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Pyrolysis and co-composting of municipal organic waste in Bangladesh: a

quantitative estimate of recyclable nutrients, greenhouse gas emission, and

economic benefits

Shamim Mia1,2*, Md. Ektear Uddin3, Md. Abdul Kader4,5,6, Amimul Ahsan7, 8,M. A. Mannan9,

Mohammad MonjurHossain10 and Zakaria M. Solaiman11

1Centre for Carbon, Water and Food, The University of Sydney, Camden, Australia

2Department of Agronomy, Patuakhali Science and Technology University, Bangladesh

3Department of Agricultural Extension and Rural Development, Patuakhali Science and

Technology University, Bangladesh

4Department of Soil Science, Bangladesh Agricultural University, Bangladesh

5School of Veterinary and Life Sciences, Murdoch University, WA, Australia

6School of Agriculture and Food Technology, University of South Pacific, Samoa

7Department of Civil Engineering, Uttara University, Dhaka 1230, Bangladesh

8Department of Civil and Construction Engineering, Swinburne University of Technology,

Melbourne, Australia

9Department of Agronomy, Bangabhandhu Sheikh Mujibur Rahman Agricultural University,

Bangladesh

10Department of Agronomy, Bangladesh Agricultural University, Bangladesh

11School of Agriculture and Environment, University of Western Australia, Perth, Australia

*Corresponding author’s email: [email protected]

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mailto:[email protected]

Supplementary information

Sl. No. Ttitle Page1. Table S1. List of literatures used in this study 32. Table S2. Definition of solid waste composition 4-53. Table S3. Per capita GDP, GNI growth, national GDP growth and

inflation rate of Bangladesh6

4. Survey for PWGR determination 75. Fig. S1. The calculation trajectory of nutrient recycling in the biochar

enriched composting.8

6. Table S4. Fraction of organic waste for different cities of Bangladesh 97. Table S5. Nutrient content in fresh waste of different cities of Bangladesh 108. Table S6. Nutrient composition of biochar produced from different

biomass including MW11

9. Table S7. Nutrient recycling potentials for GDP based calculations while composting and pyrolysing singly and co-composting

12

10. Emission factor calculation for the proposed method 1311. Table S8. Reduction of emission due to composting of biochar with

composting biomass 15

12. Fig. S2. Logarithmic (y=a×ln(x0-x)) fitting of literature emission data 1613. Table S9. Emission factor calculation for the proposed method 17-19

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Table S1. List of literatures used in this study

Sl. NO. Category References1. Per capita waste

calculationAlamgir and Ahsan, 2007a; Enayetullah et al., 2005; JICA, 2005

2. Waste composition Ahmed and Rahman, 2000; Ahmmad and Haque, 2014; Alam and Bole, 2001; Alamgir and Ahsan, 2007a; BMDF, 2012; Chowdhury et al., 2014; Das et al., 2014; Halder et al., 2014; Hossain et al., 2014; Khan, 1999;Islam et al., 2015; JICA, 2005; Lavangnolo, 2014; Mili, 2000; Rahman et al., 2013; Rouf, 2011; Sujaufddin, 2008; Uddin and Mojumder, 2011; Yousuf and Rahman, 2007; Zakir Hossain et al., 2014.

3. Nutrient recycling estimation

Alamgir and Ahsan, 2007; BCSIR, 1998; BMDF, 2012; Enayetullah and Sinha, 2002; Hasan et al., 2012; Lavangnolo, 2014; Pituello et al., 2015; Zhao et al., 2013; Zurbrugg et al., 2005

4. Greenhouse gas emission calculation

Agyarko-Mintah et al., 2017 a and b; Awasthi, et al., 2016; 2017 a and b; Boldrin et al., (2009) Chen et al., 2017; Chowdhury et al., 2014 a and b; Czekała et al., 2016; Friedrich and Trois, 2013; Han Weng et al., 2017; IPCC, 2006; Islam, 2017; Malińska, et al., 2014; Roberts et al., 2010; Sánchez-García et al., 2015; Steiner et al., 2010; Vandecasteele, et al., 2016; Wang et al., 2016.

