DEPRAVED URBAN SCAPES | Inhabiting Subnature by Vanessa Panagiotopoulou

DEPRAVED URBAN SCAPES Inhabiting Subnature Xinyi Li Ziyi Yang Vanessa Panagiotopoulou

The Bartlett School of Architecture UCL MArch Urban Design B-PRO

DEPRAVED* URBAN SCAPES Inhabiting Subnature

Xinyi Li Ziyi Yang Vanessa Panagiotopoulou

Portfolio - BENVGU22 RC 14 Tutors: Roberto Bottazzi Tasos Varoudis

The Bartlett School of Architecture, UCL MArch Urban Design

B-PRO

*de-pravus [latin]: perverted

THE BARTLETT SCHOOL OF ARCHITECTURE

MArch Urban Design

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

â&#x20AC;&#x153;The advance of civilization produced barbarity as an unavoidable waste product, as essential to its metabolism as t h e g l e a m i n g s p i re s a n d c u l t i va t e d t h o u g h t o f p o l i t e s o c i e t y â&#x20AC;?. Engels

CONTENTS

_INTRODUCTION

AIM

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

AIR POLLUTION IN THE UNITED KINGDOM

1 3 t h t o 1 9 t h C e n t u r y

The “London Smogs”

A i r Q u a l i t y To d a y

INTERACTION BETWEEN AIR POLLUTION AND WIND

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

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

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

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

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

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

TRAFFIC CONGESTION ON SITE

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

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

URBAN GREEN AND AIR POLLUTION

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

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

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

_DEPRAVED URBAN SCAPES 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

_ 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

F I LT E R I N G

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

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 86

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

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

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

F I LT E R I N G U N I T S â&#x20AC;&#x2122; PA C K I N G

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

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

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

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

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

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

K-MEANS CLUSTERING

100

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

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

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

AND PCA

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

112

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

Growth Driven from Risk Data

Form Development and Wind Pattern

Materiality of Filtering Units

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

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

122

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

128

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

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

130

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

136

LEVELS

PM2.5 Concentration

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

Subdivision of Cells

Overlay of PM2.5 Increase and

W i n d P a t t e r n

RISK ASSESSMENT

_REFERENCE LIST

155

INTRODUCTION

AIM

The

current

relationship and

air

project, between

pollution.

aims the

reestablish

urban

fabric,

specifically,

the

its

users

our

urban

approach and intervention is driven by the idea that â&#x20AC;&#x153;aspects

the

seemingly

subhuman

conditions

of contemporary urbanization and its subcultural peripheriesâ&#x20AC;&#x153; (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

inhabit

space

that

could

driven

potentially

re g e n e r a t e t h e c i t y.

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

Our

urban

visualising, concerning

strategy analysing air

pollution

and

collecting,

simulating

concentration

data

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

natural

systems, which we consider as the basic elements anticipating air pollution.

40.000 premature deaths a year in the UK linked with air pollution

£20 billion how much air pollution costs the UK economy

“The great smog”, The Guardian, London, 1952

Air pollution, Widnes, 19th century

“The great smog”, The Guardian, London, 1952

2.091 schools, nurseries, further education centres and after-school clubs are within 150 metres of a road w i t h i l l e g a l l e v e l s o f n i t r o g e n d i o x i d eâ&#x20AC;Ż

11 million cars

worldwide

designed to cheatâ&#x20AC;Żair pollution tests

South London Press, London, 2015

The Evening Standard, London, 2014

London reaches annual legal air pollution limit less than a month into 2018

AIR POLLUTION IN THE UK

13th TO 19TH CENTURY

A ccounts

of air pollution in the United Kingdom,

date back to the 13th century with main causes the rapid population growth, the urbanisation of the cities and the extensive use of charcoal instead of wood in domestic fireplaces. During the 17th century onwards, cold winter weather periods presented the first severe pollution episodes, termed as smogs, a combination of smoke and fog. Boilers, domestic fireplaces, coal combustion and industrial

furnaces

where

increased

dramatically

d u r i n g t h e I n d u s t r i a l i z a t i o n o f t h e 1 9 t h c e n t u r y. During of

the

the urban

20th

century,

metropolis

and

the the

de-urbanisation moving

the

residential areas from the center of the cities had as a consequence a decrease of poor air quality incidents. The gradual replacement of coal use with gas and electricity, especially in terms of domestic use, contributed further to an improvement of air quality which did not last long, until the â&#x20AC;&#x153;Great Smogâ&#x20AC;&#x153; of 1952 occurred in the city of London.

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

THE “LONDON SMOGS”

I n 1 9 5 2 , L o n d o n ’s a i r p o l l u t i o n h i s t o r y i s s t i g m a t i z e d by a severe pollution episode, known as the “Great Smog“. A combination of windless weather conditions and the increase of air pollutants from excessive coal use with the anticyclone phenomenon, had as a direct effect the generation of a thick layer of smog where smoke concentrations reached 56 times the normal level in some areas of the capital. This severe episode, in between eight others that occurred from 1948 to 1962, cost according to governmental medical records over than 4.000 deaths and 100.000 severe respiratory complications. The severe widespread health impacts and the public concern that aroused from these episodes, led to the Clean Air Acts of 1956 and 1968. These Acts set regulations on domestic sources of pollution and introduced

“smoke

control

areas”.

