Effects of vegetation on the urban thermal environment and climate adaptation a case study in hanoi

VIETNAM NATIONAL UNIVERSITY, HANOI VIETNAM JAPAN UNIVERSITY TRAN HUYEN CHI EFFECTS OF VEGETATION ON THE URBAN THERMAL ENVIRONMENT AND CLIMATE ADAPTATION: A CASE STUDY IN HANOI MAJOR

VIETNAM NATIONAL UNIVERSITY, HANOI

VIETNAM JAPAN UNIVERSITY

TRAN HUYEN CHI

EFFECTS OF VEGETATION ON

THE URBAN THERMAL ENVIRONMENT

AND CLIMATE ADAPTATION:

A CASE STUDY IN HANOI

MASTER’S THESIS

Hanoi, 2020

VIETNAM NATIONAL UNIVERSITY, HANOI

VIETNAM JAPAN UNIVERSITY

TRAN HUYEN CHI

EFFECTS OF VEGETATION ON

THE URBAN THERMAL ENVIRONMENT

AND CLIMATE ADAPTATION:

A CASE STUDY IN HANOI

MAJOR: CLIMATE CHANGE AND DEVELOPMENT

PLEDGE

I assure that this thesis is the result of my own research and has not been published

The use of other research’s results and other documents must comply with

regulations The citations and references to documents, books, research papers, and

websites must be in the list of references of the thesis

Author of the thesis

Tran Huyen Chi

ACKNOWLEDGMENTS

Being a student of Vietnam Japan University (VJU) and the Master’s Program in Climate Change and Development (MCCD) is one of, if not the, best experience of mine

There are a lot of people to whom I would like to express my great appreciation: my supervisor, Prof Hiroyuki Kusaka from the University of Tsukuba, and my sub-supervisor, Prof Phan Van Tan from Hanoi University of Science, for instructing and giving me advice for this final project, the professors from MCCD and other programs and universities for providing me a lot of useful knowledge throughout the course, Prof Nguyen Thi Kim Cuc from Thuyloi University for guiding my fieldwork group in Xuan Thuy and inspiring me a lot by her enthusiasm for her work, D&L Technology Integration and Consulting Joint Stock Company and Vietnam Meteorological and Hydrological Administration for providing me meteorological data, people on the Internet for helping me with coding, the sponsors for offering me scholarships, and of course, my family and friends for supporting and encouraging

me throughout my study Last but not least, I would also like to extend my thanks to the 3rd intake students of VJU, especially brothers and sisters in MCCD, who are young despite their ages, enthusiastic, and brilliant in their own way, for spending valuable time with me during two years I wish to continue to accompany them in the future

I could not mention all on this page, but everyone I met during this two-year course truly gave me great experience

TABLE OF CONTENTS

CHAPTER 1 INTRODUCTION 1

1.1 Overview of the study 1

1.1.1 Background 1

1.1.2 Significance of the study 2

1.1.3 Purpose of the study 3

1.1.4 Scope of the study 3

1.1.5 Research questions and hypotheses 3

1.2 Urban thermal environment 3

1.2.1 Urban area 3

1.2.2 Urban atmosphere 4

1.2.3 Urban heat island effect 6

1.2.4 Climate change and cities 8

1.3 Urban vegetation and its cooling effects 10

1.3.1 Cooling effects of vegetation 10

1.3.2 Review of studies on the cooling effect of vegetation 12

1.4 Climate of Hanoi 14

1.4.1 Background climate of Hanoi 14

1.4.2 Urbanization in Hanoi 16

1.4.3 Climate change in Hanoi 17

CHAPTER 2 METHODOLOGY 19

2.1 Data collection 19

2.1.1 Air temperature and humidity data 19

2.1.2 Vegetation cover data 22

2.2 Data analysis 23

2.2.1 Tools 23

2.2.2 Calculating air temperature and humidity 23

2.2.3 Calculating green fractions 24

2.2.4 Estimating correlations between green fraction and temperature, humidity and THI 27

CHAPTER 3 RESULTS AND DISCUSSIONS 29

3.1 Air temperature and humidity 29

3.1.1 Changes in air temperature and humidity during the day 29

3.1.2 Monthly mean air temperature and humidity 31

3.1.3 Mean daytime and nighttime air temperature and humidity 34

3.1.4 UHI magnitude 36

3.2 Green fractions 38

3.3 Correlations of the green fraction with air temperature, humidity, and THI 40

CHAPTER 4 RECOMMENDATIONS 49

CHAPTER 5 CONCLUSIONS 50

REFERENCES 53

APPENDIXES 56

LIST OF TABLES

Table 2.1 Stations and remarks 21

Table 3.1 Air temperature, relative humidity, and THI classified by green fraction

in June 2020 44

LIST OF FIGURES

Figure 1.1 Schematic of climatic scales and vertical layers found in urban areas

PBL – planetary boundary layer, UBL – urban boundary layer, UCL – urban canopy

layer (Oke, 2006, modified from Oke, 1997) 5

Figure 1.2 Illustration of the UHI effect 7

Figure 1.3 Reflection, transmission, and absorption of solar radiation by plant leaves (Brown and Gillespie, 1995, modified by Kong et al., 2017) 11

Figure 1.4 Temperature and precipitation in Hanoi 16

Figure 1.5 Map of Hanoi districts 18

Figure 2.1 Device PAS-OA318 20

Figure 2.2 Distribution of the study sites The x-axis refers to the longitudes, and the y-axis refers to the latitudes 22

Figure 2.3 Types of vegetation cover The top row refers to the side view of the plant The bottom row refers to the bird’s-eye view of the plant The orange highlights represent the area included in each type of cover 24

