Table A1: GHG emissions from industrial processes and waste treatment assuming

5% GDP growth (in thousand tonCO2eq); source: Decree no. 7,390/2010 (Brazil,

2010)

 Time (year) Greenhouse Gases, GHG (in thousand tonCO2eq)1

2006 123,648

2007 131,105

2008 137,805

2009 137,552

2010 144,361

2011 151,507

2012 159,006

2013 166,877

2014 175,138

2015 183,807

2016 192,905

2017 202,454

2018 212.476

2019 222.993

2020 234.031

Note:

1 The estimates made by the Vector Error Correction (VEC) models for the years

Table A1: GHG emissions from industrial processes and waste treatment assuming

5% GDP growth (in thousand tonCO2eq); source: Decree no. 7,390/2010 (Brazil,

2010)

 Time (year) Greenhouse Gases, GHG (in thousand tonCO2eq)1

1990 to 2005 used data from the Second Brazilian Inventory for GHG emissions for

that period. Moreover, to capture the effects of economic activity on emission

levels, data from the Central Bank World Wide Web Site (Series 7326 of the System

Manager of Temporal Series) were used in the Gross Domestic Product (GDP).

Chart A1: The objectives of the PSCP UNIFEI/Itajubá campus (Barros, 2008;

2010)

To estimate the physical and financial resources needed to form a framework for

supporting the initial implementation of a model that facilitates the consolidated

separation, at the source, of recyclable waste that is discarded by the campus

community of the Federal University of Itajubá.

To demonstrate to the community the commitment and socio-environmental

responsibility of UNIFEI to propagating sustainable initiatives.

To implement educational programs and raise the awareness of the academic

community on the UNIFEI campus.

To reduce the negative impact inherent to SW by returning SW to the production

chain or by disposing of SW using environmentally appropriate means in

Chart A1: The objectives of the PSCP UNIFEI/Itajubá campus (Barros, 2008;

2010)

accordance with Brazilian legislation.

To promote initiatives that are aligned with the abovementioned objectives and

articulate the issues of research, teaching, extension and ordinary management at

UNIFEI.

To evaluate potential energy savings in GHG emissions for different scenarios of

solid waste management for the UNIFEI campus in Itajubá-MG through the use

and comparison of results via the WARM software program (USEPA, 2006).

Table A2: Estimated amount of organic matter in the SW generated by the UNIFEI

restaurant; source: Barros (2006; 2008)

Index Unit Value

IGMOR kgMO/meal 0.054

Number of meals Units 200

Amount of organic matter Kg/day 10.80

Chart A2: Different treatment types used for composting (MOURA, 2006;

MOURA et al., 2007)

Treatment 1, J1Windrow: The windrow was prepared in early March 2007, with regular turnings

every 2 or 3 days on average, and water was added as needed

Treatment 2, C1 Windrow: The windrow was prepared in early March 2007, without periodic

Chart A2: Different treatment types used for composting (MOURA, 2006;

MOURA et al., 2007)

turnings and without additional water.

Treatment 3, J2 and J3Windrows: The windrows were prepared in mid-April 2007, with turnings

every 2 or 3 days on average, and water was added as needed.

Treatment 4: C2 and C3Windrows: The windrows were prepared in mid-April

2007, without periodic turnings and without additional water.

Chart A3: Methodology utilized for composting parameters measurement for

different treatment types used for composting (MOURA, 2007; MOURA et al.,

2009; BARROS et al., 2009; and OKABAYASHI, 2007)

pH level, with a Digimed® DM-20 pH meter.

Temperature, which was measured in oC every three days with a digital thermometer, accompanied

by the turning of the windrows every 3 days or when there was too much rainfall, which could result

in high humidity.

Moisture, by an analytical balance in an oven at 550 °C for two hours and in a stove

at 100 °C for 24 hours.

BOD and COD, which were measured in mg/L following Standard Methods for the Examination of

Water and Wastewater (APHA, 1998).

Escherichia coli, which was measured using Durham tubes, following the procedure of Almeida et

al. (2005).

Chart A4: The buildings of the UNIFEI/Itajubá campus

C – Institute of Exact Sciences (Instituto de Ciências Exatas, ICE, in Portuguese).

F – Campus Hall.