4 Return calculation Zeng et al., 2005 and Alamgir and Ahsan, 2007b.

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Table S2a.Definition of solid waste composition

Waste category Waste component

OrganicOrganic waste includes any wastes originated from food and vegetables, straw, seasonal fruit leftover and refuses, garden wastes except wood

PaperAny material made of paper such as paper bags, cardboard, box board etc.

Plastic Any material made of plastic such as bags, bottles or containers Textile and wood

Any materials originated from yuan, natural fibres such clothes, apparel industry by-products (Zote), and wood, bamboo etc.

Leather and rubber

Any material made of lather such as shoes, bags etc. and rubber such as tires, sponge, cork sheet etc.

Metal Any materials made of iron or other metalsGlass Any materials made of glasses such as bottles, glassware, light or bulbsCeramics Any porcelain materials such as styles, ceramic plates etc.Others Unidentified materials, sand, dust and rocks

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Table S2b. Composition of compostable and pyrolysable organic waste

Properties Compostable organic waste

Pyrolysable organic waste

Composition Waste that is decomposed easily within short period of time and usually contain high moisture percentage, for example, food waste.

Dried and semi-dried fibrous organic waste including coconut shell, dried leaves, branches, straws, large trashes, slaughter house wastes etc.

Moisture content >50% <50%

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Table S3. Per capita GDP, GNI growth, national GDP growth and inflation rate of Bangladesh (World Bank, 2016)

Year GNP (US $ persion-1)

GNP growth rate* (%)

GDP (US$ persion-1)

Per person GDP growth rate (%)

GDP growth rate

Inflation (%)

2005 470 486 6.0 7.0

2006 510 8.7 496 2.1 6.7 6.8

2007 560 7.1 543 9.5 7.1 9.1

2008 650 16.3 618 13.8 6.0 8.9

2009 710 13.4 684 10.6 5.0 5.4

2010 780 6.7 760 11.2 5.6 8.1

2011 870 8.4 839 10.3 6.5 10.7

2012 950 14.7 859 2.4 6.5 6.2

2013 1010 4.8 954 11.1 6.0 7.5

2014 1080 7.5 1087 13.9 6.1 7.0

2015 1130 5.5 1212 11.5 6.6 6.2

2016 1416 16.9 7.1** 5.6***

Average 9.4 10.3 6.3

* GNP growth rate was calculated as real income basis after subtraction of inflation* Data were collected from Asian Development Bank * * Data were collected from Bangladesh Bank

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Survey to determine per capita waste generation rate

We conducted a survey during May 2016 in Dhaka, Barisal, Mymensingh and Patuakhali to

determine the amount of waste dumped in the dumping sites. We acknowledge that we could

not conduct survey in the same day in all locations of four cities due to limited work force.

We followed a structured table to record the number of vehicle, that dumped waste with its

capacity. From these data, we calculated the waste generation in the dumping sites. From our

experience, we did know that all generated waste were not collected and dumped. Therefore,

we did interview waste collectors and waste pickers and recorded the fraction of waste would

have not been collected. A total of 50, 25, 20, and 10 personnel were interviewed in the

Dhaka, Barisal, Mymensingh and Patuakhali respectively. From these, we calculated waste

generation of a city. Since the waste generation may vary with seasons, we collected waste

generation data from the respective city corporation. We then, averaged these two waste

generation capacity, which was used to calculate the per capita waste generation rate. We

would like to also mention that if any unexceptional waste generation data was provided by

any personnel (particularly city corporation officers), we excluded this from calculation.

