DEPRAVED URBAN SCAPES - Inhabiting Subnature

DEPRAVED URBAN SCAPES Inhabiting Subnature

Xinyi L i | Ziyi Yang | Vanessa Panagioto po u lo u The Bartlett School of Architecture UNIVERSITY COLLEGE LONDON MArch Urban Design B-PRO | RC14

DEPRAVED URBAN SCAPES Inhabiting Subnature

Portfolio - BENVGU22 RC 14 Big Data City: Machine Thinking Urbanism Group Topic : Wind and Pollution Xinyi Li Ziyi Yang Vasileia Panagiotopoulou

Tutors: Roberto Bottazzi Tasos Varoudis

The Bartlett School of Architecture University College London MArch Urban Design B-PRO

Research Cluster 14 MArch Urban Design Big Data City: Machine Thinking Urbanism Research Cluster 14 explores the role of big data and learning algorithms in urban design. Big data - commonly defined as the possibility to aggregate and mine large datasets by employing computers - is often understood as a series of abstract techniques without spatial or visual qualities.

We challenge this perception by developing a research agenda in which the capabilities provided by ever-more powerful computation to mine data are used to question the role of urban design in the light of the ever-thinning distinction between man-made and natural environments.

Here, computation is aimed at including important elements of urbanity - which are either invisible or have been playing a peripheral role in the design process - in the design conversation. Through digital tools we can widen the range of what can be sensed, expanding to factors beyond human perception. Likewise, algorithms provide a means to mine data to augment the limits of our cognition, deeply changing how we interpret space. This shift allows us to collapse the distinction between natural and man-made artifacts, as the massive influence of human actions on Earth and its biosphere no longer allow us to maintain this separation.

Image on Previous Page: Picture from NASA satellite of severe smog incidents over China, 2013.

“The advance of civilization produced barbarity as an unavoidable waste product, as essential to its metabolism as the gleaming spires and cultivated thought of p lite society�.

Engels

CONTENTS

_INTRODUCTION

1

AIM

2

U R B A N S T R AT E G Y

2

AIR POLLUTION IN LONDON

6

INTERACTION BETWEEN AIR POLLUTION AND WIND

8

_ C O L L E C T I N G A N D A N A LY S I N G D ATA

15

SITE INTRODUCTION

L a n d U s e a n d R o a d I n t e g r a t i o n |

16

G r e a t e r L o n d o n

Land Use and Road Integration |

S t r a t f o r d

18

20

S PAT I A L M O D E L I N G A N A LY S I S 2 0

A n g u l a r S e g m e n t I n t e g r a t i o n A n a l y s i s

Angular Segment Choice Analysis

22

A I R P O L L U T I O N C O N C E N T R AT I O N L E V E L S |

G R E AT E R LO N D O N

25

A I R P O L L U T I O N C O N C E N T R AT I O N L E V E L S | S I T E

26

N i t r o g e n D i o x i d e |

Annual Mean Concentration

Particulate Matter 2.5 |

Annual Mean Concentration

28

TRAFFIC CONGESTION ON SITE

30

H E AT C O N S U M P T I O N O N S I T E

32

U r b a n H e a r I s l a n d s o n S i t e

34

36

URBAN GREEN AND AIR POLLUTION

P o l l u t i o n A b s o p t i o n f r o m T r e e s

40

B e s t Tre e S p e c i e s fo r P o l l u t i o n

A b s o r p t i o n

42

B l o o m i n g p e r i o d

44

W I N D S P E E D A N D D I R E C T I O N G R E AT E R LO N D O N | 2 0 1 6 DOMINANT WIND SPEED AND DIRECTION ON SITE

Wind Pattern on Site

46

_DEPRAVED URBAN SCAPES

85

50 52

E M E R G E N C E O F V E RT I C A L A N D H O R I Z O N TA L

F I LT E R I N G

86

R e t h i n k i n g t h e S t r a t f o r d

_ M A C H I N E L E A R N I N G D ATA A N A LY T I C S

DIMENSIONALITY REDUCTION METHODS

M e t r o p o l i t a n M a s t e r p l a n n i n g

86

D E S I G N S T R AT E G Y F O R H O R I Z O N TA L F I LT E R I N G

90

55 56

C AT E N A RY S T R U C T U R E S

P l o t t i n g D a t a s e t s

92

58

F I LT E R I N G U N I T S ’ PA C K I N G

D a t a C r o s s i n g s

96

M AT E R I A L S T U D I E S

98

62

P R I N C I PA L C O M P O N E N T A N A LY S I S

64

K-MEANS CLUSTERING

F i l t e r i n g U n i t s a n d

66

C O M B I N AT I O N O F R I S K A S S E S S M E N T, C L U S T E R I N G

D i f f e r e n t i a l G r o w t h

98

AND PCA

100

68

Materiality of Filtering Units

D E S I G N S T R AT E G Y F O R V E RT I C A L F I LT E R I N G

110

F R A C TA L T Y P E S O F I N T E R V E N T I O N

112

T y p e A | P u b l i c F i l t e r i n g I n s t a l l a t i o n

_ C O M P U TAT I O N A L U R B A N P R O T O C O L S O F INTERVENTION

BROADER AREA OF INTERVENTION

G O V E R N M E N TA L P R E D I C T I O N S F O R A I R Q U A L I T Y

73 74

Ty p e B | Fr a c t a l s Fo r m i n g I n t e r i o r

S p a c e

114

116

P E R F O R M A N C E O F S PA C E F I L L I N G F R A C TA L S 1 1 6

Growth Driven from Risk Data

Form Development and Wind Pattern

LEVELS

112

O R G A N I C D E F O R M AT I O N O F F R A C TA L S

118 120

76 N O 2 C o n c e n t r a t i o n

S o l a r R a d i a t i o n A n a l y s i s p e r A n n u a l S e a s o n

[ 2 0 1 3 , 2 0 2 0 , 2 0 2 5 , 2 0 3 0 ]

122

128

[ 2 0 1 3 , 2 0 2 0 , 2 0 2 5 , 2 0 3 0 ]

76

PM2.5 Concentration 78

Subdivision of Cells

M AT E R I A L I T Y O F V E RT I C A L F R A C TA L S

130

L A N D S C A P E F O R M AT I O N

136

Overlay of PM2.5 Increase and

W i n d P a t t e r n

80

82

RISK ASSESSMENT

_REFERENCE LIST

155

INTRODUCTION

AIM

The current project, aims to reestablish the relationship between the urban fabric, its users and air pollution. More specifically, our urban approach and intervention is driven by the idea that “ aspects of the

seemingly subhuman conditions of contemporary urbanization and its subcultural peripheries “ (Gissen, 2009) should not be confronted exclusively as a threat but as an existing dynamic element of potential design inspiration. The negative definition of these urban conditions as subnatural, could be inversed through design and urban interventions inspired by natural systems and processes.

Therefore, the main aim of our approach, is to produce a new spacial milieu between pollution and urban spaces, and render the space that we avoid to inhabit as space that could potentially regenerate the city.

U R B A N S T R AT E G Y

Our urban strategy is driven by collecting, visualising, analysing and simulating data concerning air pollution concentration and its components, the element of human factor in the city, and wind movement around our site of interest. The data analysis defines zones of intervention with particular characteristics that indicate different typologies of intervention.

These typologies are defined by the approach of the structural complexity and morphology of natural systems, which we consider as the basic elements anticipating air pollution.

Right Image: Widnes, England, during the late 19th century

2

3

4

5

A I R P O L LU T I O N I N LO N D O N

London is a city undergoes a long history of air pollution, even after the Great Smog of 1952 and the Clean Air Act in 1956, the air pollution in the twenty- first century is still challenging. We may not suffer from massive smog from burning coal, but contemporary urban by-products have become more complex in their chemical properties and more invisible.

Even though specific air pollutants present a certain decrease in comparison to

1950s, during the summertime of 1976, pollution

episodes, stated as “photochemical smogs“ that resulted from groundlevel ozone formed by its precursors, had as an impact the increase of mortality in London up to 7%. During the 1990s several winter smog episodes occurred during calm winter days, causing the death of 100180 people.

The main air pollutants of concern in Greater London today are particles, with PM2.5 and PM10 being considered as the most hazardous, nitrogen oxides (NOx), volatile organic compounds (VOCs) and carbon monoxide (CO). These air pollutants in between others such as Sulfur Dioxide (SO2) or Ozone (O) arise apart from vehicle emissions, from commercial, industrial and domestic emissions or the fossil fuel power generation.

Nowadays, mathematical models used with monitoring data from 1600 monitoring sites across the UK predict up until 2025 improvements in air quality and decreasing values for major air pollutants. However this is not the case for Nitrogen Dioxide (NO2) which currently exceeds the European Environmental limits, and Particulate Matters which present a severe concentration in several areas of Greater London.

ides rticles ameter of 0 µm) inly ossil fuel sources. ce of l

(kilotonnes)

d SO2 now between the main had an

VOCs, PM10 and CO

3000 3000

12000 12000

2500 2500

10000 10000

2000 2000

8000 8000

1500 1500

1000 1000

NO2 NO2 VOC VOC PM10 PM10 CO CO

6000 6000

4000 4000

500 500

00 1970 1970

2000 2000

1975 1975

1980 1980

1985 1985

Year

1990 1990

1995 1995

emissions (kilotonnes) CO CO emissions (kilometers)

UK Annual Emissions emissions of NO2, VOCs, PM10of andNO CO 2, UK annual

NO2, VOC, PM10 PM emissions (kilometers) NO 2, VOC, 10 emissions

2

0 2000 2000

Source: National Atmospheric Emissions Inventory (http://www.naei.org.uk) 6

Health problems Air pollution legislation has mainly been and still remains focused on reducing the adverse human health effects of

Image on Previous Page: Collage of newspaper headlines during the Great Smog in London.