G – Central Library.

I – Electrical and Energy Systems Institute (Instituto de Sistemas Elétricos e Energia, ISEE, in

Portuguese) / Systems Engineering and Information Technology Institute (Instituto de Engenharia

de Sistemas e Tecnologias da Informação, IESTI, in Portuguese).

K – Electrical Engineering Labs.

EXCEN – Center for Excellence in Energy Efficiency (Centro de Excelência em Eficiência

Energética, in Portuguese).

Block L: there are three blocks inside the L set (consisting of the Mechanical Engineering

Laboratories and the Natural Resources Institute (Instituto de Recursos Naturais, IRN, in

Portuguese)), namely,

L1 Block (for the Environmental Engineering undergraduate program).

L2 Block (for the Hydrologic Engineering undergraduate program).

L3 Block (for the Mechanical Engineering labs).

B – Mechanical Engineering Institute (Instituto de Engenharia Mecânica, IEM; in

Portuguese) and Production and Management Engineering Institute (Instituto de

Engenharia de Produção e Gestão, IEPG, in Portuguese).

Chart A5: The specific objectives of the PPSS UNIFEI campus / Itajubá-MG, Brazil Action

Plan (Barros, 2008; 2010)

To demonstrate to the community the commitment and the social and environmental

responsibility of UNIFEI to propagate sustainable initiatives.

To establish procedures that subsidize the implementation and maintenance of a PSCP on the

Chart A5: The specific objectives of the PPSS UNIFEI campus / Itajubá-MG, Brazil Action

Plan (Barros, 2008; 2010)

UNIFEI / Itajubá campus by a shared management process.

To collaborate systematically on the application and development of practices related to

environmental issues and the importance of selective collection.

To implement educational programs and raise the awareness of the academic community of the

UNIFEI campus.

To cooperate in addressing the concerns and expanding the socio-environmental care of the

students being educated at UNIFEI.

To reduce the negative impact of SW, by returning SW using appropriate means, to the

production chain or the environment, in conformity with legislation.

To encourage the UNIFEI community to incorporate values aimed at increasing effective internal

community participation and collaboration to value the worker known as a "recycler".

To establish partnerships for expanding the PSCP program.

To promote initiatives that encompass the objectives mentioned above and articulate research,

teaching, extension and ordinary management issues at UNIFEI.

Chart A6: Actions planned for the PPSS UNIFEI campus / Itajubá-MG (Barros, 2008; 2010).

Implementation of educational courses, thematic meetings and lectures through courses and at

internal (and external) events on recycling and SW integrated management.

Presentation of the PSCP UNIFEI / Itajubá-MG campus to the media (print, electronic and radio /

TV).

Dissemination of the study results in scientific journals, conferences, seminars,

etc.

Implementation of shared and integrated management.

Implementation and optimization of collection logistics.

Chart A6: Actions planned for the PPSS UNIFEI campus / Itajubá-MG (Barros, 2008; 2010).

Creation of a SW management committee within the PSCP, UNIFEI / Itajubá-

MG campus that meets regularly.

Conduction of interviews with the internal community to diagnose and evaluate

the PSCP.

Promotion of a reduction in SW generation at the UNIFEI/Itajubá-MG campus, with disclosure

of results.

Chart A7: The first-order kinetic equation 1 in the LandGEM (source USEPA, 1997; 2005)

QCH4=∑

i=1

n

∑j=0,1

1

k .L0[ M i

10 ]e−k . tij(4)

where

QCH4: the annual methane generation [m3];

n: the difference between the year of the calculation and the first year of

landfill operation;

k: the methane generation rate, which varies from 0.003 to 0.21 [1/year],

although under Brazilian conditions, the magnitude of this factor should

range from 0.05 to 0.15;

L0 : the potential capacity for methane generation, which is proportional to

the percentage of organic matter in the waste and varies from 0 (no

degradable material) to 300 [m³/t]. Because organic SW material from

UNIFEI was verified to constitute a small portion of the total SW evaluated,

Chart A7: The first-order kinetic equation 1 in the LandGEM (source USEPA, 1997; 2005)

a L0 value of 96 [m³/t] was used;

Mi: the bulk waste received in year i [t]; and

ti is the age of the jth section of the waste mass (Mi) entering the landfill in

the ith year.