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Mass of fresh solid organic waste

Mass of dried solid organic waste (MSOW)

70% moisture content

Mass of pyrolysable waste, PW (35% of MSOW)

Mass of compost (25% of CW)

Mass of compostable waste, CW (65% of MSOW)

Mass of biochar (35% of PW)

% C, N, P and K content in dried solid waste

Amount of C, N, N and P retained from compost

Amount of C, N, N and P retained from biochar

Co-composting effect on C, N, P and K retention

Fig.S1. The calculation trajectory of nutrient recycling in the biochar enriched composting.

This scheme was developed using the data from Ahmad et al., (2014);BMDF, (2012);Eghball

et al., (1997);Enayetullah and Sinha, (2015); Hamer, 2004; Hermann et al., (2011); Mia et al.,

(2015);Pitman, (2006); Pituello et al., (2015); Pognani et al., (2012); Themelis and Kim,

(2002); Uddin and Mojumder, 2011)); Tiquia et al., (2002); Yousuf and Rahman, (2007) and

Zhao et al., 2013).

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Table S4. Fraction of organic waste for different cities of Bangladesh

Year Waste composition (%)

DCC

GCC* NCC* RCC RaCC BCC CCC KCC SCC CoCC** MyCC Other urban areas

Total

2016

Compostable 2756 90 60 130 49 33 825 229 44 55 32 3423 7725

Pyrolysable 1484 48 32 70 27 18 444 123 24 29 17 1843 4159

Fuel for pyrolysis 643 21 14 17 5 5 213 46 10 9 3 527 1514

2035

Compostable 4964 162 107 234 89 59 1485 412 80 99 58 5089 12837

Pyrolysable 2673 87 58 126 48 32 800 222 43 53 31 2740 6912

Fuel for pyrolysis 1159 38 25 31 9 9 384 82 18 16 6 784 2561

DCC= Dhaka city corporation, GCC=Gazipur City Corporation, NCC=Narayanganj City Corporation, RCC= Rajshahi city corporation, RaCC= Rangpur city corporation, BCC=Barisal city corporation, CCC= Chittagong city Corporation, KCC= Khulna city corporation, SCC= Sylhet City Corporation, CoCC=Comilla City Corporation, MyCC=Mymensingh City Corporation. * Indicates data were used from DCC. ** indicates data were used from BCC while *** indicates data were used from RaCC as there were no study available for these cities.

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Table S5. Nutrient content in fresh waste of different cities of Bangladesh

Name of city Organic Carbon C/N Total N P (%) K (%) Reference

Average of six cites* 25.00 11.92 0.82 0.13 0.55 Alamgir and Ahsan, 2007a

Magura 24.96 20.80 1.20 0.39 1.69 Lavangnolo, 2014Rajshahi 2.90 Das et al., 2014Chittagong 13.51 18.44 1.26 0.85 1.25 BDMF et al., 2012Chittagong 14.57 13.77 1.82 0.76 1.42 BDMF et al., 2012Chittagong 11.76 8.50 2.38 0.58 1.58 BDMF et al., 2012Chittagong 14.04 15.68 1.54 0.61 1.17 BDMF et al., 2012Chittagong 15.44 9.98 2.66 0.88 1.92 BDMF et al., 2012Rajshahi 11.06 13.59 1.40 0.66 1.50 BDMF et al., 2012Rajshahi 13.70 10.52 2.24 0.84 2.00 BDMF et al., 2012Rajshahi 12.46 10.20 2.10 0.58 1.83 BDMF et al., 2012Rajshahi 13.00 17.75 1.26 0.55 1.58 BDMF et al., 2012Rajshahi 15.27 9.87 2.66 0.82 2.25 BDMF et al., 2012Rajshahi 13.16 13.48 1.68 0.75 2.08 BDMF et al., 2012Rangpur 14.04 12.32 1.96 0.63 2.33 BDMF et al., 2012Rangpur 11.06 10.45 1.82 0.82 2.17 BDMF et al., 2012Rangpur 12.46 9.57 2.24 0.67 1.75 BDMF et al., 2012Rangpur 12.81 19.67 1.12 0.64 1.33 BDMF et al., 2012Rangpur 14.74 9.05 2.80 0.81 2.25 BDMF et al., 2012Rangpur 11.93 10.47 1.96 0.60 2.00 BDMF et al., 2012Patuakhali 13.34 14.90 1.54 0.75 2.67 BDMF et al., 2012Patuakhali 11.23 8.62 2.24 0.81 1.83 BDMF et al., 2012Patuakhali 11.58 9.48 2.10 0.67 2.50 BDMF et al., 2012Patuakhali 13.86 9.46 2.52 0.80 1.75 BDMF et al., 2012Patuakhali 14.57 13.77 1.82 0.66 2.42 BDMF et al., 2012Dhaka 26.06 16.00 1.62 BCSIR, 1998Dhaka 17.81 39.00 0.46 BCSIR, 1998Dhaka 9.90 17.00 0.58 BCSIR, 1998Dhaka 12.70 20.50 0.62 BCSIR, 1998Average 14.50 14.10 1.77 0.68 1.83