Images on the Right: Contemporary pollution source and chaos.

7

INTERACTION BETWEEN AIR POLLUTION AND WIND Wind environmental conditions play a major role on the levels of air quality of Greater London, since it is the element that carries pollution

contaminants

through

its

movement

(especially

dust

particles categorised within Particulate Matter 2.5), and depending the meteorological conditions, contributes in the increase or decrease of air quality. Wind speed, wind direction and atmospheric stability can contribute in the dilution of pollution particles or can create conditions which speed up chemical reactions in the air with the impact of converting pollutants into new compounds.

Nowadays, the level of control of pollution concentration is challenged even more from the fact that 40% of these pollutants are not emitted exclusively by local pollution sources but travel through wind from different countries, something that is most apparent in the case of dust generated in the Mainland Europe and Africa.

Global Wind Pattern Source: NASA

8

9

10

C O L L E C T I N G A N D A N A LY S I N G D ATA

SITE INTRODUCTION L AND USE AND ROAD INTEGRATION| GREATER lONDON Given the fact that the major causes of air pollution are vehicle emissions as well as industrial, commercial and domestic emissions, a visualisation of the current land use and traffic network density values gives a more clear view of areas of higher exposure.

The level of Integration of the roads of Greater London as represented in the map, illustrates the traffic volume, which also affects the level of pollution created by transportation.

The traffic network density as well as the density of the urban tissue, demonstrate high risk areas which tend to appear towards the Eastern side of the city. This is where the site of interest is located, around the Olympic Park of Stratford. The increased levels of deprivation and low air quality in comparison to other urban areas of London on our site, is partially attributed to the fact that the prevailing winds are westerlies and blowing east. As a result, a considerable amount of various pollution contaminants is transfered through east blowing wind to the eastern areas of London.

Land Use Industrial Commercial Public Space Residential

Land Use and Spatial Analysis Road Integration | Greater London

Nature Site

0

Integration R8000m Low

12

High

2 km

Source: Edina Digimap

13

S PAT I A L M O D E L I N G A N A LY S I S ANGUL AR SEGMENT INTEGRATION ANALYSIS [CLOSENESS CENTRALITY] A spatial network Integration and Choice Analysis in the broader area around our site is run, in order to detect the way that the centrality and density of the traffic network might contribute in the coexistence of air pollution with the human factor at the same time.

The maps below represent an Angular Segment Analysis for Closeness

Integration r500 metric

Centrality of four different radii [500,1000,2000,4000]. The Closeness

Quantile [Equal count]

Centrality [Integration Analysis] represents the average of all shortest paths from a segment to all others in the given radii.

Integration r1000 metric Quantile [Equal count]

Integration r2000 metric Quantile [Equal count]

14

Integration r4000 metric Quantile [Equal count]

Site Low

High

15

A I R P O L L U T I O N C O N C E N T R AT I O N L E V E L S | G R E AT E R LO N D O N [ 2 0 0 7 - 2 0 1 6 ]

A visualisation of the major air pollutants’ values per month from 2007 to 2016 in Greater London demonstrates the fact that despite the European regulations on pollution environmental limits, the concentration has not decreased the past years and some areas of the capital exceed the annual mean NO2 concentration values.

NO (µg/m3)

180,9 μg/m3

NOx (µg/m3)

2016

2016

2009

NO2 (µg/m3)

2016

2009

2007

129 μg/m3

75,9 μg/m3 36,5 μg/m3

30,8 μg/m3

rs

a ye

J

16

rs

a ye J

J D months

Nitrogen Oxide

41,1 μg/m3

rs

a ye

D months

D months

Nitric Oxide

Nitrogen Dioxide

2016

2007

2016

PM 10 (µg/m3)

PM 2.5 (µg/m3)

O3 (µg/m3)

2016

2007

2007

36,9 μg/m3 29,9 μg/m3

46,3 μg/m3

6,8 μg/m3

10,9 μg/m3

D months

D months

D months

Ozone

J

J

J

14,1 μg/m3

Particulate Matter 2.5

Particulate Matter 10

Data Source: London Air | King’s College London

17

A I R P O L L U T I O N C O N C E N T R AT I O N L E V E L S | S I T E NITROGEN DIOXIDE ANNUAL MEAN CONCENTRATION ON SITE |2013 Monitoring stations around our site provide annual mean pollution concentration measurements for 2013.

A spatial visualization of data points with geolocated values of NO2 and PM2.5 concentration on our site of interest in Stratford, specifies areas where pollution concentration is higher. The concentration presents higher values within the area’s dense road and railway network due to vehicle emissions and also where the density of the urban fabric is higher and subsequently energy consumption and heating emissions reach high levels.

NO2 Cause burning of fossil fuels

forest fires

vehicles emissions

NO2 Effect respiratory infections & asthma

18

lung disease

formation of effect on fine particles (PM) vegetation

smog

acid rain

Nitrogen Dioxide Concentration on site [μg/m3] Data Source: London Air | King’s College London

19

H E AT C O N S U M P T I O N O N S I T E

Increasing levels of heat consumption, lead to augmented emissions of pollution particles and greenhouse gas emissions. The primary pollutants linked with heat consumption include: Sulfur dioxide (SO2) Nitric oxides (NOx) Particulate matter (PM) Carbon monoxide (CO) and Mercury (Hg) A visualisation of the average yearly heat consumption per building on our site, demonstrates its contribution in the air quality levels. This dataset is categorized based on the types of use of the buildings. The lowest blue points are the residential houses (average consumption of 19.5 MWH), the pink points are apartments and flats (average consumption of 2000 MWH) and the purple points are commercial buildings and high-rise offices (average consumption of 5100MWH).

Heat consumption per building use Residential House 10.5 mwh ~99mwh Public Buildings 102 mwh ~3323mwh Apartments and Commercial Buildings 89 mwh ~5530mwh

20

5100 MWH (Large Shopping Mall)

19.5 MWH (3 bedrooms standard residential house)

Annual Heat Consumption per Building [mwh] Data Source: Open Street Map and London Datastore

21

URBAN HEAT ISL ANDS ON SITE

The reintegration of the heat consuption dataset into our site, presents areas of potential formation of the heat island effect. The peaks are spotted in dense residential areas whereas the lowest levels of the heat island effect, are spotted close to open spaces around the Olympic Park.

On a hot, sunny summer day, roof and pavement surface temperatures can reach up to 27–50°C higher t h a n t h e a i r ’s t e m p e r a t u re , w h i le s h a d e d o r m o i s t

Residential District Dense residential area

surfaces—often in more rural surroundings—remain close

to

air

temperatures.

These

environmental

conditions, particularly during the summer months, have multiple impacts and contribute to the formation of atmospheric urban heat islands. Air temperatures in cities, particularly after

the sunset, can be as

much as 12°C warmer than the air in neighboring, less developed regions. Elevated temperatures from urban

heat

islands,

particularly

during

summer,

c a n a f fe c t a c o m m u n i t y ’s e n v i ro n m e n t a n d q u a l i t y of life.

22

Qu

Shopping Malls & High-rise Buildings

ueen Elizabeth Olympic Park

23

URBAN GREEN AND AIR POLLUTION

After detecting some of the basic elements that contribute in the diminishment of air quality, we attempt to identify existing conditions that anticipate and interact with the already analyzed elements.

Natural elements of the biosphere, with the predominant role of urban trees around our site are considered as a part of the man-made urban environment and at the same time are identified as urban lungs.

Relevant collected data sets, reveal the existence of forty four thousand individual trees, including their georeferenced location and twenty two different tree types. The most common host species in Stratford include

24

Hawthorn

Lime

Maple

Cherry

Alder

Acacia

Apple

Ash

Locust

Cypress

Gum

Plane

Whitebeam

Pear

Birch

Chestnut

Shrub

the species of maple, horse chestnut, cherry, alder, birch and plane.

Tree Types on Site Data Source: Mayor of London

25

26

27

POLLUTION ABSORPTION FROM TREES

Trees contribute significantly to the improvement of air quality by reducing air temperature (thereby lowering ozone levels), by filtering pollutants within the atmosphere, by absorbing them through their leaf surfaces and by intercepting particulate matter (eg: smoke, pollen, ash and dusts). Trees can also contribute in the reduction of energy demand in buildings, resulting in fewer emissions from gas and oil red burners and reduce the energy consumption demand from power plants.

The data visualisation presents georeferenced average values of CO2 and SO2 absorption from different tree types on site.

Carbon Dioxides Absorption unit: g/sqm a day Sulfur Dioxide Absorption unit: g/g(dry leaf) Sulfur Dioxide Absorption MAX: 2.74 g/g(dry leaf) MIN: 0.62 g/g(dry leaf) Carbon Dioxide Absorption MAX: 12.10 g/sqm*d MIN: 4.78 g/sqm*d

28

Absorption levels per tree (g/sqm*d ) Data Source: Mayor of London

29

BEST TREE SPECIES FOR POLLUTION ABSORPTION As a next step, we layout the best tree species for the reduction of pollutants. The first to forth layer from the top are species that specialise in the absorption and reduction of Sulfur and Nitrogen Oxide, PM10, Ozone and Carbon Monoxide. The last layer shows the trees that contribute the most to the reduction of the atmospheric temperature.

The most dominant species on our site of intervention are maples, gums, chestnuts and birches.