Figure 2.4 HSV colormap for OpenCV The x-axis represents H in [0,180), the y-axis 1 represents S in [0,255], the y-y-axis 2 represents S = 255, while keep V = 255 25

Figure 2.5 Sample of estimating green fraction at Ly Thuong Kiet 26

Figure 3.1 Changes in air temperature and humidity during the day in January 2020 Lines with the same style are the same category 30

Figure 3.2 Changes in air temperature and humidity during the day in June 2020 Lines with the same style are the same category 31

Figure 3.3 Monthly mean air temperature (left) and humidity (right) in January 2020 (Unit: °C and %) The x-axis refers to longitudes, and the y-axis refers to latitudes 32

Figure 3.4 Monthly mean air temperature (left) and humidity (right) in June 2020 (Unit: °C and %) The x-axis refers to longitudes, and the y-axis refers to latitudes 33

Figure 3.5 Mean daytime air temperature and humidity (top) and mean nighttime air temperature and humidity (bottom) in January 2020 (Unit: °C and %) The numbers of locations are the same as in Figure 3.3 The x-axis refers to longitudes, and the y-axis refers to latitudes 35

Figure 3.6 Mean daytime air temperature and humidity (top) and mean nighttime air temperature and humidity (bottom) in June 2020 (Unit: °C and %) The numbers of locations are the same as in Figure 3.4 The x-axis refers to longitudes, and the y-axis refers to latitudes 36

Figure 3.7 Air temperature and UHI between urban sites and outskirts in January (left) and June (right) 2020 at 1:00, 7:00, 13:00, and 19:00 “Outskirts” is the average of Son Tay and Ba Vi “UHI” is equal to “Urban” minus “Outskirts” 37

Figure 3.8 Green fractions in 23 locations (Unit: %) The x-axis refers to longitudes,

and the y-axis refers to latitudes 39

Figure 3.9 Hang Quat (left) and Ly Thuong Kiet (right) 40 Figure 3.10 Correlations of green fraction with air temperature, relative humidity,

and THI in January 2020 at 14 locations The shaded areas on the horizontal axes refer to the distribution of green fraction, and those on the vertical axes refer to the distributions of mean air temperature, relative humidity, and THI 41

Figure 3.11 Correlations of green fraction with air temperature, relative humidity,

and THI in January 2020 during daytime (top) and at nighttime (bottom) at 14 locations The shaded areas on the horizontal axes refer to the distribution of green fraction, and those on the vertical axes refer to the distributions of mean air temperature, relative humidity, and THI 42

Figure 3.12 Correlations of the green fraction with air temperature, relative

humidity, and THI in June 2020 at 20 locations The shaded areas on the horizontal axes refer to the distribution of green fraction, and those on the vertical axes refer to the distributions of mean air temperature, relative humidity, and THI 42

Figure 3.13 Correlations of the green fraction with air temperature, relative

humidity, and THI in June 2020 during daytime (top) and at nighttime (bottom) at 20 locations The shaded areas on the horizontal axes refer to the distribution of green fraction, and those on the vertical axes refer to the distributions of mean air temperature, relative humidity, and THI 43

Figure 3.14 Correlation models of the green fraction with mean air temperature,

humidity, and THI in June 2020 45

Figure 3.15 Correlation models of the green fraction with mean daytime air

temperature, humidity, and THI in June 2020 46

Figure 3.16 Monthly mean temperature and green fraction in the locations classified

by districts and categories in June 2020 48

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CHAPTER 1 INTRODUCTION 1.1 Overview of the study

1.1.1 Background

Hanoi, like other cities in the world, has been facing a rise in temperature due to urbanization and global climate change It is the capital of and the second largest city

in Vietnam, with an area of 3,359 km2 and a population of 7.52 million people (as of

2018, according to GSO, 2020) The city has more than a thousand-year long history, but it has significantly changed after the reformation – Doi Moi in 1986 Particularly, during the period of 1990 – 2010, the urban population in Hanoi grew from about 0.9 million to over 3 million people (Labbe, 2010), and the urban land area extended from

50 km2 to 190 km2 due to the conversion of a large area of natural lands and water cover into built-up areas (Doan et al., 2019) Land-use change and anthropogenic heat which have been found to have an impact on the urban heat island (UHI) effect (Kusaka et al., 2000) are factors that have been increasing the temperature in Hanoi (Doan et al., 2019) In addition, global warming is another factor that has been making the city warmer (Lee et al., 2017) As projected by Lee et al (2017), global warming along with land use change will further increase the temperature in the current urban areas in Hanoi by up to 2.1°C in the 2030s, not to mention the impact

of anthropogenic heat

Temperature rise matters since it may affect human comfort and health High temperatures are uncomfortable for residents, and they can be dangerous as they may cause illnesses such as heat cramps, heat stroke, and even death, especially for young children, the elderly, people with sickness, and people working outdoor Big cities are densely populated, so many people may be affected Illness due to excessive heat can be more serious in moderate regions where people are familiar to cool weather For example, a heatwave in 2003 caused 70,000 deaths in Europe (WHO, 2018 , and

a heat wave in 1995 caused 600 deaths in Chicago, US (EPA, 2017) However, even

in tropical cities like Hanoi, extremely or long-lasting hot days can still be a serious problem to public health if they exceed the adaptability of the residents It is recorded that in summer many people are hospitalized due to heat-related illnesses The

as in Hanoi most people travel outside by motorbikes, which increases the exposure

to the sun

Taking advantage of ecosystem services is one of the solutions that can be considered Many studies have shown the cooling effects of vegetation, including shading effects and transpiration (Lin and Lin, 2010; Lee et al., 2013; Georgescu et al., 2014) Plant leaves reduce radiation by reflection and transmission They also release water vapor

to the air, cooling the ambient air (Brown and Gillespie, 1995) Not only that, but green also makes the landscape more beautiful and visually pleasant For these reasons, increasing vegetation appropriately could be a good solution to temperature rise due to urbanization and climate change in Hanoi among various solutions Research on the effects of vegetation on temperature has been carried out in many cities such as Rosario in Argentina (Coronel et al., 2015), Lisbon in Portugal (Oliveira

et al., 2017), Shenzhen in China (Qiu et al., 2017), and Kuala Lumpur in Malaysia (Isa et al., 2018), showing the areas with more vegetation were cooler than the areas with less vegetation According to the study in China by Qiu et al (2017), the cooling effect of vegetation may even be better than water cover