*DCC, CCC, RCC, BCC, SCC and KCC

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Table S6. Nutrient composition of biochar produced from different biomass including MW

Waste type Pyrolysis temperatur

e

Nutrient composition (% of dry weight basis)

Reference

N P KMunicipal Solid waste 250 4.10 0.56 1.78 Pituello et al., 2014

Municipal Solid waste 350 4.00 0.65 2.05 Pituello et al., 2014



Manure Cow 550 1.99 0.65 1.20 Zhao et al., 2013

Pig 550 2.32 4.39 3.56 Zhao et al., 2013

Chicken 550 1.62 0.88 1.44 Zhao et al., 2013

Sawdust 550 0.30 0.06 1.19 Zhao et al., 2013

Treeleaf 550 1.36 0.13 0.93 Zhao et al., 2013

Grass 550 2.83 0.59 5.15 Zhao et al., 2013

Crop waste Rice straw

550 1.58 0.24 4.09 Zhao et al., 2013

Wheat straw

550 0.53 0.04 5.18 Zhao et al., 2013

Peanut shell

550 2.56 0.17 1.73 Zhao et al., 2013

Corncob 550 2.05 0.08 3.67 Zhao et al., 2013

Rice husk

550 1.23 0.12 0.68 Zhao et al., 2013

Food waste Sugarcane

550 1.43 0.19 2.01 Zhao et al., 2013

Pollyseed hull

550 1.35 0.13 3.25 Zhao et al., 2013

Shrim hull

550 5.63 2.59 1.90 Zhao et al., 2013

Bone dregs

550 3.06 10.86 0.44 Zhao et al., 2013

Eggshell 550 0.18 0.13 0.15 Zhao et al., 2013

Aquatic plants Chorella 550 3.37 0.72 13.67 Zhao et al., 2013

Waster weeds

550 1.56 0.51 3.22 Zhao et al., 2013

Municipal waste

Waste paper

550 1.62 0.12 0.08 Zhao et al., 2013

Waste water Slude

5502.86 1.70 0.26

Zhao et al., 2013

Average 1.96 0.43 2.10

The values that highlighted with bold are excluded from averaging as they are either extremely high or may be not present in the MW.

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Table S7. Nutrient recycling potentials for GDP based calculations while composting and pyrolysing singly and co-composting

Recycling process Nutrient GDP based projection2016 2035

Fresh C 1604 2666

N 63 105

P 24 40

K 65 108

Composting Weight of compost* 3862 6419

C 521 867

N 18 31

P 11 18

K 17 28

Pyrolysis Weight of biochar 437 726

C 286 475

N 9 14

P 2 3

K 9 15

Ash from fuel Weight 45 77

C 0 0

N 0 0

P 0 1

K 13 23

Co-composting effects C loss estimation 66 110

N retention increased 7 11

Total recyclable C 741 1231

Total recyclable N 34 56

Total recyclable P 13 22

Total recyclable K 39 66

*Compost weight wast calculated weight basis while all other measurement was

presented as dry weight basis.

