Co-benefits assessment in wastewater treatment plants, The case of fish processing industry in Indonesia

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Co-benefits assessment in wastewater treatment plants, The case of fish processing industry
in Indonesia
Hooman Farzaneh
Interdisciplinary Graduate School of Engineering Sciences, Kyushu University, Fukuoka, 816-
8580, Japan
Introduction
This study was led by the Energy and Environmental Systems (EES)Laboratory of the
Interdisciplinary Graduate School of Engineering Sciences, Kyushu University in order to quantify
the expected climate co-benefits from improving the water treatment process of the fish
processing industry in Indonesia. This research is developed based on a project which was done
in 2011 to reduce water pollution in public water by introducing wastewater treatment
technologies in the fish processing industry in Indonesia. Following scenarios are considered in
this study:
 Reference Scenario (BAU): the current situation of wastewater treatment with an anaerobic
lagoon.
 Scenario I (Baseline): A pre-stage aerobic lagoon treatment followed by activated sludge
method.
 Scenario II (Swimbed): This includes Swim-bed technology involving the novel acryl-fiber
material, biofringe, as a new concept for the treatment of organic wastewater.
 Scenario III (UASB): This scenario includes the Up-flow Anaerobic Sludge Blanket (UASB)
technology, which is a type of anaerobic digester. This system uses an anaerobic process and
forming a blanket of granular sludge which is processed by the anaerobic microorganism.
 Scenario IV (Combined UASB and Swimbed): This scenario is a combination of the UASB and
Swimbed.
The BAU scenario was based on the data which were derived from a questionnaire that was given
to a sample of several fish processing companies. The only technology that was installed on a
pilot basis was swimbed technology. Evaluation of the environmental benefits from introducing
new wastewater treatment scenarios was carried out with the help of a spreadsheet simulation
model developed at the EES Laboratory. The results of the environmental benefit assessment are
shown in Figure 1. All intervention scenarios perform well in COD reduction. The results also
reveal that, the best method to be implemented to remediate the fisheries industrial wastewater
and reduce the GHG emissions from the system is “UASB + Swimbed (Scen IV).” Deploying this
scenario will result in a reduction of as much as 75% in GHG emissions and 98% in COD discharge
load, respectively [1-2]. The results revealed that there is scope to do more to quantify additional
benefits (other than reducing environmental pollutants and greenhouse gases) such as improving

health conditions, increasing employment, and strengthening energy security[3-4]. Hence, the
following section will address the detailed methodology which can be used to quantify the health
and economic benefits of improving wastewater management in Indonesia’s fish processing
industry.
Fig 1. GHG emissions reduction Vs. COD removal
Health Impact Assessment
This section aims to evaluate the effects on human health related to the implementation of
different wastewater treatment trains. The impact on human health associated with water
pollution was considered disability-adjusted life years (DALYs) and converted into economic value
(social cost). As defined by World Health Organization, DALY represents the health gap measure
comprising the potential years of life lost due to premature death and the equivalent years of
‘healthy life lost by virtue of being in states of poor health or disability. Here, the contribution of
wastewater-borne disease is calculated by estimating DALYs caused by different diseases [5-6].
Three main scenarios of Swimbed, UASB, and UASB+Swimbed, were compared by quantifying
their effects on human health derived from the water pollution load discharged into the recipient
water body.
First of all, impacts were quantified according to the “Population Equivalent”, PE, or unit per
capita loading for each scenario, indicating the ratio of the sum of the pollution load produced
during 24 hours by the fish processing industry to the individual pollution load in household
sewage produced by one person in the same time. In other words, it refers to the amount of

oxygen demanding substances whose oxygen consumption during biodegradation equals the
average oxygen demand of the wastewater produced by one person:
𝑃𝐸,[𝑖𝑛ℎ𝑎𝑏] = ,[
]×[
]
[ 
 .] (1)
Where,
BOD: Biological oxygen demand
Q : Wastewater influent flow rate during the period t (m3)
n refers to the selected technology such as UASB, Swimbed, etc.
It is assumed that one unit equals 54 grams of BOD per 24 hours for practical calculations.
In this study, the total generated load was considered as PE, including independent industrial
discharges, and also the COD removal efficiency was taken into account in order to determine
the actual extent of contribution of wastewater treatment systems, as follows:
𝑃𝐸, = 𝑃𝐸, × η
(2)
Where,
𝑃𝐸,: Amount of PE, which is treated by the selected technology.
η : COD removal efficiency (%) and COD =1.25 BOD.
The actual extent of each scenario in wastewater treatment is measured based on the amount
of PE which can be treated by each scenario, as follows:
𝐶𝐹
=,
∑,