The

increased

use of electric and gas use instead of coal and solid fuels, the use of chimney stacks on power stations and the relocation of industries in rural areas, in between

others,

contributed

the

reduction

smoke pollution and specifically of Sulfur Dioxide concentration at the same time.

Weekly mortality and SO2 Concentration for Greater London during the ”Great Smog” 5.000

0.4

Weekly mortality SO2

4.000

Weekly mortality

3.000 0.2 2.000 0.1

1.000 0 Oct 19 1952

0 Nov.1

Nov.15 Nov.29

Dec.13 Dec.27 Jan.1o 1953

Week ending

Jan.24

Feb.7

Feb.21

Mar.7

Mar.21

SO2 (ppm)

0.3

AIR QUALITY TODAY

From the 1950s onwards, the main reasons causing

Nowadays,

air pollution in big cities have shifted from coal

monitoring data from 1600 monitoring sites across

combustion to traffic emissions which are considered

the

as having the major impact on the urban air quality

air

levels.

pollutants. However this is not the case for Nitrogen

mathematical

predict

quality

and

until

decreasing

models 2025

used

with

improvements

values

for

major

in air

Dioxide (NO2) which currently exceeds the European air

Environmental limits, and Particulate Matters which

pollutants present a certain decrease in comparison

present a severe concentration in several areas of

Greater London.

recent

times,

even

though

specific

1950s, during the summertime of 1976, pollution

episodes,

stated

“photochemical

smogs“

that

resulted from ground-level ozone formed by its postnote p r e c uNovember r s o r s , 2002 h a dNumber a s 188 a n Airiquality m p a cint thet hUK e Page i n c2r e a s e o f s e vquality e r a l w i n ttoday er smog Air

7%.

During

the

1990s

episodes occurred during calm

w ipollutants n t e r d a y s ,and c a utheir s i n g sources the death of 100-180 people. Air Emissions causing air pollution have changed considerably the T h e m a i n since air p o l l1950s. u t a n t s With o f csmoke o n c e r nand i n SO G2r enow ater regulated and a six-fold increase in road traffic between London today are particles, with PM2.5 and PM10 1955 b e i n gand c o 2001, n s i d e r ecoal d a combustion s t h e m o s t ish no a z alonger r d o u s ,the n i tmain rogen cause. Instead, motor vehicle emissions have had an oxides (NOx), volatile organic compounds (VOCs) increasing impact on urban air quality. and

carbon

monoxide

(CO).

These

air

pollutants

in between others such as Sulfur Dioxide (SO2) or The main pollutants of concern are nitrogen oxides O z o ( O ) a r iorganic s e a p a rcompounds t f r o m v e h i(VOCs), c l e e m iparticles ssions, from (NOx)2n,evolatile c o m m e r c PM i a l , i,nwhich d u s t r i are a l aparticles nd dome s t i c a ediameter m i s s i o n sof o r with (especially 10 t h ethan f o s sone i l f uhundredths e l p o w e r gof e nae rmillimetre, ation. less i.e. 10 µm) and carbon monoxide (CO). All of these are mainly emitted by road transport, but also arise from fossil fuel power generation and domestic and industrial sources. Solvent and petrol vaporisation is a major source of VOCs. The figure opposite shows trends in total emissions of the main substances over the last 30 years. While some pollutants such as CO and PM10 have declined, nitrogen dioxide (NO2) and VOCs increased until 1990, but have decreased since then.

Other pollutants of interest Other routinely monitored pollutants include lead and 10 complex molecules such as 1,3-butadiene, benzene and polycyclic aromatic hydrocarbons (PAHs). Road transport is the predominant source of many of these substances.

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

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)

London

(kilotonnes)

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

mortality

0 2000 2000

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

Health problems Air pollution legislation has mainly been and still remains focused on reducing the adverse human health effects of air pollutants, although during the 1980s acid rain and ecosystem damage were a principal concern. The levels of air pollutants measured today can still give rise to significant health impacts. In 1992, the Department of Health (DH) set up a Committee on the Medical Effects of Air Pollutants (COMEAP) to examine the potential toxicity and effects on health of air pollutants. In their 1998 report COMEAP concluded that up to 24,000 deaths were ‘brought forward’ in the UK in 1995/1996 due to the short term effects of air pollution5,6. Research

INTERACTION BETWEEN AIR POLLUTION AND WIND Wind environmental conditions play a major role on

the

since

levels it

the

contaminants

air

quality

element

through

that

its

Greater

London,

carries

movement

pollution (especially

dust particles categorised within Particulate Matter 2.5), and depending the meteorological conditions, contributes

the

increase

decrease

air

q u a l i t y. W i n d s p e e d , w i n d d i re c t i o n a n d a t m o s p h e r i c 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

concentration

level

challenged

control even

of more

pollution from

the

fact that 40% of these pollutants are not emitted exclusively

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

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

well

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

London

Integration represented

the

roads

the

map,

Greater

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

appear

towards

the

Eastern

side

o f t h e c i t y. T h i s i s w h e re t h e s i t e o f i n t e re s t i s 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

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

Integration R8000m Low

High

2 km

Source: Edina Digimap

L AND USE AND ROAD INTEGRATION | STRATFORD A land use map of the site of intervention located around the Olympic Park of Stratford, demonstrates a significant amount of industrial and commercial uses. This condition appears to be more dense around the London Stadium and therefore we concentrate on this specific site and on a next level, attempt to detect its current condition through data analysis.

