Model for Assessment Sustainability Indicators in
biofuels.
Danny Ibarra Vega" ”, Gerard Olivar Tost’ J ohan Manuel Redondo”
1. Universidad Sergio Arboleda, Escuela de Ciencias Exactas e Ingenieria.
2. Universidad Nacional de Colombia-Sede Manizales-Facultad de Ingenieria y Arquitectura
Abstract: A method for representing systems that allows the study of change and of necessity is
required for sustainability assessment. Therefore, in this study a model for dynamic and
prospective assessment of the sustainability in biofuels, was developed. The proposal of this
work is shown with an example in the bioethanol production from sugarcane in Colombia. First
a model of a bioethanol supply chain is developed and it is linked with associated variables
which represent sustainability indicators, these indicators were proposed by the Global
Bioenergy Partnership- GBEP. Then desired regions of the state of the system and indicators
are suggested, which were defined for some Time of Evaluation of sustainability such as:
Desired Scenario, Alert Scenario and Non Desired Scenario. This model allowed important
findings for monitoring and evaluation of sustainability in biofuels production.
Keywords: Sustainability, Modeling, System Dynamics, Viability Theory, Biofuels.
1. Introduction
Sustainability is currently one of the most important concepts in scientific research and
government programs of different countries (Nabavi et al 2017). This applies to all
productive sectors that grow in local and global economies. The application of
sustainability principles into supply chains is also an evolving research area currently
suffering from a scarcity of established theories, models, and frameworks (Ahi & Searcy
2015). One of the most noteworthy sectors for the implementation and assessment of
sustainability is the biofuel sector, because in recent years its production specifically
bioethanol has increased worldwide, due to the implementation of measures and policies
that encourage local production (Scarlat and Dallemand, 2011). Currently, these production
policies have focused on the construction of projects and sustainability standards, thus,
encouraging many countries to investigate, implement or consider the opportunity to
introduce the production of biofuels from different feedstocks in their national energy
systems (Pacini, et al 2013). All this is also encouraged because biofuels have been
considered as an option for reducing emissions of greenhouse gases, increasing the
diversity of the energy mix, creating jobs and promoting rural development (Scarlat and
Dallemand, 2011). However concems remain about the potential direct and indirect impacts
with respect to sustainable development, specifically the contribution of greenhouse gases,
food safety, environmental effects and economic development, which are still discussed in
different contexts (Valencia and Cardona 2014).
Hence the pursuit of sustainable development as an adaptive process of learning-by-doing
may benefit from using sustainability indicators, (Pupphachai y Zuidema, 2017).
Accordingly, sets of indicators have been developed to approach the assessment of
sustainability in biofuels production (Diaz-Chavez, 2011). In this direction, a set of
sustainability indicators was proposed by the Global Bioenergy Partnership (GBEP), which
consists of 24 indicators for sustainable bioenergy production assessment. This was the first
global consensus of governments to assess the sustainability in the use of bioenergy
through indicators (GBEP, 2011). These are based on the three pillars of sustainability:
economic sustainability, social sustainability and environmental sustainability. GBEP
indicators focus on a national and / or regional market level, as well as throughout the life
cycle of the biofuel (Hayashi and Ierland Zhu, 2014), i.e. throughout the supply chain. The
use of indicators provides a tool for generating and analyzing information. They are useful
for sharing and comparing and to facilitate decision-making to the different stakeholders
(Diaz Chavez, 2011) in building sustainability policies in different contexts. However the
assessment and monitoring of these indicators is made based on historical data and present
and past behaviors. Since this is a weakness of current methodologies, as it is necessary to
link the structure of the system and to define the rules of evolution in order to visualize the
different scenarios of future projection of biofuel production and the behavior of the
sustainability indicators in the future, i.e., a prospective evaluation of sustainability is
necessary.
In this vein, a method for representing systems that allow the study of change and of
necessity is required, and that also shows emergent behaviors that demonstrate the
existence of adaptation. Thus, the main goal with this study is to make an original
contribution to the biofuel sector, developing a tool that involves the ideas of change,
necessity and adaptation, developed in the context of the Methodology of System
Dynamics and the Viability Theory to prospectively evaluate the sustainability indicators
established by the GBEP.
The proposal of this work for the evaluation of sustainability in biofuels is shown with a
specific example in the production of bioethanol from sugarcane in Colombia.