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Emission factor calculation for the proposed method

In Bangladesh, municipal waste (MW) contributes 18.76% of the greenhouse (GHG)

emissions (WRI, 2014). We performed emission factor calculation for the proposed method

considering one tonne of fresh MW (Table S9). First, we considered a 30 km of travel

distance to take the waste from the collection sites to the co-composting plants. The CO2

emission for each km travel was considered as 0.19 kg CO2/t (Friedrich and Trois, 2013).

Second, we considered that 10 L diesel would be sufficient for waste processing. This

assumption was made based on personal communication with MW composting company.

The emission from diesel-burning is accounted to be 2.7 kg CO2/L (Boldrin et al., 2009;

Friedrich and Trois, 2013). In our method, 26.82% of the MW was calculated to be

pyrolysable. Third, to calculate emission factor for biochar production, we used the emission

factor of the yard waste, that was calculated for slow pyrolysis; a similar pyrolysis technique

we proposed (Roberts et al., 2010). Since, we use fuel (9.76% of the MW) in the pyrolysis

systems, we calculated biogenic CO2, CH4 and N2O emissions for fuel biomass following the

methods of Boldrin et al. (2009) and Islam (2017).

Composting process emits GHG while returning nutrients to agriculture abate

emissions. However, studies investigating GHG emissions from co-composting biochar with

biomass shown to overall reduction of emissions than composting alone although this

reduction has been an apparent estimation. We collected literature data (listed in the Table

S8) and fitted a logarithmic model (Fig. S2) to predict the possible reduction of our method,

i.e., 19% biochar addition to composting mix. Several other models were also considered and

this model (y=a×ln(x0-x)) appear to be better having the least sums of squared error. These

reductions were then adopted for both biogenic and GHG emissions from composting method

(Friedrich and Trois, 2013). For agricultural applications, we considered a combined factor of

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100 km travel distance for transport and spreading of compost. Since biochar C stabilisation

is considered in the biochar technology, we only calculated the C sequestration from

composting biomass (63% of the total biochar blended compost C). However, we considered

a 20% C sequestration for 100 yrs compared to 8% proposed by Boldrin et al. (2009), since

biochar accelerates soil organic matter stabilisation, particularly when applied to the clay-rich

soils (Han Weng et al. 2017). We considered that the moisture content of biochar compost as

40%, while N, P and K were calculated at 2.92%, 1.13% and 3.14% respectively, from

nutrients balance calculation. The emissions abatement for supplied nutrients were calculated

following the methods of Friedrich and Trois (2013).

Emissions factors of other MW recycling was also calculated using the methods of

Friedrich and Trois (2013) while emissions factor for drying and processing of plastic,

metals, glass, leather and rubber were adopted for Bangladesh.

Since most of the MW in Bangladesh is either managed by landfilling and burning, we

considered the relevant emissions reduction for our method. In a recent study, Islam (2013)

calculated an emission factor of 420.88 CO2/t of MW (wet basis). We calculated emission

abatement for avoiding landfilling using this emission factor. However, only 63.2% of the

MW are considered for landfilling while the rest was calculated in the biochar production

(Roberts et al., 2010).

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Table S8. Reduction of emission due to composting of biochar with composting biomass

CO2 CH4 N2O NH3 Biochar feedstock Biochar production

Application rate (%)

Composting biomass Reference

- 26.10 - 24.80 Cornstalk biochar 450-500 10 Layer manure and sawdust (1:0.33) Chen et al., 2017

- 15.50 - 9.20 Bamboo 450-500 10 Layer manure and sawdust (1:0.33) Chen et al., 2017

- 22.40 - 20.10 Woody 450-500 10 Layer manure and sawdust (1:0.33) Chen et al., 2017

- 10.80 - 14.20 Layer manure 450-500 10 Layer manure and sawdust (1:0.33) Chen et al., 2017