(3)
Where, 𝐶𝐹
 refers to the technology contribution in total wastewater treatment (n=1,2,3,…, N).
Based on the results obtained from the assessment of the environmental benefits, the actual
extent of each scenario in wastewater treatment in the fish processing industry was given in
Table 1. According to the results, the UASB+Swimbed scenario represents the most significant
contribution to wastewater treatment in the fish processing industry.

Tab 1. Impact of each scenario on wastewater treatment in Indonesia fish processing industry
Influent Effluent Treated
PE
Share in
total
wastewater
treatment
(%)
Wastewater Balance PE PE
Anaerobic 802,952 401,476 401,476 11.8%
Aerated Activated Sludge 802,952 134,652 668,300 19.8%
Swimbed 802,952 62,099 740,853 22.0%
UASB
802,952
26,930
776,021
2
3.0
%
UASB+Swimbed
802,952
15,525
787,427
23.
3
%
In the next step, Impacts were quantified according to the main diseases associated with
wastewater discharges, which are further linked to specific biological/microbiological assays. The
diarrhoeal disease was found to be the most relevant waterborne disease. In the weighting step,
the effects were expressed in a measurable unit such as DALYs. For this epidemiological analysis,
a similar method to those used over the last decade to examine the relationship between daily
exposure to wastewater health effects has been adopted. The morbidity and mortality are
calculated as a function of relative risk. Basically, relative risk is used to find the population
attributable risk fraction (PAF) for Diarrhoeal disease. PAF, in this instance, is the fraction of
background disease due to wastewater exposures, and is defined in as:
𝑃𝐴𝐹, =,
, (4)
Where,
𝑃𝐴𝐹, : Population Attributable risk Fraction for the diarrhoeal disease (D) due to unsafe and
untreated (W)
𝑅𝑅, : Relative Risk of the diarrhoeal disease (D) due to unsafe and untreated (W) at the
exposure level of interest
PAF is then multiplied by the DALYs of that disease to arrive at the quantity of DALYs attributable
to diarrhoeal disease as follows:
𝐷𝐴𝐿𝑌𝑠,[
] = 𝐷𝐴𝐿𝑌𝑠[
] × 𝑃𝐴𝐹, (5)

𝐷𝐴𝐿𝑌𝑠, indicates the DALYs attributable to the diarrhoeal disease (D) due to unsafe and
untreated (W) and 𝐷𝐴𝐿𝑌𝑠 is the total DALYs attributable to diarrhoeal disease (D).
In this way, if the benefits (on the waterside) derived from the application of more effective
treatment alternatives are measured through averted DALYS, the reduction of the diarrhoeal
diseases can be calculated proportionally, and, similarly, the health co-benefits:
𝐴𝐷𝐴𝐿𝑌𝑠,,[
]=𝐶𝐹
× 𝐷𝐴𝐿𝑌𝑠,[
] (6)
Where, 𝐴𝐷𝐴𝐿𝑌𝑠,, is the averted DALYs due to wastewater treatment by using the selected
scenario (n).
Economic Benefit Assessment
The DALYs can be translated into economic value by multiplying this factor by the gross domestic
product (GDP). It is noted that the term “economic benefit” here indicates the level of the
average GDP in which one person in perfect health would fully accomplish in each country in one
year [7]. Finally, the impacts were normalized over the corresponding load in PE. For a better
understanding of the calculation, the following equation summarizes the way to calculate the
economic benefit from the adverted DALYs due to wastewater treatment from using the new
scenario:
𝑆𝑎𝑣𝑖𝑛𝑔,,[$
 .] = [$/]×,,[
]
[] (7)
Where, 𝑆𝑎𝑣𝑖𝑛𝑔,, and 𝐺𝐷𝑃 are the saving from the averted DALYs from using the selected
technology (n) and Gross Domestic Production in the selected region, respectively.
Relationship between the unemployment rate and the economic benefit
To investigate the statistical relationship between the unemployment rate and the economic
benefit from the adverted DALYs, Okun’s law was used in this study. The relation between growth
and unemployment (a proxy for the labor force) has been discussed among classical economists
since 1960. Okun’s law has been a widely studied subject in advanced economies. It defines an
inverse association between cyclical fluctuations in the output gap and the unemployment gap,
where the value of the coefficients varies from country to country and from one time period to
another. Okun’s law can be expressed by using the following formula:

∆𝑢,[%] = ×,,
 × 100 (8)
∆𝑢, : Change in the unemployment rate by introducing the new wastewater treatment
technology.
𝛽: Okun’s Law coefficient, which is estimated by using historical data on unemployment rates
and GDP.
Pop: Population
𝛽 is estimated based on the historical data on the unemployment rate, GDP, and population in
Indonesia which were collected over the last 30 years. A multiple linear regression analysis was
performed to estimate the value of this coefficient which indicates 𝛽 = 0.404 for the case of
Indonesia.
Energy Security Benefits
Energy security benefits were estimated based on the amount of electricity saving in each
proposed intervention scenario. To quantify the amount of electricity-saving the following
formula can be used:
𝐸𝑆[𝐺𝑊ℎ]= (𝐸𝐸) + 𝐸
(9)
𝐸= (Amount of Biogas) x (Methane calorific value) x (Power generation efficiency) (10)
Baseline refers to the “Aerated Activated Sludge”. Intervention indicates the swimbed, UASB and
Swimbed+UASB scenarios. ES, E and 𝐸 refer to the total electricity-saving, amount of
electricity consumption in each scenario, and the amount of electricity that can be generated
from the biogas utilization (only for UASB Scenarios), respectively.
The overall calculation flow in this estimation is shown in Figure 2.

Fig. 2. Calculation flow
The calculated impacts on human health for each scenario are given in Table 2. The information
on total diarrhoeal DALYs and PAF in the different regions of Indonesia were collected from the
Global Health Data Exchange [8].
Tab 2. Health benefits from the wastewater treatment in Indonesia fish processing industry (different regions)
Region Total DALYs
attributable
to the
diarrhoeal
disease
PAF DALYs
attributable to
the diarrhoeal
disease due to
unsafe and
untreated water
Averted DALYs
Anaerobic Aerated
Activated
Sludge
Swimbed UASB UASB+
Swimbed
Aceh 51,653 0.730
37696 4485 7466 8277 8670 8797
Bali 34,205 0.738
25240 3003 4999 5542 5805 5890
Bangka-Belitung Island 10,752 0.751
8070 960 1598 1772 1856 1883
Banten 90,195 0.745
67159 7991 13302 14746 15446
15673
Bengkulu 15,847 0.704
11148 1327 2208 2448 2564 2602
Central Java 274,630 0.702
192708 22930 38169 42313 44322
44973
Central Kalimantan 20,561 0.734
15096 1796 2990 3315 3472 3523
Central Sulawesi 36,596 0.704
25745 3063 5099 5653 5921 6008
East Java 371,984 0.721
268126 31904 53107 58873 61668
62574
East Kalimantan
28,283
0.697
19719
2346
3906
4330
4535
4602
East Nusa Tenggara
74,033
0.711
52615
6261
10421
11553
12101
12279
Gorontalo
19,457
0.707
13750
1636
2723
3019
3162
3209
Jakarta 62,831 0.730
45847 5455 9081 10067 10545
10700

