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A multi-criteria decision analysis of solid waste treatment options in Pakistan: Lahore City—a case in point

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Abstract

Population of the world is increasing day by day, resulting in enormous amount of waste production. In the modern age of great technological advancements, there needs to be a systematic method to keep the environment clean. The waste management activities, i.e., collection, transport and disposal, pose a great challenge to the waste managers as they have to factor in various eclectic factors such as land availability, facilities available, budget, time required and the impact it would have on the environment, while tackling this problem. Lahore, despite being the most developed city of Pakistan, does not have a suitable solid waste management system. An increasing population leads to more waste generation, and in Lahore the situation is no different. Several waste management companies are working in the city, but as of yet they have not been able to make significant inroads to completely eradicate the problem. The aim of this paper is to suggest a suitable way for dealing with the waste. To accomplish this aim, a hierarchy-based model is used, considering six criteria and five alternatives. We used multi-criteria decision analysis to decide among different waste management alternatives. Forecasting has been used to find the population and waste produced over the years. Analytical hierarchy process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) are used to rank the feasible alternative. The results show that the population and waste were increasing drastically. Aerobic digestion was ranked as the best alternative for waste management according to AHP and TOPSIS, but there is great variation among the rank of other alternatives.

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Abbreviations

AD:

Anaerobic digestion

AHP:

Analytical hierarchy process

COMP:

Composting

EPA:

Environmental Protection Agency

INC:

Incineration

LWMC:

Lahore Waste Management Company

MCDM:

Multi-criteria decision methods

MCDA:

Multi-criteria decision analysis

MSW:

Municipal solid waste

MSWM:

Municipal solid waste management

SW:

Solid waste

SWM:

Solid waste management

TOPSIS:

Technique for Order Preference by Similarity to Ideal Solution

UNEP:

United Nations Environmental Programme

WtE:

Waste to energy

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Author information

Authors and Affiliations

  1. Department of Management Science and Humanities, Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Topi, Swabi, KPK, Pakistan

    Yousaf Ali & UbaidUllah Mumtaz

  2. Faculty of Mechanical Engineering, Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Topi, Swabi, Pakistan

    Zain Aslam & Hammad Sajid Dar

Authors
  1. Yousaf Ali
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  2. Zain Aslam
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  3. Hammad Sajid Dar
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  4. UbaidUllah Mumtaz
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Corresponding author

Correspondence to Yousaf Ali.

Appendices

Appendix 1

1.1 Forecasting tables

Population in Lahore

Year

x

Population (y)

x 2

xy

y 2

1981

1

2,953,000

1

2,953,000

8.72021E+12

1998

18

5,144,000

324

92,592,000

2.64607E+13

2004

24

5,943,000

576

14,2632,000

3.53192E+13

2005

25

6,131,000

625

153,275,000

3.75892E+13

2016

36

8,741,000

1296

314,676,000

7.64051E+13

Σ = 104

28,912,000

2822

706,128,000

1.84494E+14

ℜ = 2.888888889

∅ = 803111.1111

r2 = 0.953211217

  1. Forecasted value for 2017 = 9225582.44

Waste produced in Lahore

Year

x

Waste produced (y)

x 2

xy

y 2

2008–2009

1

977,318

1

977,318

9.5515E+11

2009–2010

2

1,182,009

4

2,364,018

1.39715E+12

2010–2011

3

1,163,481

9

3,490,443

1.35369E+12

2011–2012

4

1,231,380

16

4,925,520

1.5163E+12

2012–2013

5

1,481,580

25

7,407,900

2.19508E+12

2013–2014

6

1,884,600

36

11,307,600

3.55172E+12

Σ = 21

7,920,368

91

30,472,799

1.09691E+13

ℜ = 3.5

∅ = 1320061.333

r2 = 0.919855631

  1. Forecasted value for 2017–2018 = 2342051.13 tonnes

1.2 AHP work tables

Normalized matrix

Priority vector (P.V)

Matrix multi. (MM)

MM/P.V

CI

RI

CR

0.485175

0.4

0.506329

0.516129

0.428571

0.467240955

2.377946829

5.089337

   

0.097035

0.08

0.063291

0.064516

0.095238

0.080016081

0.401565672

5.018562

   

0.242588

0.32

0.253165

0.258065

0.238095

0.262382382

1.331439374

5.074424

   

0.121294

0.16

0.126582

0.129032

0.190476

0.145476906

0.733045201

5.038911

   

0.053908

0.04

0.050633

0.032258

0.047619

0.044883676

0.225653081

5.027509

   

Cost

     

λ max

5.049749

0.012437

1.12

0.011105

0.489796

0.380952

0.516129

0.444444

0.510638

0.468392015

2.386976833

5.096109

   

0.061224

0.047619

0.032258

0.037037

0.042553

0.044138366

0.221181106

5.011085

   