- 17.10 - 11.80 Coir 450-500 10 Layer manure and sawdust (1:0.33) Chen et al., 2017

8.94 - - 7.77 Pine Wood 400 5 Poultry litter Steiner et al., 2010

-1.56 - - 43.36 Pine Wood 400 20 Poultry litter Steiner et al., 2010

27.13 73.28 43.33 34.04 Hard and soft wood (4:1) 500-700 14 Digested solid Chowdhury et al., 2014 a

33.87 89.22 36.67 42.55 Hard and soft wood (4:1) 500-700 25 Digested solid Chowdhury et al., 2014 a

24.29 79.69 9.68 63.73 Hard and soft wood (4:1) 500-700 27 Hen manure and straw (3.5:1) Chowdhury et al., 2014 b

22.86 83.59 16.13 35.29 Hard and soft wood (4:1) 500-700 27 Hen manure and straw (3.5:1) Chowdhury et al., 2014 b

51.37 81.48 14.55 60.00 Green waste 550 10 Poultry litter and sugarcane bagasse Agyarko-Mintah et al., 2017 a

- - - 55.00 Poultry litter 550 10 Poultry litter and sugarcane bagasse Agyarko-Mintah et al., 2017a

53.00 95.00 14.00 - Oak 650.00 10 Food waste and green waste (1:1) Vandecasteele et al., 2016

78.00 67.00 - Wheat straw 500-600 12 Sewage sludge and straw (1:1) Awasthi et al., 2016

8.37 -169.77 -58.33 - Holm Oak 650 3 Poultry manure and barley straw Sánchez-García et al., 2015

-6.90 - - - Wood chip - 5 Poultry manure Czekałaet al., 2016

-7.40 - - - Wood chip - 10 Poultry manure Czekała et al., 2016

- 82.61 72.41 - Green waste 500-600 10 Poultry litter and straw Awasthi et al., 2017a

- 78.26 65.02 - Poultry litter 550 10 Poultry litter and straw Agyarko-Mintah et al. 2017b

-1.59 - - 65.50 Wood - 4 Sewage sludge and straw Malińska et al., 2014

- - 25.90 - Bamboo - 3 Pig manure, wood chip and sawdust Wang et al., 2016

- - - - Holm Oak 650 4 Olive mill waste and Sheep manure López-Cano, et al., 2016

-84.95 - - - Wheat straw 500-600 12 Sewage sludge Awasthi et al, 2017b

Values indicated in bold letters are excluded from the analysis.

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Biochar addition rate (%)

10 15 20 25 30

CH

4 em

issi

on re

duct

ion

(%)

0

20

40

60

80

100

120

Y=28.04 In(19-4.13)r2=0.22p=0.24


0 5 10 15 20 25 30

CO

2 em

issi

on re

duct

ion

(%)

-20

-10

0

10

20

30

40

50

60

Y=9.07 In(19-3.14)r2=0.17p=0.079


0 5 10 15 20 25 30

N2O

em

issi

on re

duct

ion

(%)

-80

-60

-40

-20

0

20

40

60

80

100

Y=13.77 In(19-2.6)r2=0.20p=0.002


0 5 10 15 20 25 30

NH

3 em

issi

on re

duct

ion

(%)

0

20

40

60

80

100

Y=14.9 In(19-3.38)r2=0.33p=0.076

A B

C D

Fig. S2. Logarithmic (y=a×ln(x0-x)) fitting of literature emission data- (a) CO2, (b) CH4, (c)

N2O, and (d) NH3 at different application rate of biochar in the composting biomass. Data are

taken from Agyarko-Mintah et al., 2017 a and b; Awasthi, et al., 2016; 2017 a and b; Chen et

al., 2017; Chowdhury et al., 2014 a and b; Czekała et al., 2016; Sánchez-García et al., 2015;

Malińska, et al., 2014;Steiner et al., 2010; Vandecasteele, et al., 2016; Wang et al., 2016.

