# Deconstruction Wood Sensitivity Analysis

### An evaluation of deconstruction salvage data using OSU maximum softwood density factor

#### Andey Nunes, MS

#### Research Analyst 2

#### Oregon DEQ

#### October 24, 2018

# Introduction

The 2018 Deconstruction Data Analysis used project data from residential single family homes removed under a City of Portland Deconstruction permit. The goal of the data analysis is to quantify the net environmental benefits resulting from avoided disposal of materials due to salvage/reuse (measured as Global Warming Potential and Primary Energy Demand impacts).

# Data Sources and Description

Project specific materials data originated from the following (pdf, png, or jpeg) receipts and inventory sources: * Receipts from dropbox and recycling haulers (pdf and pictures of printouts) * Pictures of handwritten sale or donation slips * Inventory forms and lists (as pdf)

All receipts were summarized in an excel spreadsheet, then reported quantities were converted to kilograms of materials. Energy and carbon impacts are calculated on the weight of materials specific to the end-of-life fate (or disposition), which is categorized as: reused, recycled, burned for energy recovery, or landfilled. Materials salvaged were treated as reused in the deconstruction scenario, while for the demolition equivalent scenario, those materials were assigned a disposition in accordance with the Metro region wide waste composition data. Materials disposed in dropboxes were standardized by material type and percentage of the dropbox weight and then assigned the waste composition dispositions under both the deconstruction and demolition scenarios.

## Data Set Description

# Results

#### Projects/Houses

There are 36 projects in the data set. The average house was 112.33 years old, roughly 1176.81 square feet, and yielded approximately 10587.17 lbs of salvage materials.

Figure 4

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Figure 5 – total weights of each material category SALVAGED, ranked from highest to lowest, with labels (material name and total kg)

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Figure 6 total weight of material disposed, demo only, grouped by the 4 broad categories (recoverable wood, other, non-recoverable wood, metal).

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## Carbon and Energy main impacts tables (Report Section 4.2)

per house average global warming potential impacts (kg CO2 equivalent)
impact_type | Deconstruction | Demolition | Difference | Per_SqFt_Difference
--- | --- | --- | --- | ---
Material impacts | -15,104 | -7,175 | 7,929 | 6.74
Material transport (to MRF or reuse) | 177 | 118 | -59 | -0.05
Material transport (MRF to EOL) | 462 | 585 | 123 | 0.10
Worker transport | 617 | 62 | -555 | -0.47
Equipment use on site | 0 | 241 | 241 | 0.20
Total | -13,849 | -6,170 | 7,679 | 6.53

per house average primary energy demand impacts (MJ)
impact_type | Deconstruction | Demolition | Difference | Per_SqFt_Difference
--- | --- | --- | --- | ---
Material impacts | -106,180 | -129,839 | -23,659 | -20.10
Material transport (to MRF or reuse) | 2,584 | 1,724 | -860 | -0.73
Material transport (MRF to EOL) | 6,751 | 8,548 | 1,797 | 1.53
Worker transport | 7,851 | 785 | -7,066 | -6.00
Equipment use on site | 0 | 3,516 | 3,516 | 2.99
Total | -88,994 | -115,266 | -26,272 | -22.33

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5. CHART THAT SHOWS CONTRIBUTION TO MATERIALS REUSE scenario on an average home. Which materials contribute most to the salvage benefits? Do for carbon and energy

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## Correlations

The research team was interested in the relationship between the weight of salvaged and disposal materials and the size (square feet) of the house. To explore this in Figure 2 the salvaged materials weight is expressed on the y-axis to the dropbox weight. This is depicted where the size of the point is relative to the total weight of the house (total salvage weight + dropbox weight), contractors are assigned to the point color, and the median crosshairs have been added to assist in differentiating the better than average performers in the upper left quadrant (with higher salvage weight and lower dropbox weight), from the lower than average performers in the lower right quadrant (lower than average salvage weight and higher dropbox weight.)