2. Bioethanol production in Colombia
Colombia is the tenth producer country of bioethanol in the world, and the third in Latin
America. In Colombia, bioethanol production comes from sugarcane and the installed
production capacity increased from 1,250,000 liters / day in 2013 (CUE, 2012) to
1,650,000 liters / day at present (Fedebiocombustibles, 2016).
In 2014 406.5 million liters of bioethanol were produced and in 2015 almost 450 million of
liters (See Figure 1.), intended for mixing with gasoline at an E8 ratio, 8% ethanol and
92% gasoline (Fedebiocombustibles, 2015). The growth of this industry in the country has
had both positive and negative impacts on the economic, environmental and social fields
since these production systems are quite complex and have many factors influencing the
sustainability of their production (Janssen and Rutz, 2011). Biofuels are expected to
account for a substantial part of the diversification of energy sources, but it is necessary to
assess the sustainability of its market to explore its effects on the economic, social, political
and environmental dimensions (Espinoza et al, 2017).
Production of bioethanol (Thousands of liters)
2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
Figure (1). Bioethanol production in Colombia. Modified from: (Asocafia — Balance
azucarero 2012 y 2016)
21 inability of bioethanol in Colombi
Colombia has made significant progress in the production of bioethanol from sugar cane,
being the best ranked biofuel in the national market. Currently, the production of bioethanol
is located in three departments of Colombia: Risaralda, Cauca and Valle del Cauca.
Significantly, the supply chain of the sugar industry was adapted for the production of
bioethanol and the sugar that was destined for export, is now used to produce ethanol
(Valencia, 2012).
Although the directive of the national government is to continue increasing this production
capacity, there is uncertainty about the true environmental, social and economic impacts
that this increase could bring. These impacts generated in the production of bioethanol, are
associated with different stages of the supply chain.
The commitment of the goverment is to increase production in the short term, but
considering the sustainability guidelines that were established in the CONPES 3510 (2008),
which wants the country to achieve an efficient and sustainable production in the economic,
social and environmental fields. Therefore, it is necessary to develop rigorous tools to link
the environmental aspects and impacts related to the production process of bioethanol along
the supply chain.
In the Colombian context, the most important study that had been conducted to evaluate the
environmental impacts of biofuels was made as a requirement of the private sector and the
national government, see (CUE, 2012), where the authors used the Life Cycle Analysis to
conclude that biofuels are environmentally friendly in Colombia. However, this study does
not allow to see future scenarios that consider increasing production.
In this paper, we seek to link and model proposed social and environmental indicators to
assess the sustainability in different contexts. The complete set of indicators that are
required for the evaluation and monitoring of sustainability are presented in the following
section.
3. Methodology for Assessment Sustainability Indicators
Due to the dynamic nature of supply chains and the complexity of the the production
process of biofuels and specifically sugarcane bioethanol, the modeling is perceived as a
natural and important tool for analysis and design of supply chains and chain management
(Tako, A. and Robinson, S. 2012). System Dynamics is among the modeling and
simulation methodologies. How is widely known SD is a methodology for analysis and
problem solving, which attempts to simulate the behavior of systems over time. In System
Dynamics, any aspect of the world is conceived as the causal interaction between attributes
that describe it. Thus, systemic representations are built with arrows and nodes, called
causal diagrams that capture all scenarios proposed by the modeler, from those which you
can leam from the system to act upon it in the exercise of decision (Ibarra and Redondo,
2015). With System Dynamics several researches have been conducted for the assessment
of sustainability in different sectors (Nabavi, et al 2017, Zhang, et al 2017, Dacea, et al
2015 Banos-Gonzalez, 2016). This methodology has also been used for evaluating
sustainability in the biofuels sector (Musango, et al 2012, Robalino, et al 2014, Demczuk &
Padula, 2017).
After developing the model, it is necessary to know if the system evolves through desired
states that correspond with the objectives of sustainability of the sector or to understand the
behavior of to understand the behavior of sustainability indicators, for this, some concepts
of Viability Theory have been linked.
The Viability Theory designs and develops mathematical and algorithmic methods to
investigate the adaptation of the states of complex systems to their viable evolution sets. It
involves interdisciplinary research covering fields that have traditionally been developed in
isolation. The aim of the theory of viability is to provide "control maps" associating any
state of the complex system, with the subset of controls or regulations governing viable
evolutions, possibly empty (Aubin, 1992). For the assessment of sustainability in the
biofuels, we have defined the scenarios that are shown in Figure (2).
