Minimizing Environmental Impact of Urban Districts with Life Cycle Assessment - Berlin Tegel Airport Complex

HafenCity University Hamburg | REAP

Material Flow Analysis and Life Cycle Assessment | WS 2015/16

Minimizing Environmental Impact

of Urban Districts with Life Cycle Assessment

Berlin Tegel Airport Complex

Submitted to / on:

Professor Gregor C. Grassl March 31st, 2016

Contributing Authors:

Gabriel Nießen – 6029039 Heather Troutman – 6028601

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REAP-M-308-100| Nießen - Troutman 2

Abstract:

The life cycle assessment of single buildings but also of urban districts becomes more valuable over time; particularly with recent challenges in regard of the predicted climate change. The following paper determines certain potentials for more sustainable quarters over a lifespan of 50 years along the case study of Berlin Tegel Airport Complex which is being under soon refurbishment. It distinguishes between solely material matters and district scale adjustments and discusses their respecting influences on the performance. The DGNB-LCA-tool for urban districts served as an example tool.

Table of Contents

1. Introduction ..................................................................................................................................................... 4

1.1. Berlin Tegel Airport Complex ................................................................................................................... 4

1.2. Life Cycle Assessment ............................................................................................................................... 4

1.3. DGNB LCA Tool ......................................................................................................................................... 4

2. Criteria for Sustainable Urban Districts ........................................................................................................... 5

3. Analysis of Built Area ....................................................................................................................................... 5

3.1. Land Use ................................................................................................................................................... 5

3.2. Built Area .................................................................................................................................................. 5

3.3. Materials ................................................................................................................................................... 5

3.3.1. Proposition of Building Materials and Areas ..................................................................................... 5

4. LCA Results of Proposed Design ...................................................................................................................... 6

5. Design Optimization for Enhanced Sustainability ........................................................................................... 8

5.1. Material adjustments ............................................................................................................................... 8

5.1.1. Optimization 1: Wooden Window Frames........................................................................................ 9

5.1.2. Optimization 2: as Optimization 0 substituting EPS with Mineral Wool ........................................ 10

5.1.3. Optimization 3: as Optimization 0 without Insulation .................................................................... 11

5.2. Adjustment of district parameters ......................................................................................................... 12

5.2.1. Optimization 4: District Heating ...................................................................................................... 13

5.2.2. Optimization 5: 30% On-Site Renewable Energy Generation ......................................................... 14

5.2.3. Optimization 6: Increased Green Surfaces ..................................................................................... 15

5.3. Optimization 7: Wooden Window Frames, District Heating, On-Site RE-Gen. and Increased Green

Surfaces ................................................................................................................................................................ 16

5.4. Comparison of all Optimizations ............................................................................................................ 17

6. Conclusion and Recommendations ............................................................................................................... 17

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Diagrams, Figures and Tables

Figure 1: study area: Berlin Tegel Airport (Grassl 2016) ............................................................................................ 4 Figure 2: DGNB criteria catalogue for urban districts (DGNB 2012) .......................................................................... 4 Figure 3: perspective of study area: built up space and open space (own depiction, based on design proposal .... 5 Table 2: energy and water benchmarks (Grassl 2016) .............................................................................................. 6 Table 1: environmental impact factors according (own depiction based on DGNB-tool) ........................................ 6 Figure 5: environmental impact factors with district specific values (own depiction based on DGNB-tool) ........... 7 Figure 4: environmental impact factors with standard reference values (own depiction based on DGNB-tool) .... 7 Figure 6: Ratio of district specific values to reference values for optimization 0 (own depiction based on DGNB-

tool) ...................................................................................................................................................................... 8 Figure 7: environmental impact factors with district specific values for optimization 1(own depiction based on

DGNB-tool) ........................................................................................................................................................... 9 Figure 8 : Ratio of district specific values to reference values for optimization 1 (own depiction based on DGNB-

tool) ...................................................................................................................................................................... 9 Table 3: results of Optimization 2 relative to Optimization 0 (own depiction based on DGNB-tool) ..................... 10 Figure 9: environmental impact factors with district specific values for optimization 2 (own depiction based on

