International Journal of Research In Science & Engineering
Volume: 1 Issue: 1
e-ISSN: 2394-8299
p-ISSN: 2394-8280
ESTIMATION OF DESKTOP E-WASTES IN DELHI USING
MULTIVARIATE FLOW ANALYSIS
Sumay Bhojwani1, Ashutosh Chandra2, Akriti Bhogal3, Mamita Devaburman4
1
Sumay Bhojwani, Dept. of Chemical Engg., IIT Delhi, sumay.25@gmail.com
Ashutosh Chandra, Dept. of Chemical Engg., IIT Delhi, ashu.chandra10@gmail.com
3
Akriti Bhogal, Dept. of Chemical Engg., IIT Delhi, bhogal.akriti@gmail.com
4
Mamita Devaburman, Dept. of Chemical Engg., IIT Delhi, mamitadb@gmail.com
2
ABSTRACT
This article uses the Material flow analysis for estimating e-wastes in the Delhi/NCR region. The Material flow
analysis is based on sales data obtained from various sources. Much of the data available for the sales is
unreliable because of the existence of a huge informal sector. The informal sector in India accounts for more
than 90%. Therefore, the scope of this study is only limited to the formal one. Also, for projection of the sales
data till 2030, we have used regression (linear) to avoid complexity. The actual sales in the years following 2015
may vary non-linearly but we have assumed a basic linear relation. The results of this study can be used to install
a treatment plant for e-wastes in Delhi. The purpose was to know an approximate quantity of e-wastes that we
will have by the year 2030 so that we start preparing ourselves for the ineluctable investment in the treatment of
these ever-rising e-wastes.
Keywords: E-wastes, Estimation, New Delhi, Multivariate flow analysis etc.
----------------------------------------------------------------------------------------------------------------------------1. INTRODUCTION
Driven by a strong economic growth in the country, the quantities of e-wastes are expected to rise sharply in the
coming years. The term e-waste or WEEE (Waste Electrical and Electronic equipment) includes any appliance that
has reached its end-of-life and uses an electric supply or batteries. In an emerging economy like India, it is a very
grave concern that the issue of e-wastes has not been investigated deeply by the authorities and proper care is still
awaited. The ever growing amounts of wastes poses a huge environmental risk in an already late-bloomer like India
(in terms of environmental protection). Currently in India, e-waste is recycled in two sectors: formal & informal. Of
the total e-waste recycled in India, 95% is recycled in the Informal sector and only 5% in the formal one. The ewaste is often recycled in the informal sector due to the lack of proper legislations and lack of awareness. There is a
lack of proper collection centers and procurement facilities. Therefore it is recommended to establish a formal
recycling system, in order to manage e-waste from all user groups in a complete manner, to prevent damage to the
environment and human health. [2]
The benefits include implementing norms that promote the manufacture of longer-lasting products and using
recyclable environmentally friendly raw materials. This also ensures that the products manufactured comply with the
limitations imposed on hazardous substances. In India, this is slowly becoming a policy principle but the presence of
informal sectors hampers its implementation. The formalization of the production, collection and recycling streams
is a very important step for the assessment of e-wastes in India. Also, the lack of awareness among the general
population regarding the repercussions on health and environment due to landfilling and improper handling of ewastes is a factor that hampers implementation of EPR in India. Another important reason is the lack of knowledge
of the quantity of e-wastes generated each year and recycled. As a result, any government action taken on handling
of e-wastes has been rendered useless. The following section will take you through the estimation process wherein
we have used Material Flow analysis for estimating the quantities.
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International Journal of Research In Science & Engineering
Volume: 1 Issue: 1
e-ISSN: 2394-8299
p-ISSN: 2394-8280
2. METHODOLOGY
The methodology is centered around the consumer behaviour and the sales of various electrical equipment in DelhiNCR. For understanding the process, we need to focus on two aspects: one is the sales, i.e. the rate at which various
products are being manufactured and bought in Delhi and the second being the consumer behaviour, as in the usage
characteristics of various products. The rate at which an equipment finds its way back to the waste stream (reaches
its end-of-life) determines the amount of e-waste to be procured in each year.