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Table S9. Emission factor calculation for the proposed method

Emission factor calculation Assumptions Positive feedback (CO2 e)

Negative feedback

Reference

A. Waste collection, sorting and biochar production

1.Waste transport1 t×30 km×0.190 kg CO2 e/t/km = 5.7 kg CO2 e

30 km travel distance is considered

5.70 Friedrich and Trois, (2013)

2. Waste processing and sorting1 t×10 L diesel ×2.7 kg CO2 e/L = 27 kg CO2 e

10 L/t MW diesel is considered for waste processing and sorting

27.00 Boldrin et al., (2009); Friedrich and Trois, (2013)

3. Biochar production1 t×0.2682×0.30×-885 kg CO2 e /t = -71.21 kg CO2 e

26.82% of MW is pyrolysed (including fuel for pyrolysis)

70% moisture considered Slow pyrolysis has an emission

factor of -885 kg CO2 e/ t Yard waste

71.21 Roberts et al., (2010)

4. Biogenic CO2 emission for fuel biomass burning during pyrolysis1 t×0.098×0.30×0.45×44/12×1000=48.51 kg CO2 e

9.76% of the MW is considered as fuel for pyrolysis

48.51* Boldrin et al. (2009)

5. CH4 emission for fuel biomass burning during pyrolysis1 t×0.098×0.30×6.5 kg/t×25 kg CO2 e/kg=4.78 kg CO2 e

6.5 kg of CH4/t emission biomass 4.78 IPCC, (2006); Islam, (2017)

6. N2O emission for fuel biomass burning during pyrolysis1 t×0.098×0.30×0.15 kg/t×298 kg CO2 e/kg =1.3 kg CO2 e

0.15 kg of N2O/t emission biomass

1.31 IPCC, (2006); Islam, (2017)

B. Co-composting 1. Compost processing and storing1 t×3 diesel×2.7 kg CO2 e/L = 8.1 kg CO2 e

3 L diesel is considered for compost processing and storing


2. Biogenic CO2 emission1 t×0.50×0.30×0.5×0.45× (100-25.06)/100×0.973×1000×44/12= 90.23 kg CO2 e

50% of the total MW 70% moisture content 50% C emitted 45% C in the MOW 25.06% emission reduction due to

co-composting.

90.23* Friedrich and Trois, (2013)

3. CH4 emission 1 t×0.50×0.30×0.5×0.45×0.027×(100-75.70)/100×1000×16/22 kg CH4×25 CO2 e/kg CH4=4.03 kg CO2 e

2.7% of C loss by CH4 emission 75.70% CH4 emission reduction


4. N2O emission 1 t×0.50×0.30×1.18/100×0.0118×(100-38.52)/100×1000×44/28 kg×298 CO2 e/kg N2O= 4.45 kg CO2 e

1.18% N in the biomass 1.8% N loss as N2O 38.52% N2O emission reduction


5. Compost transport and spreading1 t×100 km×0.33×0.190 kg CO2 e/t/km = 6.27 kg CO2 e

33% biochar compost production on fresh basis

6.27

6. Biogenic CO2 emission after soil application Moisture content 40% 367.33* Friedrich and Trois, (2013)

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Negative feedback

Reference

1 t×0.33×0.60×0.65×(100-20)/100×0.973×1000×44/12=367.33 kg CO2 e We assumed 20% C retention in the soil due to stabilisation of soil organic matter with biochar