Therefore, I would like to discover how green can actually help cool the air and provide better thermal comfort in Hanoi by evaluating the effects of vegetation on air temperature and humidity

1.1.2 Significance of the study

There has not been sufficient research on the effects of vegetation on the urban thermal environment in Hanoi so far; therefore, the present study could provide some understanding about this topic The results from the study are expected to provide a case study for the benefits of vegetation in climate change adaptation, contribute to

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policy-making in urban planning, especially in the context of climate change, and also contribute to green conservation in urban areas

1.1.3 Purpose of the study

The study aims to: (1) evaluate the effects of vegetation on air temperatures and humidity, and (2) propose some recommendations on how to take advantage of vegetation for an effective cooling effect

1.1.4 Scope of the study

The study focuses on the difference in air temperature and relative humidity at 23 urban sites with different green fractions in Hanoi Due to limited available data, the study time is January (winter) and June (summer) of 2020

1.1.5 Research questions and hypotheses

The research is to try to answer the following questions:

(1) How different are air temperature and relative humidity at locations with different green fractions?

(2) What is the correlation between green fraction and air temperature, relative humidity and THI (temperature humidity index)?

(3) How much green fraction is needed to tackle future warming in Hanoi?

It is expected that air temperature is lower, and relative humidity is higher at locations with much vegetation than locations with less vegetation Similarly, the green fraction

is expected to be negatively correlated to air temperature and THI, and positively correlated to relative humidity

1.2 Urban thermal environment

1.2.1 Urban area

There are many ways to define urban areas In Vietnam’s Law on Urban Planning (2009), an urban area is defined as an area with a high density of population mainly involved in non-agricultural sectors, and is the political, administrative, economic, cultural, or specialized center which plays a role in promoting the socio-economic development of a country, a territory or a locality Urban areas include cities, towns, and suburbs

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The urban area is a complex system with diverse elements, such as buildings, houses, and trees, which are considered as “the primary 3D urban unit” Urban elements are comprised of smaller flat units called facets, which are made of homogenous fabric They are roofs, walls, leaves, and road fragments The repetition of urban elements forms urban blocks, which combine in a similar pattern to create a neighborhood that

is usually characterized by specific land use, e.g industrial, residential, commercial, major parkland, and undeveloped land (Oke et al., 2017) Therefore, the city, basically, is the synthesis of different neighborhoods

1.2.2 Urban atmosphere

The urban atmosphere is divided into different scales based on horizontal scales and its vertical structure (Oke et al., 2017)

Climatic scales

● Microscale: atmospheric phenomena ranging from millimeters to 1 km

in length from minutes to hours, including surface-layer turbulence, flow around buildings and in urban canyons, and climate of urban elements, neighborhoods and parks

● Local-scale: atmospheric phenomena which have horizontal dimensions from 100 m to 50 km, lasting less than 1 day, e.g land/sea, mountain/valley winds, and urban heat island

● Mesoscale: atmospheric phenomena occurring throughout the city, with horizontal dimensions from 20 to 200 km, e.g urban heat island, country breezes, sea breezes, valley winds, squall lines, and thunderstorms

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Figure 1.1 Schematic of climatic scales and vertical layers found in urban areas

PBL – planetary boundary layer, UBL – urban boundary layer, UCL – urban

canopy layer (Oke, 2006, modified from Oke, 1997)

Vertical structure

The atmospheric boundary layer (ABL) is an atmospheric layer of 100 to 3000 m in thickness that is in direct contact with the Earth’s surface, so it is affected by roughness, thermal mixing and moisture and air pollutants from the Earth's surface The upper atmosphere is not affected by the Earth's surface, so it is called the free atmosphere Part of ABL in a large city is called the urban boundary layer (UBL) During the day, the heated surface creates the thermal soaring due to buoyancy that brings the surface influences to the top of the UBL floor, stopped by capping inversion (the statically stable layer) The lowest 10% of ABL is the surface layer, the rest is called a mixing layer (ML, potential temperature, water vapor, wind speed and direction are almost uniform in height)

At night, the earth's surface is cooled to create an air layer of 200 – 400 m thick to the ground called a nocturnal boundary layer (NBL) From the upper NBL to the top

of the ABL during the day is the residual layer - an atmosphere layer that has been preserved since the afternoon

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The surface layer is the closest to the ground so it is most affected by the surface influences The urban surface layer consists of two layers: the inertial sublayer (ISL) and the roughness sublayer (RSL) The urban canopy layer (UCL) is a layer from the surface to the mean height of the urban elements (buildings and trees), where the vertical exchanges of momentum, heat and moisture occur in cities (Oke, 2006) The vertical structure of the urban atmosphere can be linked to horizontal scales The climate in the UCL is considered microscale with the perspective of a pedestrian walking on the street or looking out from a ground floor window The climate in the ISL is considered local-scale with the perspective of someone looking down from a roof or a tall building The climate in the ML and the complete UBL is considered mesoscale with the perspective of someone looking out of an aircraft

