)

174
Table 4 Existing trees sidewalk average temperature (Jan at 12pm)
Figure A6 New trees full site (Jan at 12pm)

175
Figure A7 New trees sidewalk pixel (Jan at 12pm)
Table 5 New trees sidewalk average temperature (Jan at 12pm)
Full site February:
Figure A8 Existing trees full site (Feb. at 12pm)

176
Figure A9 Existing trees sidewalk pixel (Feb. at 12pm)
Table 6 Existing trees sidewalk average temperature (Feb. at 12pm)

177
Figure A10 New trees full site (Feb. at 12pm)
Figure A11 New trees sidewalk pixel (Feb. at 12pm)
Table 7 New trees sidewalk average temperature (Feb. at 12pm)
Full site March:

178
Figure A12 Existing trees full site (March. at 12pm)
Figure A13 Existing trees sidewalk pixel (March. at 12pm)

179
Table 8 Existing trees sidewalk average temperature (March. at 12pm)

180
Figure A14 New trees full site (March. at 12pm)
Figure A15 New trees sidewalk pixel (March. at 12pm)

181
Table 9 New trees sidewalk average temperature (March. at 12pm)
Full site April:
Figure A16 Existing trees full site (Apr. at 12pm)

182
Figure A17 Existing trees sidewalk pixel (Apr. at 12pm)
Table 10 Existing trees sidewalk average temperature (Apr. at 12pm)

183
Figure A18 New trees full site (Apr. at 12pm)
Figure A19 New trees sidewalk pixel (Apr. at 12pm)

184
Table 11 New trees sidewalk average temperature (Apr. at 12pm)
Full site May:
Figure A20 Existing trees full site (May at 12pm)

185
Figure A21 Existing trees sidewalk pixel (May at 12pm)
Table 11 Existing trees sidewalk average temperature (May at 12pm)

186
Figure A20 Existing trees full site (May at 12pm)
Figure A21 Existing trees sidewalk pixel (May at 12pm)
Table 11 Existing trees sidewalk average temperature (May at 12pm)
Full site June:

187

188

189

190
Full site July:

191

192

193

194
Full site August:

195

196

197
Full site September:

198

199

200
Full site October:

201

202
Full site November:

203

204

205
Full site December:
Figure A4 Existing trees full site (Dec. at 12pm)
Figure A5 Existing trees sidewalk pixel (Jan at 12pm)

206
Table 4 Existing trees sidewalk average temperature (Dec. at 12pm)
Figure A6 New trees full site (Dec. at 12pm)

207
Figure A7 New trees sidewalk pixel (Dec. at 12pm)
Table 5 New trees sidewalk average temperature (Dec. at 12pm)

208
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When using ChatGPT3.5, I first asked ChatGPT to generate key points by entering
keywords, then searched literature on the Internet according to the key points given by
ChatgPT, formed my own views and improved the content of arguments, and finally
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For example, ask ChatGPT 3.5: Give the mainly effects of Urban Heat Island. Then
ChatGPT 3.5 will give some key points like increased energy consumption, negative
health impacts, poor air and water quality, changes to the microclimate, economic stress.
Then for each point, enter the key words in google scholar such as urban heat island,
energy consumption. And try to find some articles discussing about these points, then
paraphrase the content of the articles using our own words to generate a paragraph in
thesis.

209
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https://www.envi-met.com/microclimate-simulation-software/, 2024/01/22

Abstract (if available)

Abstract With the continuous development of cities, the scale of cities continues to expand, and the rapid increase of urban populations are followed by many urban environmental problems, such as urban heat island effects, urban air pollution, urban light pollution, and so on. Especially the urban heat island effect, increasing extreme heat in urban areas will create longer periods of unhealthy and dangerous conditions for humans and other species. Focusing on studying the thermal environment problems related to the urban heat island effect and the use of trees to mitigate the problem. Using ENVI-met to simulate different factors that can affect the cooling ability of trees which include the different time of the year, the building shade, transpiration, the location of the trees, and the spacing of trees are the main variables studied. Based on tree-planting strategies that can improve the cooling ability of trees obtained from the above tests, a new tree-planting scheme is designed, and the thermal comfort condition inside the new scheme can also be analyzed. Finally, the influence of the new planting scheme on the thermal environment in the sidewalk area of the study area compared with the existing tree planting scheme in different months of the year is compared, so as to evaluate whether the new tree planting scheme can alleviate the heat island effect in the study area. This study found that, for the area under consideration in Los Angeles, the new tree planting scheme can decrease the UTCI temperature by 2.2 degrees Celsius on the sidewalks on the hottest day of the year, 0.97 degrees Celsius on the coldest day of the year, and 1.52 degrees Celsius for the entire year comparing with the existing tree planting scheme.

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Asset Metadata

Creator Zhu, Yuzhou (author)

Core Title Mitigating the urban heat island effect: thermal performance of shade-tree planting in downtown Los Angeles

School School of Architecture

Degree Master of Building Science

Degree Program Building Science

Degree Conferral Date 2024-05

Publication Date 06/12/2024

Defense Date 06/11/2024

Publisher Los Angeles, California (original), University of Southern California (original), University of Southern California. Libraries (digital)

Tag ENVI-met,OAI-PMH Harvest,thermal environment,tree cooling effect,tree planting strategy,tree shading,Urban Heat Island (UHI),UTCI

Format theses (aat)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Kensek, Karen M. (committee chair), Brower, Anthony (committee member), Margulies, Esther (committee member)

Creator Email 1624809520@qq.com,yuzhouz@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-oUC1139963AK

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