Best Species for Reduction of Sulfur and Nitrogen Oxides Ash Chestnut Birch Plane

MAPLE Specialised in Ozone [O] Absorption

Best Species for Reduction of Particulate Matter (PM10) Cypress

Best Species for Reduction of Ozone

GUM Specialised in Air Temperature Cooling

Maple Ash Chestnut Birch

Best Species for Reduction of Carbon Monoxide Ash Chestnut Birch

Best Species for Air Temperature Cooling Plane Gum Cypress

CHESTNUT Specialised in Sulfur [SO] and Nitrogen Oxides [NO] and Carbon Monoxide [CO] Absorption

BIRCH Specialised in Ozone [O] and Carbon Monoxide [CO] Absorption

PLANE Specialised in Sulfur [SO] and Nitrogen Oxides [NO] Absorption and Air Temperature Cooling

30

31

BLOOMING PERIOD

The role of urban vegetation on our site is dual. Specific tree types are not only specialized in absorbing pollution contaminants, but also release pollen which causes allergic reactions such as hay fever.

A visualisation of the blooming period of the trees of our site, inserts the factor of the time of the year when the allergens level is relatively higher in the air. In isometric view we can see more clearly the chronicle location of the blossoming period per tree from March to May. This is to be added as an extra parameter that affects air quality in a natural sense, mostly than a chemical sense.

Dec Nov Oct Sep Aug Jul Jun May Apr Mar

Months of blossoming period

Feb Jan

Blooming Period | North Elevation Dec Nov Oct Sep Aug Jul Jun May Apr Mar Feb Jan

Blooming Period | Western Elevation Right: Blooming Period | Axonometric View Data Source: Mayor of London

32

33

WIND SPEED AND DIRECTION | G R E AT E R LO N D O N | 2 0 1 6

Wind conditions such as speed, humidity, direction and stability are considered as the most influencing factors of air quality levels.

A diagrammatic visualisation of wind particles, represents their speed and direction for each of the twenty four hours of a day in the year of 2016. The x and y axis are time-based and every hour of a day is remapped in a linear order in order to illustrate the most prevailing wind conditions.

On the diagram underneath, the height of each point represents the value of wind speed and the vector lines illustrate the direction of wind during 2016.

Speed m/s

tion

ec Dir

Time Jan.

Digrammatical Visualisation of Wind Speed and Direction | 2016 Data Source: MeteoBlue

34

Feb.

Mar.

Apr.

May.

Jun.

Jul.

Aug.

Sep.

Oct.

Nov.

Dec.

N

Winter

N

Spring

E

W

W

E

S

S

mo

h arc rs M April u o h y Ma

Speed m/s

Speed m/s

er mb ece ry D a u rs Jan uary hou r b e F

mo

nth

nth

s N

Autumn

N

Summer

E

W

E

W

s

S

S

mo

nth

s

s our

h Speed m/s

Speed m/s

s hour

ber tem r obe Oct er b em Nov

Sep

mo

e Jun y Jul ust Aug

nth

s

Wind Speed and Direction of Greater London per Annual Season Data Source: EnergyPlus

35

36

Wind Speed and Direction of Greater London | 2016 Data Source: MeteoBlue

37

WIND PATTERN ON SITE

In order to detect in a more exploratory way the spatial substance of

Wind blowing from the south west generates a more dense wind pattern

the environmental wind conditions on our site, we use the output of the

when it reaches our site due to low building density, and is deformed

precedent wind rose diagrams as an input in computational simulations.

by the density of the urban infrastructure at the point when it reaches

Two dominant directions and velocity levels of wind per season are

the built environment. Hence, the pattern formed by wind blowing from

simulated in order to generate a more accurate wind pattern.

northeast and west is of lower density in contrast to the one blowing from the other direction.

