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The model of risk assessment of greywater discharges from the
Danube River ships
Dragana Makajic-Nikolic
a
, Natasa Petrovic
a
, Marko Cirovic
a
*, Mirko Vujosevic
a
and Vladanka Presburger-Ulnikovic
b
a
Faculty of Organizational Sciences, University of Belgrade, Belgrade, The Republic of
Serbia;
b
Faculty of Ecology and Environmental Protection, Union University, Belgrade,
The Republic of Serbia
(Received 23 June 2014; final version received 6 November 2014)
Having in mind the immense value of the Danube River and knowing that risk
assessment of its pollution is one of the key elements for ecology and the health
of people in its region, in this paper we emphasized the importance of risk
assessment of ship-generated wastewater –particularly in the case of greywater
discharges. Although, a number of methods for measuring and analysing differ-
ent environmental risks have been developed, previous research shows that the
failure mode and effect analysis (FMEA) method is applicable in solving envi-
ronmental issues. Therefore, we conducted our research with the main purpose
to develop a model of FMEA method application for assessing the risks of ship-
generated greywater discharges based on estimated data for total quantity of
greywater, the size of the exposed population to the pollution of greywater and
the possibilities of this pollution detection. Risk analysis was performed on offi-
cial data for nine ports on the Danube River on inland waterways of the
Republic of Serbia. Based on the obtained results, we concluded that measures,
recommendations and risk prevention strategies for ship-generated greywater dis-
charges should go into two major directions: (1) decreasing the pollution caused
by greywater discharges; (2) increasing the number of water quality monitoring
stations.
Keywords: The Danube River; greywater discharges; FMEA
1. Introduction
‘Water is the primary life-giving resource’(UNESCO 2006) and a key driver of eco-
nomic and social development, and has a fundamental function in maintaining the
environmental integrity (UN 2013). In other words, ‘freshwater of adequate quality
is not only a prerequisite for human societies, but also for natural ecosystems that
perform functions essential for human existence and life on Earth’(Costanza and
Daly 1992). Rivers are bodies of freshwater and a source of water supply that pro-
vide many services that contribute to human well-being, particularly to those people
who are living near wetlands and highly depend on these services, and who are
directly harmed by their degradation (Millennium Ecosystem Assessment 2005).
The Danube River is the longest river in the European Union. It is the second
longest river in Europe after Volga, and the twenty-first by its length in the world. It
*Corresponding author. Email: marko.cirovic@fon.bg.ac.rs
© 2014 Taylor & Francis
Journal of Risk Research, 2016
Vol. 19, No. 4, 496–514, http://dx.doi.org/10.1080/13669877.2014.988286
is 2850 km long and flows through several major cities of Europe, before it finally
reaches the Black Sea. The Danube flows through and forms the borders of 10 Euro-
pean countries. The Danube’s water flow is of the immense importance for the entire
Europe and especially for the region and the countries through which it flows (Mihic
and Andrejevic 2012). It has the navigable length of 2415 km out of which 588 km
lies within the Republic of Serbia, from Bezdan to Prahovo (EMS 2010; Mihic,
Golusin, and Mihajlovic 2011).
Approximately 5200 ships, with 4.3 million tons of carrying capacities, as well
as an increasing number of big river cruise ships sail through the Republic of Serbia
(Danube Strategy in Serbia 2011). The continuous growth of the shipping (including
river traffic) has led to a multiple increase in environmental pressures, particularly
those in the area of waste (Butt 2007; Georgakellos 2007; Zuin, Belac, and Marzi
2009). There is a wide range of various types of ship-generated waste: wastewater,
oily wastewater, ballast water, food waste, chemical waste, sewage, garbage, unused
medications or drugs, hazardous waste, and so on (e.g. Carpenter and Macgill 2005;
Lin, Lin, and Jong 2007; Presburger-Ulnikovic, Vukic, and Nikolic 2012). Conse-
quently, there is a need to consider ships’waste stream discharges and their possible
impact on environment and water quality (EPA 2013). This concern is of global
importance and the subject of research in many international institutions and organi-
zations. Thus, United States Environmental Protection Agency –EPA (2013)is
especially evaluating sewage and greywater discharges from cruise ships.
Greywater is non-sewage wastewater that results from showers, sinks, dishwash-
ing, laundries and kitchens (e.g. Butt 2007; EPA 2013), that in some cases may con-
tain chemical and microbiological contaminants (Widiastuti et al. 2008; Mourad,
Berndtsson, and Berndtsson 2011). Even though it was calculated that a ship can
generate between 120 and 300 L of greywater per person a day, there are no dis-
charge restrictions under MARPOL 73/78 for greywater discharges (Butt 2007; IMO
2013). The reason for this may be the fact that all types of greywater have a good
biodegradability. It should also be noted that too often ships retain waste on board
and simply discharge them in ports or terminals at the end of each voyage (Zuin,
Belac, and Marzi 2009). Also, greywater is often legally disposed overboard almost
anywhere (Dowling 2006).
This paper presents the model of failure mode and effect analysis (FMEA)
method application in analysing and evaluating the risk of the Danube River ship-
generated greywater discharges in the Republic of Serbia. FMEA is an inductive
scenario-based risk assessment technique which uses design knowledge in order to
provide ranking of the identified risks (Groso, Ouedraogo, and Meyer 2012). The
aims of the study were to consider some of the issues related to greywater generated
by ships: assessing the risk of greywater discharges and determining the risk ranking
for observed ports of the Danube River and along its flow. Special attention is given
to the impact of greywater on the population living in the ports, because every anal-
ysis of pollution risk must include users who depend on water supply (Hou et al.
2013). The boundaries of our research were the lack of data not only on the total
amount of ship-generated waste but also on the amount of greywater discharges in
the Republic of Serbia. This paper attempts to give a contribution in the scientific
examination and implementation of the FMEA method in creating the framework
for a model of risk assessment of greywater discharges and their impact on water
resources and population living in the area of the Danube River.