    ##     salvage      salvage_quantity_units    dropbox      dropbox_units
    ##  Min.   : 2883   Length:36              Min.   : 9580   Length:36
    ##  1st Qu.: 7205   Class :character       1st Qu.:16080   Class :character
    ##  Median : 9795   Mode  :character       Median :30720   Mode  :character
    ##  Mean   :10587                          Mean   :28775
    ##  3rd Qu.:12821                          3rd Qu.:36900
    ##  Max.   :20830                          Max.   :54740
    ##  total_house_weight total_house_weight_units percent_salvaged
    ##  Min.   :17948      Length:36                Min.   : 7
    ##  1st Qu.:27541      Class :character         1st Qu.:18
    ##  Median :40120      Mode  :character         Median :28
    ##  Mean   :39362                               Mean   :29
    ##  3rd Qu.:47188                               3rd Qu.:36
    ##  Max.   :61490                               Max.   :64
    ##    project           contractor          house_age     house_size
    ##  Length:36          Length:36          Min.   : 90   Min.   : 640
    ##  Class :character   Class :character   1st Qu.:107   1st Qu.: 897
    ##  Mode  :character   Mode  :character   Median :111   Median :1132
    ##                                        Mean   :112   Mean   :1177
    ##                                        3rd Qu.:118   3rd Qu.:1338
    ##                                        Max.   :137   Max.   :2341
    ##    est_weight     missing_house_est percent_missing
    ##  Min.   : 32000   Min.   :-12668    Min.   :-40
    ##  1st Qu.: 44850   1st Qu.:  5053    1st Qu.: 11
    ##  Median : 56575   Median : 14871    Median : 24
    ##  Mean   : 58840   Mean   : 19478    Mean   : 28
    ##  3rd Qu.: 66888   3rd Qu.: 25260    3rd Qu.: 51
    ##  Max.   :117050   Max.   : 82070    Max.   : 76

# Discussion

# References

reference EPDs & conversion factor sources here

Wickham, H. (2016) ggplot2: Elegant Graphics for Data Analysis, second edition. Springer. Switzerland.

Wickham, H., Grolemund, G. (2017) R for Data Science. O’Reilly Media, Inc. Sebastopol, California.

Xie, Y. (2016) Dynamic Documents with R and knitr, second edition. CRC Press, Taylor & Francis Group, LLC. Boca Raton, Florida.

# Appendix

## Supplemental Graphics

Relation of salvaged material weight to house size

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Relation of disposed material weight to house size

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Compute impacts on a sq ft basis.

## Assumptions log

Key data assumptions
x
Assumptions Log
Unless contractor specifies a hardwood species in the receipt line item, all dimensional lumber is assumed to be Softwood (fir, pine, cedar, redwood, hemlock)
Unless contractor specifies a softwood species (fir, pine) in the receipt line item, all wood flooring is assumed to be Hardwood
Wood flooring specified as fir is categorized as softwood lumber
Wood products specified as fir or other softwood variety are categorized as softwood lumber regardless of dimension
All light fixtures are the same whether they are an elaborate glass chandelier, a light/ceiling fan combo, a wall sconce, can light, or an 8 ft garage flourescent with a ballast
All plumbing fixtures not specified as cast iron, steel, or fiberglass products are ceramics, except bathtubs which are fiberglass unless specified otherwise
Unless specified otherwise, all pipe and ducting is assumed to be steel products
Missing data on doors: the type is assumed to be Inner (solid wood)
Missing data on windows: the type is assumed to be single pane wood
All windows are calculated as 12 sq ft window with 3’ x 4’ frame dimensions
Default weights for doors and fixtures are taken from previous data collection events
All fractions of pounds for metal and lineal, board, or square feet for wood & finish materials are rounded to the nearest whole unit (LBS or LF/BF/SF) where <0.5 is rounded down and 0.5 and >0.5 is rounded up
Cabinets specified as salvaged where quantity & type information is missing are assumed to be 3LF of lower cabinet
Where quantity information is missingfor salvage fixtures (lights, plumbing, etc), it is assumed that there is only 1 (count) of the default type
All dumpster tickets are reported in short tons: 2000 LBS = 1 TON = 907.185 kg
Appliances and furniture (which includes medicine cabinets and ‘built-in shelving’ not specified as wood in LF,SF,BF, or LBS) are not included in the raw data summary