Note:

1 Two summations are used: one to account for new loads (with a counter variable i) and one to

account for the degradation of ever-decreasing loads remaining from previous years (with a counter

variable j).

Table A3: Characterization of simulated scenarios; source: Lage (2012)

Material Scenario

C1

(15%)1

C2

(30%)1

C3

(45%)1

C4

(60%)1

C5

(75%)1

C6

(90%)1

C7

(100%)1

Rec

ycla

bles

(t

ons)

Lan

dfill

(ton

s)

Rec

ycla

bles

(t

ons)

Lan

dfill

(ton

s)

Rec

ycla

bles

(t

ons)

Lan

dfill

(ton

s)

Rec

ycla

bles

(t

ons)

Lan

dfill

(ton

s)

Rec

ycla

bles

(t

ons)

Lan

dfill

(ton

s)

Rec

ycla

bles

(t

ons)

Lan

dfill

(ton

s)

Rec

ycla

bles

(t

ons)

Lan

dfill

(ton

s)Aluminum cans (metal)

0.10 0.54 0.19 0.44 0.29 0.35 0.38 0.25 0.48 0.16 0.57 0.06 0.63 -

Mixed paper (general)

0.54 3.05 1.08 2.52 1.62 1.98 2.16 1.44 2.70 0.90 3.23 0.36 3.59 -

Mixed plastics

0.27 1.53 0.54 1.26 0.81 0.99 1.08 0.72 1.35 0.45 1.62 0.18 1.80 -

Mixed organic matter

NA1 0.80 NA1 0.80 NA1 0.80 NA1 0.80 NA1 0.80 NA1 0.80 NA1 0.80

Total 6.82 5.76 4.70 3.63 2.57 1.51 0.80

Note:

1 Recycling efficiency for selective collection

Table A3: Characterization of simulated scenarios; source: Lage (2012)

Material Scenario

C1

(15%)1

C2

(30%)1

C3

(45%)1

C4

(60%)1

C5

(75%)1

C6

(90%)1

C7

(100%)1

Rec

ycla

bles

(t

ons)

Lan

dfill

(ton

s)

Rec

ycla

bles

(t

ons)

Lan

dfill

(ton

s)

Rec

ycla

bles

(t

ons)

Lan

dfill

(ton

s)

Rec

ycla

bles

(t

ons)

Lan

dfill

(ton

s)

Rec

ycla

bles

(t

ons)

Lan

dfill

(ton

s)

Rec

ycla

bles

(t

ons)

Lan

dfill

(ton

s)

Rec

ycla

bles

(t

ons)

Lan

dfill

(ton

s)

2NA: Not Applicable

Table A4: Apparent specific weight values, determined using the procedure for

the physical characterization of SW from the UNIFEI campus; source: Pieroni

(2009); Vieira (2009); and Barros (2008; 2010)

CharacterizationTotal weight RS

analyzed (kg)

Apparent specific

weight (kg/m3)

1ª 11/21/2007 13.11±0,66 28.94

2ª 11/27/2007 8.43±0,42 26.20

3ª 12/07/2007 6.69 20.07

4ª 12/10/2007 5.49 20.16

5ª 12/12/2007 14.02 25.29

6ª 03/15/2007 19.6 81.29

7ª 05/20/2008 10.09 54.77

8ª 05/27/2008 8.64 37.38

9ª 06/03/2008 8.79 35.23

10ª 06/10/2008 9.5 30.31

11ª 07/24/2008 10.31 52.77

12ª 07/25/2008 8.49 35.08

13ª 07/30/2008 10.04 44.92

14ª 090/9/2008 8.26 59.54

15ª 03/10/2009 10.7 53.54

16ª 03/12/2009 7.58 35.38

Average 9.984 40.054

Table A4: Apparent specific weight values, determined using the procedure for

the physical characterization of SW from the UNIFEI campus; source: Pieroni

(2009); Vieira (2009); and Barros (2008; 2010)

CharacterizationTotal weight RS

analyzed (kg)

Apparent specific

weight (kg/m3)

Standard deviation 3.337 16.662

Table A5: SW types generated by sets of UNIFEI campus buildings; source: Pieroni (2009);

Vieira (2009); and Barros (2008; 2010)