Journal of Risk Research 497
2. Materials and methods
2.1. Study area
The Danube River is a very important transport corridor –Pan-European corridor
VII (the only inner channel between ten Pan-European corridors). The flow of the
Danube River in the Republic of Serbia covers the surface of 82,000 km
2
, which is
10% of the total Danube River flow. A part of the Danube River’sflow involves the
middle (Panonian) and the upper (Vlachian) part. Out of that, 137.6 km represents a
natural border with the Republic of Croatia and 299.35 km with the Republic of
Romania. There are a total of 11 ports: Bezdan, Apatin, Bačka Palanka, Novi Sad,
Titel, Beograd, Pančevo, Smederevo, Veliko Gradište, Kladovo and Prahovo. The
most important ports are Beograd, Pančevo, Smederevo and Prahovo (EMS 2010)
(Figure 1).
The Republic of Serbia has the estimated population of 7181,505 people (exclud-
ing Kosovo –data unavailable) (SORS 2013). As much as 2410 local governments
have a direct access to the Danube River (EMS 2010). The total area of local gov-
ernment units is 9228 km² or 10.44% of the total territory of the Republic of Serbia.
Eleven such units of local government are located within nine administrative dis-
tricts.
In addition, considering the importance of the Danube region in all of Europe, it
is important to analyse the limiting factors of its future development. Water quality
and pollution of the Danube River represent one of the great issues, which require
appropriate measures of protection and revitalization of the Danube region in order
Figure 1. The Danube River ports in the Republic of Serbia (My Ship 2014).
498 D. Makajic-Nikolic et al.
to achieve further development perspectives in this region. This is valid for the
Republic of Serbia, because of its active role in regional cooperation on water pro-
tection (United Nations Economic Commission for Europe 2007):
The Republic of Serbia ratified the Danube River Protection Convention.
The Republic of Serbia is a member of International Commission for the Pro-
tection of the Danube River –ICPDR.
2.2. FMEA method
The FMEA is a method for assessing the ways and the effects of potential failure of
subsystems, assemblies, components or functions that may adversely affect the over-
all functioning of the system. The FMEA was originally developed for the US Army
as a formal analysis technique. The main goal of this method is the determination of
the number of priority risks (risk priority number –RPN) for each failure mode.
The basic FMEA elements of assessment could be defined as (Ayyub 2003;
Ericson II 2005):
Occurrence rating (Occurrence –O). This rating of the risk represents the fre-
quency with which a given risk occurs. It is directly proportional to the proba-
bility of occurrence of the risk.
Severity rating (Severity –S). This rating represents the importance of the risk
effect on end-user requirements. It is directly proportional to the severity of
the consequences of the risk.
Detection rating (Detection –D). This rating represents a measure of capability
of current controls. It is inversely proportional to the probability of detection
and prediction of the risk before causing effect.
The ratings of O, S and D can have values ranging from one to ten and they are
determined based on the universal scales or scales that are formulated for the spe-
cific system being analysed. In our research, specific scales were formulated and
used.
The risk priority number –RPN is a product of the numerical occurrence (O),
severity (S) and detection (D) ratings (MakajićNikolićet al. 2011):
RPN = O SD (1)
The ranking of risks is based on the values of individual risk RPNs, and repre-
sents the base for prioritizing corrective actions. The rule of the thumb is to take into
consideration RPNs which are greater than 125 (Ayyub 2003).
Previous research shows that the FMEA method is also applicable in solving
environmental issues:
Pollution. The hazardous anomalous events associated with biomass combus-
tion processes with potentially catastrophic consequences such as explosion,
fire and poisoning from the combustion gases (Thive, Bultel, and Delpech
2008). Kikukawa, Mitsuhashi, and Miyake (2009) investigated different
scenarios of accidents on liquid hydrogen fuelling stations where safety of
personnel, customers and environment could be jeopardized. Oraee,
Journal of Risk Research 499
Yazdani-Chamzini, and Basiri (2011) investigated hazards of working under-
ground in mines, which can be related to safety, health and environment.
Wang et al. (2012) used FMEA to analyse three types of consequences caused
by failure in petrochemical plants: personnel safety effect, environmental threat
and economic loss.
Waste disposal. Pollard et al. (2006) used the FMEA method for the perfor-
mance assessments of waste induced in chemical, oil, nuclear and water sec-
tors. Mousseau et al. (2003) used the FMEA to determine consequence of
failures related to hazardous and radioactive waste. Ho and Liao (2011) evalu-
ated the potential risk in the process of infectious medical waste disposal.
Water risk assessment. Analysis of threats in water supply system using the
FMEA is presented in Rak and Tchórzewska-Cieślak (2010). Cicek and Celik
(2013) utilized FMEA to formulate solutions to prevent crankcase explosion
in marine diesel engines which is critically important for crew health and ship
safety.
There are several modifications of the FMEA method created in order to adjust
the method to the environmental research requirements. Environmental FMEA (E-
FMEA) method was used to identify and evaluate potential environmental impacts
in all life-cycle phases of the product (Lindahl 1999). The result of such analysis
can be the redesign of the product or changes of materials. Jozi, Saffarian, and Sha-
fiee (2012) applied E-FMEA to assess environmental risk of a gas power plant in
Iran, more precisely for identification of the activities and their environmental
aspects and consequences. The methodology of E-FMEA follows exactly the same
procedure as FMEA with the following differences: failures to achieve environmen-
tal objectives are checked, the definition of the severity refers to environmental
effects while detection likelihood is obtained according to detection methods avail-
able for environmental considerations (Quella and Schmidt 2003). The E-FMEA can
also be found in the literature as environment effect analysis, a method designed for
the analysis of environmental aspects of product in the early phases of its develop-
ment (Lindahl and Tingström 2001). In order to identify bio-invasion hazards in eco-
logical systems, Hayes (2002) adjusted FMEA into IMEA by replacing the failure
modes with infection modes and failure effect with environmental suitability.
3. Results
We conducted our research for the purpose of developing the model for assessing
the risk of ship-generated greywater release with the emphasis on the impact of
greywater discharges on the population living in the Danube River ports and down-
stream in the Republic of Serbia. In our paper, we used the traditional FMEA
method for risk-assessment analysis.