1.2.3 Urban heat island effect

The phenomenon that cities are warmer than the surrounding areas is called the urban heat island (UHI) effect (illustrated in Figure 1.2) The UHI was recorded for the first time by Luke Howard in 1818 in his study on London’s climate that discovered “an artificial excess of heat” in the city compared to the country (Howard, 1818) After that, many studies were conducted to examine the urban effects on local climates

In 1977, Lowry proposed a framework stating that the weather (temperature, humidity, wind speed, etc.) at a location depends on three components:

Thus, according to Lowry’s framework, the urban climate is composed of weather values in the urban area, affected by the background climate, local landscape, and human activities, in which human impacts are obvious Whereas, human impacts are less dominant in rural areas and negligible in natural areas

The UHI effect is a result of the change in energy balance in cities, which is attributed

to urban form and function (Oke et al., 2017)

Each fabric has its own radiative, thermal, and moisture properties, which affect the absorption, reflection and emission of radiation, and also the uptake, transfer and storage of heat and water In urban areas, the cover of construction materials (asphalt, concrete, brick, stone, etc.) is large, so urban areas can store more sensible heat and release it more slowly The urban structure, i.e dimensions of buildings and the spaces between them, street width and street spaces, determines the aerodynamic roughness and the albedo of the city

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The urban function is associated with anthropogenic emissions, which can have direct impacts and indirect impacts on the atmosphere As for direct impacts, anthropogenic emissions create the anthropogenic heat which is released into the atmosphere and warms the ambient air Anthropogenic heat is the heat which is converted from chemical or electrical energy created by human activities, e.g space cooling and heating, lighting, cooking, transportation, industry, and also the metabolism of human and animals As for indirect impacts, anthropogenic emissions create air pollutants and greenhouse gases, changing the Earth’s radiation balance, which will be discussed more later on

1.2.4 Climate change and cities

Climate change is also a growing problem in cities Climate change is defined by IPCC (2014) as “a change in the state of the climate that can be identified (e.g by using statistical tests) by changes in the mean and/or the variability of its properties and that persists for an extended period, typically decades or longer” The causes of climate change may vary from natural processes to human activity (IPCC, 2014); however, when we talk about modern climate change, it is more likely to refer to human-induced climate change, which is a global problem resulting from the increase

of greenhouse gases (GHG, e.g CO2, NH4, N2O, water vapor, etc.) in the atmosphere due to human activities, especially fossil fuel combustion The change in GHG concentration in the atmosphere modifies the global climate by altering the radiative balance of the Earth As a natural process, the Earth’s surface gets warm after receiving solar shortwave radiation, then emits some of its heat in the form of infrared radiation, bringing the heat back to space The Earth’s temperature is stable when the absorbed energy at the Earth's surface is balanced with the emitted energy from the Earth’s surface; i.e., the incoming energy is balanced with the outgoing energy However, as the GHG molecules in the atmosphere absorb longwave radiation, they prevent some of the Earth’s infrared radiation from escaping to space, and spread the heat back to the land and the oceans, resulting in warming up the Earth The more GHG emissions are, the warmer the Earth is A significant increase in the average global temperature since 1880 has been observed in tandem with a rise in CO2

concentration described by the WMO as the result of “the growing use of energy and expansion of the global economy” (UNFCCC, 2011) Global warming leads to many

if the global CO2 concentration increased from 323 to 645 ppm, the UHI would increase by below 0.5 K, much lower than the global warming of 3 K; however, the difference at extremely hot nights between urban and rural areas would significantly rise Nevertheless, the change in the UHI due to global climate change may vary in different cities For instance, a projection in the Paris region by Lemonsu et al (2013) showed that temperatures in the region would increase, but the temperature rise in rural areas was greater than that of urban areas due to a precipitation reduction leading

to dryer soils and less thermal admittance; as the result, the UHI would be decreased Still, an increase in urban temperatures associated with climate change is beyond doubt

It is a concern that climate change will continue in the future, depending on economic development and climate policy (IPCC, 2014) The IPCC Fifth Assessment Report (AR5) published in 2014 established four future climate scenarios based on potential GHG emissions until 2100, known as representative concentration pathways (RCPs) Even if no more anthropogenic GHG are emitted, climate change will continue for many centuries (IPCC, 2014) This means that climate change will inevitably go on in the future no matter what emissions reduction policies are implemented and that we have to face the immediate and long-term warming in cities due to global climate change Accordingly, besides climate change mitigation efforts

socio-to minimize impacts, adaptation socio-to the changing climate in cities is also important

is necessary to counteract the effects of local urbanization to compensate for the effects of global climate change That is, to make appropriate policies on urban planning and design as a climate change adaptation solution

1.3 Urban vegetation and its cooling effects

1.3.1 Cooling effects of vegetation

Vegetation is always an indispensable element in urban areas because of its wide range of services Street trees, grass, gardens and parks are not only for decoration of urban space, entertainment and relaxation, but also known for climate regulation, i.e regulation of radiation exchange, airflow, air pollutants, temperature, evaporation, runoff, and noise The climatic effects of plants differ depending on their architecture (canopy form, foliage density, branch, and root systems) and physiology, and between deciduous and evergreen species (Oke et al., 2017)

Vegetation can affect the urban thermal environment by modifying air temperature and humidity through shading and transpiration