Types of Solid Waste Buildings

B C F G I K L1 L2 L3 Excen

Paper X X X X X X X X X X

Plastics X X X X X X X   X X

PET bottles X X X X X X X X X X

Metals X X X     X     X X

Glass X X X X X X X X X  

Stacks X X X   X X   X X  

Batteries   X X   X X X   X  

Wood X X X   X X X   X  

Rubber X X X X X       X  

Ceramics             X      

Fiber X X         X     X

Food scraps X X X X X X X X    

Healthcare service X         X X X X  

Toilet paper X X X X X X X X   X

Construction debris X     X X     X    

Note: “X” denotes waste generation in the building

Table A6: SW disposal in each UNIFEI campus building; source: Pieroni (2009); Vieira (2009);

and Barros (2008; 2010)

Solid wastes Buildings

B C F G I K L1 L2 L3 Excen

Papers C/S C C/S C/R C/S C C S S C

Plastics, glasses, and

metals

C/S C C/S C C C C S S C

Cells and Batteries C A S   C C C C C  

Healthcare service       C/I     C C C  

Organics C C C C C C C C C  

Note: C represents conventional collection; S represents selective collection; C/S indicates that the

SW is sent to conventional collection when it occurs in a large quantity but is sent to selective

collection under normal circumstances; R represents reuse of the SW; and I indicates that the SW

is incinerated.

Table A7: Quantity of SW generated per week in each UNIFEI campus building;

source: Pieroni (2009); Viera (2009); and Barros (2008; 2010)

Building

Quantity of SW bags per week SW Quantity (kg) per week

Recyclable Not Recyclable Recyclable Not Recyclable

B 2 + 5 kg 28 11.73 94.25

C   15   50.49

F   10   33.66

G   25   84.15

I   45   151.47

K   5   16.83

L1   6   20.20

L2 5 5 16.83 16.83

L3 3 3 10.098 10.10

Table A7: Quantity of SW generated per week in each UNIFEI campus building;

source: Pieroni (2009); Viera (2009); and Barros (2008; 2010)

Building

Quantity of SW bags per week SW Quantity (kg) per week

Recyclable Not Recyclable Recyclable Not Recyclable

Excen   3   10.10

Table A8: Composting area designed for UNIFEI campus interior; source: Barros

(2006; 2008; 2010)

Parameter Unit Value

height m 1.50

width m 2.50

superficial area, Sst m2 1.875

bulk density for composting

(BIDONE & POVINELLI, 1999)

t/m3 0.800

volume of windrow composting,

VL

m3 0.27

windrow length, L m 0.144

windrow base area, Sba m2 0.36

clearance area for windrow

turning, Sfo

m2 0.72

total area occupied by windrow m2 0.72

Table A8: Composting area designed for UNIFEI campus interior; source: Barros

(2006; 2008; 2010)

Parameter Unit Value

composting time period

(BIDONE & POVINELLI, 1999)

days 120

composting yard area, Sup m2 86.40

increase in area for circulation % 10.00

Total area composting yard, Sup m2 95.04

Figure A1: Temporal variation in the differences between BOD and COD values; source:

Moura (2007); Barros et al. (2009)

Table A9: Results from presence (+) /absence (-) tests for total coliforms and Escherichia Coli (E.

coli); source: Moura (2007); Barros et al. (2009)

Samples Dilution Dilution

-3 -4 -5 -3 -4 -5Total coliforms E. coli

J1 (+) (+) (-) (-) (-) _J2 (+) (+) (-) (-) (-) _J3 (+) (+) (-) (+) (-) _C1 (-) (+) (-) _ (-) _C2 (+) (-) (-) (-) _ _C3 (+) (+) (-) (-) (-) _

Figure A2: Control parameters for the 1st stage of the maturation of the composting process in the

prepared windrows: a) DBO/DQO radio (upper plot); and b) statistical analysis of E. coli. presence

(lower plot); source: Okabayashi (2007); Okabayashi et al. (2009)

Figure A3: Control parameters for the 2nd stage maturation of the composting process in the prepared

windrows: a) BOD/COD ratio (upper plot); and b) statistical analysis of E. coli. presence (lower plot);

source: Okabayashi (2007); Okabayashi et al. (2009)