Indicator
LZ
\
Figure (2): Prospective evaluation of sustainability indicators. The figure shows three scenarios: desired scenario in green,
alert scenario in yellow and non-desired scenario in red. We also see the evolution of an indicator of sustainability, from a
certain initial condition in alert scenario. Note that from some evaluation time ¢,, to a final time ¢,, the indicator is in the
desired scenario (hatched area).
QA
te tr Time
We will say that the system has the desired values t, for the time of evaluation t,, when for
the time of evaluation and any future value after it in a well-defined time interval, we have
the system state in the desired scenario (Vt € [te, tr] x(t) € Ap)
4. Description and modeling of the System
Bioethanol is a type of biofuel produced from the fermentation of sugars from agricultural
crops or crop residues. This is by far the most technologically mature biofuel derived from
microorganisms and a good candidate to replace fossil fuels (Zerva A. et al 2014). In
Colombia it is produced from sugar cane, because the production of this type of plant is
consolidated in the country and has higher energy efficiency compared to other raw
materials from which bioethanol is produced. Its production in Colombia takes place
mainly in the Cauca River Valley, in the departments of Cauca, Valle, Risaralda and
Caldas, covering 47 municipalities (CUE 2012). For this article we took as base a supply
chain of sugarcane bioethanol generally presented in (CUE, 2012 and Valencia and
Cardona, 2014) The main links in the chain of bioethanol are producing sugar cane
(Hectares of sugarcane), processing of raw materials, production and transportation (Ibarra
2016).
Below are shown and defined the key attributes that were identified to build and define the
system to be studied, which describe the parts of the supply chain of bioethanol.
e Hectares of Sugarcane: The amount of hectares of sugarcane planted for the
production of bioethanol.
e Net Increase: Increase rate of hectares for sugarcane production.
e Harvested: Number of Hectares harvested and destined for the production of
bioethanol.
e Enlistment of sugar cane: Cleaning and grinding process of harvested sugarcane
e Installed Capacity: Production potential or maximum production volume of
bioethanol in the country.
e Sugarcane juice: Amount of sugarcane juice intended for fermentation.
e Bioethanol Production: Production process in function of production rate of
fermentable juice and installed capacity
e Produced bioethanol: The accumulation of liters of produced bioethanol.
e Distribution: A mount of bioethanol for blending with gasoline.
e Productivity: An economic indicator that shows the amount of volume produced per
hectare of sugarcane.
e Impact on social indicator: Positive repercussions on social indicators
e Environmental Impact indicator: Negative repercussions on environmental
indicators
From the identification of the system attributes, we proceed to the construction of the basic
causal diagram of a simple supply chain:
Net Increase Planted B2 ) Harvested,
: Bl ae NY \
+ +
Ne A, Enlisment of
Sugarcane
sudmcane 48) Installed Capacity +
ie F Suntec
+
+ +
‘Productivity ge of
x Bioethanol
B ioatianol to a aan
+) (2)
sg Distribution
inventory —————
Figure (3) Causal loop diagram of the supply chain of bioethanol.
% Harvest (w)
Net Increase (IN)
Fraction aimed at
Bioethanol (f)
Capacity
Sugarcane Enlistment
L nlistmen
Demand pare A) of sugarcane.
Factor (d) (a)
Harvest Yield (R)
‘Sugarcane
Tuice (j)
SZ Bioethanol
Bioetfahol to Inventory (IB)
Sell (V)
Produced
Bioethanol
Production of
Bioethanol
Distribution (DTS)
Sales rate (Tv)
Distribution
rate (Td)
Production Rate
Figure (4) Stocks and Flows Diagram of the supply chain of bioethanol.
From the diagram of Stocks and flows, we make equations representing the evolution in
time of the state variables of the system. Thus we can say that the hectares of planted
sugarcane are given by:
ue = IN -C, a)
where IN is the net increase given by changing a demand factor, in relation to the time and
hectares of planted sugarcane and it is defined by a piecewise function:
Ha + (Ha.k) Sit<ti
IN = (2)
Ha+(Ha.k1).d Sit>tj
The harvested flow variable C is the number of hectares of sugar cane that are harvested per
a fraction of hectares w. This is given by:
C =Ha.w (3)
Flow variables IN and C are measured in hectares of sugarcane Ha.