Spring SW 25 °

Spring NE 250 °

Summer SW 35 °

Summer NE 185 °

Autumn S8°

Autumn NE 220 °

Winter SW 30 °

Winter W 270 °

Planar Simulations of Dominant Wind Conditions per Season

38

Right: Overlay of Wind Patterns per Season Data Source: MeteoBlue

39

38425 183955 0.094118 0.098039 0.039216 0.047059 0.090196 0.098039 0.07451 0.070588 0 0 0 0 0 0 0 0.215686 0.431373 0.980392 0.941176 538425 183955 38420 183960 0.094118 0.098039 0.039216 0.047059 0.094118 0.098039 0.07451 0.070588 0 0 0 0 0 0 0 0.215686 0.427451 0.952941 0.886275 0.094118 0.098039 38435 183940 0.094118 0.098039 0.039216 0.047059 0.090196 0.098039 0.07451 0.070588 0 0 0 0 0 0 0 0.227451 0.427451 0.886275 0.964706 0.039216 0.047059 38425 183960 0.098039 0.098039 0.039216 0.047059 0.090196 0.098039 0.07451 0.070588 0 0 0 0 0 0 0 0.211765 0.431373 0.937255 0.882353 0.090196 0.098039 38430 183960 0.098039 0.098039 0.039216 0.047059 0.090196 0.098039 0.07451 0.070588 0 0 0 0 0 0 0 0.203922 0.431373 0.890196 0.862745 0.07451 0.070588 0 38440 183940 0.094118 0.098039 0.039216 0.047059 0.090196 0.098039 0.07451 0.070588 0 0 0 0 0 0 0 0.223529 0.427451 0.835294 0.92549 0 0 0 38440 183935 0.094118 0.098039 0.039216 0.047059 0.090196 0.098039 0.07451 0.070588 0 0 0 0 0 0 0 0.231373 0.419608 0.807843 0.901961 0 0 0 38420 183965 0.094118 0.098039 0.039216 0.047059 0.094118 0.098039 0.07451 0.070588 0 0 0 0 0 0 0 0.211765 0.431373 0.905882 0.792157 0.215686 0.431373 38425 183965 0.094118 0.101961 0.039216 0.047059 0.090196 0.098039 0.07451 0.070588 0 0 0 0 0 0 0 0.203922 0.435294 0.882353 0.780392 0.980392 0.941176 38435 183960 0.094118 0.098039 0.039216 0.047059 0.090196 0.098039 0.07451 0.070588 0 0 0 0 0 0 0 0.2 0.439216 0.819608 0.807843 538420 183960 38445 183940 0.094118 0.098039 0.039216 0.047059 0.090196 0.101961 0.07451 0.070588 0 0 0 0 0 0 0 0.215686 0.431373 0.752941 0.854902 0.094118 0.098039 38430 183965 0.094118 0.098039 0.039216 0.047059 0.090196 0.098039 0.07451 0.070588 0 0 0 0 0 0 0 0.196078 0.439216 0.831373 0.74902 0.039216 0.047059 38445 183945 0.098039 0.101961 0.039216 0.047059 0.090196 0.098039 0.07451 0.070588 0 0 0 0 0 0 0 0.203922 0.435294 0.745098 0.839216 0.094118 0.098039 38445 183935 0.094118 0.101961 0.039216 0.047059 0.090196 0.098039 0.07451 0.070588 0 0 0 0 0 0 0 0.223529 0.423529 0.745098 0.843137 0.07451 0.070588 0 38945 184380 0.094118 0.098039 0.039216 0.054902 0.090196 0.098039 0.07451 0.070588 0 0 0 0 0 0 0 0.356863 0.333333 0.807843 0.784314 0 0 0 38445 183930 0.098039 0.101961 0.039216 0.047059 0.090196 0.098039 0.07451 0.070588 0 0 0 0 0 0 0 0.239216 0.415686 0.721569 0.819608 0 0 0 38445 183950 0.094118 0.098039 0.039216 0.047059 0.090196 0.101961 0.07451 0.070588 0 0 0 0 0 0 0 0.203922 0.439216 0.729412 0.8 0.215686 0.427451 38420 183970 0.101961 0.098039 0.039216 0.047059 0.090196 0.098039 0.078431 0.070588 0 0 0 0 0 0 0 0.203922 0.435294 0.815686 0.701961 0.952941 0.886275 38945 184385 0.094118 0.098039 0.039216 0.047059 0.094118 0.098039 0.07451 0.070588 0 0 0 0 0 0 0 0.345098 0.341176 0.792157 0.756863 538435 183940 38425 183950 0.094118 0.098039 0.039216 0.047059 0.090196 0.101961 0.07451 0.070588 0 0 0 0 0 0 0.466667 0.231373 0.423529 0.996078 0.988235 0.094118 0.098039 38425 183945 0.094118 0.098039 0.039216 0.047059 0.090196 0.098039 0.07451 0.070588 0 0 0 0 0 0 0.466667 0.239216 0.419608 0.988235 0.996078 0.039216 0.047059 38420 183950 0.094118 0.098039 0.039216 0.047059 0.090196 0.098039 0.07451 0.070588 0 0 0 0 0 0 0.466667 0.239216 0.419608 1 0.972549 0.090196 0.098039 38425 183970 0.101961 0.098039 0.039216 0.047059 0.090196 0.098039 0.07451 0.070588 0 0 0 0 0 0 0 0.192157 0.439216 0.780392 0.690196 0.07451 0.070588 0 38420 183945 0.094118 0.098039 0.039216 0.047059 0.090196 0.098039 0.07451 0.070588 0 0 0 0 0 0 0.466667 0.247059 0.411765 0.984314 0.976471 0 0 0 38430 183945 0.094118 0.098039 0.039216 0.047059 0.090196 0.098039 0.07451 0.070588 0 0 0 0 0 0 0.466667 0.231373 0.423529 0.956863 1 0 0 0 38455 183420 0.094118 0.101961 0.039216 0.047059 0.090196 0.101961 0.07451 0.07451 0 0 0 0 0 0 0 0.239216 0.407843 0.686275 0.788235 0.227451 0.427451 38430 183950 0.094118 0.098039 0.039216 0.047059 0.094118 0.098039 0.07451 0.070588 0 0 0 0 0 0 0.466667 0.215686 0.431373 0.960784 0.980392 0.886275 0.964706 38460 183420 0.098039 0.105882 0.039216 0.047059 0.090196 0.101961 0.078431 0.094118 0 0 0 0 0 0 0 0.231373 0.415686 0.698039 0.768627 538425 183960 38465 183420 0.105882 0.113725 0.039216 0.047059 0.090196 0.101961 0.082353 0.094118 0 0 0 0 0 0 0 0.227451 0.415686 0.709804 0.74902 0.098039 0.098039 38435 183965 0.094118 0.098039 0.039216 0.047059 0.090196 0.098039 0.07451 0.070588 0 0 0 0 0 0 0 0.192157 0.439216 0.745098 0.701961 0.039216 0.047059 38450 183940 0.094118 0.098039 0.039216 0.047059 0.090196 0.098039 0.07451 0.070588 0 0 0 0 0 0 0 0.207843 0.431373 0.686275 0.768627 0.090196 0.098039 38470 183420 0.105882 0.109804 0.039216 0.047059 0.090196 0.098039 0.082353 0.090196 0 0 0 0 0 0 0 0.215686 0.423529 0.721569 0.729412 0.07451 0.070588 0 38440 183960 0.094118 0.101961 0.039216 0.047059 0.094118 0.101961 0.07451 0.070588 0 0 0 0 0 0 0 0.196078 0.439216 0.72549 0.721569 0 0 0 38450 183420 0.094118 0.098039 0.043137 0.047059 0.094118 0.098039 0.07451 0.070588 0 0 0 0 0 0 0 0.243137 0.403922 0.666667 0.796078 0 0 0 38455 183415 0.094118 0.098039 0.039216 0.047059 0.090196 0.098039 0.07451 0.070588 0 0 0 0 0 0 0 0.243137 0.403922 0.694118 0.764706 0.211765 0.431373 38425 183940 0.098039 0.098039 0.039216 0.047059 0.090196 0.098039 0.07451 0.070588 0 0 0 0 0 0 0.466667 0.247059 0.411765 0.952941 0.976471 0.937255 0.882353 38455 183425 0.101961 0.101961 0.039216 0.047059 0.090196 0.098039 0.078431 0.090196 0 0 0 0 0 0 0 0.227451 0.415686 0.658824 0.784314 538430 183960 38450 183935 0.094118 0.098039 0.039216 0.047059 0.090196 0.098039 0.07451 0.070588 0 0 0 0 0 0 0 0.215686 0.431373 0.67451 0.772549 0.098039 0.098039 38465 183415 0.098039 0.101961 0.039216 0.047059 0.090196 0.098039 0.078431 0.082353 0 0 0 0 0 0 0 0.239216 0.407843 0.72549 0.721569 0.039216 0.047059 38450 183425 0.101961 0.101961 0.039216 0.047059 0.090196 0.098039 0.078431 0.078431 0 0 0 0 0 0 0 0.239216 0.403922 0.647059 0.796078 0.090196 0.098039 38460 183415 0.094118 0.098039 0.039216 0.047059 0.090196 0.098039 0.07451 0.078431 0 0 0 0 0 0 0 0.243137 0.403922 0.705882 0.741176 0.07451 0.070588 0 38460 183425 0.109804 0.117647 0.039216 0.047059 0.101961 0.101961 0.082353 0.098039 0 0 0 0 0 0 0 0.223529 0.415686 0.670588 0.764706 0 0 0 38430 183940 0.094118 0.101961 0.039216 0.047059 0.090196 0.098039 0.07451 0.070588 0 0 0 0 0 0 0.466667 0.239216 0.415686 0.933333 0.984314 0 0 0 38470 183415 0.098039 0.101961 0.039216 0.047059 0.090196 0.101961 0.078431 0.078431 0 0 0 0 0 0 0 0.227451 0.411765 0.737255 0.698039 0.203922 0.431373 38950 184385 0.094118 0.098039 0.039216 0.047059 0.094118 0.101961 0.07451 0.070588 0 0 0 0 0 0 0 0.34902 0.337255 0.764706 0.717647 0.890196 0.862745 38465 183425 0.109804 0.113725 0.039216 0.047059 0.105882 0.101961 0.082353 0.098039 0 0 0 0 0 0 0 0.211765 0.427451 0.682353 0.745098 538440 183940 38475 183420 0.105882 0.105882 0.039216 0.047059 0.090196 0.098039 0.078431 0.098039 0 0 0 0 0 0 0 0.211765 0.427451 0.72549 0.701961 0.094118 0.098039 38445 183425 0.101961 0.101961 0.043137 0.047059 0.090196 0.098039 0.078431 0.082353 0 0 0 0 0 0 0 0.247059 0.403922 0.635294 0.8 0.039216 0.047059 38445 183925 0.094118 0.101961 0.039216 0.047059 0.090196 0.098039 0.07451 0.070588 0 0 0 0 0 0 0 0.247059 0.415686 0.682353 0.760784 0.090196 0.098039 38450 183415 0.094118 0.098039 0.043137 0.047059 0.090196 0.098039 0.07451 0.070588 0.133333 0.188235 0.592157 0.152941 0.184314 0.137255 0 0.254902 0.4 0.662745 0.772549 0.07451 0.070588 0 38470 183425 0.109804 0.113725 0.043137 0.047059 0.109804 0.101961 0.078431 0.094118 0 0 0 0 0 0 0 0.207843 0.427451 0.690196 0.72549 0 0 0 38945 184390 0.094118 0.098039 0.039216 0.047059 0.090196 0.098039 0.07451 0.070588 0 0 0 0 0 0 0 0.329412 0.356863 0.756863 0.705882 0 0 0 38475 183415 0.101961 0.101961 0.039216 0.047059 0.090196 0.098039 0.078431 0.078431 0 0 0 0 0 0 0 0.215686 0.423529 0.737255 0.67451 0.223529 0.427451 38450 183945 0.094118 0.101961 0.039216 0.047059 0.090196 0.101961 0.07451 0.070588 0 0 0 0 0 0 0 0.203922 0.439216 0.670588 0.745098 0.835294 0.92549 38445 183420 0.094118 0.098039 0.054902 0.047059 0.090196 0.098039 0.07451 0.070588 0 0 0 0 0 0 0 0.254902 0.403922 0.643137 0.8 538440 183935 38450 183930 0.094118 0.098039 0.039216 0.047059 0.094118 0.098039 0.07451 0.070588 0 0 0 0 0 0 0 0.223529 0.419608 0.654902 0.74902 0.094118 0.098039 38480 183420 0.105882 0.117647 0.039216 0.047059 0.090196 0.101961 0.078431 0.105882 0 0 0 0 0 0 0 0.203922 0.431373 0.717647 0.678431 0.039216 0.047059 38430 183970 0.101961 0.098039 0.039216 0.047059 0.094118 0.101961 0.07451 0.070588 0 0 0 0 0 0 0 0.192157 0.439216 0.72549 0.658824 0.090196 0.098039 38475 183425 0.109804 0.113725 0.039216 0.047059 0.109804 0.101961 0.082353 0.105882 0 0 0 0 0 0 0 0.203922 0.431373 0.686275 0.701961 0.07451 0.070588 0 38430 183955 0.098039 0.101961 0.039216 0.047059 0.090196 0.098039 0.07451 0.070588 0 0 0 0 0 0 0.466667 0.211765 0.431373 0.937255 0.92549 0 0 0 38440 183425 0.094118 0.101961 0.05098 0.047059 0.090196 0.101961 0.07451 0.07451 0 0 0 0 0 0 0 0.254902 0.396078 0.619608 0.788235 0 0 0 38435 183945 0.098039 0.098039 0.039216 0.047059 0.090196 0.098039 0.07451 0.070588 0 0 0 0 0 0 0.466667 0.223529 0.427451 0.898039 0.964706 0.231373 0.419608 38480 183415 0.098039 0.101961 0.039216 0.047059 0.090196 0.101961 0.07451 0.086275 0 0 0 0 0 0 0 0.211765 0.427451 0.729412 0.654902 0.807843 0.901961 38950 184390 0.094118 0.098039 0.039216 0.047059 0.090196 0.101961 0.07451 0.070588 0 0 0 0 0 0 0 0.337255 0.34902 0.745098 0.686275 538420 183965 38300 183950 0.098039 0.098039 0.039216 0.047059 0.090196 0.098039 0.07451 0.07451 0 0 0 0 0 0 0 0.117647 0.470588 0.87451 0.454902 0.094118 0.098039 38445 183415 0.094118 0.098039 0.047059 0.047059 0.094118 0.098039 0.07451 0.070588 0.380392 0.380392 0.741176 0.219608 0.219608 0.258824 0 0.254902 0.396078 0.631373 0.764706 0.039216 0.047059 40 38465 183410 0.094118 0.098039 0.039216 0.047059 0.094118 0.098039 0.07451 0.070588 0 0 0 0 0 0 0 0.247059 0.403922 0.713726 0.67451 0.094118 0.098039 38300 183945 0.094118 0.098039 0.039216 0.047059 0.094118 0.098039 0.07451 0.070588 0 0 0 0 0 0 0 0.117647 0.470588 0.890196 0.431373 0.07451 0.070588 0 38445 183430 0.105882 0.101961 0.039216 0.047059 0.094118 0.101961 0.078431 0.098039 0 0 0 0 0 0 0 0.243137 0.407843 0.611765 0.772549 0 0 0 38460 183410 0.098039 0.098039 0.039216 0.047059 0.090196 0.098039 0.07451 0.070588 0 0 0 0 0 0 0 0.25098 0.4 0.694118 0.690196 0 0 0 38450 183430 0.105882 0.109804 0.039216 0.047059 0.090196 0.101961 0.078431 0.098039 0 0 0 0 0 0 0 0.231373 0.411765 0.611765 0.764706 0.211765 0.431373