DGNB-tool) ......................................................................................................................................................... 10 Figure 10 Ratio of district specific values to reference values for optimization 2 (own depiction based on DGNB-

tool) .................................................................................................................................................................... 11 Table 4: comparison of EPS, mineral wool and no insulation (own depiction based on DGNB-tool) ..................... 11 Figure 11: environmental impact factors with district specific values for optimization 3 (own depiction based on


tool) .................................................................................................................................................................... 12 Figure 13: environmental impact factors with district specific values for optimization 4 (own depiction based on



DGNB-tool) ......................................................................................................................................................... 14 Figure 16: Ratio of district specific values to reference values for optimization 5 (own depiction based on DGNB-





tool) .................................................................................................................................................................... 16 Figure 21: Ratio of district specific values to reference values, comparison (own depiction based on DGNB-tool)

........................................................................................................................................................................... 17

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Figure 1: study area: Berlin Tegel Airport (Grassl 2016)

1. Introduction

This report aims to give a brief overview about the way how to perform a life cycle assessment along the

future project of the refurbishment of Berlin Tegel Airport. Subject is an excerpt of the study area around the

main terminal (s. Figure 1).

1.1. Berlin Tegel Airport Complex

The district around the airport Berlin Tegel has recently undergone an urban planning study to analyze the

potential for an after use after the regular routine will have ended. It is planned to install an innovation centre

intertwining innovation, production and energy whereas addressing sustainable requirements at the same time.

The urban quarter aims a mixed use with a projected balance of open and built up space as well as a good

integration into the remaining urban space and existing structures. (State of Berlin & reicher haase associierte

GmbH 2014)

1.2. Life Cycle Assessment

The instrument of a life cycle assessment aims to quantify the environmental impact of all constructed

buildings in a district including emissions, efficiency of appliances or impact of renewable energies (Bott, Grassl

und Anders 2013) (Huber 1995).

1.3. DGNB LCA Tool

The DNGB LCA tool for urban districts is the

only tool that equalizes ecological, economical,

socio-cultural and technical aspects. It

particularly concentrates on the location, the

open space and the infrastructure. It includes all

emissions and costs from claiming the resource,

producing the material, constructing the

building, using it until its end of life.

There are three different stages of

certificates and four different ratings; one in an earlier design phase, another one in the planning and

Figure 2: DGNB criteria catalogue for urban districts (DGNB 2012)

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developing phase and the third one for the completed district. The ratings are categorized into Bronze, Silver,

Gold and Platinum.

2. Criteria for Sustainable Urban Districts

The main criteria are of economic, ecological and socio-cultural manner. They aim to grant living comfort of

the users, environmental friendly and conscious use of natural resources as well as to reduce the occurring

costs over a lifecycle. All of them are being rated evenly with 22,5%. Another criteria is the technical quality

which also accounts to 22,5% as the aforementioned. The concluding item is the process quality which reflects

participatory and citizen integration, marketing and monitoring to mention a few ones.

3. Analysis of Built Area

3.1. Land Use

29% of the area is built space, 71% open space of which 62% is sealed and 9% green. Another 7% are

considered as an additional green space as every newly constructed building accommodates a green roof (own

mass estimations based on given aerial view).

3.2. Built Area

All new constructed buildings seem to be functioning as office or commercial buildings; there is no

residential use recognizable. The existing and refurbished terminals are intended as technology and research

development areas (customized docking zones) (Berlin 2013).

3.3. Materials

All buildings were considered as fully insulated (EPS) reinforced concrete skeleton buildings with a solid

technical concrete tower which equips stairs and piping. Concrete walls are coated with plaster, not load-

bearing walls consist of drywall. The façade layer is before the concrete columns. Windows were assumed as

double-pane with an aluminum frame (Reference value).

3.3.1. Proposition of Building Materials and Areas

A standardized medium standard office building accounts 87.2% of the gross area as net floor area and

12.8% as construction area (BKI 2015). The construction volume accounts to 10% of the gross building volume;

this value has been set up. The major construction material is reinforced concrete with 79% of the entire

Figure 3: perspective of study area: built up space and

open space (own depiction, based on design proposal

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construction volume (Pick und Streit 2011). 15% of construction volume is insulation material with a thickness

of 20 cm, 1% steel (Pick und Streit 2011), 2% masonry, 1% timber/aluminum, 0.8% glass, 0.2% plaster and 2%

drywall (ABW Recycling 2013).