We emphasized the importance of greywater because of the fact that:
greywater represents the largest source of cruise ship-generated liquid waste
(90–95%) (Dowling 2006; Thomas 2008),
generation rates of greywater are not measured in general (EPA 2008),
in the Republic of Serbia, there is no registry of waste types and ship-gener-
ated waste (Presuburger-Ulnikovic, Vukic, and Nikolic 2012),
500 D. Makajic-Nikolic et al.
there is no required purification treatment for ship-generated greywater before
their discharge (except in Alaska), even though their impact cannot be
neglected as they may contain: bleach, foam, high pH, hot water, nitrate, oil
and grease, oxygen demand, phosphate, salinity, soaps, sodium, suspended sol-
ids, turbidity, bacteria, hair, food particles, odour, organic matter, metal, medi-
cal and dental waste (ADEC 2000;EPA2008; Thomas 2008).
Additionally, we featured the impact of greywater on the people since greywater
(Dowling 2006):
contaminates drinking water;
contaminates vegetables, shellfish and other food products;
contaminates irrigation;
it has an effect on fishing and swimming and other recreational activities;
has the potential to transmit disease;
can contain bacteria, for example, faecal coliform, which has a serious effect
on human health.
Given the facts that too often ships simply discharge their waste in ports (Zuin,
Belac, and Marzi 2009)aswellassignificant probability of uncontrolled wastewater
discharges from ships sailing through the Republic of Serbia (Presuburger-Ulnikovic
et al. 2011) we assume that most likely scenario is undesired event in which entire ship-
generated greywater is released at once in nearby observed ports. Our study is done for
ships and cargo ships that were docking at nine ports on the Danube River on inland
waterways of the Republic of Serbia: Bezdan, Apatin, Bačka Palanka, Novi Sad, Beo-
grad, Smederevo, Veliko Gradište, Kladovo and Prahovo. These nine ports were
selected since the official figures for the ports Titel and Pančevo were not available.
3.1. Determining the assessments for the probability of occurrence
The scale for assessing the probability of occurrence of greywater discharges per
port is based on the estimated annual total quantity of generated greywater by cruise
and cargo ships that were docking at the observed ports (Table 1, Presburger-
Ulnikovic 2012).
Table 1. The estimated annual total number of cruise and cargo ships per port.
Port
Estimated annual total number of
cruise ships
Estimated annual total number of
cargo ships
Bezdan 172 2480
Apatin 0 202
Bačka
Palanka
0 173
Novi Sad 371 3594
Beograd 400 79
Smederevo 2 1108
Veliko
Gradište
319 4173
Kladovo 0 661
Prahovo 14 416
Journal of Risk Research 501
The estimated annual total quantity of generated greywater per port is calculated
as a product of the total number of passengers and crew members, the average quan-
tity of greywater generated per person a day and an average number of days that a
passenger or crew member spend on a ship. The estimation of the total number of
passengers and crew members per year was based on the data and the prediction
model gained from Presburger-Ulnikovic (2012) on the types of ships, annual num-
ber of ships and an average number of passengers and crew members on each ship
type. The average quantity of daily greywater generated per person according to
Butt (2007) is 210 L (respectively 0.21 m
3
) and an average period that a passenger
spends on the Danube River ships in the Republic of Serbia is 3 days, while for a
crew member it is a period of 5 days (Vukićet al. 2009; Presburger-Ulnikovic et al.
2011). This calculation is done for all nine observed ports. The calculated data for
the estimated annual total quantity of ship-generated greywater per port are pre-
sented in Table 2.
Considering the estimated annual total quantity of greywater generated by the
ships docking at the observed ports, we formed the scale for assessing the greywater
discharges occurrence (Table 3). Because of the fact that the total annual quantity of
greywater generated is estimated and having in mind minimal and maximal esti-
mated annual total quantities of greywater from Table 2,wedefined relative scale
with increments of 8000 m
3
.
3.2. Determining the severity ratings
The severity of the effects of greywater was analysed based on the estimated total
population size exposed to pollution caused by greywater discharges. To determine
this population size, we analysed local and systemic pollution effects. For the pur-
pose of our research, we defined the local pollution effect as the pollution effect on
residents of the municipality in which a port was exposed to greywater discharges.
Table 2. The estimated annual total quantity of ship-generated greywater per port.
Port
Estimated annual
total quantity of
ship-generated grey-
water by cruise ship
passengers (m
3
)
Estimated annual
total quantity of
ship-generated
greywater by
cruise ship crew
(m
3
)
Estimated annual
total quantity of
ship-generated
greywater by cargo
ship crew (m
3
)
Estimated
annual total
quantity of
ship-generated
greywater
(m
3
)
Bezdan 13,008.240 5420.100 26,044.830 44,473.170
Apatin ––2116.170 2116.170
Bačka
Palanka
––1816.500 1816.500
Novi Sad 28,082.880 11,701.200 27,065.010 66,849.090
Beograd 30,214.800 12,589.500 3466.470 46,270.770
Smederevo 115.920 48.300 11,631.270 11,795.490
Veliko
Gradište
24,086.160 10,035.900 43,814.400 77,936.460
Kladovo 1053.360 438.900 4363.800 5856.060
Prahovo ––6945.330 6945.330
502 D. Makajic-Nikolic et al.
For the determination of the systemic pollution effect, we estimated the number
of exposed people living downstream from each observed port. Since the greywater
decomposes over time and the Danube River has high self-purification capacity
(Bloesch 2009), the pollution impact decreases with increasing distance from the
port. The Swedish Study (Karlgren et al. 1967) has shown that after five days
of storage, greywater has achieved 90% of decomposition, that is, the rate of decay
of greywater is almost 65% per day (Tal, Sathasivan, and Krishna 2011). The rate of
pollutants that remain undiluted after one day (r) is then equal:
r¼10:91=5¼0:369 (2)
Since the average speed of the Danube River in the Republic of Serbia is 4.5
kilometre per hour (Ćurčićet al. 2011), greywater discharged in the river passes a
distance of approximately 100 km during one day. Considering this, downstream
zones of about 100 km were determined for each port in order to calculate the popu-
lation size exposed to the pollution (Table 4). For each zone, the population size
was calculated based on the number of people (SORS 2011) residing in the Danube
municipalities within observed zone. Within the same zone of the flow, it is assumed
that the concentration of greywater discharges is approximately the same.
The population living in the determined zone of the water flow.
Table 3. Rating criteria for the assessment of greywater discharges occurrence.