538545 183715 0.094118 0.101961 0.623529 0.780392 0.070588 0 0 0 538455 183410 0.094118 0.098039 0.039216 0.047059 0.090196 0.098039 0.07451 0.070588 0.380392 0.380392 0.741176 0.219608 0.219608 0.258824 0 0.254902 0.4 0.439216 0.537255 0.262745 0.67451 0.709804 538560 183740 0.094118 0.098039 538305 183950 0.094118 0.101961 0.039216 0.047059 0.090196 0.098039 0.07451 0.070588 0 0 0 0 0 0 0 0.121569 0.470588 0.070588 0 0 0 0.843137 0.478431

0.439216 0.545098 0.254902 04 0.47451 0.521569 0.356863

538575 183780 0.094118 0.098039 538320 183930 0.098039 0.101961 0.039216 0.047059 0.094118 0.101961 0.078431 0.070588 0 0 0 0 0 0 0 0.12549 0.466667 0.070588 0 0 0 0.545098 0.341176 0.427451 0.454902 0.341176 538340 183960 0.094118 0.098039 0.039216 0.047059 0.090196 0.098039 0.07451 0.070588 0 0 0 0 0 0 0 0.164706 0.447059 538830 183435 0.098039 0.101961 0.423529 0.482353 0.070588 0 0 0 538455 183445 0.121569 0.109804 0.047059 0.062745 0.12549 0.109804 0.078431 0.117647 0 0 0 0 0 0 0 0.203922 0.431373 0.47451 0.396078 0.305882 0.423529 0.529412 538970 184500 0.094118 0.101961 538420 183445 0.109804 0.101961 0.043137 0.054902 0.117647 0.105882 0.086275 0.105882 0 0 0 0 0 0 0 0.254902 0.407843 0.070588 0 0 0 0.490196 0.47451

0.482353 0.368627 0.447059 538905 183840 0.094118 0.101961 0.05098 0.047059 0.094118 0.098039 0.07451 0.070588 0 0 0 0 0 0 0.266667 0.12549 0.462745

538980 184405 0.105882 0.101961 0.709804 0.447059 0.078431 0 0.109804 0.164706 538905 183860 0.094118 0.098039 0.039216 0.047059 0.094118 0.098039 0.07451 0.070588 0 0 0 0 0 0 0.266667 0.133333 0.462745 0.4 0.498039 0.307843 0.694118 0.458824 537810 184225 0.094118 0.098039 538945 183850 0.094118 0.098039 0.039216 0.047059 0.090196 0.098039 0.07451 0.070588 0 0 0 0 0 0 0 0.113725 0.490196 0.070588 0 0 0 0.482353 0.411765 0.490196 0.356863 0.294118 538970 184415 0.101961 0.098039 0.043137 0.047059 0.105882 0.101961 0.078431 0.07451 0 0 0 0 0 0 0 0.270588 0.415686 538415 183930 0.101961 0.098039 0.588235 0.392157 0.082353 0 0 0 538445 184170 0.094118 0.098039 0.039216 0.047059 0.090196 0.098039 0.07451 0.070588 0 0 0 0 0 0 0 0.109804 0.47451

0.407843 0.756863 0.721569 0.564706 0.309804

538415 183990 0.109804 0.098039 538450 184165 0.094118 0.098039 0.039216 0.047059 0.090196 0.098039 0.07451 0.070588 0 0 0 0 0 0 0 0.109804 0.47451 0.070588 0 0 0 0.513726 0.360784 0.443137 0.439216 0.356863 538465 183910 0.094118 0.098039 0.039216 0.047059 0.090196 0.098039 0.07451 0.070588 0 0 0 0 0 0 0 0.25098 0.411765 538435 184195 0.094118 0.109804 0.541176 0.411765 0.090196 0 0 0 538470 183850 0.105882 0.145098 0.039216 0.054902 0.133333 0.141176 0.105882 0.082353 0 0 0 0 0 0 0 0.286275 0.392157 0.47451 0.564706 0.152941 0.462745 0.513726 538475 184165 0.094118 0.098039 538650 183320 0.094118 0.098039 0.039216 0.047059 0.101961 0.101961 0.07451 0.070588 0 0 0.080392 0.064706 0 0 0 0.07451 0.545098 0.070588 0 0 0 0.470588 0.439216

0.47451 0.352941 0.384314 538305 183980 0.101961 0.113725 0.039216 0.047059 0.105882 0.105882 0.082353 0.098039 0 0 0 0 0 0 0 0.145098 0.458824

538495 183445 0.094118 0.098039 0.533333 0.360784 0.07451 0 0 0 538330 183940 0.094118 0.098039 0.039216 0.058824 0.098039 0.098039 0.07451 0.070588 0 0 0 0 0 0 0 0.137255 0.458824 0.45098 0.337255 0.435294 0.447059 0.45098 538595 183835 0.094118 0.098039 538495 183400 0.094118 0.098039 0.039216 0.047059 0.090196 0.098039 0.07451 0.086275 0 0 0 0 0 0 0 0.227451 0.419608 0.070588 0 0 0 0.541176 0.396078 0.415686 0.435294 0.356863 538405 183945 0.094118 0.098039 0.05098 0.054902 0.090196 0.098039 0.07451 0.070588 0 0 0 0 0 0 0.666667 0.254902 0.407843 538640 183360 0.109804 0.113725 0.858824 0.776471 0.129412 0 0 0 538290 183920 0.094118 0.098039 0.039216 0.047059 0.090196 0.098039 0.07451 0.070588 0 0 0 0 0 0 0 0.113725 0.470588

0.482353 0.411765 0.313726 0.705882 0.164706

538835 184295 0.094118 0.098039 538300 183920 0.098039 0.098039 0.039216 0.047059 0.094118 0.098039 0.07451 0.070588 0 0 0 0 0 0 0 0.117647 0.470588 0.070588 0 0 0 0.682353 0.192157 0.25098 0.427451 0.380392 538340 183965 0.098039 0.098039 0.039216 0.047059 0.090196 0.098039 0.07451 0.070588 0 0 0 0 0 0 0 0.168627 0.447059 538950 183830 0.094118 0.101961 0.439216 0.462745 0.070588 0 0 0 538395 183980 0.12549 0.098039 0.066667 0.062745 0.172549 0.109804 0.117647 0.07451 0 0 0 0 0 0 0 0.223529 0.419608 0.490196 0.427451 0.294118 0.521569 0.45098 538275 183920 0.094118 0.098039 538415 183985 0.121569 0.098039 0.07451 0.07451 0.164706 0.113725 0.113725 0.07451 0 0 0 0 0 0 0 0.188235 0.439216 0.070588 0.180392 0.341176 0.458824 0.529412 0.447059

0.47451 0.592157 0.117647 538595 183795 0.094118 0.098039 0.039216 0.047059 0.090196 0.098039 0.07451 0.070588 0 0 0 0 0 0 0.266667 0.227451 0.435294

538420 184180 0.094118 0.101961 0.603922 0.611765 0.070588 0 0 0 538460 184185 0.094118 0.098039 0.039216 0.047059 0.090196 0.098039 0.07451 0.070588 0 0 0 0 0 0 0 0.109804 0.47451 0.47451 0.568627 0.137255 0.580392 0.290196 538440 183410 0.101961 0.101961 538470 183840 0.094118 0.129412 0.039216 0.047059 0.105882 0.113725 0.086275 0.086275 0 0 0 0 0 0 0 0.286275 0.392157 0.070588 0 0 0 0.45098 0.513726 0.403922 0.576471 0.67451 538470 183920 0.094118 0.098039 0.039216 0.047059 0.090196 0.098039 0.07451 0.070588 0 0 0 0 0 0 0 0.227451 0.419608 538460 183390 0.094118 0.098039 0.509804 0.423529 0.070588 0 0 0 538470 183925 0.094118 0.101961 0.039216 0.047059 0.090196 0.101961 0.07451 0.070588 0 0 0 0 0 0 0 0.219608 0.427451

0.415686 0.447059 0.341176 0.490196 0.443137

538465 183895 0.094118 0.098039 538940 183840 0.094118 0.098039 0.039216 0.047059 0.094118 0.098039 0.07451 0.070588 0 0 0 0 0 0 0 0.113725 0.486275 0.070588 0 0 0 0.505882 0.380392 0.396078 0.517647 0.278431 538965 184420 0.094118 0.098039 0.043137 0.047059 0.098039 0.101961 0.07451 0.070588 0 0 0 0 0 0 0 0.266667 0.411765 538540 183715 0.094118 0.098039 0.568627 0.4 0.070588 0 0 0 538340 183955 0.094118 0.098039 0.039216 0.047059 0.090196 0.098039 0.07451 0.070588 0 0 0 0 0 0 0 0.160784 0.45098 0.439216 0.537255 0.258824 0.403922 0.490196 538550 183715 0.094118 0.098039 538450 183830 0.098039 0.101961 0.05098 0.058824 0.101961 0.098039 0.078431 0.070588 0 0 0 0 0 0 0 0.254902 0.415686 0.070588 0 0 0 0.47451 0.501961

0.435294 0.517647 0.262745 538935 183835 0.094118 0.098039 0.039216 0.047059 0.090196 0.098039 0.07451 0.070588 0 0 0 0 0 0 0 0.113725 0.478431

538600 183785 0.094118 0.101961 0.521569 0.352941

MACHINE LEARNING D ATA A N A LY T I C S

DIMENSIONALITY REDUCTION METHODS ON SITE

On a next level, we attempt to approach the spatial interaction of the collected and analysed data in a more extensive and exploratory way through machine learning. The data used as an input concerns geolocated values of concentration of NO2 and PM2.5 particles, and the equivalent level of pollution absorption from the trees of our site and also values concerning the most pervasive wind conditions per annual season. Furthermore, an Integration and Choice Analysis of three different radii [1200,2400,5000] and also a Visibility Graph and Isovist Analysis including Visual Occlusivity and Visual Compactness were imported as a major element in our analysis, representing the role of the human factor on our site.