Hackney Central

Land Use Industrial

London Fields

Commercial Public Space Residential Nature

Land Use and Spatial Analysis Road Integration | Stratford

Other

Choice R8000m Low

High

500 m

Source: Edina Digimap

2.1.1 Uses

STRATFORD

HACKNEY WICK

Stratford International Station Queen Elizabeth Olympic Park

Victoria Park

London Stadium

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 CENTRALIT Y]

A spatial network Integration and Choice Analysis in

The

maps

below

represent

the broader area around our site is run, in order to

Analysis for Closeness Centrality of four different

detect the way that the centrality and density of the

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

traffic network might contribute in the coexistence

[Integration Analysis] represents the average of all

of air pollution with the human factor at the same

shortest paths from a segment to all others in the

time.

given radii.

Integration r500 metric

Integration r1000 metric

Integration r2000 metric

Quantile [Equal count]

Angular

Segment

Site

Integration r4000 metric Quantile [Equal count]

Low

High

ANGUL AR SEGMENT CHOICE ANALYSIS [BET WEENNESS CENTRALIT Y]

The maps represent an Angular Segment Analysis for Betweenness Centrality in four different radii [500,1000,2000,4000]. [Choice

Analysis]

The

Betweenness

indicates

the

Centrality

centrality

node in the given area and calculates the number of shortest paths from all the vertices that pass through that node.

Choice r500 metric

Choice r1000 metric

Choice r2000 metric

Natural Color Breaks [Jenks]

Site

Choice r4000 metric Natural Color Breaks [Jenks]

Low

High

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 v i s u a l i s a t i o n o f t h e m a j o r a i r p o l l u t a n t s’ v a l u e s per

month

from

2007

2016

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. Meteorological actors such as temperature, humidity conditions and wind speed are the three major factors that affect the level of pollution concentration per annual season.

NO (µg/m3)

NOx (µg/m3)

2016

2009

NO2 (µg/m3)

2016 180,9 μg/m3

2009

2007

129 μg/m3

75,9 μg/m3 36,5 μg/m3

30,8 μg/m3 s ar

a ye

J D months

Nitrogen Oxide

41,1 μg/m3

D months

Nitric Oxide

D months

Nitrogen Dioxide

The diagrams present a significant increase of Nitric Oxides,

especially

during

wintertime,

something

that can be ascribed to the fact that during cold winter

days

increase.

domestic

This

leads

and to

commercial

the

formation

emissions of

winter

smogs when temperature inversion - occurring when a thin atmospheric layer becomes colder- traps on the ground level harmful pollutants and prevents them from diluting.

2016

2007

2016

PM 10 (µg/m3)

PM 2.5 (µg/m3)

O3 (µg/m3)

2016

2007

36,9 μg/m3 46,3 μg/m3

29,9 μg/m3

6,8 μg/m3

10,9 μg/m3

J D months

Ozone

14,1 μg/m3 J

D months

Particulate Matter 2.5

D months

Particulate Matter 10

Data Source: London Air | King’s College London

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

Stratford,

specifies

areas

where

p o l l u t i o n c o n c e n t r a t i o n i s h i g h e r. T h e c o n c e n t r a t i o n p re s e n t s h i g h e r v a l u e s w i t h i n t h e a re a â&#x20AC;&#x2122;s d e n s e ro a d 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

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

PARTICUL ATE MATTER 2.5 ANNUAL MEAN CONCENTRATION ON SITE |2013

PM2.5 Cause sprayers & liquid jets

power plants & industries

construction demolition

forest fires

vehicle emissions

domestic heating

PM2.5 Effect premature death increases

birth defects

cardiovascular disease

respiratory infections & asthma

lung disease

greenhouse gas

weather changes

PM2.5 Concentration on site [Îźg/m3] Data Source: London Air | Kingâ&#x20AC;&#x2122;s College London

TRAFFIC CONGESTION ON SITE

The average traffic congestion on the area around our site was calculated during peak hours, giving as an output areas of higher congestion and therefore higher pollution concentration. This occurs mostly in areas of dense road and railway traffic network, around underground and overground railway stations, and also in areas of high building density where the urban irregularity of the road network might increase the potentiality of traffic congestion.

Traffic Congestion | Contours per 1 meter

Traffic Congestion | Heat map Low

High

Traffic Volume on Site 0

Traffic Congestion | Points Concentration

500 m

Data Source: Geofabrik Data

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

Increasing

levels

heat

augmented

emissions

consumption, pollution

lead

particles

to 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

the

average

yearly

heat

consumption per building on our site, demonstrates its

contribution

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

MWH),

pink

the

(average

points

are

consumption apartments

and

19.5 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

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

URBAN HEAT ISL ANDS ON SITE

The reintegration of the heat consumption 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 surfaces—often in more rural surroundings—remain close

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 12°C

warmer than the atmospheric 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.

Residential District

Shopping Malls & High-rise Buildings

Queen Elizabeth Olympic Park

URBAN GREEN AND AIR POLLUTION

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

elements

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 the species of maple, horse chestnut, cherry,

Hawthorn

Lime

Maple

Cherry

Alder

Acacia

Apple

Ash

Locust

Cypress

Gum

Plane

Whitebeam

Pear

Birch

Chestnut

Shrub

alder, birch and plane.

