URBAN MINING ALMERE Prospecting the steel and copper content of residential, commercial and industrial buildings
Capita Selecta - GIS assignment Ebe Blok - s1133640 Alex Colloricchio - s1841491 Lodewijk Luken - s1917544
March 2017
Msc. Industrial Ecology CML Leiden University Capita Selecta - GIS assignment Urban mining
Ebe Blok Alex Colloricchio Lodewijk Luken Tutors: Ester van der Voet Maarten van ‘t Zelfde
March 2017
URBAN MINING ALMERE
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Table of contents 1. Introduction 4 2. Methodology and data 6 3. Analysis and results 9 4. Conclusion and discussion 16 References 17 Appendices 18
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1. Introduction since it is a relatively young city. The first dwellings were built in 1976. The expected life cycle of buildings can range from 20100 years (longlife-world.eu). The stock of buildings that currently reside in Almere can therefore be expected to be demolished in the coming decades. The aim of this report is to quantify the stocks of copper and steel in Almere. This will be done by examining the municipality of Almere’s local districts. This will generate a geological map, in which the availability of the copper and steel are assessed and the economic value of each district is calculated. The maps generated in this process serve as a basis for prospective urban mining strategies. The goals of this report are:
ASK It is estimated that the building industry consumes around 40% of the world’s extracted materials. This makes the sector the largest consumer of natural resources. One of the biggest challenges in reforming the building industry into a sustainable practice has been finding materials that are environmentally friendly. The extraction of most materials currently in use is energy intensive and often based on nonrenewable energy sources (Van Bueren et.al., 2012). This calls for a transition from traditional materials extraction. With an increasing depletion of natural resources and the consequent rise of metals prices, there is a need for a more circular approach. In this circular approach waste is seen as a resource. Construction and demolition (C&D) waste should therefore be seen as a resource from which raw materials can be extracted, i.e. an urban mine. The goal of this report is to prospect the urban mine of the city Almere. Almere provides an interesting case study,
• To quantify the stocks of copper in residential buildings and of steel in residential, industrial, and commercial buildings. • To assess the economic value of the building stocks of copper and steel per local district.
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1.1 Scope Table 1.1 provides an overview of the detail level for a geographical study on urban mining for Almere. The dark grey cells of the table define the scope of the research.
and steel from buildings. The most accurate assumptions on metal content related to the building characteristics are available for residential buildings, therefore these buildings will be the first be analysed. An estimation of steel stocks for commercial and industrial buildings will also be carried out. This study is intended to guide policy and decision in developing new strategies for C&DW (construction and demolition waste) management.
Considering the relevance of mining a certain material and the data availability, as it has been described in the PUMA project (Huele et al, 2016), the focus of this research will be on the mining of copper
Table 1.1: Scope of the research
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2. Methodology and data 2.1 Area of study The geographical area of this study is the municipality of Almere. The municipality is divided into six districts: • • • • • •
these stocks are approximated using assumptions, and calculations are based on those assumptions. The following parameters are used to determine the metal content per building: • Building height of the buildings • Floor area of the buildings • Building function Building age is not taken into account, considering that no old buildings are present in Almere. Generalizations and categorizations are made based on these parameters. The following assumptions are used, grouped by building function.
Almere Haven Almere Stad Almere Buiten Almere Poort Almere Hout Almere Pampus
The latter district, Almere Pampus, will be excluded from the area of study since the district hardly contains any buildings. It is therefore assumed that it has no hibernating stocks of copper or steel.
RESIDENTIAL BUILDINGS: STEEL AND COPPER The assumptions made for the steel and copper contents of residential buildings in the municipality of Almere for this report are based on the same assumptions made by the PUMA report. The same point system has been used. An overview can be found in the figure below. The steel content of each category is an average number of the minimal and the maximum weight per square meter (for A that would be the average between 500 and 900 kg; 700 kg).
ACQUIRE 2.2 Assumptions In this study, a top-down approach is used. This approach is based on the methodology of Drakonis et. al. (2006) and is needed because there is no direct information available on the hibernating stocks of steel and copper in the city of Almere. Therefore
± Wijk 03 Almere Buiten
Wijk 02 Almere Stad
Wijk 04 Almere Poort
Wijk 05 Almere Hout
Wijk 01 Almere Haven
Figure 2.1: Local districts in Almere
Table 2.1: Metal content estimations (Huele, R. et al, 2016) 6
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COMMERCIAL BUILDINGS: STEEL CONTENT In a study by Wang et.al. (2015) the authors counted the iron and steel stocks in the chinese economy. One of the assumptions made was that the steel intensities for commercial buildings lays between 43 kg and 65 kg per square meter. The assumptions for this study on the steel content in commercial buildings for Almere are based on the assumptions made by Wang et al (2015). A content in between their assumptions is assumed; 54 kg per square meter.