0.122449

0.190476

0.129032

0.148148

0.12766

0.14355303

0.722950014

5.036118

   

0.081633

0.095238

0.064516

0.074074

0.06383

0.075858148

0.380991341

5.022418

   

0.244898

0.285714

0.258065

0.296296

0 255319

0.268058441

1.357623296

5.064654

   

Socio Culture

     

λ max

5.046077

0.011519

1.12

0.010285

0.489796

0.380952

0.516129

0.444444

0.510638

0.270490906

1.375788724

5.086266

   

0.061224

0.047619

0.032258

0.037037

0.042553

0.076668969

0.386291434

5.038433

   

0.122449

0.190476

0.129032

0.148148

0.12766

0.145245453

0.734136853

5.054457

   

0.081633

0.095238

0.064516

0.074074

0.06383

0.046242491

0.231877611

5.014384

   

0.244898

0.285714

0.258065

0.296296

0 255319

0.461352182

2.367027056

5.130629

   

Environmental

     

λ max

5.064834

0.016208

1.12

0.014472

0.114943

0.137931

0.12931

0.101124

0.217391

0.140139762

0.710935319

5.073045

   

0.057471

0.068966

0.051724

0.08427

0.086957

0.069877421

0.354009858

5.066155

   

0.229885

0.344828

0.258621

0.252809

0.26087

0.269402377

1.379615249

5.121021

   

0.574713

0.413793

0.517241

0.505618

0.391304

0.48053389

2.499720926

5.201966

   

0.022989

0.034483

0.043103

0.05618

0.043478

0.04004655

0.201306263

5.026807

   

Tech

     

λ max

5.097799

0.02445

1.12

0.02183

0.256881

0.247788

0.275862

0.318182

0.258065

0.27135535

1.37168891

5.054954

   

0.513761

0.495575

0.482759

0.363636

0.516129

0.474372141

2.418488921

5.098295

   

0.06422

0.070796

0.068966

0.090909

0.064516

0.071881476

0.361935135

5.035166

   

0.036697

0.061947

0.034483

0.045455

0.032258

0.042167904

0.211225992

5.009165

   

0.12844

0.123894

0.137931

0.181818

0.129032

0.140223129

0.706928407

5.041454

   

Land Required

     

λ max

5.047807

0.011952

1.12

0.010671

0.258621

0.251046

0.344828

0.272727

0.229885

0.271421326

1.395819834

5.142631

   

0.517241

0.502092

0.413793

0.363636

0.574713

0.474295108

2.484380451

5.238048

   

0.051724

0.083682

0.068966

0.090909

0.057471

0.070550404

0.358387664

5.079881

   

0.043103

0.062762

0.034483

0.045455

0.022989

0.041758153

0.209952133

5.027812

   

0.12931

0.100418

0.137931

0.227273

0.114943

0.141975009

0.722436266

5.088475

   

Time required

     

λ max

5.115369

0.028842

1.12

0.025752

0.460695

0.301075

0.525164

0.364742

0.498812

0.28125

0.405289679

2.753427871

6.793728

  

0.065814

0.043011

0.026258

0.015198

0.041568

0.125

0.052807959

0.318341575

6.028288

  

0.115174

0.215054

0.131291

0.243161

0.124703

0.1875

0.169480441

1.190341261

7.023473

  

0.076782

0.172043

0.032823

0.06079

0.049881

0.15625

0.091428288

0.599065506

6.552299

  

0.230347

0.258065

0.262582

0.303951

0.249406

0.21875

0.253850244

1.75944888

6.931051

  

0.051188

0.010753

0.021882

0.012158

0.035629

0.03125

0.027143389

0.168174284

6.195773

  

Criteria versus criteria

     

λ max

6.587435

0.117487

1.24

0.094748

Appendix 2

Towns

Population (millions)

Area (km2)

Approx waste (tonnes/day)

Iqbal Town

0.80

515

520

Aziz Bhatti Town

0.59

70

383

DGB Town

1.01

32

656

Gulberg Town

0.81

40

526

Nishtar Town

1.04

491

676

Ravi Town

1.65

29

1072

Samanabad Town

1.03

39

669

Shalimar Town

0.55

25

357

Wagah Town

0.68

445

442

Lahore Cantt

–

93

–

Σ

8.16

1780

5301

Appendix 3

Waste

Mass generated/year (tons/year)

Percentage

Vegetable, putrescible

726,036

31

Wood, bones, straw

585,512.75

25

Paper, cardboard

702,62

3

Textile, rags

187,364

8

Glass, ceramics

23,420.51

1

Plastics, rubber

140,523

6

Metals

23,420.51

1

Debris

632,354

25

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Ali, Y., Aslam, Z., Dar, H.S. et al. A multi-criteria decision analysis of solid waste treatment options in Pakistan: Lahore City—a case in point. Environ Syst Decis 38, 528–543 (2018). https://doi.org/10.1007/s10669-018-9672-y

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