Tree Types on Site Data Source: Mayor of London

POLLUTION ABSORPTION FROM TREES

Tre e s c o n t r i b u t e s i g n i f i c a n t ly t o t h e i m p ro v e m e n t 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 ( e g : s m o ke , p o l le n , a s h a n d d u s t s ) . Tre e s c a n a l s o 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

average

visualisation

values

CO2

presents and

SO2

georeferenced

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

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

BEST TREE SPECIES FOR POLLUTION ABSORPTION As a next step, we layout the best tree species for the

the trees that contribute the most to the reduction

reduction of pollutants. The first to forth layer from

of the atmospheric temperature.

the top are species that specialise in the absorption

The most dominant species on our site of intervention

and reduction of Sulfur and Nitrogen Oxide, PM10,

are maples, gums, chestnuts and birches.

Ozone and Carbon Monoxide. The last layer shows

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

BLOOMING PERIOD

The role of urban vegetation on our site is dual.

when the allergens level is relatively higher in the

Specific

a i r. I n i s o m e t r i c v i e w w e c a n s e e m o r e c l e a r l y t h e

pollution contaminants, but also release

chronicle location of the blossoming period per tree

absorbing

tree

types

pollen which causes

are

not

only

specialized

allergic reactions such as hay

f ro m M a rc h t o M a y.

f e v e r.

This

added

extra

parameter

that

A visualisation of the blooming period of the trees

affects air quality in a natural sense, mostly than a

of our site, inserts the factor of the time of the year

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

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 y axis are time-based and every hour of a day is

and stability are considered as the most influencing

remapped in a linear order in order to illustrate the

factors of air quality levels.

most prevailing wind conditions.

diagrammatic

visualisation

wind

particles,

On the diagram underneath, the height of each point

represents their speed and direction for each of the

represents the value of wind speed and the vector

twenty four hours of a day in the year of 2016. The x

lines illustrate the direction of wind during 2016.

tion

Speed m/s

ec Dir

Time Jan.

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

Feb.

Mar.

Apr.

May.

Jun.

Jul.

Aug.

Sep.

Oct.

Nov.

Dec.

Diagrammatical Visualisation of Wind Speed and Direction per day | 2016

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

DOMINANT WIND SPEED AND DIRECTION | SITE

An environmental analysis was run on our site, in

indicate the

order to specify wind conditions and the way wind

After running the analysis using as an input the most

flow interacts with the built environment and the

recent weather conditions on site (each month of

open spaces.

2017) from weather monitoring stations, we detected

The

output

represented

demonstrate

the

environmental

wind

rose

distribution

analysis

diagrams, of

wind

winter wind intensity as the most prevailing. On a

which

speed

next level, we

and

wedge

percentage

the

time

diagrams

that

wind

represents

flows

towards

set as a conditional statement the

maintenance of wind velocity values that are greater

d i r e c t i o n o v e r a s p e c i f i c p e r i o d o f t h e y e a r. Each

level of wind velocity per direction.

than 2.60 m/s, in order to visualise conditions where the

wind speed is higher and therefore creates the most

the

interesting wind patterns and conditions within the

visualised direction. The different level of colors

urban fabric.

W NE

12,90>

11,87

10,84

9,81

7,75

5,69

> 2.60

m/s

Wind rose diagram Dominant wind conditions on site

Winter

Spring

h arc rs M April y Ma

hou Speed m/s

Speed m/s

er mb ece ry D ua rs Jan uary hou r b Fe

nth

s N

Autumn

Summer

Speed m/s

nth

hou Speed m/s

s our

ber tem p e S r obe Oct er b em Nov

e Jun y Jul st u Aug

nth

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

WIND PATTERN ON SITE

In order to detect in a more exploratory way the spatial

Wind blowing from the south west generates a more

substance

conditions

dense wind pattern when it reaches our site due to

on our site, we use the output of the precedent

low building density, and is deformed by the density

wind rose diagrams as an input in computational

of the urban infrastructure at the point when it

s i m u l a t i o n s . Tw o d o m i n a n t d i re c t i o n s a n d v e lo c i t y

reaches the built environment. Hence, the pattern

levels of wind per season are simulated in order to

formed by wind blowing from northeast and west is

generate a more accurate wind pattern.

of lower density in contrast to the one blowing from

the

environmental

wind

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

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

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 0.039216 0.047059 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 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 538440 183935 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 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 38465 183410 0.094118 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 54 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

0.623529 0.780392

0.070588 0 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 0.0 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 0.843137 0.478431 0.439216 0.545098 0.254902 04 0.47451 0.521569 0.356863 538575 183780 0.094118 0.098039 0.0 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 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 0.0 0.423529 0.482353 0.070588 0 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 0.0 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 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 0.0 0.709804 0.447059 0.078431 0 0.109804 0.164706 0.0 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 0.0 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 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 0.0 0.588235 0.392157 0.082353 0 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 0.0 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 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 0.0 0.541176 0.411765 0.090196 0 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 0.0 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 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 0.0 0.533333 0.360784 0.07451 0 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 0.0 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 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 0.0 0.858824 0.776471 0.129412 0 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 0.0 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 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 0.0 0.439216 0.462745 0.070588 0 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 0.0 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 0.0 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 0.0 0.603922 0.611765 0.070588 0 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 0.0 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 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 0.0 0.509804 0.423529 0.070588 0 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 0.0 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 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 0.0 0.568627 0.4 0.070588 0 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 0.0 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 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 0.0 0.521569 0.352941 0.070588 0 0 0 0

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

extensive

and

exploratory

way

machine

learning.