The preprocessing, processing and analysis of our datasets through Machine Learning techniques and algorithms will help us identify in a more exploratory way spatial patterns and similarities between different areas of our site and therefore be used as a guideline to detect areas that require different types of intervention. These areas can either be exposed and vulnerable in terms of pollution or they might present a low value of the human factor and require the equivalent intervention of regenerating subdued zones through new proposed uses.

PM25 Concentration PM25 Trees’ Absorption NO2 Concentration NO2 Trees’ Absorption Visibility Graph Analysis Integration Analysis R500 Integration Analysis R800 Integration Analysis R3000 Choice Analysis R500 Choice Analysis R800 Choice Analysis R3000 Winter Wind SW 30 ° Winter Wind W 270 ° Autumn Wind S 8 ° Autumn Wind NE 220 ° Summer Wind NE 185 ° Summer Wind SW 35 ° Spring Wind SW 25 ° Spring Wind NE 250 °

Data Source: MeteoBlue, Mayor of London, King’s College London

42

43

D ATA CROSSINGS EXPLORING AREAS OF HIGH INTEGRATION

A

spatial

correlation

Integration of

PM25

that

the

network

values

and

with

NO2

higher

between

pollution

pollutants,

the

value

accessibility,

the

the

of

interaction

concentration

clarifies the

higher

of

human the

the

fact

fa c t o r ’s pollution

The

output

areas

where

from the

this

correlation,

interaction

between

identifies pollution

concentration and the human factor, demonstrates specific areas of intervention.

At

the

same

time,

areas

with

concentration is. The blue dots indicate a crossing

values

and

correlation between the input values with common

concentration

characteristics.

priority of intervention could be a proposal of uses

subsequently

lower

Integration

lower

pollution

identify spatial sectors where the

that regenerate subdued zones with low value of

Integration R5000

Integration.

120 110 100 90 80 70 60 50 40 30 20 10

0.2

0.3

Correlation of Integration R5000 PM2.5 Concentration

44

0.4

0.5

0.6 PM2.5 Concentration

Spring Wind

Winter wind

0.6 0.5

0.7 0.6

0.4

0.5

0.3

0.4

0.2

0.3 0.2

0.1

0.3

0.2

0.4

0.5

0.1

0.6

0.04

0.1

0.16

0.22

0.28

0.34

Correlation of Spring Wind and NO2 Concentration

1

1

0.9

0.9

PM25 Absorption

NO2 Absorption

Correlation of Winter Wind and PM2.5 Concentration

0.4

NO2 Concentration

PM2.5 Concentration

0.8 0.7 0.6 0.5

0.8 0.7 0.6 0.5

0.4

0.4

0.3

0.3

0.2

0.2

0.1

0.1 0.04

0.1

0.16

0.22

0.28

0.34

0.4

NO2 Concentration

0.46

0 0.2

0.3

0.4

0.5

0.6

PM2.5 Concentration

Correlation of NO2 Absorption and NO2 Concentration

Correlation of PM2.5 Absorption and PM2.5 Concentration

45

P R I N C I PA L C O M P O N E N T A N A LY S I S [ P C A ]

After detecting the spatial interaction of the imported datasets through simple scatterplos, georeferenced and non georeferenced distribution plots, we detect the interactions between the data through machine learning algoithms. These are the Principal Component Analysis [PCA] and K-Means Clustering.

PCA is a machine learning algorithm through which the datasets used as an input, undergo a mathematical procedure that transforms a set of correlated variables to a set of uncorrelated variables, which are called the principal components. The PCA algorithm will give us as an output areas that present the highest variation and differentiation, the hot spots with the highest variance.

The values chosen as an input were these of PM2.5 particles concentration, values concerning the major wind speed and direction during wintertime, PM2.5 absorption from the trees of our site and Integration values representing the role of the human factor. The reason we chose these values was the previously detected preexisting interaction between them. Absorption from trees is anticipating pollution concentration and winter wind -being the one detected with the highest values of velocity- has a double impact on PM2.5 concentration, since on the one hand it generates dust [major component of PM25], on the other hand, high wind speed contributes in the dilution and dispersion of pollution particles.

PCA [Principal Component Analysis] Areas with highly interactive data

High

46

Low

Interactions Level 1 T-SNE Algorithm Visualisation of data Interactions

Interactions Level 2

PCA values projected on site Interactions Level 3

Interactions Level 4 Point cloud representing the level of data interaction High

Low

Interactions Level 5

Urban Context

T-SNE Algorithm Visualisation t-Distributed Stochastic Neighbor Embedding Algorithm, visualises the gradual development of interactions between data values up until they are distributed in subgroups with common levels of interactions and similar characteristics.

47

K- MEANS CLUSTERING

After detecting areas with unique characteristics, we proceed with an unsupervised machine learning algorithm, called K-Means Clustering, which predicts and detects subgroups with similar caracteristics within the imported dataset.

The input used was similar to the one of the

Principal Component Analysis, since the output of the two algorithms, on a next level, will be combined in order to specify areas of high interest and therefore reasons to intervene.

More specifically, the values used as an input were pollution concentration, the equivalent absorption from trees, values of the major wind speed and velocity during wintertime and Integration values. The data are organized during the clustering process in three clusters with common characteristics.

K-Means Clustering Clustered areas with similar characteristics Cluster A Cluster B Cluster C

48

K-Means Clustering values projected on site

Spatial Distribution of Clustering Values Cluster A Cluster B Cluster C

Urban Context

49

C O M B I N AT I O N O F H I G H R I S K A S S E S S M E N T, PCA AND CLUSTERS

By

taking

into

consideration

the

importance

of

the human factor on our site and the impact that high

or

low

Integration

values

have

to

pollution

concentration, we proceed by filtering our data and overlaying

three

elements,

clustered

areas

with

similar characteristics, high risk areas and areas with high value of interaction between the imported datasets.

This gave us as an output the fact that high risk areas are overlapping with the ones where the PCA gave as an output high level of interaction between the data and at the same time are categorized in b e t w e e n c l u s t e r s w i t h d i f f e re n t t y p e s o f s i m i l a r i t y.

50

Risk Assessment

PCA [Principal Component Analysis]

K-Means Clustering

Filtered high risk values

Areas with highly interactive data

Clustered areas with similar characteristics

Overlaying of Dimensionality Reduction and Risk Assessment Area of intervention High risk areas High interaction a High interaction b Cluster A Cluster B Cluster C

51

52

PCA Interactions 53

54

C O M P U TAT I O N A L U R B A N P R OTO C O L S OF INTERVENTION

OVERLAY OF FUTURE PM 2.5 INCREASE AND WIND PATTERN [2013,2020,2025,2030]

Wind Pattern

Future PM2.5 Increase and PCA Values

56

Overall Data Map High PCA Medium PCA Predicted PM2.5 Increase High Wind Speed Medium Wind Speed Low Wind Speed

57

RISK ASSESSMENT

After using the output of dimensionality reduction and future predicted

Since each of these data values do not have common measurement

air quality values, the characteristics of the site of intervention, are

units, before using them as an input in the risk assessment equation,

narrowed down by specifying different levels of risk assessment. This

the data is digitally remapped and normalised under a common ratio.

value derives from an equation including PM2.5 and NO2 concentration,

In order to organise our data, a combined data map is generated

PM2.5 and NO2 absorption, network analysis Integration values and

following a risk equation which compresses all the data throughout the

spring wind’s intensity values. Values describing wind intensity

analysis process. The site is divided into a 5x5 grid system, and each

during spring months, are chosen since the precedent wind analysis,

value of risk assessment is redistributed on the grid and categorised

demonstrates lowest values around this time of year. Therefore, areas

into three levels which helped us approach our further design in a

exposed to low wind velocity are more exposed to air pollution, since

detailed urban scale.

increasing moisture levels speed up chemical reactions in the air, and prevent pollution contaminants from dispersing and diluting.

ÎŁ

Risk Assessment

Sum of

=

+

+

+

-

[PM2.5_Con + PM2.5_Abs + NO2_Con + NO2_Abs + Integration_800 – Spring_wind]

58

Risk Assessment Levels Low Risk Medium Risk High Risk

59

60

DEPRAVED URBAN SCAPES

61

EMERGENCE OF VERTICAL AND H O R I Z O N TA L F I LT E R I N G Rethinking the Stratford Metropolitan Masterplanning We proceed in the detected areas with design interventions which are defined by the structural complexity and morphology of natural systems, considered as the basic elements anticipating air pollution.

According to the risk assessment levels, we develop design strategies on the horizontal and the vertical sense, and propose filtered public and private spaces being in a permanent dialectic with air toxicity.

The site of intervention is included in the area within which the Stratford Metropolitan Masterplan (SMM) (including urban planning interventions such as the Olympic Park) is proposed and approved in 2011. Future proposals include the establishment of over 20.000 residencies and 1.800 workspaces as well as the expansion of the road network through conventional constructing methods.

The current design proposal is enabling the replacement of

these

future interventions, with the emergence of structures specialised in filtering air pollution and at the same time providing and replacing public and private spaces already proposed in the Stratford Metropolitan Masterplan. The interventions, apart from filtered public spaces, propose 3.355 sm space capacity for residence as well as 1.076 sm space labs where structural details of the design proposal are produced.