Table 2.2: Data input
INDUSTRIAL BUILDINGS: STEEL CONTENT In a report by the website of steelconstruction.info (2013) the weights of the steel frameworks used in industrial buildings are calculated. The assumptions in the report are based on the eaves the buildings. It is assumed that buildings with low eaves, that is between 4 and 8 meters, contain 40 kg of steel per square meter. For buildings with high eaves, that is above 8 meters, the weight of the steel frameworks is assumed to be 50 kg per square meter (Steelconstruction.info, 2013).
EXAMINE Some actions need to be taken to make this data workable input as starting point for the analysis. • In order to create the shapefile that will be used to work with, two databases are joined: ‘Adreslocatie’ and ‘BAG_3D_2017_Almere’. The addresses from ‘Adreslocatie’ are linked with the buildings using a spatial join, and the buildings are sorted by height. This is needed in order to filter out the buildings with a height below 3 meters. • The buildings are selected according to their gebruiksfunctie. The gebruiksfunctie has 10 different categories; woonfunctie, industriefunctie, onderwijsfunctie, kantoorfunctie, gezondheidszorgfunctie, bijeenkomstfunctie, winkelfunctie, sportfunctie, and overige gebruiksfunctie.. For the residential sector the woonfunctie is selected. The commercial sector is an aggregation of the categories onderwijsfunctie, kantoorfunctie, gezondheidszorgfunctie, bijeenkomstfunctie, winkelfunctie, sportfunctie, and overige gebruiksfunctie.
ECONOMIC VALUE: STEEL AND COPPER In order to calculate the economic value of the stocks of copper and steel in the buildings of Almere assumptions are made about the price of copper and steel per kg. These assumptions are based on the prices according to the website kh-metals. nl. The price for copper is set at €4,- per kg. The price for steel is set at €0,16 per kg (khmetals.nl, 2017). 2.3 Methodology 2.3.1 Data input The data sources that are used in this report are provided in the following table. 7
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It is assumed that these buildings have the same steel content. For the industrial sector the industriefunctie is selected, as the buildings that are used in this function are often different from the commercial functions. Often these buildings (industrial halls) have larger spans than the typical reinforced concrete structures (Steelconstruction.info, 2013). This is done in order to maintain a clear endresult. All buildings labelled as a <NULL> are filtered out. â&#x20AC;˘ While generating the density maps, a distortion was found for the location where the Flevoziekenhuis is located showing a very low value. This was due the fact that in the BAG database, the building area of the hospital falls under the residential function and was therefore assigned a consistently lower metal content then to commercial buildings (which include also the gezondheidszorgfunctie). Furthermore, according to the same database, the building had a surface area of doubtful size. A Google Earth search showed that this surface area was actually referring to the entire property of the hospital which hosts a mix of commercial activities, parking lots and general infrastructure. It was therefore decided to exclude this area from the results of the research.
data source were expressed in kilograms per housing unit. Based on the (average) surface per housing unit and the height of the building, steel categories are assigned to the individual building according to the point system created by Huele et.al. (2016). However, instead of assigning points, a decision was made to take as reference the average value of the weight ranges of each category. This decision was prompted by the consistent gap between minimum and maximum value caused by the high uncertainty of the data. The total steel content of the building is calculated by multiplying the number of housing units times their average steel content. The same is done for the calculation of the copper contents per building. The result is the layer BAG_3D_2017_Almere_filtered_residential which enabled us to visualize steel and copper content per building (kg). 2.3.3 Industrial buildings In order to calculate the steel content for the industrial sector, the layer Bag_3D_2017_ Almere_filtered_industrial is created, filtering the buildings on the function industriefunctie. Two categories are created for the steel content, based on the height of the buildings and the surface area. This generates an overview of steel content per industrial building, based on steel weight per square meter.
2.3.2 Residential buildings For the residential sector, the floor area per dwelling and the number of floors per building are calculated. The total shape area of the building is calculated by multiplying the shape area by the number of floors, under the assumption that each floor has a standard height of 3 meters, and then divided by the number of addresses in the building. Note that these passages were required because the values of our
2.3.4 Commercial buildings The calculations of the steel content for commercial buildings is done by creating the layer BAG_3D_2017_Almere_filtered_ commercial. The steel content per square meter is assigned according to the assumptions made before. 2.3.5 Local districts In order to create the maps for the steel 8
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3. Analysis and results content per local district, the residential, commercial and industrial layers are merged into a single one. The local districts of Almere are shown by clipping the geographical area of Almere out of the wijk_2016_v1 database. Using a “sum” spatial join between these two files, the sum of all steel content is obtained. Only for the residential function, the same procedure is done for copper.