Bioethanol production is estimated annually, it accumulates in the level of bioethanol
produced variable, B which is given by:
& = Production of B— DIS, (4)
The production rate parameter p Rate is a percentage production parameter and goes from 0
to 1. It allows calibration of the model.
In turn the sugarcane juice j is defined by the product between performance R and the
auxiliary variable Sugarcane Enlistment A, which is a function of crop yield Re, the milling
rate TM and the fraction for Bioetanol f, expressed as folows:
j=A.R, where A= (R.C).f. (5)
The installed capacity in this model is represented by an auxiliary variable with an annual
increase u as follows:
X=Xo+ X.u (6)
The variable flow Distribution DJS, is given by:
DIS = B.Td (7)
The amount of inventory of Bioetanol Ib is represented by the difference between what is
distributed DIS to stock and what is sold V:
dib
a = DIS-V. (8)
Sales relate to a constant sale rate Tv:
V =IB.Tv (9)
To estimate the net increase, it is associated to a demand factor d, which is based on the
Productivity. This is defined by the amount of Bioethanol produced B on the number of
hectares of sugarcane aimed at production Ha:
iB
Productivity = a (10)
di siP>n
Demand factor = (11)
d2 siP<n
The general initial conditions for the simulation of the model and the water consumption
indicator are presented in the following table:
Table 1.
Initial conditions. Modified from: (Ibarra, 2016, CUE 2012).
Variables Values
Hectares. 14000 Ha
Harvest Yield 118 Ton/Ha
Fraction aimed at bioethanol 62%
Installed Capacity 100.000 L/day
Biothanol Production Sugarcane area planted
200M Litros 30,000
200M Liters
25,000
100M Litos sear
100M Liters = 20,000 el
jx wee
on ae 15,000 |
==
0 Litros
ans 10,000
a oa ae 2018 a ane eee : 2006 2010 2014 «2018 2022 2026 ©2030-2034 «2038
‘Time (Year) _
"Biocthanol Inventory (Ib) 0" New
“Installed Capacity (x)" : New
Liters
Figure (5) Simulation of bioethanol production and sugarcane hectares.
4.1 Sustainability Indicators.
The Global Bioenergy Partnership (GBEP) developed a set of twenty-four indicators for the
assessment and monitoring of sustainability of bioenergy at national levels.
The GBEP indicators are intended to inform policymakers in countries on environmental,
social and economic aspects of bioenergy industry in their countries and guide them
towards policies that promote sustainable development, see Table (1). These are presented
in detail in Hayashi et al. (2014) and GBEP (2011).
Table 1.
List of GBEP indi S.
Envir 1 Indi s Social Indi s Economic Indicators
1. Lifecycle GHG emissions 2. Allocation and tenure of land for new 3. Productivity
production
4. Soil quality 5. Price and supply of a national food 6. Net energy balance
basket Ratio
7. Harvest levels of wood 8. Change in income Local currency 9. Gross value added
resources
10. Emission of non-GHG air 11. Jobs in the bioenergy sector 12. Change in
pollutants consumption of fossil
fuel and traditional
biomass
13. Water use and efficiency 14. Change in unpaid time spent by women 15. Training and re-
and children collecting biomass qualification of the
workforce
16. Water quality 17. Bioenergy used to expand access to 18. Energy diversity
modem energy services
19. Biological diversity and 20. Change in mortality and burden of 21. Infrastructure and
landscape disease attributable to indoor smoke logistics for
distribution of
bioenergy
For this study, the environmental indicator water use and social indicator employment was
modeled and evaluated, as are explained below:
e Use and water efficiency Indicator
This indicator defined by the GBEP as the volume of water extracted from certain
watersheds nationwide, used for production and processing of raw materials for bioenergy
per unit of bioenergy produced, in this way for this case we modeled the indicator,
considering the estimated water consumption for growing sugar cane intended to produce
bioethanol, this is a function of the hectares of sugarcane. The causal diagram that
complements the one presented in Figure (3), and that shows the link of the water usage
indicator is shown in Figure (6a). In tun the Levels and Flows diagram that complements
the one presented in Figure (4) and that models the indicator, is shown in Figure (6b).