Shading

Vegetation can reduce incoming solar radiation by reflection and transmission Due

to higher albedo, vegetation is able to reflect more radiation than dark, artificial surfaces Generally, vegetation can reflect 10% of visible and 50% of infrared radiation, and transmit 10% of visible and 30% of infrared radiation That is, only 80% of visible and 20% of infrared radiation is absorbed in vegetative surfaces (Brown and Gillespie, 1995) Therefore, air and surface temperatures are lower in the areas shaded by trees than in unshaded areas

is vitally important for the plants themselves since the process of transpiration along with the root pressure are drivers of the transportation of water and mineral ions from roots up into leaves and other above-ground parts of the plants The opening of stomata also allows the diffusion of CO2 from the air into leaves for photosynthesis The change of water into the gas state, or evaporation, also reduces the leaves’ temperature and also the ambient air’s temperature, known as “evaporative cooling” This effect is especially useful in hot weather and climate

Evaporative cooling is a physical process Water molecules absorb heat from leaf surfaces, increasing their kinetic energy If water molecules have high kinetic energy enough to break the hydrogen bonds between molecules and overcome the pressure from the ambient air, they can escape to the air and become gas As the highest kinetic energy water molecules escape, the average kinetic energy, or the temperature, of the remaining water is going to decrease This process brings the heat away, thus cooling down the leaf surfaces

The rate of transpiration is affected by various factors, including light, temperature, soil moisture, humidity, and wind speed Light is the main driver of opening stomata The water availability in soil, or soil moisture, is associated with transpiration as the

In the context of climate change, vegetation provides co-benefits to climate change response because along with the above cooling effects, it also contributes to climate change mitigation through the uptake of CO2 during photosynthesis The reduction

of CO2 in the atmosphere by vegetation can be considered as an indirect cooling effect

1.3.2 Review of studies on the cooling effect of vegetation

Studies on the effects of urban vegetation on temperature have been carried out in many cities around the world A systematic review of many studies by Bowler et al (2010) presented that a park was 1 °C cooler than a non-vegetative urban area on average Results from the studies varied, but some significant points can be pointed out as shown below

The cooling effect of vegetation impacts on air temperature not only under the canopy but also in the surrounding area Bowler et al (2010) noted in their systematic review that the temperature difference at night between a park and its surrounding areas (0.65

°C; 95% CI = 0.43–0.87) was lower than that between the park and another urban site (2.26 °C; 95% CI = 1.14–3.37)

Also, the cooling effect has been found to vary with the area of greenspaces For example, a study on air temperatures in 61 parks during summer noon in Taipei by Chang et al (2007) suggested that parks over 3 ha were usually cooler than their surroundings, while the difference between parks less than 3 ha and their surroundings varied Studies in London on warm and calm nights showed similar results on the increase of cooling intensity with green size, and also yielded the same

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trend for the distance of cooling effect from the greenspaces The reduction of temperature and the cooling distance of large green spaces (over 100 ha) were nearly 1.5 °C and 440 m, respectively (Doick et al., 2014) The results for small to medium greenspaces (0.5 to 12 ha) ranged from 0.4 to 1 °C and 30 to 330 m, respectively; and

no statistically significant difference was found for very small greenspaces (less than 0.5 ha) (Vaz Monteiro et al., 2016)

Features of greenspaces also have an impact on the cooling effectiveness Bar et al (2009) conducted an experiment over 45 days to compare the impacts of using trees, grass, and shading mesh on near-surface air temperature in courtyards It was reported that courtyards with trees were the most effective as they were up to 2

Shashua-K cooler than the bare soil, while courtyards with only grass showed an ineffectiveness This is attributed to the shading effect and the evaporative cooling of trees, while grass has evaporative cooling but it does not provide shades (Oke et al., 2017)

In Hanoi, there are limited studies on this topic One of them is research by Pham et

al in summer 1992 They measured air temperatures in Hanoi and found that Thu Le and Bach Thao Parks were 1 – 3 °C cooler than residential areas in North Thanh Xuan and Bach Khoa (Pham et al., 2019) Another experiment was conducted by the Faculty of Architecture and Planning, National University of Civil Engineering (2015) to assess the role of street trees on the reduction in urban heat Surface and air temperatures were measured on 6 streets in Hanoi, including 4 streets with few trees (Nguyen Trai, Khuat Duy Tien, Nguyen Chi Thanh, and Hoang Dao Thuy) and 2 streets with many trees (Kim Ma and Hoang Dieu) at 11 a.m and 3 p.m on June 1,

2015 Three locations were selected on each street, two of which were unshaded by trees, and one was shaded by trees at the time of measurement Results showed that the surface temperatures under the shade of trees were much lower than those in unshaded areas with the difference of 6 – 15 °C The average air temperatures on the streets having many trees were 0.4 - 0.6 °C lower than on the streets having few trees Even the air temperatures in unshaded areas on the two streets with many trees were also lower than those in unshaded areas on the streets with few trees, implying that trees have a spatial cooling effect

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Although research on the effects of vegetation on the urban thermal environment has been conducted elsewhere, there have not been many studies related to this topic in Hanoi so far Furthermore, the previous studies in Hanoi only focused on the air temperature, which could not totally reflect the thermal comfort of residents Therefore, the present study aims to seek some further understanding about the relations of vegetation to air temperature, relative humidity, and also discomfort index

1.4 Climate of Hanoi

For a better understanding of the climate of Hanoi, this section reviews the background climate, the urbanization process, and climate change in Hanoi

1.4.1 Background climate of Hanoi

Hanoi is located in the North Delta (or the Red River Delta in some materials) of Vietnam, so its background climate is the climate of this region The climate of this region is affected by solar radiation and atmospheric circulation as below (Pham, 2019)