Ranking Quantity of ship-generated greywater (m
3
)
10
21–8000
3 8001–16,000
4 16,001–24,000
5 24,001–32,000
6 32,001–40,000
7 40,001–48,000
8 48,001–56,000
9 56,001–64,000
10 >64,001
Table 4. The population living in the determined zone of the water flow.
Port
Population
Zone 1 Zone 2 Zone 3 Zone 4 Zone 5 Zone 6
Bezdan 73,000 491,474 1102,911 150,800 38,814 36,879
Apatin 99,707 628,796 1018,023 61,184 57,514
Bačka Palanka 628,796 1018,023 61,184 57,514
Novi Sad 944,611 380,580 38,814 36,879
Beograd 372,419 46,975 36,879
Smederevo 168,979 57,514
Veliko Gradište 46,975 36,879
Kladovo 36,879
Prahovo 0
Journal of Risk Research 503
To estimate the total relative population size exposed to the pollution caused by
greywater discharges in given ports and downstream, we introduced the following
notations:
bdj–the number of downstream zones of 100 km for j-th port, j=1,…,9
bkj –the population size of the k-th zone of j-th port, k=1,…,bdj
aj–the number of residents of the municipality whose port is port j.
For each j-th port, the estimation of total relative population size exposed to the
pollution of greywater (buj), can be calculated using the equation:
buj¼ajþX
bdj
k¼1
bkj rk(3)
where ris defined by the Equation (2) and r
k
represents a weighting coefficient
which shows the decrease of pollution of greywater after a knumber of days. This
weighting coefficient consequentially decreases the relative calculated number of
people exposed to the pollution proportionally to the greywater decomposition over
time.
The weighted population size downstream (Table 5, column 2) and the total
number of residents exposed to the greywater discharged nearby observed ports
(Table 5, column 3) are obtained using the data from the Tables 4and 6and the
Equation (3).
The total relative population size exposed to the pollution of greywater per port.
The severity assessment was obtained considering the total relative population
size exposed to the pollution caused by greywater discharges. The scale for the
assessment of severity, shown in Table 7, was formed based on the recommenda-
tions for the settlement classification based on population size defined by United
Nations Statistics Division (2013).
Rating criteria for the assessment of the severity based on population size
exposed to pollution
Table 5. The total relative population size exposed to the pollution of greywater per port.
Port
Weighted population size downstream
exposed to the pollution of greywater
Total relative population size
exposed to the pollution of grey-
water
Bezdan 152,465 238,034
Apatin 175,129 203,783
Bačka
Palanka
374,842 430,203
Novi Sad 403,069 738,770
Beograd 145,690 826,138
Smederevo 70,194 177,722
Veliko
Gradište
22,358 39,917
Kladovo 13,610 34,245
Prahovo –36,879
504 D. Makajic-Nikolic et al.
3.3. Determining detection ratings
A number of water quality monitoring stations downstream are used for assessing
the possibilities to detect greywater discharges in observed ports. Monitoring sta-
tions enable rapid detection of an already occurred pollution event (Hou et al.
2013). The layout of water quality monitoring stations on the Danube River in the
Republic of Serbia is given in the Figure 2(RHMZS 2013).
The number of water quality monitoring stations downstream of the observed
ports is given in Table 8.
Based on the number of water quality monitoring stations, a scale for assessing
detection ratings was established (Table 9). This scale is relative as in the case of
the ratings scale for the probability of occurrence. This is due to the fact that relative
range is made based on the current number of downstream water quality monitoring
stations.
3.4. FMEA RPN calculation
Considering the established scales for assessing the probability of occurrence
(Table 3), severity of effects (Table 7) and the possibility of detection (Table 9) and
data presented in Tables 2,5and 8, RPN has been calculated for the assessment of
Table 6. Number of residents of the municipality exposed to greywater discharges (SORS
2011).
Port Number of residents of the municipality
Bezdan 85,569
Apatin 28,654
Bačka Palanka 55,361
Novi Sad 335,701
Beograd 680,448
Smederevo 107,528
Veliko Gradište 17,559
Kladovo 20,635
Prahovo 36,879
Table 7. Rating criteria for the assessment of the severity based on population size exposed
to pollution.
Ranking Population size exposed to pollution
10–400
2 500–999
3 1000–1999
4 2000–4999
5 5000–9999
6 10,000–19,999
7 20,000–49,999
8 50,000–99,999
9 100,000–499,999
10 >500,000
Journal of Risk Research 505
the risks of greywater discharges in nearby observed ports. These results are pre-
sented in the FMEA worksheet (Table 10).
The FMEA RPN rank per port is given in Table 11.
4. Discussion
Previous research has often investigated the significance of evaluating and assessing
possible impacts of wastewater discharges from ships (e.g. Loehr et al. 2006) as well
Figure 2. Monitoring water quality stations in the Danube Basin in the Republic of Serbia.
Table 8. The number of downstream water quality monitoring stations per port.
Port Number of downstream water quality monitoring stations
Bezdan 15
Apatin 14
Bačka Palanka 12
Novi Sad 11
Beograd 8
Smederevo 7
Veliko Gradište 5
Kladovo 2
Prahovo 1
506 D. Makajic-Nikolic et al.
as the contribution of ship-generated greywater to the increase in pollution of river
waters (e.g. EPA 2008), still little research has specifically assessed the risk of the
release of greywater from ships. Thus, the purpose of our study has been to define
and analyse the application of the model-based FMEA method in evaluating the risk
of the Danube River ship-generated greywater discharges in the Republic of Serbia.
More precisely, our intention has been not only to assess the risk of greywater
Table 9. Rating criteria for the assessment of the detection based on number of downstream
water quality monitoring stations.
Rating Number of downstream water quality monitoring stations
1>9
28
37
46
55
64
73
82
91
10 0
Table 10. FMEA RPN calculation.
Port Occurrence Severity Detection RPN
Bezdan 7 9 1 63
Apatin 2 9 1 18
Bačka Palanka 2 9 1 18
Novi Sad 10 10 1 100
Beograd 7 10 2 140
Smederevo 3 9 3 81
Veliko Gradište 10 7 5 350
Kladovo 2 7 8 112
Prahovo 2 7 9 126
Table 11. FMEA RPN rank per port.