Water Consumption Tob! Consumption
mop. water Tol
+ OF consumption
water (Y
ts) Nin arigytgts vacant
saving by imigation. comsumption change (i) = — incrop(A)
change .
Diference in maximum a
consumption Water consumption Dae Raa
by Ha) consumption (Dif) Maximum
—_comsumption
Figure 6a) Causal Diagram of water consumption Indicator. 6b) Stocks and Flows Diagram of water
consumption Indicator.
The general initial conditions for the simulation of the model and the water consumption
indicator are presented in the following table:
Table 2.
Initial conditions . Source: (Ibarra, 2016, CUE 2012).
Variables Values
Hectares 14000 Ha
Harvest Yield 118 Ton/Ha
Fraction aimed at bioethanol 62%
Water consumption in crop
7,2 m3/Ha-year
As a result of the simulation model it is evidenced that the evolution in time of the annual
water consumption variable without any intervention for a first evaluation time te = 2035 is
within the Non-Desired scenario (Red color), as the amount of water consumed is not
within the range defined as desired (< 100,000 Green color). Thus it is necessary to
implement a strategy or policy that allows moving the indicator state to the desired region.
So we implemented the saving strategies for water consumption in which is considered the
greater consumption activity, sugarcane cultivation. These strategies seek savings in water
consumption by 20%, 30% and 60%, with the combination of improved irrigation
techniques of cultivation (see Table 3).
Table 3. Information about water-saving techniques. Source CUE (2012)
Saving strategy Technical description of savings 4 % Savings
irrigation
/ year
NA BAU-Business As Usual 7200 m* NA
Savings 1 CAR (administrative control of irrigation) 6000 m? 20
Savings 2 CAR and altemate groove 5000 m? 30
Savings 3 CAR, alternating groove and pipe with gate 3000m° 60
The results of the evaluation of the annual water consumption indicator show that the
intervention of the system by implementing saving strategies, would improve the outlook
and would lead the system within the desired region, but only for the system that includes
savings strategies 2 and 3. (See Figure 7) For its temporal evolution it is in the Desired
scenario in te=2035 and for t,-> te, Fulfilling the proposal in section 4.2 of this paper.
Flow Water consumption A Flow Water consumption
140,000 140,000
112,500 _==—camnnn 112,500 anaes
85,000 85,000
57,500 57,500
30,000 30,000
2014 2018 2022 2026 2030 2034 2038 2014 2018 2022 2026 2030 2034 2038
Flow Water consumption Flow Water consumption
140,000 140,000
112,500 112,500
| aes
85,000 TL 85,000
57,500 57,500
30,000 30,000
2014 2018 2022 2026 2030 2034 2038 2014 2018 2022 2026 2030 2034 2038
‘Time (Year) ‘Time (Year)
te=2035 “We * Cunent
GER) 4 @) > 115000 m7 A Gt) = 100000 < 115000 I 4 ¢) >0< 100000
Figure 7 Prospective evaluation of indicators. Water consumption.
« Employment Generation Indicator
This indicator defined by the GBEP as net job creation as a result of the production and use of bioenergy. For
this article, we used the employment indicator, measuring it as the number of jobs generated throughout the
duction chain of bioett in Figure (3, 4).
The causal diagram that complements the one presented in Figure (3), and that shows the link of the number
of jobs indicator is shown in Figure (8a). In tum the Levels and Flows diagram that complements the one
presented in Figure (4) and that models the indicator, is shown in Figure (8b).
as Sc
A . co emt san ®
naga iio pegrsojee ial
ie \ Ratio
| 2) | oe Hemp ie To
i i
CratonofDeied goes wa tre nob fr Prodan Ines (Ds)
bee ‘Production Increase
= aioe Creation of Deired (ae (co
Job Seis
Figure 8a) Causal Diagram of Jobs Indicator. 8b) Stocks and Flows Diagram of the Jobs Indicator.
The initial conditions for the simulation of the model are the same as those presented in Table 2. With an
employment relationship of 10,000 existing jobs for every 33 million bioethanol liters produced annually.
The results of the evaluation of the indicator, without any intervention of government policies, show that with
the initial diti and with the i production of bioeth 1 with
s of scale the
number of jobs would be reduced, leading the time evolution to a Non-desired region (Red) on it t, - 2025.