4. LCA Results of Proposed Design

Following environmental impact factors were basis for the life cycle assessment of the district:

Abbreviation Name Unit Reference Unit

AP Acidification potential kg SO2-Equiv. kg EP Eutrification potential kg Phosphate-Equiv. kg GWP Global Warming Potential kg CO2-Equiv. kg ODP Ozone Depletion Potential kg R11-Equiv. kg POCP Photochemical Ozone Creation Potential kg Ethene-Equiv. kg PE-NR Primary energy non-renewable MJ kg PE-RE Primary energy renewable MJ kg PE-Total Primary energy total MJ kg

The initial step was to determine the environmental impact according to standard reference values given by

ökobaudat-sheet for construction, energy, water and piping, using water and energy intensities specified by

Grassl (2016), as shown in Table 2.

Table 2: energy and water benchmarks (Grassl 2016)

Table 1: environmental impact factors according (own depiction based on DGNB-tool)

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Our second action was to set district specific values for water and energy intensities, shown as “TXL” in

Table 2, and assess the change in environmental impacts from reducing energy and water use in the district.

The optimized district (Optimization 0) results in 20 to 30% reductions in all environmental impact categories, as

shown in Figure 6. Additionally, the distribution (as a percent) of environmental impact categories relative to

district components (building construction, building operation, sealed surfaces, green spaces, media) shifted so

that building construction accounts for a larger share in each of the environmental impact categories than in the

reference scenario, as shown in Figure 5. This demonstrates decreased environmental burdens from building

operations, which is logical as Optimization 0 is an energy and water efficient urban district. As an example, in

the reference scenario, building construction and operations accounted for 12% and 87%, respectively, of

district total global warming potential (GWP); these values were shifted to 17% and 82% shares for building

construction and operations in Optimization 0.

Figure 5: environmental impact factors with district specific values (own depiction based on DGNB-tool)

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GWP ODP POCP AP EP PE.nr PE.r PE.total

District Specific Values (unit/m2 GFA) | Reference

Building Construction Building Operations Sealed Surfaces Green Spaces Media

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District Specific Values (unit/m2 GFA) | Optimization 0


Figure 4: environmental impact factors with standard reference values (own depiction based on DGNB-tool)

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Figure 6: Ratio of district specific values to reference values for optimization 0 (own depiction based on DGNB-tool)

5. Design Optimization for Enhanced Sustainability

The following optimization steps attempted to determine the most significant changes. Part One aims

adjustment in building materials, Part Two adjustment in district parameters. Part Three takes all together and

Part Four shows the overall performance relative to each other.

5.1. Material adjustments

Aluminum is known to be an energy intensive material to transform with harsh environmental impacts

associated to mining the raw resource. The first optimization reviewed the change in environmental impacts

from replacing all aluminum window frames with wooden frames, at an estimated 1,850 m length of window

frames in the entire district. As shown in Figures 7 and 8, Optimization 1 shows virtually no change in

performance or shift in environmental burdens from Optimization 0.

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GWP ODP POCP AP EP PE.nr PE.re PE.total

Ratio of District Specif ic Values to Reference Values (unit/m2 GFA) | Optimization 0

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5.1.1. Optimization 1: Wooden Window Frames

Figure 7: environmental impact factors with district specific values for optimization 1(own depiction based on DGNB-tool)

Figure 8 : Ratio of district specific values to reference values for optimization 1 (own depiction based on DGNB-tool)

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5.1.2. Optimization 2: as Optimization 0 substituting EPS with Mineral Wool

Expanded polystyrene (EPS) is a high performing insulation material. EPS is 98% by volume petroleum or

natural gas (EUMEPS 2014). This material, similar to concrete, has a large environmental footprint for the

production of the material (which is reflected in the building construction category of the DGNB District LCA

calculation tool) but then reduces the needed energy for thermal comfort in the building over its life (assumed

to be 50 years), which is reflected in the building operation category in the tool. Optimization 2 replaced all EPS

with mineral wool as an insulation material in the district under the assumption that mineral wool has a smaller

environmental footprint for production, and that the district LCA calculation tool is unable to reflect enhanced

building performance from a single material. It is assumed that approximately 16,500 m³ of insulation is used in

the total district. The 1% enhancement in GWP seems not to be much but for a single material with such a low

contribution it is worth mentioning it.