The

data

concerns

geolocated

values

used of

through an

input

concentration

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,

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.

datasets through Machine Learning techniques and algorithms will help us identify in a more exploratory spatial

patterns

and

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 °

The preprocessing, processing and analysis of our

way

PM25 Concentration

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

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 °

require the equivalent intervention of regenerating subdued zones through new proposed uses.

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

PLOTTING D ATA SETS

High

values

wind

the

dilution

and

subsequently

speed

dispersion the

have

decrease

impact

NO2

particles

air

pollution

concentration around the areas exposed to intense wind and wind gusts. The decrease of wind intensity prevents pollutants from

dispersing

and

diluting,

high

temperatures

speed up chemical reactions in the air and therefore NO2 concentration increases. The fact that the spatial distribution of wind values of high intensity and Nitrogen Dioxide concentration do not overlap on our site, is ensuring that during wintertime high wind speed enables the dilution and the dispersion of pollution contaminants. However, it generates high levels of dust categorized within the Particulate Matters. Furthermore, Machine

the

Scatter

Learning

Graphs

processes,

plotted

through

represent

the

distribution of NO2 concentration values in relation to the equivalent absorption from trees and wind conditions.

0.0

0.1

0.2

0.3

0.5

0.4

185000

184800

184600

184400

184200

184000

183800

183600

183400

183400 537800 538000 538200 538400 538600 538800 539000

0.4

0.6

0.8

1.0

537800 538000 538200 538400 538600 538800 539000

Georeferenced Distribution of NO2 Concentration

Georeferenced Distribution of NO2 Absorption NO2 Absorption

Spring wind intensity

0.2

Distribution of NO2 Absorption values

Distribution of NO2 Concentration values

0.7 0.6 0.5

1.0 0.8

0.4

0.6

0.3

0.4

0.2

0.1 0.0

0.0

0.1

0.2

0.3

Correlation of winter wind values and NO2 Concentration

0.4

0.0 NO2 Concentration

0.0

0.1

0.2

0.3

0.4 NO2 Concentration

Correlation of NO2 Concentration and NO2 Absorption

Wind intensity plays an active role in the dilution of pollution contaminants, however in the occasion of PM2.5 wind intensity generates dust and therefore identifies vulnerable areas in need of intervention. Falling

temperature,

increasing

moisture

low levels

wind

velocity,

during

and

springtime,

speeds up chemical reactions in the air and prevents pollution contaminants from dispersing and diluting. This results in the increase of concentration of NO2 values and other air pollutants, however prevents the spreading of dust particles in the atmosphere.

35 30

25 15

20 15

0.2

0.3

0.4

0.5

0.6

Distribution of PM2.5 Concentration values

0.4

0.6

0.8

1.0

Distribution of PM2.5 Absorption values

185000

184800

184600

184400

184200

184000

183800

183600

183400

537800 538000 538200 538400 538600 538800 539000

Georeferenced Distribution of PM2.5 Concentration

Georeferenced Distribution of PM2.5 Absorption

PM2.5 Absorption

Winter wind intensity

0.2

0.6 0.8 0.4 0.3 0.2 0.1

1.0 0.8 0.6 0.4 0.2

0.0 0.0

-0.1 0.20

0.25

0.30

0.35

0.40

0.45

0.50 0.55 0.60 PM2.5 Concentration

Correlation of winter wind values and PM2.5 Concentration

0.20

0.25

0.30

0.35

0.40

0.45

0.50

0.55

0.60

PM2.5 Concentration

Correlation of NO2 Concentration and PM2.5 Absorbtion

D ATA CROSSINGS EXPLORING AREAS OF HIGH INTEGRATION

spatial

correlation

Integration of

PM25

that

the

network

values

and

with

NO2

higher

between

pollution

pollutants,

the

value

accessibility,

the

interaction

concentration

clarifies the

higher

human the

the

fact

fa c t o r â&#x20AC;&#x2122;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

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

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

0.4

0.3

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

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

imported

the

datasets

georeferenced

spatial

interaction

through

simple

the

scatterplos,

and non georeferenced distribution

plots, we detect the interactions between the data through machine learning algorithms. 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

set

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

the

h u m a n f a c t o r. T h e r e a s o n w e c h o s e t h e s e v a l u e s was the previously detected preexisting interaction between them. Absorption from trees is anticipating

PCA [Principal Component Analysis]

pollution concentration and winter wind -being the

Areas with highly interactive data

one detected with the highest values of velocityhas since

double on

the

impact one

hand

on it

PM2.5

concentration,

generates

dust

[major

component of PM25], on the other hand, high wind speed contributes in the dilution and dispersion of pollution particles.

High

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.