Port Rank RPN
Veliko Gradište 1 350
Beograd 2 140
Prahovo 3 126
Kladovo 4 112
Novi Sad 5 100
Smederevo 6 81
Bezdan 7 63
Apatin 8 18
Bačka Palanka 9 18
Journal of Risk Research 507
discharges but also to determinate the risk priority for observed ports with special
attention to the impact that greywater has on people living in the ports of the Dan-
ube River and along its flow within the borders of the Republic of Serbia.
In light of the aims of our research, Table 10 presents the central results of our
study. The second column shows values of the occurrence ratings of greywater dis-
charges per port. The third column shows values of the severity ratings based on the
population size exposed to pollution per port. The fourth column provides values of
detection ratings based on the number of downstream water quality monitoring sta-
tions per port. Finally, values of the risk priority number (RPN) per port are dis-
played in the fifth column. Observed ports were ranked according to their
vulnerability to ship-generated greywater discharges, based on their calculated corre-
sponding RPN values (Table 11). Our findings suggest that three ports belong to the
group of the most jeopardized ports with the highest values of calculated RPN val-
ues (RPN higher than 125). These ports are Veliko Gradište, Beograd and Prahovo.
The port of Veliko Gradište is ranked as the most vulnerable one, with its calcu-
lated RPN of 350. This arose due to the fact that is assigned high value to its occur-
rence rating (O= 10). The high value of occurrence represents the result of the
estimated annual total quantity of ship-generated greywater. This quantity is a direct
consequence of the large number of ships docking in this port. The annual number
of ships docking is estimated around 4500 (Presburger-Ulnikovic 2012), of which
93% are cargo ships, which generate much larger quantities of greywater consider-
ing the average crew’s ship residing time (five days).
The port of Beograd takes the second place with its RPN value of 140. High
RPN is in relation with high given value to severity rating (S= 10). The reason for
this is the large total relative number of people exposed to the pollution of greywater
caused by a large number of residents in Beograd’s Danube municipalities and
downstream (826,138) (Table 5). This population constitutes as much 42% out of
the total population of the Republic of Serbia that has a direct access to the Danube
River. Also it is important to add that Beograd itself is a home for 680,448 people
relaying and depending on the Danube River.
The port of Prahovo comes in third place with its calculated RPN value of 126.
This is due to high value assigned to its detection rating (D= 9). The reason for this
is the existence of only one monitoring water quality station installed and operating
in Prahovo port and downstream.
Further on, a few interesting observations came to be noted in the study. The
results have also shown that although port of Novi Sad gained maximum values
assigned to its occurrence and severity ratings (O= 10, S= 10), it maintained its
RPN value under 125 (RPN = 100). This is due to the fact that the value assigned to
detection rating for this port is minimal (D= 1). This minimal value is based on the
number of monitoring water quality stations operating downstream of Novi Sad port
(11 of them). Hence, it can be concluded that determined detection rating represents
an important FMEA element of the proposed model of risk assessment for detection
and prediction of the risk before causing effects of ship-generated greywater dis-
charges. Also, our research pointed out that all observed ports have high severity rat-
ings (from seven to ten). This reflects the high population density in the areas along
the Danube River in the Republic of Serbia and therefore a large number of people
who may be exposed to the pollution caused by the discharges. The number of peo-
ple living in these areas is estimated around 1979,447 (Tables 3and 6) which consti-
tutes 27.6% out of entire population of the Republic of Serbia.
508 D. Makajic-Nikolic et al.
Based on the obtained results we concluded that the major causes which had
effects on the group of the most jeopardized ports with the highest values of calcu-
lated RPN are: the estimated annual total quantity of generated greywater caused by
a large number of cargo ships, the total relative population size exposed to the pollu-
tion of greywater caused by a large number of residents and a small number of
downstream water quality monitoring stations. As it is almost impossible to influ-
ence the population size living in the Danube River ports and downstream and on
the number of ships docking, we can recommend two directions of future measures,
recommendations and risk prevention strategies for the Danube River ship-generated
greywater discharges in the Republic of Serbia. These major directions are: decreas-
ing the pollution caused by greywater discharges and increasing the number of water
quality monitoring stations.
Considering the decrease of the pollution caused by discharges of greywater, we
can recommend:
the possibility of re-routing ships to minimize and limit the pollution of ports,
bodies of water and health risks;
improving waste handling on board and in ports;
legally required installation of holding tanks for greywater on ships;
legally required facilities for the treatment of greywater on ships;
required transfer to port-based reception facilities and infrastructure for grey-
water –transfer, treatment, recycling, incineration and deposition in all major
ports along the Danube River;
introducing ‘no-discharge zones’, meaning that discharge of greywater is pro-
hibited in these zones, especially in the areas with high population density.
When it comes to the number of water quality monitoring stations, the recom-
mendation is to increase their number, in particularly in jeopardized ports and down-
stream in the Republic of Serbia.
Both of these directions require a detailed analysis of technological, organiza-
tional and financial capacities of individual ports and the Republic of Serbia. Also,
it should be noted that the importance of signing, implementation, obeying and
improving international, regional and national laws, conventions and agreements
related to water protection and ship-generated waste discharges (e.g. the National
waste management strategy 2010–2019 of the Republic of Serbia (Working Group
for Revision of the National Waste Management Strategy 2009), the Danube River
Protection Convention (1994) and International Convention for the Prevention of
Pollution from Ships (MARPOL) (International Maritime Organization 2013) by the
Republic of Serbia which will provide grounds for further studies on these important
issues.
Although this study contributes to the research fields in risk assessment of ship-
generated waste, in particular, greywater discharges from the Danube River ships on
inland waterways of the Republic of Serbia, it has its limitations. First of them being
the unavailability of data for two additional ports operating on the Danube River in
the Republic of Serbia: Titel and Pančevo. Obtaining the data for ports of Titel and
Pančevo would contribute this research as well as future research on this subject.
The data for the port of Pančevo would be important because of its proximity to the
port of Beograd. Furthermore, discharges conducted in the port of Titel would have
its strongest impact in the port of Belgrade as it is the first downstream port.