Thus, the implementation of sectoral policies by the government is needed to increase the number of jobs in
the production of bioethanol and to monitor the existing relationship between the amount of ethanol produced
or ii production and the ion of new job opportunities. With the aim to discuss social benefits.
Thus, the results of the Jobs indicator evaluation, show an improvement in the time evolution of the indicator,
since we implemented in the model a policy that seeks to increase 10%, 50% and 80% of jobs for t.= 2025.
Defining as the Desired Scenario an amount of more than 15,000 jobs.
Figure 9 shows the evaluation with the three policies. It is concluded that the implementation of policies to
increase employment by 80% would improve the outlook and lead the system within the desired region,
because its evolution is in the Desired scenario in t, - 2025 and fort, -> t..
Number of Jobs ©) Number of Jobs )
LL
2006 2010 201 2018 2022 2026 2030 2034 2038
Taw (Year)
Namber of Jobs ©)
20,000 oA
iy
i Bee
Bee
3 $1250
#750
3000
2006 2010 2014 2018 2022-2026 2030-2034 2038 2006 2010 2014 «2018 2022 26 2030-2034 2038
Tine (Yew) Tine (Yea)
“Nimneto de Epos "|, ——
HE = ¢) < 10000 [EE] £@)< 15000210000 = BD & (t.) > 15000
Figure (9). Prospecti’ ‘ion of Water C: ion Indicators.
After of the simulations and de indicators assessment. We present de the causal diagram
that complements the one presented in Figure (3). This new causal diagram, represents the
whole supply chain and the way for integrate the sustainability indicators. See Figure (10).
Age
‘Total consumption
oe ) 7
fi Loa “on
CORE Area
oo ‘in ‘/
x Jbentof
-
An) wo Capacity
feeu ne Q va
+
NX Boo
an 4 Y of (* ‘Concentration (a i
ioethanol. ——m
Wastewater
— on BOD renal
= Bioethanol.
} + 4
na ‘Creation of desired }
sy vacancies BOD Discharged
i a) A Regulatory
Job Generation a ¢ restriction
“ y (mp Difference in Jobs for
Bioethanol: Distribution production increase
inventory ——___—— "i 2
‘Number of Jobs
Figure (10) Causal loop diagram of the supply chain of bioethanol with sustainability indicators.
5. Conclusions
In this paper a model was developed, with the purpose of evaluating sustainability
indicators in the biofuels sector, the methodological proposal, involves the ideas of change,
necessity and adaptation, developed in the context of Viability Theory , These ideas were
represented within a theoretical supply chain of bioethanol of sugar cane, for an installed
capacity of 100,000 liters / day, starting from the Methodology of Systems Dynamics and
defining desired regions for some evaluation times.
Although the model constructed using Systems Dynamics methodology is based on first-
order ordinary differential equations, it can represent very closely the sustainability
indicators (Water use and Jobs) required for the prospective assessment of sustainability in
the production of biofuels.
The model proposed in this study, tested with the sugarcane bioethanol in Colombia, shows
that the methodology can be used for the prospective evaluation successfully. For this it is
necessary to model the production chain to be evaluated defining the raw material, the
installed production capacity, the annual increase in biofuel production, also we choose and
model the sustainability indicators that we want to evaluate, later and after developing the
model, it is required to know if the system evolves through the desired regions, raising
evaluation times and interval values where we want the state of the indicator to evolve,
according to the policies and interests of the context in which it is developed.
Modeling with system dynamics allows the intervention of the system with strategies that
the decision makers can implement, in order to be able to lead the state of the indicators to
desired regions or sustainability goals.
5.1 Future results
Future work should link the sustainability indicators that apply to each production context.
As an example, the modeling and simulation of the water quality indicator, described in the
amount of BOD discharged to surface water, from the waste water of the production is
presented below.
Avergage .
concentration BOD Removal Efficiency Time of restriction
Rate generation
O BOD
Wastewater Removal (RO)
A Ne
<Produced ©
Bioethanol (B)>
BOD Discharged Regulatory
___g restriction
BOD Discharged
50,000
37,500
12,500
2006 2010-2014.» «-2018 2022-2026 -«2030 «20342038
e (Year)
BOD Disses
Figure (11a) Stock and flow diagram of the water quality indicator. (11b) Initial simulation of the indicator
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