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 characteristics within the imported dataset. similar

the

one

the

The input used was Principal

Component

Analysis, since the output of the two algorithms, on a next level, will be combined in order to specify areas

high

interest

and

therefore

reasons

intervene. More specifically, the values used as an input were pollution concentration, the equivalent absorption from

trees,

values

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

K-Means Clustering values projected on site

Spatial Distribution of Clustering Values Cluster A Cluster B Cluster C

Urban Context

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

taking

into

consideration

the

importance

the human factor on our site and the impact that high

low

Integration

values

have

pollution

concentration, we proceed by filtering our data and overlaying

three

elements,

clustered

areas

with

similar characteristics, high risk areas and areas with high values 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.

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

Visualisation of Machine Learning Data Interactions and Clustering 71

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

G O V E R N M E N TA L P R E D I C T I O N S FOR AIR QUALITY LEVELS NO2 CONCENTRATION [2013,2020,2025,2030] After detecting areas with highly interactive data, the

minimum

median

maximum

analysis of predicted future values of NO2 and PM2.5 concentration on the site of intervention, specify in higher detail, areas in need of intervention. As a first step, the mean concentration of the year 2013 is subdivided in three groups consisting of low, medium and high values. The high values are filtered and represented both in whisker plots as well as on a spatial georeferenced context. A

visualisation

NO2

concentration

over

2013,

NO2 Concentration 2013 [min, mid, max]

2020, 2025, 2030, presents decreasing values of this specific pollutant.

160 140 120 100 80 60 40 20 0

2013

2020

2025

2030

NO2 Concentration [2013, 2020, 2025, 2030]

183500

183400

183300

183200

539000

183600

183500

183400

183300

183200

NO2 Concentration 2025

539000

183600

538900

183700

538800

183700

538700

183800

538600

183900

183800

538500

183900

538400

184000

538300

184000

538200

184100

538100

184100

538000

183200

539000

183200

538900

183200

538800

183200

538700

183200

538600

183200

538500

183200

538400

183200

538300

183200

538200

183200

538100

183200

538000

183200

537900

183200

537800

183200

537900

NO2 Concentration 2020

537800

NO2 Concentration 2013

539000

183500

538900

183600

538800

183600

538700

183700

538600

183800

183700

538500

183800

538400

183900

538300

183900

538200

184000

538100

184000

538000

184100

537900

184100

537800

183200

538900

183200

538800

183200

538700

183200

538600

183200

538500

183200

538400

183200

538300

183200

538200

183200

538100

183200

538000

183200

537900

183200

537800

183200

NO2 Concentration 2030

PM2.5 CONCENTRATION [2013,2020,2025,2030]

minimum

the

contrary,

visualisation

PM2.5

median

maximum

concentration over 2013, 2020, 2025, 2030, presents increasing values and is detected in higher density around

central

further

the

road

arteries.

material

This

research

will

affect

the

design

proposal, specialized in matter with high absorptive performance on PM2.5 contaminants.

PM2.5 Concentration 2013 [min, mid, max]

200

180 160 140 120 100 80 60 40 20 0 2013

2020

2025

2030

PM 2.5 Concentration [2013, 2020, 2025, 2030]

183400

183300

183200

539000

183600

183500

183400

183300

183200

PM2.5 Concentration 2025

539000

183600

538900

183700

538800

183700

538700

183800

538600

183800

538500

183900

538400

183900

538300

184000

538200

184000

538100

184100

538000

183200

184100

539000

183200

538900

183200

538800

183200

538700

183200

538600

183200

538400

183200

538500

183200

538300

183200

538200

183200

538100

183200

538000

183200

537800

183200

537900

183200

537900

PM2.5 Concentration 2020

537800

PM2.5 Concentration 2013

539000

183500

538900

183500

538800

183600

538700

183600

538600

183700

538500

183800

183700

538400

183800

538300

183900

538200

183900

538100

184000

538000

184000

537900

184100

537800

184100

538900

183200

538800

183200

538700

183200

538600

183200

538500

183200

538400

183200

538300

183200

538200

183200

538100

183200

538000

183200

537900

183200 183200

537800

183200 183200

PM2.5 Concentration 2030

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

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

Wind Pattern

Future PM2.5 Increase and PCA Values

RISK ASSESSMENT

After using the output of dimensionality reduction

speed up chemical reactions in the air, and prevent

and

the

pollution contaminants from dispersing and diluting.

are

Since each of these data values do not have common

narrowed down by specifying different levels of risk

measurement units, before using them as an input in

assessment. This value derives from an equation

the risk assessment equation, the data is digitally

including PM2.5 and NO2 concentration, PM2.5 and

remapped and normalised under a common ratio.

NO2 absorption, network analysis Integration values

a n d s p r i n g w i n d â&#x20AC;&#x2122;s i n t e n s i t y v a l u e s . Va l u e s d e s c r i b i n g

map is generated following a risk equation which

wind intensity during spring months, are chosen

compresses

since

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

future

predicted

characteristics

the

precedent

air

quality

site

wind

values,

intervention,

analysis,

demonstrates

order

organize all

the

our

data

data,

combined

throughout

the

data

analysis

l o w e s t v a l u e s a r o u n d t h i s t i m e o f y e a r. T h e r e f o r e ,

and each value of risk

assessment is redistributed

areas exposed to low wind velocity are more exposed

on the grid and categorised into three levels which

to air pollution, since increasing moisture levels

helped us approach our further design in a detailed urban scale.