Results from Optimization 2 show significant changes in environmental footprint from Optimization 0,

reflected in Table 3, and shifts in environmental impacts relative to district category, as shown in Figure 9.

Ozone depletion potential (ODP) is reduced by 8%, and acidification potential (AP) is reduced by 42%.

Alternatively, Optimization 2 resulted in a 1% increase in GWP and non-renewable energy use (PEnon-renew),

and a 2% increase in photochemical ozone creation potential (POCP) and eutrophication potential (EP). The 8%

reduction in ODP, which reflects all of the building construction related emissions, is likely because EPS is

commonly treated with hexabromocyclodecane (HBCD) as a flame retardant (EUMEPS 2011). HBCD is an inert

molecule that has been shown destructive to tropospheric ozone (Jones 2013).

Table 3: results of Optimization 2 relative to Optimization 0 (own depiction based on DGNB-tool)

Figure 9: environmental impact factors with district specific values for optimization 2 (own depiction based on DGNB-tool)

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5.1.3. Optimization 3: as Optimization 0 without Insulation

The DGNB-tool calculates thermal performance of an urban district as an energy intensity measure

(kWh/m2/a) and reflects this performance in the building operations category. Accordingly, optimizations of

materials in the tool can only reflect changes in environmental impacts in the building construction category.

Optimization 3 demonstrated this by removing any form of insulation as a building material. This optimization

resulted in the best performing building between the three insulation materials (EPS, mineral wool and no-

insulation), as shown in Table 4, because the total mass of materials was reduced and there forth all of the

associated environmental burdens. For example, Optimization 3 has a 29% reduction in GWP, 38% reduction in

ODP, 21% POCP, 54% reduction in AP, 21% reduction in EP and 27% reduction in total primary energy demand,

relative to the reference district, see Figure 10.

More information is needed concerning changes in thermal energy performance of the building during

operations between the three insulation scenarios in order to be able to accurately demonstrate changes in

environmental impact of the entire urban district over its anticipated functional life, 50 years.

Table 4: comparison of EPS, mineral wool and no insulation (own depiction based on DGNB-tool)

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Figure 12 Ratio of district specific values to reference values for optimization 3 (own depiction based on DGNB-tool)

5.2. Adjustment of district parameters

As previously discussed, the DGNB district LCA tool is most suited for demonstrating optimizations at

the district scale, rather than at the material level. This analysis optimized the urban district in three ways. First,

in Optimization 4, all thermal energy demands are met through a district heating grid. Optimization 5 reverts

back to the Optimization 0 energy mix for thermal heating, but displaces 30% of electricity supplied to the

district with on-site renewable energy generation. Optimization 6 reverts all energy supplies (electricity and

thermal) to Optimization 0 standard mixed values and increases the green surfaces in the district from 9% to

25%, thus reducing sealed surfaces.

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5.2.1. Optimization 4: District Heating

Although the thermal energy demand was is being entirely covered through a district heating grid, there is

no significant changes visible compared with other already made adjustments. The only remarkable spike

expresses the share of primary renewable energy sources. It remains on the table whether a district heating is

considerable as a renewable energy source.


Figure 14 Ratio of district specific values to reference values for optimization 4 (own depiction based on DGNB-tool)

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Ratio of District Specif ic Values to Reference Values (unit/m2 GFA) | Optimization 4 (adjusted)

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5.2.2. Optimization 5: 30% On-Site Renewable Energy Generation

Only a 30%-on-site energy generation results in starkly better performance of the district; particularly

regarding the Global Warming Potential and the Ozone Depletion Potential.ODP is reduced by half, GWP by

more than 40% after all. That reflects the high share of fossil fuels in the standard energy mix as well as the high

potential for CO2-reduction through renewables. R11, as the equivalent for ODP (United Environoment Nairobi

2000) resembles beside others the occurrence of NOx which is a byproduct at the combustion of fossil fuels in

order to generate energy.