Table A10: Material resource requirements for various operations of selective collection

on the UNIFEI campus; source: Barros (2006; 2008; 2010)

Material resource Investment phase Cost (R$) Maintenance

costs

(R$)/Month

Waste containers for

selective collection

Initial / eventual replacement

(annual)

138,100.78 2,036.97

Dissemination materials Semiannual 5,100.00 850.00

Total – start of implementation of selective collection1

program

157,520.85 3,332.01

Total – selective collection implementation2 160,852.07

Note:

1 Initial total cost (R$ 143,200.78) to which a risk of 10% is added, as recommended by Von

Table A10: Material resource requirements for various operations of selective collection

on the UNIFEI campus; source: Barros (2006; 2008; 2010)

Material resource Investment phase Cost (R$) Maintenance

costs

(R$)/Month

Sperling (2005), resulting in R $ 157,520.85.

2 1/12th of the result from 20% of the total annual cost (R$ 29,784.16) for the acquisition of

equipment, dissemination and annual maintenance costs (R$ 10,200.00), as recommended by Von

Sperling (2005), resulting in R$ 3,332.01

Figure A4: Quantities of SW sent to ACIMAR by IRN/UNIFEI and quantities of SW

commercialized by ACIMAR

Figure A5: Marketing logo designed for the UNIFEI campus, which was designed and generously

given to the PSCP by the engineer Adriano Silva Bastos

Figure A6: Flowchart of actions and expected results for the PSCP UNIFEI campus;

source: Barros (2008)

Table A11: Materials in samples of SW generated by UNIFEI that were used in simulations by

WARM software (USEPA, s.d.); source: Lage (2012)

Material Recycables

(tons)

Landfill

(tons)

Combustibl

e (tons)

Composting

(tons)

Generatio

n (tons)

Aluminum

cans (metal)

0 0.6342 0 NA 0.6342

Mixed

paper

(general)

0 3.5938 0 NA 3.5938

Mixed

paper (from

offices)

0 1.057 0 NA 1.057

Mixed

plastics

0 1.7969 0 NA 1.7969

Mixed

organic

matter

NA 1.3741 0 0 1.3741

Note:

1NA: Not Applicable

Table A12: GHG emissions and changes in increasing GHG emissions relative to the baseline

scenario; source: adapted from Lage (2012)

Scenario% of

Recycling

Total GHG emissions for alternative

scenarios for SW generation and

management (tCO2e)

Increase in GHG

emissions (tCO2e)

C1 15 1 -1

Table A12: GHG emissions and changes in increasing GHG emissions relative to the baseline

scenario; source: adapted from Lage (2012)

Scenario% of

Recycling

Total GHG emissions for alternative

scenarios for SW generation and

management (tCO2e)

Increase in GHG

emissions (tCO2e)

C2 30 -2 -7

C3 45 -6 -10

C4 60 -9 -14

C5 75 -12 -17

C6 90 -16 -20

C7 100 -18 -23

Table A13: Energy consumption and change in the energy increase for each scenario; source:

adapted from Lage (2012)

Scenario% of

Recycling

Total energy use in alternative scenarios

for SW generation and management (GJ)

Increase in energy

use (GJ)

C1 15 -39.04 -42.20

C2 30 -71.74 -74.91

C3 45 -110.78 -113.94

C4 60 -147.70 -150.87

C5 75 -183.57 -186.74

C6 90 -222.61 -225.77

C7 100 -247.93 -251.09

Table A14: Equivalence of GHG emissions reduction for the simulated scenarios; source:

adapted from Lage (2012)

Scenario% of

RecyclingOil barrels Gallons of gas

X families annual energy

consumption

C1 15 7 319 0

C2 30 12 573 1

C3 45 19 866 1

C4 60 25 1148 1

C5 75 31 1427 2

C6 90 37 1721 2

C7 100 41 1911 2

Note:

Oil barrels (1 barrel = 158 liters); gallons of gas (1 gallon = 3.78 liters)

Figure A7: Methane emissions for each scenario determined from simulations by LandGEM

(USEPA, 2005); source: Lage (2012)

Figure A8: Carbon dioxide emissions for each scenario determined from simulations by LandGEM

(USEPA, 2005); source: Lage (2012)