Journal of Risk Research 509
Secondly, lack of data caused the presented model of the FMEA method to be based
on the estimated annual total quantity of generated greywater by cruise and cargo
ships that were docking at the observed ports. Consequently, future research could
be improved by the addition of enhancing the reliability of the data relating to the
exact quantities of ship-generated greywater. These limitations lead to the conclusion
that the development and the implementation of port management information sys-
tem designed with real-time data services and fully integrated with ship waste man-
agement and detailed statistical reporting on various types of ship-generated waste is
needed. Developing and applying ship waste management in ports represent the
most effective tools for minimizing and avoiding impact of waste discharges from
ships (Palabiyik 2003).
Despite these shortcomings, our paper investigated and applied the model of risk
assessment of greywater discharges from the Danube River ships in a way that pro-
vided the scientific information on issues for future corrective actions, measures, rec-
ommendations and risk prevention strategies, as well as a meaningful perspective on
risk analysis and risk assessment related to the pollution caused by ship-generated
waste, not only in the case of the Republic of Serbia, but also in other countries
which the Danube River flow through.
In addition, our focus in this paper was on the impact of ship-generated grey-
water on the population. However, a similar approach could be used to analyse other
risks created by ship-generated waste in order to maintain and improve the availabil-
ity, quality and health safety of water in the Danube River Basin. e.g. the black
water discharges from ships are more harmful than greywater because of its compo-
sition and time of the decomposition. Also, having in mind that in our research for
assessing the severity, we observed the impact on people in the Republic of Serbia,
it would be needed to investigate risk assessment of greywater discharges and their
impacts on residents of areas along the Danube River in the border area of the
Republic of Serbia with: Hungary, the Republic of Croatia and the Republic of
Romania, so far as there are trans-boundary impacts of greywater.
5. Conclusions
It is undeniable that continued degradation and pollution of riverside wetlands, espe-
cially the continued decrease of river water quantity and quality, will result in further
increasing health problems of the population, in particular, in case of vulnerable peo-
ple in developing countries, where technological fixes and technological alternatives
are unavailable (Millennium Ecosystem Assessment 2005). Bearing in mind that
environmental water management and water resources management are fundamental
for achieving sustainable development (UNESCO 2013), as well as the fact that
shipping industry has increased dramatically resulting with increasing ship waste
(Butt 2007; Georgakellos 2007; Zuin, Belac, and Marzi 2009), our research provides
the suitable information about the possible model for improving risk assessment as a
feature of modern environmental decisions and planning in ship waste management.
Although, a number of authors have discussed a need for continual risk assess-
ments and risk analysis of ship-generated waste and emissions, little research has
been carried out to examine the risk assessment of ship-generated greywater dis-
charges. This study is one of the first attempts in succeeding to apply the FMEA
method in the risk assessment of the greywater discharges from the Danube River
ships. In our paper, we have analysed the risk priority number in relation with the
510 D. Makajic-Nikolic et al.
probability of releasing ship-generated greywater through a case study of the
observed ports in the Republic of Serbia. The assessments of discharge occurrence,
severity and detection per ports are founded respectively on: the estimated annual
total quantity of ship-generated greywater, the total relative population size exposed
to the pollution of greywater and the number of downstream water quality monitor-
ing stations.
We calculated that ports with the highest risk priority numbers are Veliko Gra-
dište, Beograd and Prahovo. The reasons for this are the following: the estimated
annual total quantity of generated greywater caused by a large number of cargo
ships in the case of Veliko Gradište, the total relative population size exposed to the
pollution of greywater caused by a large number of residents in the case of Beograd
and a small number of downstream water quality monitoring stations in the case of
Prahovo.
Based on the obtained results, we concluded that the measures, recommendations
and risk prevention strategies for ship-generated greywater discharges should go into
two major directions: (1) decreasing the pollution caused by greywater discharges;
(2) increasing the number of water quality monitoring stations. Both directions
require obeying and improving of international, regional and national regulations for
river water protection and ship-generated waste discharges.
In light of the above, the model of risk assessment presented in this paper has
clearly pointed out that the FMEA method can be used in the risk assessment of
ship-generated greywater discharges and moreover in making proposals for qualita-
tive improvements of ship waste management. Also, our preliminary results demon-
strated that FMEA method could be applied in creating the framework of risk
assessment and ports ranking for the various types of ship-generated waste and their
impacts.
As previously mentioned, future research needs to encompass a broader range of
different ship-generated waste. Research is also needed to assess the importance of
risk assessment and the adoption of risk prevention strategies for ship-generated
waste in the border area of the Republic of Serbia with: Hungary, the Republic of
Croatia and the Republic of Romania, and to examine the effects of this waste on
population of these countries.
Acknowledgements
This research was partially supported by the Ministry of Science and Technological Develop-
ment, the Republic of Serbia, Project numbers: TR35045 and III44007.
References
ADEC (Alaska Department of Environmental Conservation). 2000. Alaska Cruise Ship Initia-
tive Part 1 Final Report. Juneau, AK. Accessed August 24, 2013. www.dec.state.ak.us/
water/cruise_ships/pdfs/finreportp10808.pdf.
Ayyub, B. M. 2003. Risk Analysis in Engineering and Economics, 600. Boca Raton, FL:
Chapman & Hall/CRC.
Bloesch, J. 2009. “The International Association for Danube Research (IAD) –Portrait of a
Transboundary Scientific NGO.”Environmental Science and Pollution Research 16 (Sup-
pl 1): S116–S122. doi: 10.1007/s11356-009-0151-3.
Butt, N. 2007. “The Impact of Cruise Ship Generated Waste on Home Ports and Ports of
Call: A Study of Southampton.”Marine Policy 31 (5): 591–598.
Journal of Risk Research 511
Carpenter, A., and S. M. Macgill. 2005. “The EU Directive on Port Reception Facilities for
Ship-generated Waste and Cargo Residues: The Results of a Second Survey on the Provi-
sion and Uptake of Facilities in North Sea Ports.”Marine Pollution Bulletin 50 (12):
1541–1547.
Cicek, K., and M. Celik. 2013. “Application of Failure Modes and Effects Analysis to Main
Engine Crankcase Explosion Failure On-board Ship.”Safety Science 51: 6–10.
Costanza, R., and H. E. Daly. 1992. “Natural Capital and Sustainable Development.”Conser-
vation Biology 6: 37–46.