62

Building Capacity (each) Residence: 3355 m2 Filtering Units Lab:1076 m2

7800 Large Units

7800 Large Units

5974 Small Units

5974 Small Units

7800 Large Units 5974 Small Units

Project’s Proposal

SMM Proposal

200.000 sqm retail, leisure, education 2.900 sqm residential properties

63

75.69

78.43

Masterplan of Overall Proposal related to Risk Assessment A

77.55

78.37

150.25

150.25

150.25

150.25

150.25

150.25

172.67

150.25

150.25

150.25

177.23

170.34

B

Vertical Filtering

Building Capacity (each) 3355 m2 for Residence 1076 m2 for Filtering Units Lab

B

Vertical Filtering

C

Horizontal Filtering

150.25

172.04

153.94

122.56

150.25

134.09

154.84

127.67

133.01

134.99

136.18

145.85

Increased Surface Structures with space for open public uses

Canopies with 7800 Large Units 5974 Small Units Low Risk Medium Risk High Risk

64

150.25

A

B 130.93

142.22

143.36

148.53

136.77

140.78

129.89

136.48

146.23

145.08

142.22

146.80

136.83

128.48

A 155.37

164.56

166.47

153.06

157.37

150.78

170.37

172.68

125.84

145.37

150.27

171.98

87.37

96.47

141.93

143.08

144.48

153.94

174.67

156.39

144.96

123.58

111.46

C133.55

124.86

150.25

150.25

150.25

150.25

150.25

150.25

108.96

111.62

C

171.18

172.04

150.25

150.25

150.25

150.25

150.25

150.25

144.85

145.98

122.09

123.57

150.25

150.25

150.25

150.25

114.70

C

123.59

146.09

135.92

145.45

149.82

160.66

175.59

110.62

152.49

155.93

1167.36

149.93

150.25

150.25

150.25

108.47

65

153.59

152.15

144.20

145.16

148.38

137.30

111.47

D E S I G N S T R AT E G Y FO R H O R I Z O N TA L F I LT E R I N G

The structural generation of the horizontal filtering system is based on catenary arches. We start by testing different methods which generate different shapes of canopies. By controlling the amount and location of anchor points, we can control the complexity and extent of the catenary filtering structure.

As a next step, the structural logic is applied in the urban context.

Firstly, the horizontal structure is emerged according to the risk grid, then the density of canopies is reduced by preserving exclusively the ones developed on the edges of the risk zones. In that way, the structure is acting like a filtering safeguard within the public space. As a final step, individual structures are merged together, and the output is reconstructed and adapted in the urban environment.

66

Generation 1 Emerging Different types of canopies are generated according to risk assessment values.

Generation 2 Reduction Canopies developed on the edges of the risk zones are preserved as safeguards of the area.

Generation 3 Amalgamation Individual structures are merged together by selected anchor points according to the urban context.

67

CATENARY STRUCTURES

Canopy 1

Variation 1

Anchor Points: 160 Arches: 1

Canopy 2 Anchor Points: 8 Arches: 1

Canopy 3 Anchor Points: 20 Arches: 12

68

Variation 2

Variation 3

Prototype 1 Open Space Canopy

Prototype 3 Parasitic Structure

Suited for areas with open spaces.

The coverage is driven by risk level and creates environmentally controlled microclimates.

Suited for building facades which overlap with high-risk zones The tensile structure can behave as a parasite on the existing buildings which act as hosts.

Prototype 2

Prototype 4

Street Canopy

Double Layer

Suited for traffic networks.

Suited for areas of the highest risk assessment. A second layer with smaller filtering units|modules is added in order to maximise the

The anchor points are set on the edges of the street in cooperation with vehicle and pedestrian movement.

filtering effect.

69

Structure

F I LT E R I N G U N I T S ’ PA C K I N G

Circle Packing

Shadow

Canopy Surface

220 Hexagon 70% Luminance

150 Hexagon

220 Hexagon

Filtering Ability

150 Hexagon 80% Luminance

72

Hexagonal Packing

Structural Skeleton

300 Hexagon 65% Luminance

440 Hexagon 50% Luminance

300 Hexagon

440 Hexagon

73

M AT E R I A L S T U D I E S F I LT E R I N G U N I T S A N D D I F F E R E N T I A L G R O W T H

One of the core elements of the proposed intervention, is the assemblage of modules which imitate the morphological complexity of organic elements, through Differential Growth. Differential Growth describes the space filling behavior of a geometry which grows at different rates in different locations. In the same way, through mathematical subdivision and differentiation, biological systems produce structures with specific, reproducible forms and functions.

After generating the structure of the horizontal filtering system, we start to develop filtering units|modules which will be attached on the catenary skeleton. The units are developed under the rules of differential growth which generates the maximum possible surface area within a given volume, and at the same time, provides perforation which enables air flow.

Plan V iew

Growth Phase 2

Growth Phase 3

Axonometric View

Plan View

Front View

Growth Phase 1

74

Axonometric V iew

75

M AT E R I A L I T Y O F F I LT E R I N G U N I T S

The filtering units are proposed to be constructed by geotextile and coated with moss seeds and fertilizer. The growth with absorptive performance, will be activated by the elements in the surrounding environment like rain and sunlight. These living units will eventually be dominated by natural growth which absorbs air pollution.

Steel frame & Geotextile fabric

Layer Materiality of Filtering Modules

76

Coating with seeds and fertilizer

Growth activated by rain, absorbing contaminants and dust

77

78

1:1 Filtering Unit Physical Model

79

80

81

82

83

D E S I G N S T R AT E G Y F O R V E RT I C A L F I LT E R I N G

The structural generation of the vertical filtering system is based on the principles Differential Growth, such as the filtering module of the intervention on the horizontal sense, which is scaled up and applied on a human scale. The generation and further morphological output of the vertical filtering structure, is based on natural growth algorithms since these are considered the most appropriate to engage with the flux of living systems inherent within subnatural zones and produce infinite variations according to local environmental conditions and in situ datasets.

84

ZIYI RENDER

85

F R A C TA L T Y P E S O F I N T E R V E N T I O N T Y P E A | P U B L I C F I LT E R I N G I N S TA L L AT I O N

The emerging vertical filtering is developed under two spatial types of use. Type A is generated in open spaces with high risk assessment values and creates a landscape with its own microclimate, while offering spaces for public uses.

The infinite variations created during the digital growth process, provide a variety in the level of complexity which is correlated with the local air quality conditions. Furthermore, NO2 and PM2.5 concentration levels, appear in a density of 100% on the ground level, and decrease by 20% every 3 meters upwards. According to that, the level of complexity of the space filling fractal and therefore its absorptive performance is reduced per height.

35

30

25

20

15

10

5

0

Space Filling Curves

86

5

Planar View of Emerging Structure

10

15

20

25

30 m.

T+1

T+20

T+40

T+60

T+80

T+90

T+100

T+120

T+140

T+160

T+180

T+200

T+220

T+240

T+260

T+280

Growth Process

87

T YPE B|FRA CTALS FORMING INTERIOR SPA CE

Type B, is emerged within the landscape of the space filling fractals in areas of high risk assessment. This structural type proposes enclosed inhabitable spaces with a spatial capacity of 13.320 sm for residence and 4.304 sm for spaces provided for vertical urban farming and labs producing the filtering space filling modules of the horizontal structures.

As mentioned before, the spatial capacity of the buidings is calculated in a way which when expanded

as a typology in the context of high

risk zones of the broader area,will replace partially the proposal of the Stratford Metropolitan Masterplanning for residential and commercial infrastructure.

35 m.

30

25

20

15

10

5

0

Space Filling Curves

88

5

10

15

Planar View of Emerging Structure

20

25

30

35

40

45 m.

T+1

T+20

T+40

T+60

T+80

T+90

T+100

T+120

T+140

T+160

T+180

T+200

T+220

T+240

T+260

T+280

Growth Process

89

P E R F O R M A N C E O F S PA C E F I L L I N G F R A C TA L S GROW TH DRIVEN FROM RISK D ATA

TYPE A

40

35

30

25

20

15

10

5

0

5

10

15

20

25

30

35

14 12 10 8 6 4 40 2 0

35 5

10

15

20

5

25

10

20

15

25

30

40

14 12 10 8 6 4 2 0

35 5

10

15

20

Space Filling Curves

Low Risk

20

25

30

30

Risk Assessment

10

5

25

15

30

Increased Emerging Structure

Medium Risk High Risk

TYPE B 40

40

35

35

30

30

25

25

20

20

15

15

10

10

5

5

0 35

30

25

20

15

10

Risk Assessment Low Risk Medium Risk High Risk

90

5

0

5

10

15

20

25

30

5

0 10

15

20

25

30 0

Space Filling Curves

5

10

15

20

25

30

35

40

45

5

10

15

20

25

30 0

5

10

15

Increased Emerging Structure

20

25

30

35

40

45

40

TYPE B

14

14

10

10

6 2 45 40 35 30 25 20 15 10

5

0

10

15

20

25

30

35

Structural Surface Subdivision

40

6 2 45 40 35 30 25 20 15 10

0

5

10

15

20

25

30

35

40

14 10

6 2 45 40 35 30 25 20 15 10

Decrease of Curvature per Height Low

14 10

5

0

10

15

20

25

30

35

40

2 45 40 35 30 25 20 15 10

Absorbent Capacity

Curvature Graph Low

High

6

5

0

10

15

20

25

30

35

40

High

TYPE B

40

40

40

40

35

35

35

35

30

30

30

30

25

25

25

25

20

20

20

20

15

15

15

15

10

10

10

25

5 0

20 5

10

15

10 20

25

0

15

5 0

Structural Surface Subdivision

25

5

20 5

10

15

10 20

25

0

5

20 5

10

15

0

Decrease of Curvature per Height Low

15

25

5

High

10 20

25

5 0

25

5 0

20 5

10

15

10 20

25

15

5 0

Absorbent Capacity

Curvature Graph Low

15

10

High

91

FORM DEVELOPMENT AND WIND PATTERN

TYPE A

Massing

Ground Openings | Merging with Public Space

Morphing according to Wind Flow

Organic Deformation

Wind Velocity Low

92

High

TYPE B

Massing

Morphing according to Wind Flow

Ground Openings | Merging with Public Space

Organic Deformation

Wind Velocity Low

High

93

O R G A N I C D E F O R M AT I O N O F F R A C TA L S

The massing of the vertical filtering fractals is organically deformed according to solar radiation values. As a first step we run a solar radiation analysis for each emerging structure and then digitally set attractor points on smaller areas with higher radiation values. The interaction between the attractor points and the pattern development leads to a subdivision of three types of organic cells whose main characteristic is the level of morphological complexity and the amount of available surface.