Finding sales data and consumer behaviour was extremely difficult due to lack of proper logistics and logbooks
available on the internet. This was one of our major concerns while estimating the e-wastes. Thus, we had to device
a way to approximate the amount of e-waste by considering the sales in all over India and then consider a fraction of
it to be Delhi’s share. Some of this data was available for various components.
MFA consists of the following aspects: (a) analysis of the system and the steps/processes involved right from the
sale/production till the disposal and recycling of the product. (b) estimation of mass flows through the use of
transport coefficients (c) Calculation of disposed/recycled e-wastes (d) Analysis of results via graphs, tables etc.
To begin, we have to understand some terms: ‘Processes’, ‘Residence time’, ’flows’ ‘Transport coefficients’ and
‘Lifespans’. Processes are transformations, storage steps, reuse of any material or any stage where a material will
stay for a certain time, known as residence time. Residence time is the time for which a material stays in a particular
process. Flow is defined as transfer of material from one process to the other. Transport Coefficients are used to
divide flow into multiple streams. It is the fraction of the total flow from a process. Lifespan is the measure of the
end-of-life reaching ability of a product.
2.1 Model Description & Basic Analysis
The model approximated for the scope of this analysis is as shown in Fig 1. The sales of a particular component
(say, Desktops) are assumed to go through three processes - Businesses, Households and Government.
Fig-1: Primary sorting and dismantling of e-waste
Business process includes the components being bought for firms or businesses. Similarly, government process
includes the components being purchased for government institutions and organizations. The household sector
comprises those being purchased for personal use of the general population.
Each Desktop (in any of the three sectors) can go through a Re-use step, Storage step or proceed directly towards
Recycling/Disposal. However, a major chunk goes through to the re-use step. In the Storage process, computer
equipment kept in storage for a period of time before it is disposed of or recycled is accounted. It has been assumed
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International Journal of Research In Science & Engineering
Volume: 1 Issue: 1
e-ISSN: 2394-8299
p-ISSN: 2394-8280
that when the product enters the storage step, it may go towards recycling after a residence time or go to a second
storage process where against faces a small residence time before finally ending up in the recycling step. If the
product enters the re-use process, it can either flow to a second storage or proceed towards the recycling step.
The various assumptions in the model have been listed below:
1. There is no back-flow in any case considered here.
2. If an infinite time scale is considered, we can assume that there is no accumulation whatsoever. There exists a
residence time though, but it has been assumed that all the products proceed to further steps once its residence
time has reached.
3. At the beginning of the model analysis, all the units were empty. Then, due to sale of various computer
equipment, the flow established and grew. The materials kept getting pushed ahead once their residence time
reached.
4. Computer equipment flows in annual time steps. Monthly variations have not been considered.
Based on the principle of mass conservation, we have a basic equation for the flows coming out of each process in
terms of the transport coefficients and the amount of material incoming. All the flows coming out of a step in the
year t, FX,out(t) , are equal to all the flows that entered the same process X in the year t - r, FX,in(t-r), where r is the
residence time of computer equipment in that process.