Ćurčić, N., N. Pavlovića, Ž. Bjeljac, and S. Medić. 2011. “Danube as a New Strategic Prod-
uct and the Serbian Tourism.”Paper presented at: Proceeding of 14th Contemporary
trends in Tourism and Hospitality: Via Danube, the main street of Europe: 19–28. Novi
Sad, Serbia. Accessed September 20, 2013. http://www.researchgate.net/publication/
231614611_Danube_as_a_New_Strategic_Product_and_the_Serbian_Tourism.
Danube Strategy in the Republic of Serbia. 2011. “Navigation and Transportation.”Accessed
August 30, 2013. http://www.dunavskastrategija.rs/en/?p=188
Dowling, R. K. 2006. Cruise Ship Tourism, 441. Oxfordshire: CAB International.
Ericson II, C. A. 2005. Hazard Analysis Techniques for System Safety, 528. New Jersey, NJ:
Wiley.
European Movement in Serbia –EMS. 2010. “Danube.”[In serbian]. Accessed November 1,
2013. www.emins.org/sr/aktivnosti/projekti/dunav/dunav_istrazivanje.pdf.
Georgakellos, D. A. 2007. “The Use of the Deposit-refund Framework in Port Reception
Facilities Charging Systems.”Marine Pollution Bulletin 54: 508–520.
Groso, A., A. Ouedraogo, and T. Meyer. 2012. “Risk Analysis in Research Environment.”
Journal of Risk Research 15 (2): 187–208.
Hayes, K. 2002. “Identifying Hazards in Complex Ecological Systems. Part 2: Infection
Modes and Effects Analysis for Biological Invasions.”Biological Invasions 4 (3): 251–
261.
Ho, C., and C. Liao. 2011. “The Use of Failure Mode and Effects Analysis to Construct an
Effective Disposal and Prevention Mechanism for Infectious Hospital Waste.”Waste
Management 31: 2631–2637.
Hou, D., X. Song, G. Zhang, H. Zhang, and H. Loaiciga. 2013. “An Early Warning and Con-
trol System for Urban, Drinking Water Quality Protection: China’s Experience.”Environ-
mental Science and Pollution Research Res 20: 4496–4508. doi10.1007/s11356-012-
1406-y.
International Commission for the Protection of the Danube River. 1994. “Convention on
Cooperation for the Protection and Sustainable Use of the Danube River (Danube River
Protection Convention).”Viena, Austria: ICPDR Secretariat. Accessed August 8, 2013.
http://www.icpdr.org/main/icpdr/danube-river-protection-convention.
International Maritime Organization. 2013. “International Convention for the Prevention of
Pollution from Ships (MARPOL).”Accessed August 17. http://www.imo.org/About/Con
ventions/ListOfConventions/Pages/International-Convention-for-the-Prevention-of-Pollu
tion-from-Ships-(MARPOL).aspx.
Jozi, S., S. Saffarian, and M. Shafiee. 2012. “Environmental Risk Assessment of a Gas Power
Plant Exploitation Unit Using Integrated TOP-EFMEA Method.”Polish Journal of Envi-
ronmental Studies 21 (1): 95–105.
Karlgren, L., V. Tullander, T. Ahl, and E. Olsen. 1967. “‘Residential Waste Water’Classic
Swedish Study. Swedish National Board for Building and Research Funded the Study.”
Accessed August 4, 2013. www.greywater.com/pollution.htm.
Kikukawa, S., H. Mitsuhashi, and A. Miyake. 2009. “Risk Assessment for Liquid Hydrogen
Fueling Stations.”International Journal of Hydrogen Energy 34: 1135–1141.
Lin, B., C. Y. Lin, and T. C. Jong. 2007. “Investigation of Strategies to Improve the Recy-
cling Effectiveness of Waste Oil from Fishing Vessels.”Marine Policy 31 (4): 415–420.
Lindahl, M. 1999. “E-FMEA –A New Promising Tool for Efficient Design for Environ-
ment.”In First International Symposium On Environmentally Conscious Design and
Inverse Manufacturing. EcoDesign ’99, 734–739, Tokyo, February 1–3. Accessed Octo-
ber 10, 2013. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=747416.
Lindahl, M., and J. Tingström. 2001. A Small Textbook on Environmental Effect Analysis, 66.
Kalmar: University of Kalmar.
512 D. Makajic-Nikolic et al.
Loehr, L. C., C. J. Beegle-Krause, K. George, C. D. McGee, A. J. Mearns, and M. J. Atkin-
son. 2006. “The Significance of Dilution in Evaluating Possible Impacts of Wastewater
Discharges from Large Cruise Ships.”Marine Pollution Bulletin 52 (6): 681–688.
Makajić, NikolićD., S. Jednak, S. Benković, and V. Poznanić. 2011. “Project Finance Risk
Evaluation of the Electric Power Industry of Serbia.”Energy Policy 39 (10): 6168–6177.
Mihic, S., and A. Andrejevic. 2012. “European Policy for the Promotion of Inland Waterway
Transport –A Case Study of the Danube River.”Chap. 2 In Sustainable Development –
Policy and Urban Development –Tourism, Life Science, Management and Environment,
edited by C. Ghenai, 23–40. Rijeka: InTech. doi: 10.5772/26716.
Mihic, S., M. Golusin, and M. Mihajlovic. 2011. “Policy and Promotion of Sustainable
Inland Waterway Transport in Europe –Danube River.”Renewable and Sustainable
Energy Reviews 15 (4): 1801–1809.
Millennium Ecosystem Assessment. 2005. Ecosystems and Human Well-being: Wetlands and
Water Synthesis, 109. Washington, DC: World Resources Institute.
Mourad, K. A., J. C. Berndtsson, and R. Berndtsson. 2011. “Potential Fresh Water Saving
Using Greywater in Toilet Flushing in Syria.”Journal of Environmental Management 92
(10): 2447–2453.
Mousseau, J., J. Jansen, D. Janke, and C. Plowman. 2003. “Waste Minimization Improve-
ments Achieved through Six Sigma Analysis Result in Significant Cost Savings.”Waste
Management Symposium, WM’03, Tucson, AZ. Accessed December 6, 2013. http://
www.wmsym.org/archives/2003/pdfs/195.pdf
My Ship. 2014. Accessed February 14, 2014. [In serbian]. http://mojaladja.com/component/
content/article/8-moja-ladja-section/razni-tekstovi/65-karas-raj-za-ribolovce.html.