Jambi
26,742
0.701
18738
2230
3711
4114
4310
43
73
Lampung
62,314
0.710
44249
5265
8764
9716
10177
10327
Maluku 26,903 0.702
18875 2246 3739 4144 4341 4405
North Kalimantan 3,540 0.672
2378 283 471 522 547 555
North Maluku 12,745 0.691
8811 1048 1745 1935 2026 2056
Nort
h Sulawesi
18,514
0.715
13228
1574
2620
2905
3042
3087
North Sumatra
121,945
0.700
85300
10150
16895
18730
19619
19907
Papua
85,461
0.765
65378
7779
12949
14355
15037
15258
Riau
48,244
0.731
35276
4197
6987
7746
8113
8233
Riau Islands 16,190 0.733
11872 1413 2351 2607 2730 2771
South Kalimantan 43,872 0.693
30421 3620 6025 6680 6997 7099
South Sulawesi 82,515 0.718
59246 7050 11735 13009 13626
13826
South Sumatra 61,999 0.697
43238 5145 8564 9494 9944 10091
Southeast Sulawesi
36,646
0.716
26220
3120
5193
5757
6031
6119
West Java
392,347
0.725
284530
33856
56357
62475
65441
66402
West Kalimantan 53,664 0.720
38654 4599 7656 8487 8890 9021
West Nusa Tenggara 64,022 0.787
50353 5991 9973 11056 11581
11751
West Papua 6,557 0.714
4682 557 927 1028 1077 1093
West Sulawesi
18,047
0.728
13138
1563
2602
2885
3022
3066
West Sumatra
52,238
0.698
36452
4337
7220
8004
8384
8507
Yogyakarta
32,297
0.702
2
2676
2698
4491
4979
5215
5292
The spreadsheet model (WWT)provides a full assessment of environmental-health-economic
benefits from introducing proposed intervention scenarios in Indonesia’s fish processing industry.
The main inputs include the technical specifications of each scenario. The model was developed
in which the complexity was as low as possible and simple to represent, though still able to
predict the biological processes accurately. The tool allows for the different scenarios to be
evaluated. The co-benefits quantification is based on comparing the “reference scenario” and
alternative policy scenarios “interventions” through estimating impacts for each scenario (See
Figure 3)

Fig 3. Overall view of the WWT spreadsheet tool
As expected, impacts related to water pollution gradually decline as a consequence of the
implementation of high-performing technologies such as UASB+Swimbed. The health and
economic co-benefits and reduction in the unemployment rate from deploying the high-
performing wastewater treatment technologies in Indonesia's whole fish processing industry are
given in Table 3. This calculation assumes that around 3.4 Mt/day untreated wastewater polluted
the water resources was discharged from the fish processing industry [2].
Tab 3. Mid-term multiple benefits from the wastewater treatment in Indonesia fish processing industry
Scenario Reduction
in GHG
emissions
(Mt/y)
Reduction
in COD
(kt/y)
Averted
DALYs
(1000)
Savings
($/PE/Y)
Reduction in the
Unemployment
rate (%)1
Energy
Security
(GWh/y)
Swimbed 5.0 352 372 50.6 0.573 32.1
UAS
B
5
.
4
3
70
390
5
3
.
0
0.571
4
3
.
6
2
UASB+Swimbed
6.3
37
4
39
6
53.
8
0.580
48
.
8
2
1 Indonesia's unemployment rate is 5.28% in 2019
2 Including electricity-saving (comparing with the Baseline) and electricity generation from the biogas utilization (PF=350
working days)
This analysis can quantify how human health and economic benefits can be increased over
Indonesia’s territory thanks to the technological advance in wastewater purification. Overall, the
assessment of wastewater treatment impact on water pollution in the Indonesia fish processing
industry shows that the use of advanced treatment technologies, such as Swimbed and UASB,
ensures improvement of climate change and water pollution and the human health and local

economy status. It can be observed from Figure 4 that about 53.8 $/PE/y of savings in health
damage and about 0.58% reduction in the unemployment rate from the UASB-Swimbed scenario
would be achievable in the fish processing industry. The unemployment-reducing effect is a direct
consequence of the local GDP growth, indicating that the employment development in the
Indonesia fish processing industry could be stimulated through GDP per capita growth. It is noted
that, the major reduction in the unemployment rate driven by the economic growth (GDP) is a
result of ongoing increases in the size of the labor force and the level of productivity in the whole
society and, therefore, the impacts of new jobs from manufacturing technology itself will be
minor.
Fig 4. Expected multiple benefits from wastewater treatment in the Indonesia fish industry (for a total wastewater
flow rate of 5.6 Mt/day)
As general remarks, it has to be underlined that the interpretation of health and economic
benefits largely depends on the level of confidence uncertainty of various input factors. The main
driver of model variability can be identified in the averted DALYs characterization factors, in
particular, difficulties in the estimation of PAF in the different regions.
Finally, this appraisal framework can also be used as a conceptual tool, in order to highlight the
evolution of wastewater management and associated benefits on human health and the local
economy in Indonesia.

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