If a process has more than one outflow, transport coefficients T xy1 to Txyn have been used to calculate partitioning of
flows coming out from process X to the downstream processes Y 1 to Yn. Suppose for example, the flow value for
equipment going out of the household process can be given as:
where THs1, THr and THrd are the transport coefficients for the flow coming out from the household process and going
to the storage 1, re-use and recycled processes. Also, FPH(t-r) = FH,out(t) and that the sum of the above three transport
coefficients equal to 1. Also, for the recycling process:
The transport coefficients of various processes are listed below. Note that T xy1 has been used to calculate partitioning
of flows coming out from process X to the downstream processes Y 1. Also, the transport coefficient data for Mobiles
and Tablets have been assumed to be that of laptops because of the similarity in the increasing trends of usage due to
‘no’ data available in this regard. The transport coefficients listed are based on studies in emerging economies like
Chile. [1]
From
To
Parameter
Transport Coefficients
Desktop
Production
& Sale
Government
Government
TPg
0.10
Business
TPb
0.55
Households
TPh
0.35
Households
TPh
0.35
Storage 1
TGs1
0.20
Re-use
TGr
0.50
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International Journal of Research In Science & Engineering
Volume: 1 Issue: 1
Business
Households
Re-use
Storage 1
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p-ISSN: 2394-8280
Disposal/Recycling
TGrd
0.30
Storage 1
TBs1
0.14
Re-use
TBr
0.72
Disposal/Recycling
TBrd
0.14
Storage 1
THs1
0.25
Re-use
THr
0.54
Disposal/Recycling
THrd
0.21
Storage 2
TRs2
0.50
Disposal/Recycling
TRrd
0.50
Storage 2
TS1S2
0.10
Disposal/Recycling
TS1rd
0.90
Table-1: Transport Coefficient Data for various Processes in the model assumed [1]
Process
Parameter
Residence Time
Desktops
Government
rg
4
Business
rb
4
Households
rh
6
Re-use
rr
3
Storage 1
rs1
3
Storage 2
rs2
1
Table-2: The residence times for various processes [1]
3. RESULTS & DISCUSSIONS
Year
Sales in India
(in numbers)
Delhi Percentage
Share
Sales in Delhi
(in numbers)
Generated EWaste
(in numbers)
Weight of e-waste
generated (in
T/yr)
1997
574400
24.15%
138717
-
-
1998
799058
22.86%
182664
-
-
1999
1027190
23.21%
238410
-
-
2000
1405327
22.95%
322522
-
-
2001
1881640
22.78%
428637
-
-
2002
2293643
20.28%
465150
-
-
2003
3035591
10.99%
333611
-
-
2004
3632619
15.72%
571047
-
-
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International Journal of Research In Science & Engineering
Volume: 1 Issue: 1
e-ISSN: 2394-8299
p-ISSN: 2394-8280
2005
4614724
10.50%
484546
-
-
2006
5490591
11.96%
656675
171488
4630
2007
5451656
12.21%
665790
268727
7256
2008
5992779
11.97%
717352
363512
9815
2009
5878916
12%
703722
400939
10825
2010
5740761
12%
687185
431558
11652
2011
5576002
12%
667463
486525
13136
2012
5393666
12%
645637
514084
13880
2013
5191943
12%
621490
574133
15502
2014
4985304
12%
596755
628565
16971
2015
4774924
12%
571572
664464
17940
2016
4551458
12%
544822
688034
18577
2017
4322974
12%
517472
685572
18510
2018
4083482
12%
488804
674315
18206
2019
3842965
12%
460013
654211
17664
2020
3602779
12%
431263
631835
17059
2021
3364275
12%
402713
607946
16414
2022
3136850
12%
375490
582905
15738
2023
2917898
12%
349280
556875
15036
2024
2708101
12%
324167
530064
14312
2025
2506619
12%
300049
502398
13565
2026
2308846
12%
276375
474267
12805
2027
2116750
12%
253381
445953
12041
2028
1930688
12%
231109
417767
11280
2029
1750748
12%
209569
390216
10536
2030
1579700
12%
189094
363454
9813
Table-3: Desktop Sales in India & Delhi & Weight of waste stream. [2]
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International Journal of Research In Science & Engineering
Volume: 1 Issue: 1
e-ISSN: 2394-8299
p-ISSN: 2394-8280
Fig-2: Desktop Sales & Recycled units vs year (1997-2030)
Fig-3: Weight of Recycled units in T/yr (for 2006-2030)
3.1 Composition of a Personal Desktop Computer
(based on a typical desktop computer weighing ~ 27 kg)
Material
Percentage
Plastics
22.99%
Lead
6.29%
Aluminium
14.17%
Iron
20.47%
Tin
1.00%
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Volume: 1 Issue: 1
e-ISSN: 2394-8299
p-ISSN: 2394-8280
Copper
6.92%
Zinc
2.20%
Silica/Glass
24.88%
Nickel
0.85%
Table-4: Composition of a personal Desktop Computer [4]
[Note that the compositions have been assumed to be constant due to lack of data in this regard. However, recent
reports show the decreasing use of iron and increasing use of polymeric composites]
Fig-4: Desktop Composition Pie-Chart [4]
Based on the above composition, we have the following table which details the weights of
materials disposed in various years:
(note that for convenience, the weight disposed have
been shown for some of the years and also, the metals have been grouped together).