Oraee, S., A. Yazdani-Chamzini, and M. Basiri. 2011. “Evaluating Underground Mining
Hazards by Fuzzy FMEA.”SME Annual Meeting, Denver, CO. Accessed December 20,
2013. https://www.academia.edu/2740293/Evaluating_Underground_Mining_Hazards_by
_Fuzzy_FMEA.
Palabiyik, H. 2003. “Waste Management Planning for Ship Generated Waste.”Journal of
Naval Science and Engineering 1 (2): 151–159.
Pollard, S., R. Smith, P. Longhurst, G. Eduljee, and D. Hall. 2006. “Recent Developments in
the Application of Risk Analysis to Waste Technologies.”Environment International 32:
1010–1020.
Presburger-Ulnikovic, V. 2012. “Integrated Model of Vessel-produced Waste Material Man-
agement in Regular and Emergency Situations.”[In Serbian]. PhD thesis, University of
Belgrade, The Republic of Serbia.
Presburger-Ulnikovic, V., M. Vukic, R. Jancic-Heinemann, and D. Antonovic. 2011. “Ship
Waste Quantities Prediction Model for the Port of Belgrade.”Chemical Industry and
Chemical Engineering Quarterly 17 (2): 239–248.
Presburger-Ulnikovic, V., M. Vukic, and R. Nikolic. 2012. “Assessment of Vessel-generated
Waste Quantities on the Inland Waterways of the Republic of Serbia.”Journal of Envi-
ronmental Management 97: 97–101.
Quella, F., and W. P. Schmidt. 2003. “Integrating Environmental Aspects into Product Design
and Development the New ISO TR 14062.”The International Journal of Life Cycle
Assessment 8 (2): 113–114.
Rak, J., and B. Tchórzewska-Cieślak. 2010. “The Possible Use of the FMEA Method to
Ensure Health Safety of Municipal Water.”Journal of Konbin 14–15 (1): 143–154.
Republic Hydrometeorological service of Serbia. 2013. “Hydrological Data.”Accessed
December 22. http://www.hidmet.gov.rs/eng/hidrologija/povrsinske/sliv_dunav.php.
Statistical Office of the Republic of Serbia. 2011. “Population –Database.”Accessed Sep-
tember 16, 2013. http://popis2011.stat.rs/?page_id=1221&lang=en.
Statistical Office of the Republic of Serbia. 2013. “Data|Latest Indicators|Republic of Serbia.”
Accessed September 2. http://webrzs.stat.gov.rs/WebSite/Public/PageView.aspx?pKey=2.
Tal, T., A. Sathasivan, and K. C. B. Krishna. 2011. “Effect of Different Disinfectants on Grey
Water Quality During Storage.”Journal of Water Sustainability 1 (1): 127–137.
Thivel, P., Y. Bultel, and F. Delpech. 2008. “Risk Analysis of a Biomass Combustion Process
Using MOSAR and FMEA Methods.”Journal of Hazardous Materials 151: 221–231.
Thomas, S. V. 2008. Water Pollution Issues and Developments, 208. New York: Nova
Science.
Journal of Risk Research 513
United Nations. 2013. “Integrated Water Resources Management (IWRM)|International Dec-
ade for Action ‘Water for Life’2005–2015.”Accessed September 10. http://www.un.org/
waterforlifedecade/iwrm.shtml.
United Nations Economic Commission for Europe. 2007. 2nd Environmental Performance
Review: Republic of Serbia, 169. Geneva: United Nations Publications, United Nations.
(UNESCO) United Nations Educational Scientific and Cultural Organization. 2006. “The 2nd
UN World Water Development Report: ‘Water, a Shared Responsibility’.”Accessed Sep-
tember 10, 2013. http://webworld.unesco.org/water/wwap/wwdr/wwdr2/pdf/wwdr2_ch_1.
pdf.
(UNESCO) United Nations Educational Scientific and Cultural Organization. 2013. “IWRM
Guidelines at River Basin Level –Part 1: Principles.”Accessed January 20, 2014. http://
unesdoc.unesco.org/images/0018/001864/186417e.pdf.
United Nations Statistics Division. 2013. “Population Density and Urbanization.”Accessed
September 14. http://unstats.un.org/unsd/demographic/sconcerns/densurb/densurbmeth
ods.htm.
United States Environmental Protection Agency –EPA. 2008. Cruise Ship Discharge Assess-
ment Report, 162. Washington, DC: Environmental Protection Agency, Oceans and
Coastal Protection Division, Office of Wetlands, Oceans, and Watersheds, Office of
Water.
United States Environmental Protection Agency –EPA. 2013. “Water: Vessel Water Dis-
charge|Cruise Ship Discharges.”Accessed August 16. http://water.epa.gov/polwaste/vwd/
cruise_ships_index.cfm.
Vukić, M., R. Nikolić, S. Urošević, P. Galić, I. Jovanović, and J. Popović. 2009. The Devel-
opment of an Integrated Management Model of Collection, Transport and Disposal of
Waste from Ships in the Waterway Network of the Republic of Serbia. Keynote Ahead of
the Delegation of Serbia, Expert Group Meeting on ‘Waste from Operation of Ships’.
Budapest: Danube Commission.
Wang, Y., G. Cheng, H. Hu, and W. Wu. 2012. “Development of a Risk-based Maintenance
Strategy Using FMEA for a Continuous Catalytic Reforming Plant.”Journal of Loss Pre-
vention in the Process Industries 25: 958–965.
Widiastuti, N., H. Wu, M. Ang, and D. Zhang. 2008. “The Potential Application of Natural
Zeolite for Greywater Treatment.”Desalination 218: 271–280.
Working Group for Revision of the National Waste Management Strategy. 2009. “National
Waste Management Strategy 2010–2019.”Accessed August 18, 2013. http://www.ta-hcw.
rs/resurs/library/eng/National%20Waste%20Strategy%202010-2019.pdf.
Zuin, S., E. Belac, and B. Marzi. 2009. “Life Cycle Assessment of Ship-generated Waste
Management of Luka Koper.”Waste Management 29 (12): 3036–3046.
514 D. Makajic-Nikolic et al.
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