7. Stabilisation of carbon 1 t×0.33×0.60×0.65×(100-80)/100×1000×0.973×44/12=89.01 kg CO2 e

63% labile C in the biochar compost

89.01 Han Weng et al., (2017); Friedrich and Trois, (2013)

8. CO2 abatement for N supply1 t×0.33×0.60×0.0292×0.5×1000×13 kg CO2 e =37.58 kg CO2 e

2.92% N content in the biochar-compost and 50% in available for plant

13 kg CO2 e/kg N

37.58 Boldrin et al. (2009);Friedrich and Trois, (2013)

9. CO2 abatement for P supply1 t×0.33×0.60×0.0113×1000×3.095 kg CO2 e =6.92 kg CO2 e

1.13% P content in the biochar-compost

3.095 kg CO2 e/kg P

6.92 Boldrin et al., (2009);Friedrich and Trois, (2013)

10. CO2 abatement for K supply1 t×0.33×0.60×0.0314×1000×1.53 kg CO2 e =9.51 kg CO2 e

3.14% K content in the biochar-compost

1.53 kg CO2 e/kg K

9.51 Boldrin et al., (2009); Friedrich and Trois, (2013)

C. Emission abatement for other recycled materials

i. Plastic

1. Collection, separation and sorting1 t×0.0352×45kg CO2 e/t=1.59 kg CO2 e

3.52% of MW was plastic 1.59 Friedrich and Trois, (2013)

2. Transport1 t×0.0352×100×0.19 kg CO2 e/t/km =0.67 kg CO2 e

100 km transport 0.67 Friedrich and Trois, (2013)

3. Washing, drying and final palletisation1 t×0.0352×0.7×600×1.03=15.25 kg CO2 e = 15.23

30% loss considered 600 kWh/t considered 1.03 CO2 e/ kWh considered


4. Substitution of new plastic and fuel1 t×0.0352×0.7×2100 CO2 e=51.74 kg CO2 e

2100 CO2 e/t plastic renewal 51.74 Friedrich and Trois, (2013)

ii. Leather and rubber

1. Collection, separation and sorting1 t×0.00092×45CO2 e/t=0.41 kg CO2 e

0.92% of the MW was leather and rubber

0.41


100 km transport 0.12

3. Washing, drying and final palletisation1 t×0.0092×0.7×600×1.03=2.78 kg CO2 e


2.78

4. Substitution of new leather and rubber and fuel1 t×0.0092×0.7×1000 kg CO2 e/t=6.4 kg CO2 e

We assumed 1000 kg CO2 e/t for leather and rubber

6.4

iii. Metals


1.18% metals in the MW 0.53 Friedrich and Trois, (2013)

S18


Negative feedback

Reference


100 km travel distance 0.16 Friedrich and Trois, (2013)

3. Washing, drying and final palletisation1 t×0.0118×0.7×600×1.03 CO2 e=5.10 kg CO2 e



4. Substitution of new Iron1 t×0.0118×0.7×0.8×2890 CO2 e=19.02 kg CO2 e

80% of metals is considered as Iron

2890 CO2 e/t steel


5. Substitution of new aluminium1 t×0.0118×0.7×0.2×20375 CO2 e=33.66 kg CO2 e

20% of metals is considered as aluminium

20375 CO2 e/t aluminium


iv. Glass


2.68% glass in MW 1.21 Friedrich and Trois, (2013)

2. Transport1 t×0.0268×100×0.190 kg CO2 e/t/km=0.51 kg CO2 e

100 km travel distance is considered for reaching glass processing plant


3. Washing, drying and final palletisation1 t×0.0268×0.7×600×1.03 CO2 e=11.61 kg CO2 e



4. Substitution of new glass1 t×0.0268×0.7×400 CO2 e=7.59 kg CO2 e

400 CO2 e/t glass substitution 7.59 Friedrich and Trois, (2013)

v. Others 1 t×0.503×100 CO2 e=5.3 kg CO2 e

5.03% other waste in the MW We considered 100 CO2/t other

MW

5.3

D. Emission abatement for exclusion of landfilling1 t×0.6342×420.88 CO2 e/t=267 kg CO2 e

63.42% of MW considered for landfilling calculation since the rest was considered in the biochar production and recycling of other materials.

An emission of 420.88 CO2 e/t MW was recently calculated for Bangladesh

267 Islam, (2017)

Overall balance 102 605

* Indicates that biogenic CO2is not included in the emission factor calculation (IPCC, 2006).

S19

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