Solar radiation

Stretching from the latitude of 23°22′ N to the latitude of 8°30′ S, the mainland of Vietnam lies completely between the Tropics (23°27′ N-23°27′ S), so it has the solar features of this area Every year the Sun passes through the zenith twice; therefore, the elevation of the Sun is pretty high, and so the daytime is long, even in winter months In Northern Vietnam, the daytime lasts over 10 hours and a half in winter (6:30-6:40 to 17:20-17:30), and 13 to 13 hours and a half in summer (before 5:30 to after 18:30) A large amount of solar radiation results in the hot climate in this region

Atmospheric circulation

Vietnam lies in the Asian monsoon area, which includes three main monsoon systems: Northeast Asian monsoon, South Asian monsoon, and Southeast Asian monsoon However, the territory of Vietnam does not belong completely to any of these systems but lies in the intersection of them

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In winter, Northern Vietnam is influenced by the Siberian High and South China High The former’s influence is weaker than that of the latter in the early and late winter but is more dominant in the middle of winter (December, January, February) The early spells of the Northeast monsoon often take place in late September, while the late spells are in May or June of the next year The Northeast monsoon has two origins with different features The continental Northeast monsoon developing from Northeast Asia brings cold and dry air, while the tropical Northeast monsoon developing from Southeast Asia brings warmer and moist air

In summer, Vietnam is influenced by the Southwest monsoon The early spells of the Southwest monsoon are in April, and the late spells are in early September In the early hot season, the Southwest monsoon is derived from the Bay of Bengal, coming

to Vietnam in the Southwest direction This wind passes through the plains of Myanmar and Thailand and meets the Truong Son Range (on the border of Vietnam and Laos) The hot and moist air in the foot of the mountain range becomes cool and less moist when going up to the peak, then becomes hotter and dryer when going down to the other foot of the mountain range The hot dry Southwest monsoon is often called “Laos wind” In the middle of the hot season, the Southwest monsoon is derived from the South Hemisphere trade wind, passing through the sea, so it is moist and not too hot In Northern Vietnam, this wind is mainly in the North and Northeast directions

Vietnam has a tropical monsoon climate; however, Hanoi, like other cities in Northern Vietnam, has a cold winter Overall, the climate of this region is hot and moist

The climate of Hanoi is clearly differentiated into seasons The hot season is from late May to September, and the cold season is from November to the end of March April is the transitional season from the cold season to the hot season, and October is the transitional season from the hot season to the cold season The coldest month is January, and the hottest months are June and July

The cold season in Northern Vietnam is cold and moist, unlike in some other tropical countries which is cold and dry In the early cold season (September to January), it is

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cold and dry, but the humidity can reach up to 85% In the late cold season, it is moister The hot season has a large amount of heat and high humidity

Figure 1.4 Temperature and precipitation in Hanoi

(modified from https://nchmf.gov.vn)

1.4.2 Urbanization in Hanoi

Hanoi is an old city that was established as the capital of Imperial Vietnam in 1010

by King Ly Thai To under the name “Thang Long” (meaning “the Rising Dragon”) The urban area of Hanoi has appeared since the monarchical period with the formation of “streets” for living and commercial purposes, known as the busiest area

in the capital at the time These streets are now the “Old Quarter”, which is situated mainly in Hoan Kiem District

When France occupied Hanoi (1873-1945), often called the French colonial period, they filled many lakes in the city and rebuilt it into a new Western-style urban area The “chessboard planning” has appeared in Hanoi since this period In this period, the French attached great importance to urban vegetation They planted trees along the streets, and also built many urban gardens

Before 1990, there were 4 urban districts in Hanoi, including Hoan Kiem, Ba Dinh, Dong Da, and Hai Ba Trung The other areas at the time were mainly rural In 1995 and 1996, Tay Ho, Cau Giay, Thanh Xuan consecutively turned into urban districts

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In 2003, two more urban districts, Long Bien and Hoang Mai, were established In

2008, a big change happened when Hanoi was merged with Ha Tay Province, Me Linh District of Vinh Phuc Province, and four communes of Hoa Binh Province, making the total area of Hanoi increase by 3.6 times Most of these are rural areas, only Ha Dong District and Son Tay Town, which belonged to Ha Tay Province, are urban areas Bac Tu Liem and Nam Tu Liem are the most recently established urban districts after the split of Tu Liem District in 2014

In summary, from only 4 urban districts before 1990, the urban area in Hanoi expanded to 10 urban districts and a town during 1990-2008, and 12 urban districts and a town currently (Figure 1.5) Because of its history of urbanization, Hanoi is a little bit different from many other cities in the world, as the center of Hanoi (the four old districts) mainly includes low-rises, but not tall buildings like other cities Tall buildings and apartments are mostly distributed around new districts

Urbanization is a factor that has raised the temperature in Hanoi by 0.35 °C during 1990-2010, mainly due to land-use change The mean surface air temperature in Hanoi is expected to increase by 0.7 °C during 2010-2030, in which the increase in anthropogenic heat will contribute 30-50% of the total warming (Doan et al., 2019)

1.4.3 Climate change in Hanoi

Along with other cities in Vietnam, Hanoi is impacted by climate change Lee et al (2017) projected the existing urban areas in Hanoi will be 2.1 °C warmer by 2030s compared to 2013, in which 0.6 °C accounts for land-use change, and 1.5 °C accounts for global warming The increase in temperature will be 0.2 °C larger in the prospective urban areas that are expected to be developed in the Hanoi Master Plan

to 2030

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Figure 1.5 Map of Hanoi districts

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CHAPTER 2 METHODOLOGY

The main objective of the research is to find the effects of vegetation on the urban thermal environment by comparing the differences in air temperature and relative humidity among locations with different green fractions

The research was conducted with the following main tasks:

● Calculation of the air temperature and humidity in the urban sites in January and June 2020

● Estimation of the green fractions in the sites

● Estimation of the correlation between green fraction and temperature, humidity and THI (temperature humidity index)

● Estimation of the green fraction to tackle future warming in Hanoi

2.1 Data collection

2.1.1 Air temperature and humidity data

The air temperature and humidity data used in the study were collected from PAM Air, a community project of D&L Technology Integration and Consulting Joint Stock Company that provides real-time air quality and air pollution warning in Vietnam, officially launched in February 2019 As of June 2020, it has around 50 stations in Hanoi All the data of PAM Air are observed data that are collected from PAM Air’s network of sensors and from reference air quality monitoring stations

The device used for measurements of PAM Air is PAS-OA318 (Figure 2.1) They are installed at outdoor positions at the height of at least 3 m above the surface where are well-ventilated and far enough from heat sources (e.g kitchens, vents, air-conditioning units, votive incinerators and other material burning locations, barbecue restaurants, smoking, etc.) Data are recorded every 5 minutes The devices are calibrated before launch and periodically during operation

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Figure 2.1 Device PAS-OA318

23 PAM Air stations in different urban districts in Hanoi were chosen in the present study Hourly air temperature and humidity data from February 2019 to June 2020 were collected However, the launch dates of the stations are different: 5 locations were installed since summer 2019, 9 locations since fall 2019 (August-September), 3 locations since winter 2019, and 6 locations since 2020 Also, there were some missing data and errors at some time Therefore, data in 25 days in January (2nd, 3rd,

4th, 7th, 8th, 18th excluded) at 14 locations, and 27 days in June (3rd, 15th, 28th excluded)

of 2020 were used in the present study to estimate the effects of vegetation in the cold season and the hot season The data were documented 24 hours a day

For ease of comparison, 23 stations were grouped into 6 categories based on their housing types, including apartments, buildings, schools, low-rises, in the middle of residential areas, and houses near the roads The stations and their remarks are listed

in Table 2.1

Besides, temperature and humidity data at Son Tay and Ba Vi stations, which are on the outskirts of Hanoi, were collected from the Meteorological and Hydrological Administration as reference stations to compare to the sites in urban districts to estimate the UHI magnitude These data were measured at the height of 1.5 m above the surface four times a day at 1:00, 7:00, 13:00, and 19:00

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Table 2.1 Stations and remarks

1 Cau Dien Nam Tu Liem (21.0326, 105.7603) Apartments houses, buildings, few

trees

houses, grass/field

6 Ly Thuong Kiet Hoan Kiem (21.0215, 105.8555) Buildings low-rises, road, trees

trees

near road trees, road

13 Tran Quang Khai Hoan Kiem (21.025, 105.859) Low-rises low-rises, roads, trees

strip with vegetation

about 150m from Hoan Kiem Lake

residential areas

houses, few trees

19 Tran Quoc Toan

Primary School Hoan Kiem (21.0269, 105.8505) School low-rises, trees, garden, near Hoan

Kiem Lake

1: Cau Dien, 2: Gamuda Gardens, 3: Times City, 4: Ha Dinh, 5: Ecolife, 6: Ly Thuong Kiet, 7: Pham Tuan Tai, 8: Thai Ha, 9: To Hieu, 10: Nguyen Che Nghia, 11: Hang Quat, 12: Hang Bun, 13: Tran Quang Khai, 14: Kim Ma, 15: Ly Thai To, 16: Hoang Hoa Tham, 17: Hang Thiec, 18: Doi Can, 19: Tran Quoc Toan School, 20: Genesis School, 21: Quang An School, 22: Trung Hoa School, 23: Nguyen Trai School

Figure 2.2 Distribution of the study sites The x-axis refers to the longitudes, and

the y-axis refers to the latitudes

2.1.2 Vegetation cover data

To estimate the green fractions in these areas, aerial images were used Aerial images

of the locations were collected from the U.S Geological Survey (USGS) on https://earthexplorer.usgs.gov, which provides very clear and bright high-resolution images, based on their coordinates, except that the image of Cau Dien was collected from Google Earth since the image of Cau Dien on the USGS contained clouds Other

2.2 Data analysis

2.2.1 Tools

The data were analyzed and visualized using the Python 3 programming language with the Jupyter Notebook and the Anaconda distribution, which is a free and open-source Python distribution for data science, and Microsoft Excel

2.2.2 Calculating air temperature and humidity

Air temperatures and relative humidity at 14 locations in January and 20 locations in June of 2020 were calculated in 3 categories (monthly average, daytime, and nighttime) Daytime is considered from 6:00 to 18:00, and nighttime is considered from 19:00 to 5:00 the next day

After that, the results of air temperature at 1:00, 7:00, 13:00, 19:00 were compared to Son Tay and Ba Vi to figure the UHI magnitude Due to the elevation difference among Son Tay (15.9 m), Ba Vi (30.3 m), and the urban area (around 6 m), the air temperatures at Son Tay and Ba Vi were adjusted by 0.0594 °C and 0.1458 °C respectively using the rate of temperature decrease with elevation at 0.6 °C per 100

m to eliminate the impact of elevation on air temperature The UHI magnitude was found by subtracting the average air temperatures of Son Tay and Ba Vi from the air temperatures at the urban sites

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2.2.3 Calculating green fractions

In the present study, the characteristics of vegetation used to estimate its effects on the urban thermal environment is green fraction, or vegetation cover, which is usually defined as the proportion of ground surface that is covered by vegetation There are different types of vegetation cover as seen in the bird’s-eye view: canopy cover, foliar cover, and basal cover (Coulloudon et al., 1999), illustrated in Figure 2.3