ANALYZE On the next page, an overview is provided of how the data analyis was executed. This is a simplified flowchart of the steps that have been taken. The extensive flowchart, including all tools that have been used in ArcGIS, can be found in appendix A.
2.3.6 Rasters In order to get an idea of the density of the hibernating stocks of copper and steel in the municipality, the metal content per area needs to be shown.This is done by changing the polygons of the maps to rasters. The parameter used for the rasters are first set at 1 square meter and then aggregated at 100 by 100 meters. The result is a density map, displaying the accumulation of metal content per area (kg/m2).
ACT The results of the analysis are presented subsequently, presented in the following order: 1. building scale • copper content (residential) • steel content (all buildings; the seperate maps per building function can be found in appendix B)
2.3.7 Economic value As the final step, the two datasets that have been generated (metal content per building and per local district), are now used to determine the economic value. This is done by simply multiplying the steel and copper content per building times the respective market value and adding them up. The same procedure described above is taken to get the aggregated economic value per local district and the density maps.
2. local district scale • copper content (residential) • steel content (all buildings) 3. density maps (1 ha raster) • copper content (residential) • steel content (all buildings) 4. economic value (steel and copper aggregated) • on building scale • on local district scale • density map (1 ha raster)
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Shape file with residential buildings
Shape file with industrial buildings
Shape file with commercial buildings (all other)
Calc total shape area and floor area/ dwelling Assign steel content per catagory, based on dwellings
Catagorize the data in 3 catagory groups
Assign copper content per catagory, based on dwellings
Assign steel content per catagory, based on floor area
Assign steel content, based on floor area
Steel and copper content data
Catagorize the data in 2 catagories
Figure 3.1: Simplified flowchart of the analysis
Select by function
Shape file with relevant Almere data
Filter out: - sheds by building height - addresses - <null> functions
Shape file with Almere data
Spatial join
Point file address BAG 3D locations Almere
Residential: Map with copper content/building
Cu
Residential: Map with steel content/building
St
Industrial: Map with steel content/building
St
Commercial: Map with steel content/building
St
Merge
Polygon to raster
raster aggregation to 100x100
Assign economic value
Map with copper content/ hectare
Cu
Map with copper content/ district
Cu
Assign economic value
Economic value data
Map with steel content/ hectare
St
Map with steel content/ district
St
raster aggregation to 100x100
Spatial join
Polygon to raster
Map with steel content/ building
St
Spatial join
Shape file with local districts Almere
Data set with economic value/building (copper)
Data set with economic value/district (copper)
Data set with economic value/building (steel)
Data set with economic value/district (steel)
Map with economic value/ hectare
â&#x201A;Ź
Polygon to raster
Map with economic value/ building
â&#x201A;Ź
Map with economic value/ district
â&#x201A;Ź
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Almere - residential buildings - copper content
±
Copper content (kg/building) 0 - 100 100 - 500 500 - 1000 1000 - 5000 5000 - 12000
0
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2 km
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Almere - all buildings - steel content Steel content (kg/building) 0 - 10.000 10.000 - 50.000 50.000 - 100.000 100.000 - 1.000.000 1.000.000 - 2.300.000
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Almere - all buildings - copper content Copper content (tonnes/local district) 0 - 100 100 - 250 250 - 500
747,448
500 - 1000 1000 -
1509,1
43,233
21,013 347,055
Esri, HERE, DeLorme, MapmyIndia, ©
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0,5
1
2 km Esri Nederland & Community Maps Contributors
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Almere - all buildings - steel content Steel content (tonnes/local district) 0 - 10.000 10.000 - 25.0000 25.000 - 50.000
71076,19
50.000 - 100.000 100.000 -
103673,3
3153,989
2101,776 22891,08
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Almere - all buildings - copper density Copper density (1 ha raster) High: 41064
Low : 20
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2 km
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Almere - all buildings - steel density Steel density (1 ha raster) High : 2222531
Low : 190
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2 km
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Almere - all buildings - economic value Steel and copper value density (euro/building) 0 - 5000 5000 - 15000 15000 - 30000 30000 - 70000 70000 - 450000
0
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1
2 km
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Almere - all buildings - economic value density Steel and copper value (euro/local district) 0 - 500.000 500.000 - 1.000.000 1.000.000 - 5.000.000
14361982
5.000.000 - 15.000.000 15.000.000 - 25.000.000
22624122
677570
420336 5050793
0
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2 km
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Almere - all buildings - economic value density Steel and copper value density (1 ha raster) High 422281
Low : 36
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2 km
15
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4. Conclusion and discussion The maps that are generated as the output of this research provide a ‘geographical map’ of Almere, showing the quantities of hibernating stocks of copper and steel in the built environment. Prospecting the urban mines of Almere has shown that the highest steel content for residential buildings is found in Almere Stad, while the highest steel content in industrial buildings is found in Almere Buiten. The highest copper content can be found in Almere Stad. The economic value of these materials is highest in Almere Stad. In order for the municipality of Almere to incorporate the results of this and further research, more detail has to be put on the planning aspect of its built environment. Assuming buildings in Almere will last around 80 years, it could be fruitful to extract resources from its existing urban environment. The prerequisite for this is that the demolition- and construction plans of the local government are integrated. Another important prerequisite is costeffectiveness; extraction of raw materials from urban mines has to be cheaper than the traditional approach. This is hard for areas where there is a low- to medium density of metal content. These are often heterogeneous residential areas. In order to cost-effectively ‘mine’ these areas the buildings need to have the same end-oflife periods. This requires careful planning as well.