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Ratio of District Specif ic Values to Reference Values (unit/m2 GFA) | Optimization 5 (adjusted)

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5.2.3. Optimization 6: Increased Green Surfaces

An increase of green space by almost 20% as compared to optimization 0 results in almost no improvement

of any of the environment impact factors whatsoever. The saved excavated soil underneath sealed surfaces or

even hazardously presumed aggregates for asphalt don’t have an influence here. Another explanation would be

the compensation of a comparatively small surface in regard of the energy consumption over time over the

operation of an entire district.



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Ratio of Specific Values to Reference Values (unit/m2 GFA) | Optimization 6

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5.3. Optimization 7: Wooden Window Frames, District Heating, On-Site RE-Gen. and Increased Green Surfaces

After all optimization steps has been taken together it results quite logically in the best performance of all

previously and individually performed optimizations. All environment impact factors show the largest reduction

where as the share of renewable energy rises alone ( s. Figure 21).



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Ratio of District Specif ic Values to Reference Values (unit/m2 GFA) | Optimization 7 (adujusted)

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5.4. Comparison of all Optimizations

Figure 21: Ratio of district specific values to reference values, comparison (own depiction based on DGNB-tool)

6. Conclusion and Recommendations

For future investigation and understanding of impacts of certain environment impact categories it is crucial

to analyze detailed data of respecting parameters. For a brief overview it was a good exercise but to merely

understand major impacts and utilize their potential could serve better for professional application of this tool.

In this sense it has not be

One thing that is worth of reconsidering is the lifetime of the district. 50 years should be an aimed ambition

but regarding the speed of technological advancements and the inevitable abundance of certain resources can

lead to an adjustment into longer time spans.

A given sample with comparable values and results would have had a deeper understanding and a more

sustainable learning effect.

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Ratio of District Specific Value to Reference Value (unit/m2 GFA)

Ref. Op. 0 Op. 1 Op. 2 Op. 3 Op. 4 Op. 5 Op. 6 Op. 7

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References

ABW Recycling. "Vorlesung C/Kapitel 5: Bauwerksspezifische Kennzahlen." 2013.

Berlin, Senatsverwaltung für Stadtentwicklung und Umwelt. "Tegel Airport Future Use." Masterplan Berlin TXL.

Berlin, 2013.

BKI. BKI Baukosten Teil 1: Statistische Kennwerte für Gebäude 2015. 2015.

Bott, Helmut, Gregor Grassl, and Stephan Anders. Nachhaltige Stadtplanung. DETAIL, 2013.

DGNB. Handbuch für Nachhaltiges Bauen: Neubau Stadtquartiere. Stuttgart: DGNB, 2012.

EUMEPS – European Manufacturers of Expanded Polystyrene Association (2011) “Environmental Product

Declaration (EPD): Expanded Polystyrene (EPS) Foam Insulation (with infra red absorbers, density 20

kg/m3)” Edited by Environmental Construction Products Organization (ECO)

Grassl, Gregor. 2016.

Huber, Joseph. "Nachhaltige Entwicklung." archplus, 1995: 79-81.

Jones, Philippa Nuttall (2013) “End in Sight for Flame Retardant HCBD?” Chemical Watch: Global Business

Briefing. Accessed online 28.03.2016 < https://chemicalwatch.com/16534/end-in-sight-for-flame-

retardant-hbcd>

Pick, Jürgen, and Wilfried Streit. "Kalkulation." In Zahlentafeln für den Baubetrieb, by Manfred Hoffmann.

Weisbaden: Vieweg+Teubner Verlag, 2011.

State of Berlin & reicher haase associierte GmbH. Städtebauliche Vorqualifizierung. Berlin: Tegel Projekt GmbH,

2014.

https://chemicalwatch.com/16534/end-in-sight-for-flame-retardant-hbcd

https://chemicalwatch.com/16534/end-in-sight-for-flame-retardant-hbcd

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Appendix 1: Building Volumes and Masses

Key

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Appendix 2: Energy and Water Use

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Appendix 3: Optimizations and Results

Key

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Engineering

Minimizing Environmental Impact of Urban Districts with Life Cycle Assessment - Berlin Tegel Airport Complex