Canopy cover, also known as crown cover, is the ground area covered by a vertical projection of the outermost perimeter of the plant canopy Canopy cover does not pay attention to the small gaps between leaves and between crowns, which is the difference between canopy cover and foliar cover Foliar cover, in contrast, is the amount of cover provided only by the leaves, or the foliage of the plants The last one, basal cover, is the cover provided by the base of the plants, i.e the width of basal cover is defined by the diameter of the base of the plants

Figure 2.3 Types of vegetation cover The top row refers to the side view of the

plant The bottom row refers to the bird’s-eye view of the plant The orange

highlights represent the area included in each type of cover

(Source: https://learn.landscapetoolbox.org)

In the present study, the aerial cover was estimated from images, so vegetation cover

in the research can be understood as either canopy cover or foliar cover, since it was the cover of plant leaves and the distance was so high to know if the gaps between leaves were taken into account or not

The method used to estimate vegetation cover from aerial images of these areas was based on color using OpenCV, a library of programming functions that solves computer vision problems First, all the images were cropped to have the same size

Figure 2.4 HSV colormap for OpenCV The x-axis represents H in [0,180), the axis 1 represents S in [0,255], the y-axis 2 represents S = 255, while keep V = 255

y-(Source: https://stackoverflow.com) However, a color image has three channels corresponding to three components of the color space, so it is necessary to take only a single channel to count the non-black pixels Therefore, before counting green pixels, or non-black pixels, one of the three channels was split from the resulting images The value channel, whose 0 is black, was used Non-zero pixels (non-black pixels) were counted to find the vegetation area, then divided by the total number of pixels (the image size) to get the green fraction It is noteworthy that the HSV range in OpenCV is different from other softwares For a better understanding of HSV, please refer to Appendix A

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Figure 2.5 Sample of estimating green fraction at Ly Thuong Kiet

All of the above tasks were performed with available functions in OpenCV This method is based on the method of detecting an object by a specific color using OpenCV The breakdown of the Python code is as described below

# Bitwise-AND mask and the original image

result = cv2.bitwise_and(i, i, mask=mask)

# Split the HSV result to get separate channels

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enough For that reason, it is also cost-free in case there are available aerial images

of the study area, which in most cases can be obtained on the USGS or Google Earth like in the present study This is especially useful when the spatial resolutions of free satellite images are not high enough to examine small-scale areas (e.g the resolution

of LANDSAT 8 is 30 m, which is too large to see clearly an area of 1 km2, and some more work to sharpen it may be needed if available)

In addition, it is a straightforward way to detect vegetation as it uses human sense, specifically the sense of color in this case, to set the threshold to separate vegetation and non-vegetation, while in the use of vegetation indices such as the normalized difference vegetation index (NDVI), finding the threshold for that task is more complicated Since the vegetation in the study sites was all green, the HSV range above only detects the vegetation in green but not in other colors, but in the case of multicolor vegetation such as a park including flowers, a different HSV range can be selected to get the ideal result

This method, however, still has some limitations that may affect its accuracy There were some noises of other green objects, such as green-colored roofs, and some vegetation pixels were not taken into account, especially when covered by clouds or shades Although these noises are not large, for other research with more accuracy, this method will need to be improved Despite that it may not be optimal, I think this method is sufficient for the present study

2.2.4 Estimating correlations between green fraction and temperature, humidity and THI

To better analyze the effects of vegetation on the urban thermal environment, the research also considered the effects of vegetation on thermal comfort, which is defined as “the condition of mind that expresses satisfaction with the thermal environment and is assessed by subjective evaluation” (ANSI/ASHRAE Standard 55-2010) There are various indices that are used to describe thermal comfort Since the available data were air temperature and relative humidity, the THI (temperature humidity index) was selected to describe thermal comfort The THI (also known as the discomfort index) was first developed by E C Thom (1959) with dry-bulb and wet-bulb temperatures (°F) For ease of calculation, in the present study, the THI was

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calculated from air temperature and relative humidity using the equation proposed by

Kyle (1994) (Equation 1)

𝑇𝐻𝐼 = 𝑇 − (0.55 − 0.0055𝑅𝐻) × (𝑇 − 14.5) (1) where: T is air temperature (°C), and RH is relative humidity (%)

The correlations of the green fraction with air temperature, humidity, and THI were

figured out with the following tasks

(1) Estimating the correlation coefficients to check if there were any relations

between green fraction and the observed values

The correlation coefficients of the green fraction with the observed air

temperature, humidity, and THI in January and June 2020 were estimated As the

non-linear correlations were found, the Spearman correlation coefficient was

used The alpha value was chosen to be 0.05, so analyses with a p-value less than

0.05 (or the chance of error less than 5%) were accepted

(2) Identifying the differences among different green fractions

This task comprised three steps

The study sites were grouped into 5 groups by their green fractions: 2.4 - 4.8%,

9.1 - 9.7%, 15 - 18.1%, 22.4 - 24.9%, and 42.5% To see if there were statistical

differences among the group means, a statistical test was performed Because the

data did not have a normal distribution, so it did not meet the assumptions of a

parametric statistical test Therefore, a non-parametric test was used instead

Specifically, a Kruskal–Wallis one-way analysis of variance test (or Kruskal–

Wallis test in short) was used to test the differences in air temperature, humidity,

and THI among the groups

After that, a Dunn test was performed to find out which groups were significantly

different from each other

Finally, the differences between the groups that were significantly different from

each other were drawn from the Tukey test

(3) Estimating correlation models for the statistically significant correlations

obtained

The correlation models were automatically estimated in Microsoft Excel