3.2 Material Wise Analysis
Year
Plastics
Aluminium Copper
Iron
Zinc
2013
3563817
2196576
1072710
3173177
341035
3856797
2014
3901693
2404828
1174411
3474017
373368
4222450
2015
4124528
2542173
1241485
3672427
394692
4463604
2016
4270833
2632349
1285523
3802695
408692
4621937
2017
4255549
2622929
1280922
3789086
407230
4605396
2018
4185677
2579862
1259890
3726873
400543
4529780
2019
4060887
2502948
1222329
3615761
388602
4394731
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Glass
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2020
3921990
2417338
1180521
3492090
375310
4244416
2021
3773702
2325940
1135886
3360055
361120
4083936
2022
3618266
2230136
1089100
3221658
346246
3915723
2023
3456691
2130549
1040466
3077793
330784
3740865
2024
3290263
2027970
990371
2929608
314858
3560755
2025
3118535
1922125
938681
2776704
298424
3374909
2026
2943917
1814498
886120
2621226
281715
3185936
2027
2768162
1706170
833218
2464735
264896
2995731
2028
2593207
1598336
780557
2308959
248154
2806394
2029
2422186
1492926
729079
2156683
231788
2621313
2030
2256071
1390541
679078
2008777
215892
2441542
Table-5: Weight of different materials disposed over the years
Overall Waste Trends
Weight of Material disposed (kg/yr)
5000000
4500000
4000000
3500000
Plastics
3000000
Aluminium
2500000
copper
2000000
iron
1500000
zinc
1000000
glass
500000
0
2010
2015
2020
2025
2030
2035
Years
Fig-5: Overall Trend of different materials disposed over the years
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Volume: 1 Issue: 1
e-ISSN: 2394-8299
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CONCLUSION
Plastics
Plastics are used in almost every electronic equipment – in the LCD, in the CRT, the outer body
covering etc. Since the production of electronic equipments has increased over the years, weight
of plastics found in the dumped electronic waste has also increased.
Aluminium
Magnesium is 33% lighter than aluminium. It therefore replaces aluminium alloys for virtually
anything you want to make lighter and stronger. Since these days, everyone wants lighter and
thinner devices that are durable enough to withstand the daily wear and tear, magnesium can
make a big difference for these fragile products. Thus a decrease in the use of aluminium in
electronic equipments has been predicted.
Copper
Copper is vital to the production of electronic connectors, circuitry wiring, printed circuit boards,
micro-chips etc. It is cheaper and abundant than silver and solder will stick to it, unlike
aluminium. Since usage of electronic devices is increasing day by day, use of copper is bound to
increase over the years.
Glass
Many other types of glass like scratch resistant sapphire glass have been developed to replace it.
But these are still not used more than say, gorilla glass, because it is expensive and has been used
till now only in watches. Also, though the use of glass for touch screen devices has increased, it
is compensated by the decline in the use of desktops which used to have 25% glass in their
composition. Therefore, the weight of glass in e wastes is predicted to be roughly the same.
REFERENCES
Bernhard Steubing, H. B., “Assessing computer waste generation in Chile using material flow analysis” Waste
Management , 30, pp. 473-482, 2009
[2] GTZ & BIRD, “e-Waste Assessment in India: Specific Focus on Delhi”, Delhi, 2007
[3] Feng Wang, J. H. “Enhancing e-waste estimates: Improving data quality by multivariate Input–Output
Analysis” Waste Management (33), pp. 2397-2407, 2013
[4] ewasteguide.info. Retrieved from www.ewasteguide.info/valuable-substances, 1996
[1]
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