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
## Saving 7 x 5 in imageFigure 5 – total weights of each material category SALVAGED, ranked from highest to lowest, with labels (material name and total kg)
## Saving 7 x 5 in imageFigure 6 total weight of material disposed, demo only, grouped by the 4 broad categories (recoverable wood, other, non-recoverable wood, metal).
## Saving 7 x 5 in imageCarbon and Energy main impacts tables (Report Section 4.2)
| 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 |
| 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 |
## Saving 7 x 5 in image
## Saving 7 x 5 in image
## Saving 7 x 5 in image
## Saving 7 x 5 in image- 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
## Saving 7 x 5 in image
## Saving 7 x 5 in image
## Saving 7 x 5 in image
## Saving 7 x 5 in image
## Saving 7 x 5 in image
## Saving 7 x 5 in imageCorrelations
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
## Saving 7 x 5 in image
Relation of disposed material weight to house size
## Saving 7 x 5 in image
## Saving 7 x 5 in image
## Saving 7 x 5 in imageCompute impacts on a sq ft basis.
Assumptions log
| 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 |