stake when it is combined with unreliable datasets. The BAG has proven to be helpful for conducting a spatial analysis of the urban mines of Almere, but it has to be noted that there were some discrepancies in the data. This was the case for the hospital in the Almere-Stad, which had to be left out of the analysis. A solution to these problems could come from a combination of a more time consuming but precise bottom-up approach and a top-down one. Sampling a sufficient number of buildings in order to hone the proxies and parameters of the analysis might be the most efficient procedure to tackle this issue. Field research is needed in order to make spatial data useful for local policy-makers. In a relatively young city, such as Almere, it can be easier for urbanists, architects, engineers and recyclers to team up and proactively design the (expansion of) city for its future mining along with plans and strategies to reclaim these stocks. However, again, for these actors and the local government to elaborate solutions, more hands-on data and accurate projections are needed. On the one hand, the emerging idea of material passports could bridge the actual gap in data availability and transparency especially along the actors of the value chain. On the other hand, these buildingspecific data need to be integrated into databases for geoprocessing (such as the BAG) in order for analysts to provide solid scenarios and effective management insights. The related strategies should aim at embracing the whole lifecycle of buildings: from the design phase (modularity, design for recycling,..), to the use phase (ease of maintenance, retrofitting) to the end-of-life (revamping, rematerial passports).
Discussion The outputs of this brief study are a result of applying the top-down approach to the concept of urban mining which shows the stocks by using rough assumptions and estimations. This adds uncertainty and could damage the reliability of this research. The reliability of the top-down approach is at 16
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References Bueren, van E., H. van Bohemen, L. Itard, H. Visscher (2012), Sustainable Urban Environments: An Ecosystem Approach, Springer Science + Business Drakonis K, K. Rostkowski, J. Rauch, T.E. Graedel, and R.B. Gordon (2007), “Metal capital sustaining a North American City: Iron and copper in New Haven, CT”, Resources, Conservation and Recycling, vol. 49 (2), pp: 406-420. Huele, R. E. van der Voet, A. Kouramanis, B. van Reijn, E. van Bueren, J. Spierings, T., Demeyer, G. Roemers, and M. Blok (2016). Prospecting the Urban Mine of Amsterdam. Amsterdam Institute for Advanced Metropolitan Solutions. Krommenhoek Metals (2017, march 19). Schrootprijzen. Retrieved from: http://www.khmetals.nl/nl/schrootprijzen/. Shresta, S. (2010, april 29). Longlife Design Class - Building Life Cycle. Retrieved from: http:// www.longlife-world.eu/res/dnl/en/Shritu_Shrestha_Building%20life%20cycle.109.pdf. Steelconstruction.info (2013, february). Steel Insight - Industrial Buildings. Retrieved from: http:// www.steelconstruction.info/uploads/ftpin/Steel_Insight6/?pdfPath=Steel_Insight6#/1/. Wang, T., D.B. Muller, and S. Hashimoto (2015), “The Ferrous Find: Counting Iron and Steel Stocks in China’s Economy”, Journal of Industrial Ecology, vol. 19 (5), pp: 877-889.
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Appendices Appendix A Detailed flowchart of the analysis, using the model builder in ArcGIS.
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Appendix B Seperate maps per building function of the steel content, for residential, commercial and industrial.
Almere - residential buildings - steel content Steel content (kg/building) 0 - 1.000 1.000 - 5.000 5.000 - 25.000 25.000 - 50.000 50.000 - 205.000
0
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20
Âą
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Almere - commercial buildings - steel content
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Steel content (kg/building) 0 - 10.000 10.000 - 50.000 50.000 - 100.000 100.000 - 1.000.000 1.000.000 - 2.300.000
0
0,5
1
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Almere - industrial buildings - steel content Steel content (kg/building) 0 - 10.000 10.000 - 50.000 50.000 - 100.000 100.000 - 1.000.000 1.000.000 - 2.300.000
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