ÎŁ

Risk Assessment

Sum of

[PM2.5_Con + PM2.5_Abs + NO2_Con + NO2_Abs + Integration_800 â&#x20AC;&#x201C; Spring_wind]

Risk Assessment Levels Low Risk Medium Risk High Risk

DEPRAVED URBAN SCAPES

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

the

detected

areas

with

design

interventions which are defined by the structural complexity

and

morphology

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 a i r t o x i c i t y. The

site

intervention

included

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

replacement

design of

these

proposal future

enabling

the

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.

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

7800 Large Units

5974 Small Units

7800 Large Units 5974 Small Units

Projectâ&#x20AC;&#x2122;s Proposal

SMM Proposal

200.000 sqm retail, leisure, education 2.900 sqm residential properties

75.69

78.43

Masterplan of Overall Proposal related to Risk Assessment A

77.55

78.37

150.25

172.67

150.25

177.23

170.34

Vertical Filtering

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

Vertical Filtering

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

86 150.25

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

108.96

111.62

171.18

172.04

150.25

144.85

145.98

122.09

123.57

150.25

114.70

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

108.47

87 153.59

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

canopies.

controlling

the

amount

and location of anchor points, we can control the complexity

and

extent

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

reduced

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.

Generation of Horizontal Filtering Driven by Local Risk Assessment

Risk Assessment

Emergence

Reduction

Amalgamation

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.

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

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.

Structure

F I LT E R I N G U N I T S â&#x20AC;&#x2122; 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

Hexagonal Packing

Structural Skeleton

300 Hexagon 65% Luminance

440 Hexagon 50% Luminance

300 Hexagon

440 Hexagon

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

morphological through

modules

complexity

Differential

which

Growth.

imitate

organic

the

elements,

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,

structures

with

biological

specific,

systems

reproducible

produce

forms

and

functions. After

generating

filtering

system,

the we

structure start

the

develop

horizontal 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

Plan V iew

possible surface area within a given volume, and at the same time, provides perforation which enables a i r f lo w. Growth Phase 2

Growth Phase 3

Axonometric View

Plan View

Front View

Growth Phase 1

Axonometric V iew

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

f e r t i l i z e r. T h e g r o w t h w i t h a b s o r p t i v e p e r f o r m a n c e , will be activated by the elements in the surrounding environment

rain

and

sunlight.

These

living

units will eventually be dominated by natural growth which absorbs air pollution.

Steel frame & Geotextile fabric

Layered Materiality of Filtering Modules

Coating with seeds and fertilizer

Growth activated by rain, absorbing contaminants and dust

Days Precipitation amount Sum of a day (mm/m2)

31 29 27 25 23 21 19 17 15 13 11 9 7 5 3 1 31 29 27 25 23 21 19 17 15 13 11 9 7 5 3 1 31 29 27 25 23 21 19 17 15 13 11 9 7 5 3 1

Relative Humidity 2 m. above ground

Average Temperature 2 m. above ground

Jan. Feb. Mar.

Apr.

May. Jun. Jul.

Life Circle and Performance of Filtering Units

100

Aug. Sept. Oct.

Nov. Dec.

101

102

103

104

105

106

107

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

human

morphological

scale.

The

output

generation the

and

vertical

further 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.

108

ZIYI RENDER

109

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

complexity which is correlated with the local air

t w o s p a t i a l t y p e s o f u s e . Ty p e A i s g e n e r a t e d i n

quality

open spaces with high risk assessment values and

concentration levels, appear in a density of 100%

creates a landscape with its own microclimate, while

on the ground level, and decrease by 20% every

offering spaces for public uses.

3 meters upwards. According to that, the level of

The infinite variations created during the digital

complexity of the space filling fractal and therefore

growth process, provide a variety in the level of

its absorptive performance is reduced per height.

conditions.

Furthermore,

NO2

and

PM2.5

Space Filling Curves

110

Planar View of Emerging Structure

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

111

T YPE B|FRA CTALS FORMING INTERIOR SPA CE

Ty p e B , i s e m e rg e d w i t h i n t h e l a n d s c a p e o f t h e s p a c e

structures.

filling fractals in areas of high risk assessment.

capacity

This structural type proposes enclosed inhabitable

which when expanded

spaces

with

residence

and

spatial 4.304

capacity

vertical

urban

farming

filtering

space

filling

for and

labs

modules

the

mentioned buildings

before,

the

calculated

spatial in

way

as a typology in the context

for

of high risk zones of the broader area,will replace

provided

for

partially the proposal of the Stratford Metropolitan

the

Masterplanning

13.320

spaces

producing the

for

residential

and

commercial

infrastructure.

horizontal

35 m.