On an already extended space filling structure, the increase of the amount of available surfaces which act as hosts for growth, amplifies their absorptive performance.

kwh/m2 247.65 222.68 198.12 173.35 148.59 123.82 99.06 74.29 49.53 24.76 <0.00

Annual Solar Radiation Analysis m Points fro Attractor es lu Va n iatio High Rad

ell ic 2D c y solar Organ riven b d t n e em arrang es on valu ti ia d ra

94

ttern

nic

Orga

n pa drive data

n of

latio

Simu

y

pacit

nt Ca

rba Abso

Diagrammatic Representation of Solar Radiation driven Organic Deformation Data Source: Energy Plus

95

S O L A R R A D I AT I O N A N A LY S I S PER ANNUAL SEASON

TYPE A

Winter

Summer

kwh/m2 <0.00 24.76 49.53 74.29 99.06 123.82 148.59 173.35 198.12 247.65<

Solar Radiation Analysis

96

TYPE B

Winter

Summer

kwh/m2 <0.00 24.76 49.53 74.29 99.06 123.82 148.59 173.35 198.12 247.65<

Solar Radiation Analysis Data Source: Energy Plus

97

8 m.

T YTPY EP EA A

6 4

8 m.

8 m.

6

6

4

4

2

2

0

0

5 10

5

15 20

Cells Type A

25

10

Cells Type AA Cells Type

30 15

Lofted Lofted

Solar Radiation > 198.12 kwh/m2 8 m.

6 4 8 m.

8 m.

6

6

4

4

2

2

0

0

5 10

5

15 20

25

Cells Type B

10 30 15

Cells Type BB Cells Type Lofted Lofted

74.29 kwh/m2 < Solar Radiation < 198.12 kwh/m2 8 m.

6 4 8 m.

8 m.

6

6

4

4

2

2

0

0

5 10

5

15 20

98

25

10 30 15

Cells Type C

Cells Type CC Cells Type

0 kwh/m2 < Solar Radiation > 74.29 kwh/m2

Lofted Lofted

124

99

TYPE B TYPE B

35 m.

35 m.

30

30

25

25

20

20

15

15

10

10

5

5

0

0

Cells Type A

CellsCells TypeType A A

Solar Radiation > 198.12 kwh/m2

Lofted Lofted

35 m.

35 m.

30

30

25

25

20

20

15

15

10

10

5

5

0

0

CellsCells TypeType B B

Cells Type B 74.29 kwh/m2 < Solar Radiation < 198.12 kwh/m2

35 m.

Lofted Lofted

35 m.

30

30

25

25

20

20

15

15

10

10

5

5

0

0

Cells Type C 0 kwh/m2 < Solar Radiation > 74.29 kwh/m2

Cells Type C Lofted

Cells Type C Lofted

100

126

101

SUBDIVISION OF CELLS

The space filling vertical structures, are organically deformed in three types of cells according to the local solar radiation values. The available surface increases where the solar radiation is lower and

therefore

enables the growth of a type of moss specialised in the absorption of air pollutants. This type of moss also presents high resistance and higher growth potential in areas with humidity, increased shadow and low solar radiation.

Cells Type A Solar Radiation > 198.12 kwh/m2 Solar Radiation Level Moss Growth Absorbent Capacity

Cells Type B 74.29 kwh/m2 < Solar Radiation < 198.12 kwh/m2 Solar Radiation Level Moss Growth Absorbent Capacity

Cells Type C 0 kwh/m2 < Solar Radiation > 74.29 kwh/m2 Solar Radiation Level Moss Growth Absorbent Capacity

102

103

SUBDIVISION OF CELLS-FINAL DIGITAL MODEL

104

105

M AT E R I A L I T Y O F V E RT I C A L F R A C TA L S

In terms of materiality, we have focused on 7 types of material which

Recyclability

Cost-effective MATERIAL CHARACTERISTIC

can provide effective filtering results towards toxic airborne pollutants.

The infiltration ability of Carbon(C), Copper(CU) and PAN(C3H3N) is significantly affected by static electricity, and they all present

Durability 1.0

resistance and lond durability. However, these materials present poor performance when filtering urban exhausts and are of high cost. On the contrary, biological materials such as moss and liches have a greater particulate matter.

Removal efficiency

flexibility across all the factors and can digest both urban exhausts and

0.8 Urban Exhausts 0.6 PM2.5

0.4

PM10 0.2

0.0

CARBON CARBON

106

Affecting Factors

Nanoparticles/SEM

C C

Materials

Affecting Factors

Static Electricity

COPPER COPPER

Cu Cu

B I O C O BL O rN aOs [Li tPO eaN sr]IaZsAi tTeI O I ONCI ZOAL TOI O N INZ A TI O IaOC s ]N [ P B[ P PM2.5 PM10-2.5

C R Y P T O G ACMRSY P T O G A M S CO2

Funghi [Mycelial growth] PAN PAN

(C3H3N)n (C3H3N)n

Temperature

F uAnlg ghaie [Mycelial growth] Algae PP (C3H6)n

N | SO2

S | NO2

L i c h e n sF u n g hLi i c h e n s [Mycelial growth]

Fungi PVP (C6H9NO)n

Wind

Lichens PVA [CH2CH(OH)]n

CRYPTOGA SO2

Moss

L i c h eM n

Moss PS

(C8H8)n

Humidity

107

108

109

110

111

Detail of Landscape and Absorptive Fractals Configuration A

Vertical Filtering

Building Capacity (each) 3355 m2 for Residence 1076 m2 for Filtering Units Lab

B

112

Vertical Filtering

Increased Surface Structures with space for open public uses

113

L A N D S C A P E F O R M AT I O N

Landscape Contour

Wind Curl Particels

Wind Pattern

114

Earthworks & Trees

Seating & Urban Gardening

Pedestrian Path

Pedestrian Path

Earthworks

Trees

Seating Area & Urban Gardening

115

Wester Field Ce

Increased Surfaces for vertical filtering

BB A Masterplan of Overall Proposal A

Vertical Filtering

Building Capacity (each) 3355 m2 for Residence 1076 m2 for Filtering Units Lab

B

Vertical Filtering

C

Horizontal Filtering

Increased Surface Structures with space for open public uses Canopies with 7800 Large Units 5974 Small Units

116

A

B

enter

A

C C

C

117

118

119

Risk Assessment Data

Horizontal Filtering Canopies with 7800 Large Units 5974 Small Units

120

Section A-A

Vertical Filtering Building Capacity (each) 3355 m2 for Residence 1076 m2 for Filtering Units Lab

Vertical Filtering Increased Surface Structures with space for open public uses

Section B-B

121

O R G A N I C D E F O R M AT I O N O F F R A C TA L S PHYSICAL MODEL ASSEMBLING

Individual 3D printing units

Assembled pices

122

123

124

125

F I LT E R I N G U N I T S 1:1 PHYSICAL MODEL ASSEMBLING

126

127

128

129

130

131

132

133

134

135

REFERENCE LIST

137

D ATA SOURCES

Mayor of London.Air quality and health. [Online], a v a i l a b l e a t h t t p s : / / w w w. t o n d o n . g o v. u k / w h a t - w e .

Tr e e s d a t a : m a p s . l o n d o n . g o v. u k / t re e s /

do/environment/polludon-and-air-quality/air-

H e a t c o n s u m p t i o n : d a t a . l o n d o n . g o v. u k

quality-and-health [Accessed 20/ 01 /2018]

Wind data: meteoblue.com m e t o f f i c e . g o v . u k

L o n d o n i Tre e e c o p ro j e c t ( E n g l a n d ) . [ o n l i n e ] Ava i l a b le

P o l l u t i o n d a t a : l o n d o n a i r. o r g . u k / L o n d o n A i r /

a t : h t t p s : / / w w w. f o re s t r y. g o v. u k / l o n d o n - i t re e

Tra f f i c d a t a : g e o fa b r i k . d e

[Accessed

23/02/2018]

Meteorological data: energyplus.net/weather Cruz, M. Beckett, R. [2016], Bioreceptive Design: a novel approach to biodigital materiality

PAPERS AND ARTICLES Irving, G. Segerman, H. [2013], Developing Fractal M o r t o n , T. ( 2 0 1 2 ) . E c o l o g y w i t h o u t n a t u r e . R e t r i e v e d f ro m w w w. e c o lo g y w i t h o u t n a t u re . b lo g s p o t . com,https/ecologywithoutnature. WogspoLCo. u k / 2 0 / 2 / 1 2 / w h a t - d o e s - h y p e ro b j e c t s - s a y. h t m l [Accessed 05/03/2018] S t o p p a n i , T. ( 2 0 0 7 ) , ’ D u s t r e v o l u t i o n s . D u s t , informe, architecture, (notes for a reading of Dust in Bataille),’ The Journal of architecture, v o l u m e 1 2 ( 4 ) . h t t p s : / / w w w. t a n d fo n l i n e . c o m / doi/pdf/10.1080/13602360701614714 [Accessed 05/03/2018] Cleaner Air for London. History of air pollution in L o n d o n . [ O n l i n e ] , a v a i l a b le a t h t t p s : / / w w w. museumoflondon.org.uk/discover/londons-past-air [Accessed 05/03/2018] NASA Visible Earth SAHARAN DUST ON THE MOVE. [Online], a v a i l a b le a t h t t p s : / / v i s i b le e a r t h . n a s a . g o v / v i e w. php?id=83966 [Accessed 01/03/2018]

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Curve