Space Filling Curves

112

Planar View of Emerging Structure

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

113

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

14 12 10 8 6 4 40 2 0

35 5

14 12 10 8 6 4 2 0

35 5

Space Filling Curves

Low Risk

Risk Assessment

Increased Emerging Structure

Medium Risk High Risk

TYPE B 40

0 35

Risk Assessment Low Risk Medium Risk High Risk

114

0 10

30 0

Space Filling Curves

30 0

Increased Emerging Structure

TYPE B

6 2 45 40 35 30 25 20 15 10

Structural Surface Subdivision

6 2 45 40 35 30 25 20 15 10

14 10

6 2 45 40 35 30 25 20 15 10

Decrease of Curvature per Height Low

14 10

2 45 40 35 30 25 20 15 10

Absorbent Capacity

Curvature Graph Low

High

TYPE B

5 0

20 5

10 20

5 0

Structural Surface Subdivision

20 5

10 20

20 5

Decrease of Curvature per Height Low

High

10 20

5 0

20 5

10 20

5 0

Absorbent Capacity

Curvature Graph Low

High

115

FORM DEVELOPMENT AND WIND PATTERN

TYPE A

Massing

Ground Openings | Merging with Public Space

Morphing according to Wind Flow

Organic Deformation

Wind Velocity Low

116

High

TYPE B

Massing

Morphing according to Wind Flow

Ground Openings | Merging with Public Space

Organic Deformation

Wind Velocity Low

High

117

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

the

vertical

filtering

fractals

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

l cell nsiona -Dime 2 ic n y solar a b Org driven t, n e em arrang es on valu ti ia d ra

118

siona

n Dime en 3iv r d Data ttern ic pa n a g r o

n of

latio

Simu

pacit

nt Ca

rbe Abso

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

119

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

Organic Deformation

Spring

Winter

Summer

Autumn

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

120

TYPE B

Winter

Spring

Autumn

Summer

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

Organic Deformation Data Source: Energy Plus

121

8 m.

TYPE A

6 4

8 m.

5 10

15 20

Cells Type A

30 15

Lofted

Solar Radiation > 198.12 kwh/m2 8 m. 6 4 8 m.

8 m.

5 10

15 20

Cells Type B

10 30 15

Cells Type B Lofted

74.29 kwh/m2 < Solar Radiation < 198.12 kwh/m2 8 m. 6 4 8 m.

8 m.

5 10

15 20

10 30 15

Cells Type C

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

Lofted

122

123

TYPE B

35 m.

Cells Type A

Solar Radiation > 198.12 kwh/m2

Lofted

35 m.

Cells Type B

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

35 m.

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

124

Lofted

Cells Type C Lofted

125

SUBDIVISION OF CELLS

The space filling vertical structures, are organically

the growth of a type of moss specialised in the

deformed in three types of cells according to the

absorption

local solar radiation values. The available surface

also presents high resistance and higher growth

of the bioreceptive host material, increases where

potential in areas with humidity, increased shadow

the solar radiation is lower and

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

126

therefore enables

air

pollutants.

This

type

moss

127

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 seven types

material

which

can

provide

effective

filtering results towards toxic airborne pollutants. The

infiltration

ability

and

P A N (C3H3N) i s

Carbon(C),

significantly

Copper(CU)

affected

static

electricity, and they all present resistance and long d u r a b i l i t y. H o w e v e r , t h e s e m a t e r i a l s p re s e n t p o o r performance when filtering urban exhausts and are of high cost. On the contrary, biological materials such as moss and lichens have a greater flexibility across all the factors and can digest both urban e x h a u s t s a n d p a r t i c u l a t e m a t t e r.

128

Host]

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 a r a B[ P

Recycling Capacity Cost-effective MATERIAL

CHARACTERISTIC

PM2.5 PM10-2.5

Durability

TYPES

1.0

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

CRYPTOGAMS

0.8

PM2.5

CO2

0.6

N | SO2

S | NO2

SO2

0.4

Algae

0.2

0.0

CARBON CARBON

Algae COPPER COPPER

Cu Cu

Funghi [Mycelial growth] PAN PAN (C3H3N)n (C3H3N)n

F uAnlg ghaie [Mycelial growth] PP Algae

(C3H6)n

L i c h e n sF u n g hLi i c h e n s PVP Funghi

(C6H9NO)n

[Mycelial growth]

PVA Lichens

[CH2CH(OH)]n

Moss

L i c h eM no s ss

PS Moss

(C8H8)n

Nanoparticles/SEM

a ldl ual b e o lsee B a s e d Sc e teenr Si a l s Mra

PM10

Materials Biodegradable Hydrogel ScreenS

Affecting Factors Affecting Factors

Removal efficiency

Urban Exhausts

Static Electricity

Temperature

Static Electricity

CO2

Wind

Temperature

CO2

N|SO2

Wind

NO | S2 O 2 C

|N S | N O 2 N | SO 2O2

Humidity

O22 S | NSO

SO2

129

130

131

132

133

Detail of Landscape and Absorptive Fractals Configuration A

Vertical Filtering

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

134

Vertical Filtering

Increased Surface Structures with space for open public uses

135

B A Masterplan of Overall Proposal A

Vertical Filtering

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

Vertical Filtering

Horizontal Filtering

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

136

C C

137

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

Landscape Contours

Wind Curl Particels

Wind Pattern

138

Earthworks & Trees

Seating & Urban Gardening

Pedestrian Path

Earthworks

Trees

Seating Area & Urban Gardening

139

140

141

Risk Assessment Data

Horizontal Filtering Canopies with 7800 Large Units 5974 Small Units

142

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 143

144

145

146

147

148

149

150

151

152

153

154

155

156

157

158

159

â&#x20AC;&#x153; Dirt is a social category that we assign to specific types of social relations. It lacks any fundamental physical quality. Instead it is a relationship.â&#x20AC;? Erik Swyngedouw, 2004